Human Capital Development HCD - World Bank€¦ · HCDHuman Capital Development Working Papers...

I6 29

Human Capital Development

HCDWorking Papers

Human Capital Underdevelopment:The Worst Aspects

HCDVP

November 1996

HCDWP 76

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Papers in this series are not formal publications of the World Bank. They present preliminary and unpolished results ofanalysis that are circulated to encourage discussion and comment; citation and the use of such a paper should take account of itsprovisional character. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author(s)and should not be attributed in any manner to the World Bank, to its affiliated organizations, or to members of its Board ofExecutive Directors or the countries they represent.

Human Capital Underdevelopment:

The Worst Aspects

HCDVP

This report is the result of a team effort led by George Psacharopoulos andanchored by Robert Mattson, with contributions from Keiichi Ogawa,Anthanassios Katsis, and Hideo Akabayashi.

Preface

Today it is widely agreed that human capital development is the key factor underpinning acountry's effort for economic and social development. Although much progress has been maderecently in the developing world regarding health and education, much more remains to be done,especially at the sub-regional level.

The purpose of this report is to bring to the surface some of the worst aspects of humancapital underdevelopment, by dissaggregating to the extent possible the relevant indicators. (Fora summary, see Annex Table 2 starting on page 44.) After all, when the illiteracy rate in aparticular country is of the order of 70 percent, more than 80 percent of the children suffer frommalnutrition, of less than 15 percent of the primary school age children are in school, it becomesobvious what are the investment priorities in that country.

The report is produced in the hope that dissemination of such statistics would sensitizeand instigate further action from governments and international assistance agencies to close theappalling gaps.

George PsacharopoulosSenior Advisor

Human Capital Development

P. S. This is the last paper in the Human Capital Development series under HCDVPmanagement. A new series will start under the HID Network.

Contents

I. I ntroduction ............................................. 1

II. Human Capital Development Indicators ............................................. 1

III. Poverty: The Catch-all Factor ............................................. 3

IV. Education ............................................. 6

V. Health...... .. 18

VI. Public Spending ............................................ 30

References and Sources ............................................ 35

Annex Table 1: The Poorest Countries ............................................ 43

Annex Table 2: Selected Worst Aspects Indicators by Country ............................................ 44

I. Introduction

Development of human capital is a leading priority in the international communityencompassing governments, intemational organizations, and academia. Increasing human capitallevels is one part of a three-part strategy for alleviating poverty. The strategy aims to reducepoverty by encouraging broad-based growth while increasing the levels of human capital andproviding safety nets (World Bank, 1990). Research abounds identifying issues affecting thedevelopment of human capital. As a result, governments have become more aware of, andfocused on, problem areas for the development of their people's human capital, and significantprogress has been made (Psacharopoulos, 1995).

It is well known that poverty and human capital are closely related. Typically, where thereis poverty one can find a poor stock of human capital. Increasing human capital levels has beenassociated with higher wages, better health conditions, and a myriad of other benefits-thereforeincreasing human capital levels is an integral part of the poverty alleviation strategy. In addition,human capital has been estimated to be over 50 percent of a country's total capital stock, 80percent in the most developed countries (Becker, 1988 and 1995). Thus, human capital is a majorasset of a country, and should be treated as such.

Human capital development indicators reflect a country's investment in its people. Theseinvestments include schools, hospitals, books, teachers' services, medical equipment and otherphysical capital, along with recurrent expenditures associated with human capital investments-the mere construction of a school or hospital does little to increase literacy or treat illnesseswithout the staff to provide instruction or medical care. The identification of problem areas isanother way of identifying where there is under-investment in human capital. After all, it is highlyunlikely that an area has an 80 percent illiteracy rate because large sums of money were investedin its educational system. On the contrary, it is more probable that it was the result of under-investment in staff, materials, and other basic necessities for providing education. Once problemareas have been identified, they can be addressed by acting upon the constraints to thedevelopment of human capital.

The purpose of this report is to identify and document some worst aspects of humancapital development. This is done on the hope that dissemination of such statistics may instigatefurther action by governments and international donors to close the gaps. In the following sectionwe discuss a taxonomy of human capital indicators and then present some broad statistics ofpoverty. The remaining sections deal with education, health, nutrition, and governmentexpenditures on the social services.

II. Human Capital Development Indicators

Human capital indicators can be separated into two groups: monetary and physical.Monetary indicators fall into two categories: government expenditure and private expenditure.Government expenditures include those for the construction of facilities, infrastructure, and

2

recurrent outlays. Private expenditures are those incurred by individuals, which include tuitionfees, books, drugs, and medical fees. Using expenditures as an indicator to human capital levelsprovides an insight into the investment priorities of governments and their public. However, adrawback to using expenditure indicators is that they tell little about the actual output, oreffectiveness of those investments.

A better proxy to assess human capital development is the use of physical indicators, forexample, literacy or vaccination rates. The advantage of physical indicators is that most of themare the end product of human capital investments. For example, suppose we want an idea of theinvestment in expanding primary education. One investment indicator would be the number ofschools built. While this tells us the potential capacity of the primary education system, is doesnot tell us the actual usage. In other words, is the additional capacity being utilized? A betterindicator for this would be the primary school enrollment ratio. If a school system has 100,000places, but only 50,0000 students are enrolled, it might not be a good decision to build moreschools, at least not in that area. Though there is an investment in the form of extra capacity, thebenefits are not necessarily being realized. Therefore a better proxy to the investment would beenrollment ratios.

Even with the limitations of monetary indicators one cannot ignore them, for they are acomplement to the physical indicators. For example, by looking at human capital expenditure as apercentage of GNP, one can get a feel for the investment priorities of society at large. Inaddition, the percentage of the government budget allocated to human capital investmentsprovides a sense of the government's investment priorities. But the effectiveness of theinvestments is gauged by associating a monetary value with a quantified non-monetarymeasurement, in other words using both monetary and physical indicators (Pearce, 1986).

Each of the physical indicators can be broken down into two sub-categories: those thatlook at the current human capital stock, and those that show the future potential of human capitalstock, in terms of the current investment flows that build it, Figure 1. Examples of current stockindicators are literacy rates, educational attainment levels, life expectancy, or access to safe water.Examples of flow indicators include current enrollment levels, vaccination rates, and prenatal care.

Looking at human capital indicators, monetary and physical, at the aggregate level of acountry, though insightful, can be misleading. In some situations the indicators at the aggregatelevel are similar to dissaggregated indicators. Many countries have relatively encouragingaggregate level indicators, but after dissaggregating the data and looking at the regional anddemographic breakdown, it is revealed that there are serious problems with the current and futurehuman capital stock.

3

Figure 1: Human Capital Indicators Taxonomy

| Human Capital Development |Stock FlwFIndicatorsl

| Monetary | Physical l(Gov't Expenditure)Il

(Private Expenditure)II

|Educafion || Heath l

Stock Flow Stock Flow(Literacy Rate) (Enrollment Ratios) (Life Expectancy) (Vaccination Rates)

(Attainment Levels) (Student-Teacher Ratio) (Access to Safe Water) (Pre Natal Care)

To exemplify the point, take the case of Brazil, and let us use the indicator of illiteracy.As a whole, Brazil's illiteracy rate is 19 percent.' However, the geographic distribution ofilliteracy in Brazil is not uniform. Areas such as Brasilia and Sao Paulo have relatively low ratesof illiteracy, while the state of Piaui has an illiteracy rate over 60 percent. Thus, Piaui would beidentified as a human capital development hot spot.

III. Poverty: The Catch-all Factor

As a general rule, where there is poverty there is low human capital development(Psacharopoulos, et.al, 1994; De Geyndt, 1996). Since the correlation of low human capitallevels to poverty is strong, the identification of poverty is a good place to start. However, itshould again be pointed out that human capital is just one part of the welfare equation. The otherpart is economic growth (macroeconomic conditions). Therefore, the use of poverty as anindicator is not always indicative of low human capital levels. For instance, take the case of SriLanka. The incidence of poverty in Sri Lanka is 22 percent, but the overall human capitalindicators are better than in other countries with the same incidence of poverty. This is a result ofbroad-based growth being poorly implemented. Cuba, Russia, and other former Soviet countriesare also good examples of high levels of human capital and low economic growth.

1 All indicators listed in this paper are from World Bank sources that appear in the references section, unlessotherwise stated.

4

Another limitation to using poverty as an indicator of human capital development, is thatpoverty levels are determined by establishing a minimum income and/or consumption level thathas been deemed appropriate for survival. Though much thought and debate goes into theestablishment of the poverty line, it is still an arbitrarily established line, or value, below which oneis declared impoverished. This is further compounded by poor measurements of income inhousehold surveys.

Even with the limitation of using poverty incidence as an indication of human capitallevels, it is a good place to start, particularly since we are interested in the alleviation of poverty.Identifying the poverty in a country is seldom as simple as looking at the overall national leveldata-though this would give the relative rating of a country to other countries, it provides littleother useful information in targeting investments.2 The most useful information for targetinginvestments is derived from dissaggregated analysis. In the dissaggregation of the data, there aremany sub-categories that have common patterns. One of these sub-categories is comparing theurban and rural populations-other sub-categories include various demographic, geographic,gender, and ethnic comparisons. Take for example Mozambique, arguably the poorest country inthe world with a 1994 per capita GNP of $90, where one-third of the urban population isimpoverished compared to two-thirds of the rural population. In Nicaragua, 75 percent of therural population is poor compared to 32 percent of the urban population. Twenty percent of theurban population in Malawi is impoverished compared to 42 percent in the rural areas.

Though the rural and urban poverty incidence may differ, it fails to adequately show thedistribution of poverty between the rural and urban areas. For this reason we are also interestedin the distribution of the poor. For example, the absolute poverty levels in Tanzania, are 48percent in the rural areas and 11 percent in the urban areas is poor. However, 91 percent of thepoor live in rural areas compared to 9 percent in the urban areas.

An even more detailed and revealing dissaggregation is obtained by breaking the statisticsinto regions, states, or municipalities within a country. A good example of this is Indonesia,where the national poverty rate is 17 percent. Breaking this down by region, East Nusa Tenggarahas the highest incidence of poverty at 45.6 percent. Or Malawi which has a 20 percent nationalincidence of poverty, but 51 percent of the population in the Southern regions are poor. Pakistanhas a national poverty incidence of 33 percent, with the highest incidence of poverty being insouth rural Punjab, 48 percent.

High national levels of poverty do not inhibit an uneven distribution of poverty. Whereasthe national incidence of poverty in Eritrea is 69 percent, the urban areas have a 62 percentincidence of poverty compared to 71 percent in the rural areas.3 Within the rural areas of Eritreathere is a wide variation in the incidence of poverty. The rural highlands have an 83 percentincidence compared to 52 percent in the lowlands.

A good example that highlights this point is again Brazil. Though Brazil is considered anupper-middle income country, it has some pockets of poverty that rival areas in low-income

2 Annex 1 shows the poorest countries in the world by per capita GNP ranked in ascending per capita GNP.3 Based on Table 1.2 World Bank, 1996i.

5

countries. For example, in Sao Paulo, 6.9 percent of the population lives below the poverty line,while in the North East, 32.4 percent live below the poverty line, Figure 2. Put another way, SaoPaulo accounts for 21.9 percent of the population and 8.6 percent of the poor, while the northeastaccounts for 29.6 percent of the population and 55 percent of the population living in poverty.One state in the northeast of Brazil is particularly bad off, Piaui, with 50 percent of the state'spopulation living in poverty. In the rural areas of Piaui, 67.8 percent of the rural population isimpoverished.

