Journal ofnirdpr.org.in/NIRD_Docs/jrd/jan-mar-2015.pdfthat during 2005-06 more than 99.5 per cent of...

Journal ofRural Development

Vol.34 January - March 2015 No. 1

CONTENTS

1. Employment Pattern in the Unorganised Manufacturing 1Sector in AssamDilip Saikia

2. Informal Finance- A Case Study of North East India 17Tiken Das

3. Microfinance Livelihood Initiatives and Women Empowerment in 31Selected Villages of Andhra Pradesh L. Vachya

4. Retail Operating Models in Indian Agri-Business Sector 49Vishwas Gupta

5. Efficiency of Khadi and Village Industries in India– Data Envelopment 61Approach (DEA)M. Manonmani *

6. Organic Farming and Quality of Food 71E. Thippeswamy

7. An Empirical Study of Women Labourers at Workplace and 85at Home in Rural PunjabDharam Pal* and Gian Singh

8. A Study of Mid-Day Meal Scheme Implementation in Nalgonda 101District For Improving School AttendanceSambi Reddy Vippala

9. Meaningful Financial Inclusion 115Shruti Sarma

BOOK REVIEW

1. Essays in Economics and Other Cheerful Themes 121Dr.R.Murugesan

Journal of Rural Development, Vol. 34, No. 1, January - March : 2015

1

Introduction

Creation of gainful employment hasbeen one of the major challenges for thepolicymakers in India, especially in the post-reform period.1 This is partly because theemployment situation during the post-reformperiod has not been encouraging and theorganised manufacturing sector failed togenerate employment opportunities, whereasthe unorganised manufacturing sectorwitnessed remarkable rise in both the numberof units and workers (Rani and Unni, 2004; Sahu,2007). The unorganised manufacturing sectorof India is huge and quite diversified, includinga wide range of manufacturing units, dispersedall over the country both in rural and urbanareas (Saik ia, 2011). The unorganised

EMPLOYMENT PATTERN IN THEUNORGANISED MANUFACTURINGSECTOR IN ASSAM

Dilip Saikia*

ABSTRACT

This paper examines the employment situation in the unorganisedmanufacturing sector in Assam. The findings suggest that though the unorganisedsector has got immense employment potential, the role of the sector in generatingproductive employment opportunities in the State is doubtful. The sector has sufferedsharp decline in employment during 1994-95 to 2000-01, especially full-time workersin the OAMEs segment. Though the period 2000-01 to 2005-06 witnessed significantemployment generation in the sector, a large proportion of these new jobs were part-time workers, again in the OAMEs segment. Additionally, the share of female workersincreased in both the part-time and full-time workers category. Thus, the recent growthin unorganised sector employment in the State has taken place largely throughcasualisation and feminisation of workers, which is not an encouraging trend as thisimplies decline in the quality of employment in the sector. Adding to this, the sectorcontinues to suffer abysmally low level of productivity, leading to poor performanceof the sector. Therefore, the paper emphasises on reformulating the existingindustrialisation strategy in the State and calls for special policy attention towardsproductivity growth, modernisation and technology upgradation of the sector.

manufacturing sector is largely labourintensive, and thus, holds the promise forgeneration of vast employment opportunities,especially in developing countries like India,which are labour abundant. Recognising therole of the unorganised sector, the 11th Plan(2007-2012) and 12 th Plan (2012-2017)emphasised the sector as the most potentialsector for rapid employment generation.

Assam is known to be one of the mostbackward States in India in industrialdevelopment. This is in spite of the fact thatthe State has a vast stock of natural resourcessuch as mineral oil, natural gas, coal, limestone,water and forest resources (Goswami, 1981;Sarma and Bezbaruah, 2009). Themanufacturing sector contributes only 4.77 per

Journal of Rural Development, Vol. 34 No. (1) pp. 1-16

NIRD & PR, Hyderabad.

* Assistant Professor of Economics, Department of Commerce, Darrang College, Tezpur, Assam - 784 001,

India E-mail: [email protected]


2

cent to the net state domestic product (NSDP,at constant 2004-05 prices) in 2011-12, ofwhich 2.18 per cent is contributed by theorganised segment and 2.58 per cent iscontributed by the unorganised segment (RBI,2012). More seriously the sector’s contributionto NSDP continued to shrink from 9.17 percent in 2004-05, which is largely caused bydecline in the contribution from the organisedsegment from 6.82 per cent during the same,while the unorganised segment’s contributionhas marginally increased. Looking into theindustrial structure in the State, it can be saidthat the industrial sector is largely confined tothe unorganised manufacturing sector. During2005-06, the unorganised manufacturingsector with about 370.8 thousand units, whichis more than 99.5 per cent of totalmanufacturing units in the State,accommodated about 632.5 thousandworkers, which is more than 83 per cent ofmanufacturing workers in Assam. In spite ofthe crucial role played by the sector inindustrial development and employmentgeneration, the sector has not received dueattention in the policy sphere and researchcommunity in the State. While there is a largebody of literature on growth, productivity andother aspects of the sector for the country as awhole (Kabra, 2003; Rani and Unni, 2004;Mukherjee, 2004; Sahu, 2007; Kathuria et al,2010), these issues have not been addressedin the context of Assam. For India as a whole,studies have shown that the unorganisedmanufacturing sector witnessed sharp declinein number of units and employment duringmid-1980s to mid-1990s and the period sincemid-1990s experienced significant rise of thesector in terms of both number of units andemployment (Rani and Unni, 2005; Sahu, 2007).However, the performance of the sector inAssam is not discussed yet. In this paper wehave made an attempt to fill this void in theliterature by analysing the pattern ofemployment in the unorganisedmanufacturing sector in the State.

The specific objective of this paper isto analyse the employment growth of theunorganised manufacturing sector in Assam.We specifically examine the growth andpattern of employment by nature and qualityof work and male-female category. We havealso looked at growth in factor productivitiesand capital intensity of the sector in order tounderstand the efficiency of the sector. Theanalysis has been carried out for the overallunorganised manufacturing sector as well asfor different enterprise types.

Data Source

The National Sample SurveyOrganisation (NSSO) is the principal agencythat collects information about variousdimensions like output, employment, capital,gross value added, etc., of the unorganisedmanufacturing sector in India. The NSS surveyscover all the units of unregisteredmanufacturing sector and provide a largevariety of estimates for the entire unregisteredmanufacturing sector at differentgeographical scales, viz. State, region anddistrict. While published NSS reports provideinformation only up to the State level, the unitlevel data (available on CD-ROMs supplied bythe NSSO New Delhi) provide information atthe sub-regional and district level.

The unorganised (or unregistered)manufacturing sector, in the NSS framework,covers all the manufacturing enterprises thatare not covered by Annual Survey of Industries,conducted by the Central StatisticalOrganisation. Per se, the sector includes all themanufacturing enterprises except (a) thoseregistered under section 2m(i) and 2m(ii) ofFactories Act, 1948 and Bidi and Cigar Workers(conditions of employment) Act, 1966 and (b)those run by Government (CentralGovernment, State Governments, LocalBodies)/Public Sector Enterprises.

In this paper data for the unorganisedmanufacturing sector in Assam are drawn

Dilip Saikia


3

from the latest three quinquennial rounds ofNSS survey on unorganised manufacturingsector, viz. 51st round (July 1994 June 1995),56th round (July 2000 June 2001) and 62nd

round (July 2005 June 2006). 2 These rounds,however, differ from each other in terms ofindustrial classification and coverage, whichleads to a few conceptual and methodologicalinconsistencies in different rounds of data. Forinstance, the 51st round, 56th round and 62nd

round data are based on the National IndustrialClassification (NIC) of 1987, 1998 and 2004,respectively. Therefore, we have to makenecessary adjustments to the industry groupsunder the NIC 1987 and NIC 1998, to makethe industry groups comparable with theindustry groups under NIC 2004. Secondly,some industrial categories such as repairservices, repair of capital services, etc., areincluded in the 51st round, but excluded inthe 56th and 62nd rounds, and some industrialcategories such as cotton ginning, cleaningand baling, recycling, etc., are included in the56th and 62nd rounds, but excluded in the 51st

round. These industrial categories have beenexcluded from the analysis in order to makevalid comparison among all the three NSSrounds.

Why Emphasise on UnorganisedManufacturing Sector?

The unorganised manufacturing sectorrepresents an important part of the economy,especially in developing countries like India.The sector is regarded as the growth engineof many developing economies and is one ofthe fastest growing industrial sectors all over

the world. The strategic role of the sector isthe creation of a wide variety of gainfulemployment opportunities at a very lower costof capital, along with its contribution toproduction, income generation, exports andcapital accumulation.

The size of unorganised manufacturingsector in Assam is huge both in terms ofnumber of units and workers. Table 1 showsthat during 2005-06 more than 99.5 per centof manufacturing enterprises were in theunorganised segment and the dominance ofthe sector remained since 1994-95. Thepredominance of the unorganised segment istrue in respect of employment as well. In1994-95, the segment accommodated about83.3 per cent of the workers engaged inmanufacturing, which declined 81.6 per centin 2000-01 and then increased to 83.1 per centin 2005-06. In other words, the organisedsector accounted for only 0.50 per cent ofmanufacturing units during 1994-95 to 2005-06 and about 16.7 per cent of manufacturingemployment in 1994-95, 18.4 per cent in2000-01; and 16.9 per cent in 2005-06. Thus,it is apparent that the unorganisedmanufacturing sector approximately sums upthe total manufacturing sector in the State,especially from the view point of number ofunits and workers. However, the sector’scontribution to manufacturing gross valueadded is very meagre, which was 26.17 percent in 1994-95, 33.8 per cent in 2000-01 and24.9 per cent in 2005-06. This is because ofabysmally low level of productivity due to lowlevel of technology-in-use in the sector, whatwe will discuss in a later section.

Employment Pattern in the Unorganised Manufacturing Sector in Assam


4

Structure of Unorganised ManufacturingSector in Assam

Before we discuss the pattern andgrowth of employment in unorganisedmanufacturing sector, it is worthwhile toanalyse the structure of the sector. In India,the unorganised manufacturing sector is sub-divided into three enterprise types- ownaccount manufacturing enterprises (OAMEs),non-directory manufacturing establishments(NDMEs) and directory manufacturingestablishments (DME). As per the definitionfollowed by the NSSO, OAMEs are enterprisesrun without a hired worker on a fairly regularbasis; NDMEs are establishments employingup to six workers, at least one of them being ahired worker employed on a fairly regular basis;and DMEs are establishments employing sixor more (but less than ten) workers, at leastone of them being a hired worker.

The composition and structural changesin unorganised manufacturing sector in Assamduring 1994-95 to 2005-06 is shown in Table2 in terms of three indicators, namely, numberof units, number of workers and gross valueadded, separately for different enterprises

types. It is obvious from Table 2 that a verylarge proportion of unorganisedmanufacturing industries in Assam has beencontinued to be constituted by the OAMEs,which are the tiniest self-employingenterprises. The dominance of the OAMEssegment is true in respect of each of the threeindicators. For example, in 2005-06, 88.5 percent of the units, 74.6 per cent of workers and55 per cent of gross value added in theunorganised manufacturing sector areconcentrated in the OAMEs segment. On theother hand, these percentages are only of 10.3,18.1 and 31.1, respectively for NDMEs segmentand 1.2, 7.3 and 13.8, respectively for DMEssegment. Thus, it is apparent that unorganisedmanufacturing sector in Assam has beendominated by the OAMEs, especially in termsof number of units and workers. On the otherhand, the presence of NDMEs and DMEs,which are regarded as the modern segmentof unorganised manufacturing, in Assam’sunorganised manufacturing sector has beenvery marginal in terms of number of units andworkers, but in terms of gross value added theyhave fairly respectable shares.

Table 1: Structure of Manufacturing Sector in Assam: 1994-95 to 2005-06

Year Industry Type No. of Units No. of Workers Gross Value Added*

% share % share ` Lakh % share

Organised 1514 0.49 124885 16.70 114535 73.83

1994-95 Unorganised 307200 99.51 622814 83.30 40592 26.17

Total 308714 100.00 747699 100.00 155127 100.00

Organised 1435 0.51 112542 18.41 160468 66.24

2000-01 Unorganised 278449 99.49 498800 81.59 81781 33.76

Total 279884 100.00 611342 100.00 242249 100.00

Organised 1864 0.50 128662 16.90 410918 75.11

2005-06 Unorganised 370781 99.50 632481 83.10 136169 24.89

Total 372645 100.00 761143 100.00 547087 100.00

Note: * Values are at Current Prices.

Source: NSSO (1998a, 1998b, 2002a, 2002b, 2002c, 2008a and 2008b) and CSO (1994/95, 2000/01and 2005/06).

Dilip Saikia


5

From the data presented in Table 2 wecan identify some changes that have takenplace in the structure of the unorganisedmanufacturing sector between 1994-95 and2005-06. In terms of number of units, the shareof OAMEs remained unchanged throughout,the share of NDMEs has marginally declined,which has gained by the DMEs. In terms ofnumber of workers, the share of OAMEsremained same during 1994-95 to 2000-01and then declined marginally during 2000-01to 2005-06, whereas the share of NDMEsdeclined during 1994-95 to 2000-01 and thenincreased during 2000-01 to 2005-06 and thatof DMEs significantly increased throughout theperiod. In terms of gross value added, the shareof OAMEs marginally increased during 1994-95 to 2000-01 and then declined during 2000-01 to 2005-06, while that of NDMEs declinedduring 1994-95 to 2000-01 and then increasedduring 2000-01 to 2005-06. The DMEssegment, in contrast, experienced increase in

its share in gross value added throughout theperiod 1994-95 to 2005-06.

Going by absolute numbers, we can seethat there has been overall improvement innumber of units during 1994-95 to 2005-06.However, a break-up in the period shows thatduring 1994-95 to 2000-01, the number ofunits drastically declined, particularly forOAMEs and NDMEs segments, while numbersof DME units increased; and then during 2000-01 to 2005-06, the number of units increasedin each segment of the unorganisedmanufacturing sector. But the absolute figuresfor NDMEs units in 2005-06 are lower thanthose for 1994-95, while in the other twosegments the figures in 2005-06 are higherthan those for 1994-95. In terms of numbersof workers there has been improvement inthe absolute numbers during 1994-95 to2005-06 for the overall unorganisedmanufacturing sector, but except for DMEs

Table 2: Structure of Unorganised Manufacturing Sector in Assam

Year EnterpriseType No. of Units No. of Workers Gross Value Added*

in ’000 % share in ’000 % share ` Lakh % share

OAME 262.9 85.6 489.5 78.6 21526 58.8

NDME 42.0 13.7 116.3 18.7 12472 34.11994-95

DME 2.2 0.7 17.0 2.7 2586 7.1

ALL 307.1 100.0 622.8 100.0 36583 100.0

OAME 247.4 88.9 392.5 78.7 26197 59.6

NDME 28.2 10.1 81.1 16.3 10927 24.92000-01

DME 2.8 1.0 25.2 5.1 6813 15.5

ALL 278.4 100.0 498.8 100.0 43937 100.0

OAME 328.1 88.5 472.1 74.6 35619 55.0

NDME 38.2 10.3 114.4 18.1 20132 31.12005-06

DME 4.4 1.2 45.9 7.3 8959 13.8

ALL 370.8 100.0 632.5 100.0 64712 100.0

Note: * Values are at Constant (1993-94) Prices.Source: NSSO (1998a, 1998b, 2002a, 2002b, 2002c, 2008a and 2008b).



6

segment, the OAMEs and NDMEs segmentssuffered sharp decline in workers between1994-95 and 2005-06. In the OAMEs andNDMEs segments the absolute decline innumber of workers during 1994-95 to 2000-01 was much sharper than the increase innumber of workers during 2000-01 to 2005-06. Contradictorily, the DMEs segmentexperienced significant increase in number ofworkers throughout the period 1994-95 to2005-06. In terms of gross value added, theunorganised manufacturing sectorexperienced significant rise during 1994-95to 2005-06.

Employment in UnorganisedManufacturing Sector in Assam

Growth of Employment : The compoundannual growth rate of employment inunorganised manufacturing sector in Assamduring 1994-95 to 2005-06 is illustrated inTable 3. The employment in unorganisedmanufacturing sector witnessed an annualdecline of 3.63 per cent during 1994-95 to

2000-01. The closure of as large as 28.7thousand units during this period (Table 2) waslargely responsible for this employmentsetback. However, the sector experiencedsignificant growth of 4.86 per cent per annumduring 2000-01 to 2005-06. This employmentsurge was again accompanied byestablishment of about 92.4 thousand newunits during this period (Table 2). For the entireperiod (1994-95 to 2005-06), the sector hasmanaged an annual growth rate of 0.14 percent in employment. Looking at the growth ofemployment in the sub-sectors, the OAMEs andNDMEs sector suffered sharp decline of 3.61per cent and 5.83 per cent, respectivelybetween 1994-95 and 2000-01. However, boththe sectors experienced considerableimprovement (3.76 and 7.12 per cent,respectively) during 2000-01 to 2005-06, buta marginal decline for the entire study period.Contrarily, the DMEs segment enjoyedsignificant growth in workers throughout thestudy period.

Table 3: Compound Annual Growth Rate (%) of Employment in

Unorganised Manufacturing Sector

Enterprise Type 1994-95/2000-01 2000-01/2005-06 1994-95/2005-06

OAME -3.61 3.76 -0.33

NDME -5.83 7.12 -0.15

DME 6.78 12.74 9.45

ALL -3.63 4.86 0.14

Source: Same as in Table 2.

Dilip Saikia


7

Composition of Workers by EmploymentType : Though the employment situation of theunorganised manufacturing sector in Assamis found to be improved in recent years (Table3), it is also important to look at thecomposition of employment in terms of part-time and full-time workers, separately for maleand female category. This is because theexisting evidence for India as a whole suggeststhat in recent years, the unorganisedmanufacturing sector witnessed significantincrease in the part-time workers, while thefull-time workers sharply declined (Sahu,2007).3 This could also be a case for Assam’sunorganised manufacturing sector. Therefore,we analyse the size and composition ofworkers in terms of the nature of employment:part-time and full-time, separately for male andfemale category.

Table 4 shows the composition ofworkers by employment type for the period1994-95 to 2005-06. It shows that the shareof full-time workers for the overallunorganised manufacturing sector declinedfrom 77.38 per cent in 1994-95 to 73.58 percent in 2005-06. This implies the share of part-time workers has increased from 22.62 to26.42 per cent during this period. The share isnot uniform among the three layers of theunorganised manufacturing sector. The shareof part-time workers was more for the OAMEssegment and this is true for throughout thestudy period. In 1994-95, the share of part-time workers stood at 27.11 per cent forOAMEs, 5.58 per cent for NDMEs and 9.41 percent for DMEs, which increased to 31.16, 11.36and 15.25 per cent, respectively for theOAMEs, NDMEs and DMEs segments in 2005-06.

During 1994-95 to 2000-01, there hasbeen sharp decline in both full-time and part-

time workers, but the decline was higher forthe full-time workers (101.5 thousands)compared to part-time workers (22.5thousands). The decline in full-time workerswas largely contributed by the decline inOAMEs and NDMEs segments, while the DMEssegment experienced improvement (Table 4).The decline in part-time workers was mainlycontributed by the OAMEs segment, while theNDMEs and DMEs segments experiencedincrease in part-time workers during thisperiod. During 2000-01 to 2005-06, there hasbeen increase in both full-time as well as part-time workers in all the segments ofunorganised manufacturing sector. But, theincrease was more in case of part-timeworkers compared to full-time workers (maynot be in terms of absolute numbers, but interms of growth rates) for the overallunorganised manufacturing sector as well asits all the three sub-sectors.

In terms of the absolute numbers, acomparison of employment situation in 2005-06 with that in 1994-95 reveals that thenumber of full-time workers for the overallunorganised manufacturing sector as well asfor OAMEs and NDMEs segments was lowerin 2005-06 than those figures in 1994-95(Table 4). Contrarily, the number of part-timeworkers was higher in 2005-06 than thosefigures in 1994-95. Only for the DMEs segmentthe number of both full-time and part-timeworkers was higher in 2005-06 than thosefigures in 1994-95. This implies that the sizeof increase in full-time workers during 2000-01 to 2005-06 was lower than the size ofdecline during 1994-95 to 2000-01, leadingto overall decline in full-time workers for theoverall unorganised manufacturing sector andOAMEs and NDMEs segments during theperiod 1994-95 to 2005-06.



8

Tab

le 4

: W

ork

ers

by

Emp

loym

ent

Typ

e in

Un

org

anis

ed M

anu

fact

uri

ng

Sec

tor

OA

ME

ND

ME

DM

EA

LL

Fu

ll-

Pa

rt-

Tota

lF

ull

-P

art

-To

tal

Fu

ll-

Pa

rt-

Tota

lF

ull

-P

art

-To

tal

tim

eti

me

tim

eti

me

tim

eti

me

tim

eti

me

Co

mp

osi

tio

n o

f Wo

rker

s (in

Th

ou

san

ds)

1994

-95

356.

813

2.7

489.

510

9.5

6.8

116.

315

.41.

617

.048

1.9

140.

962

2.8

2000

-01

283.

810

8.7

392.

574

.26.

981

.122

.42.

825

.238

0.4

118.

449

8.8

2005

-06

325.

014

7.1

472.

110

1.4

13.0

114.

438

.97.

045

.946

5.4

167.

163

2.5

Shar

e o

f Fu

ll-ti

me

and

Par

t-ti

me

Wo

rker

s (in

per

cen

t)

1994

-95

72.8

927

.11

100.

0094

.15

5.85

100.

0090

.59

9.41

100.

0077

.38

22.6

210

0.00

2000

-01

72.3

127

.69

100.

0091

.49

8.51

100.

0088

.89

11.1

110

0.00

76.2

623

.74

100.

00

2005

-06

68.8

431

.16

100.

0088

.64

11.3

610

0.00

84.7

515

.25

100.

0073

.58

26.4

210

0.00

Incr

emen

t/D

ecre

men

t (in

Th

ou

san

ds)

2000

-01/

1994

-95

-73.

0-2

4.0

-97.

0-3

5.3

0.1

-35.

27.

01.

28.

2-1

01.5

-22.

5-1

24.0

2005

-06/

2000

-01

41.2

38.4

79.6

27.2

6.1

33.3

16.5

4.2

20.7

85.0

48.7

133.

7

2005

-06/

1994

-95

-31.

814

.4-1

7.4

-8.1

6.2

-1.9

23.5

5.4

28.9

-16.

526

.29.

7

Co

mp

ou

nd

An

nu

al G

row

th R

ate

(in p

er c

ent)

1994

-95/

2000

-01

-3.7

4-3

.27

-3.6

1-6

.28

0.24

-5.8

36.

449.

786.

78-3

.87

-2.8

6-3

.63

2000

-01/

2005

-06

2.75

6.24

3.76

6.45

13.5

17.

1211

.67

20.1

112

.74

4.12

7.13

4.86

1994

-95/

2005

-06

-0.8

50.

94-0

.33

-0.7

06.

07-0

.15

8.79

14.3

69.

45-0

.32

1.56

0.14

Sou

rce

: Sam

e as

Tab

le 2

.

Dilip Saikia


9

Composition of Workers by Sex andEmployment Type : Figure 1 illustrates thecomposition of unorganised manufacturingworkers separately for male and female forthe period 2000-01 to 2005-06.4 It reveals thatin 2000-01, about 71.85 per cent of totalunorganised manufacturing workers in Assamwere male. The share of male workers declinedto 65.17 per cent in 2005-06. This implies thatthe share of the female workers inunorganised manufacturing sector increasedfrom 28.15 to 34.83 per cent during 2000-01

to 2005-06. The male-female composition ofworkers is not uniform among the threesegments of unorganised manufacturingsector. The OAMEs segment accommodatedthe highest female unorganisedmanufacturing workers, followed by the DMEssegment. Between 2000-01 and 2005-06, theOAMEs and NDMEs segments haveexperienced increase in the share of femaleworkers, whereas in the DMEs segment theshare of female workers declined.

Going by the absolute numbers, as Table5 depicts, the size of male workers increasedfrom 358.4 thousand in 2000-01 to 412.1thousand in 2005-06, while size of femaleworkers increased from 140.4 thousand to220.3 thousand during the same period. Thus,between 2000-01 and 2005-06, there hasbeen increment of about 53.74 thousand maleworkers and 79.94 thousand female workers,with the compound annual growth rate of 2.83and 9.43 per cent for the male and femalecategory, respectively. In the case of maleworkers, the highest increment in terms ofnumber of workers was in the NDMEs segment

(27.04 thousand) followed by DMEs segment(17.56 thousand), while in terms of growthrate it was the DMEs segment (13.93 per cent)followed by NDMEs segment (6.29 per cent).The absolute increment/growth rate was verylow for the OAMEs segment. In the femalecategory, the OAMEs segment contributed thelargest increment in the number of workers(70.58 thousand), followed by NDMEssegment (6.18 thousand), but growth rate washighest for the NDMEs segment (16.48 percent) followed by OAMEs (9.13 per cent) andDMEs (8.77 per cent) segments.


Source : NSSO (2002a, 2002b, 2002c, 2008a and 2008b).


10

Table 5 also illustrates the compositionof workers by employment type for male andfemales separately. It is obvious that the shareof part-time workers was high in the femalecategory for the overall unorganisedmanufacturing sector as well as its three sub-sectors. Of the total female unorganisedmanufacturing workers, about 42.17 per centwere part-time workers in 2000-01 and 35.13per cent in 2005-06. The corresponding figuresfor male part-time unorganised manufacturing

Table 5 : Workers by Sex and Employment Type in Unorganised Manufacturing Sector

Year Male FemaleFull-time Part-time Total Full-time Part-time Total

Composition of Workers (in Thousands)

OAME 2000-01 211.5 52.1 263.6 72.4 56.5 128.92005-06 194.6 78.1 272.6 130.4 69.0 199.5

NDME 2000-01 69.6 6.1 75.8 4.6 0.8 5.42005-06 93.3 9.6 102.8 8.1 3.4 11.6

DME 2000-01 18.2 0.9 19.1 4.2 1.9 6.12005-06 34.6 2.0 36.7 4.3 5.0 9.3

ALL 2000-01 299.2 59.2 358.4 81.2 59.2 140.42005-06 322.5 89.7 412.1 142.9 77.4 220.3

Percentage Share of Full-time and Part-time WorkersOAME 2000-01 80.24 19.76 100.00 56.17 43.83 100.00

2005-06 71.36 28.64 100.00 65.40 34.60 100.00NDME 2000-01 91.94 8.06 100.00 85.19 14.81 100.00

2005-06 90.67 9.33 100.00 70.43 29.57 100.00DME 2000-01 95.29 4.71 100.00 68.85 31.15 100.00

2005-06 94.54 5.46 100.00 46.24 53.76 100.00ALL 2000-01 83.48 16.52 100.00 57.83 42.17 100.00

2005-06 78.24 21.76 100.00 64.87 35.13 100.00Increment/Decrement (in thousands) in 2005-06 over 2000-01OAME -16.91 25.96 9.05 58.04 12.54 70.58NDME 23.66 3.48 27.04 3.54 2.64 6.18DME 16.42 1.14 17.56 0.11 3.08 3.19ALL 23.26 30.48 53.74 61.70 18.25 79.94Compound Annual Growth Rate (in per cent) between 2000-01 and 2005-06OAME -1.65 8.42 0.68 12.50 4.09 9.13NDME 6.03 9.45 6.29 12.10 33.84 16.48DME 13.72 17.78 13.93 0.54 21.23 8.77ALL 1.51 8.66 2.83 11.97 5.52 9.43

Source: NSSO (2002a, 2002b, 2002c, 2008a and 2008b).

workers were 16.52 and 21.76, respectively.The decline in the share of female part-timeworkers was mainly due to decline of the shareof female part-time workers in the OAMEssegment, while the share of female part-timeworkers in the NDMEs and DMEs segmentsincreased significantly. On the other hand, theshare of male part-time workers increased inall the three segments of the unorganisedmanufacturing sector in Assam.

Dilip Saikia


11

In terms of absolute numbers, the sizeof male part-time workers increased from 59.2thousand in 2000-01 to 89.7 thousand in 2005-06, while size of female part-time workersincreased from 59.2 thousand to 77.4thousand during the same period. Within theunorganised manufacturing sector, all thethree sub-sectors experienced increase inpart-time workers for both male and femalecategory between 2000-01 and 2005-06; thehighest increment being witnessed by theOAMEs segment, about 25.96 thousand in themale category and 12.54 thousand in thefemale category.

The size of full-time workers alsoincreased for both the male and femalecategories for the overall unorganisedmanufacturing sector during 2000-01 to 2005-06. However, the increment in full-time workerswas much higher in the female category (61.70thousand), whereas the increment in the malefull-time workers was about 23.26 thousand.Thus, whatever the increase in full-time workers(about 85 thousand) we have observed between2000-01 and 2005-06, more than two-thirds ofthese workers were female workers. Thisphenomenal increase in the female full-timeworkers was mainly occurred in the OAMEssegment (about 58.04 thousand increment),while NDMEs and DMEs segments witnessedmarginal increment in female full-time workers.On the other hand, though the overallunorganised manufacturing sector witnessedincrease in number of male full-time workersduring 2000-01 to 2005-06, the OAMEs segmentexperienced a decline of about 16.91 thousandmale full-time workers. The NDMEs and DMEssegments, however, experienced significantincrement in male full-time workers during thesame period.

To summarise, whatever theimprovement in employment of theunorganised manufacturing sector in Assam hasbeen observed between 2000-01 and 2005-06,was largely contributed by the increase of part-

time workers (both male and female) andfemale full-time workers, especially in theOAMEs segment. Though the size of both full-time and part-time workers increased for bothmale and female category, the absoluteincrement was high for the female workers,especially in the OAMEs segment. Thus, therecent increase in employment in theunorganised manufacturing sector in Assam hastaken place mainly through casualisation ofworkers within the unorganised manufacturingsector and also through feminisation of workers.

Productivity of UnorganisedManufacturing Sector in Assam

The unorganised manufacturing unitsuse very low level of technology, which causeslow productivity, low profits and stagnation ofthe sector. There is unanimous evidence to claimthat the factor productivity in the unorganisedmanufacturing sector in India is very low(Mukharjee, 2004; Sahu, 2007; Kathuria et al.,2010). However, there is little evidence about itfor the State of Assam. Nayak and Dey (1996),based on the Second All India Census of SmallScale Industrial Units (1988), conclude that thelevel of labour and capital productivity was verylow in the small scale industrial sector in Assam.Kathuria et al. (2010) observe that labourproductivity in unorganised manufacturingsector in Assam was 9.4 times lower than that inthe organised manufacturing sector during1994-2005, whereas for the country as a wholeit was 4.4 times lower in the unorganised sectorcompared to organised sector. They also find thatcapital-labour ratio in the unorganisedmanufacturing sector is much lower than thatin the organised manufacturing sector - whilefor the country as a whole it was 3.5 times lower,for Assam it was 15.4 times lower during 1994-2005. Saikia (2013) also reports low level offactor productivity in the unorganisedmanufacturing sector in Assam during 1994-95to 2005-06. With such low level of productivity,the unorganised manufacturing sector has beenfacing serious challenges in the post-reform



12

period, as the sector has to meet stiff competitionfrom the large-scale units. In such situation, themajor challenge for the sector is to create gainfulemployment with increasing the level of factorproductivity.

Although we are aware about the factthat the partial productivity measures presentonly a partial picture of the efficiency in factor-use, in this study the productivity of unorganisedmanufacturing sector is discussed by using twopartial productivity measures, viz. labourproductivity - measured as gross value addedper worker and capital productivity - measuredas gross value added per unit of fixed capital. Wehave also looked into the capital intensity,measured as capital-labour ratio, which gives theinvestment per worker. The level and compoundannual growth rate of labour and capitalproductivities and capital intensity of theunorganised manufacture sector in Assam arereported in Tables 6 and 7, respectively.

The labour productivity (at constant1993-94 prices) for the overall unorganisedmanufacturing sector increased from ̀ 5874 in1994-95 to ` 10231 in 2005-06. Within theunorganised sector, labour productivity is lowestin the OAMEs segment. The labour productivityincreased in all the three segments ofunorganised manufacturing sector, except forDMEs between 2000-01 and 2005-06. The annualgrowth in real labour productivity for the overallunorganised manufacturing sector recorded at6.99 per cent during 1994-95 to 2000-01, whichslowed down to 3.04 per cent during 2000-01to 2005-06. For the entire period 1994-95 to2005-06, the annual growth rate was 5.17 percent. All the three segments within theunorganised manufacturing sector recorded

growth in real labour productivity during theentire period and two sub-periods, except theDMEs segment during 2000-01 to 2005-06.

The capital productivity increased during1994-95 to 2000-01 for the overall unorganisedmanufacturing sector and its OAMEs segment,while it declined for NDMEs and DMEs segmentduring the same. On the other hand, during2000-01 to 2005-06, capital productivitydeclined for the overall unorganisedmanufacturing sector as well as OAMEs and DMEssegments, but increased for NDMEs segment.Compared with the year 1994-95, the capitalproductivity in 2005-06 was lower for the overallunorganised manufacturing sector as well asNDMEs and DMEs segments, but higher in OAMEssegment.

Table 6 also reveals that capital intensityin the unorganised manufacturing sector inAssam has been abysmally low. The capitalintensity (at constant 1993-94 prices) for theoverall unorganised manufacturing sector, stoodat ̀ 4180 in 1994-95, which increased to ̀ 8040in 2005-06. The capital intensity is highest forthe DMEs segment, which is relatively capitalintensive within the unorganised manufacturingsector, while it is lowest in the OAMEs segment,which is the tiniest segment of the unorganisedmanufacturing sector. The real capital intensityof the overall unorganised manufacturing sectorrecorded an annual growth of 5.27 per centduring 1994-95 to 2000-01, 7.17 per cent during2000-01 to 2005-06 and 6.13 per cent during1994-95 to 2005-06. All the sub-sectors ofunorganised manufacturing sector recordedsignificant growth in capital intensity during theoverall study period as well as the sub-periods.

Dilip Saikia


13

Table 6 : Factor Productivities and Capital Intensity in Unorganised Manufacturing Sector

Enterprise Type Year Labour Capital CapitalProductivity (`)* Productivity Intensity (`)*

OAME 1994-95 4398 1.39 3163

2000-01 6675 1.68 3982

2005-06 7545 1.40 5392

NDME 1994-95 10720 1.34 7990

2000-01 13473 1.21 11172

2005-06 17595 1.33 13274

DME 1994-95 15207 2.06 7377

2000-01 27001 1.85 14587

2005-06 19502 0.88 22202

All 1994-95 5874 1.41 4180

2000-01 8808 1.55 5688

2005-06 10231 1.27 8040

Note: * Values are at Constant (1993-94) Prices.Source: Same as in Table 2.

Table 7: Compound Annual Growth Rate (%) of Factor Productivities and CapitalIntensity in Unorganised Manufacturing Sector

Enterprise Type Year Labour Capital CapitalProductivity Productivity Intensity

OAME 1994-95/2000-01 7.20 3.17 3.91

2000-01/2005-06 2.48 -3.55 6.25

1994-95/2005-06 5.03 0.06 4.97

NDME 1994-95/2000-01 3.88 -1.76 5.74

2000-01/2005-06 5.48 1.91 3.51

1994-95/2005-06 4.61 -0.11 4.72

DME 1994-95/2000-01 10.04 -1.78 12.03

2000-01/2005-06 -6.30 -13.85 8.76

1994-95/2005-06 2.29 -7.46 10.53

ALL 1994-95/2000-01 6.99 1.63 5.27

2000-01/2005-06 3.04 -3.85 7.17

1994-95/2005-06 5.17 -0.90 6.13

Source: Same as in Table 2.



14

Conclusion

The unorganised manufacturing sectoroccupies a place of great significance in theindustrial economy of Assam, especially in termsof number of units and employment. Within theunorganised manufacturing sector, the OAMEssegment plays a dominant position. Therefore,any policy towards development of the industrialsector in Assam should consign utmost focus tothe unorganised manufacturing sector and tothe OAMEs segment within the unorganisedmanufacturing sector. In this context, the presentpaper examines the employment pattern of theunorganised manufacturing sector in Assam forthe period 1994-95 to 2005-06.

