A study of the impacts of land fragmentation on ...131275/FULLTEXT01.pdf · from the Vietnam...

DEPARTMENT OF ECONOMICS Uppsala University Bachelor thesis Authors: Patrik Sundqvist, Lisa Andersson Supervisors: Javad Amid, Erik Jakobsson Autumn 2006

A study of the impacts of land fragmentation on

agricultural productivity in Northern Vietnam

ABSTRACT This study examines the relationship between land fragmentation and agricultural productivity in Vietnam, as well as the outcomes of land consolidation programs on productivity. Data from the Vietnam Household Living Standard Survey 2004 and data on the land consolidation process was used for the regression analysis. The results show weak correlations between fragmentation and productivity. Land fragmentation seems to be positively correlated to productivity due to more use of fertilizers and labour input. The communes that have consolidated their land are more productive, but this seems to be explained by initial differences in productivity. Our results suggest that there are no immidiate gains in land consolidation. Keywords: Vietnam, land reforms, land fragmentation, land consolidation, agricultural productivity, number of plots, farm size, Simpson’s index

FIGURES AND TABLES Figure 1 The prevalence of land fragmentation in Vietnam Table 1 Land-use change in Vietnam since the 1990s Table 2 Impact of land fragmentation on land productivity Table 3 Impact of land fragmentation on land productivity (using Simpson’s index) Table 4 Impact of land fragmentation on labour productivity (using Simpson’s index) Table 5 Determinants of land consolidation Table 6 Impacts of land consolidation on productivity Table 7 Correlation between land fragmentation and use of fertilizers Table 8 Correlation between land fragmentation and use of machinery Table 9 Correlation between land fragmentation and on-farm working hours Table 10 Correlation between LC communes and land productivity ABBREVIATIONS LUC- Land Use Certificate GSO- General Statistical Office LC- Land consolidation VHLSS- Vietnam Household Living Standard Survey ACKNOWLEDGEMENT This study was performed in Vietnam during May and June 2006, as a Minor Field Study financed by SIDA. First of all we would like to thank SIDA for the financial support that enabled us to perform this study. We would also like to thank Toan Do at the World Bank for providing us with statistical data and guidance during our stay in Vietnam. We are also grateful for the help we got from Björn Hansson at Ramboll Natura and John Sowdow at Mekong Economics. Many thanks to Gunilla Krantz for providing us with valuable contacts, and to Vung Nguyen Dang for helping us with contacts and giving us a very nice welcome in Hanoi. Finally, thank you Sara and Ellinor for great support.

TABLE OF CONTENTS 1. INTRODUCTION 1

2. LAND FRAGMENTATION AND ITS CONSEQUENCES 3

2.1 Disadvantages 3 2.2 Advantages 4 2.3 Consolidation programs 5 2.4 Previous studies 6

3. LAND FRAGMENTATION IN VIETNAM 8

3.1 Land consolidation in Vietnam 10 3.2 Following land consolidation 11

3.3 Previous studies concerning land fragmentation and land consolidation in Vietnam 12

4. THE IMPACTS OF LAND FRAGMENTATION

AND CONSOLIDATION IN THE NORTH OF VIETNAM 14

4.1 Data 14 4.2 Method and Model 15 4.3 Results 19 4.3.1 Impacts of land fragmentation on land productivity 19 4.3.2 Consolidation programs 24

5. ANALYSIS AND CONCLUSIONS 28

REFERENCES 30

APPENDIX 32

Table 2 (complete)

Table 3 (complete)

Table 4 (complete)

Table 7, 8, 9, 10

Map of Vietnam

1. INTRODUCTION

“Vietnam’s experience in land consolidation remains poorly studied. Consolidating

experience gained and lessons learned have been recognized as critical for further policy

development in this area.”1 – The World Bank 2006

Land fragmentation, where a single farm consists of a large number of separate land plots, is a

common agricultural phenomenon in many countries. Land fragmentation is said to be a

constraint to efficient crop production and agricultural modernization, and in several countries

this has resulted in the implementation of land consolidation programs.

The land reform that started in the late 1980s in Vietnam is one of the most radical in modern

times. The country switched from a socialistic method of agricultural production, with large

collective farms, to an individual farm-household system where the farmers have been granted

long-term use rights over land and greater freedom over production choices. The most

important principle of the land allocation under the 1993 land law was to maintain equity. As

a result many farmers, especially in the Northern provinces, received several diffrent land

plots often scattered over a wide area. 2 The government of Vietnam considers land

fragmentation to be “a significant barrier to achieving further productivity gains in

agriculture3.” So far no nationwide consolidation policy has been implemented in the country,

but the government encourages land consolidation, and a national policy is under

consideration. Voluntary, decentralized programs have been carried out in some communes in

the north, although progress is slow4.

The Vietnamese government seems to regard land fragmentation as something negative, but

the research in this area has not been consistent in that land fragmentation is an obstacle to

agricultural productivity. Theory points out a number of disadvantages related to land

fragmentation, but also advantages. Few investigations have been carried out when it comes

to evaluating the costs of fragmentation and the results of consolidation programs

1 The World Bank, 2006, p.25

2 Ravallion et al, 2003, p.4

3 The World Bank, 2005, p. V

4 Van Hung, T. MacAulay Gordon, and Marsh P Sally, 2006, p.5

1

implemented so far in Vietnam. Studies from other countries show mixed results regarding

the impact of land fragmentation on agricultural productivity.

The purpose of this study is to examine the issues of land fragmentation and land

consolidation in the Northern provinces of Vietnam. We aim to investigate what possible

impact land fragmentation in Vietnam has on land productivity, and what effect the

implemented land consolidation programs has had on productivity.

The study includes quantitative analysis of the effects of land fragmentation on productivity,

and of the link between productivity and communes that have completed consolidation

programs. Regression analysis will be carried out, using the Vietnam Household Living

Standard Survey 2004. We will limit the study to Northern Vietnam, where land

fragmentation is most widespread. We also restrict the investigation to look only at the link

between land fragmentation and productivity of planting crops, defined as annual-, perennial-,

industrial-, and fruit crops.

The paper is organized as follows: First we present the theoretical framework. We define what

we mean by land fragmentation, and give a brief description of the advantages and

disadvantages discussed in literature. We also discuss some costs and benefits of

consolidation programs, and some reports of what previous studies have concluded. Part three

contains a description of the origin and prevalence of land fragmentation, the recent land

reforms in Vietnam, and governmental policies regarding land consolidation. In section four

we specify our model and present our empirical results, followed by the last section where we

analyze our findings.

2

2. LAND FRAGMENTATION AND ITS CONSEQUENCES

Land fragmentation can be defined as a situation where a farming household possesses several

non-contiguous land plots, often scattered over a wide area. It is an observed phenomenon in

many countries around the world, and is often viewed as an obstacle to agricultural

productivity and modernization. Land fragmentation used to be closely associated with

Europe, but it has been documented in all parts of the world. Examples are Taiwan, Malaysia,

Japan, the United States, Kenya, Uganda, Peru and Mexico. The absence of a real standard

objective measure of land fragmentation makes comparisons between countries difficult, and

makes it hard to decide when a farm is too fragmented5.

Literature proposes several explanations of land fragmentation. These explanations are often

divided into two main categories; demand-side and supply-side factors. The supply-side factor

treats fragmentation as an exogenous imposition on farmers, which has an adverse effect on

agricultural production, while the demand-side factors assume that the farmers voluntarily

choose beneficial levels of fragmentation. 6 The disadvantages of land fragmentation are

mostly associated with inefficient allocation of recourses (labour and capital) leading to

increased costs of production, and with the hindering of agricultural modernization. The

recognized advantages are closely related to the demand-side causes of fragmentation.

