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
2.2 Advantages
2.3 Consolidation programs
2.4 Previous studies
3. LAND FRAGMENTATION IN VIETNAM
3.1 Land consolidation in Vietnam
3.2 Following land consolidation
3.3 Previous studies concerning land fragmentation
and land consolidation in Vietnam
3
4
5
6
8
10
11
12
4. THE IMPACTS OF LAND FRAGMENTATION
AND CONSOLIDATION IN THE NORTH OF VIETNAM
14
4.1 Data
4.2 Method and Model
4.3 Results
4.3.1 Impacts of land fragmentation on land productivity
4.3.2 Consolidation programs
14
15
19
19
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
agriculture 3 .” 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 slow 4 .
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 fragmented 5 .
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 makers 11 .
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
30 Van Hung, MacAulay, Marsch, 2006, p. 11
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
300
Total
Index
250
200
Cereals
150
Annual industrial
crops
100
Perennial
industrial crops
50
Fruit crops
03
02
20
01
20
00
20
99
20
98
19
97
19
96
19
95
19
94
19
93
19
92
19
91
19
19
19
90
0
Year
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
process 35 . 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
34 Van Hung, MacAulay, Marsch, 2006, p. 5
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 provinces 40 .
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 plots 41 . 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
size 42 :
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 Rwanda 45 . 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)
(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)
i = 1 to n
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-values 48 . 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
Independent variables
log no of planting plots per household
log farm size
log average distance to plot
log percentage plots with LUC
log Area devoted to fruit crops
log Area devoted to industrial crops
log Use of seeds per area
log Use of fertilizers per area
Dummy for use of machinery
log on-farm working hours
Dependent variable: (log) Land productivity (total value/total area)
1
2
3
4
5
6
7
-0.096*** 0.090*** 0.128*** 0.052*
0.124*** 0.054**
0.030
[0.012]
[0.027]
[0.039]
[0.025]
[0.038]
[0.022]
[0.022]
-0.335*** -0.321*** -0.236*** -0.332*** -0.263*** -0.294***
[0.036]
[0.033]
[0.027]
[0.034]
[0.024]
[0.027]
-0.036** -0.033** -0.030
-0.014
-0.011
[0.018]
[0.014]
[0.018]
[0.012]
[0.012]
0.078*
0.070**
0.076*
0.015
0.017
[0.044]
[0.026]
[0.044]
[0.018]
[0.017]
0.031*** 0.034*** 0.032***
[0.008]
[0.008]
[0.007]
0.001
0.005
0.004
[0.004]
[0.003]
[0.003]
0.048
0.047
[0.036]
[0.035]
0.150*** 0.143***
[0.025]
[0.024]
0.130***
[0.010]
0.055***
[0.012]
Commune characteristics control
no
no
Region fixed effects
no
no
Household characteristics control
no
no
Observations
3629
3629
R-squared
0.02
0.32
Standard errors in brackets, clustered at province level
* significant at 10%; ** significant at 5%; *** significant at 1%
Source: Calculations based on VHLSS 2004
no
no
no
yes
yes
yes
3202
0.33
no
no
no
2754
0.46
no
no
no
3202
0.35
no
no
no
3133
0.45
0.037
[0.025]
-0.298***
[0.029]
-0.017
[0.014]
0.023
[0.018]
0.031***
[0.007]
0.004
[0.003]
0.047