The findings of the paper suggest thatthe performance of the unorganisedmanufacturing sector has been abysmal over theyears. During 1994-95 to 2000-01, many of theunorganised manufacturing units closed down,especially in the OAMEs and NDMEs segments.Similarly, there has been significant decline inworkers in the OAMEs and NDMEs segments ofunorganised manufacturing sector, while theDMEs segment, being bigger in scale ofoperation, has considerably contributed ingenerating employment during this period.However, the sector has shown some sort ofincrease in employment during 2000-01 to2005-06. All the three segments within thesector witnessed significant increase inemployment during this period. However, theincrease in employment during 2000-01 to2005-06 was not sufficient to compensate theloss of employment during the previous period(1994-95 to 2000-01) in the OAMEs and NDMEs

segments, resulting in loss of employment inthese two segments during the entire studyperiod.

The most disturbing fact is that anoverwhelming proportion of workers losingtheir jobs during 1994-95 to 2000-01 was full-time workers, especially in the OAMEs segment.But, a large proportion of the new jobs createdduring 2000-01 to 2005-06 were part-timeworkers, again in the OAMEs segment. Thus,there is a tendency for the proportion of part-time workers to increase in the unorganisedmanufacturing sector, and this is true for all thethree segments of the sector. Adding to this, theshare of female workers increased in both thepart-time and full-time worker category, in allthe three segments of the sector, especiallyagain in the OAMEs segment. Thus, it can be saidthat the recent increase in employment ofunorganised manufacturing sector in Assam hastaken place largely through casualisation andfeminisation of workers. Adding to this, the levelof productivity is found to be very low inunorganised manufacturing sector in Assam,leading to poor performance of the sector. Withinthe unorganised manufacturing sector, theproductivity level is lowest in the OAMEssegment. Thus, though the unorganisedmanufacturing sector has got immenseemployment potential, the impending role ofthe sector in creating productive employmentopportunities is doubtful in Assam. Therefore, itis important to provide special policy attentionfor increasing productivity throughmodernisation and technological upgradationof the sector.

Notes

1. Here, we refer to the macro-economic reforms of July 1991 and the period thereafter. UntilJuly 1991, India has followed an inward-looking restrictive policy regime, characterised byeconomic planning, state-led industrialisation, high protectionism, imports substitution,exchange control, and extensive state-regulation on different spheres of the economy. Themacro-economic reforms initiated in the economy following the balance of payment crisisof July 1991 broadly covered the areas of industrial licensing, foreign trade, foreign investment,

Dilip Saikia


15

exchange rate management, tax reforms and financial sector reforms. This economic reformwas a crucial turning point in economic policy history and has become one of the landmarksfor the recent spate of economic growth in India.

2. The NSS data are not available at yearly basis, rather at an interval of five years. Starting in1958–1959 it has completed, till date, nine-rounds of survey on unorganised manufacturingindustries (the other rounds are during 1968–1969, 1974–1975, 1978–1979, 1984–1985,1989–1990, 1994–1995, 2000–2001 and 2005–06).

3. To the best of our knowledge, no study has so far focused on this issue in the context ofAssam, and hence, there is dearth of information about it.

4. For the year 1994-95, the composition of workers by employment type is not available formale and females differently in the NSS published reports (NSSO 1998a, 1998b). Therefore,we are not able to carry out analysis for the year 1994-95.

References

1. Goswami, A. (1981), “Assam’s Industrial Development: Urgency of New Direction”, Economicand Political Weekly, Vol. 16, No. 21, pp. 953-956.

2. CSO (1994/95), Annual Survey of Industries (1994-95) - Summary Results for Factory Sector,Central Statistical Organisation, Government of India, New Delhi.

3. CSO (2000/01), Annual Survey of Industries (Factory Sector) - 2000-01, Central StatisticalOrganisation, Government of India, New Delhi.

4. CSO (2005/06), Annual Survey of Industries (Factory Sector) - 2005-06, Central StatisticalOrganisation, Government of India, New Delhi.

5. Kabra, K.N. (2003), “The Unorganised Sector in India: Some Issues Bearing on the Search ForAlternatives”, Social Scientist, 31(11/12): 23-46.

6. Kathuria, V., R. Raj and K. Sen (2010), “Organised versus Unorganised ManufacturingPerformance in the Post-Reform Period”, Economic & Political Weekly, 45(24): 55-64.

7. Mukherjee, D. (2004), “Productivity in the Informal Manufacturing Sector: Regional Patternsand Policy Issues”, MPRA Paper No. 4859, University of Munich Library, Germany.

8. Nayak, P. and N.B. Dey (1996), “Productivity in Small Scale Industry in Assam”, Yojana, Vol. 40,No.5.

9. NSSO (1998a), Unorganised Manufacturing Sector in India: Its Size, Employment and SomeKey Estimates, NSS 51st Round (July 1994–June 1995), Report No. 433(51/2.2/1), NationalSample Survey Organisation, Govt. of India, New Delhi.

10. NSSO (1998b), Unorganised Manufacturing Enterprises in India: Salient Features, NSS 51st

Round (July 1994–June 1995), Report No. 434(51/2.2/2), National Sample Survey Organisation,Govt. of India, New Delhi.

11. NSSO (2002a), Unorganised Manufacturing Sector in India: 2000–2001, Key Results, NSS 56th

Round (July 2000–June 2001), Report No. 477(56/2.2/1), National Sample Survey Organisation,Govt. of India, New Delhi.



16

12. NSSO (2002b), Unorganised Manufacturing Sector in India 2000–2001: Employment, Assetsand Borrowings, NSS 56th Round (July 2000–June 2001), Report No. 479(56/2.2/3), NationalSample Survey Organisation, Govt. of India, New Delhi.

13. NSSO (2002c), Unorganised Manufacturing Sector in India 2000–2001: Input, Output andValue Added, NSS 56th Round (July 2000–June 2001), Report No. 480(56/2.2/4), NationalSample Survey Organisation, Govt. of India, New Delhi.

14. NSSO (2008a), Unorganised Manufacturing Sector in India- Employment, Assets andBorrowings, NSS 62nd Round (July 2005–June 2006), Report No. 525(62/2.2/2), NationalSample Survey Organisation, Govt. of India, New Delhi.

15. NSSO (2008b), Unorganised Manufacturing Sector in India: Input, Output and Value Added,NSS 62nd Round (July 2005–June 2006), Report No. 526(62/2.2/3), National Sample SurveyOrganisation, Govt. of India, New Delhi.

16. Rani, U. and J. Unni (2004), “Unorganised and Organised Manufacturing in India: Potential forEmployment Generating Growth”, Economic and Political Weekly, 39(41): 4568-4580.

17. RBI (2012), Handbook of Statistics on Indian Economy 2012, Reserve Bank of India.

18. Sahu, P.P. (2007), “Expanding Productive Employment Opportunities: Role and Potential ofthe Micro and Small Enterprises Sector”, Working Paper 2007/05, Institute for Studies inIndustrial Development, New Delhi.

19. Saikia, D. (2013), “Entrepreneurship and Micro and Small Enterprises Growth in Assam”, IUPJournal of Entrepreneurship Development, 10(2):54-64.

20. Saikia, D. (2011), “Unorganised Manufacturing Industries in India: A Regional Perspective”,African Journal of Marketing Management, 3(8): 195-206.

21. Sarma, A. and M.P. Bezbaruah (2009), “Industry in the Development Perspective of NortheastIndia”, Dialogue, 10(3): 55-64.

Dilip Saikia


17Journal of Rural Development, Vol. 34 No. (1) pp. 17-30


* Assista

Introduction

Broader access to finance helps aneconomy produce more, and distribute it fairly.Both consumers and producers benefitbecause their welfare and productivity areraised. Without access to credit, one avenueof opportunity--- self-employment - is shut off.As a result, the poor are doubly damned --- notonly because they lose an option but alsobecause their bargaining power, when theywork for those who have resources, isweakened. Indeed, access to credit allows allthose with talent to obtain the resourcesnecessary to carry out their ideas, and societyis the richer for it. But financial markets,because of their special features, often servepoor people badly, since poor people haveinsufficient traditional forms of collateral suchas physical assets to offer. They are oftenexcluded from traditional financial marketsbecause transaction costs are often highrelative to the small loans typically demandedby poor people. Apart from that, because ofthe Information Asymmetry, the bank facestwo types of risk-voluntary and involuntary.

INFORMAL FINANCE- A CASESTUDY OF NORTH EAST INDIA

Tiken Das *

ABSTRACT

Based on the various ‘All India Debt and Investment Survey’ reports, NSSO surveyand primary data conducted in Indo-Myanmar border area, this study reviews thenature and domination of informal finance in the study area. In addition, by usinglogistic regression model this study analyses the factors influencing the borrowingdecision of the household in the study area. The study found the non-existence ofbank loan and domination of relatives and friends as a loan giver in the study area,which signals the presence of vicious circle i.e. borrowing money � and loss �borrowing money. Apart from that, the study provides an indication whether the ruralfamily’s employment or self-employment is related with money borrowing.

These risks make the acceptance of collateralnecessary for the lenders. In case of thosewho are living below poverty line, have littleor no asset to be provided as collateral. Thus,the poor are generally excluded from theformal financial institutions and have todepend on informal sector. This raises thequestion what the best opportunity informalsources are providing which can not beprovided by formal sources.

In India, since the early national plans,successive governments have emphasised thelink between improving finance access andreducing poverty. The need to improvefinancial access for India’s poor motivated theestablishment of a vast network of ruralcooperative credit banks in the 1950s,followed by a drive to nationalise commercialbanks, launched in 1969. In 1980, subsidisedcredit programmes were attempted, notableexamples being the Integrated RuralDevelopment Programme (IRDP) in India. The1990s saw the partial deregulation of interestrates, increased competition in the bankingsector and new micro-finance approaches that

* Ph.D. Scholar, Department of Economics, Sikkim Central University, email : [email protected].


18

combine the safety and reliability of formalfinance with the convenience and flexibilityof informal finance. Access to finance for therural poor has improved somewhat over thepast decades. But the vast majority of India’srural poor still do not have access to eitherformal finance or micro-finance. The Reportof the ‘Task Force on Credit Related Issues ofFarmers’ (GoI, 2010) submitted to the Ministryof Agriculture in June 2010 looked into theissue of a large number of farmers, who hadtaken loans from private moneylenders. Thereport mentioned: “in recent years, policyinterventions have led to doubling ofagricultural credit, but the limited access ofsmall and marginal farmers to institutionalcredit continues to be a matter of concern.What is worrying is that the proportion of suchfarmers is increasing and they form more thanfour-fifths of the operational holding”. At thesame time, micro-finance institutions havebeen criticised for seeking higher interest rateand mostly confined to the States with fairlywell-developed banking system and alsocompeting for same target group (Pradhan,2013: 2-3). In recent times, Government ofIndia and the Reserve Bank of India have givenstrong emphasis for financially including themajority of the population of the country. Thisis more so for the regions like the North EastRegion (NER) which has a large number ofpopulation still outside the fold of the formalfinancial agencies. More than 95 per cent ofthe households are financially excluded fromthe formal sources in the NER. Majority of theseexcluded households belonged to the smalland marginal farmers. At a disaggregated level,the situation is much more acute with morethan 70 per cent of the districts in Assamhaving an exclusion which ranges from 96.1-98.5 per cent. Thus, the financial exclusion isvery high. The rural financial market istherefore, dominated by the informal sectorinstitutions, offering services to the area,especially the poor. The 59th round of NSSOclearly reports that almost 80 per cent of the

households in Assam were indebted to theinformal sector as compared to only 60 percent in the country as a whole. And a largeportion of these suppliers are the communitybased traditional financial institutions (Sharma,2011:1-18). Thus, we can point out what arethe community based traditional financialinstitutions working in NER and secrets behindtheir success.

In the light of the above theoreticalliterature, the researcher attempts tounderstand the dominance of informal financeamong rural people of Indo-Myanmar borderarea in NER of India. The following issues havebeen considered as the specific objectives ofthe paper:

� To understand the presence of informalfinance at all India level.

� Whether the situation has been changedafter nationalisation of banks.

� To document the existence of informalfinance in the study area.

� To identify the factors influencing theborrowing decision of rural people in thestudy area.

Materials and Methods

Source of Data : The study was conducted byusing both primary and secondary data. Thesecondary data cover both ‘All India RuralCredit Survey 1951-52’ (RBI, 1954) and ‘All-India Rural Debt and Investment Survey 1961-62, 1971-72, 1981-82, 1991-92 and 2001-02’conducted by Reserve Bank of India and fourrounds of All-India Debt and InvestmentSurveys by ‘National Sample SurveyOrganisation’ of the Government of India from1971-72 to 2002-03. The primary survey wascarried out during the period of March, 2013.

The north east region is connected tothe rest of India by a 20 km wide land in Bengal,known as the Chicken’s Neck but shares over2,000 km of international border with Bhutan,China, Myanmar and Bangladesh. In recent

Tiken Das


19

times, the Governmen of India had takenvarious initiatives to connect the north eastwith the ASEAN countries. By introducing ‘LookEast Policy’, the Government of India tried tomake the rural people living in the border areaeconomically progressive. Now the questionarises, are the people living in the border areafinancially ready to welcome the developmentinitiatives? Do they have sufficient opportunityto access finance? The present study wascarried out in four north eastern States namely,Arunachal Pradesh, Mizoram, Manipur andNagaland which were chosen purposively

since all these four States are sharing theirborder with Myanmar. After selecting States,one district per State namely, Changlang inArunachal Pradesh, Champai in Mizoram,Ukuril in Manipur and Mon in Nagaland wereselected which are adjacent to Myanmarborder. Four villages were selected from eachdistrict according to their distance from theborder and population composition and fromeach village twelve households were takenrandomly. Table 1 documented clearly theprofile of villages chosen and samplerespondents interviewed.

Table 1 : The Villages Chosen and the Sample Covered

Villages/ Age 20-29 30-39 40-49 50-59 60+ Total

State Tribe M F M F M F M F M F M F Total

Arunachal Pradesh

Therimka Thangsa 0 0 6 0 6 0 0 0 0 0 12 0 12

Injan Thangsa 2 0 5 2 3 0 0 0 0 0 10 2 12

Khonsa Wangcha 7 6 2 0 2 0 0 0 0 0 11 6 17

Chongkow Wangcha 1 0 14 2 0 0 0 0 0 0 15 2 17

Manipur

Bun Kullen Tangkhul 0 2 0 2 1 0 1 0 3 2 5 6 11

Kangpat Tangkhul 1 1 1 1 3 1 0 0 3 0 8 3 11

Monnaph Kuki 0 0 3 1 6 0 0 1 1 0 10 2 12

Monjol Kuki 1 0 3 2 0 1 0 1 2 1 6 5 11

Mizoram

Ngaizawl Mizo 1 1 2 1 2 2 0 0 3 2 8 6 14

Zotlang Paitei 4 1 1 0 4 0 0 0 4 0 13 1 14

Zote Mizo & Paitei 2 0 3 0 2 1 0 0 0 0 7 1 8

Ngur Mizo & Paitei 0 0 0 1 2 3 0 0 6 2 8 6 14

Nagaland

Longwa Konyak 9 2 0 0 1 0 0 0 0 0 10 2 12

Wangti Konyak 8 4 0 0 0 0 0 0 0 0 8 4 12

Chen Moho Konyak 8 4 0 0 0 0 0 0 0 0 8 4 12

ChLoisho Konyak 6 6 0 0 0 0 0 0 0 0 6 6 12

Total Total 50 27 40 12 32 8 1 2 22 7 145 56 201

Informal Finance- A Case Study of North East India


20

Given the sampling design, the sampleframe was modest in size as it is, is arguablyrepresentative of the situation of the Indo-Myanmar border only.

Methodology : The data of ‘All India Debtand Investment Survey’ (AIDIS) were used toknow the dominance of informal finance inIndia and apart from that, ‘Analysis of VarianceMethod’ (ANOVA) was used to understand thesituation of post- nationalisation periodcompared to pre-nationalisation era. A set ofdescriptive statistics has been used to knowthe variation in the share of institutional andnon-institutional finance among differentStates of India. In addition, field data werereported to understand the depth of informalfinance among the rural people in Indo-Myanmar border area. A Logistic RegressionModel was developed to identify the factorsinfluencing the borrowing decision of the ruralpeople.

Persistence of Informal Credit in RuralIndia- AIDIS Surveys

Although India inherited a basicnetwork of credit cooperatives from thecolonial era, the Reserve Bank’s first decennial‘All India Rural Credit Survey’ (AIRCS) 1951-52(RBI, 1954) found that 92.8 per cent of ruralhouseholds relied on informal financial sector.The investigation extended over to nearly 1,30,000 families having residents in 600villages and all types of credit agencies in 75districts. During 1951-52, an increase in debtwas recorded in all the 75 districts. Themoneylenders continued dominance in thebeginning of plan period (around 70 per centof rural credit) despite all measures to controlthem, suppress or supplant had led to thesuggestion that ‘any realistic system of ruralcredit should seek to incorporate him in itselfrather than compete with him or wishfullyexpect to eliminate him’ (RBI, 1954). In thesecond survey by Reserve Bank of India (1961-62), the outstanding loans owed to agriculturist

moneylenders accounted for about 46 percent of the aggregate outstanding of all ruralhouseholds, nearly double the share comparedto first survey. The share of outstanding loansowing to professional moneylenders was nexthighest though their share declinedconstituting 15 per cent of the aggregateoutstanding. As per the survey findings on all-India basis, the share of cooperatives was at9.1 per cent, ‘others’ at 8.9 per cent, tradersand commission agents at 7.7 per cent,relatives at 6.8 per cent and government at5.3 per cent in the total outstanding debt. Theshares of landlords and commercial banks inthe aggregate outstanding were negligible at9.0 and 0.4 per cent, respectively. This factsignifies the continuance of informal financein rural India that might have prompted thenationalisation of commercial banks in 1969in the first phase.

Although the post-nationalisationperiod is different from the pre-nationalisationperiod, an ANOVA model with time dummyhas been fitted to determine the position ofinstitutional and non-institutional finance inpost- nationalisation period as follows-

Yi= β

0 + β

1 D

i + U

i

Yj= a

0 + a

1 D

j + U

j

Where,

Yi is the percentage share of institutional credit

out of total rural credit.

Yj is the percentage share of non-institutional

credit out of total rural credit.

‘D’ is dummy variable such that, D= 1 for post-nationalisation period (1981-2002) and D=0for pre-nationalisation period (1951-1971)

‘i’ is a period of study (1951-2002)

‘j’ is a period of study (1951-2002)

Ui and U

j are well behaved error terms.

Tiken Das


21

The ANOVA results pointed out that theestimated dummy coefficient of non-institutional rural credit is negativelysignificant which shows the share of non-institutional rural credit decline by an averageof 43.71 per cent in post- nationalisationperiod compared to pre-nationalisation period,whereas in case of institutional credit thedummy coefficient is positively significantwhich indicates the increasing share ofinstitutional credit in post-nationalisationperiod.

It can be assessed that the informal/non-institutional finance was graduallydeclining during the 1960s, was very nearlybroken during the 1970s, with the institutionalagencies making steady inroads into the ruralscene. The share of institutional creditagencies in the outstanding cash dues of therural households at the all-India levelincreased from 29 per cent in 1971 to 61 percent in 1981 and then the pace of increase

Table 2: ANOVA Results of Institutional and Non-Institutional Credit Agencies

Agencies Estimated dummy coefficients

Institutional 43.70**(6.75)

Non-Institutional -43.71**(6.76)

Source: Self Estimates based on Various Reports of All India Debt and Investment Survey, RBI.Note: ** 5% Level of Significance.

was arrested rising to 64 per cent in 1991.During the following decade, the sharedeclined by about 7 per cent points andreached 57 per cent in 2002. It seems thatcredit cooperatives, commercial banks, andother formal financial sector programmes inrural areas have not displaced informal sourcesof credit, altogether. The 2002 AIDIS revealedthat 43 per cent of rural households continueto rely on informal finance, which includesprofessional moneylenders, agriculturalmoneylenders, traders, relatives and friends,and others. Thus, it remains questionablewhether various government measures likenationalisation of banks (1969), coverageexpansion through introduction of RegionalRural Bank (RRB) and lead bank scheme,financial sector reform (1991) andtransforming banking structure from traditionalbrick-and-mortar branches to mechanisedbanking through technological upgradationare successful for transformation of bankingstructure from class banking to mass banking.



22

Tab

le 3

: D

escr

ipti

ve S

tati

stic

s o

f S

har

e o

f In

stit

uti

on

al a

nd

No

n-i

nst

itu

tio

nal

Fin

ance

Am

on

g V

ario

us

Sta

tes

of

Ind

ia

BH

JK

KM

OP

RSt

atis

-A

GU

AH

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Tiken Das


23

Table 3 shows that during the period of1961-2002, the share of institutional credit was80 per cent on an average in Maharashtrawhich is highest amongst all States in Indiawhereas it is lowest (29 per cent) in AndhraPradesh. Likewise, during the same timeperiod, the share of non-institutional creditwas lowest in Maharashtra and highest inAndhra Pradesh. This raises the questionwhether domination of informal financeprovided an opportunity to develop micro-finance programme in India which is evidentfrom Andhra Pradesh, most successful Statefor providing financial facilities to poor peopleby micro-finance programme.

Depth of Informal Finance Among RuralPeople in Border Area

Individual respondents were askedabout the amount of money they borrowedfrom different informal sources, in the absenceof access to formal financial markets. Out ofthe 201 respondents, 186 (93 per cent)responded that they borrowed money. The rest15 (8.06 per cent) have not borrowed anymoney and 11 of them from Manipur nearMoreh where business and job opportunitiesexist. Is it a consequence of ‘Look East Policy’?The biggest number of loans is from relativesand friends (36.56 per cent) [Fig.1] and it istrue mainly about Mizoram that accounts for27 (75 per cent) out of 36 such cases. Bank

loans are almost non-existent; only 7 (4 percent) [Fig.1] cases were reported out of 186respondents, who borrowed money frombanks. It indicates the failure of formalagencies to introduce flexible products andservices for rural masses. One sees smallcooperatives emerging in two villages ofManipur while as many as 31 (11.83 per cent)persons from among the Wangcho inArunachal Pradesh have borrowed moneyfrom self-help groups. Moneylenders are notyet a menace that they are in the rest of Indiabut one notices a beginning of it in all fourStates, particularly among the Wangchoa.There is a regional difference in the patternof moneylending but with somecommonalities. Formal banking systems areall but non-existent. Where they exist they donot reach the poor. That pushes them into thehands of local informal sources, many ofwhom are merchants who come outside toexploit them. The fact that most lending is byrelatives and friends may not change thesituation much because studies indicate thatmuch land is alienated within the tribe torelatives and friends who lend money in anemergency and get land at a low price(Fernandes & Pereira, 2005). It was found that31.18 per cent respondents borrowed moneywithin the range of `1000-5000 [Fig.2], andit indicates the domination of small borrowersin the study area. On the other side, 19.89 percent of the respondents borrowed money

Fig. 1: Percentage Distribution of Borrowers Borrowed from Different Sources

Corporation

Banks

PHRY

SHGs

Moneylenders

NGO

Relatives and friends



24

within the range of ` 60,000-1,00,000 whichindicates the presence of some businessmen,may be from outside. Field note indicates thatmost of them paid high interest and becauseof the delicate nature of the issue they do notwant to divulge it. Field notes also indicatethat most of those who paid less than 10 percent [Fig.3] interest refer to monthly interestpaid to local moneylenders and merchants. Inother words, their annual rate is high. Thosewho paid 10-15 per cent interest are personswho got loans from the SHGs, AIDA and othercivil society groups. But one significant pointis that 33.87 per cent respondents do not payany interest while repaying loans. This may bebecause most of the borrowers borrowedmoney from relatives and friends. The amount

of interest paid gives an indication of the typeof problems faced by the people because ofthe loan. Out of 77 (41.39 per cent) personswho faced some problems while or beforerepaying the loan, 36 (46.75 per cent) lostsome of their land. Most of it happened notwhile repaying the loan but while borrowingthe money which was mainly for healthemergencies or to send children away forcollege studies. 24 (31.16 per cent) borrowerslost other assets and 17 (22.08 per cent) otherswere forced to give free labour under businesspersons and moneylenders. Moneylenderswho exploit them are coming slowly into theirvillages. Their number is bound to grow ifalternatives are not provided.

Fig. 2 : Percentage Distribution of Borrowers Borrowed Various Amounts of Money

60000-100000035000-600000

20000-3500010000-20000

5000-100001000-5000

500-1000

Fig. 3 : Percentage Distribution of Borrowers Paying Different Rates of Interest

Zero per cent

Fiftgeen to twenty per cent

Ten to fifteen per cent

Below ten per cent

Tiken Das


25

Factors Influencing Borrowing Decision

What induces rural household to borrowmoney? It was found from the field note thatrural people borrow money to finance theirlow income, children’s education, for medicalcheckups, for daily needs, etc. Apart from that,they borrowed money for agriculture andsmall business, for marketing their products,to compensate their land lost, to getgovernment job and it provides an indicationwhether there is a link between their

employment and money borrowed i.e. withself-employment. Incorporating the abovementioned explanatory factors, a logisticregression model has been developed toexplain the decision of borrowing money ofsample households.

Construction of the Variables-DependentVariable : Decision of borrowing money (Y):whether the particular family borrowedmoney or not.

Table 4: Explanatory Variables Included in the Regression Model for BorrowingDecision

Explanatory Notation Definition ExpectedVariables Sign

Income INCb” Income of the family in terms of rupees -

Dependent DMFb” Number of dependent members in the family +members

Education EoEb” Expenditure of the family in education per month +expenses

Medical EoMb” Medical expenditure of the family per month +expenses

Occupations AGRb” Dummy variable, D=1 if the household depends on +agriculture and 0, otherwise

Small business SBUSb” Dummy variable, D=1 if the family depends on +small business and 0, otherwise

Land lost LLOSb” Dummy variable, D= 1 if the family lost any +land and 0, otherwise

Education E1b”,

E2i,

E3b”

Education of the respondent. Illiteracy has +/-dummies been taken as reference category, thus

E1=

1 if primary education, 0 otherwise;

E2= 1 if upper primary, 0 otherwise;

E3= 1 if matriculate and undergraduate,

0 otherwise



26

Pi = 1 if the family borrow money

= 0 otherwise

Ui is well behaved error term

Here, { is the odd ratio in favour of money

borrowed i.e., the ratio of the probability thatthe household borrowed money to theprobability that the household did not borrowmoney. Each slope coefficient in the aboveequation is a partial slope coefficient andmeasures the change in the estimated Logitfor a unit change in the value of the givenregressor (holding other repressors constant).Finally, the maximum likelihood estimates ofthe parameters have been obtained using SPSS16. Results of the regression analysis havebeen summarised in Tables 5 & 6. In Table 5,classification tablea presents the results withonly the constant included before anycoefficients are entered into the equation. TheTable suggests that if we knew nothing of ourvariables and guessed that a household wouldnot borrow money, we would be correct 60.2per cent of time. Likewise, classification tableb

shows how the classification error rate haschanged from the original 60.2 per cent. Byadding the variables we can now predict with100 per cent accuracy. We have used Omnibustests of model coefficients to test whether theconstant only model is good fitting model ornot. Here; H

0= the model is a good fitting model,

H1=

the model is not a good fitting model (i.e.the predictors have a significant effect).

Here Yi = 1, if the household borrowed

money and Yi = 0, if the household has not

borrowed money.

Explanatory Variables: Table 4 shows theexplanatory variables included in the modelalong with their notations, definitions and theexpected signs of the coefficients.

Functional Specification of the Model : Sincethe dependent variable is dichotomous, wecannot predict a numerical value for it usinglogistic regression, so the usual regressionleast squares deviation criteria for best fitapproach of minimising error around the lineof best fit is inappropriate. Instead, logisticregression employs binomial probabilitytheory in which there are only two values topredict: that probability (p) is 1 rather than 0,i.e. the event/person belongs to one grouprather than the other. Logistic regression formsa best fitting equation or function using themaximum likelihood method, whichmaximises the probability of classifying theobserved data into the appropriate categorygiven the regression coefficients.Incorporating the explanatory variablesmentioned above, the logistic regressionmodel can be fitted as follows.

Li = = β

0 + β

1INCb” + β

2DMFb” +

β3EoEb” + β

4EoMb” + β

5AGRb” + β

6SBUSb” +

β7LLOSb” + β

8E

1b” + β

9E

2i + β

10E

3b” + Ub”

Where,

Li is the Logit function

Tiken Das


27

In our case model chi square has 10degrees of freedom, a value of 270.22 and aprobability of p<0.000. Thus, the indication isthat the model has a poor fit, with the modelcontaining only the constant indicating thatthe predictors do have a significant effect andcreate essentially a different model, whereasNagelkerke R2 in our case it is 0.783, indicatinga moderately strong relationship of 78.3 per

cent between the predictors and theprediction. Apart from that, H-L goodness-of-fit test statistic is greater than .05, as we wantfor well-fitting models; we fail to reject thenull hypothesis that there is no differencebetween observed and model-predictedvalues, implying that the models estimates fitthe data at an acceptable level.

Table 5: Classification Table and Goodness of Fit of Logistic Regression Model

Classification tablea

Observed Predicted Decision to borrow money Percentage correct

Money not borrowed Money borrowedDecision to borrow money 0 80 .0

0 121 100.0Overall percentage —- —- 60.2

Classification tableb

PredictedDecision to borrow money Percentage correct

ObservedMoney not borrowed Money borrowed

Decision to borrow money 80 0 100.00 121 100.0

Overall percentage —- —- 100.0

Omnibus test of model coefficients

Chi-square df Sig.270.223 10 .000

Goodness of fit by Cox & Snell, Nagelkerke R2

-2Log Likelihood Cox & Snell R2 Nagelkerke R2

150.00 .539 .783

Hosmer and Lemeshow test

Chi- square dj Sig.85.00 5 .630



28

In Table 6 Wald statistic and associatedprobabilities provide an index of thesignificance of each predictor in the equation.Here variable ‘dependent members’ in thefamily and one dummy coefficient ‘E

3i’ are not

showing statistically significant. Thus these twovariables do not provide any influence inborrowing money. Besides, all the variablesbear the expected signs. From the value ofEXP (â) we can say that family expenditure onmedical purposes influences more forborrowing money. Hence when medicalexpenditure is raised by one unit, the odd ratiois 13.81 times as large and therefore,householders are 13.81 more times likely tobelong to the category of money borrowing.Likewise, expenditure on children’s educationshowing as one unit increase in expenditureon education leads that householders are10.08 more times likely to belong to thecategory of money borrowing. Thus, peopleare borrowing more money for financing theirchildren’s education and health. This questionsthe government social sector policy, speciallyeducation and health policy. The third largestinfluencing factor is whether the family’soccupation is agriculture or not and it indicatesone unit increase of family whose occupation

is agriculture, leads to 9.05 more times likelyto belong to the category of money borrowingin comparison to non-agriculture occupationfamilies. Similarly, one unit increase in thenumber of family members whose primaryoccupation is small business, leads to 6.04more times likely to belong to the category ofmoney borrowing compared to other families.These findings provide an indication whetherthe rural family ’s employment or self-employment is related with money borrowing.One significant point is that the families whohave lost their land because of some reasonswere forced to borrow money for theirlivelihood. There may be a signal of viciouscircle i.e. borrowing money � land loss �borrowing money. As per expectation, thevariable income of the family is negativelyrelated with money borrowing. This indicatesthat most of the people borrow money forfinancing their daily needs. In addition, theresults of education dummies show that oneunit increase of household where the head offamily’s education is primary level, leads to.020 more times likely not belong to thecategory of money borrowing in comparisonto illiterate household, whereas one unitincrease in the number of respondents with

Table 6: Wald Test and its Significance Value

Variables β’s S.E Wald df Sig. Exp(β)

INCb” -.005 3.81 6.53 1 .032 .070

DMFb” .010 10.71 8.32 1 .132 3.01

EoEb” .083 60.90 4.26 1 .042 10.08

EoMb” 2.626 2.25 10.98 1 .053 13.81

AGRb” 22.926 1.01 5.72 1 .001 9.05

SBUSb” 33.304 2.62 12.47 1 .023 6.04

LLOSb” 7.56 2.73 7.58 1 .000 2.92

E1b”

-3.89 9.14 3.42 1 .001 .020

E2i

-.741 8.38 11.39 1 .004 .099

E3b”

.950 9.79 17.38 1 .350 4.38

Constant -50.85 2.10 14.13 1 .052 .000

Tiken Das


29

upper primary education leads to .099 moretimes likely not belong to the category ofmoney borrowing in comparison to illiteratehousehold.

Conclusions with Policy Implications

The continuance of domination ofinformal finance in rural areas has questionedthe measures the government of India havetaken during various times to reach theunreached people. The beginning of planperiod (around 70 per cent of rural credit) andlead to the suggestion that any realistic systemof rural credit should seek to incorporate himin itself rather than compete with him orwishfully expect to eliminate him. Onesignificant point is that in north east India,unlike formal institutions, the communitybased traditional financial institutions areworking successfully and this raises thequestion what is the story behind their success.During the period of 1961-2002 AndhraPradesh has shown highest share of non-institutional finance and indicated the positivelink between domination of informal financeand progress of micro-finance programme. Instudy area we found that out of 186households, only seven households borrowedmoney from formal sources which shows howinformal sources are dominating in these

places. Most of the borrowers borrowedmoney from relatives and friends, and they aresmall borrowers to fulfill their small needs. Thefact that most of the lending is by relativesand friends may not change the situation muchbecause studies indicate that much land isalienated within the tribe to relatives andfriends who lend money in an emergency andget land at a low price. One important point isthat out of the total number of householdswho borrowed money (8.06 per cent), 83.33per cent are from Manipur near Moreh wherebusiness and job opportunities exist.

Apart from that, people are borrowingmore money for financing their children’seducation and health. This questions thegovernment social sector policy, speciallyeducation and health policy. In addition, mostof the people are borrowing money forfinancing their livelihood, i.e. agriculture, smallbusiness etc. These findings provide anindication whether the rural family ’semployment or self-employment is relatedwith money borrowing. One significant pointis that the families who have lost their landbecause of some reasons are forced to borrowmoney for their livelihood. There may be avicious circle of sustainable livelihood i.e.borrowing money � land loss � borrowingmoney.

References

1. Basu, Priya (2006), Improving Access to Finance for India’s Rural Poor, The World Bank,Directions in Development (36448).

2. GoI (2010), Report of the Task Force on Credit Related Issues of Farmers (Chairman: U.C.Sarangi), Submitted to the Ministry of Agriculture, Government of India, June.

3. NABARD (2013), Status of Microfinance in India 2011-12.

4. Pradhan, Narayan Chandra (2013), Persistence of Informal Credit in Rural India-Evidencefrom ‘All-India Debt and Investment Survey’ and Beyond, WPS (DEPR): 05/2013, RBIWorking Paper Series, Department of Economic and Policy Research.

5. RBI (1954), All India Rural Credit Survey, Bombay, Reserve Bank of India.

6. RBI (1965), All India Rural Debt and Investment Survey 1961-62, RBI Bulletin, September,Bombay, Reserve Bank of India.



30

7. RBI (1977), All India Debt and Investment Survey- Cash Dues Outstanding Against RuralHouseholds as on June 31, 1971, Bombay, Reserve Bank of India.

8. RBI (1987), All India Debt and Investment Survey 1981-82, Assets and Liabilities ofHouseholds as on June 30, 1981, Bombay, Reserve Bank of India.

9. RBI (1999), All India Debt and Investment Survey 1991-92- Salient Features, RBI Bulletin,May, Bombay, Reserve Bank of India.

10. RBI (2000), All India Debt and Investment Survey, 1991-92- Incidence of Indebtednessof Households, RBI Bulletin, February, Bombay, Reserve Bank of India.

11. RBI (2006), Report of the Technical Group to Review Legislation on Money Lending,(Chairman: S.C. Gupta), Mumbai, Reserve Bank of India.