2.1 Disadvantages

Land fragmentation is said to harm productivity in a number of ways. First, fragmented land

holdings can increase transport costs. If the plots are located far from the home, and far from

each other, there is a waste of time for the workers spent on travelling in-between the plots

and the home. Management, supervision and securing of scattered plots can also be more

difficult, time consuming, and costly. Small and scattered plots waste land area and require

more land for fencing, border constructions, and paths and roads. Land fragmentation might

also increase the risk of disputes between neighbours.7

5 Bentley 1987, p.32

6 Blarel et al. 1992 p. 234,

7 Mwebaza Rose, Gaynor Richard, 2002, s.23f

3

Small fragmented land holdings might also cause difficulties to grow certain crops, and

prevent farmers from changing to high profit crops. More profitable crops, like for example

fruit crops, require larger plot areas, so if the farmers only posess small and fragmented plots

they may be forcet to grow only less profitable crops.8

Other costs associated with land fragmentation include the hindering of economies of scale

and farm mechanization. Small and scattered plots hamper the use of machinery and other

large scale agricultural practices. In small fields operating machines and moving them from

one field to another can cause problems. Small land holdings might also discourage the

development of infrastructure like transportation, communication, irrigation, and drainage.9

Finally it is noticed that banks are sometimes unwilling to take small, scattered land holdings

as collateral, which prevents farmers from obtaining credit to make investments.10

2.2 Advantages

Even though policy makers often point out the drawbacks of fragmentation there is no

consensus that fragmentation is strictly a negative phenomenon. Bentley argues that the harm

caused by fragmented land holdings is overrated and that the farmers own views often are

neglected by policy makers11.

A benefit associated with land fragmentation is the variety of soil and growing conditions that

reduce the risk of total crop failure by giving the farmer a variety of soil and growing

conditions. Many different plots allow farmers access to land of different qualities when it

comes to soil, slope, micro-climatic variations etc. Fields with high yields one year may the

following year generate much lower yields, thus several plots of the same crop also spreads

out the risk. In addition, a holding with several plots facilitates crop rotation and the ability to

leave some land fallow.12

8 The world Bank, 2005, p.8-9

9 Mwebaza and Gaynor, 2002, p.26, Lam Mai Lan, 2001, p. 75-77

10 Mwebaza and Gaynor, 2002, p.26

11 Bentley, 1987, p.50f

12 Ibid.

4

Another benefit of land fragmentation is the use of multiple eco zones. Different plots enable

farmers to grow a wider mix of crops. Since crops ripe at different times when the plots are in

different altitudes, spreading out the agriculture work like harvest and sawing during a longer

period of time helps farmers to avoid household labour bottlenecks. This is especially

important when the growing season of the crop is short and easily creates seasons of peak

labour demand.13

Farmers may also prefer fragmented land holdings when there are diseconomies of scale with

respect to the size of the parcels. This phenomenon might be a result of labour market failure.

The farmers might be unable to gather enough labour to meet seasonal peaks on large

parcels.14 Labour market failure, i.e. the lack of off-farm job opportunities, can also result in a

large amount of unproductive family members working on the farm due to their low

opportunity cost. The resulting high ratio in labour to land makes the productivity per acre of

land high. This could be an explanation of the existence of diseconomies of scale.15

2.3 Consolidation programs

Policy makers often propose land consolidation programs as a solution to the costs associated

with land fragmentation. Land consolidation means that farmers surrender their scattered plots

in order to receive an equivalent area or value of land in fewer and more continuous plots.

Most consolidation programs are results of government policies, ranging from large-scale

mandatory programs to decentralized small programs encouraging consolidation on a more

voluntary basis. The interventions vary in levels of formality and government involvement.

Programs often include new roads, irrigation systems, settlement schemes and related

services.16

Large, full-scale, mandatory programs are very expensive and time-consuming and the results

are not always satisfying. The implementation requires extensive political, legal and logistical

requirements. Some programs have failed to defeat the underlying causes of fragmentation

and the holdings have therefore been re-fragmentized after some time. Voluntary programs

13 Bentley, 1987, p.50f

14 Ibid.

15 Heltberg, 1998,16 Mwebaza and Gaynor, 2002, p.27

5

take even longer to implement and gathering an adequate number of farmers who are willing

to participate in the program to make it meaningful has been proven difficult.17

The costs of consolidation include surveying and detailed mapping of location, elevation, size,

soil type, value etc. of every parcel. A rearranging scheme of plots into larger ones is required,

followed by a second mapping. Legal and experienced people with local knowledge are

required to work with mapping and hear out the farmers view. Consolidation often requires

new roads and other infrastructure investments.18

Sceptics claim that it is the farmer who bears the indirect costs of consolidation, even if

programs are government sponsored. Consolidation can interrupt the crop cycle for several

years, and can also disrupt the ecological benefits of land fragmentation. Consolidation is also

said to benefit larger farms because larger farms tend to have lower ratio of labour to land and

thus the most to gain by decreasing their travel time through land consolidation. Land

consolidation may therefore increase rural social stratification, as it benefits the wealthy.19

2.4 Previous studies

Previous studies concerning the impacts of land fragmentation and land consolidation on

productivity show mixed results. In one survey using household data from Ghana and Rwanda,

the authors investigate the relationship between fragmentation and land productivity. An

econometric model was used, and the null hypothesis was that fragmentation is inefficient and

reduces yield. The results rejected the hypothesis that fragmentation is inefficient. The authors

found that the private benefits of fragmentation seemed to be at least as large as the private

costs, and that consolidation programs were unlikely to lead to a significant increase in land

productivity. The authors instead suggested that policymakers focus on what really causes

fragmentation, namely inefficiencies in land, labour, credit, and food markets.20

These results contradict another study, performed in China, which found land fragmentation

to have a significant economic cost. Average plot size was used as a measure of land

17 Mwebaza and Gaynor, 2002, p.27

18 Bentley, 1987, p. 55f

19 Ibid. p. 57

20 Blarel et. al. 1992

6

fragmentation. Production functions were estimated for all the major grain crops, and the

hypothesis tested was that plot size (which the authors distinguish from ownership size) is

positively related to output. The study found a statistically significant positive relationship

between plot size and output for all crops, indicating that there are gains in plot consolidation

but that the consolidation process also involves costs. The conclusion was that to reduce

economic costs, land consolidation should be undertaken with less government intervention.

The importance of the establishment of markets for land and improvements in rural credit and

grain markets was pointed out.21

Regarding the farmers own views, a household survey in Uganda based on interviews

revealed that the farmers themselves felt that fragmentation had both advantages and

disadvantages. The most frequent advantage according to the interviewed farmers was the

ability to grow different crops based on different soil fertility. The most frequent

disadvantages stated were difficulties of managing fragmented land holdings and time waste

when travelling between plots. A lot of the respondents, in one district as many as 94 %, saw

some advantage with possessing fragmented land. When it comes to consolidation, the

majority of the respondents believed that consolidation of their land holdings would increase

productivity.22

21 Nguyen, Tin, Cheng, Enjiang Cheng, 1996

22 Mwebaza and Gaynor, 2002, p. 31-32

7

3. LAND FRAGMENTATION IN VIETNAM

During the French Colonial period land in Vietnam was very unevenly distributed and 60% of

farmers were landless in the mid 1940s. However, after the North gained its independence a

major land reform was carried out. Land and ownership rights were distributed to farmers,

followed by a rapid increase in productivity and agricultural output.23

The communist ideology gained strength and land began to be collectivized in the late 1950s.

As a result, by the mid 1960s, 90% of all peasant households in the north were working in

cooperatives. When the war ended and Vietnam reunified in 1975, land collectivization

started in the south, but the results were quite weak. In 1986, only 5,9% of farmers in the

Mekong Delta and 20% in the South-eastern region belonged to cooperatives. During the

period, agricultural yields were extremely low, and faced with an economic crisis the

government introduced a reform in 1986, called “Doi Moi”. Since then there have been two

major changes concerning the regulatory environment of land rights, one in 1988 and the

other in 1993.24

When Resolution 10 of the 1988 land law was passed by the government the aim was to

transfer control and cash-flow rights from the cooperatives to the individual households. Land

was allocated to the households with a 15 year security of tenure and implicit renewal, output

markets were privatized and investment decisions were decentralized and left to the

households. Private property was practically instituted. However the land-use rights were not

tradable and a proper land market did not develop.25

The 1993 land law granted five rights to the households: the right to transfer, exchange,

inherit, rent and mortgage land. This law is therefore seen as setting the foundation for a

formal market for land. Similarly to previous land reforms, the 1993 land law was unevenly

implemented throughout the country. But by 2000, 90% of rural households had been granted

a land use certificate (which gave farmers tenure security to the land) and the process was

23 Do and Iyer, 2006, p. 3

24 Ibid. p. 3-4

25Ibid. p. 6

8

expected to be completed in 2001.26 The land law also increased the duration of the tenure

security to 20 years for land used for annual crops and aquaculture, and 50 years for land used

for perennial crops, and it could now also be renewed.27

The most important principle of land allocation was to maintain equity, but there were

different procedures in different provinces. Land was allocated depending on several factors:

the number of persons in the household, the land quality, irrigation system, and capacity for

crop rotation. Annual crop land, for example, was divided into six categories according to

land quality. To maintain equity each household was given a plot of land of each category.

