long-term farming and rural demographic...
TRANSCRIPT
BACKGROUND PAPER
FOR THE WORLD DEVELOPMENT REPORT 2008
Long-Term Farming and
Rural Demographic Trends
Gustavo Anríquez and Genny Bonomi
The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Development Report 2008 Team, the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
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Version: May 9, 2007
Long-Term Farming and
Rural Demographic Trends
Background Paper for World Development Report 2008
by: Gustavo Anríquez and Genny Bonomi*
Food and Agriculture Organization of the UN Agricultural and Development Economics Division
* The authors thank the useful comments from Karen Macours, Benjamin Davis, Kostas Stamoulis, Gero Carletto, and Alberto Zezza. Also, the efficient research assistance of Stefano Trento is kindly acknowledged. The usual disclaimer applies: all errors and omissions are the exclusive responsibility of the authors.
Table of Contents
I.- Introduction ...........................................................................................................................1
II.- Global Farming Trends ........................................................................................................2
A.- Global Trends in Agricultural Land ................................................................................2
B.- Land Distribution and Inequality .....................................................................................4
Bimodal Land Distributions...............................................................................................4
Land Inequality ..................................................................................................................6
Evolution of Land Inequality .............................................................................................6
Land Inequality and Landlessness .....................................................................................8
C.- Evolution of the Small Farm............................................................................................9
Fragmentation ..................................................................................................................10
Crop Diversification.........................................................................................................11
D.- The Inverse Relationship in Agricultural Censuses.......................................................12
III.- Global Rural Demographic Trends...................................................................................14
A.- Economically Dependent Groups and Rural Dependency ............................................16
B. Rural Feminization..........................................................................................................18
The Role of HIV/AIDS....................................................................................................21
The Role of Migration .....................................................................................................22
C.- Rural Ageing..................................................................................................................23
IV.- Population Movements in Latin America and East Africa...............................................25
A.- Estimating Population Movement .................................................................................25
B.- The Results ....................................................................................................................28
V.- Conclusions........................................................................................................................30
Tables.......................................................................................................................................32
Figures......................................................................................................................................47
Appendix I. Countries and Censuses or Sample Surveys Included in the Global Population
Database...................................................................................................................................58
Appendix II. Maps ...................................................................................................................61
Appendix III. Additional Net-Migration Tables......................................................................63
References................................................................................................................................67
i
List of Figures
Figure 1. Farm Land ................................................................................................................47
Figure 2. Evolution of Total Farm Land..................................................................................47
Figure 3. Evolution of Agricultural Land ................................................................................48
Figure 4. Land Distributions in Sub-Saharan Africa and Latin America ................................49
Figure 5. Land Distributions in Bangladesh ............................................................................50
Figure 6. Change in Land Under Small Holders......................................................................50
Figure 7. Small Farm Fragmentation .......................................................................................51
Figure 8. Specialization in Main Crops Small Farmers vs. Larger Farms...............................51
Figure 9. Correlation between Specialization in Main Crops and Openness ..........................52
Figure 10. Bean Yields in Chile...............................................................................................52
Figure 11. Sorghum Yields in Botswana .................................................................................53
Figure 12. Maize Yields in Brazil............................................................................................53
Figure 13 Rice Yields in Vietnam (Kg/Ha) .............................................................................54
Figure 14. Economically Dependent Population and Income .................................................54
Figure 15. Rural Dependency Ratio and Income.....................................................................55
Figure 16. Changes in the Femininity Ratio by Initial Femininity ..........................................55
Figure 17. Rural Ageing Developing Countries ......................................................................56
Figure 18. Rural Ageing in Sub Saharan Africa ......................................................................57
ii
List of Tables
Table 1. Agricultural Censuses Considered in this Study........................................................32
Table 2. Determinants of the Evolution of Farm Land............................................................32
Table 3. Evolution of Inequality Mean Farm Size and Population..........................................33
Table 4. Changes in Farm Land Inequality (GINI)..................................................................33
Table 5. Relative Prevalence of the Small Farm......................................................................34
Table 6. Main Crops and their Land Shares by Country .........................................................35
Table 7. Determinants of the Share of Land under Main Crops..............................................36
Table 8. Population Age Composition and Development .......................................................36
Table 9. Dependency Ratios and Dependency Ratio Changes by Global Regions and
Condition of Rurality .......................................................................................................37
Table 10. Femininity Ratios.....................................................................................................38
Table 11. Rural Gender Inequality...........................................................................................39
Table 12. Femininity Ratio of the International Migrant Stock by Development Region ......39
Table 13. Gender of International Migrants Leaving Latin American Countries....................40
Table 14. Rural Household Head Age and Wealth..................................................................40
Table 15. Correlation Matrices of Marginality Quartiles ........................................................41
Table 16. Population Movement, Initial Population Characteristic, and Changes by Housing
Quality Quartiles..............................................................................................................42
Table 17. Characteristics of the Adult Cohort, and Changes by Population Movement
Quartiles...........................................................................................................................44
Table 18. Intercensal Net Migration Rate................................................................................46
Table 19. Population Movement, Initial Population Characteristic, and Changes by Average
Education Quartiles.........................................................................................................63
Table 20. Population Movement, Initial Population Characteristic, and Changes by Distance
Quartiles...........................................................................................................................65
iii
I.- Introduction
When it is estimated that 3 out of 4 persons in the poorest fifth of the world population (i.e.
the poorest 1.2 billion persons) live in rural areas (IFAD (2001)), where agriculture is the
main source of livelihood, any serious analysis of the prospect of reducing rural and overall
poverty requires a clear understanding of this farming population, their characteristics and the
obstacles they face.
Among development specialist several trends have been mentioned as possible threats
to agricultural and rural development in developing nations. Many of these threats are
endogenous, and with the right policy environment, or with an “investment push” can be
solved, like low levels of public and private investments in infrastructure and human capital.
Other threats however, are exogenous and therefore more worrisome. It is generally argued
that farms are fragmenting to a point in which their own economic viability is at risk. Other
examples of these exogenous threats refer to disadvantageous demographic developments,
like ageing of population or higher female to male ratios due to war or new epidemics like
AIDS, which can basically cut the supply of new labor and block the prospects of rural
development.
In this paper we attempt to provide a long-term and global view at these exogenous
trends. This effort is divided into two parts. In the first section we examine a sample of
agricultural censuses from different developing regions of the world and explore what are the
main trends. We want to see if the agricultural censuses support the generalized concern of
fragmenting small farms. A related question is whether the limit on available agricultural land
has been reached, and hence agricultural output will be constrained by the lack of availability
of agricultural land. Furthermore, we want to study the evolution of the small farm. For
example, have small farms been able to move away from staple crops into higher value crops,
and how specialized are they in staple crops.
In the second part of this paper, we explore the validity of the concern of adverse
exogenous rural demographic developments. These questions are addressed with an analysis
of a global database of population census figures. The goal of this effort is to identify
inherent differences in the demographic characteristics of rural populations as compared to
their urban counterparts. In this section we explore the issue of ageing, economic
dependency, and feminization; both in terms of current levels and observed trends. As
identified below, one of the main forces determining the future demographic characteristics
of rural populations will be migration.
In the fourth section of this paper we carry out a cross-country net-migration study for
a sample of four Latin American countries and two East African countries. Here we explore
how migration is acting in rural communities, that is, how the demographic and socio-
economic characteristics of the population change due to migration. In addition of searching
for common trends in feminization, ageing, education, and other welfare indicators, we
explore the hypothesis that migration is increasing duality at a national level separating those
who do well and those who stay behind.