12. Sharma, Abhijit (2011), An Exploratory Study on Traditional Financial Institutions of LowerAssam, Centre for Microfinance Research, Bankers Institute of Rural Development &Indian Institute of Bank Management, Guwahati.

Tiken Das


31

MICROFINANCE LIVELIHOOD INITIATIVESAND WOMEN EMPOWERMENT IN SELECTEDVILLAGES OF ANDHRA PRADESH

L. Vachya*

Introduction

Poverty has been one of the mostresearched areas in social sciences. However, alot of challenges lie in understanding povertyfrom the socio-political and social empowermentperspectives. This study has therefore, attemptedto bring forth those socio-political questions ofempowerment1 and capacity building, while alsoattempting to examine the measurableeconomic outcomes.

One way of understanding poverty is tolook at exclusion of the poor from access to

ABSTRACT

Developing countries have large number of their population living below povertyline. Therefore, it becomes imperative to formulate situation-specific poverty alleviationpolicies and programmes to generate a minimum level of income for rural poor, whoform a substantial percentage of national population in developing societies. In India,the Central and State governments initiated several programmes to eradicate poverty.Provision of microfinance is one such programme which gained importance in recenttimes. This programme has fully involved the SHGs (Self-Help Groups). Microfinancesystem can only supplement the role of banks and financial institutions to alleviatepoverty and unemployment in the country. In this process, the Central and Stategovernments introduced several women-specific schemes to empower them in generaland rural women in particular. In this connection there is a need to study the impact ofmicrofinance on rural women, whether the benefits of this developmental programmehave percolated down or not, particularly at micro level. This paper is an attempt to assessthe impact of microfinance on the changes in the level of income and employment ofmembers after joining the SHGs at micro level. To fulfill the above objective, six villages inthe three regions (Coastal Andhra, Rayalaseema and Telangana) of former united AndhraPradesh were taken up for an in-depth study. The major findings of the study are: (i)microfinance activities have altered the living condition of the SHG members, (ii) theseactivities have also contributed to social empowerment of women.

financial institutions, which were primarilymeant to foster the socio-economicdevelopment of masses, especially in thedeveloping economies. The panacea suggeststhat Bangladesh model of microfinanceeradicates poverty through the adoption offinancial inclusive policies. Microfinance is oftenconsidered as one of the most effective andflexible strategies in the fight against poverty. Itis sustainable and can be implemented on amassive scale necessary to respond to the urgentneeds of those subsisting on less than $1 a day.The last twenty years have shown that

* School of Economics, University of Hyderabad, Hyderabad-500 046. e-mail : [email protected]

The author is grateful to Prof. B. Kamaiah, School of Economics, University of Hyderabad, for his

support and guidance in conducting this study. Without his help this paper would not have been

completed.




32

microfinance is a proven development tool andcapable of providing sustainable livelihoods to avast number of the poor, particularly women(Ghate, 2007; David, 2004; Joy and Murthy, 2007;Karmakar, 2010). The 2005 State of the MicroCredit Summit Campaign reports thatmicrofinance institutions reached over 92 millionclients and benefited 333 million familymembers. The success of microfinancerepresents a paradigm shift in the developmentindustry. The poor are no longer to be treated asmere recipients of charity-but as customers tobe served. Women make up approximately 83per cent, or 66 million, of reported microfinanceclients. They not only make good clients but alsothey have a better track record than men inrepayment of loans. They are also key drivers ofdevelopment (Samuel et al. 2005; Sam Daley-Harris, 2005). Investing in women, literally, hasproved to be the most effective way to increaseindividual family expenditure on health andeducation, improved nutrition and food security,protection against emergencies, and begin theslow process of tackling the gender inequalitiesthat hinder development in many countriesaround the world. Funding for microfinanceprogrammes is set to increase further in the yearsto come (Bali Swain et al. 2009; Yunus, 2004).Microfinance is being promoted as a key povertyalleviation strategy to enable poor women andmen to cope with the adverse economic andsocial impacts of structural adjustment policiesand globalisation (Shetty, 2002; Frances, 2005;Satish, 2005).

It has been a challenge for the Indianbanking system to provide financial services toevery strata of society. At present, more than 50per cent of total population continues to beexcluded from its ambit. Such exclusions are dueto many direct and indirect reasons. Many studieshave substantiated the argument that most ofthe loans disbursed by commercial and regionalrural banks go to the economically well-offfarmers or to those who have considerableamount of assets (mostly landholdings). Those

who do not possess land or any other collateralassets seem to lose out to access credit. Post-1990s, many developing countries haveembarked on deregulating their financialsystems and transforming their financialinstitutions into effective intermediaries toextend viable financial services to all segmentsof the population. These services all over theworld are being provided through group-baseddelivery mechanisms, which have the twinadvantages of low transaction costs and the useof joint liability as social collateral. Presently, self-help group bank linkage programmes (SHG-BLP)are playing a major role in poverty alleviation inrural India. SHGs are fast emerging as a powerfultool for socio-economic empowerment of thepoor in the rural areas. In India, SHGs represent aunique approach to financial intermediation. Theapproach combines access to low-cost financialservices with a process of self-management anddevelopment for the women who are SHGmembers. SHGs are seen to confer many benefits,both economic and social. It enables women toexpand their savings and access to credit whichbanks are increasingly willing to lend. SHGs canalso be community platforms from whichwomen become active in village affairs, standfor local elections or take action to address socialor community issues. The present study attemptsto examine the socio-economic impact of theSHGs members. It examines how far theprogramme helped in raising the income andemployment levels of the rural women. In thelight of the above, the focus of the present studyis to identify the impact of microfinance onincome and employment levels of memberborrowers in three different regions of AndhraPradesh. With this broad issue in mind, thefollowing objectives were set for the study, toexplore socio-economic condition analysis ofSHG members in selected villages, to assess thechanges in the level of income and employmentof members after joining the group and finallyto search for the dynamics of women’sparticipation in the decisions made in thehouseholds.

L. Vachya


33

Review of Literature

The research works pertaining to the verytheme of inter-dependence betweenmicrofinance and some socio-economic factorsis very rich and it is imperative to sketch a briefnarrative of this literature. Deshmukh (2004) inhis study on the impact of SHGs in AndhraPradesh showed that there has been an increasein the economic activity, savings, income, andfinancial assets of the group members afterjoining SHGs. Apart from economic progress,joining SHGs has made the members sociallyaware. National Commission for Women (2004)reveals that 30 per cent of the householdsreported increase in assets after joining SHG,mainly in Rajasthan and Tamil Nadu. Wadiniale(2004) reported that SHG programme led to anincrease in monthly household income, 66 percent women converted their houses intopermanent structures. In addition, there was apositive impact in areas like health, social andcultural values. A survey of 29 districts (Thanas)in Bangladesh was undertaken by Khandker etal. (1998) for the World Bank and the BangladeshInstitute of Development Studies (BIDS). Theyconcluded that microfinance, as delivered byGrameen Bank, Bangladesh Rural AdvancementCommittee (BRAC) and Rural Development-12(RD-12), accelerated the shift from wageemployment in the informal rural sector to self-employment among the poor participants. Butthey added that absence of technologicaldevelopment slowed down the overall increasein production and employment. Some otherstudies, for instance, Rahman, et al. (2010), alsoassessed the impact of microfinance of ruraldevelopment schemes on the livelihoods of therural poor in Bangladesh. The results show thathousehold income, productivity of crops andlivestock, expenditure, and employmentincreased significantly due to the productive useof the finance availed of by the beneficiaries. Inthis study, the logit-model showed that socio-economic factors like age, number of familymembers in farming, total land size and clients’

ethics and morals of the clients had a positiveand significant influence on household income.They concluded that micro investmentprogramme brought about significant andpositive changes in the lives of the clients. Microinvestment programmes helped in significantlyreducing rural poverty. They recommended thatthe programme should be replicated in otherrural areas of Bangladesh, in order to accelerateeconomic activities of the poor. Such a finding isalso supported by Latif (2001), who measuredthe effects of microcredit on the householdsaving of Bangladeshi borrowers. He observedthat land size and family size had influence onconsumption and calorie intake of ruralBangladeshi households. The study found thatsaving-income ratio was significantly higher forthe participants than for the non-participants.Akter, et al. (2010) surveyed the impact ofcommunity-based organisation (CBO) loans onlivelihood improvement. The study foundsignificant improvements in the socio-economicconditions of the beneficiaries. These included;greater awareness, increase in family income,assets, better clothing and food intake andimproved sanitation. It was also found that thewomen participation in the household decisionmaking increased.

Kumar et al. (2008) explored the impactof microfinance on employment, income andempowerment in Himachal Pradesh. They foundthat the first round loan impact is observed inthe easy and timely availability of small amountof loans to rural poor women to meet their day-to-day urgent consumption requirements. Thishas been the single most remarkableaccomplishment of the formation of SHGs. Inaddition, the womenfolk felt more empowered,in terms of decision making at the householdlevel. The impact is that microfinanceempowered people and improved theireconomic condition, improved skills, enhancedproduction skills and increased income andemployment, and some people are educatedand started petty businesses, small-scaleindustries, etc.

Microfinance Livelihood Initiatives and Women Empowerment in Selected Villages of ...


34

An attempt to analyse the SHGs withspecial reference to social and economicempowerment was made by Puhazhendhi andSatyasai (2001). They observed that theinvolvement of the rural poor in SHGs significantlycontributed to their social empowerment, asmeasured by improvement in their confidence,their position within the family, improvedcommunication skills and other behaviouralchanges. They found that SHG, as an institutionalarrangement, could positively contribute to theeconomic and social empowerment of the ruralpoor and the impact on the latter was morepronounced than the former. Further, a fewstudies (Sharma 2001) focused on the role ofSHG in women empowerment and found thatsignificant changes in the living standard of SHGmembers have taken place, in terms of increasein income levels, assets, savings, borrowingcapacity and income generating activities.

The socio-economic empowerment ofwomen through SHGs was explored byVijayalakshmi and Valarmathi (2008). Theypointed out that poverty and empowerment arethe major problems in India, and they suggestedways to overcome these two problems throughmicrocredit, i.e., through SHG which is consideredas a potential instrument for combating povertyin a sustainable manner. They also found thatmany expressed the desire of an improvementin their level of income, assets, wealth, andstandard of living. The contribution made by themicrofinance programme initiated by SahyadriGrameen Bank in Thyagarthi village in Shimogadistrict of Karnataka was evaluated byRaghavendra (2001). The analyses revealed asignificant change among the group membersin diversifying income-generating economicactivities. The researcher found that themicrofinance programme was financiallysustainable. The members reported that they nomore borrowed from moneylenders. It was foundthat the members of the SHG formed by theforward community created their own capitalbase. It was observed that there was a great

potential for implementing various programmesfor the rural poor through SHGs.

The impact of SHGs on women in Goawas examined by Gaonkar (2001). The studyrevealed that SHGs had a lasting impact on thelives of the poor and their quality of life improvedin terms of increase in income, savings,consumption expenditure, self-confidence,productive use of free time, getting anopportunity to show hidden talents, and gettingmore importance in the family. He concludedthat the SHG movement could significantlycontribute towards the reduction of poverty andunemployment in the rural area. Accordingly, ithad its impact on decision making in householdmatters as well. As argued by Bali Swain et al.(2009), improvement in nutrition of children leadto greater efficiency in woman’s role in thehousehold but it also falls within the existingrole of women under the prevailing norms ofthe society. When a woman is better able toperform such activities, it leads to an increase inher self-confidence and feeling of well-being.This might create conditions leading to womanempowerment, but is not empowering on theirown. In their research paper, Dahiya et al. (2001)conducted a socio-economic analysis of theSHGs in Solan district of Himachal Pradesh. Thestudy found that there was a considerableincrease in annual income in the post-SHGperiod. The social impact was considerable withregard to empowerment of women, educationaldevelopment of children, and emancipation fromsocial evils like drunkenness of male householdmembers.

Scope of the Study : The basis of the concept ofmicrofinance is self-organisation of the poor atthe community level driven by a desire and aninherent capacity to improve their livingconditions by themselves. Microfinancerevolution in recent times played a pivotal rolein women empowerment and povertyalleviation, through SHGs increasing the incomeand employment. In this paper, some importantrelated literature on microfinance and its impact

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35

on income and employment generation isreviewed. A number of studies have so far beenundertaken to examine the SHGs and theirperformance in various parts of India and abroad.Several authors demonstrated how SHGs areplaying an important role in extendingmicrofinance to the rural poor. The functioningof SHGs is based on participatory mechanismand the impact of SHGs on its members, in termsof empowerment, accessibility to credit, socio-economic change, etc., has been found to bepositive. A few studies have been undertaken,to assess the impact of microfinance on thesocio-economic empowerment. Several studiesdiscussed in this section systematically analysedthe various aspects of microfinance. Whileanswering the various aspects of microfinance,the borrower’s viewpoint is frequently missing.There is hardly any study which deals with thevarious forms of microfinancing or SHGs andtheir sustainability aspects, except a studyconducted by NABARD and a few others. It istrue that the concept of SHG itself is a very recentone and hence it is quite possible that in futuremany studies might emerge. This is a testableproposition for the State of Andhra Pradesh inIndia since it has been witnessing a massivegrowth in SHGs in last few years. This study isundertaken to analyse the structure, conductand performance of self-help groups and theirimpact on women in six villages of AndhraPradesh. This paper will seek to bridge the gapsin the empirical literature about the seeminglylittle or no evidence on the effectiveness andthe impact of microfinance on income andemployment levels of member borrowers inthree different regions of the erstwhile State ofAndhra Pradesh. This paper will, therefore,contribute to the empirical literature on howfar the programme helped in raising the incomeand employment levels of rural women andexamine the process of women empowermentand changes in the economic status of SHGmembers, in particular, and rural women, ingeneral.

Data and Methodology

Geographically Andhra Pradesh2 isdivided into three distinct regions, namely,Coastal Andhra consisting of nine districts,Rayalaseema four districts and Telangana tendistricts. The coastal Andhra is socio-economicallymore advanced than the other two regions. Thestudy is based on primary data and used multi-stage stratified proportionate random samplingtechnique for selection of representativedistricts, mandals, villages and households. At thefirst instance, three districts, namely, EastGodavari, Chittoor and Karimnagar were chosenon the basis of one district from each region, i.e.,Coastal Andhra, Rayalaseema and Telangana onthe existence of highest number of SHGsrespectively. In the second stage, from eachdistrict, one mandal was selected on the basis ofhighest number of SHGs. These are Rajahmundry(Rural) from East Godavari district, Madanapallefrom Chittoor and Peddapally from Karminagar.In the third stage, from each mandal, two villageswere selected on the basis of highest and lowestsize of existing SHG. A total of six villages, namely,Torredu and Rajavolu from Rajahmundry Mandal,Chinnathippasamudram (CTM) and Ankisettipallifrom Madanapalle Mandal, and Raghavapoor andBrahmanpalle from Peddapalle Mandal wereselected. In the fourth stage, SHGs were selectedin consultation with VOs3. SHGs linked to thebanks for the past two years were selected forthe study, assuming that the benefits from theSHG-bank linkage programme would have beenfairly well-stabilised by then. In addition, 33 percent of total members were covered from eachselected village (covering not less than 40members from each village). At the fifth stage,SHG members were randomly selected fromeach of the selected SHGs. Members werestratified by random sampling, based on thegroup and caste from selected villages. The totalsample size is 582, out of which 176 areScheduled Castes/Scheduled Tribes (SC/STs), 242are Backward Castes (BCs) and 164 belong tothe Other Castes (OCs) community. The primary



36

data pertain to the year 2010-11. The study isbased on a field survey which was conductedduring the period May 1 to August 31, 2011.

Empirical Analysis : In the following section, abrief note on logit model is given. Since twodependent variables, namely, income/employment generation activity and womendecision making are qualitative variables, logisticregression model has been employed foranalysis of impact of some socio-economicfactors such as self-help group members’ age,education, marital status, annual income, typeof family, spouse education, spouse income,household income, frequently borrowing,borrowed loan amount and participating intraining programme by members, size of landand household assets which a play pivotal rolein empowering women members andpromoting employment/income generationactivities.

The Logit Model : Logistic regression wasproposed in the year 1970 as an extension oftraditional ordinary least square method and toovercome its limitation in incorporatingdichotomous or binary variables as dependentvariables. The logit models became extremelypopular in the fields of social sciences becausea large number of variables like gender, caste,marital or educational status, etc., coming underits domain are qualitative in nature. Socialscientists (Afifi and Clerk, 1990; Ryan, 1997; andTabachnick and Fidell, 2001) recognised andacknowledged the importance of logit modelas an useful alternative to linear regressionmodeling technique. A brief note on logit modelis given below.

Let us consider a binary response variableY and an explanatory factor, say, X

i. A logit model

predicts the log of odd ratio of Y frompredetermined variable X

i. A simple logit model

can be represented as:

= α + β x = log (odds) —(1)

Where e = probability (Y= outcome of

interest| X=x) =

Extending the logit model for multiplepredictors, a complex logistic model can beconstructed as follows-

= α + β1x

1 + β

2x

2 + …… + β

kx

k ——

(2)

Where e is the probability of occurrenceof the event and all β

i are slope coefficients

capturing marginal effect of each explanatoryvariable and á is intercept term. All theparameters are estimated by maximumlikelihood (ML) method. (e/1-e) is called odd ratioin favour of the event under consideration.Interpretations of all β

i coefficients are a little

tricky in logit models. For example,interpretation of coefficient β

2 in equation 2 is

as follows- a unit change in x2

will cause (eβ2 -1)*100 percentage change in odd ratio in favourof the event under consideration and the nextof the slope coefficients can be interpreted inthe similar way.

An attempt is made to examine theimpact of microfinance on income/employmentgeneration activity and women decision makingin household matters. More specifically, thissection explores the proximate determinants ofthe two dimensions mentioned above. Logisticregression model has been employed foranalysis of impact of some socio-economicvariables. Since income/employment generationand women decision making have been definedby the dummy variables, the study employedthe logit regression analysis as the OrdinaryLeast Squares (OLS) (multiple regression) is notappropriate to the present context.

Microfinance Impact on Income/EmploymentGeneration Activity : Microfinance helps thepoor in financial distress to become self-employed through rural non-farm sectoractivities of their choice. Moreover, microfinance

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is not limited to rural society or developingcountries only. But also it is equally applicable tosemi-urban areas and of developing anddeveloped countries. Unemployment andpoverty pose major challenges for anydeveloping country and India is no exception tothis phenomenon. This is because of the gapbetween demand for and supply of credit forthe poor to start economic activities. The ruralnon-farm employment has acquired greaterimportance in economic development in recenttimes. It plays a positive role to alleviate povertyand generation of employment. The growth ofthe rural non-farm sector had a positive impacton poverty alleviation and benefited manysections of the rural society. The growth of ruralnon-farm sector can provide assets, employmentand income to the rural poor. It also provides anopportunity for rural women to take upremunerative work beyond casual labour andhousehold labour.

The income/employment generationactivity has undergone changes after the SHGrespondents were exposed to the microfinanceactivities. It is observed that factors such as age,education, marital status, annual income, spouseeducation, household income, borrowed loanamount, training programme participation,household size of land and household assetsexert considerable influence on the membersgenerating income/employment. Accordingly, itis hypothesised that the income/ employmentgeneration by the SHG members is influencedby their age, marital status, spouse’s age, frequentborrowing, borrowed loan amount and trainingprogramme participation.

In the light of this background, thefollowing logit model is formulated:

ACTIVITYi= β

0+ β

1 SMAGE+ β

2 SMEDCN

+ β3 MSTATUS + β

4 SMINCOME + β

5 HEDCN

+ β6 HINCOME + β

7 LOANAMT + β

8TRAINING

+ β9 SIZEOFLAND+ β

10 HHASSETS+U

t —— (3)

In the above model, the variables aredefined as follows:

ACTIVITY= 1 if SHG members started income andemployment activity.

0 = otherwise

SMAGE= SHG members’ age (in years)SMEDCN= SHG members’ education (in years)MSTATUS= 1 if SHG member married

0 = otherwise (widows)

SMINCOME = SHG members’ yearly income

(in `)

HEDCN= SHG member’s spouse education

(in years)

HINCOME= SHG member’s householdincome (in `)

LOANAMT= SHG member borrowedloan amount (in ̀ )

TRAINING= 1= if SHG member participatingtraining programme

0 = otherwise

SIZEOFLAND = Size of landHHASSETS = Household assetsU

t= Error term

Microfinance Impact on Women DecisionMaking Proximate Determinants : The decisionmaking behaviour has undergone changes afterthe SHG respondents were exposed tomicrofinance activities. It is observed that factorssuch as age, marital status, spouse education andtraining exert considerable influence on thedecision making. Accordingly, it is hypothesisedthat the decision making by the SHG membersis influenced by factors such as, age, education,marital status, yearly income, type of family,spouse education, spouse income, householdincome, frequently borrowing, borrowed loanamount, participating in training programme etc.Accordingly, the following logit model isformulated:

DESMAKi= β

0+ β

1 SMAGE+β

2 SMEDCN

+ β 3 MSTATUS + β

4 SMINCOME + β

5FTYPE

+β 6HEDCN+ â

6 HINCOME + β

7 HHINCOME

+ β 8

NOOFTIMES + β 9

LOANAMT + β 10

T RAINING + Ut

—————— (4)



38

In the above model, the variables aredefined as follows:

DESIONMAK= 1 if decision taken by SHG memberin household matters

0 = otherwise

SMAGE= SHG members’ age (in years)

SMEDCN= SHG members’ education (in years)

MSTATUS = 1 if SHG member married

0 =otherwise

SMINCM= SHG members’ yearly income (in `)

FTYPE = 1 if size of family is more than 3.

=0, otherwise

HEDCN= SHG member’s spouse education(in years)

HINCOME= SHG member’s spouse income(in `)

HHINCOME = SHG member householdincome (`)

NOOFTIMES= frequency of taking loan(in times)

LOANAMT= Loan amount (in ̀ )

TRAINING= 1 if SHG member participating intraining programme

0 = otherwise

Ut= Error term

The above model is estimated through astep-wise procedure.

The following are the results obtainedfrom the estimated logit regression model.

Socio-economic Profile of the SampleMembers

An important aspect of the study is thesocio-economic profile of the members of theSHGs. For this study, total 582 members wereinterviewed with the help of comprehensivelystructured schedules. The socio-economic profileof the members include their age, marital status,

education, type of household, access status ofration cards, source of drinking water, source ofenergy, family size, husband’s education,landholdings of the members and their husbands.The study reports that majority of the membersof groups in all the selected villages are in theage groups between 26-35 and 36-45 years. Itshows that middle aged women join the groups,which are more productive age groups. The studyfound that nearly 88 per cent of members weremarried and are the important bread winners intheir families. The number was more inRaghavapur and less in Ankisettipalle village. Itwas the highest in the SC community and lowestin the ST community in all the selected villages.Similarly, the education level of the membersshows that nearly 68 per cent are literate andrest of the members are illiterate. It is observedthat the number of members having primaryeducation is quite large in the ST community,while none have acquired intermediate leveleducation in the ST community. Also, due tobeing married at a relatively younger age andweak financial conditions, many of them wereunable to continue with their education. Thestudy observed that, in all selected villages,majority of the member families are nuclear andthe percentage was highest in Raghavapur.Similarly, about 86 per cent of the membersreported that their spouses are the heads of thehousehold and only 11 per cent of the membersreported that they are managing the householdsthemselves. Interestingly, the study observes thatall the members own ration cards in all theselected villages. Out of them, about 99 per centof the members have white cards, while onlyone per cent of the members have pink (Sugarcard) card. Similarly, it is observed that nearly 70per cent of the members live in pucca houses,followed by 27 per cent in semi-pucca and only3 per cent live in kutcha houses. The study alsofinds that about 45 per cent of the membershave toilet facilities in their houses. Accessingtoilet facility is more common in Ankisettipalle,while it is less frequent in Raghavapur village.Non-availability of sanitary facilities is reported

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highest in ST families, followed by SC, BC, OCand minority communities. Same is the positionregarding the source of energy for cooking,where it is found that only 43.3 per cent of themembers have gas for cooking. It is alsoobserved that most of the SC and ST membersare dependent on firewood for cooking. Thefamily size of the household is less than fourmembers (76 per cent) in the selected villagesand it was registered highest in Rajavolu andlowest in Ankisettipalle village. The study findsthat 80 per cent of the members’ husbands areliterates. It had been observed that the membersand their husbands have different levels ofeducation in selected households and on anaggregate level, the SCs and BCs are moreilliterate when compared to OCs, STs and minoritycommunity members’ husbands. The fieldobservation also shows that husbands of STmembers have in general studied up tointermediate level, but in other community, they

have completed up to graduation, post-graduation or technical education. Thelandholding size of respondents’ families hadbeen reported that 43.3 per cent of themembers are landless and the remaining aremarginal, small and semi-medium farmers. Thelandholding size of respondent’s families hasbeen reported that majority of land owningfamilies belong to marginal, small and semi-medium and almost one-third of members arelandless. The members who do not have landsare more in Torredu and CTM villages, and lesserin Brahmanpalle, and Ankisettipalle villages. It isimportant to note that among all social groupmembers, minority members are at highestnumber followed by SCs, STs, BCs and OCs inselected villages.

Results and Discussion

The study formulated two equations[equation (3) and (4)] and estimated with logistic

Table 1: Estimates of Impact of Social and Economic Factors on Income/Employment Generation Activity

Variables Coefficient Std. Err P>Z

smage 0.0644 0.1455 0.658

smedcn -0.8505 0.2969 0.004

mstatus -1.8906 0.7208 0.009

smincome 0.0018 0.0001 0.000

hedcn -0.6965 0.3535 0.049

hincome -0.0000 0.0000 0.000

loanamt 0.0003 0.0000 0.001

training 0.0261 0.3020 0.931

sizeofland -0.1304 0.0956 0.172

hhassets 4.7100 0.0000 0.013

Cons 0.7597 1.3310 0.568

Number of observations 582

LR chi2 (10) 332.6900

Prob> chi2 0.0000

Pseudo R2 0.4210

Log likelihood 228.7764



40

regression technique since in both cases thedependent variables are qualitative in nature.When standard logistic regression technique isemployed for both equations, log of odd ratio infavour of the event under considerationbecomes the dependent variable in bothequations. For example, in the third equation,dependent variable is the log of odd ratio infavour of starting employment/income creationactivity against non-generation is the dependentvariable. Similarly, in the fourth equation, log ofodd ratio in favour of taking decision by themember in respect of household activities againstnon-taking decision. Estimates of equation (3)are given in Table 1.

The data in Table 1 show that out of tensocio-economic factors, seven are statisticallysignificant as the p values are less than 0.05 thusindicating impact income/employmentgenerating activity of the SHG member. From ourestimated logit model, the results of which arepresented in Table 1 analysis, the study foundthat seven socio-economic factors namely, SHGmember’s education (smedcn), marital status(mstatus), income (smincome), spouse education(hedcn), household income (hincome),borrowed loan amount (loanamt) and householdassets (hhasset) out of ten explanatory variableshave shown statistically significant impact onincome and employment generating activity ofthe SHG member. From the Table it can be inferredthat log of odd ratio in favour of income/employment generation goes up by 0.0644 unitsand 0.0018 units if age of SHG member increasesby one year and income of member goes up byone rupee, respectively. Age is one of theimportant variables which determines theexercise of authority among men and women. Itimplies that middle-aged women, those who arein the more productive age groups join the SHGsand get engaged in micro-enterprises. They aremore matured in decision making, and want totake up income-generating activities to increasethe income of their families. Age can be animportant factor in the decision taking ability of

women. An unmarried or newly marriedwoman may not have much maturity toconfidently manage domestic issues, especiallyfinancial ones. However, as she grows older andgains experience on her own, as well as byobserving others she can become an importantplayer in the decision-making process. Forinstance, a middle aged woman can play animportant role in, say, selecting her daughter-in-law. On the other hand, once she becomesfairly aged, there is every possibility that hermarried children may not give her the sameimportance which she enjoyed when they werestill unmarried. Increase in members’ householdincome has detrimental impact on log of oddratio in favour of employment/incomegeneration by 0.0000 units whereas one unitincrease in household asset has small positiveeffect of 4.7100 units on log of odd ratio. Apartfrom these, educational status of husband (hedn)of the SHG member has near-significant impacton income/employment generation. Similarly,if the concerned SHG member is married, thenlog of odd ratio in favour of the employmentactivity would be insignificant. If a member takesloan, there is a high probability of income/employment activity going up. Any programmedispensing credit normally will aim at ‘creditwidening’ by increasing the clientele base and‘credit deepening’ by enhancing the quantumof loans per borrower. It shows that microfinanceapproach to financing rural poor helped in both‘credit widening’ and also ‘credit deepening’. Itis also observed that among the social groupmembers who took loans more often, theirsaving is more and they repay more amountthrough higher monthly instalments. If themember participates in training programme,then the log of odd ratio in favour of member tostart income/employment generation activitywill go up by 0.0261 units. Training programme(as in the participating training programme) alsoinfluences the income and employmentgeneration activity. The microfinanceprogramme has made a positive impact on thegeneration of employment and income and it

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generated more employment for the member’shouseholds (Table 1).

In recent times, government andvoluntary organisations have beenendeavouring to tap the latent entrepreneurialtalents of women. To that end, a number oftraining capsules on subjects like agriculturalpractices, fisheries, poultry, weaving, candle-making, pickle-making, and basket-weaving etc.,are being organised for women. The intentionbehind such initiatives is to empower womenso that they can gain a greater degree of financialindependence. It should be logical to expect thatwith enhanced self-confidence and own income,women would be more articulate in expressingtheir views in household matters.

Other socio-economic factors are foundto be statistically significant. The members in theinitial stages normally borrow for consumptionpurpose. The members then slowly shift their

focus to income and employment generationpurpose, which require higher amount of loans.There is significant variation in level ofemployment of the member households afteravailing of microfinance and joining self-helpgroup-bank Linkage programmes. In short, thedata in Table 1 imply that the level ofemployment of the member householdsincreased after receiving benefits from themicrofinance programme. It concluded thatmicrofinance programme helped the womenparticipants to increase their contribution to thehousehold income. There is a substantialincrease in income of the participant womendue to adoption of the programme. Thecontribution to the family income helps womento become economically independent and adecision making in the household expenditure.Similarly, estimates of equation (4) are given inTable 2.

Table 2: Estimates of Impact of Social and Economic Factors on Decision Making

Variables Coefficient Std. Err P>Z

smemage -0.1826 0.1087 0.093

smemedcn -0.0485 0.2212 0.826

mstatus 0.8757 0.5230 0.094

smincome 0.0005 0.0001 0.000

ftype 0.5720 0.2257 0.011

hedcn -0.0200 0.2586 0.938

hincome 0.0004 0.0000 0.002

hhincome -0.0000 0.0000 0.000

nooftimes 0.0250 0.0896 0.779

loanamt 0.0010 0.0000 0.199

training 0.1858 0.2268 0.413

Cons 0.9307 0.9339 0.319

Number of observations 582

LR chi2 (11) 43.0000

Prob> chi2 0.0000

Pseudo R2 0.0561

Log likelihood -361.8331



42

A woman is an important player inhousehold matters. It is she who primarily runsthe kitchen and looks after the family members-her husband, children, younger siblings andelders (in case it is a joint family). Since she hasthe basic responsibility of running the household,she can logically expect to have a reasonablesay in matters concerning the family. The issuesmay include: food, clothing, education ofchildren, saving and expenditure to meet socialobligations. Furthermore, according to the SHGmembers, the subordinated role of women andunbalanced gender power relations in the studyarea are fast becoming things of the past, thanksto microfinance. In other words, access tomicrofinance brought substantial changes togender power relations at the household andcommunity levels. Indeed, the study finds thatthe gender power relations gaps are beingbridged because women who benefited frommicrofinance improved contribution tohousehold incomes and this earned themrespect among their male counterparts. Thus,their male counterparts now see women aspartners in decision making at all levels.

Microfinance through SHGs cancontribute to women empowerment.Empowerment is a slow and continuous processand the mere joining of SHG does not lead toempowerment. It takes some time to get thefull benefits of the programme. This study showsthat microfinance programme helped womenin increasing their economic empowerment andas a result, they started taking some householdfirm decisions independently. Table 2 presentsestimates of impact of social and economicfactors on decision-making. The results show thatout of eleven socio- economic factors, six havestatistically significant impact on decision makingof SHG members. Reported coefficients signifychange in log of odd ratio in favour of thedecision of members to participate in SHG dueto one unit change in each explanatory variable.The results show that if the concerned SHGmembers are married, then log of odd ratio in

favour of taking decision by the women will goup by 0.8750 units. Hence, marital status alsoplays an important role for the SHG members’decision making. Usually, married womendepend on their spouse’s income for meetingthe household expenses. A rural womannormally does not have a scope to earn moneywithout the permission of her in-laws. Now, withrapidly increasing cost of living both in rural andurban areas, it is becoming increasingly difficultto run a household with a single person’s income.In addition, when the family size is large, awoman may not be able to manage the familyexpenditure with her spouse’s income alone.Hence, after marriage, out of necessity, womentend to undertake some income-generatingactivities to enhance their family income.

The regression estimates in Table 2 showthat when SHG member’s income goes up byone unit (rupee), log of odd ratio in favour oftaking the decision by women will go upmarginally by 0.0005 units. In a family wherethe male is the sole earner, he may assert that healone is ‘empowered’ to decide on family issues(especially the financial ones). Today, financialstringency is forcing many women to startearning money. There is every possibility thatsuch women would feel that they should have areasonable say in household issues. Theirargument may be that since they too are earningmoney and are not solely dependent on the malemembers of the family, they should be allowedto have an important role in household matters.Similarly, log of odd ratio goes up by 0.5720 unitsif family size under consideration is large. Familyis usually of two types, joint family and nuclearfamily. Similarly, the study observes that most ofthe nuclear families have been having moremonthly saving than joint families. Log of oddratio also increases marginally by 0.0004 units ifhusbands’ income of the SHG membersincreases by one rupee. The level of income of afamily can have an important bearing on itsquality of life. When the income level is low, theexpenditure pattern should logically be

L. Vachya


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expected to be low as well (granted that thefamily does not take loans to meet its expensesand fall into further financial stringency).However, a high level of income does not alwaysguarantee a high quality of life. There is alwaysthe risk of the family indulging in wastefulexpenditure-at the cost of meeting the morepressing needs. A possible scenario is that inlow income families, most of the decisions are‘enforced’ ones- to primarily meet the pressingneeds. On the other hand, in a high incomefamily, women could enjoy a greater say inhousehold matters.

Further, if a member takes loanfrequently, then log of odd ratio in favour oftaking decision making will go up by 0.0250units. If the member’s borrowed loan amountgoes up by one rupee, then the log of odd ratioin favour of taking decision by the woman willinch up by 0.0010 units. The SHGs provide loansto their members for investing and to get accessto income-generating opportunities. Asemployment opportunities increase,automatically purchasing power will increasewhich in turn will lead to economic growth. Anamazing fact noticed in the recent decades isthat Financial Institutions are finding womenborrowers to be more diligent than their malecounterparts. No wonder, the success ofBangladesh’s Grameen Bank is largely attributedto the sincerity of its women clients. Anincreasing number of SHGs are reporting thatwomen in general tend to utilise the borrowedamounts for income generating activities.

The training of SHG members plays acrucial role because it is training which makesthem financially literate. In detail, undoubtedly,training plays an important role in sharinginformation, carrying out financial transactions,decision making and enhancing the bargainingpower of the SHG members. Compulsoryattendance of members in the training is one ofthe prerequisites for smooth functioning of thegroup. It is one of the indicators to ensure theactive participation of members in the business

of the group in a democratic manner. The resultsshow that the log of odd ratio in favour of takingdecision by the women will go up by 0.1858units when member is trained. Therefore,participation in training programme is animportant indicator of being practicallyempowered, especially when the SHG memberis sufficiently aware of the basic bankingoperations and is able to handle her savingsaccount on her own. The guideline of thegovernment is that members of the group shouldvisit the bank at least once in a month andminimum of two women members should gotogether to the bank. The rationale for such policyis that the women should not confine themselveswithin the four walls of the house and at thesame time should get some exposure to financialmatters. By this, they improve contacts, can voicetheir views and get some idea of financialtransactions.