[0.037]
0.131***
[0.027]
0.124***
[0.012]
0.061***
[0.012]
0.030
[0.023]
-0.287***
[0.026]
-0.020
[0.014]
0.025
[0.015]
0.030***
[0.007]
0.006*
[0.004]
0.047
[0.037]
0.105***
[0.027]
0.116***
[0.013]
0.069***
[0.012]
10
0.023
[0.022]
-0.282***
[0.027]
-0.018
[0.013]
0.026
[0.016]
0.029***
[0.007]
0.006
[0.004]
0.053
[0.036]
0.095***
[0.027]
0.104***
[0.012]
0.071***
[0.012]
yes
no
no
yes
yes
no
yes
yes
yes
8
3133
0.48
9
2695
0.50
2695
0.51
2695
0.52
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)
Independent variables
Simpson_index
log farm size
log average distance to plot
log percentage plots with LUC
log Area devoted to fruit crops
log Area devoted to industrial crops
log Use of seeds per area
log Use of fertilizers per area
Dummy for use of machinery
log on-farm working hours
Dependent variable: (log) Land productivity (total value/total area)
10
1
2
3
4
5
6
7
8
9
-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]
-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]
-0.024*
-0.024** -0.018
0.007
0.004
0.002
-0.004
-0.003
[0.014]
[0.011]
[0.014]
[0.009]
[0.009]
[0.011]
[0.010]
[0.010]
0.080*
0.071**
0.078*
0.016
0.018
0.023
0.025
0.025
[0.044]
[0.026]
[0.044]
[0.018]
[0.017]
[0.017]
[0.015]
[0.015]
0.031*** 0.034*** 0.032*** 0.032*** 0.031*** 0.030***
[0.008]
[0.008]
[0.007]
[0.007]
[0.007]
[0.007]
0.003
0.006*
0.004
0.004
0.006*
0.006
[0.004]
[0.003]
[0.003]
[0.003]
[0.004]
[0.004]
0.048
0.049
0.050
0.050
0.056
[0.035]
[0.034]
[0.037]
[0.036]
[0.036]
0.158*** 0.149*** 0.139*** 0.111*** 0.100***
[0.025]
[0.024]
[0.026]
[0.027]
[0.027]
0.136*** 0.131*** 0.122*** 0.109***
[0.012]
[0.014]
[0.015]
[0.013]
0.056*** 0.062*** 0.070*** 0.072***
[0.012]
[0.012]
[0.012]
[0.012]
Commune characteristics control
no
no
Region fixed effects
no
no
Household characteristics control
no
no
Observations
3775
3775
R-squared
0.01
0.46
Standard errors in brackets, clustered at province level
* significant at 10%; ** significant at 5%; *** significant at 1%
Source: Calculations based on VHLSS 2004
no
no
no
yes
yes
yes
3202
0.33
no
no
no
2754
0.46
no
no
no
3202
0.35
no
no
no
3133
0.45
yes
no
no
3133
0.48
yes
yes
no
2695
0.50
yes
yes
yes
2695
0.51
2695
0.52
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)
Independent variables
Simpson_index
log farm size
log average distance to plot
log percentage plots with LUC
log Area devoted to fruit crops
log Area devoted to industrial crops
log Use of seeds per area
log Use of fertilizers per area
Dummy for use of machinery
Dependent variable: (log) Labour productivity (total value of crops / on-farm work hours)
10
1
2
3
4
5
6
7
8
9
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]
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]
-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]
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]
0.016*
0.020** 0.018** 0.016** 0.016** 0.013**
[0.009]
[0.007]
[0.007]
[0.007]
[0.007]
[0.006]
-0.035*** -0.028*** -0.027*** -0.023*** -0.012
-0.011
[0.009]
[0.008]
[0.007]
[0.008]
[0.008]
[0.008]
0.034
0.039
0.027
0.020
0.044
[0.035]
[0.034]
[0.036]
[0.033]
[0.033]
0.272*** 0.245*** 0.209*** 0.120*** 0.077**
[0.029]
[0.029]
[0.033]
[0.033]
[0.031]
0.237*** 0.227*** 0.186*** 0.178***
[0.048]
[0.038]
[0.035]
[0.034]
Commune characteristics control