II.- Global Farming Trends
In this section we try to uncover the main trends in the development of farming operations in
the different developing regions of the planet. For this purpose we constructed a database of
farming characteristics of 17 countries across 43 different agricultural censuses, from three
different continents. The complete list of agricultural censuses considered is displayed in
Table 1.
With this organized data source we explore different questions relating to the ability
of agriculture to act as an enabler of development, or the possible barriers to the growth of
agriculture, with a special emphasis in the small and usually poor farm. First we explore the
question of land availability, is there still land available for agricultural and food output to
grow, or has the limit been reached? Next we explore the issue of land distribution to show
regional differences, and to expose possible barriers to agricultural development. Next we
identify the small farmers and the landless rural population to discover not only how
prevalent they are, but what are the differences in their access to land, and their ability to
diversify in to higher value output. Finally, we show the differences in productivity between
farm sizes, with special attention to the evolution of productivity.
A.- Global Trends in Agricultural Land
Before we can start describing the trends in farm land, we have to be clear in what we are
defining as farm land. With the aid of Figure 1 we can describe the different components of
what we usually call farm land. A farm holding may contain forests, grassland, unusable land,
and agricultural or arable land2. These differences are very important, as different types of
land may display different patterns of evolution. For example, in Chile in the inter-censal
period 1975-1997 agricultural land contracted, while total farm land expanded. The 2 The term “arable land” is generally used to refer to two related but different things: (i) potentially cultivable land; and (ii) land most of the time under temporary crops. In this paper “agricultural land” is comparable to the second definition.
2
difference is explained by an expansion in both grasslands (livestock) and forestry operations.
Any measure of inequality of holdings will vary depending on the type of land considered.
For example, if one considers total land, and includes large cattle farms and forestry
operations, one would probably observe higher measures of inequality than by calculating
inequality using agricultural land alone.
The general trend in the available agricultural censuses is to have the different types
of lands described in Latin America, while in Africa agricultural censuses generally describe
only planted land, and in Asia results are presented for agricultural land. As much as possible,
in what follows we try to compare results for agricultural land, the focus of our study.
The evolution of unused agricultural land is also relevant, because agricultural land
may be unused due to agro-ecological reasons, when it is left as fallow, or to economic
reasons, when there are not enough resources or the relative prices do not make profitable the
use of all the available agricultural land. Finally, when farms are smaller, like in Asia, the
difference between these different types of land is less relevant.
In Figure 2, we present the evolution of total farm land. The figure shows big
expansion in total land in Latin America, while in South Asia there is a mixed picture, almost
no changes in India, a contraction of total land in Bangladesh, and small expansion in
Pakistan. In Figure 3, we describe the evolution of agricultural land. The figure shows that in
both Brazil and Chile, the latest trend is toward reduction in agricultural land, while we
observe huge increases of agricultural land in most Sub Saharan countries (SSA), with the
exception of Malawi, as well as in Mexico. In the case of Mexico, we can compare the
evolution of total land, and observe that most of the expansion of agricultural land has
occurred within the holdings, as total farm land has had a more modest growth compared to
the more than doubling of agricultural land. Results for Ethiopia should be read with care, as
not exactly the same area was sampled in each census. Agricultural land has expanded
considerably in North Africa, as the evolution of agricultural land in Egypt and Algeria
(DZA) shows.
In Table 2, we attempt to uncover the determinants of the evolution of farm land.
Before commenting the results, we have to admit we are skating on thin ice. Our first
limitation is the scarcity of degrees of freedom. We also suffer from missing variable bias, as
probably the availability of land that can be transformed into agricultural production is a key
3
variable that explains the evolution of farm land3. Additionally, our holding inequality is
inconsistent because it measures inequality of different types of land. In spite of these severe
limitations, the results are sensible and appealing. In the first panel, we see that our proxy of
availability of land is positively correlated with total farm land expansion. The agricultural
openness (Agricultural Imports + Exports over Agricultural GDP) in the ending period also
pushes total farm land expansion. This result makes sense because our sample is made of
developing countries, which should have a comparative advantage in the export of
agricultural goods. Finally, inequality also pushes total farm land expansion. In the second
column, we observe that openness does not promote expansion of agricultural land. This
result seems reasonable, because the comparative advantage could be in the livestock sector,
which expansion would not be accompanied by growth of agricultural land. Finally, we see
that inequality is also positively correlated with the expansion of agricultural land.
B.- Land Distribution and Inequality
Bimodal Land Distributions
During the 1970’s the economist Bruce Johnston disseminated the notions of
unimodal and bimodal agricultural development strategies, and unimodal and bimodal
institutions (Johnston and Kilby (1975); Johnston and Clark (1982)). In a bimodal
development strategy there is agricultural development through increases in productivity and
output of a minority of large farmers using capital intensive technologies imported from
developed countries, while a unimodal development strategy allows the majority of
households to enjoy increased output through productivity growth of labor using and capital
saving technology, like the Asian “Green Revolution.” Johnston and Kilby (1975) use
Taiwan and Colombia as two diverging examples of unimodal and bimodal agricultural
development respectively. A different issue is the institutional setting which enables either
development strategy. This is related to the land distribution. If land is extremely unequally
distributed, most farms would be small and a few would be large. The manifestation of this
structure in a land distribution plot would be bimodal in the sense that land would be
distributed with a mode at large land plots, while the distribution of farms would exhibit a
different mode at lower levels of land. If on the contrary land was equitably distributed both
the distribution of farms and land would share a mode around the mean plot level. A bimodal
land structure predisposes a political economy environment in which the privileged few can 3 Although we do not have this variable, we use ( Country Area / Country Population ), i.e. the inverse of the population density, as a proxy of agricultural land available.
4
steer public policy in their favor. This is why bimodality is an institutional framework, more
than just an asset distribution.
The corollary of this dual characterization of the rural world is that unimodal
strategies are much more successful at reducing poverty. The weakness of Johnston’s
analysis is that unimodality is not a policy, but a result; and the institutional setting is not
deterministic, for example it is perfectly conceivable to have unimodal agricultural
development within a bimodal institutional setting. In spite of these shortcomings, Johnston’s
analytical framework was very influential, even as a justification for land reform, and can still
be applied to understand different development outcomes.
In Figure 4 we show the land distribution of a selection of four countries, two from
Sub Saharan Africa, and two from Latin America. In Panels a) and b) we can see that the land
distributions of Botswana and Ethiopia are unimodal, in the sense that both the farm and land
distribution follow the same pattern across land categories (although strictly speaking the
distribution of land in Botswana is bimodal, with two modes). These countries reflect a
general trend of the seven Sub Saharan countries analyzed; they show, in general terms,
unimodal land distributions in the Johnston sense. Panels c) and d) show the land
distributions of two Latin American countries, Brazil and Chile. Strictly speaking the land
distributions are multimodal, but they are bimodal in the Johnston sense, the distribution of
farms in both cases is concentrated in the lower tail, while the distribution of land is
concentrated in the upper tail. This bimodality was the characteristic of the other Latin
American countries considered in this study (Panama and Ecuador). In spite of its bimodality,
the Latin American countries in Figure 4 where much better agricultural performers with per
capita agricultural GDP growth of 20 and 48% (for Brazil and Chile respectively) while
agricultural GDP per capita fell both in Botswana and Ethiopia (-45 and -16% respectively)
during the period of 1980-2000.