The result was that households ended up with a large number of plots, often scattered over a

large area.28

In the south of Vietnam, land fragmentation is not as widespread as in the north. Farmers

were allocated larger parcels, and land allocation was also more likely to be based on land

held prior to re-unification in 1975. For example many farmers in the Mekong Delta hold only

one or two land plots.29

Table 1: The prevalence of land fragmentation in Vietnam

Region No of observations Average no. of plots Highest no of plots

Red River Delta 1560 5,9 41

North East 1119 6,7 32

North West 393 7 31

North Central Coast 848 5,8 19

South Central Coast 579 4,4 40

Central Highland 450 3,9 19

South East 561 2,8 10

Mekong River Delta 1246 2,7 31 Source: Calculations based on VHLSS 2004

26 Do and Iyer, 2006, p. 7

27 Van Hung, MacAulay, Marsch, 2006, p. 3

28 Ibid. p 3

29 Marsch, MacAulay, 2006(1), p 4

9

Table 1 is based on the VHLSS 2004 and confirms that land fragmentation is more

widespread in the northern parts of Vietnam. On average it seems that households in the north

(the first four regions in the table) have about twice the amount of plots compared to

households in the south.

Due to the fragmented land and poor rural infrastructure, the level of mechanization in the

agriculture is still very low. Low income and the abundance of cheap household labour also

discourage households from investing in machinery. Of the food crops, rice production has

the highest level of mechanization, particularly in the Mekong Delta where land holdings are

larger and less fragmented.30

3.1 Land consolidation in Vietnam

The disadvantages of excessive fragmentation of land are recognized by the Vietnamese

government. There is not yet any national land consolidation policy, but it is under

consideration. In 1998 the government issued a policy to promote exchange of plots to

encourage larger plot sizes. Since then, northern provinces have started the implementation of

plot exchanges, though progress is still quite slow. In reports made to central and local

governments the conclusion is that plot exchange should be implemented where farmers

realise that there is a problem with land fragmentation. This is to avoid new conflicts related

to land allocation. The most important principle is that farmers should voluntarily exchange

the land. However this is not always the case and many farmers are unaware of their right to

be involved in the land allocation process.31

Along with the land consolidation, infrastructure investments in roads and irrigation are

implemented. The best results have been achieved when land consolidation has been

accompanied by a process of land-use conversion in line with the farmers´ demands, and

when this has been coordinated and well funded.32


31 Ibid.

32 The World Bank, 2005, p.vii

10

3.2 Following land consolidation

The possibility to change the land use towards more profitable crops might be one of the main

impacts of land consolidation in Vietnam. In the last decade there has been a substantial

increase in the production of commercial cash crops. The land area dedicated to annual

industrial crops (cotton, jute, sugarcane, peanut, soybean, tobacco) increased by 53% between

1990 and 1999. The area dedicated to perennial industrial crops (tea, coffee, rubber, pepper,

coconut) increased by 130%, and the area dedicated to fruit crops increased by 156% during

the same period. Meanwhile land area dedicated to food crops (such as rice) only increased by

29% (see Figure 1 below).33

Figure 1: Land-use change in Vietnam since the 1990s

Planted area of crops

0

50

100

150

200

250

300

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Year

Inde

x

Total

Cereals

Annual industrialcrops

Perennialindustrial crops

Fruit crops

Source: Statistical Yearbook 2004, published by the GSO

There exists some government policies related to the use of land in Vietnam. All land should

be used, and land should be farmed efficiently with appropriate crops and rotations and

attention paid to maintaining the fertility of the land. In practice, this is determined by

restrictions on land use that are specified on the land use certificate. Production targets are set

33 Statistical Yearbook, 2004

11

at local level in response to government directives and the households may have to grow

crops as directed. Four million hectares of land in Vietnam is required to grow rice.34

3.3 Previous studies concerning land fragmentation and land consolidation in Vietnam

There have been some previous studies about land fragmentation and land consolidation

performed in Vietnam. In a pre study made by the World Bank, a number of authors

investigated the impacts of land fragmentation and the outcomes of the land consolidation

process35. They find a strong relationship between land fragmentation (measured by number

of plots) and rice cultivation (measured by percentage of land dedicated to paddy). The fact

that land fragmentation is associated with rice cultivation is interesting. It might indicate that

fragmented land makes it more difficult to grow more valuable crops.

The authors have also investigated labour allocation. Their theory was that a transition to

more valuable crops and more mechanization would translate into labour savings, which

could be reallocated towards non-farm activities. They did find that land fragmentation is

associated with more labour devoted to farming. However, they did not find a correlation

between land fragmentation and off-farm employment, which could suggest that labour

markets in rural areas are poorly developed and that the labour savings translate into leisure

instead of off-farm work.36 This is important, if there are no off-farm labour opportunities the

benefits of mechanization decrease and one of the theoretical advantages of land

consolidation becomes less important. The paper also investigates outcomes of the land

consolidation process using case studies. It finds that the take off of land consolidation has

been slow; but that where it has taken place the results appear promising. Land consolidation

seems to favour diversification away from rice. However, there seems to be few opportunities

for displaced labour to be absorbed in the non-farm market.37

In another paper, Marsch and MacAulay find that there is an active market for land use rights

in Vietnam, but that the level of activity varies considerably between provinces. 38 They also

find that a more active market for land use rights appears to be associated with opportunities


35 The World Bank, 2005, p. 8-9

36 The World Bank, 2005, p. 8-9

37 Ibid. p. 33

38 Marsch, MacAulay, 2006(1), p. 2

12

for land use changes which lead to more profitable production activities. Another paper by the

same authors, using survey data from 179 households in the north of Vietnam, finds that the

number of plots per household does not appear to be correlated with the rice yields or revenue

earned from various types of rotations. 39 However, labour use does appear to be related to the

number of plots. The paper also reports that land fragmentation is not a significant

determinant of output risk spreading, but it does appear to be a significant factor for crop

diversity.

39 Van Hung, MacAulay, Marsch, 2006

13

4. IMPACTS OF LAND FRAGMENTATION AND CONSOLIDATION IN THE

NORH OF VIETNAM

4.1 Data

Vietnam has quite extensive state controls and restrictions when it comes to the openness of

official documents. Reliable and objective information can therefore sometimes be hard to

obtain. The data used in this paper derives from the extensive and recently performed Vietnam

Household Living Standard Survey (VHLSS) of 2004. The survey is preformed by the

Vietnam General Static Office (GSO), and is based on interviews with 9000 rural households

in Vietnam, a representative sample. The VHLSS 2004 has just been released and, because of

the large sample and the detailed information on agricultural activities and household

characteristics, it is well suited for this study. Household living standard surveys have earlier

been performed in Vietnam in the years 92/93, 98, and 2002. The survey has been extended,

modified and improved each time, and it is conducted in cooperation with the World Bank.

Therefore we consider it reliable.

The survey covers two types of questionnaires; household interviews and commune

interviews. The household questionnaire covers a wide rage of topics, divided into 10 sections.

These include, for example, information about household members, education, health,

employment, agricultural activities, income and expenditures, assets, debts and credits. It also

contains farm characteristics such as farm size, number of plots, on-farm labour hours, and

income. In addition, characteristics of each plot such as distance from house, crops grown,

and inputs and outputs can be obtained. The commune questionnaire contains community

level information such as demographic information of the community, economy and

infrastructure.

We also received information about which communes have undertaken a consolidation

program. This data was based on the commune’s own statements of whether they had land

consolidated or not, for the years 2004 and 2006. The fact that the data is built on the

communes own statements creates a risk for biased results. Some communes might state that

they have undertaken consolidation programs to a greater extent than they actually have, since

the government has expressed their concern to consolidate.