SHG member household income and SHGmember annual income are found to affect logof odd ratio adversely. This finding is inconsonance with economic intuition, since amember of any well-off family will have lesserchance to join the SHG. Increase in loan amountaffects odd ratio adversely. Other variables likeSHG member education as well as spouseeducation status, frequency of taking loan, etc.,have no considerable impact on decision makingof SHG membership.

The Decision Maker of the Family: To understandwho is the decision maker in the family, datawere collected on purchase/sale of householdassets, family savings, children’s education andmarriage, using a 4-point scale, i.e., (i) SHGMember, (ii) Husband, (iii) Both ( Wife andHusband) and (iv) Joint Family Members. It iscommonly observed that in a family somedecisions are exclusively taken by the spousei.e., head of the family, while some are exclusivelytaken by the housewife, and the other decisionsare taken jointly by both spouse and wife. Thereare some other decisions, which are exclusivelytaken by other members in the family, like the



44

children, parents-in-law, married sons, etc. Of the582 members, at the time of joining the group,majority reported that household decisions weretaken by their husbands. Now, majority of themembers report that household decisions aretaken by them jointly with their husbands.

It reveals that women’s role in householddecisions increased after joining the SHGs. This isbecause of the experience gained by themembers by attending trainings and meetings.Undoubtedly, training and meeting plays animportant role in sharing information, carryingout financial transactions, decision making andenhancing the bargaining power of the SHGmembers. Compulsory attendance of membersin the training and meetings are one of theprerequisites for smooth functioning of thegroup. It is one of the indicators to ensure theactive participation of members in the businessof the group in a democratic manner. Table 3shows that out of 582 selected sample members,nearly 49 per cent have reported that householddecisions are taken jointly with their husbands,followed by 32 per cent who reported that theirhusbands generally take decisions in their family,12 per cent reported that household decisionsare taken by themselves (SHG members), and 7per cent of the members reported that usuallyall family members take the decisions together.According to the norms of the patriarchal society,it is usually the head or the male members of thehousehold who take certain key decisions. It wasfound that the increased participation of womenin the decision-making processes empowersthem in the true sense. Joint decision making inthe purchase of business items is observedamong all the members belonging to all thevillages under study. Thus, it can be concludedthat the involvement of spouse is more commonfor all types of decisions among all the groupmembers.

It is further observed from Table 3 thatamong all selected villages, the percentage ofmembers who reported that household decisionsare taken jointly with their husbands, is found to

be highest in Rajavolu (53 per cent) and lowestin Ankisettipalle village (38 per cent), while inRaghavapur, Brahmanpalle, CTM, and Torreduvillages they are 45.9, 45.2, 49.7 and 51 per cent,respectively. The joint decision making bywomen improved significantly in all aspectsinvestigated. The study found that in all theselected villages, majority of all social groupmembers reported that they make householddecisions jointly with their husbands in theirfamilies. The percentage is found to be highestamong the OCs, followed by the BCs. The OCand BC community members are relativelyliterate and economically sound in comparisonto the others. Further, it was observed that amongminority community members, majority of themreported that their household decisions aretaken by their husbands. It was also observedthat among all the selected villages, inRaghavapur, members playing a greater andindependent role in decision making regardingall matters related to their family is highest.

Analysis of Table 3 also reveals that highestpercentage of members whose husbandsgenerally take decisions in their family is inAnkisettipalle and lowest in Brahmanpalle village.However, a decreasing role of husbands isobserved in the process of decision making ascompared to earlier times. This is a positive trendin the process of women empowermentthrough income generation through SHG-banklinkage. It is also observed that in Ankisettipallevillage, the husbands of SC and minoritycommunity members played a major role indecision making as compared to othercommunities. Notably, among all the selectedvillages, highest joint family decisions are foundin Brahmanpalle village.

Overall, the participation of women indecision-making process increased after joiningthe SHGs. Further, it is informed that the socialindicators have been assumed to play animportant role in successful implementation ofvarious microfinance programmes. It is observedthat the impact of microfinance is relatively more

L. Vachya


45

pronounced on the social aspects than on thevalue of the economic aspects. Thus, it can beratified that social indictors have made asignificant impact on members’ decision making

by bringing a positive shift in their socialconditions. Further, the social indicators havebeen assumed to play an important role forsuccessful implementation of various

Table 3: Village-wise and Community-wise Frequency of Decision Making by SHG Members

Social Decision Name of the VillageGroups Making Raghavapur Brahmanpalle CTM Ankisettipalle Torredu Rajavolu Total

SC Self 5 (29.4) 2 (28.6) 3(12.5) 3(25.0) 12(16) 2(9.5) 27(17.3)Husband 6(35.3) 0 (0) 9(37.5) 5(41.6) 25(33.33) 8(38.1) 53(34.0)Jointly with 5(29.4) 3(42.8) 10(41.7) 2(16.7) 36(48) 10(47.6) 66(42.3)husbandJointly family 1(6.0) 2(28.6) 2(8.3) 2(16.7) 2(2.66) 1(4.8) 10(6.4)

Total 17 (100) 7 (100) 24 (100) 12 (100) 75 (100) 21 (100) 156 (100)

ST Self 0 (0) 0 (0) 1(11.1) 1(16.7) 0 (0) 0 (0) 2(10.0)Husband 1(100) 1(100) 5(55.6) 2(33.3) 2(100) 1(100) 12(60.0)Jointly with 0 (0) 0 (0) 3(33.3) 3(50.0) 0 (0) 0 (0) 6(30.0)husband

Total 1 (100) 1 (100) 9 (100) 6 (100) 2 (100) 1 (100) 20 (100)

BC Self 12(15.5) 4(12.9) 9(13.0) 2(11.8) 2(8.7) 3(12.0) 32(13.2)Husband 19(24.7) 8(25.8) 20(29.0) 5(29.4) 3(13.1) 4(16.0) 59(24.4)Jointly with 39(50.7) 15(48.4) 36(52.2) 8(47.0) 15(65.2) 16(64.0) 129(53.3)husbandJointly family 7 (9.1) 4(12.9) 4(5.8) 2(11.8) 3(13.0) 2(8.0) 22(9.1)

Total 77 (100) 31 (100) 69 (100) 17 (100) 23 (100) 25 (100) 242 (100)

OC Self 0 (0) 0 (0) 4(5.7) 1(8.3) 4(10.5) 2(11.1) 11(7.8)Husband 0 (0) 1(50) 20(28.7) 3(25.0) 12(31.6) 6(33.4) 42(29.8)Jointly with 1(100) 1(50) 44(62.8) 6(50.0) 20(52.6) 9(50) 81(57.4)husbandJointly family 0 (0) 0 (0) 2(2.8) 2(16.7) 2(5.3) 1(5.5) 7(5.0)

Total 1 (100) 2 (100) 70 (100) 12 (100) 38 (100) 18 (100) 141 (100)

Minority Husband 2(100) 0(0) 12(80) 3(100) 1(100) 1(100) 19(82.6)Jointly family 0(0) 1(100) 3(20) 0(0) 0(0) 0(0) 4(17.4)Total 2 (100) 1 (100) 15 (100) 3 (100) 1 (100) 1 (100) 23 (100)

All Self 17(17.3) 6(14.3) 17(9.1) 7(17.0) 18(13.0) 7(10.6) 72(12.4)Husband 28(28.6) 10(23.8) 66(35.3) 18(36.0) 43(31.0) 20(30.3) 185(31.8)Jointly with 45(45.9) 19(45.2) 93(49.7) 19(38.0) 71(51.0) 35(53.0) 282(48.5)husbandJointly family 8(8.2) 7(16.7) 11(5.9) 6(12.0) 7(5.0) 4(6.1) 43(7.3)

Total 98 (100) 42 (100) 187 (100) 50 (100) 139 (100) 66 (100) 582 (100)

Note: Figures in parentheses are percentages.



46

microfinance programmes. As virtually all thosewho participated in microfinance programmewere women, use of social indicators enabledthem to move ahead in their economic statuson a sustained basis.

Summary and Conclusion

The microfinance system brought aboutan unprecedented change in the lifestyles ofwomen in rural areas. The streamlining andeffective operation of credit system will entirelytransform rural lives. The empowerment throughSHGs would impact rural women by giving themimmense confidence to mould their lives andthat of their families. In this light, the presentstudy examined the financial status of womenin pre-and post-SHG scenarios and alsoinvestigated the impact of microfinancing onwomen. For the purpose, the study examinedvarious economic benefits to SHG members interms of increased asset creation, enhancedsaving and borrowing habits, increased income,higher degree of empowerment and improvedsocial lives. The empirical examination shows thatthe SHG-bank linkage programme has a positiveimpact on decision making by women inhousehold matters. In addition, it brought aboutchanges in their attitudes against social evils such

as gender discrimination and the dowry system,and it enabled them to advocate for equalproperty rights, education, the fair treatment ofgirls and widow remarriage. Empirical findingssuggest that income/employment generatingactivities, SHG member’s education, marital status,income, as well as their husbands’ education,household income, whereas the amount of loangranted, and total household assets are found toaffect the statistically significant impact onincome/employment generating activity. In caseof participation and decision making by womenmembers is positively influenced by SHGmembers’ age, marital status, income, family type,along with economic factors, have statisticallysignificant impact on decision making. The majorfindings of the study are that microfinanceactivities altered the living condition of the SHGmembers, and these activities also contributedto social empowerment of women. In the lightof these evidences, policy measures such asincrease in frequency of SHG meeting, SHGtraining programme, increase in loan amount andensuring effective utilisation of the loan, may bethe useful initiatives to enhance womenempowerment, income and employmentopportunities.

References

1. Afifi, A.A. and V Clerk, (1990), “Computer Aided Multivariate Analysis (2nd ed.)”, New York VaNostrand Reinhold.

2. Akter. R, M.A.Bashar and M.K. Majumder and Sonia B. Shahid (2010), “Effect of CommunityBased Organization Microcredit on Livelihood Improvement”, J. Bangladesh Agril.Univ. Vol.8.No.2, pp.277-282.

3. Bali Swain, Ranjula and Adel Varghese (2009), “Does Self Help Groups Participation Lead toAsset Creation”, World Development, Vol. 37, No. 10, pp. 1674-1682.

4. Dahiya, Prem Singh, N K Pandey and Anshuman Karol (2001), “Socio-Economic Evaluation ofSelf-Help Groups in Solan District of Himachal Pradesh: Impact, Issues and Policy Implications”,Indian Journal of Agricultural Economics, Vol. 56, No 3, pp. 486-487.

5. David, S Gibbons (2004), “Financing Micro Finance for Poverty Reduction”, Shelter, Vol. 7, No.1, pp. 57-60.

6. Deshmukh, Ranadive J (2004), “Women’s Self-help Groups in Andhra Pradesh: ParticipatoryPoverty Alleviation in Action”, World Bank, Washington DC.

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7. Gaonkar, Rekha R (2001), “Working and Impact of Self-Help Groups in Goa”, Indian Journal ofAgricultural Economics, Vol. 56, No. 3,p. 471.

8. Ghate, Prabhu (2007), Indian Micro Finance: The Challenges of Rapid Growth, Sage Publications,New Delhi.

9. Deshmukh Joy and Ranjani Murthy (2007), “Introduction: Linking the Triad”, in Burro et al. (Ed.),Microfinance, Poverty and Empowerment, Sage Publications, New Delhi.

10. Karmakar, K G (2010), Micro Finance in India, Sage Publications, New Delhi.

11. Khandker S R (1998), “Fighting Poverty with Micro Credit: Experience in Bangladesh”, OxfordUniversity Press.

12. Khandker, S R, S Hussain and K Zahed (1998), “Income and Employment Effects of MicrocreditProgrammes: Village-Level Evidence from Bangladesh”, Journal of Development Studies, Vol.35(2), pp.96-124.

13. Kumar, Virender, R K Sharma and H R Sharma (2008), “Impact of Micro Financing on Incomeand Employment: Micro Evidence from Himachal Pradesh”, Financing Agriculture: A NationalJournal of Agriculture and Rural Development, Vol.40 (1), pp.3-8.

14. Latif, M.A. (2001), “Income, Consumption and Poverty Impact of Infrastructure Development”,The Quarterly Journal of the Bangladesh Institute of Development Studies, Vol. 28, No. 3, pp. 1-36.

15. National Commission for Women (2004), “Effectiveness of Women SHG in Micro EnterpriseDevelopment in Rajasthan and Tamil Nadu”, New Delhi, 2004, pp.202

16. Puhazehdhi, V and Satya Sai (2001): “Economic and Social Empowerment of Rural Poor ThroughSelf-Help Groups”, Indian Journal of Agricultural Economics, Vol. 56, No. 3, pp.450-452.

17. Raghavendra, T S (2001), “Performance Evaluation of Self Help Groups: A Case Study of ThreeGroups in Shimoga District”, Indian Journal of Agricultural Economics, Vol. 56,No. 3, pp. 466-467.

18. Rahman Mizanur and Fariduddin Ahmad (2010), “Impact of Microfinance of IBBL on the RuralPoor’s Livelihood in Bangladesh: An Empirical Study”, International Journal of Islamic and MiddleEastern Finance and Management, Vol. 3. No. 2, pp. 168-190

19. Ryan, T.P (1997), Modern Regression Method, New York Wiley.

20. Onyuma Samuel O, and Albred Ouma Shema (2005),”Myths of Microfinance as a Panacea forPoverty Eradication and Women Empowerment”, Journal of Saving and Development, Vol.29.No.2, pp.199-222.

21. Sam, Daley-Harris (2005), “Microfinance Summit Campaign Reports”, Published by Washington,D.C

22. Satish, P (2005), “Mainstreaming of Indian Micro Finance”, Economic and Political Weekly, Vol.40, No. 17, pp. 1731-1739.

23. Sharma, K C (2001), “Micro Financing Through Self- Help Groups”, Indian Journal of AgriculturalEconomics, Vol.56, No. 3,pp. 460-461.



48

24. Shetty (2002), “Working and Impact of Rural Self-Help Groups and Other Forms of MicroFinancing”, Indian Journal of Agricultural Economics, Vol. 57, No. 1, pp. 31-34.

25. Sinha, Frances (2005), “Access, Use and Contribution of Micro Finance in India: Finding froma National Study”, Economic and Political Weekly, Vol. 40, No. 17, pp. 1714-1719.

26. Tabachnick, B.G. and L.S Fidell (2001), “Using Multivariate Statistics (3rd Edition)”, NeedhamHeights, MA, Allyn and Bacon.

27. Vijayalakshmi, R and G Valarmathi (2008), “Socio-Economic Empowerment of Women ThroughSelf Help Groups (SHGs)”,Daniel Lazar and P Palanichamy (Eds.), Microfinance and PovertyEradication: Indian and Global Experience, New Century Publication, pp. 243-255.

28. Wadilniale, Saroj.M (2004), “Slum Women Empowerment by SHG”, Social Welfare, Vol.51, No5,pp.31-35.

29. Yunus, M (2004), “Grameen Bank, Micro Credit and Millennium Development Goals”, Economicand Political Weekly, Vol. 39, No. 36, pp. 4077-4092.

Notes

1. Empowerment is a process of change by which individuals or groups gain power and abilityto take control over their lives. It involves access to resources, resulting in increasedparticipation in decision making and increased bargaining power, control over benefits,resources and own life, self-confidence, self-esteem, self-respect and well- being. Thus,‘empowerment’ is a multi-fold concept that includes economic, social and politicalempowerment.

2. Andhra Pradesh State is bifurcated into two States as Telangana and Andhra Pradesh in 2014.

3. For this purpose, the lists of SHGs were collected from VOs of each of the sampled villages.Separate lists of total SHGs, SHGs with two years of bank linkage and caste-wise SHGs wereconsidered. Out of these lists, those SHGs which existed for two years as on March 31, 2009were separated out to form the lists of SHGs as eligible for this study.

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RETAIL OPERATING MODELS IN INDIANAGRI-BUSINESS SECTOR

ABSTRACT

The emerging economies constitute approximately 80 per cent of the globalpopulation and 20 per cent of the world economy, are going through a transformationphase due to the effect of globalisation. The effect is visible not just in metros but inrural markets also, particularly in the retail sector in India. The paper focuses on thegrowth of rural retail in India and changing face of rural marketing. The study wasundertaken to know how the corporate giants tapped the latent potential hidden atthe bottom of pyramid. ITC has come up with an innovative idea of ‘Choupal Sagar’in rural areas of Madhya Pradesh; HUL is using its Project ‘Shakti’ initiative, leveragingwomen self- help groups to explore the rural market, which proves that big companiesare ready to experiment to gain more and more market share. These companiesunderstand that the success of organised retail in any country depends upondeveloping innovative retail formats according to the needs of that particular countryand not merely by imitating the successful formats of other countries. Following suit,in the race are major Indian players like Godrej ‘Aadhar’, DSCL’s ‘Haryali’, Cargill ‘Saathi’,Tata’s ‘KisanSansar’ etc.

Today looking at the point of saturation in the urban markets, it is wise to stepinto the rural markets to tap the vast unexploited potential at the bottom of Pyramid.So the basic rationale behind this paper is to understand the reason of venturing intothe retailing and to analyse the system of working of different models that areoperational in agri-business sector and rural marketing. This study also addressesthe factors which result in more number of footfalls like providing consultancyservices, assistance in shopping experience and providing a one-stop solution to theend consumers. Not only forward linkage but an integration of both forward andbackward linkage (like procurement of the farm produces thus giving a better earningchoice to the farmers) is the basic objective of any rural retail giant.

Vishwas Gupta*

* Assistant Professor, School of Business, Block 14, Lovely Professional University, NH-1,Phagwara, Punjab (India) 144411, [email protected]

“The farmer is the only man in our economywho buys everything at retail, sells everythingat wholesale, and pays the freight both ways”-Anonymous

Indian Retailing - An Overview

“There is nothing more powerful than anidea whose time has come” Victor Hugo rightly

said, the retail wave in India is about to sweepover every other wave. Retailing is gearing upin full vigour and now it is only wise towelcome this timely idea named “Retailing”.The key drivers fuelling the retailing boom inIndia are the growing Indian middle class(more than 300 million) with increasing




50 Vishwas Gupta

disposable income, constantly changing lifestyle and buying behaviour.

Indian organised retail market size isaround $330 billion, accounts for only 4 percent of the total retail sector. It is expected tobe more than 25 per cent in the next 10 years,a phenomenal increase in the share oforganised retail, an effect of globalisation. Theunorganised retail will not be able to servicethis growth in terms of logistics, supply chainmanagement, waste and consumer amenities.Further modernisation of retail is bound tohappen. Retail is the second largest industryand the second largest employer afteragriculture in India.

It’s very clear that Indian retail industryis basically an unorganised industry. But nowas the big corporates like Reliance, ITC, GodrejAgrovet Ltd., Tata, and DSCL venturing into theretailing (rural retailing), we can expect in thenear future retailing will be the most lucrativesector in India. Fortunes will be found at thebottom of the pyramid by tapping the ruralcommunities with retail models that suit theneeds of the new Indian consumer.

“Keys to success will be location,infrastructure and the technology required tomanage the supply chain (the back end)”

Indian Retail - Snapshots

� India tops the AT Kearney’s annual GlobalRetail Development Index (GRDI), as themost attractive market for retailinvestment.

� Indian retail market is the fifth largestretail destination globally.

� Retail contributes 10 per cent of the GDPand employs 8 per cent of Indianpopulation.

� Organised retail will create 2.5 millionjobs in the next five years.

� Organised retail currently employs anaverage of one person per 250 square feetof retail space.

� The annual compounded growth rate oforganised retail in the last three years is46.64 per cent.

� India has a young population of medianage 24 years, which spends more andsaves less, giving a huge opportunity forretailing.

Retail Market Market Size Estimated Growth Rate

Total Retail USD 330 Billion 9 per cent

Organised Retail USD 13 Billion 25-30 per cent

Phases of Retail Development in the Four Biggest Economies of the World

Indian retail is in the introductionor embryonic phase, China indevelopment stage, UK in maturity phaseand US in decline stage. This clearlyshows that Indian retail has sharp growthprospects, whereas US retaildevelopment is showing downwardstrend.


51Retail Operating Models in Indian Agri-Business Sector

Rural Retail Formats

Retailing in India can be broadlyclassified as organised and unorganisedretailing. Organised retailing can be furtherclassified into Rural Retailing and UrbanRetailing. More than 85 per cent of urbanretailing is from big metro cities.

The formats used in rural retailing by retailgiants come in three forms –

� Company Owned Company Operated(COCO)

� Franchisee Owned Franchisee Operated(FOFO)

� Company Owned Franchisee Operated(COFO)

Company Owned and Company Operated(COCO) : The company owns the retail outletsor the hubs under this category. As far as theoperations and management is concerned, thecompany itself also carries it. All the costs areborne by the company (ITC e choupal, TataKisanSansar, Godrej Agrovet’s-Aadhar andHaryali).

Franchisee Owned and Franchisee Operated(FOFO) : This type of retail outlets are owned

by the franchisee while as far as the operationsand management part is concerned it is alsodone by the franchisee. The franchisee has tobear all the costs required to open the outlet.The initial installation costs are also borne bythe franchisee. In some cases the companymay come forward to bear some part of thecosts (Mahindra Subhlabh).

Company Owned Franchisee Operated(COFO) : This type of retail outlets are ownedby the company but the day-to-day operationsare undertaken by the appointed franchisee.(Tata KisanSansar and Cargill Sathi)

A Brief Detail of Each of the Formats and thePlayers Involved :

1. Hub & Spoke -Company Owned andCompany Operated (COCO)- WorkingSystem

There is involvement of both the forward aswell as the backward linkage; Reverse tradingin case of ITC’s ‘e-chopal’ or DSCL’S ‘HariyaliKisanBazaar’ has proved to be a good source ofrevenue generation.

Evolution of Retail in India

Traditional FormatsItinerant Salesman

HaatsMelas

Mandis etc.

Established FormatsKirana Shops

Convenience /Departmental Stores /PDS / Fair Price Shops

/ PaanBeedi Shops

Emerging FormatsExclusive Retail Outlets

HypermarketInternet RetailSpecialty Malls

MultiplexesFast Food OutletsService Galleries


52

Forward Linkage: The Company uses its ownchannel in the villages to sell its own productsas well as products of many other companieswith which it has tied up. It sells FMCGproducts, consumer durables, automobiles,electronic goods etc., to the villagers right intheir villages. Through its newly launchedshopping malls in these rural areas (i.e. thechoupalsagars), the company tries to providea one-stop solution to the end consumersunder one roof. It is one of the best means offorward linkage in these rural areas. Throughthese malls ITC manages to provide a kind ofpleasing shopping ambience to even the verypoor. The farmer can shop here with fullassurance of value for his money.

Backward Linkage : The company procuresthe grains directly from the farmers withoutany involvement of the intermediaries. Thusit is able to pass on the price advantage to thefarmers. Here the sanchalak who is basically afarmer plays an important role in giving theprice related information to the farmers 12hours before selling hour. The price is normallyhigher than the Mandi prices, so farmers havea choice to exercise their own decision makingpower as to whom to sell their produce.

The sanchalak also gives informationregarding the quality standards to the farmerswho also take great care to maintain thementioned quality standards. Through thesanchalak the produces are brought to thehub or the purchase point, where the qualityof the produces are checked. To check thequality there are instruments like moisturemeter, hectoliter.

There is an electronic weighing balanceto measure the weight of the produces. If theproduces do not match the qualityspecifications, then the whole of the producesare returned. The purchased wheat is thengraded according to various grades. Thefarmers are paid across the counter right thenby the sanyojak who collects cash from the

company in the evening or afterwards. Thenthe different grades of wheat are bagged ingunny bags separately. Similar grades arestacked in similar stacks. The filled bags have adistinct colour to indicate the grade of thewheat it contains inside. The company providesgunny bags. Unloading charges are borne bythe company.

Characteristics of the Format : The farmers areable to know the prices 12 hours before thesale of their produce. The farmers are benefitedfrom accurate weighing, faster processingtime, and prompt payment, and from accessto a wide range of information, includingaccurate market price knowledge, and markettrends.

� The farmers get a higher price i.e. on anaverage 2.5 per cent higher than the dailyMandi price per unit sale of their produceto ITC.

� It empowers the village farmer i.e. thesanchalak with the responsibility ofassociating villagers of 4-5 villages for thepurpose of wheat procurement.

� The farmers are paid in cash across thecounter.

� The closeness with which they work, thesanchalak and the sanyojak are respecteda lot by the company personnel and thecompany personnel also know the farmersby their name.

Business Areas

a) Agri-Inputs : Seeds, Fertilisers, Pesticides,Insecticides

b) Outputs: Wheat, Soybean, Barley, Maize,Chilly, Fishes

c) Services: Soil testing, Price Information,Technology Dissemination, Internet facility

d) Non-Agri : Apparels, Consumer durables &FMCG, Energy-petroleum Products, Gas,Financial Services, Credit Facilities, Insurance.

Vishwas Gupta


53

2. Hub & Spoke - Company OwnedFranchisee Operated (COFO) - WorkingSystem

The company has one spoke connectedwith spokes which provides different agri-inputs and services to the farmers. The huband spoke have their own working areas butall spokes are regulated by the hub.Byintroducing the “Cargill; SaathiKrishisamadhanKendra” Cargill wants to tap the untapped ruralmarket. ‘SaathiKrishisamadhan Kendra’ isbased on the concept of franchisee models(Hub & Spoke) which works in the rural areasto procure as well as to supply the companyproducts to the rural customers.Agri-inputRetail outlets like ‘SaathiKrishiSamadhanKendra’ seeks to overcome the inefficienciesof rural market and aims to cater to all theoccupational needs of farmers under one roof.

Generally one hub is placed in onedistrict and 2-3 spokes to cover the area ofaround 25-30 kms. Where a farmer can availof all agricultural products and services, inaddition to the agricultural products andservices, SaathiKrishiSamadhan Kendra alsoprovide some other services like banking,petroleum products etc.

Retailing is a nascent industry in India:still in evolving phase. With numerous businesshouses entering in this business, competitionis picking up fast. Each one is coming up withnew strategies to entice the customers. Earliercompanies were trying to copy western modelin India but soon they realised a difference inconsumer preferences, they expect a differentkind of treatment and a feel of Indian’s, andthus new trends have started emerging in foodretailing business.

Characteristics of the Format

� To provide the farmer with a package ofinputs and services for optimum utilisationof balanced primary nutrients; plantprotection chemicals; water; seeds; post-

harvest services; and to develop a genuinepartnership with the farmer.

� To provide all agricultural inputs andservices to the farmer under one roof.

� To assist the farmers for better andprofitable agriculture.

� To provide quality products and servicesto farmers.

� To provide easy access to the market.

� To create new distribution channel foragri-inputs.

� To facilitate farmer by providing long term,low interest credit by becoming a part ofself-help group called Kisan SahyogParivar (KSP). To provide after salesservices.

� To extend output buy back ser vice(contract farming).

Business Areas

a) Agri-Inputs: Seeds, Fertilisers, Agro-chemicals, and Agri-implements.

b) Agronomy Services: Soil testing, Watertesting, Fertiliser testing, Training to the farmersabout new technology and information, andExtension services.

c) Training & Information: Training byagronomist, and Farm practice programmes.

d) Other Services: Banking services likeinsurance, and Crop insurance.

3. Hub & Spoke - Franchise OwnedFranchisee Operated (FOFO) - WorkingSystem

One hub is connected with severalspokes. Initially, hub and the spokes were tobe operated with the help of franchises.Franchises were expected to make hugeinvestments, and pay a non-refundable signup fees. Franchises were to reach the villages.The initial model’s project was approximately

Retail Operating Models in Indian Agri-Business Sector


54

1 crore rupees. This included almosteverything, the cost of property equipments,land for carrying out agri R&D, computers,tractors, other implements, office space andworking capital required. The sign-up feeswere rupees 5 lakh. This fee is non-refundable.Mahindra also had to invest to set up systemsand procedures. Mahindra expected a lot ofsign up by prospective dealers (franchises).

A wholly-owned subsidiary of tractormajor Mahindra Subhlabh came intoexistence to provide total farm solution to theIndian farmers. There were two aspects to thebusiness, one was the commodity trading andsecond one was to provide one-stop shop tothe farmers for all his needs. This is likeproviding total solution to almost all theproblems of Indian farmers. MSSL hasrevolutionised agri-business by aggregatingthe factors of production under the brandMahindra KrishiVihar through farmingsolutions specific to crop, region, and market.

Characteristics of the Format:

� To provide good quality agri-inputs andservices to the farmers.

� To provide quality assurance through farmsolutions.

� To develop a viable farm mechanisationsystem.

� To provide ser vices of financialinstitutions.

� To provide market access to the farmers.

� To remove the inefficiencies andproblems of agriculture.

� To provide commercial agriculturalextension by bi-weekly visits to thefarms.

� To provide commodity trading in finalproduce of the farmers.

Business Areas:

a) Agri-Inputs: Seeds , Fertilisers, Agro-chemicals, and Agri-implements.

b) Services: Soil testing, Water testing,Consultancy services, Insurance- cropinsurance, and Weather insurance.

Objectives of the Paper: All three formats ofHub & Spoke models are engaged in the ruralretail marketing through networking indifferent areas. The business areas are alsoalmost similar in all the formats. The farmersare involved directly or indirectly with all theformats. The farmers and other villagers aregetting several benefits from these conceptsadopted by different companies. Still, to knowthe accurate effectiveness and the efficiencylevel of these formats we have decided togauze the performance and effectiveness ofeach of the formats. We have decided thefollowing objectives:

To know the catchment areas of eachof the formats;To know the average footfallson weekdays and weekends; To know thequality of products available in each of theformats;To know the different services offeredat each of the formats; Do the farmers havethe sense of ownership at each of theformats?Are they comfortable with themanagement and day-to-day operationactivities (modus operandi) at each of theformats?Is there proper financing and costbearing facility available at each of theformats?Which of the formats has higher rateof success in terms of revenue generation?Which is more popular and why?What is thepossibility of transforming of these rural retailformats i.e. can the urban farmers get somebenefits from these formats? What are theinvestment opportunities for the othercorporate giants including foreign players inthis sector? What are the future prospects ofthese formats? and much more..

Since the topic is so much interestedand wide, various questions can be raised andresearched for a very longer period of time.So far we have focused only on the qualitativeaspects of the research and tried to measurethe effectiveness of each of the formats in

Vishwas Gupta


55

the form of (modified version of the 4 Ps ofMarketing) the popularity, the quality ofproducts available, the services offered andthe prices charged at each of the formats.

Hypotheses

H1:The average footfalls on weekdays are

equal

H2:There is no difference in the quality of

products available at each of the formats.

H3: There is no difference in the services

offered at each of the formats.

H4:The prices of the products are uniform at

each of the formats.

Methodology

The farmers do not know about theformat concept, yet they know the players

involved in these processes. So to gauge theeffectiveness of each of the formats we haveselected 45 farmers (15 from each format)randomly as per our convenience and firstobserved their activities without informingthem, after that we have discussed few issuesrelated to our research with them and got thestructured questionnaire filled. The survey wasconducted during April-May 2013. The samplewere selected from Bundi in Rajasthan (COCO),Moradabad in UP (COFO) and Sangli inMaharashtra (FOFO). Similar questionnaireswere administered to all the respondents ateach of the locations and almost samemethods were adopted to monitor theeffectiveness of the formats.

Steps Followed in Sample Selection

Levels Sampling Hierarchy Description

1 Primary Sampling Unit (PSU) Store Catchment Area2 Secondary Sampling Unit (SSU) Particular Village3 Tertiary Sampling Unit (TSU) Household4 Element Farmers as defined in the population

Analysis and Findings

Catchment area plays a significantrole in the market penetration. The greaterthe catchment area, the more difficult itis to establish the reach and it results intolesser proportion of footfalls out of thetotal population of the catchment area.So the conversion ratio also is very less inthis case. If the company is into backwardlinkage, then distance does play a greaterrole, as it would add to the transportationcosts.

So farmers would not prefer moredistant outlets to sell their produces. Therefore,the farmers generally prefer COCO, which islocated within tractable distance. It is also

The data were organised at overall aswell as format level. To test the validity of the

hypotheses, the one-way analysis of variancetechnique was applied to analyse the data.

easier for the company to provide consultancyservices to maximum farmers during peakseasons, and to develop a sense of trust amongthe people in smaller catchment area than inlarger catchment area.



56

From the graph it is understood thatmaximum footfalls during weekdays is seenin case of COCO (Choupal Sagar), followed byCOFO and FOFO. In case of Choupal Sagar, it isdoing backward linkage, so there are morenumber of footfalls. Location also plays animportant role in the number of footfalls. Asfar as farmers are concerned, they are readyto go to even far off places for quality productsfor their farming requirements. But as far astheir needs for FMCG goods are concerned,they would prefer nearer places and cheaperproducts.

Consumers rate COCO outlet very highand would prefer this to others. There aremany reasons behind such preference like -the ambience, the wide range of goodsavailable there; a good market for their ownproduces, branded and quality productsavailable. In case of COFO, though it is not

involved in backward linkage, still manyfarmers rate it second because of theambience, quality products, location andservices. FOFO is rated third mainly becauseof the narrow range of products, its location(which is far off ) and does not provide a one-stop solution. They have a very narrow rangeof products.

Results of Hypothesis testing (Footfall during weekdays - Across Formats)H

1 : The average footfalls on weekdays are equal

ANOVA

Footfall

Sum of Squares Df Mean Square F Sig.

Between Groups 1243552.133 2 621776.067 340.252 .000

Within Groups 21928.800 12 1827.400

Total 1265480.933 14

Descriptive

Footfall95% Confidence Interval for Mean

N Mean Std. Std. Lower Upper Minimum MaximumDeviation Error Bound Bound

1 5 781.00 66.558 29.766 698.36 863.64 700 880

2 5 241.20 27.289 12.204 207.32 275.08 200 275

3 5 118.00 17.536 7.842 96.23 139.77 100 140

Total 15 380.07 300.652 77.628 213.57 546.56 100 880

Vishwas Gupta


57

At 95 per cent Confidence level, thesignificance value is 0.00. Hence the nullhypothesis is rejected. We cannot confirm thatthe average footfalls on weekdays at each ofthe formats are equal, since the averagefootfall depends on catchment area covered

by the particular format, proximity of theparticular outlet to villagers and availability ofdaily need products. The COCO completes allthe requisite criteria and attracts the maximumnumber of customers followed by COFO.

Results of Hypothesis Testing (Product Quality - Across Formats)

H2: There is no difference in the quality of products available at each of the formats.

ANOVA

Quality

Sum of Squares df Mean Square F Sig.

Between Groups 84.933 2 42.467 19.586 .000Within Groups 91.067 42 2.168

Total 176.000 44

Descriptive

Quality

95% Confidence Interval for Mean


1 15 8.13 1.506 .389 7.30 8.97 5 102 15 6.07 1.486 .384 5.24 6.89 4 83 15 4.80 1.424 .368 4.01 5.59 2 8

Total 45 6.33 2.000 .298 5.73 6.93 2 10

At 95 per cent Confidence level, thesignificance value is 0.00. Hence the nullhypothesis is rejected. We cannot confirm thatthe average quality of the products at each ofthe formats is similar. As per the observation,all the formats have almost similar productsbut with different brands. There is much

difference in the ambience and layout of theformats which triggers the satisfaction levelof the customers and once again COCO formatwas the first choice among consumers due toits wide variety of the much superior qualityof the products.

Results of Hypothesis Testing (Services Offered - Across Formats)

H3: There is no difference in the services offered at each of the formats.