The difference in performance of the agricultural sector does not mean that bimodality
favors agricultural development, it just shows that there is no deterministic relationship. It
still remains true, as Johnston argues that more egalitarian land distributions will make the
benefits of agricultural development better distributed across the population, and thus make
agriculture more pro-poor. Also, the political economy of the bimodal institutional setting
provides grounds for policies that favor large scale farming, and not necessarily small farms.
For example, Brazil, which has more acute bimodality has been less successful than Chile in
reducing rural poverty (World Bank (2003); Anríquez and López (2007)).
5
Land Inequality
Furthermore, the initial land distribution matters, because it is unlikely that the land
market alone will produce the Pareto enhancing trades (Binswanger and Deininger (1997)).
Land in many settings has a value considerably above the current value of expected farms
profits4. Land may have additional value beyond its agricultural use because it provides other
services like: serve as collateral in the presence of imperfect capital markets, serve as a
source of social and political power, serve a an inflation or tax shelter, serve as a mean of
access to subsidized credit or other public benefits. Additionally, the covariance of risk of
agricultural production further promotes concentration, because during agricultural “bad”
years, the price of land goes down, and only the very wealthy or those with urban incomes
can access this cheaper land. As land markets are generally unable to replicate an efficient
distribution of land, inequality in land distribution may have important aggregate effects.
The economic literature has addressed this issue generally finding that more unequal
land distributions affect development in several ways. Deininger and Squire (1998) use a
cross section of countries to show that economies that initially have more unequal land
distributions grow less. Vollrath (2007) shows that agricultural productivity (measured as is
usually done in the literature as value of output per unit of land) grows more slowly in
countries with less egalitarian land distributions. Erickson and Vollrath (2004) show that land
inequality is correlated with weaker financial and public institutions (the Johnston hypothesis
re-visited). It is also hypothesized that land inequality may diminish long-term development
by decreasing access to education, but this relationship has not been empirically corroborated.
Evolution of Land Inequality
In Table 3 we describe the evolution of inequality of farm land distribution and the
evolution of farms. One of the results that immediately strikes from the table is the general
correlation between shrinking average farm sizes and improvements in inequality. Most of
the countries that exhibit rapid relative fall in farm average size, like Chile, Malawi,
Tanzania, Panama, and Egypt, also show improvements in farm land inequality, i.e. a fall in
the GINI index. This is an important result, because it indicates that improvements in land
inequality may actually be the bearer of bad news, land distribution becomes more equal as
farmers become more similarly smaller5.
4 See Binswanger et al. (1995), and Anríquez and Valdés (2006) for an example. 5 The simple correlation (removing the observation of Bangladesh, see below) is 0.2.
6
This general trend is contradicted precisely by the country which had the highest
relative decline in the farm average size, Bangladesh. The case of Bangladesh is very
insightful, because it exposes the limits in comparability of agricultural census data. In Table
3, we also show the evolution of landless households which is approximated by the difference
of the rural households obtained from the population census and the farm households
surveyed in the agricultural census. It shows that Bangladesh had an awe-striking reduction in
landlessness from 86.8% to 31.7%. This dramatic change is hard to believe. Part of the
reduction in landlessness is real, as there was a land reform started in the early 1970’s that did
redistribute some land (Rahman et al. (1983)). Unfortunately, most of this reduction in
landlessness is an statistical artifact, as Hossain (1986) argues. What happens is that the 1977
agricultural census, as opposed to the 1960 and 1996 agricultural censuses, did not enumerate
the very small and tiny plots, i.e. the rural household with a small family orchard. In Figure 5,
this is shown graphically, where the 1996 census based land distribution shows a big spike
just below the half hectare which is absent in the 1977 distribution. Thus, the case of
Bangladesh exposes the limitation of agricultural census based inequality measures, tiny
differences in implementation can cause big differences in observed results. The
inconsistencies of the agricultural census based inequality measures unfortunately pervades
the empirical literature that studies development and land inequality issues; which calls for
care in the evaluation of their conclusions. This is particularly true in studies that use the
evolution of inequality, which produces much weaker and inconsistent measures, much more
than pooling inequality for different types of land as is usually done6.
This positive correlation between change in average farm size and inequality requires
a more careful inspection. In Table 4 we explore this correlation in a multivariate regression.
The table shows the changes in land inequality as explained by changes in average farm size,
change in total farm land, and changes in total number of farms. The correlations are
statistically strong and sensible. We observe that the correlation between changes in average
farm size and changes in inequality, as initially argued, is statistically positive. At the same
time correlation between the change in total number of farms and land inequality is also
positive and significant; while the correlation between changes in total available farm land
and inequality is, as expected, statistically negative. However after removing the dubious
Bangladesh observation, results change strongly, and we observe that only changes in farm
6 For example Deininger and Squire (1998) fail to confirm the popular Kuznets hypothesis that relates inequality to development, however their tests which uses a dynamic panel has very low power due to the inconsistencies explained above.
7
size (as argued initially) and changes in the total land are statistically correlated with changes
in land inequality.
In the light of the results presented in Table 4 we can reinterpret the evolution of
inequality in Bangladesh. Given the reduction in average farm size, inequality should have
decreased in Bangladesh; however, this effect gets overwhelmed by the sharp (and artificial)
expansion in the number of farms, particularly in the lower tail of the land distribution, which
causes an overall increase in farm land inequality. We can generalize these results. For a
given number of farms, reductions in farm size (fragmentation) are associated with
improvements in the inequality of land distribution. For a given number of farms, expansion
in total farm land reduces inequality.
Land Inequality and Landlessness
If in one country two farmers each owns half of the total agricultural land, and the rest
of the population owned none, the land GINI would be 0, that is, perfect equality. This
happens because the way in which land inequality is measured, which only accounts for
landholders, people without access to land are not considered7. In this section we attempt to
correct this problem by accounting landless households and re-estimating inequality
considering the landless group. The first problem to solve is who should be considered as
landless. Clearly not everybody that does not own (or have access to) land is landless
(constrained) in an economic sense. Some households are landless because they are
constrained financially or culturally, while others are landless because they are employing
their assets and skills in higher-paying non-agricultural activities. For an ideal inequality
measure we would like to count just the former households; however there just does not exist
an internationally comparable source to identify households linked/dependant on agriculture.
In Table 3 we identified landless by using all rural households as a proxy of the
population linked to agriculture. From official population censuses we identify the total
number of rural households. Then we interpolate the number of households that should have
existed the year of the agricultural census, and finally we approximate as landless the
difference between all rural households and the total number of farms / households counted in
the agricultural census. The limitations of this choice are obvious, middle income countries,
all things equal will have more landless, as these countries have more households that have
moved away from agriculture; while in poorer economies, where agriculture is the main rural
economic activity the estimated landless number will be more accurate. 7 Also, as we have explained above, not even all of those with access to agricultural land get counted always.
8
The table shows that landlessness is particularly acute (>50%) in Pakistan, Botswana
and initially in Bangladesh. The land inequality GINI that accounts for landlessness is larger
than the inequality obtained from agricultural censuses in every reported case, except for
Brazil 1996 and Pakistan 1990, where the former GINI is smaller8. The direction of the
change in the inequality measure is the same in every country reported with the exception of
Brazil, where GINI with landless falls while the landholdings GINI increased. In general,
both inequality measures are surprisingly similar, with the exception of Bangladesh, Malawi,
Guinea, countries where the GINI with landless is more than 20% larger than the traditional
inequality measure. Of these latter countries, Bangladesh and Malawi are countries that
exhibited the highest reductions in their share of landless households, in the first case we
have a dubious observation as we explain above, and in the second we have measurement
issues, as we are comparing a full agricultural census with the results of a household survey.