14

We will investigate the Northern provinces of Vietnam, where fragmentation is a much more

widespread phenomenon than in the south. The province Thua Thien Hue, which is the

province located in the very south of what is considered the Northern provinces in the Living

Standard Survey, is excluded from the study because its fragmentation pattern resembles more

that of the Southern provinces40.

4.2 Method and model

There are six different parameters generally used to measure the degree of land fragmentation:

farm size, number of plots, plot size, plot shape, spatial distribution of plots, and the size

distribution of the plots41. A common measurement of fragmentation used in studies is an

average of the number of plots per farm. In an attempt to standardize measures of

fragmentation authors also use an index of fragmentation. The Simpson index used in this

paper is defined as the sum of the squares of the plot sizes, divided by the square of the farm

size42:

1 - ∑ia2i /A2

Here ai is the area of the ith plot and A is the farm size. The index has a value between 0 and 1.

Value 0 indicates complete land consolidation, i.e. the farm operates with only one parcel.

Value 1 means that the farm is very fragmented and operates a large number of plots. This

index is sensitive to the number of plots as well as the size of the plots, which means that

fragmentation will decrease as the area of the big plots increases for example.43

Although measures such as the Simpson index capture some of the effects of land

fragmentation they fail to take into account all of the six parameters mentioned earlier. Plot

shape is for example not considered. This could be an important factor because rectangular

fields make machine cultivation easier.44

40 A map indicating which provinces that are included in the study can be found in the Appendix

41 Bentley, 1987. p. 33

42 The literature cites two index; “Simpson’s” and “Simon’s”, which seem to have the same or almost the same definition. We will refer to

the index as Simpson index.

43 Van Hung, MacAulay, March, 2005, p. 8-9

44 Bentley 1987, p. 32-33, Blarel et. al. p. 238

15

In order to measure the impacts of fragmentation and consolidation on crop productivity, an

econometric model is used. We created a production model, partly based on the model

specified in a study of the effects of land fragmentation in Ghana and Rwanda45. The model

includes three factors; the level of farm fragmentation (F), the parcel-level use of direct inputs

(Li), and the current stock of land improvements on each parcel (Ii)

(1) Y = f (F, Li, Ii, X1i, X2, X 3) i = 1 to n

(2) Li = f (Ii, F, X1i, X2, X 3 )

(3) Ii = f (F, X1i, X2, X 3, Ii, t-1 )

(4) F = f (X2, X3)

X1i denotes a set of plot-specific characteristics such as; distance from home to plot, plot size

and land use right registration. Variable X2 represent a set of household-specific

characteristics like family size, education of household head, and tribe. Finally X3 captures

some commune-specific characteristics such as infrastructure, land distribution, and the

geographic region. The dependent variable crop productivity (Y) is defined as value of

harvest crops per planting area. The sign i indicates a particular plot, and there are n parcels.

Equation 1 determines the yield of each plot, depending on the use of direct inputs, the current

stock of land improvements, the level of land fragmentation, and plot-, household-, and

commune- specific characteristics. Equation (2) determines the level of direct inputs on the ith

parcel, and equation (3) determines the current stock of land improvements on the ith parcel.

In equation (4) land fragmentation is determined at the household level, and depends on the

household- and village characteristics.

Equation (4) does not include the current values of Y, Li or Ii and can therefore be written in

semi-reduced form. The yield equation then looks like this;

(5) Y = f (F, X1i, X2, X 3) i=1 to n

45 Blarel et al. 1992

16

The farm fragmentation variable now captures both the direct and indirect effects of

fragmentation on yields. The level of fragmentation is assumed to be predetermined before

yield in the model, however there is a risk that the estimated coefficient of the fragmentation

variable in equation (5) will be biased if there are unobserved variables influencing both the

level of fragmentation and yield.46

In most of our regressions we use land productivity as our y-variable. This is because land is

very scarce in Vietnam, and increasing the agricultural productivity per acre is crucial.

Therefore we use productivity instead of total output. When measuring productivity you can

either choose to make separate regressions for each crop pattern, or aggregate several crop

patterns into one. To measure land productivity (Y) we use the total value of all crops

(annual-, perennial-, industrial-, and fruit crops) divided by total planted area of these crops.

This enables us to catch the effect of farmers that have changed to more profitable crops,

which is not capture if we only use one crop pattern at a time. In one of the tables we use

labour productivity as our y-variable, which is measured as total value of all harvested crops

divided by on-farm work hours.

As a measure of fragmentation we use two different measures; number of plots and the

Simpson’s index. A great number of plots indicates a higher level of land fragmentation and is

expected to be negatively correlated with crop productivity, the same holds true for the

Simpson index. We won’t use average plot size since it is hard to define. A large average plot

size could mean a low degree of fragmentation, but it could also be a result of the total size of

the farm. If it is a result of a low degree of fragmentation we would expect it to be positively

correlated with crop productivity, but is it a result of the total farm size we might expect a

negative correlation since many previous studies have found this inverse relationship 47 .

Therefore the total size of all planting plots for the household (from now on defined as “farm

size”) will be used since this variable is easier to analyze. The use of farm size will probably

also give us a better estimation of the number of plots variable since these variables are not

correlated, as opposed to average plot size that is calculated dividing by number of plots.

46 For further discussion on the model specification see Blarel et. al. 1992 p. 243f

47 Marsch, MacAulay, 2006(2), p 14

17

The variable average distance to plot is defined as the total distance from home to plots (in

metres) divided by the number of plots for each household. However, our variable will only

catch the effect of travelling between the plots and the home, since we have no data on the

distance between the plots. The variable for Land Use Certificate states what percentage of

the plots that have been granted a LUC. Access to a formal land registration is often

considered to have a positive effect on agriculture productivity. One may therefore expect the

LUC variable to have a positive sign. The Gini coefficient is measured at commune level and

is defined as an index of land distribution among the rich and the poor in the commune. The

index ranges between 0 and 1; a higher number represents a more unequal land distribution.

The dummy for communes classified as poor derives from a question in the commune

questionnaire where the commune states if the commune has been classified as poor by the

135 program of the Government. The dummies for road to village and irrigation (the existence

of a small irrigation system in the commune) also originate from the commune questionnaire,

and are expected to carry positive signs. The excluded variable among the five regional

dummies is the High mountain area. This is probably the least productive region so we expect

the other dummies to have positive signs.

The design of the survey data can have effects on the analysis of the data. Observations are

often sampled rather as groups, or “clusters”, than independently. Within the groups

(provinces, communes and households in our case) there might also be further sub sampling.

In the VHLSS provinces are sampled, then communes within the provinces, next households

within the communes, and finally persons and plots within the households. This might mean

observations in the same cluster to be dependent of each other. Accounting for clustering is

necessary for honest estimations of the standard errors and p-values48. In all our regressions

we have clustered the standard errors at the province level. The cluster command takes into

account the fact that some observations in the same geographical area (in our case provinces)

might be correlated. The cluster option produces “correct” standard errors, even if the

observations are correlated. This enables us to relax the independence assumption, i.e. that

some observations within the provinces might be correlated. The cluster option is also

equivalent of using the “robust” option; therefore there is no need to make homoscedasticity

tests for the regressions.49

48 Stata Users guide, release 7, p. 324

49 Stata Users guide, release 7, p. 258-259

18

4.3 Results

Next follow the results from the regressions. Each table contains several regressions (5-10),

gradually adding new variables. This is useful because we can directly see if the variables are

significant or insignificant.

4.3.1 Impacts of land fragmentation on land- and labour productivity

Table 2 investigates the correlation between land fragmentation (measured as number of plots

per household) and land productivity. It is based on both equation (1) and equation (5),

specified in the method section. The first regressions are based on the semi-reduced equation

(5) and the fourth regression includes all the variables specified in this equation. Then, as we

add the current stock of land improvements (measured as area devoted to fruit and industrial

crops) and the direct inputs (measured as seeds, fertilizers, work hours, and machine-use) the

regressions are similar to equation (1). The fourth regression is the most important since it

measures the total effect of land fragmentation on land productivity, including both the direct

and the indirect effects.