ANOVAServices


Between Groups 47.511 2 23.756 21.259 .000Within Groups 46.933 42 1.117

Total 94.444 44



58

Descriptive

Services

95% Confidence Interval for Mean


1 15 8.20 .862 .223 7.72 8.68 7 102 15 7.40 1.183 .306 6.74 8.06 5 93 15 5.73 1.100 .284 5.12 6.34 4 8

Total 45 7.11 1.465 .218 6.67 7.55 4 10

At 95 per cent Confidence level, thesignificance value is 0.00. Hence the nullhypothesis is rejected. We cannot confirm thatthe various services offered at each of theformats are same. COCO format provides

much better services to its customers like,price information, weather forecast to thefarmers, option to the farmers to sell their owncrops to the company against the fast cashfacility.

Results of Hypothesis Testing (Prices Charged - Across Formats)

H4: The prices of the products are uniform at each of the formats.

ANOVA

Prices


Between Groups .711 2 .356 .511 .603

Within Groups 29.200 42 .695

Total 29.911 44

Descriptive

Prices

95% Confidence Interval for MeanN Mean Std. Std. Lower Upper Minimum Maximum

Deviation Error Bound Bound

1 15 7.93 .799 .206 7.49 8.38 7 92 15 7.67 .724 .187 7.27 8.07 7 93 15 7.93 .961 .248 7.40 8.47 6 9

Total 45 7.84 .824 .123 7.60 8.09 6 9

At 95 per cent Confidence level, thesignificance value is greater than 0.50. Hencethe null hypothesis is accepted. We canconfirm that the prices offered for variousproducts at each of the formats are same.

Since most of the formats sell thebranded items at discounted prices to thefarmers, the prices are almost same.

Vishwas Gupta


59

Conclusions and Recommendations

A distinctive feature of organisedretailing in India is that it is largely an urbanphenomenon. Organised retail has been moresuccessful in metros and cities, more so in thesouth and west of India. The reasons for thisregional variation range from differences inconsumer buying behaviour to cost of real

estate and taxation laws. Nonetheless, the casefor Indian retailers to explore rural markets isstrong. Factoring the size of the ruralpopulation and agricultural income growth inrural India, the rural market is certainly anopportunity for retailers with an innovativeretail proposition. A clear indicator of thispotential is the current share of rural marketacross major categories of consumption.

Share in Retail Market:Urban vs. Rural (per cent)

Segment Rural Urban

Food 64 36

Clothing and Footwear 61 39

Misc. Consumer Goods 57 43

Consumer Durables 50 50

Consumer Services 44 56

Entertainment 33 67

Source: NSSO and KPMG Analysis.

Globalisation affected Indian retailindustry more than any other industry. But theeffect is more visible and talked about in theurban areas, and rural India is neglected fromall discussions even though majority of Indiastill lives in the rural areas. To reap the benefitsof the globalisation and to tap the rural marketpotential, the following are recommended:

1. Further development of the organisedretail sector, as it should lead to reducedrole of middlemen. The directprocurement route results in a greatermargin for the farmers, besides facilitatingthe creation of a market with multiplesellers and buyers meeting at sameplatform, eliminating the old fashion offew buyers and many sellers, thusresulting in monopoly after some time.

2. Farm incomes in India can double iforganised retail enhances farmerrealisations on food items from the

current 30 to 35 per cent of retail priceto the international norm of over 50 percent. Thus, rural retailing should beencouraged and supported.

3. Rural organised retailing (a tool of ruralmarketing) should be encouraged by thegovernment since rural retailing has amultiplier effect on the economy. It willlead to employment generation, settingup of food based processing industries,cool chain stores, other industries, whichin turn will increase the income of peoplein rural areas and will help in stoppingthe migration of people from rural tourban areas. Once migration stops, thepressure on cities would reduce, pollutionwould reduce, and the growingdisparities of income between rural andurban areas would decrease.

4. Government should act as a nodal agencyfor the development of rural retail, since



60

the fruits of globalisation should be sharedby everyone in the country.

5. The higher purchasing power in rural andsemi-urban areas has significantlymodified people’s lifestyle; for e.g. thesachet phenomenon is a thought to reachto the bottom of the pyramid. Lot ofpeople in rural India are just not willing tobuy a whole bottle of shampoo, but thatdoes not mean they won’t buy it. Thus,the key is in slicing the relevant customersegments and developing appropriateformats. If the specific needs ofconsumers are recognised, there wouldbe a considerable market expansion,which would divert a part of retailbusiness to rural areas and help inreducing rural-urban imbalance.

6. Easy and quick availability of finance atcompetitive rates is a key enabler forgrowth in retail. Retail space availabilityand costs are the issues to which probablythe only answer is the diversification ofretail business across the country insteadof concentrating on already saturatedmarkets.

7. Organised retailing just does not meanretailing by big companies and MNC’sonly. It is much more than that. Smalltraders, farmers, self-help groups etc., canjoin hands together, form a group anddevelop their own format of organised

retailing. They too can reap the benefitsof the effect of globalisation. Banks, micro-financial institutions and governmentshould come forward to encourage suchbusiness ventures, finance them anddisseminate information about the same.

8. FDI in retail should only be allowed withcertain mandatory clauses like, apercentage of investment in rural areas,procurement of the agricultural producefrom the local farmers (India), and settingup of cool chain storages in rural areas.

9. Not moving with time but moving aheadof time is the secret of today’s success.For that, one has to take the advantage ofthe various advances made in science andtechnology. Use of IT-computers shouldbe used to maintain the logistics and otherthings. Use of GIS (global informationsystem) and use of GPS (global positioningsystem), use of remote sensing should beencouraged in rural retailing.

Limitations: The study has the limitations asall the data were collected through multiplesources and other reported measures.Therefore, common method variance may bea major problem. Responses of individualsurvey items may not be truly independent asthere has been a chance of influence of groupmembership. From this perspective, it can besaid that more research is needed to examinethe generalisation of this study.

Bibliography

1. NargundkarRajendra, (2008), Marketing Research, Tata McGraw Hill Publishing CompanyLtd., New Delhi.

2. Raghuram G.& Rangaraj N., (2010), Logistics and Supply Chain Management, MacmillanIndia Ltd., New Delhi.

3. Berman Barry & Evans Joel R., (2010), Retail Management, Prentice Hall Of India, NewDelhi.

4. WhalinGeorge, (2001), Retail Success, Willoughby Press, U.S.

5. The Economic Times (India), Retail Knowledge Series.

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61

EFFICIENCY OF KHADI AND VILLAGEINDUSTRIES IN INDIA– DATA ENVELOPMENTAPPROACH (DEA)

M. Manonmani *

ABSTRACT

The measurement of efficiency of an industry is important both for the economictheorist and economic policy maker. If economic planning is to concern itself with particularindustries, it is important to know how far a given industry can be expected to increase itsoutput by simply increasing its efficiency, without absorbing further resources. Past studieshave shown that productivity can be raised by improving efficiency, which usually is aneglected source of productivity, without increasing the resource base or withoutdeveloping new technologies.

The major objective of this study was to analyse technical, scale, cost and allocativeefficiencies of Khadi and Village industries in India between 2000-01and 2010-11. Theefficiency scores were obtained by applying Data Envelopment Approach (DEA). It couldbe found that for the entire period, technical, scale, cost and allocative efficient DMUs(Decision Making Units) were more under variable return to scale (VRS) than under constantreturns to scale (CRS) production technology. Also it is very clear that inefficiency could bedue to the existence of either increasing or decreasing returns to scale.

* Professor, Department of Economics, Avinashilingam Institute for Home Science and Higher Education for Women,

Coimbatore – 43, Tamil Nadu.

Introduction

Khadi and Village Industries have beenrecognised as one of the most important meansfor providing better economic opportunities forthe people of a developing economy like India.The importance of Khadi was triggered byGandhiji in 1908, when he perceived that thechief cause of rural poverty was destruction ofspinning wheel. The ideology of Khadi and VillageIndustries was also popularised by MahatmaGandhi and dawned upon the imagination ofthe framers of our Constitution. Promotion ofKhadi and Village Industries was speciallymentioned in our Constitution as one of theDirective Principles of the State Policy. It wasrecognised that their labour-intensive industriescould mitigate unemployment and promote

self-sufficiency. The First Five Year Plan, therefore,laid special emphasis on small scale industriesincluding Khadi and Village industries with theobjective of providing additional employmentopportunities, mobilising resources of capital andskill and providing a more equitable distributionof income and wealth. The development of Khadiand Village Industries is emerging as a stock ofparamount opportunities, diffusion of skills tothe rural areas, alleviation of regional imbalancesand a better distribution of national incomeessential to achieve the egalitarian objective ofestablishing a social welfare state. Villageeconomy cannot be complete without theessential village industries.

The common characteristic found in bothKhadi and Village Industries is that they are labour




62

intensive in nature. In the wake ofindustrialisation, and the mechanisation of almostall processes, Khadi and Village industries aresuited to a labour surplus country likeIndia.Another advantage of Khadi and VillageIndustries is that they require little or no capitalto set up, thereby making them an economicallyviable option for the rural poor. This is an importantpoint with reference to India in view of its starkincome, regional and rural/urban inequalities.

Efficiency is a very important factor ofproductivity growth especially in developingeconomies, where resources are scarce andopportunities for developing and adopting bettertechnology have also started lately. Past studiesshowed that productivity can be raised byimproving efficiency, which usually is a neglectedsource for productivity, without increasing theresource base or without developing newtechnologies. In this regard the role played byKVI should not be neglected.

Though the Khadi and Village industry hasregistered a significant increase in terms ofproduction and sales even during globalisationperiod, it undoubtedly is facing a stiff competitionin the globalisation period.In other words it hasshown the signs of withstanding it withoutconsistency. But the Khadi and Village industrieshas a long way to go as it suffers from too muchof reliance on budgetary sources, lack of adoptingnew market techniques, lack of productinnovativeness, could not market the brandimage utilising the India’s national heritage, etc.Moreover, it has the potential to solve theunemployment problem of rural India to agreater extent. If we ignore the khadi and villageindustry, it is at our own risk. In order to bringthese industries on par with other manufacturingsectors, specifically it’s efficiency level will haveto be given weightage.

Methodology

Database of the Study : This study is based onsecondary data. The reference period chosen forthe study covers ten year period between 2000-

01 and 2010-11. The data were available onlyup to this period while the analysis wasmade.The basic data source of the studyincludes, Annual Survey of Industries (ASI)published by Central Statistical Organisation(CSO), Government of India relating to fixedcapital, net value added and number of workers.The variables- fixed capital and net value added- were normalised (excepting number ofworkers) by applying Gross Domestic Product(GDP) deflator in order to convert the actualfigures into real terms.For this purpose, GDP atcurrent and constant prices were obtained fromvarious issues of Economic Survey published byGovernment of India, Ministry of Finance andEconomic Division, New Delhi to calculate GDPdeflator.

Tools of Analysis - DEA Model : This study is mainlybased on input oriented DEA model.In the input-based measure, the technical efficiency of thefirm is evaluated by the extent to which all inputscould be proportionally reduced without areduction in the output. The input oriented CCRmodel (Charnes, Cooper, Rhodes, 1978) and BCCmodel ( Banker, Charnes and Cooper, 1984) areexplained below.

Technical Efficiency

(i) CCR Model ( based on constant returns toscale) : The efficiency measure for the DMU canbe calculated by solving the followingmathematical programming problem:

max h0(u,v) =

∑

∑

=

=S

iioi

S

rror

xv

Yu

1

1

………………………………. (1)

Subject to

M. Manonmani


63

∑

∑

=

=m

iji

S

rrjr

xiv

Yu

1

1o1, j 1,2......, j ,......, n...

......................................(2)

≤ =

ur ≤ 0, r = 1,2,...., s................... (3)vi ≥ 0, j = 1,2,...., m ....................(4)

where xij is the observed amount ofinput of the ith type of the DMU (xij> 0,

i = 1,2,......n, j = 1,2,.....n) and yrj = theobserved amount of output of the rth type forthe jth DMU (Yrj > 0, r = 1,2,.....s, j = 1,2,...n).

The variables ur, and vi are the weightsto be determined by the above programmingproblem. However, this problem has infinitenumber of solutions since if (u*, v*) is optimalthen for each positive scalar α (αu*, αv*) is alsooptimal. Following the Charnes - Coopertransformation (1962), one can select arepresentative solution (u,v) for which

∑m

i=1i iov x = 1 ........................... (5)

to obtain a linear programming problemthat is equivalent to the linear fractionalprogramming problem (1) - (4). Thus,denominator in the above efficiency measureh0 is set to equal one and the transformed linearproblem for DMU can be written.

max z0 = ∑=

S

rrorYu

1 ......................... (6)

Subject to

S mi i j

r rjr 1 r 1

v x 0 ,u Y

j 1, 2 , ..., n

........ . . . . . . . . . . . . . .(7 )

= =

≤−

=∑ ∑

∑=

=m

rioi xv

1

1 ...................... (8)

ur ≥ 0, r = 1,2,...., s ...................... (9)

vi 0, i = 1,2,...., m ...................... (10)

For the above linear programmingproblem, the dual can be written (for the givenDMU) as:

min z0 = Θo ..................... (11)

Subject to

∑=

n

jrjrY

1

λ ≥ yro‘ r = 1,2,...,s ................ (12)

Θoxio – ∑=

=≥n

jijj mix

1

,...,2,1,0λ

...................... (13)

λj ≥ 0, j = 1,2,...,n ...................... (14)

Both of the above linear problems yieldthe optimal solution Θ*, which is the efficiencyscore (so-called technical efficiency or CCRefficiency) for the particular DMU and repeatingthem for each DMUj , j = 1,2,....n efficiency scoresfor all of them are obtained. The value of Θ isalways less than or equal unity (since whentested, each particular DMU is constrained by itsown virtual input-output combination too).DMUs for which Θ* < 1 are relatively inefficientand those for which Θ* = 1 are relativelyefficient, having their virtual input-outputcombination points laying on the frontier. Thefrontier itself consists of linear facets spannedby efficient units of the data and the resultingfrontier production function (obtained with theimplicit constant returns to scale assumption)has no unknown parameters.

Efficiency of Khadi and Village Industries in India– Data Envelopment Approach (DEA)


64

(ii) BCC Model ( based on Constant Returns toScale ) : Since there are no constraints for theweights λ j , other than the positivity conditionsin the problem (11) - (14), it implies constantreturns to scale. For allowing variable returns toscale, it is necessary to add the convexitycondition for the weights, λ j, i.e. to include inthe model (11) - (14) the constraint:

..................... (15)

The resulting DEA model that exhibitsvariable returns to scale is called BCC model, afterBanker, Charnes and Cooper (1984). The input-oriented BCC model for the DMU0 can be writtenformally as:

min z0 = Θo ..................... (16)

Subject to

∑=

=≥n

jrorjr srYY

1

,...,2,1λ ..................... (17)

Θoxio – ∑=

=≥n

jjij mix

1

,...,2,1,0λ

..................... (18)

∑=

=n

jj

1

1λ ..................... (19)

λj ≥ 0, j = 1,2,...,n ..................... (20)

Running the above model for each DMU,the BCC efficiency scores are obtained (withsimilar interpretation of its values as in the CCRmodel). These scores are also called “puretechnical efficiency scores”, since they areobtained from the model that allows variablereturns to scale and hence eliminate the “scalepart” of the efficiency from the analysis.

Generally, for each DMU the CCR efficiency scorewill not exceed the BCC efficiency score, whatis intuitively clear since in the BCC model eachDMU is analysed “locally” (i.e. compared to thesubset of DMUs that operate in the same regionof returns to scale) rather than “globally”,

iii. Scale Efficiency : Following the scaleproperties of the above two models, (Cooper etal., 2000), the scale efficiency is defined asfollows. For a particular DMU, the scale efficiencyis defined as a ratio of its overall technicalefficiency score (measured by the CCR model)and pure technical efficiency score (measuredby the BCC model).

iv. Cost Efficiency : The standard measure of costefficiency is obtained via a two stage process. i)Estimate the minimum price-adjusted resourceusage given technological constraints, and (ii)compare this minimum to actual, observed costs.Cost efficiency can be measured if input pricesare available in addition to output and input data.Let x =(x1, ....xk) ε R+

k denotes a vector ofinputs and y = (y1, ....ym) ε R+

m denote vectorof outputs. Formally, the cost efficiency modelcan be specified as :

Minz,x ..................... (21)

s.t. z.Y ≤ y0

z.x x0

zi 0

where Y is an n x m matrix of observed outputsfor n industries and x is an n x k matrix of inputsfor each industry. z is a l x n vector of intensityvariables and w = (w1,...wk) ε R+

k denoted inputprices. The constraints of the model (21) definethe input requirement set given by :

M. Manonmani


65

L(y) = (x. z. y ≥ y0, z x ≤ x, z i ≥ 0, ∑n

i=1iz = 1 )

..................... (22)

The input requirement set specifies aconvex technology with Variable Returns to Scale( VRS), which is imposed by the

constraint∑=

=n

iiz

1

1 . Leaving the constraint out

of the model changes the technology toConstant Returns to Scale (CRS).

v. Allocative Efficiency : Allocative efficiency isdefined as a ratio of cost efficiency score totechnical efficiency score. Both under CRSproduction technology and VRS productiontechnology, this efficiency score was estimatedfor the present study.


The results regarding the technicalefficiency estimates of the industries arepresented in Table 1.

Table 1 : Technical Efficiency (TE) Estimates

DMU CRS VRS

2000-01 1.000 1.0002001-03 1.000 1.0002003-04 0.947 0.9602004-05 0.920 0.9622005-06 0.875 0.8952006-07 0.401 0.5232007-08 0.477 0.6812008-09 1.000 1.0002009-10 0.262 0.9292010-11 0.794 1.000

Average Technical Efficiency (2001-11) 0.768 0.895Average Technical Inefficiency (2001-11) 0.302 0.112

No. of Technical Inefficient DMUs (2001-11) 3 4

Source: Calculations are based on ASI data.

Footnote: Average technical inefficiency = 1-

CRS- Constant Returns to Scale; VRS- Variable Returns to Scale.

Under Constant Returns to Scale (CRS)production technology, the average technicalefficiency score during 2001-02 to 2010-11 was0.768. This implied that the industries neededonly 76.8 per cent of the inputs. In terms ofaverage inefficiency, it would have needed 23.20per cent more inputs to produce the sameoutput, which meant waste of resources to theextent mentioned above. Whereas undervariable returns to scale production technology

(VRS),the average technical efficiency scoreduring the same reference period was 0.895.This again explained the fact that the industriesneeded only 89.5 per cent of the total inputs. Inother words,it would be waste of resources forthe industries to the extent of 10.50 per cent interms average inefficiency to produce the samelevel output if it employs more than 89.5 percent of total inputs for producing the same levelof output.



66

Based on the above findings, it could beconcluded that under VRS productiontechnology, the number of inefficient DMUsexceeded the number of efficient DMUs (If theefficiency scores across the years are exactlyequal to one, those years are referred to asinefficient DMUs or years, which explains thefact that there are no improvements inproduction). Under VRS production technology,higher average efficiency was recorded. It maybe due to the reason that DMUs that wereefficient under Constant Returns of Scale (CRS)production technology were accompanied bynew efficient DMUs that might operate underincreasing or decreasing returns to scale. Higherdegree of average technical inefficiencyparticularly under constant return to scaleproduction technology could be attributable tothe fact that the industries may not be using themost efficient technology available to transformthe inputs into output due to differences in

products produced, differences in selecting bestpractice frontiers and relatively small regionalspheres of operation of the industries might haveresulted in inefficiencies and also structuredproblems regarding staff efficiency andoperating efficiency may have prevented thefirm from improving its efficiency level.

It could be concluded that though theefficiency of the firms varied considerably onaccount of the various reasons mentioned, allthe firms were estimated to be on the frontiersat least once. In other words, both under CRSand VRS technology, the number of efficiencyscores or levels during the entire period, wasindicative of the fact that the efficiency of firmswas not strongly influenced by the size ofproduction.

b. Scale Efficiency : The scale efficiency scores ofthe industries selected under the present studyare presented in Table 2.

Table 2 : Scale Efficiency (SE) Estimates

DMU CRS (TE) VRS (TE) Scale Efficiency(SE) RTS(CRS(TE)/VRS (TE)

2000-01 1.000 1.000 1.000 CRS2001-03 1.000 1.000 1.000 CRS2003-04 0.947 0.960 0.986 IRS2004-05 0.920 0.962 0.956 IRS2005-06 0.875 0.895 0.978 IRS2006-07 0.401 0.523 0.767 IRS2007-08 0.477 0.681 0.700 IRS2008-09 1.000 1.000 1.000 CRS2009-10 0.262 0.929 0.282 IRS2010-11 0.794 1.000 0.794 IRS

Average Scale Efficiency 0.768 0.900 0.846(2001-11)

Average Scale Inefficiency 0.302 0.011 0.182(2001-11)

No. of Scale Inefficient DMUs 2 4 3(2001-11)

IRS - Increasing Returns to Scale;CRS – Constant Returns to Scale.

Source : Calculations based on ASI data.

Footnote : Average scale inefficiency = 1-

RTS - Returns to Scale;

M. Manonmani


67

DEA results applied to know the scaleefficiency of industries for the entire periodrevealed that the industries were not operatingat an optimum scale. The average scaleefficiency was 84.6 per cent. In terms of averageinefficiency, it could increase additionalproduction to the extent of 15.4 per cent, bytaking advantage of their scale characteristics.DEA allows to assess whether a firm lies in therange of increasing, constant and decreasingreturns to scale. In other words, it revealed thescale characteristics of DMUs. If market containsfirms scale, market efficiency can be increasedif more DMUs attain constant returns to scale,because fewer resources are wasted. The

Table 3 : Cost Efficiency (CE) Estimates

DMU CRS VRS

2000-01 0.975 1.0002001-03 0.857 0.8742003-04 0.812 0.8272004-05 0.904 0.9252005-06 0.867 0.8772006-07 0.397 0.4972007-08 0.449 0.6422008-09 1.000 1.0002009-10 0.201 0.7322010-11 0.506 0.750

Average Cost Efficiency(2001-11) 0.697 0.812Average Cost Inefficiency (2001-11) 0.435 0.232

No. of Cost inefficient DMUs(2001-11) 1 2

Source : Caculations are based on ASI data.Footnote: Calculations are based on ASI dataCRS- Constant Returns to Scale;VRS- Variable Returns to Scale;

measurement of economies of scale, therefore,helps assess at the same time whether highermarket concentration should be encouraged toimprove efficiency. A DMU may be scaleinefficient, if it experiences decreasing returnsto scale or if it has not taken full advantages ofincreasing returns to scale. Indeed most of theinefficient DMUs presented increasing returnsto scale characteristics which indicated thatindustries can increase the scale to effectivelyimprove that efficiency.

Cost Efficiency : Table 3 gives details regardingcost efficiency scores of selected industries forthe reference period under study.

Average cost inefficiency = 1-

Under Constant Returns to Scale (CRS)production technology, the industries wereefficient to the extent of 69.7 per cent. UnderVariable Returns to Scale (VRS) productiontechnology the same industries were moreefficient to the extent of 81.2 per cent.

Considering the cost efficient DMU’s, it was foundto be more under VRS production technology,than under CRS production technology. Theaverage cost inefficiency was more under CRSproduction technology than under VRSproduction technology.The average costinefficiency of the industries under CRS and VRSproduction technology, respectively were 43.5



68

and 23.2 per cent.The number of cost inefficientDMUs exceeded the number of cost efficienctDMUs during the reference period under study.

Allocative Efficiency : Allocative efficiencyscores of the industries under the referenceperiod are presented in Table 4.

Table 4 : Allocative Efficiency (AE) Estimates

DMU CRS VRS

2000-01 0.975 1.0002001-03 0.857 0.8742003-04 0.857 0.8612004-05 0.982 0.9622005-06 0.990 0.9802006-07 0.992 0.9502007-08 0.943 0.9432008-09 1.000 1.0002009-10 0.769 0.7882010-11 0.637 0.750

Average Allocative Efficiency(2001-11) 0.900 0.911Average Allocative Inefficiency(2001-11) 0.101 0.098

No. of AllocativeInefficient DMUs(2001-11) 1 2

Estimates revealed that over the studyperiod, the industries under CRS productiontechnology had on an average allocativeefficiency level of 90 per cent implying that theindustries were 10 per cent inefficientrespectively. In the case of VRS productiontechnology, the industries had an averageallocative efficiency of 91.1 per cent, implyingthat the industries were on an average 0.9 percent inefficient which was negligible. Moreefficient DMU’s were observed in VRSproduction technology than under CRSproduction technology.

Conclusion

It could be concluded that for the entireperiod of analysis, technical, scale, cost andallocative efficient DMUs were more underVariable Returns to Scale (VRS) productiontechnology than under Constant Returns to Scale(CRS) production technology. Also it is very clearthat inefficiency could be due to the existenceof either increasing or decreasing returns to

VRS- Variable Returns to Scale;

Average Allocative inefficiency = 1-

Source: Calculations are based on ASI data.Footnote: CRS- Constant Returns to Scale;

scale. Following measures are to beimplemented which is consistent ever-Reviewing existing KVI schemes and suggestingpolicies/ schemes for the sector for employmentgeneration, technology upgradation/modernisation and support for innovativeproducts, credit, marketing, training need ofentrepreneurs and monitorable annual targetsfor each area. For inclusive growth andsustainable development, the inefficient khadiclusters should increase their production or sales.Moreover, the khadi clusters should strengtheninterrelationships relating to infrastructure,technology, procurement, production andmarketing and should make use of the benefitsannounced by Government of India underSFRUTI (Scheme of Fund for Regeneration ofTraditional Industries). The soft and hardintervention on Cluster DevelopmentProgramme of Government of India will helpKhadi and Village Industries Clusters in India toincrease their productivity and efficiency.

M. Manonmani


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Efforts must be made to improve thequality and value of khadi production by focusingupon design inputs and improving the quality ofkhadi cloth. The government must also provideadequate finance, tax exemptions, particularlyin sales tax, octroi , purchase tax, etc., to khadiand village industries till it can stand on its ownand face globalisation. When the issue of climatechange is dominating the economic policies, theclean and green techniques of production haveto be promoted. Hence khadi and villageindustries have to be promoted. Moreover, it hasthe potential to solve the unemploymentproblem of rural India to a greater extent. If weignore the khadi and village industry it is at ourown risk!.The following policy initiatives areneeded for the development of these industriesin the near future.

1) To come out with viable technology toreduce the cost of production of khadiand village industries products.

2) The cloth required by governmentdepartments like schools, hospitals andjails, should be purchased only from thekhadi industries.

3) The government employees of all thedepartments should be compulsorilyasked to wear khadi clothes at least twicein a week.

4) Marketing techniques should be suitablyadopted .

5) Khadi and village industries should beencouraged in villages and no MNCs orbig firms should be allowed to producethose products produced by Khadi &Village industries.

References

1. Golder, B (2004), “Ownership and Efficiency in Engineering Firms: 1990-1991 to 1999-2000”,Productivity, Vol. 39, No. 5, Pp. 441-446.

2. Mitra, A. Dakies, A.V. and Varoudakis, M.A.V. (2002), “Productivity and Technical Efficiency inIndian States Manufacturing: The Role of Infrastructure”, Economic Development and CulturalChange, Vol. 50, No. 2, Pp. 395-425.

3. Kulwant Singh Pathania (2010), Khadi And Village Industries: Status, Problems And Challenges,Regal Publications, Vol.1,Pp. 252.

4. www.annualsurveyofindustries.com

5. www.reservebankofIndia.bulletin.com.



71

ORGANIC FARMING AND QUALITY OF FOOD

E. Thippeswamy*

ABSTRACT

Organic farming is becoming increasingly popular all over the world. Manyconsumers are feeling disillusioned from chemically produced foods and are makingefforts to buy organic. Extensive use of chemicals and anti-biotics in inorganic foodproduction technology compelled the health conscious people to explore and supportorganic farming methods in agriculture. In this context, this study was undertaken toanalyse the Organic Farming and Quality of Food produced in Shimoga district ofKarnataka. The survey was undertaken for collecting primary data from 420 equal numberof organic and inorganic paddy farmers during 2011-2012. The study found that theilliterate and primary education category, the percentage of respondents is more amonginorganic farmers compared to their counterparts in the organic farmers. It is quite naturalthat the knowledgeable people are more inclined towards the innovative farming practicesadded to increasing health awareness also encourages people to go for organic farming.The most important finding is that higher community people are more inclined towardsorganic farming and backward people like scheduled castes (SC) and scheduled tribes(ST) are still practising inorganic farming. It is a fact that the upper caste people have moreawareness about the negative effects of high external input based and unsustainableinorganic farming, hence large proportion of upper caste farmers are switching over toorganic farming than the lower caste farmers. Growing interest in the organic agricultureis increasing consciousness about health hazards associated with agro-chemicals. Finally,95.2 per cent of the respondents expressed the opinion that they shifted from inorganic toorganic paddy to produce the health food, however majority of them are growing organicpaddy for their self-consumption.



* Associate Professor of Economics, Field Marshal K. M. CARIAPPA College, Madikeri-571201, Kodagu (District),

Karnataka. email:[email protected], [email protected].

Background

Human communities, no matter howsophisticated, could not ignore the importanceof agriculture. To be far farm dependable sourcesof food was to risk malnutrition and starvationhence, the fundamental basis of community isagriculture, tillage of the soil (Baha, 1912).Agriculture is one of human kind’s most basicactivities because all people need to nourishthemselves daily. History, culture, and communityvalues are embedded in agriculture. Theprinciples apply to agriculture in a broad sense,including the way people tend soils, water, plantsand animals in order to produce, prepare and

distribute food and the other goods. Hence, thenational food security, nutritional security,maintenance of soil health, environment safety,ecological balance, biodiversity and qualityproduce (Ramanathan 2006) should becomevery important components in agricultural policyin order to increase the production andproductivity. Expanding proportion of consumersregard food produced through “Organic” orbiological means of safer to eat and methodsused to produce it as less polluting, better forthe soil, respecting the welfare of animals andmore hospitable to wildlife than food producedthrough conventional (Inorganic) means” (OECD,


72 E. Thippeswamy

2001c). Organic agriculture should sustain andenhance the health of soil, plant, animal, humanand planet as one and individual. In thisbackground, the present paper focuses on theadoption of organic farming and quality of foodin Shimoga district of Karnataka.

Since the introduction of greenrevolution technology, India not only becameself-sufficient in foodgrains but also became theexporter of food and other agro-based products.However, the consumption of chemical fertilisers(N.P.K) has been increasing in India during thepast thirty years at a rate of almost half a milliontonnes on an average a year. It was only 13.13kg/ha in 1970-71, 31.83 kg/ha in 1980-81 and74-81 kg/ha in 1995-96. Further, it shot up toabout 96 kg/ha during 1999-2000 (Narayanan,2005). Hence, both increased area and use ofchemical fertilisers and pesticides contributedfor increased foodgrain production. Under greenrevolution, it is a known fact that the consumptionof chemical fertilisers increased seven-fold,pesticides by 375 times while the foodproduction had just doubled during the first 20years of the launch of Green Revolution in India(Palaniappan and Annadurai 1994). Meanwhile,increased use of chemicals under intensivecultivation disturbed the harmony existingamong soil, plant and microbial population(Ghosh 1999). More importantly, the intensity oftheir use in a few regions and a few crops arecauses of serious concern to human health, soil,water, environment and thus to the sustainabilityof agriculture production in the country(Narayanan, 2005).

The damage caused through agro-chemical pollution to environment and humanhealth, directly and through the human foodchain and sustainable agriculture and foodsecurity is irreparable (Guansoon, 1998; Thakuret al., 2003; Vepa et al., 2004). In many cases,over 90 per cent of the inorganic produce ofvegetables, foodgrains, fruits, milk etc., producedunder Inorganic Farming System (GreenRevolution) contains poisonous agro-chemical

residues harmful and unsuitable forconsumption (Paroda. 2001). Obviously, thepresent chemical farming (Inorganic farming)created mismatch between resources availabilityand consumption, resulted in decline in watertable, soil health degradation, useful birdselimination, appearance of new weed bio-types,insect-pest and disease ultimately affecting theprofitability of farming offer an important optionwhich not only improve the resource but alsoensures their rational utilisation (Gill and Sarlach,2006). Thus, the Green Revolution of yester yearshas left farmers of today searching for somethingbetter, in addition, farmers are pursuing chemicalsupplements to push crop yield, which is onlyharming the earth. Farmers and communitiesfaced many socio-economic problems,particularly small farmers who found themselvesincreasingly marginalised due to lack of accessto external inputs. Their soil is depleted from theconstant application of harsh and harmfulchemicals. A placard informed farmers that inreality organic crops yield 70 per cent (DeccanHerald, 2 December, 2008). In addition to this,the low-tech sustainable agriculture is increasingcrop yield on poor farmers across the world, oftenby 70 per cent or more. This has been achievedby replacing synthetic chemicals in favour ofnatural pest control and natural fertilisers (NewScientist, 2001).

Thus, the effect of green revolution(Inorganic Farming System) advocated thenecessity of organic farming because of over-exploitation of the natural resources (land, waterand vegetation). Therefore, to keep the naturalresources afresh and to meet the national goal,organic farming using manures, legumes, cropresidue, off-farm organic wastes and bio-pesticides enable the country to produce thefoodgrains sufficiently along with theconservation of resources. In this way, extensiveuse of chemicals and anti-biotic inorganic foodproduction technology compelled the healthconscious people to explore and support organicfarming methods in agriculture. In this context,


73Organic Farming and Quality of Food

this study was undertaken to analyse the OrganicFarming and Quality of Food produced inShimoga district of Karnataka.

Methodology

The study is based on the followingspecific objectives :

� To examine the quality of food producedunder organic and inorganic farming.

� To compare the quality of paddy cropproduced under organic farming system inShimoga district of Karnataka.

The study used primary and secondarydata. Primary data were collected from therespondents practising organic and inorganicfarming in the process of paddy production. Thefield survey was conducted during 2012-13kharif season in Shimoga district of Karnataka.Four hundred and twenty respondents wereselected from the study area. Of them, 210respondents are organic and remaining are theinorganic farming respondents (210).Therelevant secondary data for this study werecollected from the various journals, reports,unpublished theses and reports published byCentral and State Government authorities. Inaddition, secondary data were also collectedfrom the Organic Farmers Association affiliatedto Karnataka Savayava Krushi Mission andDepartment of Agriculture, Government ofKarnataka helped in identifying the organicfarmers. The farmers who are using only organic,biodynamic, or natural inputs in the productionof paddy termed as organic paddy farmers.Inorganic farmers are the farmers usingchemical fertilisers and pesticides along with orwithout applying organic inputs in the processof paddy production.

RESULTS AND DISCUSSION

Quality of Organic and Inorganic Food

Many consumers are feeling disillusionedfrom chemically produced foods and are makingefforts to buy organic. Their reason may includea concern for their families, the health of the

environment, and the increase in taste andnutritional value found in organic products.Inorganic agriculture uses a wide range ofsynthetic chemicals that inevitably leave residuein the produce: There are more than 130 differentclasses of pesticides containing some 800 entries(Plimmer, 2001), pesticides residues enter thefood chain via four main routes; on-farm pesticideuse, post-harvest pesticides use (accounts forthe largest part), pesticide use on imported foodand cancelled pesticides that persist in theenvironment (Kuchler et al., 1996) According toWHO estimates, approximately one millionpeople are taken ill every year with pesticidespoisoning and up to 20,000 of them die in agonyand a variety of reproductive health impacts inwomen and pesticide exposure. Increasedincidence of miscarriage, birth malfunctions, stillbirths and delayed pregnancy are documentedamong women agricultural workers and wivesof men employed in pesticide mixing andspraying (Ranson, 2002). This is mainly due tooveruse or misuse of chemicals, particularlysynthetic insecticides, fungicides, herbicides,fertilisers, plant growth regulators etc., thatresulted in undesirable side effects not only inthe agro-ecosystems, but also on human healthand life systems of beneficial fauna andmicroorganisms. Further, the Green Revolution’sgains have come at the cost of extensiveenvironmental degradation and considerablehealth problems due to exposure to agro-chemicals. Thus, toxic residues poison the bodyslowly causing intensive damage to human body;the food products containing toxic pesticidesresidues cause heart disease, brain, kidney andliver damage and even cancer, limb deformitiesand poor eyesight.