Regional variation of poverty can also be attributed or associated with racial or ethnicgroups residing in that area. Take Guyana for example. Though poverty is widespread inGuyana, there are still regional pockets of extreme poverty. For instance 30.9 percent of thepopulation in the Upper-Demerara-Berbice region live in poverty compared to 94.8 percent ofthose in the Potaro-Siparuni region. The Potaro-Siparuni region is mostly traditional Amerindianvillages. The rate of poverty among the indigenous population is particularly severe in Guyana,where 87.5 percent of the Amerindians are categorized as poor.

As a side note to this, there are other things to consider which affect poverty levels thatmight not be readily revealed in statistical tables. They both come from war. One is the influx ofrefugees and the other is the destruction of property. Ethiopia and Mozambique, Box 1, aresuffering from the side effects of war. In Ethiopia, half of the population lives below the povertyline. In some regions 85 percent of the population lives below the poverty line. The povertyproblem is compounded by the influx of refugees from Somalia and Sudan, demobilized soldiers,orphans, and redundant public servants.

Box 1: War, Education, and Nutrition in MozambiqueArguably o-ne othe poorest countries in tie world, Mozambique is- a country with many problems

inhbiting the :development human capital. In the course. of tre recent civil wax that raged-mMozaibique; over half of the level I primary schools were t Te deion of these sools

ot ufom arsreon. Te, 90 prent of the siool e deyi, cpred to 6pcin Ca Delgado. fta is nbad enough, approxintly 70 percent of All primary schools hive beenlosed. Aitis 67 p t of the oultIon15ys or o isillwiet. With tdestrucion

closure of so any s, few will have ty to att4ini a n sol eduion, letuv sc ln. Theost re pri'' sho enrollmenratiowas6 percenit.

Besides-the need for educationacilities, Mozai ique:suf ers nutritional problems.average persnis e t to csmonly 77 f thereuir daiy cc take. It ha beeestimated that 30 to 40 percent of thechidren ini- Mozambique suer from chroiuc malnutrition, This isevidW :vnwthh- uiermortaltraJutas tling is that 50 percentof the deaths ofchildre-repoted by th hpias,e asid by le diseases.-

Source: Wol J;n, -a----cau

6

Figure 2: Poverty Incidence in Brazil, by Region and State

32.4

30

25

i20

15.5 14.6 14.1~15 1.

10 8.76.9

5

0 Z

0~~~~~~~~~~~~~~~

After the above overview of poverty conditions, we focus on the three traditional humancapital indicators: education, health and nutrition.

IV. Education

Educational Attainment. One of the most common indicators used to evaluate thecurrent human capital stock as it relates to education is literacy. Figure 3 shows the nationalilliteracy rates for those countries with some of the highest rates in the world. All of thesecountries have a per capita GNP of less than $600.

7

Figure 3: Adult Illiteracy Rates90 86

81

8073

'70 - - ~~69 69 6

65 65 64 63 62 62 62 61 60

60

~50

140-

.... .... .... .... . .. --- .... ... ... . .. ... .... .... --- Low-vircome

30 - lountry Avecge(34%1/)