C.- Evolution of the Small Farm
The small farm is a relative concept. What constitutes a small farm in the Argentinean
Patagonia is very different in size to what constitutes a small farm in Indian and Bangladeshi
Bengal; in this exaggerated example the first farm is many times larger than the second one.
The small farm as an a economically viable unit that can provide livelihood to a household
will vary in size depending on the productivity of land, the availability of public goods, and
agroecological conditions among others. In this section however, we want to provide a global
view of the small farm, so we make the crude assumption that small farms are everywhere
those units that operate less than 2 ha of land. Due to data limitations, we can not use this
same threshold in all countries, for example, in the case of Chile the threshold used is 1 ha,
and in Bangladesh 2.5 acres (1.01 ha). In Table 5 we provide a global view of the relative
prevalence of the small farm around the world. At a global level almost 90% of farms are
small farms, however there are important regional differences. For, example in Latin America
the share of small farmers is much lower (consistent with higher average farm sizes as shown
in Table 3) only 27%, compared to other regions where 80% or more of farms are small. East
Asia, has the highest prevalence of small farms at 95%, a rate strongly influenced by China,
where 98% of farms are small. In the rest of the global regions about 80% of farms are small.
The land held by small farmers has a higher variability. In Latin America where both
8 This counter-intuitive result of reducing the GINI by accounting the landless is mathematically possible. An intuitive explanation is that in both cases, Pakistan and Brazil the share of landless is large, and their inclusion causes a downward equalizing effect.
9
distributions are very unequal, and the small farm prevalence is low, small farmers hold less
than 1% of farm land. The highest share of land held by small farmers is observed in Sub
Saharan Africa, where small farmers hold 56% of farm land (an indicator of more equality in
the distribution of land). Overall, small farmers hold 15% of the global farm land.
In Figure 6 we describe the evolution of the land under small holders. We see that in
Latin America, the small holder sector is relatively shrinking, while in South Asia, and high
density SSA countries (Ethiopia and Malawi) small farms are relatively growing. In South
Asia, the relative expansion of the small holder sector has been particularly fast (even if we
ignore the dubious Bangladesh change). Of course, the relative growth of the small sector is
highly correlated with population density: we calculate a simple correlation coefficient of
0.78.
Fragmentation
An issue of concern for the rural development scholar is the fragmentation of small
farms. Small farms may continue to get smaller to a scale in which the economic viability of
the farm is at risk. To review fragmentation, we define it at the aggregate level as the case
where the average size of the small farm is decreasing, while at the same time the absolute
amount of land under small farms is increasing. This is a bit more general definition than
what is generally understood as fragmentation, i.e. the case where the number of small farms
increases, and the amount of land they hold stays constant. Figure 7 shows why this
definition is warranted. The mean size of small farms may be decreasing because the smaller
farms are being left behind, while some farms either grow or are being bought-off, as would
be the case in the South-West quadrant of the plot where Brazil and Chile lie. In the North
West quadrant, where most countries lie, we have fragmentation, both the mean size of small
farms is falling and the land under the small sector is growing. Bangladesh’s level of
fragmentation is over-stated, but it is most probable present, because it shares the
characteristic of countries where fragmentation is occurring, that is high population densities.
Furthermore, fragmentation is happening in the other South Asian countries, India and
Pakistan. Fragmentation is not observed in Sub Saharan countries with the exception of
Malawi, the Sub Saharan Africa country with the highest population density of those
considered (122 persons / km2). The south east quadrant shows an expansion of the small
sector, as land under small holders is falling and the mean size of the small farm is growing.
Only in Botswana small farms are expanding.
10
Crop Diversification
A perhaps desirable feature of agricultural development would be a diversification
from low value traditional crops towards higher value crops, in order to promote the increase
of rural households’ income. Anecdotal evidence suggests that the success stories in the small
holder sector are limited and would be hard to observe at the national level. We explore this
issue here with agricultural census data.
Defining high value crops is not a clear cut task. What is a high value crop in one
agro-ecological zone may not be such in another less productive one. Also, what was a high
value crop in 1970’s may not be a high value crop anymore. However, defining main or
staple crops is much easier, and can be done by looking at the amount of land these crops
occupy. In Table 6 we show which crops where considered main crops and the share of land
they occupy. In tropical countries with more than 1 agricultural season per year this share
could be greater than 100%, because we are calculating shares over physical land instead of
crop land (the sum of land planted in both seasons). We chose this rule because using crop
land, land shares could change just by a composition change of crops towards year round
crops, even if the amount of physical land used was not changing. The table shows that some
crops are tradables, like wheat, but none could be described as a high value crop. Thus,
observing the evolution of main crops, we can indirectly say something about diversification
into high value crops. In particular, if the share of main crops is increasing, we know there is
intensification in non high-value crops.
The first issue we explore is whether small farmers are more specialized in main crops
vis-à-vis larger farmers. In Figure 8, we plot with bars the ratio of the share of land under
main crops of small farmers over the share of land under main crops for larger farms. In the
same plot, a line tracks the share of land under small holders for each country. The first
inference that can be obtained from the figure is that in general small farms are more
specialized in staple crops than large farms. The graph also shows a clear trend: when the
share of land under small holders is low, these are highly specialized in main crops. As small
farmers become more prevalent, the farmers start to become more diversified than larger
farms. There are is one important exception, Algeria (DZA), where small farms are more
diversified, even though the share of land under small holders is relatively low.
In Figure 9, we show that there exists a negative correlation between agricultural
openness and the share of land under main crops. We explore this correlation with a simple
multivariate regression in Table 7, where we show that the share of land with main crops is
11
smaller in more open countries, but larger in those with higher population density. In spite of
its simplicity, this regression allows us to make a very important prediction: an increase in
agricultural openness, i.e. a reduction in agricultural subsidies in OECD countries as it is
currently being discussed, would increase diversification in low density countries, like
Guinea and Panama, but would do little to reduce the specialization in rice in Bangladesh or
in maize in Malawi.
D.- The Inverse Relationship in Agricultural Censuses
One of the topics that has most captivated the rural development, and the agricultural
economics literature is the issue of what was initially called the inverse relationship. This
refers to an inverse relationship between productivity and farm size, i.e. small farms are more
productive than large farms. There is no comprehensive literature survey to refer to, so we try
to introduce the main findings of this literature by providing a brief historical sketch.
The inverse relationship was first found in India. Using data from Farm Management
Studies from the mid fifties researchers found that productivity, measured as value of output
divided by land (i.e. partial land productivity) was negatively related to farm size (Sen
(1962); Sen (1964); Khusro (1964)). Some initially took these results as evidence of lack of
constant returns to scale in agriculture, when obviously one partial productivity indicator is
not an indicator of scale economies in production, which refers to all of the factors of
production.