no
no
no
yes
no
no
no
yes
yes
yes
Region fixed effects
no
no
no
yes
no
no
no
no
yes
yes
Household characteristics control
no
no
no
yes
no
no
no
no
no
yes
Observations
3801
3744
3184
2741
3184
3117
3117
2683
2683
2683
R-squared
0.02
0.05
0.03
0.30
0.05
0.17
0.19
0.24
0.28
0.32
Standard errors in brackets, clustered at province level
* significant at 10%; ** significant at 5%; *** significant at 1%
Source: Calculations based on VHLSS 2004
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
Independent variables
log planting productivity (value/area), av. for commune
log farm size, av. for commune
log no of planting plots per household, av. for commune
log percentage plots with LUC, av. for commune
log average distance to plot, av. for commune
Gini coefficient for land
Dummy for road to village
Dummy for irrigation
Dummy for communes classified as poor
Dummy for Coastal area
Dummy for Delta area
Dummy for Midland area
Dummy for Low mountain area
Observations
Standard errors in brackets, clustered at province level
* significant at 10%; ** significant at 5%; *** significant at 1%
Source: Calculations based on VHLSS 2004
Dependent variable: Dummy for communes that have initiated LC
1
2
3
4
5
1.400*** 1.490*** 1.528*** 1.049*** 0.929***
[0.193]
[0.276]
[0.294]
[0.302]
[0.316]
-0.295*
-0.329*
-0.198
0.018
[0.173]
[0.173]
[0.173]
[0.175]
0.738*** 0.543**
0.638*** 0.481*
[0.162]
[0.217]
[0.246]
[0.266]
0.012
0.143
0.236
[0.194]
[0.221]
[0.233]
0.203
0.017
-0.007
[0.140]
[0.137]
[0.135]
-8.831*** -7.265***
[1.002]
[1.064]
1.071**
0.832*
[0.467]
[0.478]
0.456*
0.498**
[0.239]
[0.247]
-1.023*** -0.341
[0.274]
[0.320]
2.359***
[0.764]
1.794***
[0.376]
1.310***
[0.424]
0.729**
[0.326]
729
724
703
626
626
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
Independent variables
Dummy for communes that have completed LC
log farm size, av. for commune
log percentage plots with LUC, av. for commune
Gini coefficient for land
Dummy for road to village
Dummy for irrigation
Dummy for Coastal area
Dummy for Delta area
Dummy for Midland area
Dummy for Low mountain area
Dependent variable: (log) Land productivity (total value/total area)
1
2
3
4
5
0.245*** 0.145*** 0.144*** 0.116*** 0.048*
[0.030]
[0.024]
[0.025]
[0.025]
[0.026]
-0.375*** -0.369*** -0.366*** -0.336***
[0.020]
[0.020]
[0.022]
[0.023]
0.125*** 0.132*** 0.116***
[0.029]
[0.030]
[0.028]
-0.345*** -0.097
[0.097]
[0.096]
0.114*** 0.055*
[0.033]
[0.032]
0.117*** 0.099***
[0.028]
[0.027]
0.065
[0.081]
0.303***
[0.036]
0.282***
[0.043]
0.202***
[0.035]
1173
1173
1113
1006
1006
0.05
0.45
0.45
0.49
0.53
Observations
R-squared
Standard 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 socioeconomic 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)
1
2
3
4
5
6
-0.096*** 0.090*** 0.128*** 0.052*
0.124*** 0.054**
[0.012]
[0.027]
[0.039]
[0.025]
[0.038]
[0.022]
-0.335*** -0.321*** -0.236*** -0.332*** -0.263***
[0.036]
[0.033]
[0.027]
[0.034]
[0.024]
-0.036** -0.033** -0.030
-0.014
[0.018]
[0.014]
[0.018]
[0.012]
0.078*
0.070**
0.076*
0.015
[0.044]
[0.026]
[0.044]
[0.018]
0.031*** 0.034***
[0.008]
[0.008]
0.001
0.005
[0.004]
[0.003]
0.048
[0.036]
0.150***
[0.025]
7
8
9
10
0.023
[0.022]
log farm size
-0.282***
[0.027]
log average distance to plot
-0.018
[0.013]
log percentage plots with LUC