20

10

0

~~~~~~ 0.~~~~~~~~~~~

Table 1 shows the percent of the population over 18 years old in Pakistan by region thatcannot read or write. Similar to the poverty indicators, the prevalence of illiteracy varies byregion. Also, again, there is wide variation between urban and rural areas.

This kind of dissaggregation can also be instructive by gender. Table 2 shows the adultilliteracy rate in Tanzania by region and gender, where particularly hard hit are females inZanzibar.

Table 1: Population over 18 Years Old That Cannot Read/Write, Pakistan (%)Urban Rural

South Punjab 29.8 77.7

North-west Frontier 57.2 77.4Province

Balochistan 64.3 80.8

Overall 47.3 73.4

Source: Pakistan Integrated Household Survey, 1991

8

Table 2: Adult Illiteracy Rates, by Region in Tanzania (%)Mainland Zanzibar

Female 31.8 58.1

Male 15.0 35.7

Total 23.8 46.7

Source: Tanzania Human Resource Development Survey, 1993

One of the primary goals of basic education is to bring about literacy. Literacy is generallybelieved to be attained after 4 to 5 years of schooling. So to have a high percentage of studentsnot reach the fourth or fifth grade is detrimental to increasing literacy. Table 3 shows the effect ofcompleting primary school on illiteracy in Tanzania. Those who complete primary school areliterate, except for roughly 1 percent, while those who do not complete primary school have ahigher probability of being illiterate. Thus the funds used to provide the education to those whodropped out were relatively ineffective.

Table 3: Effect of Education on the Adult Illiteracy Rate (percentage)

Primary School

Unfinished Finished Total

Whole Country

Female 63.6 0.9 32.2

Male 35.5 0.7 15.4

Worst Four Regions*

Female 79.3 0.7 45.1

Male 50.6 1.5 27.2

Source: Tanzania Human Resource Development Survey, 1993* The rural regions of Dodoma, Arusha, Singida, Tabora.

Years of schooling attained is another gauge of the educational level of the population. InHaiti, the average years of schooling is 1.7 years. Another area of particularly low attainmentlevels is in the northwest and northeast of Nigeria where the median years of schooling is .7 formales and .6 for females. In India, the mean years of schooling for females in the poorest incomequintile and living in rural areas is .5. Or, in urban Bolivia, the mean years of schooling for non-

9

indigenous people is 8.4 years, while the urban mono-lingual indigenous population's means yearsof schooling is .3 years, and .2 for females (Psacharopoulos and Patrinos, 1994).

Though a powerful indicator, information on educational attainment levels is not alwaysavailable, but it can be proxied in other ways. One proxy for attainment levels is to look at theproportion of the adult population that has no schooling. Table 4 presents this proxy by genderand region. Across all countries the rural areas have a higher portion of adults with no schooling.With the exception of Brazil, females fare worse than the males. The definitive hot spots in Table4 would be the rural areas of Egypt and Pakistan. Figure 4 shows the proportion of adult femaleswith no schooling. Again, Pakistan and Egypt top the list, but the lack of schooling for females isstill a major problem in the other four countries.

Table 4: Population with No Schooling, 25 + Years Old (%)Country Both Sexes Female

BoliviaUrban 13.1 18.4Rural 37.5 50.9

Brazil'Urban 13.3 14.1Rural 35.4 34.1

EgyptUrban 47.8 61.8Rural 78.4 92.6

PakistanUrban 59.2 naRural 80.4 94.1

VenezuelaUrban 17.2 19.2Rural 45.2 48.1

Source: UNESCO, 199S.' 10 + years of age

10

Figure 4: Females Over 25 Years Old with No Schooling (%)

87.990.0 78.6

80.0 68.3771.5

870.0 65.9 68.3

t 60.0 56.0

z q.-50.0

40.0

X 30.0

20.0

10.0

0.0

Source: UNESCO, 1995.

Another proxy for attainment levels is the number of people reaching or attendingparticular levels of schooling. The national primary school graduation rate in Indonesia is 60percent, but in the East Timor region it is only 20 percent. In Vietnam, 42 percent of the childrendo not reach grade 5.4 In India, only 51.3 percent of the 6 to 10 year old cohort in the state ofBihar are still in school. Figure 5 shows the percent of cohort to reach grade 4 by gender inselected countries.5

4 UNICEF (1996).5 Cohort is defined as those who had enrolled in primary school.

11

Figure 5 Percent of Cohort Reaching Grade 4

80- 74 73

70- 67 67 68

Xo~~~~~~~~~~ 63

60 ~~~~~~60 60 60

50 -45 46 4

~O'10

8 20

10 -

0_ - _ E | | E _~~U o~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~c

U Female * Male

School Enrollment. While illiteracy rates and attainment levels provide an indication ofthe current stock of human capital, enrollment ratios help provide an indication of the future stockof educated people. For example, in a recent household survey in Nicaragua, it was found that 22percent of the children 6 to 14 years of age have never attended primary school. Here there is notonly a difference within regions, but a great difference between the rural and urban areas within aregion, particularly Matagalpa (Figure 6). In Nigeria the percentage of the population that neverattended school is staggering, particularly in the rural areas where 65 percent of the females and50 percent of the males never attended school. The two worst regions in Nigeria are thenortheast and northwest with female non-attendance reaching well above 80 percent. InGuatemala, 61 percent of the indigenous people had no education. (Psacharopoulos and Patrinos,1994). Table 5 gives an indication of a country's future human capital stock by looking at theprimary school age children who have never attended school Pakistan.

12

Figure 6: Children 6 -14 Years Old That Have Never Attended Primary School, Nicaragua

50 - 46

45-40 39

40 -

35 -

30-

25-

20-

15 12 1

10 - 7 _. 5-

0Matagalpa Atlantica Norte Boaco

E Urban * Rural

Source: Nicaragua Living Standard Measurement Survey, 1993.

Table 5: Children 6 - 13 Years Old Who Have Never Attended School, Pakistan,1991 (%)

Region Urban Rural

North Punjab 16.7 29.3


Sind 30.0 56.6

Overall 25.7 45.9

Source: Pakistan Integrated Household Survey, 1991.

13

Table 6 presents the percent of children in a given age that are not attending school inBrazil. Across regions and age-groups the children of poor families are more likely not to beenrolled. In the south rural areas, a higher percent of the 7 to 9 year olds are in school than the10 to 14 year olds. In the rural northeast the opposite is true. This suggest that while the childrenin the south are enrolling earlier and then dropout, the children in the northeast are enrolling later.

Table 6: Children out of School by Area, Age Group and Poverty Level,Brazil 1990 (%)

Age Group Poor Non-Poor(Years)

All Brazil7 - 9 42.0 19.010- 14 23.6 13.47 - 14 29.5 16.0

South (Rural)7 - 9 20.0 11.010 - 14 31.9 24.97 - 14 27.2 19.5

Northeast (Rural)7-9 63.0 52.010 - 14 31.1 30.27 - 14 44.6 38.3

Countries with both high illiteracy rates and low enrollment ratios face a serious hurdlebecause their current stock of human capital is low and their future stock is not promising. Forexample, Guinea has an 86 percent illiteracy rate and only a 44 percent net primary enrollmentratio (Figure 7). In Malawi, with a 59 percent illiteracy rate, the net primary enrollment ratio forthe poorest male and female students is 34 and 31 percent, respectively. In Lao PDR, with a 36percent incidence of illiteracy, the net primary enrollment ratio is 60 percent-54 percent ruraland 78 percent urban. The net primary enrollment ratio for the lowest income quintile in LaoPDR is 44 percent, compared to 78 percent for the richest.

Figure 8 shows a comparison of the rural and urban literacy rate and net primaryenrollment ratios for Niger by household expenditure quintile. Clearly, across quintiles, the ruralareas have a poor current human capital stock and a bleak future. Compared to the urban areas,the net primary enrollment ratio and literacy rate are relatively flat across expenditure quintiles.

14

Figure 7: Illiteracy Rate and Net Primary Enrollment Ratio

90 86

80

70-2 70 62 61 60

60

50 44 44

40

30

20

10

0

Guinea Pakistan Moznmbique

MEllitrracy Rafte (%) U Net PrmBry Etmlront~ Ratki

Figure 8: Net Primary Enrollment Ratios and Literacy Rates, Urban and Rural, Niger

80 -

70 -- Urban net Primary Enrollment

60-

50

o- I | i i _ i | l | _~~~~~~~~~~-

40- -

* -lhc RabUrban Literacy30

20Rural Net Primary Enrollment

10 - …… -- …Rural Literacy

30 - __

1 2 3 4 5Expenditure Quindle

15

In Guyana, where the primary school enrollment ratio is 90 percent, 27 percent of therural interior people do not receive primary education compared to 4 percent in Georgetown. InSierra Leone the primary enrollment ratio in the Northern Province is 20 percent. Net primaryenrollment is 26 percent in Eritrea, where the highest ratios are in the Asmara and HamasienProvinces: 44 and 40 percent, respectively. The lowest ratios are found in the Sahel, Barka,Dankalia and Gash-setit Provinces; 5, 9, 13, and 15 percent respectively. In Uganda the primarygross enrollment ratio is 91 percent. However it is particularly low in the Kotido and Morododistricts-21 and 26 percent, respectively. The female enrollment ratios are especially low inthese same districts-12 and 22 percent respectively. In Chad the gross primary enrollment ratiosin Biltine, Batha and Quaddi are 10, 13 and 15 percent, respectively, while in the Lonone Occ. itis 115 percent.

Box 2: Education in Northeast BrazilThe -northeast-of EBrazil: isan area caght Grade Repetition. Even in the face of highin the trap-: ' of low ' -stock. and: flow- enrollment ratios, there can still be many obstacles toindicators of human capital.' In the rural overcome. One such obstacle is grade repetition. Inareas of Brazil 60pecent of the -poor Lesotho, the primary school repetition rate is 22household heads are illiterate, compared percent. In rural areas of Lesotho, the primaryto 25-percent-in 'the urban 'areas. In the school completion rates are as low as 42 percent innortheast this climbs- to near -70 percent.- Qacha's Nek. Overall, 54 percent of the children inEighty percent of the household heads in Lesotho do not reach grade 5 (UNICEF, 1996). Inthe:- norheast atteded between1 to- 4 Eritrea, repetition rates are almost 29 percent foryears of schooling.- This- cycle does not females compared to 18 percent for males.appear to be abating.. Sixtytree percentof the children 7 to 9'years ofae are not' Peru is reported to have a 118 percent grossattending school. Fo thse aftending primary enrollment ratio. However, the repetitionschool, the repettiton rates fior, grade,s: 1t-o rate is 32 percent in the first grade alone. This is not4 range from'' 74 to' 49 percent. It has surprising since 50 percent of the teachers arebeen estimated"'-that foar.Brazil as a whole, uncertified. In Guinea-Bissau the repetition rate forpmary schol repeition costs 30 prcent all grades, male and female, is 38 percent, but forof the current spencding for primary first and second grade it is 43 percent. This is note:ducation. surprising considering that less than 13 percent of theSource: World ak jfia - primary schools teachers have only 4 to 5 years of

lsource-. 11 .. . ..

schooling, and 33 percent have 6 to 8 years. It is nowonder that with such poor schooling, 80 percent of the children do not reach grade 5 (UNICEF,1996).

The overall indicators in Mexico are good, but the indigenous population is a hot spot.While the national primary enrollment ratio is 100 percent, for the indigenous population it is only72 percent. But the most telling figures are the primary repetition and completion rates. Theprimary repetition rate for the general population is 9 percent, but 42 percent for the indigenous.Fifty-eight percent of the general population complete primary school, whereas only 2 percent ofthe indigenous do.

16

The availability of textbooks is another crucial flow indicator. Text availability, or lackthereof, is an indication of the quality of schooling. In Lao PDR, 62 percent of the rural primarystudents have textbooks available to them. In Bolivia, it is estimated that only 20 percent of thestudents in grades 1 to 5 have textbooks available to them (Wolff, et al. 1994).