This early Indian literature, neatly summarized in Bhagwati and Chakravarty (1969)
identified the possible causes of this intriguing result that would return in the literature in
many different shapes. First, the inverse relationship could be explained by differences in soil
fertility (i.e. land quality, its natural productivity), as Khusro (1964) argued. This is one
important cause for the phenomenon, because it is reasonable to expect that more productive
land is more valuable, and therefore, more likely to be fragmented into smaller plots. The
corollary is that if land quality is inversely related to plot size, this would explain the inverse
relationship. The second explanation is that the family farmer as compared to the capitalist
farm (using Sen (1964) language) provides labor to a the level of zero cost of opportunity, or
at the very least to a level of productivity below the latter farmer. This effect was later
formalized by Bhalla (1979) by showing that the small farm faces cheaper labor costs and
therefore oversupplies labor. Barrett (1996), provides a second formal explanation for Sen’s
argument when he showed that, under uncertainty, the very poor and constrained farmer
optimally over-supplies labor to a point below its cost of opportunity. The third possibility is
12
that large farms have an absentee landlord, and therefore under-provide managerial services
and are less productive, as argued by Khusro (1964). This argument, expressed in more
updated economist talk, as the issue of moral hazard in labor contracts was later formalized
by Feder (1985). The author showed that if effort provides disutility, then hired labor will
supply less labor effort than family labor, and if supervision is costly, the larger farm would
be less efficient. Clearly these three causes for the inverse relationship have very different
policy implications. If the cause of the inverse relationship is differences in soil quality,
redistributing larger farms into smaller ones would not improve efficiency, while if small
farms are more efficient due to labor or management advantages, then there can be important
aggregate efficiency gains by redistributing land.
The work of Berry and Cline (1979), which collected several empirical studies on
farm size and productivity helped to popularize the notion of the inverse relationship in the
field, which fueled the already hot debate on land reform, particularly in Latin America. As
empirical methods for studying technology evolved, with Data Envelopment Analysis and
Stochastic Frontier models, the focus shifted from partial land productivity to total factor
productivity (TFP). Current methods consist in estimating TFP, and in a second stage
determining if a negative correlation exists between TFP and farm operation size. Even
though these latter methods measure something different, the results have been consistent
with the previous literature.
Today the inverse relationship is usually taken as an undisputed characteristic of
agricultural production in the developing world; however, a brief survey of the empirical
literature would show that this is far from true. In South Asia, the inverse relationship seems
to be prevalent: in addition to the early work in India, it has been shown to exist in
Bangladesh (Hossain (1977)) and Pakistan (Khan (1977)). Even after accounting for soil
differences, the inverse relationship is still present: in Pakistan (Heltberg (1998)); and while it
is diminished, it is still shown to be present in India (Bhalla and Roy (1988) and Lamb
(2003)). The inverse relationship does not seem to be present in modern agricultural
operations, as shown for wine grape growers in South Africa (Townsend et al. (1998)), and
New Zealand’s milk farming industries (Jaforullah and Devlin (1996)). The rest of the
empirical evidence is mixed. In Sub Saharan Africa, Dorward (1999) shows that there is a
positive relationship between farm size and productivity among small farmers in Malawi,
while in Java the inverse relationship disappears after considering differences in land quality
(Benjamin (1995)). Recent research from Brazil and Nigeria suggests that the relationship
between farm productivity and farm size is U-shaped, with an inverse relationship up to a
13
certain threshold and a positive relationship for larger farms (Hefland and Levine (2004);
Kimhi (2006)).
In Figures 10-12 we present the contribution that agricultural censuses can provide to
this discussion. Unfortunately output is not measured in agricultural censuses in Asia, and in
most African countries. This leaves us with information on yield mostly for Latin America.
In the figures we plot yields for crops that are likely be prevalent in small farms: Beans in
Chile, Maize in Brazil, and Sorghum in Botswana. In Brazil and in Chile we see that yields
display the U-shaped relationship previously described in the literature, but in Botswana this
U-shaped relationship is not clearly identifiable. We believe that this U-shaped relationship
should be the expected one, because as farms become larger, whatever the source of inverse
relationship: land quality differences, or hired/family labor productivity differential starts to
become relatively less important than the productivity advantage of highly technology and
capital intensive operations. However, what is truly an striking conclusion from this census
data, is that productivity of larger farmers is growing faster than productivity of small
farmers. This result which is common across the three countries where data is available
requires further examination, but is an important warning sign into the future ability to
compete of the small farm.
We contrast this intriguing result further by looking at the evolution of yields in Asia,
but here we have to rely on household surveys. In Figure 13 we show the evolution of rice
yields in Vietnam by farm size. Here we see that smaller farm still enjoy an advantage in
yields, but at about 2 ha yields start growing, but they never surpass the smaller farms yields,
but note however that there are no large farms in Vietnam. In Chile larger farms are more
productive in beans above 10 ha, in Botswana and Brazil the turn-around is roughly at 7 ha;
in Vietnam those type of farms do not exist. In this country, larger farms also seem to show
faster productivity gains (these differences however are not statistically significant using this
sample survey data).
III.- Global Rural Demographic Trends
As countries develop, life expectancy increases, as health services and their coverage
improve, and lifestyles change reducing the risk of death. At a later stage, as countries
continue developing, the fertility rate drops, as family planning services become available
14
and / or, due to cultural and economic reasons, females reduce the amount of children they
have during their fertility life-span (in demographic terms 15 – 49 years). These are well
established features of development in the dynamics of populations, and are generally
described together in what demographers call the demographic transition (Lee (2003)). One
manifestation of the demographic transition is a change in the age/sex population pyramid
shape, from properly a pyramid shape to a cylinder shape as countries move through the four
stages of demographic transition: as the fall in the fertility rate manifests in a lower relative
size of the younger cohorts, and the reduction of mortality rates shows in a higher relative
size of older cohorts.
In this section, we start from what is still a valid generalization, to focus in the trends
of rural population dynamics. The goal is to identify how these trends differ between rural
and urban areas. Furthermore, we want to identify demographic trends that may eventually
act as a burden for rural development. For example, ageing and the lack of adult males may
act as a barrier to agricultural development, particularly in poor, capital scarce countries,
where man-power is still an essential input for agricultural production.
To study these topics we have constructed a global database of tables of age (5 year
cohorts) / sex (male-female)/ and rurality (urban-rural) of populations. The main source of
this database is the UNSTATS common database, which at the time of this report was fairly
complete until 1998; the United Nations Demographic Yearbook 2003 (United Nations
(2006)), which at the time of this report was not yet available in print, but was available on-
line; and a dedicated search in on-line and printed sources for these tables for censuses, or
urban/rural decompositions that were not available in UN sources. In the end, we created a
database that contained population estimates, but we decided to use in this section only
official figures from de jure or de facto censuses and sample survey population counts.
Population estimates are prepared with sophisticated and time-tested demographic methods
that account for changing mortality rates, fertility rates, and migration. However, these
estimates are based in previous trends, and things can change a lot from what is predicted.
Furthermore, we decided to keep sample survey population counts, because we can not know
a priori if it is more reliable than a census. Certainly, a well done sample survey may be more
precise than a poorly carried out population census. Also, some countries, like Germany, for
historical and political reasons do not carry out population censuses. In the end, we kept the
sample surveys, and they represent 3.5% of our sample9. Finally, we chose to work with
9 A full list of countries and years included in this section can be found in Appendix I.
15
observations as old as those of 1980; we had older observations, but we want to describe the
latest demographic trends10.
A.- Economically Dependent Groups and Rural Dependency
More than 50% of the variability in the share of the old population (in demography, older
than 64 years), and the share of children in the population (14 or younger) can be explained
by differences in the level of development alone. As Table 8 A shows, more than 2 thirds of
the variability in the national share of children are explained by the differences in per capita
income. The correlation of the share of old population is not as high, but it is still
considerable.