0.026
[0.016]
log Area devoted to fruit crops
0.029***
[0.007]
log Area devoted to industrial crops
0.006
[0.004]
log Use of seeds per area
0.053
[0.036]
log Use of fertilizers per area
0.095***
[0.027]
Dummy for use of machinery
0.104***
[0.012]
log on-farm working hours
0.071***
[0.012]
Gini coefficient for land
-0.086
-0.196
[0.168]
[0.149]
Dummy for road to village
0.054**
0.014
[0.025]
[0.025]
Dummy for irrigation
0.067**
0.034
[0.027]
[0.023]
Dummy for Coastal area
0.046
-0.102
[0.091]
[0.086]
Dummy for Delta area
0.273***
0.104*
[0.060]
[0.059]
Dummy for Midland area
0.203***
0.037
[0.055]
[0.050]
Dummy for Low mountain area
0.201***
0.073*
[0.050]
[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
2695
R-squared
0.02
0.32
0.33
0.46
0.35
0.45
0.48
0.50
0.51
0.52
Standard errors in brackets, clustered at province level
* significant at 10%; ** significant at 5%; *** significant at 1%
Source: Calculations based on VHLSS 2004
Independent variables
log no of planting plots per household
0.030
[0.022]
-0.294***
[0.027]
-0.011
[0.012]
0.017
[0.017]
0.032***
[0.007]
0.004
[0.003]
0.047
[0.035]
0.143***
[0.024]
0.130***
[0.010]
0.055***
[0.012]
0.037
[0.025]
-0.298***
[0.029]
-0.017
[0.014]
0.023
[0.018]
0.031***
[0.007]
0.004
[0.003]
0.047
[0.037]
0.131***
[0.027]
0.124***
[0.012]
0.061***
[0.012]
-0.304**
[0.127]
0.028
[0.026]
0.040*
[0.022]
0.030
[0.023]
-0.287***
[0.026]
-0.020
[0.014]
0.025
[0.015]
0.030***
[0.007]
0.006*
[0.004]
0.047
[0.037]
0.105***
[0.027]
0.116***
[0.013]
0.069***
[0.012]
-0.211
[0.143]
0.021
[0.024]
0.038
[0.022]
-0.041
[0.084]
0.165***
[0.054]
0.101**
[0.047]
0.112**
[0.043]
32
Table 3 (complete): Impact of land fragmentation (index) on land productivity
Dependent variable: (log) Land productivity (total value/total area)
1
2
3
4
5
6
7
-0.327*** 0.313*** 0.445*** 0.125*
0.439*** -0.059
-0.084
[0.054] [0.073] [0.099] [0.071] [0.097] [0.063] [0.063]
log farm size
-0.394*** -0.302*** -0.229*** -0.315*** -0.254*** -0.289***
[0.030] [0.030] [0.026] [0.031] [0.023] [0.026]
log average distance to plot
-0.024* -0.024** -0.018
0.007
0.004
[0.014] [0.011] [0.014] [0.009] [0.009]
log percentage plots with LUC
0.080*
0.071** 0.078*
0.016
0.018
[0.044] [0.026] [0.044] [0.018] [0.017]
log Area devoted to fruit crops
0.031*** 0.034*** 0.032***
[0.008] [0.008] [0.007]
log Area devoted to industrial crops
0.003
0.006*
0.004
[0.004] [0.003] [0.003]
log Use of seeds per area
0.048
0.049
[0.035] [0.034]
log Use of fertilizers per area
0.158*** 0.149***
[0.025] [0.024]
Dummy for use of machinery
0.136***
[0.012]
log on-farm working hours
0.056***
[0.012]
Gini coefficient for land
-0.076
[0.175]
Dummy for road to village
0.052**
[0.025]
Dummy for irrigation
0.069**
[0.027]
Dummy for Coastal area
0.039
[0.089]
Dummy for Delta area
0.270***
[0.060]
Dummy for Midland area
0.202***
[0.055]
Dummy for Low mountain area
0.202***
[0.051]
Sex of head of household
-0.039*
[0.021]
Household size
0.012*
[0.006]
Head's education level
0.031***
[0.008]
Tribe
0.148***
[0.042]
Observations
3775
3775
3202
2754
3202
3133
3133
R-squared
0.01
0.46
0.33
0.46
0.35
0.45
0.48
Standard errors in brackets, clustered at province level
* significant at 10%; ** significant at 5%; *** significant at 1%
Source: Calculations based on VHLSS 2004