Gender Gap& Figure 9 shows some of the worst female illiteracy rates, at the nationallevel. Just like the other previous indicators, rural areas typically have worse indicators. In LaoPDR, for example, the illiteracy rate for females 36 to 55 years of age is 76 percent in the ruralareas, with the highest incidence of illiteracy in the rural south-81 percent.

Figure 9: Female Illiteracy Rate100 -

9190 86

82BD X7 7 ~ 7 75 75 7

70 6970

.8 60

50 -. Low4mcon CDuntry

. 40 Avenge (45%)

30

20

10 X

Enrollment ratios for females in developing countries are typically lower than that ofmales, and can reach extreme conditions. For example, the gross primary enrollment ratio forfemales in Ethiopia is 21 percent, and in Niger it is 23 percent. In Guinea, only 15 percent offemales aged 7 to 12 are in school.

In Mauritania the female illiteracy rate is 74 percent and the gross enrollment rate forfemales is 62 percent. As anticipated, regional disparities exist here as well. In Eritrea thenational female gross primary enrollment ratio is 44 percent. However, in the Sahel Province it is27 percent and in the Barka Province 29 percent. The lowest net enrollment ratio in Mauritaniacan be found in the rural, lowest expenditure quintile-4 percent. In Cambodia, adult illiteracy is

17

35 percent and the net enrollment ratio is 55 percent, but for females they are 50 and 43 percent,respectively.

The overall female literacy rate in Niger is 10 percent, compared to 20 percent for males.However, there are significant differences between the rural and urban areas. Table 7 shows themale and female literacy rate by an urban-rural break down.

Table 7: Literacy Rate in Niger, 1993(%)

Urban Rural

Female 25 6

Male 43 15

In the rural areas of Niger there is not much variation in the literacy rate by expenditurequintile. However, in the urban areas females in the lowest expenditure quintile have a literacyrate of 16 percent, compared to 40 percent for females in the highest in expenditure quintile. Thissame pattern exists for net primary enrollment ratios. Table 8 shows the net primary enrollmentratio in Niger. Again, the rural females are worse off relative to males or urban females.

Table 8: Net Primary EnrollmentRatios in Niger, 1993 (%)

Urban Rural

Female 61 12

Male 74 24

In Guinea, female students compose 33 percent of the primary school student population,and less than 15 percent of the girls 7 to 12 years of age are in school. Even though the ruralareas represent over half of the potential students, fewer than 20 percent of all primary studentsare from the rural areas, and only 13 percent of the female primary school student are from ruralareas.

Increasing the enrollment rates and in turn the education of females is no easy task.Various cultural attitudes or school designs can prevent or cause students not to be enrolled. InPakistan, 27 percent of the females age 5 to 10 who never attended school did so because theirparents did not want them to.

18

V. Health

Human capital is a vast web of complements and mutually reinforcing elements. Forexample, to do well in school, one needs to be able to attend school, and maintain concentration.To do this, one needs to be healthy and well fed. In turn, a well-educated person tends to live ahealthier lifestyle and is more likely to use medical facilities, prenatal care, and put their childreninto private school, all of which tend to increase children's educational attainment levels, cognitiveability and academic achievement levels, because of their parents likelihood of having healthychildren. The trap for the poor and uneducated is their tendency to live less healthy lives, not usemedical facilities, prenatal care, and their children are less likely to enroll in school-thusperpetuating the cycle of poverty.

Once a person has ceased their formal education and entered the work force, their humancapital development becomes a function of growing experience, good health, and their educationalattainment-which is complemented by additional training. Should one's health fail, their humancapital can rapidly diminish. Therefore, while education is a primary component of the person'shuman capital, good health is also necessary to be educated and also to realize the benefits ofbeing educated.

One of the primary national level indicators of the overall health conditions of a country islife expectancy. Since life expectancy is the result of many inputs it acts as a gauge to summarizethe overall health of a population. Life expectancy should only be used only as a rough indicator.It lends little insight into causes, problems, shortages, or geographic and demographic problemareas. Figure 10 shows some of the lowest life expectancies.

19

Figure 10: Life Expectancy

70

....................................................................... .................................... -L ow-nm e Coutry60 A-rape (63 years)

20 ~ ~ ~ 4

40

30

10~~~~~~~~~~~~~*

0 55 X 0 ] ] t }

Figure 11: Access to Safe Water

80 7570

7059

2 60 52

50 44

s 40-

9 30- 27

18 17 20< 20 14 1 i i _

10

0 -

* Urban * RuWal

20

Box 4: Human Capital Indicators in Tanzania* . . . . ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. .;.. ... . ..P

InTazaia 2 eren o eoleint..ailad.n.5.prcn inZ~Anzba arebelow they

or-0T0-- t ttti ii0 pnva wwr0-i ..... .. .. ...... ....... W- 00. t. 0 0 ....... ....... ...... t: :.tipovrtyline. lPovetysemsto be arural gl phnmeo: 92 0 - g peren o- the poo liv in*ua:rasadterey eaiy n gicltr. Therai incoe s five:0 tie les thtoewo reeiewae in te pbior prvt setor. -

areworse in Zanbr 9prcet5 : 9pmna noradrdwite. Thxe piay holgross erlmnratiorapidlydeclined-betwn-1980-and1993:fro86to69percentforfemales,andfr 9 ........... 9..t1........

percent for males. The grosssecondaryenrolments..... extr.me.y low:.. pecn for''-. ...emae ad C-6- p

The poor receive m .uh. .l v The wo -poorest iis r"ceive

only 0280 percent of the.total=education subsidies, while the two richesquintiles receive..7.percent. At thetsecodaan tertiayles,thetwo higest inom grup reetiv.ei 61 pece and 10 pecet fhStotal benefits,respectively. In th0et0 pvate sector, t O9 pors q spend o r ' ' e 9 $6. per'hoehold foreducation,copaedtoUSi $23 pr hosoldi| spe t-0..-204400 by0t th rihes qwti

Thefetiit ratpt. Tazani is 5.8 an oa ifeepcac s ers h vrg ubro,~~~~~~~~~~~~~. ....... ...... .. .. ....... ........

childrenbelow the age 0 of 1840 pe houisehold 0is 4.14 Inthe botom- -unie comare t o- 2. in te} rihqnuintile.: Thgle infant imortality rate jis 84 per 10010: live births, Dmatena mortality is--748 per8 100:-000 ilive.birh, and th ner $ -mortlt rat is 15 pe 1,00 liv births.000 --.00t. 0.? .

Publ00000000ic 00000Pl*ealth expniues ar-e pr-ich. Fo0r istanc, the 00poorest 40 percent recev onl00.y- 25pecn of lhospital subsidies while thetp. 20 pecet rceve32 pret- of th hoptal. bidiesIn theT fprvtesector, fthe:poorest lquintile spens ol *0US $5 per.hiou:sehold; foirheath,whereas .the: richestuintilej

can afford to spend US $39.~~~~~~~~~~f :bnw id

-. -Fortyseven percen ofchildeunr theaeo re stute an 2 pecn of th reann

iER. i..E .ER i j iiS~~. .. . i.iREE .... . i----E..E. E.--iE. ....... ... E ... .

fchldrnr manorshed. I addtin t140 pecn ofault men an 34 pret 0of adut woenar

Other indicators for gauging the health condition of a country include access to safe water,health facilities and other utilities, along with the incidence of illnesses.

Safe Water. The ability for one to have access to safe water dramatically reduces theirsusceptibility to water-born diseases, thus increasing the likelihood of their being able to show upfor work or school. Unfortunately, there are many areas in the world where access to safe wateris rare. For instance, in Ethiopia, Guinea-Bissau, and Sierra Leone 82, 75, and 67 percent of thepopulation does not have access to safe water, respectively. As with the other indicators, ruralareas are particularly hard hit. In Mozambique, 75 percent of the population does not haveaccess to safe water, but there is a heavy rural bias-83 percent of the rural population lacksaccess to safe water compared to 56 percent urban. In Cambodia, 88 percent of rural and 80percent of the urban population do not have access to safe water.

21

Health Facilities. Figure 12 shows some of lowest indicators for access to healthfacilities. It is not uncommon that those with no access to safe water are double hit with noaccess to health care. Table 9 shows a few countries where both of these indicators wereavailable. The problem of simultaneously lacking safe drinking water and health facilities canmanifest itself in the rate of deaths for treatable diseases. For instance, in Mozambique only 25and 40 percent of the population have access to water or health facilities, respectively. At thesame time 50 percent of the deaths of children in Mozambique reported by hospitals were causedby treatable diseases. In northeast Brazil, infectious and parasitic diseases are the third leadingcause of death. In Chad 74 percent of the population has no access to health care.

Figure 12: Access to Health Facilities

1818

1614

~14

12

*:- 12 10 10lo 9

6rA 6

4 3 4

2

00 4

z

22

Table 9: Access to Safe Water and Health Facilities (%)

Country Safe Water Health Facilities

Ethiopia 18 45

Guinea 40 45

Mozambique 25 40

Niger 37 30

The use or lack of health facilities is brought to light in another related set of indicatorscentered around pregnant women. For instance, Table 10 shows the proportion of pregnantwomen not receiving prenatal care in Nicaragua. Clearly, the highest incidence is in the ruralareas. It should come as little surprise that the proportion of births performed outside a hospitalhas a simnilar distribution (Table 11).

Table 10: Pregnant Women without Prenatal Care, Nicaragua (%)

Region Overall Urban areas Rural areas

Boaco 40.1 21.6 48.2

Matagalpa 42.4 17.4 52.9

Regi6n Autonoma 40.5 26.7 51.7Atlantica Norte

National 31.2 20.2 43.6

Source: Nicaragua Living Standards Measurement Survey, 1993

Table 11: Births Performed Outside of the Hospital, Nicaragua (%)

Region Overall Urban areas Rural areas

Boaco 64.6 39.6 78.8

Matagalpa 65.8 43.6 75.2

Regi6n Autonoma 64.1 41.1 83.0Atlantica Norte

National 50.7 33.0 73.0

Source: Nicaragua Living Standards Measurement Survey, 1993

23

Whereas in Nicaragua the use of prenatal care is low, particularly in the rural areas, inTanzania the prenatal care indicators are even worse. As a whole, only 3.1 percent of the womenreceive prenatal care. The difference between the urban and rural areas is less than 1 percent.Table 12 shows the relationship of prenatal care and education in Tanzania.

Table 12: Prenatal Care and Primary Education, Tanzania (%)

Primary School Country Average Worst Six Regions6

Incomplete 1.4 1.0

Completed 9.9 7.3

Source: Tanzania Human Resource Development Survey, 1993.

ilnesses. In a recent household survey in Pakistan, it was found that 80 percent of thehouseholds in the lowest expenditure quintile had at least one member sick in the 30 dayspreceding the survey.' Also, 25 percent of the children under 5 reported having diarrhea. InNicaragua, almost 40 percent of the children under 1 year old in the rural Boaco region reportedhaving diarrhea within 30 days of the survey. This is the same region where almost 40 percent ofthe children aged 6 to 14 never attended primary school.

Vaccination. Even so, many countries have extremely low vaccination rates (Table 13).It should be noted, that, according to UNICEF (1996), in Haiti, Ethiopia and Mozambique noneof the costs of vaccination are covered by the government.

6rSix ural areas of Ruvuma, Singida, Sinyanga, Kagera, Mara, and Coast.7The highest quintile had a 74 percent incidence.

24

Table 13: Children Not Vaccinated (%)

Country Measles DPT

Dominican Republic 47 69

Ethiopia 63 57

Haiti 41 69

Mozambique 77 81

Paraguay 56 67

Table 14 presents the vaccination rates for Pakistan by region. There is 63 percent morechance for children from the lower income families not to be immunized relative to the childrenfrom upper income level families. Children from the urban areas have 72 percent more chance ofbeing immunized relative to the children from the rural areas. In South Punjab this percentageincreases to 200 percent.

Table 14: Children under 5 Years Old That Have Never Been Immunized byProvince/Area, Pakistan 1993 (%)

Urban Rural


Sind 22.2 41.4

North-west Frontier 22.8 40.7