The demographic transition model tells us that countries, starting from high levels of
mortality and fertility (first stage), first experience reductions in their mortality rates (second
stage), which manifests in longer life-expectancy. Later as countries continue developing
they move into the third stage when fertility rates fall and the share of children in the
population falls as well. As people live longer, the share of older population starts increasing.
However, the fall in the share of children is much faster than the rise in the share of the older
population. This should not come as a surprise, because at earlier stages of the demographic
transition younger population are a larger share of the total population, and it takes time for
these cohort to reach the old-age cohorts, while the fall in fertility immediately manifests in
the share of children in total population.
As we compare the differences between rural and urban populations in panels B and C
of Table 8, we see that on average for the same level of income, rural populations have a
higher share of children population. This is consistent with the fact that rural populations
have a higher fertility rate. The share of children though, falls faster with income in rural
populations. Also, we see that the share of older people is also a little bit higher in rural areas,
for an equivalent level of income; but the share of old population rises slightly faster in urban
areas. These results can be observed more clearly in Figure 14, where we can see that the
predicted (by income) share of children is higher in rural areas, and statistically significant.
The share of old people, on the other hand, is higher in rural areas but not statistically
significant. Also, both gaps tend to close with development.
Four very important conclusions can be made just by looking at this picture:
10 It may be argued that a smaller time frame is required to capture the latest demographic trends; however, in Sub Saharan Africa information is very scarce, and a larger time span is necessary to capture at least two population counts.
16
• First, as the larger share of economically dependent population are children, and this
share is larger in rural areas, it follows that rural dependency ratios are higher in rural
areas (these is amply confirmed in Table 9).
• Fertility rates are higher in rural areas.
• As the larger share of economically dependent population falls with income, it
follows that the economic dependency ratio also falls with income.
• Finally, there is eventually a theoretical turn-around, at a certain income level the
dependency ratio starts growing with income. Using the numbers in Table 8, panel A,
we can make a back of the envelope prediction that this will happen at roughly
$80,000 per capita. This prediction may seem excessive, but Luxembourg for
instance, at $62,000, in 2003 is not far from this threshold.
In Table 9, we show dependency ratios by global regions, and confirm some of the
above predictions. First, the rural dependency ratio is higher in every global region when
compared to the urban dependency ratio, and this difference is statistically significant in
every region. Second, the dependency ratio is precisely higher in the least developed regions,
with rural Sub Saharan Africa showing the highest average dependency ratio. In Figure 15 we
plot the rural dependency ratio against per capita income, and confirm the strong negative
correlation between level of development and the rural dependency ratio. However, it is
important to highlight that the outliars, with higher dependency than predicted for their level
of development, in Sub-Saharan Africa, Botswana, Swaziland, and Zimbabwe are among the
countries that have highest HIV incidence (24.1, 33.4, and 20.1% of adult HIV incidence
respectively), as estimated by UNAIDS (2006).
The fact that rural dependency ratios are higher than their urban counterparts has
important welfare implications. One robust finding in poverty studies around the world is that
household dependency ratios are positively correlated with poverty. For example, López and
Váldes (2000) summarizing the evidence from 6 poverty studies in Latin America show that a
consistent determinant of poverty in all of the studies was the dependency ratio. Similar
evidence has been found in Pakistan (Malik (1996)) and Ethiopia (Bigsten et al. (2003)), just
to mention two other examples from different development regions. Therefore, part of the
observed differences in the intensity of poverty between rural and urban areas can be
explained by these demographic differences alone11.
11 Ravallion (2005) attempts to determine the contribution of differential mortality and fertility rates between poor and non-poor to the evolution of poverty incidence in the 1990s. He finds that these differences
17
In Table 9, we also show how the dependency ratio is changing. We can say that on
average it is falling on every major global region; however, there is no clear trend between
the fall in rural and urban areas. It is falling faster in urban areas of Asia, but faster in rural
areas of the Latin America and Caribbean region; however, none of these differences are
statistically significant. Also, there is no statistically significant correlation between the fall in
the rural dependency ratio and income. There is a negative correlation between the fall in the
dependency ratio and per capita GDP growth, but this relationship is not strong. It is perhaps
useful to look at the countries where the dependency ratio is actually growing; in our sample
these countries are: Armenia, Maldives, Namibia, Nepal, Paraguay, Uganda, and Zambia.
Once again, the three Sub-Saharan nations (Namibia, Uganda, and Zambia) are among those
with high adult HIV prevalence (19.6, 6.7, and 17% respectively)12. Obviously, AIDS
mortality is precisely higher in the adult, non-economically dependent cohort. In the other
three, Armenia, Maldives, and Paraguay, it is very likely that migration plays a determinant
role.
B. Rural Feminization
The issue of rural feminization can be of concern from two different perspectives. First, the
absence of adult males can be a hindrance to agricultural development, where raw man-power
is an important input in agricultural production. This risks to be a male chauvinist concern,
for, as a matter of fact, in many cultures it is females that generally attend the agricultural
activities of the household. However, in other communities the absence of able bodied adult
males can be a serious obstacle to agricultural growth.
The other aspect of feminization is at the household level. If female-headed
households are consistently over-represented among the poor, then the increase in femininity
should be of concern. With respect to this last question the evidence is mixed. Buvinic and
Gupta (1997) report that in a survey of 61 studies that addressed the question, 38 reported
higher poverty rates for female-headed households. In a cross-country study using different
household surveys, Quisumbing et al. (2001) find that in only 2 (4) out 10 surveys, female
headed households are over-represented using a 1$/day poverty line (33rd percentile poverty
line). Whether female-headed households earn less farm income is also a question with mixed
answers. For example, in Ethiopia it has been found that they earn significantly less (Holden
significantly affect poverty. However, the main demographic determinant of poverty differentials may be the differences in dependency between poor and non-poor. 12 UNAIDS (2006). Uganda, furthermore has been the victim of a civil war, see more details below.
18
et al. (2001)); while in Pakistan and Egypt there no significant differences (Anríquez and
Valdés (2006) and Croppenstedt (2006)).
Even if the empirical evidence on income and poverty studies is mixed, there are
sufficient theoretical reasons to be concerned about the disfavored position of female-headed
households. Female headed households generally have higher dependency ratios, lower
average earnings for the “bread earner”, many times are forced to take lower paying jobs to
accommodate to household-duties’ generated time constraints; all of which contribute to
higher poverty (Buvinic and Gupta (1997)). Furthermore, even if measured poverty does not
indicate that female-headed households are poorer, their welfare position is still likely to be
lower due to the leisure time sacrifices that they have to trade for equivalent income (Lipton
and Ravallion (1995)). In conclusion, although female headed households are likely in a
disfavored position, the situation varies considerably by country and developing region.
In Table 10, we present the mean femininity ratios, i.e. number of females every 100
males, by global development region, and condition of rurality, as identified from population
censuses. Overall, rural femininity is high in Europe and Sub-Saharan Asia. In general,
femininity ratios are higher in urban areas than in rural areas, with the exception of South
Asia, Middle East and North Africa (MENA) and Sub-Saharan Africa (SSA); however, in the
first two regions there are on average still more males than females in rural areas.
The femininity measure is mainly influenced by three channels. First, the rate of
renewal of the population is less gendered biased, and therefore contributes to balance the
femininity ratio. Although, as the national averages indirectly show, for cultural reasons, in
MENA, South Asia, and China, the younger cohorts tend to have more boys than in the rest
of the world13, we still find that the renewal of the population tends to balance out the overall
femininity. The second force is ageing. Older cohorts bias towards the female side, as young
male mortality is much higher than young female mortality. Hence, older populations tend to
display higher femininity ratios. Finally, there is migration, rural-urban and international,
which is the wild card in the femininity equation. We do not have an a priori about the
effects of migration in the gender composition of population, but we explore the issue below.