19

Table 2: Impact of land fragmentation on land productivity

Dependent variable: (log) Land productivity (total value/total area)Independent variables 1 2 3 4 5 6 7 8 9log no of planting plots per household -0.096*** 0.090*** 0.128*** 0.052* 0.124*** 0.054** 0.030 0.037 0.030 0.023

[0.012] [0.027] [0.039] [0.025] [0.038] [0.022] [0.022] [0.025] [0.023] [0.022]log farm size -0.335*** -0.321*** -0.236*** -0.332*** -0.263*** -0.294*** -0.298*** -0.287*** -0.282***

[0.036] [0.033] [0.027] [0.034] [0.024] [0.027] [0.029] [0.026] [0.027]log average distance to plot -0.036** -0.033** -0.030 -0.014 -0.011 -0.017 -0.020 -0.018

[0.018] [0.014] [0.018] [0.012] [0.012] [0.014] [0.014] [0.013]log percentage plots with LUC 0.078* 0.070** 0.076* 0.015 0.017 0.023 0.025 0.026

[0.044] [0.026] [0.044] [0.018] [0.017] [0.018] [0.015] [0.016]log Area devoted to fruit crops 0.031*** 0.034*** 0.032*** 0.031*** 0.030*** 0.029***

[0.008] [0.008] [0.007] [0.007] [0.007] [0.007]log Area devoted to industrial crops 0.001 0.005 0.004 0.004 0.006* 0.006

[0.004] [0.003] [0.003] [0.003] [0.004] [0.004]log Use of seeds per area 0.048 0.047 0.047 0.047 0.053

[0.036] [0.035] [0.037] [0.037] [0.036]log Use of fertilizers per area 0.150*** 0.143*** 0.131*** 0.105*** 0.095***

[0.025] [0.024] [0.027] [0.027] [0.027]Dummy for use of machinery 0.130*** 0.124*** 0.116*** 0.104***

[0.010] [0.012] [0.013] [0.012]log on-farm working hours 0.055*** 0.061*** 0.069*** 0.071***

[0.012] [0.012] [0.012] [0.012]

Commune characteristics control no no no yes no no no yes yes yesRegion fixed effects no no no yes no no no no yes yesHousehold characteristics control no no no yes no no no no no yesObservations 3629 3629 3202 2754 3202 3133 3133 2695 2695 2695R-squared 0.02 0.32 0.33 0.46 0.35 0.45 0.48 0.50 0.51 0.52Standard errors in brackets, clustered at province level* significant at 10%; ** significant at 5%; *** significant at 1%Source: Calculations based on VHLSS 2004

10

First of all it should be noted that without any other variables there is a negative correlation

between land productivity and number of plots which is significant at the 1% level. But when

we add “farm size” the correlation is positive, also significant at 1%. Adding the other parcel

specific characteristics “distance to plot”, “percentage LUC” (in regression 3) and the control

variables (in regression 4) does not change a lot except that adding the control variables

makes “no of plots” significant only at 10%. Thus, in the fourth regression that is specified

exactly as equation (5) we have a positive correlation between number of plots and

productivity. The total effect of number of plots on land productivity seems to be positive.

An increase in number of plots with 1% would increase productivity with 0,05%, ceteris

paribus.

When we add the direct input variables the significance of number of plots disappears. Use of

fertilizer, machine use and on-farm work hours seem to be positively correlated with number

of plots. Could this be the reason why number of plots seems to increase land productivity?

We have constructed three tables in Appendix (Table 7, 8 and 9) that is based on equation (2)

where these direct inputs are dependent on number of plots and other control variables.

20

According to these tables there seem to be a positive correlation between use of fertilizer and

the Simpson’s index at a 1% level (including all the control variables). The significance is

high: the Simpson’s index alone explains 11% of the variation in use of fertilizers. There is

also a positive correlation between on-farm work hours and the Simpson’s index at the 5%

level (with the control variables). The Simpson’s index alone explains 8% of the variation in

on-farm work hours. Use of machinery does not seem to be correlated with the Simpson’s

index when the control variables are included. These two positive correlations seem to be the

explanation of the significant positive correlation between land productivity and land

fragmentation that was found before adding the input variables.

The “distance to plot” variable has an expected negative correlation at 5% in the beginning

but in the later regressions this variable becomes insignificant. Longer travelling distances

does not seem to reduce land productivity, ceteris paribus. This is a contradiction to the theory

presented.

A quick glance at the other variables reveals that there is a very strong negative correlation

between farm size and land productivity, i.e smaller farms are more productive. There is a lot

of literature regarding this inverse relationship, and it is an interesting issue, but it falls

outside the aim of this study so we won’t go into details. The same holds regarding the control

variables, the complete tables can be found in the appendix.

Table 3 resembles Table 2 and the only difference is that in this table the Simpson index is

used instead of number of plots to measure land fragmentation.

21

Table 3: Impact of land fragmentation on land productivity (using Simpson´s index)

Dependent variable: (log) Land productivity (total value/total area)Independent variables 1 2 3 4 5 6 7 8 9Simpson_index -0.327*** 0.313*** 0.445*** 0.125* 0.439*** -0.059 -0.084 -0.111 -0.114 -0.129*

[0.054] [0.073] [0.099] [0.071] [0.097] [0.063] [0.063] [0.076] [0.070] [0.069]log farm size -0.394*** -0.302*** -0.229*** -0.315*** -0.254*** -0.289*** -0.292*** -0.281*** -0.276***

[0.030] [0.030] [0.026] [0.031] [0.023] [0.026] [0.028] [0.026] [0.027]log average distance to plot -0.024* -0.024** -0.018 0.007 0.004 0.002 -0.004 -0.003



[0.008] [0.008] [0.007] [0.007] [0.007] [0.007]log Area devoted to industrial crops 0.003 0.006* 0.004 0.004 0.006* 0.006





[0.012] [0.012] [0.012] [0.012]


10

The results are similar to the results in the previous table. First there is a negative correlation

between the fragmentation index and land productivity. But adding farm size the correlation

becomes positive. The total effect of land fragmentation on land productivity is positive and

significant at 10% (regression 4). However, a notable difference in this table is that the

correlation turns negative again after the input variables have been included. In the last

regression the negative correlation is even significant at 10%.

As noted before it seems that fragmented land is positively correlated with use of fertilizers

and on-farm work hours, which seem to explain the positive effect of fragmentation on

productivity. However, including these inputs to hold them constant, which we do in the final

regressions, reveal what seems to be a negative effect of land fragmentation on productivity.

This could be a direct effect of the Simpson’s index on productivity, or it could be an indirect

effect caused by a negative correlation between the Simpson’s index and an input variable

22

that is not included in the model. However, this undetermined negative effect is smaller than

the positive effect caused by more use of fertilizers and more labour input.

The labour productivity, i.e the total value of crops per on-farm working hour, is investigated

in Table 4. Fragmentation seems to imply more labour input which increases land

productivity. In this table we will see if fragmentation also increases the productivity per hour.

The variable on-farm work hours is excluded from the table because of the strong correlation

with the dependent variable.

Table 4: Impact of land fragmentation on labour productivity (using Simpson´s index)

Dependent variable: (log) Labour productivity (total value of crops / on-farm work hours)Independent variables 1 2 3 4 5 6 7 8 9Simpson_index 0.728*** 0.485*** 0.545*** -0.066 0.543*** -0.225 -0.260** -0.261* -0.256** -0.272**

[0.074] [0.157] [0.173] [0.092] [0.169] [0.134] [0.127] [0.135] [0.120] [0.115]log farm size 0.134*** 0.126*** 0.371*** 0.170*** 0.218*** 0.213*** 0.237*** 0.287*** 0.362***

[0.037] [0.043] [0.029] [0.042] [0.034] [0.033] [0.033] [0.027] [0.029]log average distance to plot -0.028 -0.021 -0.025 0.021 0.013 0.005 -0.014 -0.005

[0.032] [0.021] [0.030] [0.023] [0.021] [0.024] [0.022] [0.019]log percentage plots with LUC 0.088 0.115*** 0.098 0.021 0.022 0.079** 0.096*** 0.087***

[0.072] [0.030] [0.070] [0.040] [0.036] [0.037] [0.031] [0.030]log Area devoted to fruit crops 0.016* 0.020** 0.018** 0.016** 0.016** 0.013**

[0.009] [0.007] [0.007] [0.007] [0.007] [0.006]log Area devoted to industrial crops -0.035*** -0.028*** -0.027*** -0.023*** -0.012 -0.011


[0.035] [0.034] [0.036] [0.033] [0.033]log Use of fertilizers per area 0.272*** 0.245*** 0.209*** 0.120*** 0.077**


[0.048] [0.038] [0.035] [0.034]


10

At first it appears to be a positive correlation between labour productivity and the

fragmentation index. But when the control variables are added the significance disappears. In

the fourth regression there is no correlation between labour productivity and fragmentation.