Province


Overall population 21.1 36.4


The reasons for parents not vaccinating their children can be as simple as not havingsomeone visit them or the facilities being too far away or too expensive. From a recent Pakistansurvey, Table 15 shows the percent of people not vaccinated because a team did not visit them.As with so many other indicators, there is a rural bias here. In the rural areas, the majority of thepeople's reason for not immunizing their children was the lack of a team visiting.

25

Table 15: People That Have Indicated "No Team Visited" As the Main Reason for NotImmunizing Their Children,8 Pakistan 1993 (%)

Urban Rural

Sind 26.4 60.1


Overall 27.9 51

Source: Pakistan Integrated Household Survey, 1991.

Nutition. Nutrition plays a critical role in the development of human capital, in particularthe educational and intellectual abilities of children. This is particularly true in the earlydevelopmental stages of the child. Unfortunately, many children suffer from malnutrition. Theeffects of malnutrition at the early stages of development can be devastating to the child's futuredevelopment. In Bangladesh, 84 percent of the children under 5 are malnourished. In India andEcuador, 63 percent of the children under 5 are malnourished. In Cambodia 40 percent of thechildren under 5 are malnourished. In Ethiopia the rate is 38 percent and in Indonesia it is 39percent. In Zambia, 35 percent of the children under 5 were malnourished in the northem region.One of the worst case is in Mozambique where 30 to 40 percent of the children are estimated tobe chronically malnourished.

In Eritrea, almost 37 percent of the children under 5 are malnourished. However, themalnutrition rate climbs to almost 60 percent in the Hamasien Province and 54 percent in theGash-setit province. The prevalence of stunting for children under 5 in Lesotho is 33 percent,versus 39.8 percent in Qacha Nek. In Malawi, 49 percent of those under 5 were stunted. InNigeria, 43 percent of the children under 5 were malnourished. In Brazil, as a percentage of theage group, five time as many children were underweight in the northeast than in the south.

Figure 13 presents the proportion of children under 5 with moderate to severe stunting inAfiica. Considering that the first four years of life are the most critical to the future human capitaldevelopment of the child, the fact that a country has over 40 percent of their future studentsalready suffering from malnutrition does not bode well. This suggests that this cohort will likelyhave high repetition and dropout rates, lower educational attainment and achievement, and mostlikely be left in poverty.

N The three possible answers were "too far away", "team did not visit", "cost too much".

26

Figure 13: Children under Five with Moderate to Severe Stunting (%)

7064

60 57

5_48 485

.0 505

inr 40

E 30

20

10

0

ln -

Infant Mortality. Mortality rates, like life expectancy, are a good indicator of theoverall health conditions within a county. Mortality rates are generally broken down into infant,child, and maternal. As with the previous indictors, high mortality rates are associated with otherunfavorable human capital indicators. Figure 14 shows a geographic and demographicdecomposition of infant mortality in Tanzania. The groups with the highest mortality rate are theilliterate, living in one of the four worst regions, and those who did not finish primary school.

27

Figure 14: Infant Mortality and Education, Tanzania, Worst Four Regions(deaths per thousand live births)

The Whole Country

177

The Rest The Worst Four Regionsof the Country in the Mainland

164 250

Primary School Primary SchoolFinished Unfmished

211 275

252 284

Source: Tanzania Human Resource Development Survey, 1993

Under 5 Child Mortality. The second mortality indicator is that of children under 5. InMalawi, the overall under 5 mortality was 233 per 1000 live births-244 in the rural areas and262 in the central regions. In Eritrea, the under 5 mortality rate is 203 per 1000 live births, withthe highest rate in Dankalia, Sahel, and Sember: 318, 287, and 254 per 1000 live births,respectively.

Maternal Mortality. The third mortality indicator is matemal mortality, which also hasgreat variability within countries. For instance, in Tanzania, with a per capita GNP of $140, thematemal mortality rate is 748 per 100,000 live births, while in Benin, with a per capita GNP of$370, the matemal mortality rate is 2,500 per 100,000 live births. Mozambique, Ethiopia, Chadand Yemen, all have matemal mortality rates near 1,500 per 100,000 live births.

Fertility. The issue of fertility as a human capital development factor might not be obviouson the surface. The development of human capital is partly a function of family resources: time

28

and money.9 As children are being added to the family, the resources used to educate and feedthem are being spread out. Inherently, this means less resources per child, which in turn meanslower human capital. Higher female education levels are associated with lower fertility rates, bothof which are associated with higher educational attainment levels of children. Figure 15 presentssome the highest fertility rates in the world.

Box 5: Human Capital in EritreaErntrea: ws econonucal Vw:ell developed prt h bSa African couEtrie inte.

1950Xs. Hwever du toth&¢ irty ~0yearst ofwr ro to 0 lier tinn 191 titW i t beaeoeo theporscontie, it;ape capita icoe fabotess than$200. 0 9;:2rgu xa0

Theicdneof- wpverty $in Erie i5 ne; of th e ost; 0a tin thef wrd:6 percet. In0 rura areas,

loland. Eveninurbman ra,62 percent liv -ide the30j poet line.)- f :

5 -perceiitin mSahelPrvice;: 10percent min arka Provine 11:3 -percenltin Dankalia Provice andll5 percn

S.f SS.iE,iiS.";ff i.fTd SiS .E.S ..f.7 ..S.f .5-.f.-... S.0 ..-ff .... .. .f.av2fDD D .i.f.^D.fff.. . . ....

in Gash-Setit Province. Accorin to- UNICtEf (1996),onl 25 v percento ch5ildrenl reacheda grade fIveC in-l

The health s.taX-tuXls o Eiotre'ts populaionisoon ofisthe ooes-:1tr among low-incm icutries. ThlNe ytotalfertl^ity rateis5.7 births.perkwomenandlife epectancyat birthisoly!48 yea. The mor tlty ratenorthseunder thCe ageof fiv is 0203 pr100w 10 live birth. Conditionsithe rural ara ae mor sevre sucas in Dankalia l (318per".1,000 live e-birth) andSahel' ,1,:(28,7 pe :r l:1t00 live births). OethousandSl and fourhundredper 0Qt0,0 l birtsinEartren 1990cituted one of th o m r tl rate 0Sub-SC Afra F, 1 -996)L.-: -;f fft

Thir00:000 0ty 0t-seven perent per:fof children:.under th.e a.ge .of fiv.et are. .. alnourishd. Rgionally,the. incidence ofchild mQlalnutri intha eia sienafnd Gash-it-Provinces is mostsevr-6percent ad 54 pent

Source: UMC~~~~~~~~F The Pro s~~~~~~~~ Q N.... ion9

9See Becker and Tomes, 1986; Behrman, Pollak, and Taubman, 1989; Blake, 1989; Psacharopoulos and Matson,1996.

29

Figure 15: Fertility Rate (births per woman)

8 7.5 7.4 7.1 6.9 67 6.7 6.7 6.6 6.6 6.5

6

5

3.---- ---- Low-income3* Country Average2 - (3.3)

1-

0 - ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~

One influence on fertility rates, besides the education of females, is the use ofcontraceptives. Figure 16 presents some of the countries with the lowest rates of contraceptiveuse.

Figure 16: Contraceptive Use (%)

14- 1312 12

12 11

100)8~~~~~~~~~~~~

66- -

2 -

0 10 0)1 .

0).- 444 u L2.~

30

Table 16: Population That Did Not Use Birth Control by Area/Region,Pakistan (percent)

Urban Rural

North Punjab 66.1 87.8


Overall 64.2 80


Table 16 presents contraceptive use by region in Pakistan. The proportion of the femalepopulation that does not use birth control is higher in the rural than in the urban areas, 80 and 64percent respectively. In the rural areas of South and North Punjab the proportion of females notusing contraception is 82 and 88 percent, respectively. In Guyana, the national level ofcontraceptive use in 29 percent. However, in the lowest per capita consumption quintile it isestimated to be only 12 percent.

VI. Public Spending

Our next task is to take a look at how governments are addressing, in terms of socialspending, the issues listed above. For instance, if it is found that 95 percent of the urbanpopulation and only 10 percent of the rural population are literate, one would like to believeefforts are being made to educate those in the rural areas. So we look at the government'sspending on education. Often the distribution of the benefits is pro-rich and poorly targeted, ornot targeted at all, to the needy (Grosh, 1990: McGreevey 1980; Van de Walle and Nead, 1995).

Table 17 shows the distribution of income and social spending in Brazil. The incomedistribution follows the typical pattern, the upper quintile has the majority of the income while thelower quintile has the least. Table 17 shows that the social spending in Brazil is pro-rich, i.e. therichest receives more benefits than the poorest.

31

Table 17: Income Distribution and Public Spending, Brazil1990 (%)

IncomeQuintile Income Share Social Spending*

I Poorest 2.1 13

2 5.2 18

3 9.6 21

4 17.8 24

5 Richest 65.2 24

* Education, health, nutrition and social security.

Education. Figure 16 shows the public expenditure on education as percent of GDP in anumber of countries with the lowest such spending. This amounts to 1-2 percent of the GDPagainst 4% for developing countries.

Figure 16: Public Education Expenditure (% of GDP)

4.5 -

4.0 -.- .------ - -- --------------------------- - --- DeveeoExng Country Average

3.5

-3.0I 025

250 1.8 1.9 2.0 2.0 2.0 2.02.0~~~~~~~.

1.05 0.8 0.8 1l

0.5

0.0

*%of GNP

32

Table 18 shows the distribution of public education expenditure in Brazil. On the surface,when looking at the distribution for primary schooling, it would be commendable that the poorappear to be receiving most of the benefit. However, this could be misleading in the face of highprivate school enrollment, especially considering the wealthy typically send their children toprivate schools.

At both the secondary and higher education levels the distribution of benefits is definitivelypro-rich, and worsens from secondary to higher education.

Table 18: Distribution of Public Education Spending, Brazil (%)

Level of Schooling

Quintile Total Primary Secondary Higher

I Poorest 16 23 9 7

2 18 22 15 12

3 20 20 20 18

4 22 19 25 26

5 Richest 24 16 31 37

A similarly regressive education benefit distribution can be found in Ghana. In Ghana thepoorest quintile receives only 14.9 and 6 percent of the secondary and higher education benefits,respectively. However, the richest quintile receives 18.6 and 45.2 percent of the secondary andhigher education benefits, respectively. In Malawi, the poorest quintile receive 16 percent of theeducation subsidies, while the richest quintile receive 25 percent of the subsidies. In Lao PDRuniversity students receive 24 times the subsidy of a primary school student. In Mongolia,investment in education dropped from 5.9 percent on GNP to 1.8 percent from 1990 to 1994respectively. In 1994, 28 percent of the education budget in Mongolia was on primary educationand 62 percent on secondary education. The distribution of education benefits was also pro-richfor both the primary and secondary levels.

Tight budgets, strong teachers' unions, or simply poor management can often lead to anallocation of the education budget that deprives the students and teachers of the materialsnecessary for providing quality education. In 1991, 90 percent of the financial resources fortextbooks in Bolivia came from the parents (Wolff, et.al. 1994). In 1989, a disproportionateallocation of resources caused 98 percent of the educational budget in Bolivia to be used forsalaries. In India, it was estimated that 97 percent of the education budget for lower primary and96 percent of the budget for upper primary was for salaries.

Figure 17 shows the distribution of education expenditure in Guyana, by regional percapita consumption deciles. The four regions with the lowest average per capita consumptionreceived less per capita education expenditure than any other region. In Guyana, university

33

students receive 33 times the education subsidy of the primary student. Also, the distribution ofeducation expenditure is favored toward the rich areas.