It is therefore useful to look at the femininity of the adult cohort (15 – 49 years),
because this cohort is neither biased by ageing nor by the younger more gender balanced
population. Also, the gender composition of the adult cohort may be more relevant to study
13 This phenomenon known in the literature as the case of “the missing girls” has been amply studied in the literature. See for example Johansson and Nygren (1991); Coale and Banister (1994); and Gupta and Shuzhuo (1999). Unfortunately this phenomenon seems to worsen with income growth (Sudha and Rajan (1998))!
19
from the perspective of availability of working age males. The lower panel of Table 10 shows
the adult femininity ratio by global development region and condition of rurality. We focus
on the rural femininity and see that the high total femininity in rural Europe was a result of
ageing, as the femininity ratio of the adult population is quite balanced. On the other hand,
the table also shows that the femininity ratio in rural SSA is even higher than the total rural
femininity ratio. South Asia also has a higher femininity ratio, and it could become of
concern if it is growing. We would like to stress that these are gross averages; behind those
already high femininity ratios for SSA may lie communities that due to war or AIDS show
even higher femininity ratios. In the map of adult rural femininity one can observe that the
case of high rural femininity is an issue basically only in SSA (see Appendix II, Maps).
Given new threats like the HIV/AIDS pandemic and old ones like war, it is thus
important to explore the changes in femininity. However, the changes themselves are not
revealing; it is interesting to observe whether femininity is growing from low levels (a likely
result just from population growth), or whether it is growing from already high levels of
femininity. We explore this question graphically in Figure 16, where in the x-axis we
measure the initial femininity, while in the y-axis we measure the change in femininity, using
average yearly rates for comparability. This means that the North-East quadrant of the plot is
where both femininity is high and it is growing. In panel B of the figure, that plots adult rural
femininity, we can see that most of the observations in the right quadrants are from the SSA
region. We inspect the observations of rural SSA, to see if rural adult femininity is still
growing. We see that although adult femininity ratios are high, they are still growing in only
two of ten observations (Uganda (1991-2002) and Zimbabwe (1992-1997)).14
After identifying high rural femininity ratios in Sub-Saharan Africa, the next obvious
question is to determine whether female-headed households are over-represented among the
poor in that development region. To provide a global overview, we propose a simple way to
analyze the correlation between female headship and income by searching for consistent
differences in the share of female-headed households and income levels. In Table 11, we
tabulated the percentage of (rural) female-headed households by expenditure quintiles for 27
household surveys, most of them from the RIGA database of household surveys (Davis et al.
(2007)). To capture in a single statistic the differences between gender headship of
households of different income levels, we propose the following index:
14 The first country has been going through one of the worst internal displacement crisis in the continent, see further details below; and the second is one of the countries with highest adult HIV prevalence.
20
1
Gender Inequality (5 )i ii Q Q
i+∈ >>
= −∑ .
This gender inequality index increases only if the percentage of female headed households in
the poorer quintile is statistically higher than the percentage of female headed households in
the next (richer) expenditure quintile. It values differences in the poorest quintiles higher than
differences in the richer quintiles, this is consistent with the value judgment that gender
differences in the poor quintiles are more relevant from the policy perspective. Also, starting
to add from the poorest quintile, we stop adding if the sign is reverted and if it is statistically
significant, that is, we stop adding if a richer quintile has a statistically higher rate of female
headed households. The index has a maximum value of 10 and a minimum of 0. Table 11
shows that countries in the SSA region and South Asia, the countries with higher adult rural
femininity, are precisely the countries with higher gender inequality. In Latin America,
gender inequality seems to be lower, but Ecuador is an exception with high gender inequality.
The Role of HIV/AIDS
As Sub-Saharan Africa has higher adult rural femininity ratios, it is easy to point to
the AIDS pandemic as the main cause, but things are not as straightforward. Without AIDS,
i.e. before it appeared, and in current epidemiological models that estimate a no AIDS
scenario, adult male mortality is higher than adult female mortality, like in the rest of the
world. Thus, if the HIV/AIDS pandemic is the culprit of higher rural femininity ratios, then it
is enough for the disease to have increased male mortality by less than female mortality to
cause an increase in femininity15. Unfortunately we can not give definite answers, all we can
provide is scattered evidence. Mather et al. (2004), using household surveys from five high
AIDS prevalence countries reports death of “prime age” adults, i.e. persons in the 15 – 59
years age cohort. These numbers are revealing, because most of these deaths are AIDS
related. The authors report that in two countries, Kenya and Malawi, there were more male
deaths, while in two others (Mozambique and Zambia) there were more female “prime age”
deaths, and in the fifth country (Rwanda) there was essentially a gendered balanced death
count. In Tanzania, female AIDS mortality was higher than male AIDS mortality in 1997
(UNDP (2000)), while in Ethiopia in 2001 male AIDS mortality was higher than female
(Reniers et al. (2006)). Perhaps the most insightful way to look at the issue of gender bias of 15 It can be shown that the femininity ratio would not change if the change in mortality followed the
rule:(1 )
(1 )m
f f
dm m
dm m
−=
−m
f, where are the male and female mortality rates. Since male adult mortality is
initially higher than female adult mortality this ratio is less than one.
, mm m
21
AIDS mortality is to look at the change in Demographic and Health Surveys’ reported
mortality from an early (ideally pre AIDS period) and a later period when the pandemic is
present. This exercise was done by World Health Organization (2003). The document reports
that in Ethiopia and in Zimbabwe female mortality grew more than male mortality (both rates
growing by 400 to 600%!), which suggests that AIDS induces a reduction in the rural adult
femininity ratio; while in South Africa male mortality increased more than female.
South Africa is a useful country, because it has good data, and it shows that the gap
between the gender mortality rates changes closes, replicating what has happened in
developed countries, where initially male mortality is much higher, but later both AIDS
related male mortality fall and female mortality rises. So if South Africa trends can be
extrapolated, we can propose that AIDS likely caused initially an increase in femininity rates,
but today and towards the future the AIDS pandemic will be the causal for a decrease in SSA
femininity rates. This latter prediction is shared by the United Nations Population Division,
which estimates that in the 38 affected countries in Africa, there will be an AIDS induced
increase in female mortality of 39.8% while AIDS related excess deaths will increase male
mortality by only 33% for the period 2000-2005. This higher female AIDS mortality
differential is projected to grow and continue through 2020, the end date of the projections
(United Nations (2005)).
The Role of Migration
To close this section on femininity we refer to migration. We have discussed that
migration is the wild card in any prediction on what is going to happen with rural femininity.
To get a global perspective on what is happening, at least with international migration, we
have two avenues. One is to look at the gender of the migrant stock, which is a task that the
UN Population Division has been carrying, trying to identify the size and composition of
those who are foreign born, as measured either by population census, or using official
citizenship records. Before discussing the results, note that both methods (particularly the
second) are likely to undercount illegal migrants, and therefore undercount the gender
composition of this component of the international migrants. In Table 12, we report the
average femininity ratio of the international migrant stock by development region. There, we
observe that, with the exception of Europe, in all developing regions most international
migrants are male; that is, we can infer that the South-South migrant is predominantly male,
while in OECD countries the femininity ratio is above 100, which would suggest that most
South-North Migrants migrant are female.