The total effect of land fragmentation on labour productivity seems to be neutral. This could

indicate that the main reason for the positive correlation between land fragmentation and land

productivity found in table 2 and 3 is a higher level of labour input.

23

When the input variables are added the correlation is negative, significant at 5%. As in table 3

there seem to be an undefined negative effect of fragmentation on productivity which we see

when we hold the input variables constant. However, this effect is cancelled out by the higher

level of inputs. The total effect is neutral.

Another thing worth noticing is that there is a positive correlation between farm size and

labour productivity, while there was a negative correlation when productivity was measured

in terms of land. These results are predicted by theory and are probably explained by labour

market imperfections that results in a high labour to land ratio.

4.3.2 Consolidation programs

The reasons for why communes choose to consolidate are investigated in table 5.

Decentralised consolidation programs have been encouraged by the government, and we will

try to find possible factors that distinguish communes that have initiated consolidation from

the ones that have not. This allows us to compare the initial level of land productivity in these

communes. It is important to investigate the initial difference in land productivity between

these two groups to be able to analyze the outcomes of the land consolidation.

As the dependent variable a dummy for the communes that have initiated land consolidation

during the years 2004 to 2006 is used. The data makes it possible to separate these communes

from the ones that have not yet initiated such programs. The communes that already had

finished the LC process before 2004 are not included in the regressions since we are interested

in the initial differences between the communes that decide to consolidate the land and the

communes that have not.

Since Table 5 is calculated on the commune level, some of the variables on the household

level have been excluded. The household characteristics have been left out as well as the

stock of land improvements on the plots. There is a risk of getting inaccurate estimations

when using a mean of a mean, which would have been the case if these variables would have

been included. However, the fragmentation variable and the plot-specific variables are

included since these are regarded as important factors influencing the willingness to

consolidate.

24

Table 5: Correlation between communes that consolidate and land productivity

Dependent variable: Dummy for communes that have initiated LCIndependent variables 1 2 3 4 5log planting productivity (value/area), av. for commune 1.400*** 1.490*** 1.528*** 1.049*** 0.929***

[0.193] [0.276] [0.294] [0.302] [0.316]log farm size, av. for commune -0.295* -0.329* -0.198 0.018

[0.173] [0.173] [0.173] [0.175]log no of planting plots per household, av. for commune 0.738*** 0.543** 0.638*** 0.481*

[0.162] [0.217] [0.246] [0.266]log percentage plots with LUC, av. for commune 0.012 0.143 0.236

[0.194] [0.221] [0.233]log average distance to plot, av. for commune 0.203 0.017 -0.007

[0.140] [0.137] [0.135]Gini coefficient for land -8.831*** -7.265***

[1.002] [1.064]Dummy for road to village 1.071** 0.832*

[0.467] [0.478]Dummy for irrigation 0.456* 0.498**

[0.239] [0.247]Dummy for communes classified as poor -1.023*** -0.341

[0.274] [0.320]Dummy for Coastal area 2.359***

[0.764]Dummy for Delta area 1.794***

[0.376]Dummy for Midland area 1.310***

[0.424]Dummy for Low mountain area 0.729**

[0.326]Observations 729 724 703 626 626Standard errors in brackets, clustered at province level* significant at 10%; ** significant at 5%; *** significant at 1%Source: Calculations based on VHLSS 2004

Looking at the results it seems that the communes that have initiated the consolidation process

are more productive. Even after adding the control variables the significance is very strong.

There is a significant correlation at the 1% level. It seems that the communes that decide to

consolidate their land are more productive than the others, ceteris paribus. So it will be very

hard to prove that the communes that have consolidated are more productive, this might as

well be an effect of this initial higher level of productivity.

Table 6 shows the impacts of the land consolidation programs on land productivity. The table

is based on the semi-reduced model (5). As in table 5 many of the variables on the household

level are excluded since this table also is calculated on the communal level. The interesting

variable here is the dummy variable for the communes that stated that they had finished a land

consolidation program before the year of 2004. We want to investigate if completing the

consolidation program results in a higher level of productivity, holding the control variables

constant. In this table the fragmentation variables are not included, because we want the

25

dummy for communes that have completed consolidation to catch this effect, so we can

decide if the change in fragmentation has had an impact on land productivity.

Table 6: Correlation between communes that have consolidated and land productivity

Dependent variable: (log) Land productivity (total value/total area)Independent variables 1 2 3 4 5Dummy for communes that have completed LC 0.245*** 0.145*** 0.144*** 0.116*** 0.048*

[0.030] [0.024] [0.025] [0.025] [0.026]log farm size, av. for commune -0.375*** -0.369*** -0.366*** -0.336***

[0.020] [0.020] [0.022] [0.023]log percentage plots with LUC, av. for commune 0.125*** 0.132*** 0.116***

[0.029] [0.030] [0.028]Gini coefficient for land -0.345*** -0.097

[0.097] [0.096]Dummy for road to village 0.114*** 0.055*

[0.033] [0.032]Dummy for irrigation 0.117*** 0.099***

[0.028] [0.027]Dummy for Coastal area 0.065



[0.043]Dummy for Low mountain area 0.202***

[0.035]Observations 1173 1173 1113 1006 1006R-squared 0.05 0.45 0.45 0.49 0.53Standard errors in brackets, clustered at province level* significant at 10%; ** significant at 5%; *** significant at 1%Source: Calculations based on VHLSS 2004

Looking at the fourth regression there is a positive significant correlation at the 1% level

between communes that have consolidated and land productivity. However, adding the

regional dummies makes the consolidation dummy significant only at the 10% level.

Nevertheless, it does seem like the communes that have completed the land consolidation are

more productive than the others. But considering the result in Table 5 this could be explained

by the initial difference in productivity.

It should be noted that more communes have been included in Table 6 than in Table 5. In

Table 5 the communes that had not started to consolidate in 2006 were compared with the

communes that started the land consolidation in the period 2004-2006. But in Table 6 the

communes that had finished the consolidation before 2004, and were excluded in Table 5,

were compared with all the other communes (the ones included in Table 5). This is important

because by excluding the communes that have neither started nor finished a land

consolidating program we can correct for the fact that communes that decide to consolidate

26

are more productive from the start. Table 10 in appendix compares the communes that have

completed land consolidation with the communes that have initiated land consolidation but

not yet completed it. And the results in table 10 show no correlation between the dummy for

communes that have completed land consolidation and land productivity. The fact that

communes that have land consolidated are more producitve seems to be due to the initial

difference in productivity.

We have performed some statistical tests on the regressions in our tables. Ramsey reset tests

reveal that there seems to be omitted variables, i.e. there might be some additional variables

that should be included. This is important to keep in mind when evaluating the validity of our

results. Skewness kurtois tests reveal that the regressions do not seem to have a normal

distribution for the residuals. However, considering the large sample this should not be

regarded as a serious problem. Due to the large amount of regressions these tests won’t be

included in the appendix.

27

5. ANALYSIS AND CONCLUSIONS

In theory there are both advantages and disadvantages with land fragmentation, even though

the disadvantages seem to be more accepted. The advantages are associated with risk

spreading in terms of possibilities to grow a wider variety of crops and avoid labour

bottlenecks. The disadvantages are related to increased transport costs, difficulties to grow

more valuable crops, and difficulties in the mechanization of the agriculture.

In the case of Vietnam, land fragmentation arose as a result of the egalitarian aspects of land

allocation, and can therefore be said to have supply-side causes. In addition land is scarce and

population density is high, which might contribute to involuntary fragmented land holdings.