Healtk Figure 18 presents the public sector expenditure on health as a percent of GDP.The distribution of recurrent health expenditure in Guyana is similar to the distribution of itseducation expenditure. Figure 19 shows the distribution of recurrent health expenditure inGuyana.

In Togo, the maritime regions have 35 percent of the country's population, and yet receivea disproportionate amount of the health related resources-90 percent of the country's drugsupply, and 70 percent of the health personnel.

Figure 17: Per Capita Recurrent Education Expenditure in Guyana

8000

700570000 6635

;Ja 6000

0 ~~~~~~~~~~~4835* 5000 4479

S~4000 34033148

3000

2000- - 1477 15001226

t 1000 -- 575

0

1 2 3 4 5 6 7 8 9 10Poorest Richest

Mean Per Capita Regional Consumption Ranking

34

Figure 18: Public Health Expenditure (% of GDP)

2.5

0 2 - ................................................................................... Develp Co t ry Aveage

1.5-

Is .5 0. . . 0.9 0.9 1.0 1.0 1.0 1.0

i o5 OA| | | |

X ~ iE i .] E E j 0 -~~~~~~~rfa

E~~

Source: UNDP, 1996

Figure 19: Per Capita Recurrent Health Expenditure in Guyana

8000 7756

13 7000

.~6000

g'. 5000

X 4000

M 3000- 2825 2897

:4 2000 1741 1647

a 1000 611 ,,, 771 528128

1 2 3 4 5 6 7 8 9 10Poorest Richest

Mean Per Capita Regional Consumption Ranking

References and Sources

Becker, Gary S. 1988. "Family Economics and Macro Behavior." American Economic Review.78 (1): 1-11.

Becker, Gary S. 1995. "Human Capital and Poverty Alleviation." HRO Working Paper Number52. World Bank, Human Resource Development and Operations Policy, WashingtonD.C..

Becker, Gary S. and Nigel Tomes. 1986. "Human Capital and the Rise and Fall of Families."Journal of Labor Economics. 4 (3): S1-S39

Behrman, Jere R., Robert A. Pollak, and Paul Taubman. 1989. "Family Resources, Family Sizeand Access to Financing for College Education." Journal of Political Economy. 97 (2):398-419.

Blake, Judith. 1989. Family Size and Attainment. Berkeley: University of California Press.

De Geyndt, Willy. 1996. "Social development and Absolute Poverty in Asia and Latin America."World Bank Technical Paper 328. Washington DC: World Bank.

Demery, Lionel, Shiyan Chao, Rene Bernier, and Kalpana Mehra. 1995. "The Incidence of SocialSpending in Ghana." PSP Discussion Paper Series 82. Poverty and Social PolicyDepartment, World Bank.

Grosh, Margaret E. 1990. "Social Spending in Latin America: The Story of the 1980's" WorldBank Discussion Paper 106. Washington DC: World Bank.

McGreevey, William. 1990. "Social Security in Latin America: Issues and Options for the WorldBank." World Bank Discussion Paper 110. Washington DC: World Bank.

Pearce, David w. 1986. MIT Dictionary of Modern Economics. Cambridge: MIT Press.

Psacharopoulos, George. 1994. "Returns to Investment in Education: A Global Update." WorldDevelopment September.

Psacharopoulos, George. 1995. "Building Human Capital for Better Lives." Directions inDevelopment. Washington DC: World Bank.

Psacharopoulos, George, and Harry Anthony Patrinos. 1994. Indigenous People and Poverty inLatin America: An Empirical Analysis. Washington DC: World Bank.

36

Psacharopoulos, George, and Robert Mattson. 1996. "Family Size, Education Expenditure andAttainment in a Poor Country." Human Capital Development. Washington DC: WorldBank. (mimeo).

Psacharopoulos, George, Samuel Morley, Ariel Fiszbein, Haeduck Lee, and Bill Wood. 1996."Poverty and Income Distribution in Latin America: The Story of the 1980's" World BankTechnical Paper No. 351, Washington DC: World Bank.

Psacharopoulos, George, and Bill Wood. "Social Indicators in Latin America and the Caribbean:A Compilation of Statistics from 1970 to the Present." A View from LATHR No. 23.Latin America and the Caribbean Region, Human Resources Division, TechnicalDepartment. Washington DC: World Bank.

Summers, Lawerence H. 1992. "Investing in All the People." The Pakistan Development Review.31 (4) Winter: 367-404.

UNDP. 1996. Human Development Report 1996. New York: Oxford University Press.

UNICEF. 1996. The Progress of Nations. Oxon: P&LA.

- 1996. The State of the World's Children 1996. New York: Oxford University Press.

Van de Walle, Dominique, and Kimberely Nead, eds. 1995. Public Spending and the Poor:Theory and Evidence. Baltimore: John Hopkins University Press.

Wolff, Laurence, Ernest Schiefelbein, Jorge Valenzuela. 1994. 'Improving the Quality of PrimaryEducation in Latin America and the Caribbean: Toward the 21st Century." World BankDiscussion Paper 257. Washington D.C.: World Bank.

World Bank. 1990a, Bolivia: Poverty Report, Report No. 8643-BO, Latin America and theCaribbean Region, Country Department III, Washington DC, USA.

- 1990b, World Development Report. 1990. New York: Oxford University Press.

-1991, Indonesia: Third Nonformal Education Project, Staff Appraisal Report, Report No.9805-IND, Asia Region ,Country Department V, Washington DC, USA.

-1992a, Guyana: From Economic Recovery To Sustained Growth, Report No. 10307-GUY,Latin America and the Caribbean, Washington DC, USA.

-1992b, Mozambique: Education Sector Expenditure, Management and Financing Review,Report No. 1 1000-MOZ, Africa Region, Southern Africa, Washington DC, USA.

-1992c, Paraguay: Public Expenditure Review: The Social Sectors, Report No. 10193-PA,Latin America and the Caribbean Region, Country Operation Department IV, WashingtonDC, USA.

37

-1992d, Rwanda: Secondary and Higher Education Sector Study, Report No. 11337-RW,Africa Region, Country Department III, Washington DC, USA.

-1993a, Bangladesh: Female Secondary School Assistance Project, Staff Appraisal Report,Report No. 11386-BD, South Asia Region, Country Department I, Washington DC, USA.

-1993b, Chad. Basic Education Project, Staff Appraisal Report, Report No. 11680-CD, AfricaRegion, Sahelian Department, Washington DC, USA.

-1993c, Ethiopia: Toward Poverty Alleviation and A Social Action Program, Report No.11306-ET, Africa Region, Eastern Africa, Washington DC, USA.

-1993d, Guinea: Higher Education and Research Sector Study, Report No. 12009-GUI,Occidental and Central Africa Department, Washington DC, USA.

-1993e, Guyana: Public Sector Review, Report No. 11753-GUA, Latin America and theCaribbean Region, Country Department III, Washington DC, USA.

-1993f, Pakistan: Balochistan Primary Education Program, Staff Appraisal Report, ReportNo. 11403-PAK, South Asia Region, Country Department III, Washington DC, USA.

-1993g, Peru: Poverty Assessment and Social Policies and Programs for the Poor, Report No.11191-PE, Latin America and the Caribbean Region, Country Department I, WashingtonDC, USA.

-1993h, Sierra Leone: Policies for Sustained Economic Growth, Report No. 11371-SL, AfricaRegion, Western Africa, Washington DC, USA.

-1993i, Sierra Leone: Policies for Sustained Economic Growth and Poverty Alleviation, ReportNo. 11371-SI, Africa Region, Western Africa, Washington DC, USA.

-1993j, Uganda: Social Sector Strategy, Report No. 10765-UG, Africa Region, Eastern AfricaDepartment, Washington DC, USA.

-1993k, Uganda: Social Sector Strategy, Report No. 10765-UG, Africa Region, Eastern Afica,Washington DC, USA.

-19931, Venezuela 2000, Education for Growth and Social Equity, Report No. 11 130-VE, LatinAmerica and the Caribbean Region, Country Department I, Washington DC, USA.

- 1993m, World Development Report. 1993. New York: Oxford University Press.

-1994a, Bangladesh: Poverty Alleviation Microfinance Project, Staff Appraisal Report, ReportNo. 15431-BD, South Asia Region, Country Department I, Washington DC, USA.

-1994b, Bolivia: Education Reform Project, Report No. P-631 1-BO, Washington DC, USA.

38

-1994c, Bolivia: Education Reform Project, Staff Appraisal Report, Report No. 12863-BO,Latin America and the Caribbean Region, Country Department III, Washington DC, USA.

-1994d, Brazil., Parana Basic Education Quality Project, Staff Appraisal Report, Report No.12699-BR, Latin America and the Caribbean Region, Country Department I, WashingtonDC, USA.

-1994e, Brazil: State Of Minas Gerais Basic Education Quality Improvement Project(Proqualidade), Staff Appraisal Report, Report No. 12477-BR, Latin America and theCaribbean Region, Country Department I, Washington DC, USA.

-1994f, Eritrea: Options and Strategies for Growth, Report No. 12930-ER, Africa Region,Easter Africa Division, Washington DC, USA.

-1994g, Guinea-Bissau: Poverty Assessment and Social Sectors Strategy Review, Report No.13155-GUB, Africa Region, Sahel Department, Washington DC, USA.

-1 994h, Guyana: Strategiesfor Reducing Poverty, Report No. 12861 -GUA, Latin America andthe Caribbean Region, Country Department III, Washington DC, USA.

-1994i, Mauritania: Poverty Assessment, Report No. 12182-MAU, Africa Region, SahelianDepartment, Washington DC, USA.

-1994j, Paraguay: Country Economic Memorandum, Report No. 11723-PA, Latin America andthe Caribbean Region, Country Department IV, Washington DC, USA.

-1994k, Paraguay: Poverty and the Social Sectors In Paraguay: A Poverty Assessment, ReportNo. 12293-PA, Latin America and the Caribbean Region, Country Department IV,Washington DC, USA.

-19941, Rwanda: Poverty Reduction and Sustainable Growth, Report No. 12465-RW, AfricaRegion, South Central and Indian Ocean Department, Washington DC, USA.

-1994m, Sierra Leone: Public Expenditure Policies for Sustained Economic Growth andPoverty Alleviation, Report No. 12618-SL, Africa Region, Western Africa, WashingtonDC, USA.

-1 9 9 4 n, Tanzania: Role Of Government Public Expenditure Review, Report No. 12601-TA,Africa Region, Eastern Africa, Washington DC, USA.

-1994o, Uganda: The Role Of Nongovernmental Organizations and Community-Based GroupsIn Poverty Alleviation, Report No. 12262-UG, Africa Region, Eastern Africa, WashingtonDC, USA.

-1 995a, Brazil: A Poverty Assessment, Report No. 14323-BR, Latin America and the CaribbeanRegion, Country Department I, Washington DC, USA.

39

-1995b, Brazil: Country Assistance Strategy, Report No. 14569-BR, Latin America and theCaribbean Region, Country Department I, Washington DC, USA.

-1995c, Brazil: Northeast Rural Poverty Alleviation Program, Report No. P-6604-BR,Washington DC, USA.

-1995d, Brazil: Northeast Rural Poverty Alleviation Program; Rural Poverty AlleviationProject-Bahia, Staff Appraisal Report, Report No. 14390-BR, Latin America and theCaribbean Region, Country Department I, Washington DC, USA.

-1 995e, Ethiopia: Country Assistance Strategy, Report No. 1498-ET, Africa Region, CountryDepartment II, Washington DC, USA.

-1995f, Ghana: Growth, Private Sector, and Poverty Reduction A Country EconomicMemorandum, Report No. 1411 1-GH, Africa Region, West Central Africa, WashingtonDC, USA.

-1995g, Guinea: Education Sector Adjustment Credit, Implementation Completion Report,Report No. 14617, Africa Region, Western Africa, Washington DC, USA.

-1995h, Lao PDR: Social Development Assessment and Strategy, Report No. 13992-LA, EastAsia and Pacific Region, Country Department I, Washington DC, USA.

-1995i, Lesotho: Poverty Assessment, Report No. 13171-LSO, Africa Region, Southern AfricaDepartment, Washington DC, USA.

-1995j, Mozambique: Country Assistance Strategy, Report No. 15067-MOZ, Africa Region,Southern Africa, Washington DC, USA.

-1995k, Pakistan: Country Assistance Strategy, Report No. 15115-PAK, South Asia Region,Country Department I, Washington DC, USA.

-19951, Pakistan: Poverty Assessment, Report No. 14397-PAK, South Asia Region, CountryDepartment I, Washington DC, USA.

-1995m, Republic Of Nicaragua: Poverty Assessment, Report No. 14038-NI, Latin Americaand the Caribbean Region, Country Department II, Washington DC, USA.

-1995n, Sri Lanka: Poverty Assessment, Report No. 13431-CE, South Asia Region, CountryDepartment I, Washington DC, USA.

-1995o, Viet Nam: Poverty Assessment and Strategy, Report No. 13442-VN, East Asia andPacific Region, Country Department I, Washington DC, USA.

- 1996. African Development Indicators 1996. Washington DC: World Bank.

-1996b, Armenia: Confronting Poverty Issues, Report No. 15693-AM, Europe and Central AsiaRegion, Country Department IV, Washington DC, USA.

40

-1 996c, Bangladesh: Labor Market Policies for Higher Employment, Report No. 13 799-BD,South Asia Region, Country Department I, Washington DC, USA.

-1996d, Bangladesh: Public Expenditure Review, Report No. 15905-BD, South Asia Region,Country Department I, Washington DC, USA.

-1 996e, Brazil: Health Sector Reform Project - Reforsus, Staff Appraisal Report, Report No.15522-BR, Latin America and the Caribbean Region, Country Department I, WashingtonDC, USA.