Independent variables
Simpson_index
10
-0.129*
[0.069]
-0.276***
[0.027]
-0.003
[0.010]
0.025
[0.015]
0.030***
[0.007]
0.006
[0.004]
0.056
[0.036]
0.100***
[0.027]
0.109***
[0.013]
0.072***
[0.012]
-0.196
[0.155]
0.016
[0.025]
0.036
[0.023]
-0.090
[0.086]
0.109*
[0.059]
0.046
[0.050]
0.080*
[0.042]
-0.031
[0.021]
0.002
[0.004]
0.021***
[0.007]
0.080**
[0.036]
2695
2695
2695
0.50
0.51
0.52
8
-0.111
[0.076]
-0.292***
[0.028]
0.002
[0.011]
0.023
[0.017]
0.032***
[0.007]
0.004
[0.003]
0.050
[0.037]
0.139***
[0.026]
0.131***
[0.014]
0.062***
[0.012]
-0.300**
[0.131]
0.031
[0.027]
0.043*
[0.022]
9
-0.114
[0.070]
-0.281***
[0.026]
-0.004
[0.010]
0.025
[0.015]
0.031***
[0.007]
0.006*
[0.004]
0.050
[0.036]
0.111***
[0.027]
0.122***
[0.015]
0.070***
[0.012]
-0.209
[0.149]
0.023
[0.024]
0.040*
[0.022]
-0.027
[0.082]
0.172***
[0.054]
0.112**
[0.046]
0.121***
[0.044]
33
Table 4 (complete): Impact of land fragmentation (index) on labour productivity
Dependent variable: (log) Labour productivity (total value of crops / on-farm work hours)
1
2
3
4
5
6
7
8
9
0.728*** 0.485*** 0.545*** -0.066
0.543*** -0.225
-0.260** -0.261* -0.256**
[0.074]
[0.157]
[0.173]
[0.092]
[0.169]
[0.134]
[0.127]
[0.135]
[0.120]
log farm size
0.134*** 0.126*** 0.371*** 0.170*** 0.218*** 0.213*** 0.237*** 0.287***
[0.037]
[0.043]
[0.029]
[0.042]
[0.034]
[0.033]
[0.033]
[0.027]
log average distance to plot
-0.028
-0.021
-0.025
0.021
0.013
0.005
-0.014
[0.032]
[0.021]
[0.030]
[0.023]
[0.021]
[0.024]
[0.022]
log percentage plots with LUC
0.088
0.115*** 0.098
0.021
0.022
0.079** 0.096***
[0.072]
[0.030]
[0.070]
[0.040]
[0.036]
[0.037]
[0.031]
log Area devoted to fruit crops
0.016*
0.020** 0.018** 0.016** 0.016**
[0.009]
[0.007]
[0.007]
[0.007]
[0.007]
log Area devoted to industrial crops
-0.035*** -0.028*** -0.027*** -0.023*** -0.012
[0.009]
[0.008]
[0.007]
[0.008]
[0.008]
log Use of seeds per area
0.034
0.039
0.027
0.020
[0.035]
[0.034]
[0.036]
[0.033]
log Use of fertilizers per area
0.272*** 0.245*** 0.209*** 0.120***
[0.029]
[0.029]
[0.033]
[0.033]
Dummy for use of machinery
0.237*** 0.227*** 0.186***
[0.048]
[0.038]
[0.035]
Gini coefficient for land
-1.017***
-1.521*** -1.033***
[0.366]
[0.364]
[0.372]
Dummy for road to village
0.124*
0.177** 0.148**
[0.062]
[0.074]
[0.064]
Dummy for irrigation
0.067
0.067
0.065
[0.045]
[0.041]
[0.042]
Dummy for Coastal area
0.054
0.051
[0.143]
[0.146]
Dummy for Delta area
0.623***
0.586***
[0.094]
[0.093]
Dummy for Midland area
0.228**
0.252**
[0.092]
[0.092]
Dummy for Low mountain area
0.274***
0.277***
[0.085]
[0.083]
Sex of head of household
0.063
[0.059]
Household size
-0.080***
[0.011]
Head's education level
0.071***
[0.013]
Tribe
0.198**
[0.082]
Observations
3801
3744
3184
2741
3184
3117
3117
2683
2683
R-squared
0.02
0.05
0.03
0.30
0.05
0.17
0.19
0.24
0.28
Standard errors in brackets, clustered at province level
* significant at 10%; ** significant at 5%; *** significant at 1%
Source: Calculations based on VHLSS 2004
Independent variables
Simpson_index
34
10
-0.272**
[0.115]
0.362***
[0.029]
-0.005
[0.019]
0.087***
[0.030]
0.013**
[0.006]
-0.011
[0.008]
0.044
[0.033]
0.077**
[0.031]
0.178***
[0.034]
-1.003***
[0.347]
0.099*
[0.058]
0.040
[0.040]
-0.086
[0.141]
0.416***
[0.103]
0.084
[0.098]
0.163*
[0.089]
0.064
[0.058]
-0.083***