However, even though land was initially given out in scattered plots, the facts that the land is

still very fragmented, more than 10 years after the first land law was implemented, and that

the progress of land consolidation is slow, indicates that land fragmentation might have

demand-side explanations and thus to some extent is preferred by the farmers. This could be

an argument against land consolidation programs, but it should also be noted that there are

many market inefficiencies that might prevent farmers from consolidating land, even if they

would like to.

Our results imply that there is a positive effect of number of plots on land productivity. The

explanation seems to be that more plots of land is positively correlated with use of fertilizers

and number of hours worked on the farm. There is also a positive correlation between the

Simpson index and land productivity. However, we find no significant correlation between

labour productivity and land fragmentation.

The finding that land fragmentation is positively correlated with on-farm work and use of

fertilizers is interesting. The labour input could explain the positive correlation between land

fragmentation and land productivity. When we measured productivity in terms of labour

instead of land we found no significant correlation. The productivity per hour is not affected

by the number of plots which indicates that input of labour is in fact the deciding factor that

explains why having more plots increase land productivity. This is an important finding and it

could be the subject of further research.

28

The machine use was not found to be significantly correlated with number of plots. However,

the fact that we did not find a negative correlation is interesting. According to theory

fragmented land holdings is an obstacle to more use of machinery. This is believed to be one

of the main disadvantages with land fragmentation. However, our results suggest that this is

not the case. Previous reports have suggested that the off-farm labour market is

underdeveloped, which implies that the opportunity cost of working on the farm is very low.

If the off-farm labour possibilities are limited the household members work on the farm,

regardless of the labour returns. This market imperfection could reduce the gains in investing

in labour-saving machines.

Another disadvantage of fragmented land is that it might be an obstacle to switching to more

profitable crops that require larger areas. However the restrictions on crop choice that exist in

Vietnam imply that the farmers are forced to grow rice on plots that are suitable for growing

more valuable crops. This reduces one of the potential disadvantages of land fragmentation.

Regarding the consolidation programs, we find that communes that have consolidated are

more productive. However, we also find communes that decide to consolidate to be more

productive. The initial difference in productivity makes it difficult to draw any conclusions

regarding the effects of consolidation programs. When comparing communes that have

consolidated with communes that are going to consolidate we find that there is no difference

in productivity. This suggests that the consolidation programs have not increased productivity.

What are the implications of our results? The government of Vietnam considers land

fragmentation to be an obstacle to agricultural productivity. However, our results indicate that

land fragmentation increases land productivity. Does this mean that the government policies

regarding land fragmentation are wrong? Not necessary. In a longer time perspective the

policies might be correct. If the off-farm labour market develops this would increase the

opportunity cost of working on the farm. This would make mechanization more profitable and

there would be larger potential gains with land consolidation. Lifting the restriction on crop

choice could also increase the potential benefits with land consolidation. Nevertheless, our

study suggests that there is no negative impact of land fragmentation on land productivity in

Northern Vietnam.

29

REFERENCES

Bentley, Jeffery, 1987. “Economic and Ecological Approaches to Land Fragmentation: In Defence of a Much-Aligned Phenomenon.” Annual Review of Anthropology 16: 31-67 Blarel et. al, 1992. ” The Economics of Farm Fragmentation: Evidence from Ghana and Rwanda.”, World Bank Economic Review, vol. 6 no.2 May 1992 Do, Quy-Toan and Iyer, Lakshmi 2003. “Land rights and economic development, Evidence from Vietnam”. The World Bank, 2003. Do, Quy-Toan and Iyer, Lakshmi 2006. “Land titling and Rural Transition in Vietnam”. The World Bank, 2006. Dollar, David, Glewwe, Paul and Litvack, Jennie. “Household Welfare and Vietnam’s Transition”, The World Bank, 1998. General Statistical Office, “Vietnam Housing Living Standard Survey 2004”, General Statistical Office, 2004 General Statistical Office, “Statistical Yearbook 2004”, Statistical Publishing House, 2004 Heltberg, Rasmus, “Rural Market Imperfections and the Farm Size-Productivity Relationship:Evidence from Pakistan”, World Development, Vol. 26, No. 10, October 1998 Lam Mai Lan, "Land fragmentation - a constrain for Vietnam agriculture", Vietnam’s socio-economic development, no 26, summer 2001. March, Sally P. and MacAulay, T. Gordon, 2006(1). “Farm size and land use change in Vietnam following land reforms”, The University of Sydney, 2006 Marsh, Sally P. and MacAulay, T. Gordon, 2006(2). “Land reform and the development of commercial agriculture in Vietnam: policy and issues”, The University of Sydney, 2006 MPI/DSI and UNDP, 2001. “Assistance for the Preparation of the Socio-Economic Development Strategy for Viet Nam 2001-2010”, MPI/DSI and UNDP, 2001 Mwebaza Rose, Gaynor Richard, 2002. “Land sector analysis; land market, land consolidation, and land re-adjustment component”, Rural Development Institute, The Government of the Republic of Uganda, 2002. Nguyen, Tin, Cheng, Enjiang, 1996 “Land fragmentation and farm productivity in China in the 1990s”, China Economic Review, 1043951X, Fall 96, Vol. 7, Issue 2 Ravallion, Martin and Van de Walle, Dominique, “Land allocation in Vietnam’s agrarian transition”, The World Bank, 2003 Stata Users Guide, release 7, Stata Press, College Station, Texas

30

The World Bank, “Land Consolidation Issues in Northern Vietnam – Institutions, Implementation, Impacts”. Working paper, The World Bank, 2005. The World Bank, “Three Pillars of Rural Development”. The World Bank, Feb. 2006. Van Hung, MacAulay, Marsch, 2006, “The Economics of Land Fragmentation in the north of Vietnam”, The University of Sydney, 2006

31

APPENDIX Table 2 (complete): Impact of land fragmentation on land productivity

Dependent variable: Land productivity (total value/total area)Independent variables 1 2 3 4 5 6 7 8 9log no of planting plots per household -0.096*** 0.090*** 0.128*** 0.052* 0.124*** 0.054** 0.030 0.037 0.030 0.023

[0.012] [0.027] [0.039] [0.025] [0.038] [0.022] [0.022] [0.025] [0.023] [0.022]log farm size -0.335*** -0.321*** -0.236*** -0.332*** -0.263*** -0.294*** -0.298*** -0.287*** -0.282***

[0.036] [0.033] [0.027] [0.034] [0.024] [0.027] [0.029] [0.026] [0.027]log average distance to plot -0.036** -0.033** -0.030 -0.014 -0.011 -0.017 -0.020 -0.018



[0.008] [0.008] [0.007] [0.007] [0.007] [0.007]log Area devoted to industrial crops 0.001 0.005 0.004 0.004 0.006* 0.006





[0.012] [0.012] [0.012] [0.012]Gini coefficient for land -0.086 -0.304** -0.211 -0.196

[0.168] [0.127] [0.143] [0.149]Dummy for road to village 0.054** 0.028 0.021 0.014

[0.025] [0.026] [0.024] [0.025]Dummy for irrigation 0.067** 0.040* 0.038 0.034

[0.027] [0.022] [0.022] [0.023]Dummy for Coastal area 0.046 -0.041 -0.102

[0.091] [0.084] [0.086]Dummy for Delta area 0.273*** 0.165*** 0.104*

[0.060] [0.054] [0.059]Dummy for Midland area 0.203*** 0.101** 0.037

[0.055] [0.047] [0.050]Dummy for Low mountain area 0.201*** 0.112** 0.073*

[0.050] [0.043] [0.041]Sex of head of household -0.040* -0.031

[0.022] [0.021]Household size 0.012* 0.001

[0.006] [0.004]Head's education level 0.030*** 0.020**

[0.008] [0.007]Tribe 0.151*** 0.078**

[0.043] [0.036]Observations 3629 3629 3202 2754 3202 3133 3133 2695 2695 2695R-squared 0.02 0.32 0.33 0.46 0.35 0.45 0.48 0.50 0.51 0.52Standard errors in brackets, clustered at province level* significant at 10%; ** significant at 5%; *** significant at 1%Source: Calculations based on VHLSS 2004

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Table 3 (complete): Impact of land fragmentation (index) on land productivity

Dependent variable: (log) Land productivity (total value/total area)Independent variables 1 2 3 4 5 6 7 8 9Simpson_index -0.327*** 0.313*** 0.445*** 0.125* 0.439*** -0.059 -0.084 -0.111 -0.114 -0.129*