-1 996f, Brazil: Rural Poverty Alleviation and Natural Resources Management Project, ReportNo. P-6899-BR, , Washington DC, USA.

-1996g, Cambodia: From Recovery To Sustained Development, Report No. 15593-KH, EastAsia and Pacific Region, Country Department I, Washington DC, USA.

-1996h, Caribbean Countries: Poverty Reduction and Human Resource Development In theCaribbean, Report No. 15342-LAC, Latin America and the Caribbean Region, CountryDepartment III, Washington DC, USA.

-1996i, Eritrea: Poverty Assessment, Report No. 15595-ER, Africa Region, Eastern AfricaRegion, Washington DC, USA.

-1996j, Ethiopia: Ethiopian Social Rehabilitation and Development Fund Project, StaffAppraisal Report, Report No. 14907-ET, Africa Region, Eastern Africa Department,Washington DC, USA.

-1996k, Ghana: Basics Education Sector Improvement Program, Staff Appraisal Report,Report No. 15570-GH, Africa Region, West Central Africa, Washington DC, USA.

-19961, Guinea, Beyond Poverty: How Supply Factors Influence Girls' Education In Guinea,Report No. 14488-GUI, Africa Region, Western Africa, Washington DC, USA.

-1996m, Guinea: Public Expenditure Review, Report No. 15147-GUI, Africa Region, WesternAfrica, Washington DC, USA.

-1996n, Haiti: Country Assistance Strategy (Operations Committee Review), Latin Americaand the Caribbean Region, Washington DC, USA.

-1996o, India: Primary Education Achievement and Challenges, Report No. 15756-IN, SouthAsia Region, Country Department II, Washington DC, USA.

-1 996p, India: Second District Primary Education Project, Staff Appraisal Report, Report No.15496-IN, South Asia Region, Country Department II, Washington DC, USA.

-1996q, India: State Health Systems Development Project II, Staff Appraisal Report, ReportNo. 15106-IN, South Asia Region, Department II, Washington DC, USA.

41

-1996r, Indonesia: Dimensions Of Growth, Report No. 15383-IND, East Asia and PacificRegion, Country Department III, Washington DC, USA.

-1996s, Indonesia: East Java and East Nus Tenggara Junior Secondary Education Project,Staff Appraisal Report, Report No. 15501-IND, East Asia and Pacific Regional Office,Country Department IL Washington DC, USA.

-1996t, Indonesia: Social Sectors Strategy and Capacity Building, Technical AssistanceProject, Report No. T-6868-IND, Washington DC, USA.

-1996u, Lesotho: Country Assistance Strategy, Report No. 15510-LSO, Africa Region,Southern Africa, Washington DC, USA.

-1 996v, Malawi: Human Resources and Poverty Profile and Priorities for Action, Report No.15437-MAI, Africa Region, Southern Africa, Washington DC, USA.

-1996w, Mongolia: Poverty Assessment In A Transition Economy, Report No. 15723-MOG,East Asia and Pacific Region, China and Mongolia Department, Washington DC, USA.

-1996x, Niger: Health Sector Development Program, Staff Appraisal Report, Report No.15443-NIR, Africa Region, Human Development III, Washington DC, USA.

-1996y, Niger: Poverty Assessment, Report No. 15344-NIR, Africa Region, West CentralAfrica, Washington DC, USA.

-1996z, Niger: Primary Education Development Project, Implementation Completion Report,Report No. 15748, Africa Region, West Central Africa, Washington DC, USA.

-1996aa, Nigeria: Federal Public Expenditure Review, Report No. 14447-UNI, Africa Region,Western Central Africa Department, Washington DC, USA.

-1 996ab, Nigeria: Poverty In the Midst Of Plenty the Challenge Of Growth With Inclusion: AWorld Bank Poverty Assessment, Report No. 14733-UNI, Africa Region, Western Africa,Washington DC, USA.

-1996ac, Pakistan: Beaconhouse School System, Report No. IFV, Washington DC, USA.

-1996ad, Pakistan, Improving Basic Education: Community Participation, SystemAccountability, and Efficiency, Report No. 14960-PAK, South Asia Region, CountryDepartment I, Washington DC, USA.

-1996ae, Pakistan: Northern Health Program Project, Staff Appraisal Report, Report No.15133-PAK, South Asia Region, Country Department I, Washington DC, USA.

-1996af, Pakistan: Public Sector Adjustment Loan/Credit, Implementation Completion Report,Report No. 15758, South Asia Region, Country Department I, Washington DC, USA.

42

-1996ag, Pakistan: Punjab Private Sector Groundwater Development Project, Report No. P-6779-PAK, Washington DC, USA.

-1996ah, Paraguay: Maternal Health and Child Development Project, Staff Appraisal Report,Report No. 15610-PA, Latin America and the Caribbean Region, Country Department I,Washington DC, USA.

-1996ai, Peru: Irrigation Subsector Project, Report No. P-6958-PE, Washington DC, USA.

-1996aj, Peru: Second Social Development and Compensation Fund Project, Staff AppraisalReport, Report No. 15497-PE, Latin American and the Caribbean Region, CountryDepartment III, Washington DC, USA.

-1996ak, Rwanda: Health and Population Project, Report No. P-6943-RW, Washington DC,USA.

-1996al, Sri Lanka: Teacher Education and Teacher Deployment Project, Staff AppraisalReport, Report No. 15282-CE, South Asia Region, Country Department I, WashingtonDC, USA.

-1996am, Tanzania: The Challenge Of Reforms: Growth, Incomes and Welfare, Report No.14982-TA, Africa Region, Eastern Afica, Washington DC, USA.

- 1996an, Uganda: The Challenge of Growth and Poverty Reduction. A World Bank CountryStudy. Washington DC: World Bank.

- 1996ao, Uganda: Growing Out of Poverty. A World Bank Country Study. Washington DC:World Bank.-1996a, Taking Action for Poverty Reduction In Sub-Saharan Africa:Report Of An Africa Region Taskforce, Report No. 15575-AFR, Africa Region, HumanResources, Technical Department, Washington DC, USA.

- 1996ap, World Development Report. 1996. New York: Oxford University Press.

43

Annex Table 1: The Poorest CountriesCountry GNP/capita (dollars) Population mid-

1994 1994 (millions)Rwanda 80 7.8Mozambique 90 15.5Ethiopia 100 54.9Tanzania 140 28.8Burundi 160 6.2Sierra Leone 160 4.4Malawi 170 9.5Chad 180 6.3Uganda 190 18.6Madagascar 200 13.1Nepal 200 20.9Vietnam 200 72.0Bangladesh 220 117.9Haiti 230 7.0Niger 230 8.7Guinea-Bissau 240 1.0Kenya 250 26.0Mali 250 9.5Nigeria 280 108.0Yemen, Rep. 280 14.8Burkina Faso 300 10.1Mongolia 300 2.4India 320 913.6Lao PDR 320 4.7Togo 320 4.0Gambia, The 330 1.1Nicaragua 340 4.2Zambia 350 9.2Tajikistan 360 5.8Benin 370 5.3Central African Republic 370 3.2Albania 380 3.2Ghana 410 16.6Pakistan 430 126.3Mauritania 480 2.2Azerbaijan 500 7.5Zimbabwe 500 10.8

44

Annex Table 2: Selected Worst Aspect Indicators by Countr

Country Education Health and NutritionAngola Access to Safe Water, 19%

RuralBangladesh Child Malnutrition 84%

Benin Illiteracy 65% Maternal Mortality, Per 2500100,000 Live Births

Bolivia Educational Attainment, .3 yrsIndigenous

No Schooling, Rural 51%Females

Brazil Illiteracy, Rural PoorHeads-Brazil 60%-Northeast 70%/o

Repetition, Grades 1-4, 74 toNortheast 49%

Public EducationExpenditure Incidence-Poorest Quintile 16%-Richest Quintile 24%

Cambodia Access to Safe Water, 12 %Rural

Child Malnutrition 40%

Central Africa Access to Safe WaterRepublic -Rural 11%

-Urban 14%

Chad Primary Enrollment Ratio Maternal Mortality, Per 1594-Bilitine 10% 100,000 Live Births-Batha 13%-Quaddi 15%

Congo Access to Health Facilities 9%

Ecuador Child Malnutrition 63%

Egypt No Schooling, Rural 78% Public Health 1%Population Expenditure/GDP

- continued

45

Annex Table 2: continued

Country Education Health and NutritionEritrea Primary Enrollment Ratio Under 5 Mortality, Per

-Sahel 5% 1,000 Live Births-Barka 9% -Dankalia 318-Dankalia 13% -Sahel 287-Gash-Setit 15% -Sernber 254

Grade Repetition, Female 29% Child Malnutrition-Hamasien 60%-Gash-Setit 54%

Ethiopia Illiteracy 73% Access to Safe Water, 11%Rural

Primary Enrollment Ratio, 21%Females Access to Health Facilities 10%

Maternal Mortality, Per 1528100,000 Live Births

Child Stunting 64%

Ghana Public EducationExpenditure Incidence

-Secondary-Poorest Quintile 14.9%-Richest Quintile 18.6%

-Higher-Poorest Quintile 6%-Richest Quintile 45.2%

Guatemala No Schooling, Indigenous 61%

Guinea Illiteracy 86% Access to Health Facilities 14%

Net Primary Enrollment 44%Ratio

- continued

46


Country Education Health and Nutrition

Guinea- Grade Repetition Life Expectancy 38 yrsBissau -First and Second Level 43%

-Overall 38%Access to Safe Water 25%

Public Education .8%Expenditure/GDP

India Educational Attainment, .5 yrs Child Malnutrition 63%Rural Females

Education Budget ForSalaries-Lower Primary 97%-Upper Primary 96%

Indonesia Public Health .7%Expenditure/GDP

Lao PDR Net Primary Enrollment 54% Access to Health Facilities 4%Ratio, Rural

Public Health Expenditure1%

Madagascar Access to Health Facilities 3%

Child Stunting51%

Malawi Illiteracy 59% Child Stunting 49%

Net Primary EnrollmentRatio (Poorest)-Females 31%-Males 34%

Public EducationExpenditure Incidence-Poorest Quintile 16%-Richest Quintile 25%

Mauritania Illiteracy 81% Child Stunting 57%

Net Primary Enrollment,Rural, Female Poorest 4%Quintile

- continued

47



Mongolia Public EducationExpenditure/GDP-1990 5.9%-1994 1.8%

1994 Budget-Primary 28%-Secondary 62%

Mozambique Illiteracy 67% Access to Safe Water, 17%Rural

Primary Schools Closed 70%Access to Health Facilities 10%

Schools Destroyed, Tete 90%Maternal Mortality, Per 1512100,000 Live Births

Child Malnutrition 30-40%

Nepal Access to Health Facilities 6%

Child Malnutrition50%

Nicaragua No Schooling, Children 6- 46%14, Rural Matagalpa

Niger Illiteracy, Rural Females 94%

Net Primary Enrollment 12%Ratio, Rural Females

Nigeria No Schooling, Rural Access to Safe Water, 20%-Female 65% Rural-Male 50%

No Schooling-Northeast 82%-Northwest 83%

- continued

48



Pakistan Illiteracy, Rural 81%Balochistan

No Schooling, Rural 80%Population

No Schooling, Children 57%6-13, Rural Sind

Peru Repetition, First Grade 32%

Sierra Leone Primary Enrollment Ratio, 20% Access to Safe Water 33%Northern Province

Somalia Public Health .9%Expenditure/GDP

Sudan Public Health .5%Expenditure/GDP

Suriname Public Education .8%Expenditure/GDP

Syria Arab Public Health .4%Republic Expenditure/GDP

Tanzania Female Illiteracy, 59% Public Hospital SubsidiesZanzibar -Poorest Two Quintiles 25%

-Richest Quintile 32%Public EducationExpenditure Incidence-Poorest Two Quintiles 28%-Richest Two Quintiles 57%

Uganda Primary Enrollment Ratio-Kotido 21%-Morodo 26%

Yemen Maternal Mortality, Per 1471100,000 Live Births

Zaire Public Health .8%Expenditure/GDP

I

Human Capital Development Dissemination Notes

Title Date

1 Tobacco Death Toll February 11, 1993

2 The Benefits of Education for Women March 8, 1993

3 Poverty and Income Dist. in Latin America March 29, 1993

4 BIAS is Here April 12, 1993

5 Acute Respiratory Infections April 26, 1993

6 From Manpower Planning to Labor Market May 10, 1993Analysis

7 Enhancing Invest in Education Through May 24, 1993Better Nutrition and Health

8 Indigenous People in Latin America June 7, 1993

9 Developing Effective Employment Services June 28, 1993

10 Social Security: Promise & Pitfalls in July 12, 1993Privat. Experience from Lat Am

11 Making Motherhood Safe August 2, 1993

12 Indigenous People & Socioecon Devel in August 30, 1993LA: The Case of Bolivia

13 Participatory Poverty Assessment September 13, 1993

14 World Population Surpasses 5.5 Billion in September 27, 19931993

15 Alcohol-Related Problems October 12, 1993

16 Hidden Hunger - I October 25, 1993

17 Hidden Hunger II - Micro Mal November 8, 1993

18 Barriers/Solutions to Gender Gap November 29, 1993

19 Higher Education in Singapore December 13, 1993

20 Five Criteria for Poverty Programs January 3, 1994

21 Operations Evaluation at the Bank January 18, 1994

22 Vocational Education for Chilean Farming January 31, 1994

23 Poverty Reduction Strategy - The Grameen February 28, 1994Bank Experience

24 Social Security I - The Need for Reform March 14, 1994

25 Hidden Hunger III - Anemia March 28, 1994

26 Cross-Subsidies April 11, 1994

27 Social Security II - The Elements of Reform April 25, 1994

28 Intemational Migration and Trade Part 1 of June 20, 19942


Title Date

29 International Migration and Trade July 11, 1994

Part 2 of 2

30 Women in Higher Education August 1, 1994

31 The Unit Cost of Family Planning August 15, 1994

32 Zimbabwe: Determinants of Contraceptive August 29, 1994Use at the Leading Edge of FertilityTransition in Sub-Saharan Africa