In the world, nowadays, food safety isreceiving more attention than ever before notonly in developed countries but also indeveloping countries, by policy makers, healthprofessionals, the food industry, the biomedicalcommunity and last but not least, the public. Thepreference for organic food has been associated


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with multiple factors that, in general, reflect anincreased interest towards personal health,animal welfare and environmental protection(Makatouni, 2002). Health-related issues seemto assume greater importance than otherconcerns and notions about food safety and arefundamental for purchasing organically grownfood (Magnusson et al., 2003; Lohr, 2001; Harperand Makatouni, 2002; Beharrell and Mac Fie,1991)

. A study of data collected by the US

government found pesticide residues on 23 percent of organic fruits and vegetables and nearly75 per cent of conventionally grown produce,through the residues in all the samples wellbelow statutory limits (Baker et al., 2002). A studyof three apple production systems (organic,integrated, conventional) in Washington Stateassessed their impact on some factors in all threedimensions of sustainability. They concluded thatorganic production systems were moreprofitable, had a lower environmental impactand produced sweeter and less tart apples(Regenanold et al., 2001).

There is a widespread belief that organicfood is substantially healthier and safer thaninorganic food and consumers are willing to paysignificant price premium to obtain it. Organicagriculture uses almost exclusively biologicaland natural materials and processes to producefood. The practice aims to protect human healthand conserve or enhance natural resources, withthe goal to presume the quality of theenvironment for future generations while beingeconomically sustainable. Hence, farmers areconverting to organic methods for a variety ofreasons but the most important have to do witha general unease with the health andenvironmental impacts of conventional practice,increasing disease and pest problems and theexpectation that organic farming methods maybe more profitable (Blobaum, 1983: UnitedStates GAO, 1990: MacRae et al. 1990). A studyconducted in USA on the nutritional value ofboth organic and conventional food found thatconsumption of the former is healthier. Apple,

peas, potatoes, corn, wheat and baby foods wereanalysed to find out ‘bad’ elements such asaluminum, cadmium, lead and mercury, also‘good’ elements like boron, calcium, iron,magnesium, selenium and zinc. The organic foodin general had more than 20 per cent less of thebad elements and about 100 per cent more ofthe good elements (Narayanan, 2005).

Singh and Dinesh Kumar (2007)examined organic farming vis-à-vis humanhealth and environment. The study revealed thatorganic farming was superior to conventionalfarming or chemical farming in terms ofpollution-free environment, good quality of foodand health: conventional farming produced foodand fodder by using chemical fertilisers andpesticides, which contaminated the food, healthhazards and environment pollution. Besides,organic farming produces good quality of food,by using different plant nutrition, weedmanagement, pest and disease management sothat eating of organic food considerably reducesthe heart attacks, strokes, cancer, bowel cancerand many other diseases. However, Faido Magkoset al., (2006) reported the critical and transparentoverview of organic food safety to identifypotential drawbacks in organic food production.The results revealed that food safety of organicverses conventional produce is difficult becauseof divergent conditions prevailing in terms ofsoil, water and climatic conditions. Organic foodis not free from pesticides. However, fruits andvegetables grown under organic farming canbe found with much less agrochemical residuesthan their conventional alternatives. Besides, thehealth risk associated with dietary exposure toagrochemicals remains to be evaluated.Organically cultivated nitrophillic vegetables viz.leafy root and tuber were found with lowercontent than the respective conventional ones.Pragya Agarwal et al., (2007) compared thequality characteristics viz, differences in physicalcharacteristics , nutrient composition, cookingquality and sensory quality in fresh green peasgrown by organic, inorganic and integrated

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methods. The study found that no significantdifference was observed in terms of pod length.However, organically grown peas scored higherfor total sugar, sweetness, colour, flavour andtaste, besides in terms of minerals, organicallygrown peas had higher copper and zinc levelsas compared to inorganically grown peas andpeas grown by integrated method of cultivation.

Thakur et al., (2003) examined thecomparative economics of organic produce orOFS vis-à-vis inorganic produce or IFS at theinstances of ICAR in the backward and tribal hillyarea of Himachal Pradesh, India. The studyselected 100 farmers from different villages and100 traders and 100 consumers from differentlocal and national markets through suitablesampling techniques. The study revealed that thepoisonous and toxic inorganic chemical inputsused under IFS turned out to be highlydestructive, injurious and harmful causing largescale polluting and poisoning of soil, water, airecosystem, agro-ecosystem, environment, plantsand crop produce which in turn, induce manydeadly diseases including cancer. Besides, OFSusing organic inputs is a solution for the ills andproblems of IFS as it is found to improveconstantly the soil fertility, yields and productionof crops and sustainability of agriculture in thelong run. The organic produce or organic food isbest for health; more nutritious of better quality,free and safe from toxic inorganic chemicalresidues, looks fresh and good and tastesdelicious. Hence, the health conscious buyersand consumers are buying organic produce atvery high premium prices which are generally3-4 times higher depending upon products.

In Japan, Yukio Yokoi (2002) conducted asimilar study on the policy development onorganic agriculture and future perspectives. Thestudy revealed that the public are greatlyconcerned about food safety issues owing tothe recent incidents of mad cow disease (BovineSpongiform Encephalopathy) and the detectionof excess pesticide residues and the use ofprohibited pesticides. Hence, policies on organic

agriculture and organic food have beendeveloped in terms of the “JAS Organic”accreditation system and technological supportof organic farming. There was a potential forfurther shift to organic agriculture, thegovernment has given higher priority toconsumers, is to provide more administrative aswell as technical support for organic agriculture.

Wiebel et al., (1999) assessed fruit qualityin golden delicious apple from five organic farmsand five farms using integrated productionmethods. They found that in terms of taste,firmness, dietary fiber and phenolic compoundcontents, fruits from organic farmsoutperformed the others. Hogstad et al., (1997)examined sensory quality and chemicalcomposition of carrots from designed trials andfrom organic and conventional farms. The datawere analysed using principal components andpartial least squares regression to identify themain factors responsible for variation in quality.One of the most important factors was fertilisersapplication. Carrots grown with fertiliser, lowlevels of mineral fertiliser or with organicfertiliser, had more total sugars, stronger flavourbut less crispness, protein and carotene thancarrots grown with high levels of mineralfertiliser. Dudi et al., (2005) examined theinfluence of four levels each of nitrogen andFYM on fruit quality and yield at Ghursal villageof Hissar district in Haryana. The study found thatindividual application of 750gr N and 100-150kg of FYM/plant significantly increased the totalsoluble solids, sugars, ascorbic acid juice contentand fruit yield, besides, the highest level of Nresulted reduced quality of fruits but rindthickness increased with higher levels of appliedN. The maximum fruit yield (181 kg/ tree) wasrecorded with the application of nitrogen 750gram/ tree during 1999 whereas it was 175 kg/plant with the addition of 150 kg FYM in thesame year because of application of nitrogenand FYM in combination improved the qualityas well as the yield of fruits.

Organic Farming and Quality of Food


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Anecdotal evidence suggests, from theabove reviewed literature, that food producedusing organic methods tastes better and containsa better balance of vitamins and minerals thaninorganically produced food.

Marilyn Lynn (FP

female primary) expressed that food as anopportunity to connect to the earth and to otherpeople. “Food is a common ground for everyone.We all need to eat and we all recognise andappreciate when we are able to eat food that ishealthy and tasty and that we know was raisedin an economically and socially just way. We arecontinually reminded, because of the diversityof folk we see at the farmers; that we do allshare this common bond” (Lynn, 2003). Though,very few studies contradict that the foodgrainsproduced under organic farming alsocontaminated compared to foodgrains producedunder inorganic farming. In fact, more numberof studies revealed that products produced underorganic farming are healthy and tastier than theproducts produced in inorganic farming. Severalforms of organic farming are being successfullypractised in diverse climates, particularly inrainfed, tribal, mountains and hilly areas of thecountry. Hence, it is necessary to promote organicfarming from the point of view of healthy foodand healthy society.

The Quality of Paddy Crop Produced UnderOrganic Farming System

Organic farming has been a popular formof sustainable agriculture all over India. KarnatakaState is also bestowed with divergent climaticand soil types spread across agro-climatic zones.The physical features of Karnataka include coastalplains, Western Ghats and plateau enabling it togrow a variety of crops. The State is also knownfor its excellence in horticultural crops andanimal husbandry. In addition, many farmers ofthe State are pioneers in organic agriculture anddeveloped many different systems of cultivationthrough indigenous knowledge base. There aremany opportunities for promotion of organicfarming in Karnataka. In this background,

Shimoga district of Karnataka was selected forthe study where large number of farmersadopted organic farming practices for growingcrops. Hence, the farmers growing paddy underorganic farming were selected for the study.

Socio-economic features of the farmfamilies in general and heads of the families inparticular influence their farming practices ingrowing a crop and the level of their crop yield.Hence, socio-economic features that are relevantto crop production and adoption of organicfarming decision were chosen for the analysis.The socio-economic variables considered for thispurpose include age composition, level ofeducation, size of the family, mean values ofrespondents’ landholdings and caste-wisedistribution of the respondents. The datapertaining to these variables were collected fromthe respondents' family with special emphasison the heads of the family. The head of the familyhere need not necessarily be the senior memberof the family but the person who plays a vitalrole in decision-making process.

Age is one of the important demographicfeatures of the respondents which will influenceon the decision-making style in farming practices.The age of the respondents ranges from 22 to82 years and thus respondents were categorisedunder three groups viz young farmers (<35years), middle aged farmers (35-60 years) andold age farmers (>60 years). Frequencydistribution of the farmers across the differentage groups is given in Table 1. Distribution of thefarmers across the different age groups is givenseparately for organic and inorganic farmers.Majority of the farmers in the overall categorybelong to the middle age (268) followed by theyoung age (102). Middle age farmers accountfor 63.8 per cent of the total respondents in thiscategory. The disaggregated data for the organicand inorganic farmers are 69.1 and 58.6 per cent,respectively.

Education is a key indicator of theknowledge level of the respondents which in

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turn will influence on the decision- makingprocess in the adoption of farming practices.Respondents with higher level of education willbe the pioneers in the adoption of innovativefarming practices. Therefore, data on theeducation level of the respondents werecollected and the results are given in Table 2.The education level of the respondents is mainlyclassified into four categories viz. illiterates,respondents with primary, secondary and collegeeducation. The distribution of the organic andinorganic respondents across these differentlevels of education is given in this Table. In theoverall category, highest percentage of therespondents were having education up to thecollege level (32.4) and it was followed by theprimary (32.1) and then secondary education(28.4). Organic respondents were found to behaving relatively higher level of educationcompared to the inorganic farmers. Respondentswith college level of education are considerablymore among the organic farmers (45.2)compared to the inorganic farmers (19.5). Similardifference could be found in the secondaryeducation also. In the illiterate and primaryeducation category, the percentage ofrespondents is more among inorganic farmerscompared to their counterparts in the organicfarmers. It is quite natural that the knowledgeablepeople are more inclined towards the innovativefarming practices, added to this, increasing healthawareness is also encouraging the people to gofor organic farming.

Caste is one of the indicators of socialstatus of an individual. It influences on decision-

making status of an individual. Therefore, datawere collected from the respondents about thecaste status of their family and given in Table 1.The caste of the respondents is mainlycategorised into four groups SC, ST, OBC andGeneral category. The distribution of organic andinorganic respondents across the differentcategories is given separately for organic andinorganic farming respondents. In the overallzone category, out of 420 total respondents, 300respondents are found to be belonging to OBCfollowed by 74 belonging to General and 32belonging to SC and remaining are ST(12)category. The significant feature of results is thatgeneral category respondents account forhigher share among the organic farmers (31 percent) compared to their share is relatively less ininorganic farming group (4.2 per cent) whereasSC and ST category people account for higherpercentage in the inorganic farming groupcompared to the organic farming group. Itindicates that caste is arranged in hierarchicalorder, the higher community people moreinclined towards organic farming compared tothe backward people like SC and ST.

The most important finding of this resultis that higher community people are moreinclined towards organic farming and backwardpeople like SC and ST are practising inorganicfarming. Thus, it is a fact that the upper castepeople are more aware about the negativeeffects of high external input based andunsustainable inorganic farming, hence largeproportion of upper caste farmers are switchingover to organic farming than the lower castefarmers.



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Table 1 : Socio-economic Features of the Organic and Inorganic Respondents

S. Particulars Distribution of RespondentsNo. Organic Farmers Inorganic Farmers Overall

1 Age of the Respondentsi) Young Farmers(<35 years) 42(20.0) 60(28.6) 102(24.3)ii) Middle Age Farmers 145(69.0) 123(58.6) 268(63.8)iii) Old Age Farmers 23(11.0) 27(12.8) 50(11.9)Total 210(100.0) 210(100.0) 420(100.0)

2 Educationi) Illiterate 03(1.4) 27(12.9) 30(7.1)ii) Primary Education 43(20.5) 92(43.8) 135(32.1)iii) Secondary Education 69(32.9) 50(23.8) 119(28.4)iv) College Education 95(45.2) 41(19.5) 136(32.4)Total 210(100.0) 210(100.0) 420(100.0)

3 Caste of the Respondentsi) SC 06(2.9) 26(12.4) 32(7.6)ii) ST 04(1.9) 10(4.8) 14(3.4)iii) OBC 135(64.2) 165(78.6) 300(71.4)iv) General 65(31.0) 09(4.2) 74(17.6)Total 210(100.0) 210(100.0) 420(100.0)

4 Size of Landholdingsi) Small Holdings(<2 hectares) 92(43.8) 128(60.9) 220(52.4)ii) Large Holdings(>2 hectares) 118(56.2) 82(39.1) 200(47.6)Total 210(100.0) 210(100.0) 420(100.0)

5 Size of Familyi) Small Family 97(46.2) 95(45.2) 192(45.7)ii) Medium Family 99(47.1) 103(49.1) 202(48.1)iii) Large Family 14(6.7) 12(5.7) 26(6.2)Total 210(100.0) 210(100.0) 420(100.0)

Figures in parentheses are percentage to total.

In rural economy, land is one of theimportant socio-economic indicators. Size oflandholding influences the cropping pattern,farming practices and adoption of moderntechnology. Data relating to the size of land-holdings were collected from the respondents.Based on the size of landholdings, the samplerespondents are broadly categorised into smallfarmers (< 2 hectares) and large farmers (>2hectare). The frequency distribution ofrespondents across the different landholdingcategory is presented in the Table. In the overallsize category, 52.4 per cent of respondents arein small size category and the remaining 47.6

per cent are in large category. Considerablyhigher percentage of organic respondents (56.2)are in the large size holdings compared to theinorganic respondents (39.1). Small size ofholding is relatively more among the inorganicrespondents (60.9 per cent) compared to theorganic respondents (43.8 per cent). Largefarmers are relatively more inclined towards theorganic farming compared to the small farmers.

Size and composition of family is anotherdemographic feature that could influence on thefarming practices. Data relating to number ofmembers in the family were collected from the

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respondents. The size of respondents’ family isclassified into three categories viz. small family(<4 members), medium family (between 5 to 8members) and big family (>9 members). Thefrequency distribution of respondents’ familiesacross the different size of families’ is givenseparately for organic and inorganic farmers inthe Table. The overall zone category is thepooled data of organic and inorganicrespondents. In the overall zone category,majority of the respondents belong to mediumfamilies (202) followed by small (192) and largefamilies (26). There is no much differencebetween organic and inorganic respondents’families with respect to their distribution acrossthe different size category. Adoption of organicfarming system has nothing to do with the familysize of the respondents.

The problems associated with inorganicfarming system or attractive features of organicfarming system; influence the farmers to convertthe inorganic farming system into organicfarming system. Those who have a holisticunderstanding of organic farming are likely tobe motivated by local benefits such as improvedsoils, healthy food, fewer toxic chemicals, andself-reliance with inputs. Moreover, economicobjectives are not the only motivation of organicfarmers; their intention is often to optimise land,animal and plant interactions, preserve naturalnutrient and energy flows and enhancebiodiversity. In this context, information wascollected from the respondents about thequality of products produced under organicfarming system. For this purpose, eight factors,which could influence the respondents to adoptthe organic farming, were identified based onthe experience gained through the discussionwith the progressive farmers, agriculturalscientists as well as from the review of existingliterature.

The factors which could motivate therespondents to adopt the organic farminginclude; i) improving soil fertility, ii) food of goodquality, iii) production of crops for the self-

consumption, iv) avail of the benefit of pricepremium, v) avail of the benefit of governmentsubsidy, vi) utilise the locally available resources,vii) reduce the problems of pests and diseases,and viii) reduce the explicit cost of cultivation.The information was collected from therespondents and results are consolidated andpresented in Table 2. An individual farmer maybe motivated by several factors therefore, thesum of the column may not be equal to thesample size.

Reckless application of agro-chemicalsand pesticides leads to decline in soil qualityover the years which motivated the respondentsto convert the inorganic farming system intoorganic farming system. In Shimoga district, 199respondents expressed the opinion thatdeclining soil fertility in the inorganic farming isone of the factors that motivated the farmers toswitch over from inorganic to organic farming.Another major motivational factor that leads togrowing interest in the organic agriculture isthe increasing consciousness about healthhazards associated with agro-chemicals. Thus,the 95.2 per cent of the respondents expressedthe opinion that they have shifted frominorganic to organic paddy to produce thehealth food. Majority of them are growingorganic paddy for their self-consumption.

Agricultural products produced underorganic farming are receiving higher pricepremium than the products produced underinorganic farming in the market. About 81 percent of respondents expressed the opinion thatthe higher price to organic products was one ofthe factors that motivated them to convert theirinorganic farming methods into organic farmingmethods. Government subsidy is anotherimportant factor that could largely motivate therespondents to switch over from inorganicfarming system into organic farming system. Inthe district, majority of the farmers adopted theorganic farming system to avail of the benefitof the government subsidy. It is more evident indistrict where 85.7 per cent of the respondents



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converted their inorganic farms into organicfarms.

Organic farming has the potential toutilise the locally available resources and toprovide benefits in terms of environmentalprotection, conservation of non-renewableresources. Hence, utilisation of locally availableresource would influence the farmers. The

general opinion among the farmers of this districtis that the crops grown under inorganic farmingare more susceptible to pests and diseases andpreventing them becomes very difficult taskwhereas organic farming reduces the pests anddiseases risk and this opinion also is one of thereasons for the conversion of inorganic intoorganic paddy.

Table 2: Experience and Opinons of Organic Paddy Farming Respondents

S. No. Particulars Total (210)

1 Converted the inorganic into organic farming to improve thesoil quality 199(94.8)

2 Converted the inorganic into organic farming to producehealthy food crops 200(95.2)

3 Converted the inorganic into organic farming to producefood crops for self-consumption 195(93.0)

4 Converted the inorganic into organic farming to avail of thebenefit of price premium 170(81.0)

5 Converted the inorganic into organic farming to avail of thebenefit of Government subsidy 180(85.7)

6 Converted the inorganic into organic farming to utilise thelocally available resources 150(71.4)

7 Converted the inorganic into organic farming to reducethe problems of pests and diseases 138(65.7)

8 Converted the inorganic into organic farming to reduce theexplicit cost of cultivation 183(87.1)

Note: Figures in parentheses are percentage to the total sample respondents.

It is clear from the foregoing discussionthat all the factors are motivating at least someof the respondents to switch over from inorganicto organic farming. In district, highest percentageof respondents shifted from inorganic to organicpaddy for healthy food crops. It was followed bythe intention of improving the soil quality of theirland and then with the intention of availing ofthe benefit of government subsidy. Therefore,foodgrains produced under organic farming arebetter in quality and tasty compared to productsproduced under inorganic farming.

Conclusion

The problems associated with inorganicfarming system or attractive features of organic

farming system; influence the farmers to convertthe inorganic farming system into organicfarming system. Consumers have been worriedby a series of poisoning incidents withinorganically produced foods, and turned toorganically produced food which is safer sincethey contain very less or no pesticide residues.Hence, organic farming has been a popular formof sustainable agriculture all over India. A largenumber of farmers are switching over frominorganic farming to organic farming inKarnataka. It is a fact that the upper caste peopleare more aware about the negative effects ofhigh external input based and unsustainableinorganic farming hence large proportion ofupper caste farmers are switching over to

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organic farming than the lower caste farmers. Itis quite natural that the knowledgeable peopleare more inclined towards the innovativefarming practices, added to this, increasing healthawareness is also encouraging the people to go

for organic farming. Thus, the intention ofimproving the quality of paddy crop, soil qualityof their land and then with the intention ofavailing of the benefit of government subsidy,farmers are moving towards organic farming inShimoga district.

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2. Baker, B., C.Benbrook, E. Groth III, and K. Benbrook (2002), “Pesticide Residues in ConventionalIPM-Growth and Organic Foods: Insights from Three Data Set”, Food Additives andContaminants, Vol.19, No.5, May, pp.427-446.

3. Beharrell, B., and MacFie, J.H. (1991), “Consumer Attitudes to Organic Food,” Br. Food J., 93:25-30.

4. Blobaum, R. (1983), “Barriers to Conversion to Organic Farming Practices in the MidwesternUnited States, ”In W. Lockeretz ed., Environmentally Sound Agriculture, Praeger, New York,pp263-278.

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8. Hogstad S; Risvik E; Steinsholt K (1997), “Sensory Quality and Chemical Composition of Carrota Multivariate Study,” Acta Agriculturae Scandinivica Section B, Soil and Plant Science, 47:4,pp253-264.

9. Gill and.M.S and R.S.Sarlach (2006), “Organic Farming in Indo-Gangetic Plains-Scope, Statusand Opportunities”, Environment and Ecology, 24(2):313-326.

10. Ghosh Sailen (1999), “Principles of Organic Farming in the Tropics,” The Organic FarmingReader, Other India Press, P.53-68.

11. Guan Soon, I. (1998), “Integrated Pest Management in Developing Asia,” In G.S. Dhaliwal,N.S.Radhawa, Ramesh Arora, A.K. Dhawan (Eds.) (1998), Ecological Agriculture and SustainableDevelopment, Vol.2, Indian Ecological Society, Punjab Agriculture University, Ludhiana andCRRID, Chandigarh.

12. Harper, G.C., and Makatoumi, A (2002), “Consumers Perceptions of Organic Food Productionand Farm Animal Welfare”, Br. Food J., 104: 287-299.

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14. Lohr, M., (2001), “Factors Affecting International Demand and Trade in Organic Food Products,”In: Regmi, A., Ed., Changing Structure of Global Food Consumption and Trade, Washington,D.C, United State, Department of Agriculture (USDA), Economic Research Service, 67-79.

15. MacRae, R., S.Hill, G.Mehuys, and J. Henning (1990), “Farm-Scale Agronomic and EconomicConversion from Conventional to Sustainable Agriculture,” Advances in Agronomy, 43:155-198.

16. Magnusson, M.K., Arvola, A., and Koivisto-Hursti, UK (2001), “Attitude Towards Organic Foodamong Swedish Consumers,” Br. Food J., 103:209-226.

17. Makatouni, A. (2002), “What Motivates Consumer to Buy Organic Food in the UK? Results fromQuantitative Study”, Br.Food J., 104:345-352.

18. Marilyn Lynn. (2003), “Rama Farm Douglas County,” April 22. Society of Soil Science, Vol. 54,No.4, pp 418-425.

19. Narayanan, S., (2005), “Organic Farming in India: Relevance, Problems and Constraints”,Occasional Paper-38, Department of Economic Analysis and Research, National Bank forAgriculture and Rural Development.

20. New Scientist (2001), Editorial, February 3, 2001.

21. OECD (2001c), “Sustainable Development: Critical Issues”., Paris, France.

22. Paroda, R.S. (2001), “99 Percent Pesticide Remain in Environment”, The Tribune, July19.

23. Palaniappan, S.P., and Annadurai, K. (1999), “Organic Farming: Theory and Practice”, ScientificPublisher (India), Jodhpur, p.18.

24. Plimmer, J.R., Chemistry of Pesticides, In: Krieger, R. (2001), Handbook of Pesticide Technology,2nd Edn. San Diego, CA: Academic Press, 95-107.

25. Pragya Agrawal, Leena Bhattacharya, Kalpana Kulshrestha and Mahapatra B.S. (2007), “Effectof Organic, Inorganic and Integrated Methods of Cultivation on Quality of Fresh Green Peas,”Journal of Eco-friendly Agriculture, Vol.2 (1), pp.20-22.

26. Ramanathan, K.M. (2006), “Organic Farming for Sustainability,” Journal of the Indian Society ofSoil Science, Vol. 54, No.4, pp 418-425.

27. Ransom, Pamela (2002), “Women, Pesticides, and Sustainable Agriculture”. Paper Presentedat the Women’s Caucus for the Earth Summit 2002 Available: http://www.earthsummit2002.org/wcaucus/Caucus%20position% 2020papers/agrilture/pestces1.htm.

28. Reganold, J.P., J.D. Glover., P.K. Andrew, and H.R. Hinman (2001), “Sustainability of Three AppleProduction Systems”, Nature, 410:926-929.

29. Thakur, D.S; V. Sharma; K.D. Sharma; C.L. Bhardwaj; A.S. Saini and H.C. Chandel (2003), “EconomicImportance of Organic Farming System (OSF) for Sustainable Agriculture in Hills (HP), IndianCouncil of Agricultural Research (ICAR), New Delhi.

30. United States General Accounting Office (1990), “Alternative Agriculture: Federal Incentivesand Farmers’ Opinions”, GAO/PEMD-90-12, US General Accounting Office, Washington,February.

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31. Singh Y.V. and Dinesh Kumar (2007),”Organic Farming VIS-A- VIS Human Health andEnvironment”, Kurukshetra, The Ministry of Rural Development, pp.3-7.

32. Thakur.D.S., K.D. Sharma, D.R. Thakur and A.S. Saini (2003), “Economics of Production andMarketing of Organic Produce and the Burgeoning Niche Market for Organic Food Products”,Indian Journal of Agricultural Marketing, Conf.Spl. (Abtract).

33. Vepa, S.S.; Deepa Verma; G.S.G. Prasad;G. Anuradha; R. Manghnani;G. Sagarika; K. Anantram; B.Anandkumar; S. Chandrakala; M. Mathew and D. Bhattacharyya (2004), “Atlas of the Sustainabilityof Food Security in India”, M.S. Swaminathan Research Foundation and World Food Programme,Chennai, India.

34. Weibel-FP; Bickel-R; Lethold-s; Alfondi-T; Niggli-U; Foguelman-D; Lockeritz (1999), “AreOrganically Grown Apples Tastier and Healthier? A Comparative Field Study Using Conventionaland Alternative Methods to Measure Fruit Quality”, Proceeding of the 12th International IFOAMScientific Conference, Mar de Plata, Argentina, November 15-29, 1998. 147-153.

35. Yokio Yokoi (2002), “Organic Agriculture in Japan: Developing of a Labelling Scheme andProduction Policies”, Organic Agriculture, Sustainable Markets and Policies, CABI Publications,OECD, 2003.



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AN EMPIRICAL STUDY OF WOMENLABOURERS AT WORKPLACE ANDAT HOME IN RURAL PUNJAB

Dharam Pal* and Gian Singh**

ABSTRACT

The study attempts to analyse the problems faced by the women labourers atthe workplace and on the domestic front along with their social status and livingconditions in the rural areas of Punjab. Data were collected from 498 households ofthree districts, namely, Sangrur, Ludhiana and Hoshiarpur, selected on the basis ofwork participation rate of rural women in Punjab by using multi-stage systematicrandom sampling technique. The study reveals that the social status and livingconditions of the sampled women labourers are very miserable in the rural areas ofPunjab. Further, almost all the sampled women labourers do not find work inagriculture and non-agriculture sectors for more than 180 days in a year. A largemajority of them do not enjoy any facility at their workplace. About one-third of therespondents are not being paid equal wages for equal work with men. Further, about93 per cent of the respondents are not aware about the standard working hours fixedby the government for such labour. This is mainly because of their illiteracy andignorance towards their rights. On the domestic front, there are some encouragingresponses which reflect an element of their importance at home and theirempowerment too to some extent.

* Assistant Professor in Economics, GGDSD College, Kheri Gurna, Banur, Patiala (Punjab). Email:

[email protected]

** Professor, Department of Economics, Punjabi University, Patiala (Punjab). Email: [email protected]

Introduction

The situation of women labourers inrural India is quite deplorable. They are oneamong the worst sufferers of socio-cultural,political and economic exploitation, injustice,oppression and violence. Their woes andmiseries are boundless. They are mainlyemployed in unorganised sector of the Indianeconomy as daily wagers and marginalworkers. Lack of adequate employmentopportunities, limited skills and illiteracy madetheir mobility extremely limited andprevented them from achieving anindependent status. They do not enjoy anysocial security, maternity benefits, pensionschemes or any other kind of economic

protection. With the adoption of policies ofglobalisation in India, their employmentopportunities are likely to be further reducedas they will have to suffer stiff competitionfrom foreign technology and modern methodsof agriculture (Jaiswal, 2009).

The plight of the sampled womenlabourers is very miserable. This is because ofthe fact that they have to face many problemsat the workplace and on the domestic front.Many studies (GoI, 2008; Rajasekhar et al.,2007; Sandhu, 2002; Tuteja, 2000; Padma, 1999;Rani et al., 1990) reveal that majority of thewomen labourers are illiterate, unskilled,socially backward and economically weakwhich force them to work in the unorganised




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sector without fair wages and occupationalamenities. They have few opportunities to seekemployment in the non-agriculture sector.They find employment only in occupationswhich need low level skill. The main objectiveof the study is to discuss the problems facedby the women labourers at the workplace andon the domestic front in the rural areas ofPunjab. But to find out these problems, it isquite significant to examine their social statusand living conditions. Therefore, an attempthas been made to analyse these.

Methodology

The present study, based on multi-stage systematic random sampling technique,relates to the year 2010-11. For the purposeof this study, the whole State has been dividedinto three zones of districts on the basis ofwork participation rate of rural women inPunjab. One district from each zone wasselected on an average basis. Sangrur districtrepresents the high work participation zone,while Ludhiana and Hoshiarpur districtsrepresent the medium and low workparticipation zones, respectively. From the listof villages in each development block in eachof the selected districts, one village wasselected randomly.

From these villages, acomprehensive list of the women labour

households was prepared. From this list, 10per cent of the households were selectedrandomly. In all, 498 households were selectedfor the survey. These households were visitedpersonally to collect information on thevarious socio-economic aspects of theirfamilies. The information was recorded bypersonal interview method through a pre-tested structured questionnaire designed forthe purpose. The results were analysed byusing the mean values and percentages.


The analysis of the social status ofwomen labourers ( Table 1) reveals thatmajority of the respondents are young asabout two-thirds (64.86 per cent) of therespondents have been found to be less than45 years of age in the rural areas of Punjab. Abreak-up of this percentage shows that 47.59per cent women labourers are between theage of 30 to 44 years, 16.27 per cent arebetween the age of 15 to 29 years while justone per cent are below the age of 15 years.Slightly more than one-fourth (26.51 percent) of the women labourers are in the agegroup of 45 to 59 years. Only 8.63 per centwomen labourers fall under the age categoryof 60 and above. The field survey reveals thatmost of the women labourers representingthis category get employment underMGNREGS.

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Table 1: Social Status of Sampled Women Labourers

Age (in years)

Below 15 15 to 29 30 to 44 45 to 59 60 and above Total5 81 237 132 43 498(1.00) (16.27) (47.59) (26.51) (8.63) (100.00)

Educational status

Illiterate Primary Middle Matric Higher Graduation Totalsecondary

363 14 94 22 4 1 498(72.89) (2.81) (18.88) (4.42) (0.80) (0.20) (100.00)

Caste

Scheduled castes Backward class General Total398 94 6 498(79.92) (18.88) (1.20) (100.00)

Marital status

Married Unmarried Widow or Divorced Total441 8 49 498(88.55) (1.61) (9.84) (100.00)

Number of children

Up to 2 2 to 4 4 to 6 6 and above Total158 193 121 18 490(32.25) (39.39) (24.69) (3.67) (100.00)

Family type

Nuclear Joint Total398 100 498(79.92) (20.08) (100.00)

Note: The figures given in parentheses indicate percentages.

Education creates awareness amongwomen about their rights and prepares themfor diverse occupation jobs (Bhatia andDhindsa, 2009). The analysis of data showingthe level of education of the respondents(Table 1) indicates that almost three-fourths(72.89 per cent) of the sampled womenlabourers are illiterate. There are only 27.11per cent women labourers who have acquiredsome formal school education. The analysisfurther provides that 18.88 per cent of thewomen labourers under study are educated

up to the middle level, 4.42 per cent up to thematric level and 2.81 per cent up to theprimary level. Merely a negligible proportionof just 0.80 per cent of the respondents areeducated up to the higher secondary level.There is just a single woman labourer (0.20per cent) in the sample who has education upto the graduation level. For male workers,higher levels of education are indeedassociated with higher WPR (workparticipation rate), both in rural and urbanareas. But for women, WPR is higher for

An Empirical Study of Women Labourers at Workplace and at Home in Rural Punjab


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illiterate women than for women with higherlevels of school education -- a trend whichreverses itself only for women with technical/vocational education or graduates (Srivastavaand Srivastava, 2009).

Caste has always been a dominantfactor in the social set-up of the Indian society.The present analysis also provides thatmaximum workforce in the agriculture andnon-agriculture sectors come from the so-called lower castes of the society. Caste-wisedistribution of women labourers shows thatmost of the sampled women labourers, i.e.,98.80 per cent either belong to the scheduledcaste or the backward class categories. Thepercentage of the scheduled castes, i.e., 79.92is the highest among all the categories. TheWPR is the highest for scheduled tribe (ST)and scheduled caste (SC) women and thelowest for women from the other castes. TheSCs and STs are the most marginalised sectionsin the economy and the most impoverished.Women from these groups have higher WPRsbecause extreme poverty leaves them withlittle choice but to work, and because they donot face social taboos that disapprove of work.The converse is true for women from othercastes (Srivastava and Srivastava, 2009).

The data further reveal that 88.55 percent of the total sampled women labourersare married, 1.61 per cent unmarried, whereas9.84 per cent of them are either widows ordivorced. The data showing the number ofchildren in each family of the respondentwomen labourers highlights that slightly morethan two-thirds (67.75 per cent) of therespondents have more than two children in

their families. The fact matches the empiricalfinding of another research study (Rani, 2011)which shows that most of the womenlabourers have large families. Most of thefamilies (79.92 per cent) of the sampledwomen labourers are nuclear comprisingmainly husband and their children. Further,one-fifth (20.08 per cent) of the respondentshave joint family system. It was observed duringthe field survey that all the adult members inthe sampled households are contributing toenhance the family income.

The living conditions of the womenlabourers were observed to be quite patheticin the rural areas of Punjab. The houses wherethey live in are in bad condition; and there hasnot been a proper arrangement of evenpotable water which exposes them to manyhealth hazards. Table 2 reveals that more thaneight out of ten respondents (401 out of 498)live in semi-pucca houses, whereas more thanone out of ten (54 out of 498) live in katchahouses. Only 8.64 per cent of them (43 out of498) have pucca houses. The Table furtherreveals that slightly more than two-thirds ofthe total respondents (67.27 per cent) areliving in dilapidated houses, while in the caseof more than one-fourth respondents (27.91per cent), their houses are in a moderatecondition. Only a meagre proportion of therespondents, i.e., 4.82 per cent have relativelygood houses to live in. It clearly reflects thatthe women labourers under study have to liveunder pitiable conditions due to theireconomic compulsions. They find it hard toeven get their houses repaired.



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Table 2 : Housing Conditions of Women Labourers

Type of house

Katcha Semi- pucca Pucca Total54 401 43 498(10.84) (80.52) (8.64) (100.00)

Conditions

Good Moderate Dilapidated Total24 139 335 498(4.82) (27.91) (67.27) (100.00)

Source of drinking water

Handpump Tap Submersible pump No. Total98 259 10 131 498(19.68) (52.01) (2.01) (26.30) (100.00)

Average number of Families having access Housesrooms available to bathroom\toilet electrified

464 4911.90 (93.17) (98.59)

Note: The figures given in parentheses are percentages.