[0.054] [0.073] [0.099] [0.071] [0.097] [0.063] [0.063] [0.076] [0.070] [0.069]log farm size -0.394*** -0.302*** -0.229*** -0.315*** -0.254*** -0.289*** -0.292*** -0.281*** -0.276***

[0.030] [0.030] [0.026] [0.031] [0.023] [0.026] [0.028] [0.026] [0.027]log average distance to plot -0.024* -0.024** -0.018 0.007 0.004 0.002 -0.004 -0.003



[0.008] [0.008] [0.007] [0.007] [0.007] [0.007]log Area devoted to industrial crops 0.003 0.006* 0.004 0.004 0.006* 0.006





[0.012] [0.012] [0.012] [0.012]Gini coefficient for land -0.076 -0.300** -0.209 -0.196

[0.175] [0.131] [0.149] [0.155]Dummy for road to village 0.052** 0.031 0.023 0.016

[0.025] [0.027] [0.024] [0.025]Dummy for irrigation 0.069** 0.043* 0.040* 0.036

[0.027] [0.022] [0.022] [0.023]Dummy for Coastal area 0.039 -0.027 -0.090

[0.089] [0.082] [0.086]Dummy for Delta area 0.270*** 0.172*** 0.109*

[0.060] [0.054] [0.059]Dummy for Midland area 0.202*** 0.112** 0.046

[0.055] [0.046] [0.050]Dummy for Low mountain area 0.202*** 0.121*** 0.080*

[0.051] [0.044] [0.042]Sex of head of household -0.039* -0.031

[0.021] [0.021]Household size 0.012* 0.002

[0.006] [0.004]Head's education level 0.031*** 0.021***

[0.008] [0.007]Tribe 0.148*** 0.080**


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Table 4 (complete): Impact of land fragmentation (index) on labour productivity

Dependent variable: (log) Labour productivity (total value of crops / on-farm work hours)Independent variables 1 2 3 4 5 6 7 8 9Simpson_index 0.728*** 0.485*** 0.545*** -0.066 0.543*** -0.225 -0.260** -0.261* -0.256** -0.272**

[0.074] [0.157] [0.173] [0.092] [0.169] [0.134] [0.127] [0.135] [0.120] [0.115]log farm size 0.134*** 0.126*** 0.371*** 0.170*** 0.218*** 0.213*** 0.237*** 0.287*** 0.362***

[0.037] [0.043] [0.029] [0.042] [0.034] [0.033] [0.033] [0.027] [0.029]log average distance to plot -0.028 -0.021 -0.025 0.021 0.013 0.005 -0.014 -0.005

[0.032] [0.021] [0.030] [0.023] [0.021] [0.024] [0.022] [0.019]log percentage plots with LUC 0.088 0.115*** 0.098 0.021 0.022 0.079** 0.096*** 0.087***

[0.072] [0.030] [0.070] [0.040] [0.036] [0.037] [0.031] [0.030]log Area devoted to fruit crops 0.016* 0.020** 0.018** 0.016** 0.016** 0.013**

[0.009] [0.007] [0.007] [0.007] [0.007] [0.006]log Area devoted to industrial crops -0.035*** -0.028*** -0.027*** -0.023*** -0.012 -0.011


[0.035] [0.034] [0.036] [0.033] [0.033]log Use of fertilizers per area 0.272*** 0.245*** 0.209*** 0.120*** 0.077**


[0.048] [0.038] [0.035] [0.034]Gini coefficient for land -1.017*** -1.521*** -1.033*** -1.003***

[0.366] [0.364] [0.372] [0.347]Dummy for road to village 0.124* 0.177** 0.148** 0.099*

[0.062] [0.074] [0.064] [0.058]Dummy for irrigation 0.067 0.067 0.065 0.040

[0.045] [0.041] [0.042] [0.040]Dummy for Coastal area 0.054 0.051 -0.086

[0.143] [0.146] [0.141]Dummy for Delta area 0.623*** 0.586*** 0.416***

[0.094] [0.093] [0.103]Dummy for Midland area 0.228** 0.252** 0.084

[0.092] [0.092] [0.098]Dummy for Low mountain area 0.274*** 0.277*** 0.163*

[0.085] [0.083] [0.089]Sex of head of household 0.063 0.064

[0.059] [0.058]Household size -0.080*** -0.083***

[0.011] [0.010]Head's education level 0.071*** 0.062***

[0.013] [0.013]Tribe 0.198** 0.148*


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Table 7: Correlation between land fragmentation and use of fertilizers

Dependent variable: log Use of fertilizers per areaIndependent variables 1 2 3 4Simpson_index 1.881*** 2.553*** 2.006*** 1.574*** 1.500***

[0.086] [0.330] [0.301] [0.261] [0.265]log farm size -0.155 0.008 0.111* 0.155***

[0.092] [0.074] [0.055] [0.051]log average distance to plot -0.167*** -0.157*** -0.159*** -0.148***

[0.046] [0.040] [0.033] [0.028]

Commune characteristics control no no yes yes yesRegion fixed effects no no no yes yesHousehold characteristics control no no no no yesObservations 3749 3565 3071 3071 3071R-squared 0.11 0.17 0.39 0.48 0.50Standard errors in brackets, clustered at province level* significant at 10%; ** significant at 5%; *** significant at 1%Source: Calculations based on VHLSS 2004

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Table 8: Correlation between land fragmentation and use of machinery

Dependent variable: Dummy for use of machineryIndependent variables 1 2 3 4Simpson_index 1.465*** 1.870*** 1.311** 0.867* 0.685

[0.162] [0.457] [0.539] [0.448] [0.424]log farm size -0.068 0.052 0.172** 0.161**

[0.105] [0.091] [0.071] [0.067]log average distance to plot 0.045 0.061 0.052 0.051

[0.074] [0.079] [0.077] [0.075]

Commune characteristics control no no yes yes yesRegion fixed effects no no no yes yesHousehold characteristics control no no no no yesObservations 4125 3621 3126 3126 3126Standard errors in brackets, clustered at province level* significant at 10%; ** significant at 5%; *** significant at 1%Source: Calculations based on VHLSS 2004Logistic regression

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Table 9: Correlation between land fragmentation and on-farm working hours

Dependent variable: log On-farm working hoursIndependent variables 1 2 3 4 5Simpson_index 2.913*** -0.051 0.138 0.237** 0.263**

[0.149] [0.147] [0.118] [0.106] [0.105]log farm size 0.649*** 0.577*** 0.548*** 0.472***

[0.042] [0.043] [0.043] [0.044]log average distance to plot -0.005 -0.021 -0.015 -0.031

[0.026] [0.025] [0.022] [0.020]

Commune characteristics control no no yes yes yesRegion fixed effects no no no yes yesHousehold characteristics control no no no no yesObservations 4125 3621 3126 3126 3126R-squared 0.08 0.29 0.34 0.35 0.38Standard errors in brackets, clustered at province level* significant at 10%; ** significant at 5%; *** significant at 1%Source: Calculations based on VHLSS 2004 Table 10: Correlation between LC communes and land productivity (comparing w. communes that started LC later)

Dependent variable: (log) Land productivity (total value/total area)Independent variables 1 2 3 4 5Dummy for communes that have completed LC 0.065** 0.047* 0.044 0.037 0.011

[0.031] [0.026] [0.027] [0.029] [0.029]log farm size, av. for commune -0.274*** -0.271*** -0.280*** -0.274***

[0.026] [0.026] [0.027] [0.027]log percentage plots with LUC, av. for commune 0.039 0.048* 0.057**

[0.026] [0.027] [0.026]Gini coefficient for land -0.466*** -0.301**

[0.145] [0.139]Dummy for road to village 0.140*** 0.123***

[0.043] [0.040]Dummy for irrigation 0.062* 0.067**

[0.032] [0.031]Dummy for Coastal area 0.099



[0.072]Dummy for Low mountain area 0.236***

[0.067]Observations 761 761 711 642 642R-squared 0.01 0.27 0.25 0.29 0.32Standard errors in brackets, clustered at province level* significant at 10%; ** significant at 5%; *** significant at 1%Source: Calculations based on VHLSS 2004

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