33 Determining Contraceptive Use in Three September 12, 1994Leading Countries

34 Self Employment for the Unemployed September 26, 1994

35 Supporting Entrepreneurship by Low- October 17, 1994Income Women

36 The ORT Miracle October 31, 1994

37 Women's Health and Nutrition November 16, 1994

38 Poverty Reduction and Deregulation of November 28, 1994Argentina's Microfirms

39 Poverty, Deregulation and Microfirms Part December 12, 1994II: Mexico

40 Community-managed Schools Program in El December 19, 1994Salvador

41 Retraining of the Unemployed: What January 9, 1995Impact? The Case of PROBECAT inMexico

42 Hidden Hunger IV January 23, 1995

43 The Caribbean Public Information Center February 6, 1995

44 Bringing Market Forces to Workers' February 27, 1995Training

45 Dominican Education Reform Makes the March 13, 1995Grade

46 Cervical Cancer: Promising Approaches March 27, 1995

47 Nutrition and Early Child Development: Into April 10, 1995the Year 2020

48 Labor Market Outcomes of Technical April 27, 1995Training: The Case of Mexico

49 Community-Managed Health Care May 8, 1995Programs In Mali.

50 Do Women Workers Gain or Lose During May 22, 1995Economic Growth or Adjustment

52 Priorities and Strategies for Education June 19, 1995


Title Date

53 Does Good Economic Analysis Lead to July 10, 1998Better Projects?

54 Payroll Taxes: Why Should Workers August 14, 1995Worry About Them

55 The Ecoregional Factor: New Perspectives August 28, 1995on Malnutrition and Poverty

56 Savings and Education: Some Empirical September 18, 1995Evidence

57 Key Indicators for Family Planning September 25, 1995

58 Involving Schools and Communities in October 2, 1995Education: An Analysis of World BankExperience (FY1970 to FY 1995)

59 The Economic Value of Contraception October 10, 1995

60 Costs and Benefits of Bilingual Education in October 23, 1995Guatemala

61 Are Donor-Supported Structural Adjustment November, 6, 1995Programs Responsible for Reducaitons inPublic Spending on Health

62 Economic Justification of Training November 13, 1995Interventions: Should They Satisfy Needsor Correct Market Failures?

63 Cervical Cancer: The Case of Mexico November 20, 1995

64 Bank Lending for Labor Markets: 1991 to December 4, 19951995

65 Labor Market Outcomes, Output Growth, December 11, 1995and Population Growth

66 Appraising a Health Project: Economic January 8, 1996Benefits of the Onchoceriasis ControlProgramme in West Africa

67 Techniques for Evaluating the Impact of February 5, 1996Interventions.

68 Costs and Effectiveness of Retraining in March 4, 1996Hungary

69 What are the Effects of School Choice March 18, 1996Programs? Evidence from the United States

70 Evaluating Retraining Programs in OECD April 1, 1996Countries

71 Education Achievements and School August 5, 1996Efficiency in Rural Bangladesh

72 Reflect: Evaluating a New Approach to October 31, 1996Adult Literacy

Human Capital Development Working Papers

Contact forTitle Author Date paper

HROWP1 Social Development is Nancy Birdsall March 1993 L. MalcaEconomic Development 37720

HROWP2 Factors Affecting Achievement Eduardo Velez April 1993 B.in Primary Education: A Ernesto Schiefelbein Washington-Review of the Literature for Jorge Valenzuela DialloLatin America and the 30997Caribbean

HROWP3 Social Policy and Fertility Thomas W. Merrick May 1993 0. NadoraTransitions 35558

HROWP4 Poverty, Social Sector Norman L. Hicks May 1993 J. AbnerDevelopment and the Role of 38875the World Bank

HROWP5 Incorporating Nutrition into F. James Levinson June 1993 0. NadoraBank-Assisted Social Funds 35558

HROWP6 Global Indicators of Nutritional Rae Galloway June 1993 0. NadoraRisk (II) 35558

HROWP7 Making Nutrition Improvements Donald A.P. Bundy July 1993 0. Nadoraat Low Cost Through Parasite Joy Miller Del Rosso 35558Control

HROWP8 Municipal and Private Sector Donald R. Winkler August 1993 E. De CastroResponse to Decentralization Taryn Rounds 89121and School Choice: The Caseof Chile, 1981-1 990

HROWP9 Poverty and Structural Ishrat Husain September 1993 M. YoussefAdjustment: The African Case 34614

HROWP1O Protecting Poor Jamaicans Margaret E. Grosh September 1993 M.E. Quinterofrom Currency Devaluation Judy L. Baker 37792

M. Rodriguez30407

HROWP1 1 Operational Education George Psacharopoulos September 1993 L. MalcaIndicators 37720

HROWP12 The Relationship Between the John Clark October 1993 P. PhillipState and the Voluntary Sector 31779

HROWP13 Obstacles to Women's Access: Joseph Kutzin October 1993 0. ShoffnerIssues and Options for More 37023Effective Interventions toImprove Women's Health

HROWP14 Labor Markets and Market- Arvil V. Adams October 1993 S. KhanOriented Reforms in Socialist 33651Economies

HROWP15 Reproductive Tract Infections, May T.H. Post October 1993 0. ShoffnerHIV/AIDS and Women's Health 37023

HROWP16 Job Security and Labor Market Ricardo D. Paredes November 1993 S. KhanAdjustment in Developing 33651Countries



HROWP17 The Effects of Wage Indexation Luis A. Riveros November 1993 S. Khanon Adjustment, Inflation and 33651Equity

HROWP18 Popular Participation in Philip R. Gerson December 1993 L. MalcaEconomic Theory and Practice 37720

HROWPl9 Economic Returns from Edwin Mansfield January 1994 I. DioneInvestments in Research and 31447Training

HROWP20 Participation, Markets and Deepak Lal January 1994 L. MalcaDemocracy 37720

HROWP21 Safe Motherhood in Patricia Daly January 1994 0. ShoffnerFrancophone Africa Michael Azefor 37023

Boniface Nasah

HROWP22 Indigenous People and Poverty George Psacharopoulos February 1994 I. Conachyin Latin America Harry Anthony Patrinos 33669

HROWP23 Is Grameen Bank Sustainable? Shahid Khandker February 1994 S. DavidBaqui Khalily 33752Zahed Khan

HROWP24 Concepts of Educational Marlaine E. Lockheed March 1994 M. VerbeeckEfficiency and Effectiveness Eric Hanushek 34821

HROWP25 Scientific Research for Erik W. Thulstrup March 1994 L. MalcaDevelopment 37720

HROWP26 Issues in Education Finance Stephen P. Heyneman April 1994 B. Cassorlaand Management in ECA and 37172OECD Countries

HROWP27 Vocational Education and Julian Schweitzer April 1994 A. GonzalezTraining: The Role of the 37799Public Sector in a MarketEconomy

HROWP28 Social Security Issues and Nguyen X. Nguyen May 1994 M. EspinosaElements of Reform 37599

HROWP29 Health Problems and Policies Mary Eming Young May 1994 0. Shoffnerfor Older Women: An 37023Emerging Issue in DevelopingCountries

HROWP30 Language and Education in S.M. Cummings May 1994 M. EspinosaLatin America: An Overview Stella Tamayo 37599

HROWP31 Does Participation Cost the Jesko Hentschel June 1994 D. JenkinsWorld Bank More? Emerging 37890Evidence

HROWP32 Research as an Input into Harold Alderman June 1994 P. CookNutrition Policy Formation 33902

HROWP33 The Role of the Public and Deepak Lal June 1994 M. EspinosaPrivate Sectors in Health 37599Financing



HROWP34 Social Funds: Guidelines for Soniya Carvalho July 1994 K. LabrieDesign and Implementation 31001

HROWP35 Pharmaceutical Policies: Graham Dukes July 1994 0. ShoffnerRationale and Design Denis Broun 37023

HROWP36 Poverty, Human Development Harsha Aturupane August 1994 P. Cookand Growth: An Emerging Paul Glewwe 30864Consensus? Paul Isenman

HRO HRO Working Paper Series August 1994 M. EspinosaAbstracts: Numbers 1-35 37599

HROWP37 Getting the Most out of Helen Saxenian September 1994 0. ShoffnerPharmaceutical Expenditures 37023

HROWP38 Procurement of Denis Broun September 1994 0. ShoffnerPharmaceuticals in World 37023Bank Projects

HROWP39 Notes on Education and Harry Anthony Patrinos September 1994 I. ConachyEconomic Growth: Theory and 33669Evidence

HROWP40 Integrated Early Child Mary Eming Young October 1994 0. ShoffnerDevelopment: Challenges and 37023Opportunities

HROWP41 Labor Market Insurance and Deepak Lal October 1994 M. EspinosaSocial Safety Nets 37599

HROWP42 Institutional Development in Alberto de Capitani October 1994 S. HowardThird World Countries: The Douglass C. North 30877Role of the World Bank

HROWP43 Public and Private Secondary Marlaine E. Lockheed November 1994 M. VerbeeckSchools in Developing Emmanuel Jimenez 34821Countries

HROWP44 Integrated Approaches to T. Paul Schultz November 1994 M. EspinosaHuman Resource Development 37599

HROWP45 The Costs of Discrimination in Harry Anthony Patrinos November 1994 I. ConachyLatin America 33669

HROWP46 Physician Behavioral Nguyen X. Nguyen December 1994 M. EspinosaResponse to Price Control 37599

HROWP47 Evaluation of Integrated T. Paul Schultz January 1995 M. EspinosaHuman Resource Programs 37599

HROWP48 Cost-Effectiveness and Health Philip Musgrove January 1995 0. ShoffnerSector Reform 37023

HROWP49 Egypt: Recent Changes in Susan H. Cochrane February 1995 0. ShoffnerPopulation Growth Ernest E. Massiah 37023

HROWP50 Literacy and Primary Kowsar P. Chowdhury February 1995 M. EspinosaEducation 37599



HROWP51 Incentives and Provider Howard Barnum March 1995 0. ShoffnerPayment Methods Joseph Kutzin 37023

Helen Saxenian

HROWP52 Human Capital and Poverty Gary S. Becker March 1995 M. EspinosaAlleviation 37599

HROWP53 Technology, Development, and Carl Dahlman April 1995 M. Espinosathe Role of the World Bank 37599

HROWP54 International Migration: Sharon Stanton Russell May 1995 0. ShoffnerImplications for the World 37023Bank

HROWP55 Swimming Against the Tide: Nancy Birdsall May 1995 A. ColbertStrategies for Improving Equity Robert Hecht 34479in Health

HROWP56 Child Labor: Issues, Causes Faraaz Siddiqi June 1995 I Conachyand Interventions Harry Anthony Patrinos 33669

HCOWP57 A Successful Approach to Roberto Gonzales July 1995 K. SchraderPartcipation: The World Bank's Cofino 82736Relationship with South Africa

HCOWP58 Protecting the Poor During K. Subbarao July 1995 K. LabrieAdjustment and Transitions Jeanine Braithwaite 31001

Jyotsna Jalan

HCOWP59 Mismatch of Need, Demand Philip Musgrove August 1995 Y. Attkinsand Supply of Services: 35558Picturing Different WaysHealth Systems can go Wrong

HCOWP60 An Incomplete Educational Armando Montenegro August 1995 M. BennettReform: The Case of 80086Colombia

HCOWP61 Education with and with out the Edwin G. West September, 1995 M. EspinosaState. 37599

HCOWP62 Interactive Technology and Michael Crawford October 1995 P. WarrickElectronic Networks in Higher Thomas Eisemon 34181Education and Research: Lauritz Holm-NielsenIssues & Innovations

HCOWP63 The Profitability of Investment George Psacharopoulos December 1995 M. Espinosain Education: Concepts and 37599Methods

HCDWP64 Education Vouchers in Practice Edwin G. West February 1996 M. Espinosaand Principle: A World Survey 37599

HCDWP65 Is There a Case for Antonio Zabalza March 1996 M. EspinosaGovernment Intervention in 37599Training?

HCDWP66 Voucher Program for Alberto Calder6n Z. May 1996 M. EspinosaSecondary Schools: The 37599Colombian Experience



HCDWP67 NGO-World Bank Toshiko Hino June 1996 A. ThomasPartnerships: A Tale of Two 31151Projects

HCDWP68 The Disability-Adjusted Life Nuria Homedes July 1996 L AriasYear (DALY): Definition, 35743Measurement and PotentialUse

HCDWP69 Equitable Allocation of Ceilings Philip Musgrove August 1996 Y. Attkinson Public Investment: A 35558General Formula and aBrazilian example in the HealthSector

HCDWP70 The Economics of Language: Barry Chiswick September 1996 I ConachyThe Roles of Education and 33669Labor Market Outcomes

HCDWP71 Agricultural Growth and Rashid Faruqee September 1996 C. AnbiahPoverty in Pakistan Kevin Carey 81275

HCDWP72 Measuring the Opportunity Andrew D. Mason September 1996 D. BallantyneCost of Children's Time in a Shahidur R. Khandker 87198Developing Country:Implications for EducationSector Analysis andInterventions

HCDWP73 The Full Social Returns to Alain Mingat September 1996 J_ YangEducation: Estimates Based Jee-Peng Tan 81418on Countries' EconomicGrowth Performance

HCDWP74 Costs and Benefits of Bilirngual Hary Anthony Patrinos October 1996 I ConachyEducation in Guatemala: A Eduardo Velez 33669Partial Analysis

HCDWP75 What is Education Worth? Robert Picciotto November 1996 R. WiemannFrom Production Function to 84572Institutional Capital

HCDWP76 Human Capital HCDVP November 1996 R. MattsonUnderdevelopment: The Worst 31144Aspects

Date post:	21-Jul-2020
Category:	Documents
Upload:	others
View:	5 times
Download:	0 times

Human Capital Development HCD - World Bank€¦ · HCDHuman Capital Development Working Papers...

Documents