As far as the source of potable wateris concerned, the Table indicates that morethan half of the respondents (52.01 per cent)are using tap water followed by those whoget water from handpumps (19.68 per cent)and submersible pumps (2.01 per cent). It ispertinent to note that more than one-fourthrespondents (26.30 per cent) have no sourceof potable water. They depend upon theneighbours or other sources for potable water.The Table further depicts that the averagenumber of rooms available per household is1.90. As many as 464 respondents (93.17 percent) are having access to bathroom/toilet.However, it has been observed during the fieldsurvey that the bathrooms/toilets used by

them are not proper in any respect. ThoughPunjab is regarded as one of the prosperousStates, there is no arrangement of bathrooms/toilets for about 7 per cent of the respondents.It has also been observed that there is noarrangement of electricity in the case of 1.41per cent sampled households.

The analysis of sectoral employmentof the sampled women labourers (Table 3)indicates that majority of the respondents, i.e.,77.51 per cent get employment in both theagriculture and non-agriculture sectors, 14.26per cent of the respondents get employmentonly in the agriculture sector, and theremaining 8.23 per cent get employmentonly in the non-agriculture sector.



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Table 3 : Sectoral Distribution of Sampled Women Labourers

Sector WorkplaceIn native Outside native Sampled women

village village labourers

Agriculture 65 6 71(18.37) (4.17) (14.26)[91.55] [8.45] [100.00]

Non-agriculture 18 23 41(5.08) (15.97) (8.23)

[43.90] [56.10] [100.00]Both agriculture and 271 115 386non-agriculture (76.55) (79.86) (77.51)

[70.21] [29.79] [100.00]Total 354 144 498

(100.00) (100.00) (100.00)[71.08] [28.92] [100.00]

Note: The figures given in upper and lower brackets indicate column-wise and row-wisepercentages, respectively.

It is evident from the Table that majorityof the respondents are not able to findsufficient amount of work in the agriculturesector alone in the rural areas of Punjab. So,they have to find it in both agriculture andnon-agriculture sectors. The labour absorptioncapacity of the agriculture sector reached theupper limit and it is not able to keep the ruralworkers engaged throughout the year. Therural households also seek employmentoutside the agriculture sector to tide over theinter-year and intra-year variations inagricultural income (Bhakar et al., 2007). TheTable further reveals that 71.08 per centrespondents work in their native villages andthe remaining 28.92 per cent go out of theirnative villages in search of work. Out of total354 respondents working in their nativevillages, majority of them, i.e., 76.55 per centearn their livelihood from both agriculture andnon-agriculture sectors. Only 18.37 per centof the respondents work in the agriculturesector alone and the remaining work only inthe non-agriculture sector. As regards thesectoral composition of the sampled women

labourers working outside the native village,79.86 per cent respondents work in bothagriculture and non-agriculture sectors, while15.97 per cent work only in the non-agriculture sector and the remaining work inthe agriculture sector alone.

One of the main problems faced bysampled women labourers is of irregularity intheir work. About one-third of the respondents(32.93 per cent) work for a period of 90 to120 days in a year. Almost equal proportion ofthe respondents (32.13 per cent) getemployment for a period of 60 to 90 days. Avery small proportion, i.e., 3.42 per cent of therespondents find work for more than 180 daysin a year. The data highlight the fact thatavailability of work for the sampled womenlabourers is quite low in the rural areas ofPunjab.

Usually, casual labourers have to workat the place wherever they get employment.Data show the willingness of the sampledwomen labourers to work according to thedistance of workplace. There is an inverse



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relationship between the distance ofworkplace and the willingness of therespondents to work, i.e., as the distance ofworkplace increases, lesser respondents areinterested to work. The fact responsible forthis phenomenon is that the domestic choresforce them to work nearby. About one-thirdof the respondents (32.33 per cent) are willingto work up to a distance of 4 kilometers andmore. Whereas slightly more than half of therespondents (50.60 per cent) stated that theyare willing to work around an area of 3 to 4kilometers. However, the strength of therespondents willing to work around an area of2 to 3 kilometers increased to 82.13 per cent.It further increased to 97.19 per cent for anarea of 1 to 2 kilometers, whereas all therespondents prefer to work in an area less than1 kilometer.

Table 4 provides details about the modeof transport used by the respondents to reachtheir workplace in the rural areas of Punjab. Itis evident from the Table that the respondents

do not use any particular mode of transport.They usually go on foot to work locally; theyalso use bicycles, public transport facilities andsometimes, the employers’ transport facilities.Up to a distance of 2 kilometers, most of therespondents (98.35 per cent) go on foot totheir workplace. For an area of 2 to 3kilometers, 76.53 per cent of the respondentsgo on foot, 40.10 per cent use employers’transport facilities, and 18.58 per cent use theirown bicycles. As many as 78.17 per cent ofthe respondents reach their workplace usingemployers’ means of transport, 36.51 per centon foot, 30.16 per cent on bicycles and 16.27per cent through public transport facilities fora distance of 3 to 4 kilometers. Further, for adistance of 4 kilometers and more, 18.63 percent respondents reach their workplace onfoot, and 44.72 per cent use their own bicycles.The employers’ mode of transport is usedusually for a distance of 2 kilometers and more,whereas the public transport is viable for adistance exceeding 3 kilometers.

Table 4 : Mode of Transport Used to Reach the Workplace by Women Labourers

Distance On foot Bicycle Employer Public Total (in kms.) transport transport

Less than 1 498(100.00) 0(0.00) 0(0.00) 0(0.00) 498(100.00)

1 to 2 476(98.35) 21(4.34) 0(0.00) 0(0.00) 484(100.00)

2 to 3 313(76.53) 76(18.58) 164(40.10) 0(0.00) 409(100.00)

3 to 4 92(36.51) 76(30.16) 197(78.17) 41(16.27) 252(100.00)

4 and more 30(18.63) 72(44.72) 146(90.68) 52(32.30) 161(100.00)

Note: The figures given in parentheses indicate row-wise percentages (Multiple responses).

There is no doubt about the fact thatbasic facilities such as arrangement of toilet,canteen, creche, etc., made available to thelabourers at their workplace contribute highlytowards their involvement in the work. Out ofthe 498 sampled women labourers, only 5.22per cent enjoy some of these facilities, whilethe remaining majority of them, i.e., 94.78 percent are not provided such facilities. Only 4.42

per cent respondents were provided the toiletfacility at their workplace, while canteenfacility is available to only 3.41 per cent ofthem. A negligible proportion of therespondents, i.e., just 0.60 per cent have thefacility of first aid. Not even a single womanlabourer in the sample has the facility ofcreche at their workplace.



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In India, despite the existence of EqualRemuneration Act 1976, the wagediscrimination among men and womenlabourers for a similar type of work stillprevails, particularly in the case of labourersworking in the unorganised sector. Table 5carries data showing wage discriminationamong men and women labourers in the ruralareas of Punjab. The Table shows that 30.32

per cent respondents feel that wages are notpaid equally to both men and women labourersfor the same type of work. It implies that 69.68per cent respondents find no discriminationin this regard. The Table also reveals the reasonsand areas of work in which wagediscrimination prevails among men andwomen labourers in the rural areas of Punjab.

Table 5 : Wage Discrimination Among Men and Women Labourers

Particulars Sampled womenlabourers

Wage discrimination Yes 151(30.32)

No 347(69.68)

Total 498(100.00)

Area of work in which wage Growing vegetables 101discrimination prevails (20.28)(multiple responses) Threshing 13

(2.61)Construction work 63

(12.73)White-washing 28

(5.62)Local industry 43

(8.63)Reasons for wage Male able to do all kinds 91discrimination of work (18.27)(multiple responses) Lack of mobility 43

(8.63)Traditional way followed 47

(9.44)Less output 74

(14.86)Hard labour involved in work 65

(13.05)No response 30

(6.02)

Note: The figures given in parentheses denote percentages.



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About various types of exploitationfaced by the sampled women labourers ingetting their wages, it is evident from data that70.08 per cent respondents do not face anyproblem in getting their wages, while theremaining respondents reported that theyhave to face problems in this regard. The mainproblems expressed by them include delay inthe payment of wages (23.49 per cent),demand for commission by the contractors/agents (11.04 per cent) and overtime workwithout any additional wages (7.23 per cent).

During the field survey, it wasobserved that there is no direct link between

the employers and women labourers. Figure1 reflects the position of women labourers atthe workplace in a hierarchical manner in therural areas of Punjab. The employers usuallydeal either with the contractors or the malemembers. The women labourers fall at thebottom of the hierarchy. On the top are theemployers, followed by the contractors, malelabourers or husbands and at the lowest levelare the women labourers. It is quite rare thatwomen labourers have a direct dealing withthe employers and contractors. As a result ofthis, some of the women labourers have toface exploitation in getting their wages.

Figure 1 : Position of Women Labourers at Workplace



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Table 6 reveals that majority of therespondents, i.e., 92.77 per cent are not awareabout the standard working hours fixed by thegovernment for such labour. This is mainlybecause of their illiteracy and ignorancetowards their rights. As far as putting inmaximum hours of work is concerned, majority

of the respondents (83.33 per cent) have towork for 8 to 9 hours in a day, if the work is ondaily basis. However, where the work is oncontract basis, majority of the respondents(81.33 per cent) are required to work for morethan 10 hours a day.

Table 6 : Awareness Among Sampled Women LabourersAbout Standard Working Hours

Particulars Sampled womenlabourers

Awareness Yes 36(7.23)

No 462(92.77)

Total 498(100.00)

Working hours If work is on Below 8 34daily basis (6.83)

8 to 9 415(83.33)

9 to 10 38(7.63)

10 and above 11(2.21)

If work is on Below 8 0contract basis (0.00)

8 to 9 28(5.62)

9 to 10 65(13.05)

10 and above 405(81.33)

Note : The figures given in parentheses indicate percentages.

As the respondents selected for thestudy were women labourers, it was quitedifficult to ask them direct questions abouttheir sexual exploitation at the workplace. So,it was considered appropriate to seek answersto such questions from their husbands,employers and male co-workers. The questionswere put to them in a different way and then

inferences were drawn. It was found that 6.83per cent of the respondents faced sexualexploitation. Another 15.06 per centrespondents faced no harassment at theirwork place. However, majority of therespondents, i.e., 78.11 per cent refused torespond on this issue. It is a fact widelyacknowledged that sexual harassment



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hampers women’s constitutional rights toequality and dignity. It sabotages workperformance, affects working environment,and diminishes women’s progress (Kapur,2013).

In addition to the problems faced at theworkplace, the women labourers have to facemany problems on their domestic front also,which are discussed as below:

The marital status is an importantdeterminant of women’s employment. In therural areas, women labourers are allowed towork only on the willingness and acceptanceof their husbands. Table 7 reveals whether the

husbands of the sampled women labourersapprove that their wives should hire out theirlabour or not. The reasons for their approval ordisapproval were also highlighted. Of the total498 sampled women labourers, 441 (88.55per cent) were living with their husbands andthe remaining were either unmarried orwidowed/divorced. Thus, the response datapertain to 441 respondents only. The Tablereveals that in 92.29 per cent cases, thehusbands of sampled women labourers haveno objection if their wives hire out their labour,while the remaining 7.71 per cent hold analtogether different view in this regard.

Table 7 : Attitude of Husbands Towards Working of Their Wives as Labourers

Particulars Sampled women labourers

Acceptance Yes 407(92.29)

No 34(7.71)Total 441

(100.00)Reasons for Additional income 171acceptance (38.78)(multiple responses) To run family 215

(48.75)To pay-off debt 59

(13.38)Employment within the 94village (21.32)

Reasons for non- Longer working hours 17acceptance (3.85)(multiple responses) No proper care of children 30

(6.80)Very low wages 8

(1.81)

Note: The figures given in parentheses denote percentages.

The Table further reveals that most ofthe women labourers, i.e., 48.75 per cent hireout their labour to run their families; 38.78per cent generate additional income for theirfamilies; 21.32 per cent for the reason thatemployment is available within the village; and

13.38 per cent hire out their labour to pay-offtheir debts. In cases, where the husbandsobjected to the working of their wives, thereasons given by them included no proper careof children (6.80 per cent), longer workinghours (3.85 per cent), and very low wages(1.81 per cent).



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Generally, men hold most or all of thepower in the rural households, which leads tofierce gender discrimination. Ignorance, lowsocial status and illiteracy among the womenare the main reasons that can be attributed totheir problem. Males, especially those who arein the low strata, are in the habit of incurringunnecessary and unwanted expenditure andthis aggravates their economic problemsfurther (Balakrishnan, 2005). Here, an attempthas been made to know from the respondentwomen labourers whether they have any sortof problems against their husbands. The datareveal that 65.99 per cent respondents haveno problem against their husbands, while theremaining raised some problems faced bythem. With regard to the nature of problemsfaced by the women labourers, 27.44 per centrespondents related their problems to thedrinking and smoking habits of their husbands,while 16.10 per cent respondents complainedthat they have been the victims of domesticviolence. However, another 13.38 per centrespondents related their problems to the ill-treatment made by their husbands.

It is also quite important tounderstand the behaviour of the husbands ofthe sampled women labourers during theillness of their wives. It is a moral duty of ahusband to take care of his wife and assist herin domestic chores, whenever she falls ill. Theresponses of all the 441 respondent womenlabourers, living with their husbands, weresought in this regard. The data reveal thatabout three-fourths of the respondents, i.e.,73.47 per cent are found to be getting suchassistance from their husbands, while theremaining think it otherwise.

The women labourers were furtherenquired about the type of help and assistancereceived from their husbands at the time oftheir illness. As many as 67.35 per centrespondents revealed that their husbands takethem to a nearby medical practitioner/dispensary/hospital for treatment. However,

45.12 per cent respondents revealed thattheir husbands take care of the children at sucha critical time, while in 52.83 per cent casesthe required medicine is provided to them bytheir husbands. It was also found that in only9.75 per cent cases, the husbands share thedomestic responsibilities at such a difficulttime. With regard to the behaviour of thehusbands towards their wives during theirillness, 18.59 per cent respondents stated thattheir husbands generally neglect them at suchtime. Another 10.67 per cent reported thatthey are ill-treated by their husbands.

In most of the rural labour class families,women are encouraged by their in-laws to dowork as a labourer along with their husbandsin order to enhance the family income, whilethere are others who discourage them not todo such menial jobs for their own reasons. It ispertinent to note that only 165 respondentsare living with their in-laws. Data reveal that alarge majority of the respondents, i.e., 89.70per cent are encouraged by their in-laws todo work as labourers, and the remaining 10.30per cent are discouraged in this regard. Childrearing (77.58 per cent), kitchen work (63.64per cent), house maintenance (62.42 per cent)and washing (56.97 per cent) are thesupporting activities undertaken by the in-lawsof sampled women labourers to hire out theirlabour.

In the absence of any creche facility atthe workplace, women labourers while onwork need the help of their family membersto look after their children. The sampleincludes 176 such respondents in all. The dataclearly show that 61.36 per cent respondentsengage their own son/daughter to take careof the younger child during their stay at work.Another 32.95 per cent reported that theirhusbands look after their children. Whenprobed further, they stated that due to non-availability of work, their husbands take theresponsibility to look after their children. Someof the women labourers (31.25 per cent) leave



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their children in the Anganwadi. The jointfamily system plays a great role in this respect.It was noticed that in 25.57, 23.30 and 22.16per cent cases, the children belonging towomen labourers are looked after by theirmothers-in-law, fathers-in-law and sisters-in-law, respectively.

The decision-making role of women inthe family is as important as that of men. Infact, the degree of involvement in decision-making process related to family matters canserve as a good indicator of the status ofwomen in households which in turn,determines their status in the society(Balakrishnan, 2005). The women labourerswere asked to respond whether their familymembers consulted them on important familymatters or not. To this, slightly more than one-third of the total respondents, i.e., 33.53 percent answered negatively, while theremaining two-thirds of them, i.e., 66.47 percent confirmed their involvement in allimportant family matters. The fact matcheswith the empirical finding of another researchstudy (Sethi, 1989) which concludes that allthe major decisions are taken by men in therural women households either individuallyor sometimes jointly with women. The fieldsurvey revealed that their recognition andinvolvement in the family decision-making ismainly due to their contribution towards thefamily income. The women labourerscontributing more towards the income oftheir families had greater participation in thedecision-making process of their families.

With regard to the reasons for non-involvement in the family decision-makingprocess, 22.29 per cent of the respondentsheld the opinion that it was a customarypractice not to involve women in allimportant family matters; whereas 15.46 percent respondents regretted that their familymembers did not involve them in decision-making process just for being females.However, 7.43 per cent respondents gave noresponse in this regard.

Conclusion

The foregoing analysis clearly revealsthat the social status and living conditions ofthe sampled women labourers in the ruralareas of Punjab are very miserable. Aboutthree-fourths of the women labourers areilliterate and almost all the respondents eitherbelong to the scheduled caste or thebackward class categories. Slightly more thantwo-thirds of the respondents have more thantwo children in their families. Further, majorityof the respondents are living in semi-puccahouses and most of their houses are indilapidated condition. It clearly reflects thatthe women labourers have to live underpitiable conditions due to their economiccompulsions.

Apart from this, the women labourershave to face many problems at theirworkplace. The study reveals that a largemajority of the sampled women labourers, i.e.,96.58 per cent do not find work in theagriculture and non-agriculture sectors formore than 180 days in a year. This depicts thatthe availability of work for the sampledwomen labourers is quite low in the rural areasof Punjab. As many as 94.78 per cent of thetotal sampled women labourers do not enjoyany facility at their workplace. About one-thirdof the respondents are not being paid equalwages for equal work with men. Further, alarge majority of the respondents, i.e., 92.77per cent are not aware about the standardworking hours fixed by the government forsuch labour. This is mainly because of theirilliteracy and ignorance towards their rights.

On the domestic front, there are someencouraging responses which reflect anelement of their importance at home and theirempowerment too to some extent. Like about67 per cent of respondents reported that theyare involved in the family decision making;about 74 per cent of men assist the womenand help them at least when they are unwell;



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about 71 per cent men give their income athome; and about 90 per cent are encouragedby their in-laws to work outside home andwhen needed their children are also taken careof by them.

Policy Implications

The results of the study and field surveyhave the following important implications:

� The government should effectivelyimplement MGNREGS and ensure that poorfamilies get 100 days of work in a yearunder it.

� The programmes for establishment ofagro-based small-scale industries in therural areas should be effectivelyimplemented.

� The poor people should make aware of thevarious government programmes whichprovide loans at very low rate of interestfor the establishment of various incomegenerating ventures.

� Adult education programmes should beeffectively implemented for the labourersto curtail the illiteracy level among them.

� The poor people should make aware of therural housing programmes chalked out bythe government. These programmesshould also bring more and more needyand poor people in its ambit.

� Both the government and non-governmentorganisations need to take the necessarysteps to organise skill developmentprogrammes for the economic upliftmentof women labourers.

� The provisions of the Minimum Wages Actand Equal Remuneration Act which protectthe rights of women and provide themequality with men in relation to wagesneed to be implemented more stringently.

� The women labourers remain deprived oftheir rights due to non-existence of anyrepresentative bodies. Thus, efforts needto be made in this regard.

References

1. Balakrishnan, A. (2005), Rural Landless Women Labourers: Problems and Prospects, KalpazPublications, New Delhi.

2. Bhakar, R.; Banafar, K.N.S.; Singh, N.P.; and Gauraha, A.K. (2007), “Income and EmploymentPattern in Rural Areas of Chhattisgarh: A Macro View”, Agricultural Economics ResearchReview, Vol. 20, Jul.-Dec., pp. 395-406.

3. Bhatia, S.; and Dhindsa, P.K. (2009), “Female Work Participation in the Emerging LabourMarket: A Case Study of Sarhali Village of Tarn Taran District”, The Journal of Developmentand Agricultural Economics, Vol. 1, No. 6, pp. 127-131.

4. GOI (2008), Socio-economic Conditions of Women Workers in Selected Food ProcessingIndustries Including Sea Food and Marine Products, Ministry of Labour & Employment,Labour Bureau, Shimla/Chandigarh.

5. Jaiswal, R.P. (2009), “Plight of Dalit Women in India: A Sociological Analysis”, InternationalConference on Social Science and Humanities, Singapore, 9th to 11th Oct., pp. 366-370.

6. Kapur, N. (2013), “Workplace Sexual Harassment: The Way Things Are”, Economic andPolitical Weekly, Vol. XLVIII, No. 24, pp. 27-29.

7. Padma, K. (1999), “Changing Cropping Pattern and Employment Conditions of WomenWorkers: A Case Study”, The Indian Journal of Labour Economics, Vol. 42, No. 4, pp. 687-698.



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8. Rajasekhar, D.; Suchitra, J.Y.; and Manjula, R. (2007), “Women Workers in Urban InformalEmployment: The Status of Agarbathi and Garment Workers in Karnataka”, The IndianJournal of Labour Economics, Vol. 50, No. 4, pp. 835-846.

9. Rani, M. (2011), “Socio-economic Conditions of Female Domestic Workers in Punjab: ACase Study”, in M. Verma et al. (eds.), Women and Children Issues: National andInternational Perspectives, Women’s Study Centre, Punjabi University, Patiala, Feb. 11-12, pp. 603-613.

10. Rani, P.S.; Raju, V.T.; Ram, P.R.; and Naidu, G.M. (1990), “Wage Differentials and FactorsGoverning Employment of Women in Agriculture”, Agriculture Situation in India, Vol. XLV,No. 4, pp. 240-252.

11. Sandhu, P. (2002), “Female Labour Force in Punjab: Socio-economic Profile, ParticipationRate and Problems Faced”, The Indian Journal of Social Work, Vol. 63, No. 4, pp. 552-565.

12. Sethi, R.M. (1989), “Status and Power of Rural Women”, in R.K. Sapru (ed.), Women andDevelopment, Ashish Publication House, New Delhi, pp. 38-61.

13. Srivastava, N.; and Srivastava, R. (2009), “Women, Work, and Employment Outcomes inRural India”, Paper Presented at the FAO–IFAD- ILO Workshop on Gaps, Trends and CurrentResearch in Gender Dimensions of Agricultural and Rural Employment: DifferentiatedPathways out of Poverty, Rome, 31 March–02 April.

14. Tuteja, U. (2000), “Female Employment in Agriculture: A District-wise Analysis of Haryana”,The Indian Journal of Labour Economics, Vol. 43, No. 2, pp. 339-347.



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A STUDY OF MID-DAY MEAL SCHEMEIMPLEMENTATION IN NALGONDA DISTRICT FORIMPROVING SCHOOL ATTENDANCE

Sambi Reddy Vippala*

ABSTRACT

The erstwhile State Government of Andhra Pradesh launched the cooked Mid-Day Meal (MDM) Programme in 2003 to all primary school children in Government,local body and Government Aided Schools. The State is also implementing the MDMscheme in the high schools covering students of 9th and 10th classes from 2008 withthe State budget. Mid-Day Meal Scheme (MDMS) is being implemented by theDepartment of School Education, Government of Andhra Pradesh in the State. Theresearcher studied schools of three mandals (Nadigudem, Munagala and Kodad) inNalgonda district of A.P by using normative survey method. Students, teachers andhead teachers responded to this research study. The researcher selected ‘stratifiedrandom method’ for sampling. The present study consists of a total of 12 schools fromthree mandals. The study found that majority of the schools are receiving poor qualityof rice from the government; there is no safe drinking water for MDM, no properdistribution of bills, lack of training for administrators. The enrolment and attendanceimproved and classroom hunger avoided due to MDM programme.

* E.O, (P.R & R.D), MPP Nuthankal, Nalgonda (Dist), Telangana.

Introduction

Education is that which makes one’s lifein harmony with all existence and thus enablesthe mind to find the ultimate truth which givesus the wealth of inner light and love and givessignificance to life- Rabindranath Tagore

In India, The School Lunch Programme(SLP) was first introduced in Madras by theCorporation for children belonging to poorfamilies.SLP was introduced in some parts ofKerala in 1941 followed by Bombay in1946.Uttar Pradesh commenced theprogramme on a voluntary basis in 1953. Manya programme with assistance frominternational agencies such as UNICEF, FAO andWHO became available. Cooperative ofAmerican Relief Everywhere (CARE) initiatedits assistance to mid-day meals programme(MDMP) in 1961 in the States of Kerala andTamil Nadu. It began supply of food

commodities under PL480 TITLE II programme.During the first year of operation it benefited2.4 million children. In 1990-91, 17 Stategovernments were running the MDMP forprimary school children of 6-11 age group.Twelve States conducted the programme withtheir own resources. Andhra Pradesh andRajasthan implemented the programme withCARE assistance and discontinued the schemeafter CARE withdrew its support. The Sixth AllIndia Educational Survey, jointly conducted bythe NCERT and NIC, reported in August 1998that out of 735771 schools in the countrycovering all school stages 187016 (25.42 percent) had MDMP. Out of 22553505beneficiaries were studying in rural schools.Totally 59.02 and 40.98 per cent girls wereamong the beneficiaries, 147647(27.92 percent) primary schools and 33757(24.28 percent) higher secondary schools were operatingthe MDMP.




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In the primary schools 13669720 werebenefiting from the scheme. Among them83.79 per cent were in rural schools and 16.21per cent were in urban schools, 21.93 per centof the beneficiaries belong to SC and 16.66per cent belong to the ST categories, 61.41per cent were from other categories. Amongthem, 57.78 per cent were boys and 42.22per cent were girls. In upper primary schools7073280 students were taking advantage,60.38 per cent were boys and 39.62 per centgirls, 71.28 per cent were in rural schools and28.72 per cent in urban schools.

Programme for Nutritional Support toPrimary Education (NPNSPE) has beenimplemented since the year 1995-96. NPNSPEenvisaged a full coverage in a phased mannerover a period of three years. The programmecovered 2499 blocks during 1995-96, 4426blocks during 96-97 and 5451 blocks during1997-98. By December 31, 1998, it covered504 districts.

It was expected to cater to 974.5 lakhstudents across 6.85 lakh schools. Theprogramme provided the States with theoption of giving nutritional support in the formof following alternatives:

� Cooked meal (100 gms per day for 200school days)

� Pre-cooked meal

� Three kgs of wheat or rice per child permonth for 10 months

Presently, cooked meals are served inGujarat, Kerala, Tamil Nadu and Pondicherry. InDelhi and Chandigarh, processed food isdistributed. In the rest of the States or UTs,foodgrains are being distributed.

At the request of UNICEF, theOperations Research Group (ORG) conductedan evaluation study of the impact of NPNSPEin 1999. The study adopted a multi-stagesampling procedure and selected 10 States,

25 districts, 50 blocks, 397 villages and 397schools as its sample. It covered 1795 studentsand 1227 receiving MDM, 404 eligible but notreceiving and 164 never enrolled students. Inall, 3164 parents were contacted. Of them,1069 parents were of children receiving MDM,930 parents of children not receiving MDMand 1105 parents of children who neverenrolled or dropped out.

There was limited variation in theattendance pattern of students during the pre-programme period. The students with 80 ormore per cent of attendance rose from 59 to64 between 1994-98. The proportion of boyshaving an attendance of 80 per cent or morewas marginally higher as compared to girls.

National Council for EducationalResearch and Training (NCERT) conducted thesixth all India Educational Survey (1998) whichrevealed that out of 7, 35,771 schools in thecountry covering all school stages, 1,878,016(25.42 per cent) had mid-day mealprogramme. Out of 2, 25, 53,505 beneficiaries,78.41 per cent were studying in rural schools,59.02 per cent of boys 40.98 per cent of girlswere among the beneficiaries. And 27.92 percent of primary schools, 24.28 per cent ofsecondary schools were operating the mid-day meal programme.

United Nations International EducationForum (UNICEF) conducted a research reviewon mid-meal programme by using multi-stagesampling procedure in 1999. It revealed thatthe coverage of the programme was poor inMadhya Pradesh, moderate in Gujarat, good inOdisha, Rajasthan and Uttar Pradesh. The levelof awareness of the provision of mid-day mealprogramme at school was high among allparents, but awareness of 80 per centattendance for eligibility for MDM was lowamong parents and children too.

National Institute of Public Cooperation& Child Development (2006) conducted astudy on Mid-Day Meal Scheme in Karnataka.

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The institute surveyed all the schools whichprovide mid-day meals in Karnataka. The studyreport indicated that the Mid-Day Mealscheme improved the school attendance inmajority of the schools and reducedabsenteeism, reduced dropout rate, especiallyin the primary school stage. In addition, thereport observed that the mid-day meal schemefostered a sense of sharing and fraternity andpaved way for social equity.

Josephine & Raju (2008) studied theMid-Day Meal Programme in Andhra Pradesh.The study revealed that the programmereduced dropout rate and shownimprovement in retention, and effectivelyalleviated classroom hunger. It curbed teacherabsenteeism and narrowed social distances.Sharing of common meal enhancedsocialisation and reduced prejudices. Itmobilised women self-help groups foreffective implementation of MDM.

Andhra Pradesh

The Government of Andhra Pradeshlaunched cooked Mid-Day Meal Programmein 2003 to all primary school children ingovernment, local body and aided schools.Subsequently it was extended to childrenenrolled under Education Guarantee Scheme(EGS), Alternative & Innovative Education (AIE)centres and Madrasas / Maqtabs and NCLPschools. The State has also been financing andimplementing the MDM in the high schoolscovering students of 9th and 10th classes from2008. Mid-Day Meal Scheme (MDMS) is being

implemented by the Department of SchoolEducation, Government of Andhra Pradesh.

Commissioner cum Director SchoolEducation is the nodal officer for theimplementation of the mid-day meal schemein the State. The department of schooleducation is responsible for planning,implementation and monitoring of thescheme in the State. It also coordinates withother participating agencies like Food & CivilSupplies, FCI, Panchayati Raj, Health and UrbanDevelopment. The Andhra Pradesh State CivilSupplies Corporation is the nodal agency forlifting the foodgrains from the FCI andsupplying to the schools through Mandal LevelStorage points and fair price shops. The PAB-MDM approved Central assistance for 59.98lakh children (38.76 in primary and 21.22 lakhin upper primary) studying in 78,716 schools(59,023 primary and 19,693 upper primary).The State covered all the approved 59,023primary schools and 19,693 upper primaryschools and serving mid-day meal to 38.76lakh children in primary (class I-V) and 21.22lakh children in upper primary (class VI-VIII).

Nutrition Content under MDM Scheme :a) 450 kcal and 12g of protein which is derivedfrom 100g of foodgrains (rice/wheat), 20g ofpulses, 50g of vegetables and 5g of oil forchildren studying in primary classes and

b) 700 kcal and 20g of protein, which isderived from 150g of foodgrains (rice/wheat),30g of pulses, 75g of vegetables and 7.5g ofoil in upper primary classes.

Quantity of Food

S. No. Items Quantity per child per dayPrimary Upper Primary

1 Foodgrains 100g 100g2 Pulses 20g 30g

3 Vegetables (leafy also) 50g 75g

4 Oil & fat 5g 7.5g5 Salt & condiments As per need As per need

A Study of Mid-Day Meal Scheme Implementation in Nalgonda ...


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Engagement of Cook Cum Helpers

� One cook-cum-helper may be engaged ina school having up to 25 students, twocooks-cum-helpers for schools having 26to 100 students, and one additional cook-cum-helper for every additional 100students w.e.f. 01.04.2010.

� Provision for payment of honorarium tocook-cum-helper @ ` 1000 per month i.e.` 750 and ` 250 per month as Central andState share, respectively.

Implementing Agencies

� In rural areas-DWCRA/Self-Help-Groups/SEC/other agencies like temple, NGOs ofproven track record, charitable trusts/groupof parents (in this order of preference) areidentified by the MROs in rural areas.

� In urban areas, community developmentsocieties (CDS)/NGOs/urban SHGs/DWCUA/SEC/other agencies like temples/NGOs of proven track record/ charitabletrusts/group of parents (in this order ofpreference) are identified by a committeeheaded by the MRO.

Nalgonda District

As of 2011 India census, Nalgonda hada population of 135, 163. Males constitute 51per cent of the population and females 49 percent. Nalgonda has an average literacy rate of87.08 per cent, higher than the nationalaverage of 59.5 per cent, male literacy is 92.23per cent, and female literacy is 81.92 per centIn Nalgonda, 11 per cent of the population isunder 6 years of age.

In 2006, the Indian government namedNalgonda one of the country’s 250 poorestdistricts (out of a total of 640). It is one of thethirteen districts in Andhra Pradesh currentlyreceiving funds from the Backward RegionsGrant Fund Programme (BRGF).

Need and Importance

The 93 rd Amendment bill makeseducation for children in the 6-14 years age

group a fundamental right. The governmentshould introduce the right incentives to attractthe children to the school instead of burdeningthe parents with the fundamental duty ofproviding education opportunities to theirchildren.

The mid-day meal programme for thechildren was initially viewed as an act ofcharity. Over a period of time it came to beconsidered as an item of “child welfare”. Stilllater it was regarded as a component of childdevelopment and ushered in nutritionalapproach. In the context of extensive poverty,illiteracy, lack of awakening and want ofpopular demand for formal school education,the mid-day meal programme for the schoolchild assumed certain level of significance.

Educational researchers and plannersare obliged to suggest ways to maximise thereturns on public investment in basic educationand when the returns on certain programmesare not satisfactory, they should suggestalternative strategies for fulfilling the nationalaspirations and goals in this respect.

Objectives of the Study

1. To study the implementation of mid-daymeal Scheme with reference to threemandals of Nalgonda district (A.P) toimprove school attendance.

2. To study the differences among threemandals regarding mid-day meal schemeimplementation in their schools.

3. To elicit the perceptions of teachers,students and parents on mid-day mealscheme implementation to improve schoolattendance.

4. To assess the quality views in relation tovarious components for betterimplementation of mid-day meal scheme.

Methodology of the Study

In view of the objectives and nature ofthe study, the researcher selected ‘stratifiedrandom method’ for sampling. The presentstudy consists of a total 12 schools from three

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mandals, from each mandal four villages wereselected. From each village 1 head teacher(HM), 5 teachers, 5 students and 5 parents wereselected randomly.

Then a total of 12 HMs, 60 teachers, 60students and 60 parents were selected in thestudy included by rural and urban; male andfemale; SC/ST/BC/OC and differenteducational qualifications. The researcherselected the ‘Normative survey method’ forthe present study. This method gathers datafrom a relatively large number of samples,provides the information useful to the solutionof local problems and it is concerned not withthe characteristics of individuals but withcharacteristics of the whole population or asample.

With prior permission of the school, theresearcher visited the schools. Questionnaireswere given to the head teachers, teachers,students and parents. Researcher explainedthe purpose of the study and clarified doubtsfor filling up the questionnaire. Observationswere recorded by the researcher at the timeof data collection. The tools were designedafter deep study of review of literature relatedto present topic, question items are preparedand discussed with the experts.

Data Analysis and Interpretation

The opinion elicited from the HMs andtheir responses with percentages on MDMscheme in the schools are presented.

Table 1 : Quality of Foodgrains (rice) Received for the Mid-DayMeal Scheme in the School

S. No. Name of the Responses of the HMs TotalMandal Very Good Good Average Poor

Kodad - - 2 (50%) 2 (50%) 4 (100%)Munagala - - 1 (25 %) 3 (75 %) 4 (100%)Nadigudem - 1 (25 %) 2 (50 %) 1 (25 %) 4 (100%)

Total - 1 (8.3%) 5 (41.7 %) 6(50%) 12 (100%)

Table1 reveals that 50 per cent of theschools are receiving poor quality of rice fromthe government, 41.7 per cent of the schoolsare receiving average quality of rice and only8.3 per cent of the schools received goodquality of rice for MDM scheme in the school.

The same Table indicates that in Kodadmandal, 50 per cent of the schools arereceiving average quality of rice, 50 per centof the schools are receiving poor quality of

rice; In Munagal mandal, 75 per cent of theschools are receiving poor quality of rice and25 per cent of schools average, whereas, 25per cent schools of Nadigudem mandal arereceiving good quality of rice to the schoolsbut 50 per cent of schools in the same mandalare receiving average quality of rice, 25 percent of schools are receiving poor quality ofrice. School head masters also takeresponsibility regarding the quality of rice.