[0.010]
0.062***
[0.013]
0.148*
[0.074]
2683
0.32
Table 7: Correlation between land fragmentation and use of fertilizers
Independent variables
Simpson_index
log farm size
log average distance to plot
Dependent variable: log Use of fertilizers per area
1
2
3
4
5
1.881*** 2.553*** 2.006*** 1.574*** 1.500***
[0.086]
[0.330]
[0.301]
[0.261]
[0.265]
-0.155
0.008
0.111*
0.155***
[0.092]
[0.074]
[0.055]
[0.051]
-0.167*** -0.157*** -0.159*** -0.148***
[0.046]
[0.040]
[0.033]
[0.028]
Commune characteristics control
no
no
yes
yes
yes
Region fixed effects
no
no
no
yes
yes
Household characteristics control
no
no
no
no
yes
Observations
3749
3565
3071
3071
3071
R-squared
0.11
0.17
0.39
0.48
0.50
Standard errors in brackets, clustered at province level
* significant at 10%; ** significant at 5%; *** significant at 1%
Source: Calculations based on VHLSS 2004
Table 8: Correlation between land fragmentation and use of machinery
Independent variables
Simpson_index
log farm size
log average distance to plot
Dependent variable: Dummy for use of machinery
1
2
3
4
5
1.465*** 1.870*** 1.311**
0.867*
0.685
[0.162]
[0.457]
[0.539]
[0.448]
[0.424]
-0.068
0.052
0.172**
0.161**
[0.105]
[0.091]
[0.071]
[0.067]
0.045
0.061
0.052
0.051
[0.074]
[0.079]
[0.077]
[0.075]
Commune characteristics control
no
no
Region fixed effects
no
no
Household characteristics control
no
no
Observations
4125
Standard errors in brackets, clustered at province level
* significant at 10%; ** significant at 5%; *** significant at 1%
Source: Calculations based on VHLSS 2004
Logistic regression
yes
no
no
3621
yes
yes
no
3126
yes
yes
yes
3126
3126
35
Table 9: Correlation between land fragmentation and on-farm working hours
Independent variables
Simpson_index
log farm size
log average distance to plot
Dependent variable: log On-farm working hours
1
2
3
4
5
2.913*** -0.051
0.138
0.237**
0.263**
[0.149]
[0.147]
[0.118]
[0.106]
[0.105]
0.649*** 0.577*** 0.548*** 0.472***
[0.042]
[0.043]
[0.043]
[0.044]
-0.005
-0.021
-0.015
-0.031
[0.026]
[0.025]
[0.022]
[0.020]
Commune characteristics control
no
no
yes
yes
yes
Region fixed effects
no
no
no
yes
yes
Household characteristics control
no
no
no
no
yes
Observations
4125
3621
3126
3126
3126
R-squared
0.08
0.29
0.34
0.35
0.38
Standard 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)
Independent variables
Dummy for communes that have completed LC
log farm size, av. for commune
log percentage plots with LUC, av. for commune
Gini coefficient for land
Dummy for road to village
Dummy for irrigation
Dummy for Coastal area
Dummy for Delta area
Dummy for Midland area
Dummy for Low mountain area
Dependent variable: (log) Land productivity (total value/total area)
1
2
3
4
5
0.065**
0.047*
0.044
0.037
0.011
[0.031]
[0.026]
[0.027]
[0.029]
[0.029]
-0.274*** -0.271*** -0.280*** -0.274***
[0.026]
[0.026]
[0.027]
[0.027]
0.039
0.048*
0.057**
[0.026]
[0.027]
[0.026]
-0.466*** -0.301**
[0.145]
[0.139]
0.140*** 0.123***
[0.043]
[0.040]
0.062*
0.067**
[0.032]
[0.031]
0.099
[0.099]
0.317***
[0.063]
0.261***
[0.072]
0.236***
[0.067]
761
761
711
642
642
0.01
0.27
0.25
0.29
0.32
Observations
R-squared
Standard errors in brackets, clustered at province level
* significant at 10%; ** significant at 5%; *** significant at 1%
Source: Calculations based on VHLSS 2004
36
37