Table 2 : Kitchen Facility Available in the School

S. No. Name of the Mandal Responses of the HMs TotalYes No

1. Kodad 1 (25 %) 3 (75 %) 4 (100%)2. Munagala 2 (50%) 2 (50 %) 4 (100%)3. Nadigudem 3(75%) 1 (25 %) 4 (100%)

Total 6 (50 %) 6 (50%) 12 (100%)



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Table 2 reveals that 50 per cent of theschools have kitchen rooms for MDM scheme,and 50 per cent schools do not have a kitchen.In Kodad mandal, 75 per cent of the schoolsdo not have kitchen rooms, only 25 per cent

of the schools have kitchen. In Munagalamandal, kitchen facility is available in 50 percent of the schools; In Nadigudem mandal,kitchen facility is available in 75 per cent ofthe schools.

Table 3 : Availability of Cooking Agency for MDM Scheme in the Schools

S. No. Name of the Responses of the HMs Total

Mandal Members of School Private ParentsSHG (1) Management Agency Committee

Committee (3) (4)(SMC) (2)

1 Kodad 4 (100%) - - - 4 (100%)

2 Munagala 3 (75 %) - - 1 (25 %) 4 (100%)

3 Nadigudem 3 (75%) - - 1 (25 %) 4 (100%)

Total 10 (83%) - - 2 (17%) 12 (100%)

Table 3 reveals that, members of self-help group (SHG) are cooking food for MDM in83 per cent of the schools, parent committeemembers are cooking in 17 per cent of theschools. In Kodad mandal, members of SHGare cooking in all schools (100 per cent). In

Munagala mandal, 75 per cent of schools andNadigudem mandal 75 per cent of theschools, cooking is done by members of SHG.It is pointed out that parents committee isalso involving in cooking food for MDMscheme.

Table 4 reveals that the maximumschools (83.3 per cent) are not receivingfoodgrains (rice) in-time; only 16.7 per centare receiving rice at the school in-time. InKodad mandal, 75 per cent of the schools are

not receiving rice in-time, and the same ishappening in Nadigudem mandal too. But 100per cent of the schools are receiving rice fromthe dealers in-time.

Table 4 : Receiving Foodgrains in-time to the School

S. No. Name of the Responses of the HMs Total

Mandal Yes No

Kodad 1 (25%) 3 (75%) 4 (100%)

Munagala — 4 (100 %) 4 (100%)

Nadigudem 1 (25%) 3 (75%) 4 (100%)

Total 2(16.7 %) 10 ( 83.3 %) 12 (100 %)

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Table 5 reveals that 65 per cent ofrespondents opined that MDM schemeenhanced the enrolment of the children,

Table 5 : Increase of Children's Enrolment Due to MDMScheme Implementation in the Schools


1 Kodad 3 (75 %) 1 (25 %) 4 (100%)

2 Munagala 3 (75 %) 1 (25 %) 4 (100%)

3 Nadigudem 2 (50 %) 2 (50 %) 4 (100%)

Total 8 (65 %) 4 (35%) 12 (100 %)

whereas only 35 per cent of the respondentsopined that MDM scheme does not influenceenrolment of children.

Table 6 : Regularity of Childen Attending School


1 Kodad 4 (100%) - 4 (100%)

2 Munagala 3 (75%) 1 (25%) 4 (100%)

3 Nadigudem 3 (75%) 1 (25%) 4 (100%)

Total 10 (83 %) 2 (27%) 12 (100 %)

Table 6 reveals that 83 per cent of theschools have regular attendance of childrenbut only 27 per cent of the schools do nothave regular attendance. And 100 per centregular attendance in Kodad mandal schools,75 per cent regular attendance in Munagal andNadigudem. And that majority of therespondents (83 per cent) opined that

regularity improves in the school due to MDMscheme but only 27 per cent respondentsopined there is no change. In case of Kodadmandal, there is 100 per cent of regularity ofthe students and there is 75 per cent regularityof the students each in Munagala andNadigudem mandals.

Table 7 : Safe Drinking Water Facility in the Schools


1 Kodad 2 (50%) 2(50%) 4 (100%)

2 Munagala 1(25%) 3(75%) 4 (100%)

3 Nadigudem 1(25%) 3(75%) 4 (100%)

Total 4(33.2%) 8(66.8%) 12 (100 %)



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Table 7 reveals that there is no safedrinking water facility available in majority of

the schools (66.8 per cent). But 33.2 per centof schools had safe drinking water facility.

Table 8 : Providing MDM Bills to the Cooking Agency and Helpers

S. No. Name of the Responses of the HMs TotalMandal Yes No

1 Kodad - 4 (100%) 4 (100%)2 Munagala - 4 (100%) 4 (100%)3 Nadigudem - 4 (100%) 4 (100%)

Total - 12 (100 %) 12 (100 %)

Table 8 reveals that bills are not properlypaid to cooking agency in time. Same is the

case with three sample mandals.

Table 9 : Fulfilling the Children's Right to Food Objective Due to MDM

S. No. Name of the Responses of the HMs TotalMandal Yes No

1 Kodad 3 (75 %) 1 (25%) 4 (100%)2 Munagala 2 (50%) 2 (50%) 4 (100%)3 Nadigudem 1 (25%) 3 (75%) 4 (100%)

Total 6 (50 %) 6 (50%) 12 (100 %)

Table 9 explains that 50 per cent of therespondents opined MDM scheme fulfills theobjective of right to food for children. Another

50 per cent of the respondents opinednegatively.

Table 10 : School Enrolment, Malnutrition of Food, SchoolAttendance, Mid-Day Meal Scheme

S. No. Statement Teachers’ responses Parents’ responsesYes No Yes No

1. School enrolment increaseddue to Mid-Day Meal ( MDM) 50 (80.5 %) 10 (19.5%) 45 (72%) 15(28 %)

2. Malnutrition of childrendecreased due to MDM scheme 30 (50%) 30 (50 %) 40(64 %) 20(36 %)

3. School attendance increaseddue to MDM scheme 53(84.8 %) 07 (5.2 %) 50(80.5%) 10(19.5%)

Table 10 indicates that majority of theteachers and parents (80.5 and 72 per cent)opined that the school enrolment increaseddue to MDM scheme. But only a smallpercentage of teachers (19.5 ) and parents (28)felt there is no increase in enrolment due toMDM. The same Table reveals that the teachersopined that malnutrition in children decreased

due to MDM (50 per cent). But the majority ofthe parents (64 per cent) opined thatmalnutrition in children decreased due to MDMscheme in the schools. It reveals that majorityof the teachers and parents (84.8 and 80.5per cent) viewed school attendance to haveincreased due to MDM scheme.

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Table 11 reveals that 88 per cent ofteachers’ perception on implementation ofMDM school is good, but parents’ (35.2 percent) perceptions are not positive aboutimplementation of MDM scheme in schools.Teachers (80 per cent) viewed food norms

Table 11 : Implementation of MDM Scheme, Food Norms (Quantity), Paying Bills


1. MDM scheme implementationin our school/ village is good 55 (88%) 05 (12%) 22(35.2%) 38(64.8%)

2. Food norms (quantity) aredesigned properly 50(80%) 10(20%) 35(56%) 25(46%)

3. Paying bills to agency, cook& helper is negotiable 40(64%) 20(36%) 40 (64%) 20(36%)

designed in proper way but 56 per cent arenot satisfied with food norms. Paying bills toagency, cook and helper is negotiable asexpressed by teachers and parents (64 and 64per cent).

Table 12 : Work Load for Teachers, Utilisation of Noon Time by Students andCooking by SHG Members


1. It is additional load for teachersto maintain the MDM Scheme 44 (70.4%) 16(29.6 %) 08(12.8%) 52(86.2%)

2. Students utilise afternoon timeproperly due to MDM 54(86.4%) 06(12.6 %) 55(88%) 5(12%)

3. Cooking by SHG members in theschool is suitable 33(52.8%) 27(6.2%) 22(35.2%) 38(64.8%)

Table 12 reveals that majority teachers(70.4 per cent) opined to be additional loadfor teachers to manage the MDM Scheme, butparents viewed positively. Hence theperceptions of both are different. Majority ofteachers (86.4 per cent), parents (88 per cent)opined that students utilise afternoon timeproperly due to MDM scheme. Cooking by SHG

members in the school is suitable as expressedby 52.8 per cent teachers and 35.2 per cent ofparents. It is concluded that there is adifference between opinions of parents andteachers with regard to work load for teachersmanaging MDM Scheme and Cooking by SHGmembers in the school.

Table 13 : Supervision on MDM Scheme, Social Inequality & Social Participation Aims


1. Supervision on MDM schemeto be strengthened 55 (88%) 05 (12%) 56(89.6 %) 04 (9.4 %)

2. Social inequality & socialparticipation aims are fulfilledby MDM in the school 54(86.4%) 06 (12.6 ) 54(86.4%) 06 (12.6 )



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Table 13 reveals that majority ofteachers (88 per cent) and parents (89.6 percent) opined supervision on MDM scheme to

be strengthened and majority of respondents( 86.4 and 86.4 per cent) felt social inequality& social participation aims fulfilled by MDMin the school.

Table 14 : Taking Mid-Day Meal Daily, Encouragement of Parents, GettingAbsence Due to MDM

S. No. Statement Students’ ResponsesYes No

1. I take Mid-Day Meal (MDM) in our school daily 52 (83.2%) 8 (15.8 %)2. My parents encourage me to take MDM in

the school 50 (80%) 10 (20%)3. I am unable to be absent due to MDM

scheme in school 38 (60.8%) 22 (39.2%)

Table14 reveals that majority of therespondents (83.2 per cent) are taking MDMin their schools, only 15.8 per cent are nottaking, and 80 per cent of students’ parents

encourage students to consume MDM in theschool; 60.8 per cent of the students areunwilling to be absent due to mid-day meal.Only 22 per cent students are absent.

Table 15 : Development of Healthy and Good Habits Due to MDM

S. No. Statement ResponsesYes No

1. Healthy and good habits are developed dueto mid-day meal scheme 44 (70.4%) 16 (29.6 %)

2. I am getting proper nutrition with MDM 41 (65.6) 19 (34.4 %)

3. I am satisfied with MDM in our school 44 (70.4%) 16 (29.6 %)

Table 15 reveals that majority of thestudents (70.4 per cent) feel healthy and goodhabits are developed due to mid-day meal

scheme, 65.6 per cent of the students feelthey get proper nutrition with MDM, 70.4 percent feel satisfied with MDM.

Table 16 : Quality and Taste of MDM, Taking Two Eggs Weekly, Supervision andEating Together with Friends

S. No. Statement ResponsesYes No

1. MDM in our school is of good qualityand tasty 37 (59.2 %) 23 (39.8%)

2. I take two eggs weekly by MDMscheme in our school 39 (62.4 %) 21(37.6%)

3. Supervision to be strengthened onMDM scheme 41(65.6 %) 19(34.4%)

4. Eating with my friends mid-day mealmakes me happy 50 (80%) 10(20%)

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Table 16 reveals that 59.2 per cent ofstudents feel mid-day meal is of good qualityand tasty, but 39.8 per cent students feelotherwise. Majority of the students take twoeggs per week. Eighty per cent eat with theirfriends and mid-day meal makes them happy.

Major Findings of the Study

1. Fifty per cent of the schools receive poorquality of rice from the government, 41.7per cent of the schools receive averagequality of rice and only 8.3 per cent of theschools receive good quality of rice forMDM scheme in the school.

2. In Kodad mandal, 50 per cent of theschools received average quality of rice,50 per cent of the schools received poorquality of rice; In Munagal mandal, 75 percent of the schools got poor quality of riceand 25 per cent of schools average,whereas, 25 per cent schools ofNadigudem mandal received good qualityof rice to the schools but 50 per cent ofschools in the same mandal receivedaverage quality of rice, 25 per cent ofschools received poor quality of rice.

3. Fifty per cent of the schools have kitchen.In Kodad mandal, 75 per cent of theschools do not have kitchen but only 25per cent of the schools have kitchen; InMunagala mandal, kitchen facility isavailable in 50 per cent of the schools; InNadigudem mandal, kitchen facility isavailable in 75 per cent of the schools.

4. Members of self-help group (SHG) cookfood for MDM in 83 per cent of the schools,parent committee members cook in 17per cent of the schools. In Kodad mandal,members of SHG are cooking in all schools(100 per cent). In Munagala mandal 75 percent of schools and Nadigudem mandal75 per cent of the schools, MDM is cookedby members of SHG.

5. Cook and helper availability in all schools(100 per cent) and all schools of threemandals have cooks and helpers.

6. The maximum schools (83.3 per cent) arenot receiving foodgrains (rice) in-time;only 16.7 per cent are receiving rice atthe school in-time. In Kodad andNadigudem mandals, 75 per cent of theschools have not received rice-in time.However, 100 per cent of the schools arereceiving rice from the dealers in-time.

7. Fifty per cent of the school’s enrolmentincreased below 10 per cent; and 50 percent of the school’s enrolment increasedbetween 11 and 20 per cent due to MDMscheme. It can be concluded that there isan increase in enrolment in schools dueto MDM scheme.

8. Majority of the respondents (83 per cent)opined that regularity to the schoolimproved due to MDM scheme. In case ofKodad mandal, there is 100 per cent ofregularity of the students and there is 75per cent regularity of the students each inMunagala and Nadigudem mandals.

9. Boys and girls sit separately in 41.5 percent of the schools; About 33.2 per centof children sit in accordance with classstudying, only 24.3 per cent schoolschildren sit together for mid-day meal.

10. There is no health problem reported bychildren due to MDM scheme in theschools. However, only 33.2 per cent ofthe schools are getting health problemsdue to MDM meal.

11. Training programmes are not conductedfor HMs on MDM scheme. Paying of billsto cooking agency in time is not found inany of the three mandals. Fifty per cent ofthe respondents opined MDM scheme tobe fulfilling the objective of right to foodfor children.

12. Majority of the teachers and parents (80.5and 72 per cent) opined that the schoolenrolment increased due to Mid-Day Mealscheme. A small percentage of teachers(19.5 ) and parents (28) feel there is no



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increase in enrolment due to MDMimplementation.

13. Incidence of malnutrition among childrendecreased due to MDM scheme. But themajority of the parents (64 per cent)opined there is decrease of malnutritionamong children due to MDM scheme inthe schools. It reveals that majority of theteachers and parents (84.8 and 80.5 percent) viewed school attendance to haveincreased due to MDM scheme.

14. Eighty eight per cent of teachers’perception on implementation of MDMin the school is positive. Teachers (80 percent) said food norms are designed inproper way but 56 per cent are notsatisfied with food norms. Paying bills toagency, cook and helper is negotiable asviewed by teachers and parents (64 and64 per cent).

15. Majority of teachers (70.4 per cent) opinedas additional work load for teachers tomaintain the MDM Scheme, but not thesame opinion by the parents. Hence theperceptions of both are different. Majorityof teachers (86.4 per cent), parents (88per cent) opined of better attention andretention with the help of mealconsumed in the mid-day. Cooking by SHGmembers in the school is expressed tobe good.

16. Majority of teachers (88 per cent) andparents (89.6 per cent) opined supervisionon MDM scheme to be strengthened andmaximum respondents ( 86.4 and 86.4per cent) felt social inequality and socialparticipation aims are fulfilled by MDM inthe school.

17. Majority of the respondents (83.2 per cent)take MDM in their schools, only 15.8 percent do not take, and 80 per cent ofstudents’ parents are encouragingstudents to take MDM in the school; 60.8per cent of the students are unable to skipschool due to mid-day meal.

18. Majority of students (64 per cent) feltdrinking water facility and cleanliness tobe good in their schools, 64 per cent said

quantity provided under MDM scheme issufficient, and 60 per cent students'opinion is mid-day meal menu is properlydesigned.

19. Majority of the students (70.4 per cent)say healthy and good habits are developeddue to mid-day meal scheme. 65.6 percent of the students stated that they aregetting proper nutrition due to MDM, 70.4per cent expressed satisfaction with MDM.

20. Mid-day meal is of quality and tasty, but39.8 per cent students say it is of poorquality. Majority of students consume twoeggs weekly by MDM scheme in theirschools; 80 per cent eat with their friendsand mid-day meal makes them happy.

Conclusions and Recommendations

Safe drinking water must be used forfood preparation. Suitable water purificationsystem must be made available to all schoolsand kitchens. Government should providethese facilities for better implementation andto improve school attendance. Vegetables andpulses should be added daily in the mid-daymeal as per prescribed menu under MDMguidelines.

The School Management Committeemay be involved to decide the menu accordingto the availability of local ingredients and theliking of the school children. ManagementInformation System (MIS) launched by MHRD.is to be used and inspections and monitoringregularly carried out by the Govt. awareness toall the stakeholders and officials is essential.Periodic orientation for teachers, SMCmembers and officers is to be conducted.

Community mobilisation efforts need toundergo a qualitative shift by taking Right toEducation (RTE-act) norms into considerationwhereby communities are also empowered tomonitor the implementation of mid-day mealscheme. Improved hygienic practices througheducation in terms of hand-washing, safedrinking water etc., will enhance the healthbenefits of this scheme. MDM logo and menuchart should also be exhibited prominently inthe school.

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References

1. Best, J.W and Khan J.V (2004), Research in Education (7th edition), New Delhi, Prentice-hall of India private limited.

2. Garret, H.E (2006), Statistics in Psychology and Education, New Delhi, Cosmo Publications.

3. Raghuram Singh, M (2002), Mid-day Meal Motivates the School Children, Edutracks, April,2002.

4. Deodhar. Y (2007), Mid-Day Meal Scheme: Understanding Critical Issues with Referenceto Ahmedabad City, Indian Institute Of Management, Ahmedabad, India.

5. Deodhar, S.Y. (2004); ‘Strategic Food Quality Management: Analysis of Issues and PolicyOptions,’ Oxford IBH, NewDelhi.

6. Deodhar, S., S. Ganesh, and W. Chern (2008), ‘Emerging Markets for GM Foods: An IndianPerspective on Consumer Understanding and the Willingness to Pay,’ international Journalof Biotechnology, Vol. 10, No. 6.

7. Dreze, J. and A. Goyal (2003), ‘Future of Mid-day Meals,’ Economic and Political Weekly,Vol. 38, No. 44.

8. Yazali Josephine & Vetukuri P.S. Raju (2008), A Study of Best Practices In theImplementation of Mid-day Meals Programme in Andhra Pradesh, NUEPA, New Delhi.

Websites:· www.megpied.gov.in· www.cordindia.com· www.righttofoodindia.org· www.educationforallindia.com· www.nutritionfoundationofindia.res.in· http://www.sagepublications.co.uk· http://www.educationalhelp.com



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MEANINGFUL FINANCIAL INCLUSION

Shruti Sarma*

ABSTRACT

Presently, of the 246.7 million households in the country, only 144.8 million haveaccess to banking. This means, 75 million households or 40 per cent still do not have accessto basic banking services. The newly-launched Jan Dhan scheme seeks to plug the gap byproviding two accounts each for these 75 million households by August 2018. Thisambitious plan may achieve the targeted numbers but it needs to overcome a majorproblem that has been persistent with all financial inclusion initiatives till now, wherein amajority of the accounts opened has remained inactive with zero balance. Will the newFinancial Inclusion scheme cover the distance that the other financial inclusion effortshave missed? Moreover, can a bank account help the poor beat the vicious cycle of poverty?What are the issues plaguing the Financial Inclusion efforts and how can these areas ofconcern be addressed? How do we make the transition from dormant Financial Inclusionto Meaningful Financial Inclusion? This paper attempts to answer these questions througha field survey in Wayanad district of Kerala.

* Research Scholar, Indira Gandhi National Open University, [email protected]

Introduction

Financial inclusion is not new to India.The effort to bring people into mainstreambanking began as early as in 1904 when the co-operative movement started. Nationalisation of14 major commercial banks in 1969 provided afurther impetus with significant expansion ofbank network to unbanked areas and steppingup of lending to agriculture, small industry andbusiness. Since then, there have been variousschemes and incentives aimed at expanding thereach of banking among the poor.

However, the first ‘real’ step in financialinclusion was taken in 2005. The then Chairmanand Managing Director of the Indian Bank, alongwith the then RBI Governor met the Puducherrychief minister and suggested the idea ofproviding a bank account for every householdin Puducherry. The first circular on financialinclusion was drafted the very same evening

and Mangalam in Puducherry became the firstvillage to be introduced to Financial Inclusionon 30 December 2005.

In the first phase of this journey, banksplanned to provide banking services in everyvillage having a population of over 2,000 byMarch 2010 and covered over 75,000 unbankedvillages. In the second phase, the target wasvillages with a population of less than 2000.About 4,90,000 unbanked villages with less than2,000 population across the country wereidentified and allotted to various banks.

The focus was on establishing the basicright of every person to have access to affordablebasic banking services. This phase, therefore, ina way stressed more on the numbers –achievement meant whether unbanked nowhad a bank account. The government intensifiedthese efforts by linking the accounts to its directbenefits transfer programme. Self-help groups




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and NGOs too chipped in to help the effortsbeing led by the banks.

After nearly ten years of consistent effortsat bringing the poor to the banking mainstream,the government has now acknowledged thereis still a long way to go. The latest initiative of theGovernment in this direction is the launching ofthe scheme Jan Dhan Yojana.

However, it is observed that in spite ofthe efforts of the government and the bankingsector, a significant section of the population stillcontinues to be outside the ambit of the formalfinancial system. In fact, one needs to examinewhy the earlier efforts were found wanting? Andwill the new scheme be able to succeed whenthe other initiatives do not seem to havemeasured up? Does just opening a bank accountmean financial inclusion? Can a bank accounthelp the poor beat the vicious cycle of poverty?

Review of Literature

Financial Inclusion is regarded as aninstrument for poverty alleviation and a majorstep towards ‘inclusive development’. Studieshave tried to establish the positive link betweenfinancial inclusion and poverty alleviation.Binswanger and Khandker (1995) noted anincrease in non-agricultural employmentthrough rural credit expansion programme,which also lowered poverty.

Getting basic banking right is the firstessential step towards financial inclusion and thisinvolves a bank account for every adult. However,mere opening of accounts does not lead tosuccessful financial inclusion. A no-frills accountis not financial inclusion. It is when transactionstake place in that no-frills account regularly thatfinancial inclusion takes place. Both public andprivate sector banks play a huge role in achievingthis. However, the expectations are more frompublic sector banks. S. Ananth and T Sabri Öncü(2013) emphasise the need for a greater role forpublic sector banks in expanding financialinclusion due to their larger branch presence in

“unbanked” areas, especially if their regional ruralbank branches are included and because theyplay a much larger role in government-sponsored schemes, especially those that aresubsidy-linked.

KGK Subba Rao (2007) stressed onimproving customer service to ensure peopleof low-incomes no longer feel unfairly treatedand misunderstood. While these efforts havebeen started way back in 2007, the problemsare still there and are getting addressedgradually.

The lower strata of the unorganisedsegments have to depend on non-institutionalsources when the formal sources are notforthcoming. Banks’ processes are cumbersomeand time-consuming. And on top of that there isno guarantee if the loan would come. Here, costand easy availability of credit are the two primaryfactors why people prefer informal sources.

The ‘unviable poor’ need to be the targetof formal sources. While extending credit to thepoor, one question often asked is if the poor arenot viable, should they be given credit! It is herethat the informal sources score. They reach outto this particular group effectively, where banksfail. The role of society at large to make the poorcreditworthy and make credit available to themneeds to be the guiding spirit. This is one of themost critical aspects of financial inclusion, whereit gets linked with and goes hand in hand withfinancial literacy.

Collard et al (2003) noted that lowincome consumers, in fact, prefer to deal withlocally based community organisations, partlybecause of ease of access but also because theymistrust banks and mainstream financialproviders.

Bank Accounts are a key measure offinancial inclusion because essentially all formalfinancial activity is tied to accounts. In developedeconomies, 89 per cent of adults report that theyhave an account at a formal financial institution,

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while the share is only 24 per cent in low-income economies. Globally, 50 per cent of theadult population, more than 2.5 billion people,do not have a formal account (World Bank’sGlobal Financial Development Report 2014).

Research Methodology

With a view to exploring the issuesrelating to financial inclusion, a study wasundertaken in Wayanad district of Kerala. Arandom survey of 150 households in the districtwas carried out in June 2014, which hasachieved 100 per cent financial inclusion. Allthe households surveyed were involved in someform of financial inclusion.

All major public sector banks havebranches in Wayanad and almost all the people,including members of the tribal communities,have access to banks and ATMs. In fact, Wayanadhas bagged the national honour for becomingthe best district in the country to link maximumbank accounts with Aadhaar numbers of thebeneficiaries of the Central government’sflagship Direct Cash Transfer programme.

Further, Wayanad has finished ahead of20 districts from across the country where directcash transfer of LPG subsidies was launched. Outof the total 1,44,341 LPG customers in thedistrict, bank accounts of 1,14,384 or 79 per centhave been linked with the Aadhaar scheme.

Results

Of the 150 households surveyed, 29 percent comprised farmers while daily wageearners were the highest with 47 per cent andthe rest comprised drivers, SHG employees,carpenters, tappers, etc. The key findings of thesurvey are as follows:

� 95 per cent of the respondents had a bankaccount.

� All those who enrolled in Financial Inclusiondid so mainly to seek loans and governmentassistance (83 per cent) while savings (16.7per cent) came next on the list. However,the savings in these accounts was negligible.

� Those who availed of loans formed 64 percent, while those who did not take any loanconstituted 36 per cent.

� The loan amount mostly varied from ̀ 5,000to ` 30,000.

� Loans were taken mostly in the name ofagriculture purposes (76 per cent).

� As most were daily wage earners working infarm land or had agricultural land, theirincome was irregular. As a result, the accountsremained dormant.

� Those who invested in agriculture did notget the desired returns. Sometimes cropsfailed, sometimes quality of output was aproblem and most of the times, the loanavailed was used for other purposes ratherthan what it was taken for. These ‘agri’ loanswere mostly diverted for ‘house repairs’ andother consumption needs.

� Only in a very few cases, where therespondent had regular income or hadintelligently planned his spend, there wassome saving.

� 31per cent of the respondents did not shyaway from admitting that they did not usethe loan for the purpose they stated.

� With little or no saving, the penaltypercentage too was high - 54.62 per centhad to pay penalty.

� Getting a loan was easier from self-helpgroups (44 per cent). This was mainly due tothe network of the SHGs and also due to thegovernment’s SHG-bank linkage programme.

� Those who took loan only from a bankformed 25.64 per cent and the remainingsecured it from more than one source, forinstance, SHG and bank, moneylender andbank and moneylender and SHG.

� Borrowing from more than one sourceincluded informal sources like money-lenders. This was because it was easier toget money from these informal sources topay off the formal source of loans.

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� Banks were not encouraging and required alot of paper work to get a loan sanctioned.

� 79.56 per cent of the respondents foundSHGs better and only a meagre 5.37 per centfound banks better.

� 50 per cent of the people who had a bankaccount did not even know how to use it.

� Over 33 per cent were indifferent as bankaccounts made no difference to their lives.

� Loans taken for productive purposesconstituted 52 per cent while those whotook it for unproductive purposes comprised43 per cent.

� It was also observed that wherever the loanwas judiciously invested, it yielded results.

� But, wherever it was used for non-productivepurposes, the respondents fell in a debt trap.With income being less, the loan went forday-to-day uses and as a result, there washardly anything left to save or to repay theloan.

� Around 50 per cent were caught in a debttrap, mainly because the investment did notyield the desired returns.

Plugging the Gaps

Financial Inclusion has come to meanopening bank accounts. If a person has a bankaccount, s/he is counted to be financiallyincluded. Many a time, bank accounts areopened to meet targets of bankers or receivegovernment benefits. From the survey, it wasseen that 83 per cent opened an account fortaking loans and only 16.7 per cent used theaccount for any kind of saving. However, thesavings in these accounts was negligible and theaccount remained dormant. Merely opening no-frills accounts is not financial inclusion. FinancialInclusion needs to be a means to help the poortowards a better future. It cannot be aboutincreasing the number of accounts or thenumber of dormant accounts. Opening bankaccounts are certainly a very good first step, butin itself achieves nothing. Efforts need to be

made to keep the poor connected to the bankingsystem and banks need to come up with specialproducts and schemes that meet therequirements of the poor people. Only consistentengagement over a considerable period of timecan lead to meaningful financial inclusion. Banksneed to become a partner in the progress of thepoor. Financial Inclusion, in order to becomemeaningful, and help improve the life of the poor,needs to focus on the following aspects:

� Loans for the poor need handholding: It goeswithout saying that the poor need money toimprove their lives. Therefore, most of themopen an account to take a loan and make asincere attempt. Firstly, the amount of loansgranted to the poor is too meagre to reallybe able to make a difference to their lives.Moreover, they mostly invest in agriculture,where chances of good return aredependent on many factors beyond thecontrol of the poor. There is a need tohandhold the poor, at least initially to helpher choose the right way ahead. Self-helpgroups and NGOs can play a role here andthey could be provided incentives if they canhandhold the poor out of poverty. Openingan account and granting a loan and thenremembering the poor only when theinstalment is due will not help.

� Banks need to become friendly: The poor findit easy to get loan from a self-help group oreven a moneylender. In a district likeWayanad, where almost everybody has abank account, just about 26 per cent peopleturned to only banks for loans. Therespondents shared that banks were notencouraging and required a lot of paper workto get a loan sanctioned. The fact that 79.56per cent of the respondents found SHGsbetter and only a meagre 5.37 per cent foundbanks better is a telling statistic. And 50 percent of the people surveyed did not evenknow the use of a bank account. Andconsidering that our financial inclusion

Shruti Sarma


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programme is primarily dependent on banks,urgent remedial action is required. There is aneed to simplify the process of not justopening accounts but for availing of loanstoo. Further, loans cannot be given only whencollaterals are submitted. In most cases, thepoor reside on lands which have no legaltitle deeds and have no means to meet theirneeds for a decent livelihood. The reasongiven by the respondents for preferring SHGwas SHGs were easily approachable,information was readily available and so wasloan. With a bank one had to go through a lotof paperwork and sometimes even after that,the result was negative.

� Financial Inclusion has to be about beatingpoverty: Around 33 per cent people wereindifferent to financial inclusion because itmade no difference to their lives. FinancialInclusion needs to be a comprehensiveprogramme, which not only provides thefunds but also opens up new opportunitiesand guides the poor over a period of time tomove ahead in their lives. Financial Inclusionhas to have a social motive and has to keepthe big picture in mind. Financial Inclusioncannot be about earning money from thepoor, it has to be about enabling the poor toearn money. Loans are now given forproductive purposes only. But as it can beseen from the survey, over 31 per cent ofthe people who took loans had no qualms inadmitting that they used it for differentpurposes. If we have to achieve meaningfulfinancial inclusion, we need to be innovativeand handhold the poor to elevate them frompoverty. This would mean that the socialaspect too is brought in and is coupled withthe financial aspect. Some portion of a loancan be permitted for meeting the dailyneeds and the remaining can be used forproductive purposes. This would help poorto get on with their life legally and alsoimprove it over a period of time.

� A long-term view will bring in profits andprosperity: Banks, like most formal for-profitinstitutions, are not willing to spendinordinate amounts of time and resources tocreate a market because of highestablishment costs. Thus, the expansion ofthe banking system through BusinessCorrespondents and utilisation oftechnologies like Mobile Banking deservesgreater attention because the size of themarket at the bottom of the pyramid is verylarge and uplifting them will certainly bringhandsome profits in the long-term. Banksneed to take a long-term view to focus oncreating and developing this market.

� First mile challenges: Today we have over220,000 business correspondents, ultra smallbranches and new technologies to aidfinancial inclusion. This has taken care of thelast mile challenge. But first mile performancethrough developing new products for thepoor and financially excluded has been afailure. Banks need to have productsspecifically designed for the poor, taking intoaccount their specific needs.

Conclusion

The focus of financial inclusion has to beabout understanding the needs of the poor,creating awareness among them and helping intheir economic upliftment. Financial Inclusionshould be about partnering the poor in theirdevelopment and handholding them in theirjourney towards upliftment. The social objectiveneeds to be ahead and focus should be onmoving the marginalised to the mainstream.

Financial Inclusion needs to bedeveloped as a helping hand for the poor. Thereneeds to be consistent effort over a considerableperiod of time. A single intervention is unlikelyto succeed. What we need is a shared vision, anda partnership with the poor. Only then willmeaningful financial inclusion happen. Profit willhave to wait.

Meaningful Financial Inclusion

References

1. Ananth S and Oncu, S. T. (Feb 16, 2013), Challenges to Financial Inclusion in India: The Case ofAndhra Pradesh, EPW, Vol - XLVIII No. 07, pp 77-83.

2. Binswanger, Hans P. and Shahidur Khandker (1995), The Impact of Formal Finance on theRural Economy of India, Journal of Development Studies.

3. Collard et al (2003), Promoting Financial Inclusion: An Assessment of Initiatives Using aCommunity Select Committee Approach, Bristol: Policy Press.

4. Global Financial Development Report 2014, International Bank for Reconstruction andDevelopment (The World Bank).

5. Subba Rao, K G K (2006), ‘Indebtedness of Cultivator Households: Some Puzzling Results’,EPW, August 12-18.

Shruti Sarma

Essays in Economics and Other CheerfulThemes by S.Subramanian, SAGE Publications,New Delhi, 2014, ISBN: 978-81-1373-7(HB),pp.205, ` 850.

The book under review is based on thecollection of academic writings over a numberof years by the author, a social scientist, with aninterest to boost students and society in theeconomy and polity. The author, at large,concentrates on existing conceptual andempirical issues for development at the globallevel in general and for the nation in particularwith philosophical bases and accessible terms. Itis nothing, but something of a professionaleconomist’s ramble through territory that is bothfamiliar and important to him, but undertaken ina spirit of some leisureliness which the authorhopes will attract a readership beyond that,society, of fellow professionals.

The author organised this book intoThree Parts namely, the first ‘ Of Home and theWorld’, the second ‘Between Economics andPhilosophy’ and the third ‘MiscellaneousMistakes’. The first part contains nine essaysaccommodated in the three different segmentsviz., a) Global Deprivation and Disparity’, b)Domestic Deprivation and Disparity, and c) Polityand Society. As stated, it contains a collection offormal essays that could be said to fall within thedomain of concern of a development economist.These essays casually address the themes,directly or by hinting of global and nationaljustice; the obligations of a state to its citizens;the rights and well-being of the state’s citizen;poverty, inequality and discrimination; socialexclusion and the arbitrary exercise of power bynations and people in possession of such power.

The second part of this book isanalytically demanding than the first part. Thisconsists of four essays on themes at theintersection of economics, philosophy andpolitical science. The author’s first essay analyseson brief consideration of the relative merits of

the proportion and the number of people inpoverty as the appropriate headcount indicatorof deprivation. The second essay is a review ofAmartya Sen’s important book entitled ‘The ideaof Justice’, and the author has thrown light onthe issues of development policy that are notinformed by the normative values. His third essayseeks to rescue the credo of egalitarianism froma criticism directed at it by the moral philosopherDerek Parfit.

The third part rightly captioned as‘Miscellaneous Mistakes’ also has four essays thatinclude a) A Curmudgeon’s Complaints, b) JaiHo, Jeeves!, c) Language and Representation or,More Modestly, Mathematical Economics andPoverty and d) Writing Economics in Exactly 300words. These are more philosophical than havingethnic developmental context. In general, theauthor provided a review of literature oneconomics and development in the globe as awhole and the country in particular.

Obviously, this volume is very useful forresearch scholars, especially those engaged ineconomic research arena and developmentalprofessionals, social workers, non-governmentalorganisations, and other social scientists.

Dr.R.Murugesan

BOOK REVIEW

Journal of Rural Development, Vol. 34 No. (1) pp. 121


Journal of Rural Development, Vol. 33, No. 4, October - December : 2014

Book Reviews 521Journal of Rural Development(Quarterly Journal of NIRD&PR)

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522 Book Reviews


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Journal of Rural Development, Vol. 34 No. (1)


Journal of Rural Development, Vol. 34 No. (1)


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