22
The other avenue to get a global perspective on the gender composition of
international migrants is to look at the latest population census, which have begun asking
households to identify members that have left the country. In the 2000 round of population
census in Latin America this question was asked in Mexico, Ecuador, Panama, and
Dominican Republic. In Table 13, we show that in the countries with a high rate of
international migration, Mexico and Ecuador, international migrants are predominantly
males. This is more consistent with the anecdotal evidence available in the press of OECD
countries that reports that captured illegal migrants trying to enter the developed world are
mostly male. We posit the hypothesis that the first wave of illegal migrants are mostly male,
but when this first generation establishes, mostly female legal migrants follow; however, this
is an hypothesis we are in no position to contrast here.
We explore the gender trends in domestic migration in the section of population
movements further below.
C.- Rural Ageing
At what point does population ageing become a hindrance for rural development? This is a
question with no easy answer. The answer depends on many factors: on whether the country
has a well funded pension system, on whether older people are holding to land as insurance
blocking efficient land transactions, on whether households headed by older people are
consistently poorer. The answers to these questions will determine if ageing of the population
could block rural development.
In the section above, we have shown that ageing is highly and positively correlated
with the level of income of countries. We consider 60 years as the threshold of old
population, instead of the traditional threshold of 65 years usually used in demography. We
first see that the share of population older than 60 has a similarly high positive correlation
with income (results comparable to those presented in Table 8). We map the share of rural
old population at the global level (see Appendix II) and we discover that the rural ageing map
looks extremely similar to an income map, with older cohorts representing a smaller share of
total population in SSA.
To study rural ageing, we analyze the evolution of the old cohort. In Figure 17, we
plot the change in the rural old cohort against per capita income, and we discover that the
change in the rural cohort (an indirect measure of ageing) is also positively correlated with
income. This means that not only wealthier countries are older, but that they age faster. In
Figure 18, we repeat the exercise of plotting the change in the older cohort against income for
23
SSA. We observe that in the region this positive correlation also holds. We can conclude that
ageing behaves like a non-income measure of welfare; it captures the increases in life
expectancy associated with development16.
The HIV/AIDS pandemic undoubtedly plays a role in rural ageing. As we have shown
in the previous sections adult mortality rates have jumped dramatically, but this rise in a
region where life expectancy was already low, instead of increasing the share of the old, the
disease has most likely reduced it. This can be seen with the case of Zimbabwe, the country
with the fourth highest adult AIDS prevalence (20.1% of the adult population in 2003,
UNAIDS (2006)). In Figure 18, we can see that this country reduced the rate of growth of the
rural old from the low HIV prevalence period of 1982-1992 to the high prevalence period of
1992-1997. Another aspect to consider is whether these increased deaths are at the aggregate
levels affecting the intra-household labor supply. Mather et al. (2004) report that in none of
the five countries studied, households afflicted by death of an adult had statistically less adult
labor than non-afflicted households. This result, together with the high fertility in SSA
suggests that it is still too early to promote labor-saving technologies for the SSA region
(Jayne et al. (2005)).
We further explore the effects of ageing at the household level, searching for a
correlation between the age of household heads and wealth. In Table 14, we tabulate the
mean age of rural household heads by expenditure quintile for a set of recent household
surveys from the RIGA database (Davis, Winters et al. (2007)). A close inspection of the
table will reveal that there is no correlation with wealth to report. In some countries older
heads are more prevalent in poorer households, for example in Bulgaria, while in others older
heads are wealthier, like in Chile, while in yet other countries there is no difference in the
average age of the household head by expenditure quintile, for example in Guatemala. We
also explore the possibility that the overall share of rural old may determine the sign of the
correlation between household head age and wealth; however, we find no such relationship.
For example, both in Bangladesh and in Chile, where the share of old is more than double
that of Bangladesh, we find that older heads are wealthier.
We can conclude by stating that rural ageing may be a hindrance to rural development
in some particular communities, but at aggregate levels the share of old population, under
10% in all development regions, is unlikely to be a barrier to the development of rural
economies. This statement is more valid the less developed the country is.
16 This is why Sen (1998) suggests that mortality may be in many cases a preferred welfare indicator.
24
IV.- Population Movements in Latin America and East Africa
In this section we explore for common trends in population movements in Latin American
and East African countries with a cross-country net-migration study. Our interest is to
explore common trends that can help predict into the future how demographic composition of
population in rural areas is going to change. We would like to know whether people are
staying behind in the worst-off communities increasing duality within countries separating
successful areas and pockets of poor. We explore these questions, for a sample of four Latin
American countries: Brazil, Mexico, Ecuador and Panama and two East African countries:
Kenya and Uganda; by estimating where people are moving out from and how the
characteristics of the communities they leave behind change.
A.- Estimating Population Movement
To estimate population movement we follow the path of an age cohort across time, as
it is counted in two different population censuses. The idea is that within a constant area, if
we count people at one point in time, and then we recount people at the same exact place a
period later, we can know the net result of how many people arrived or left the place. The
problem is that people not only leave or arrive, but people are born or die. We eliminate the
problem of new people being born by counting people of a certain age. We still have to
account for mortality. In this study we follow the adult cohort 15-49 years at the first census,
and compare them to the people 25-59 that are still in the same place 10 years later in the
second census count. It makes sense to follow the adult cohort, because they form the bulk of
the migrating population, and also for the most part have finished their education (particularly
in the poorer regions), which allows us to make some inference about changes in their
education levels.
Before counting people in the second census, we predict how many should be there if
no one moved, accounting for mortality. It is well known that mortality varies by gender and
age group (higher for males, and increasing with age), so we use different mortality rates by
gender. For example, females initially in the age group 15-19 in 1990 in administrative unit i,
should be 25-29 ten year later, but less after some of them died, in particular we can estimate
their number with:
25
(1). 5 525 29 15 19 15 19 20 24
ˆ (2000) (1990) (1 (1990,1995)) (1 (1995 2000))i i F FF F m m− − − −= ⋅ − ⋅ − −
That is, the predicted size of the cohort (under the assumption that there is no net migration)
is equal to the initial size minus those who die after they suffer risk of death that corresponds
to their age group for five years. After five years the cohort “graduates” and moves to the
next age cohort, and suffers a different risk of death17.
Thus if our mortality measure was exact, we can impute exactly the net in or out
migration of the cohort between 1990 and 2000 as:
(2). 25 29 25 29 25 29 25 29 25 29ˆ ˆ(1990,2000) ( (2000) (2000)) ( (2000) (2000))i i i i iNM F M F M− − − − −= + − +
The measure would be positive if more people in-migrated than those out-migrated, and
negative if more people left the administrative unit i. Finally we can calculate the migration
rate adding the imputed migration over the 7 5-years cohorts that compose the adult cohort:
15 49 15 49
(1990, 2000)(1990, 2000)
( (1990) (1990))
ijji
i i
NMnm
F M− −
=+
∑ (3).
The problem is that death rates are not equivalent across administrative units, which would be
of no concern if the risk of death was randomly distributed with the known mean we are
working with. In Latin America for example it is very likely that in poorer, more rural
regions, mortality rates are higher than the national means, and that in richer urban settings
the mortality rate is lower than the national rate. Although we can not control this bias, we
can at least predict what its effect is: we are going to overestimate out-migration in poor
areas, and over estimate in-migration in richer areas. It is also possible that the gap between
male and female mortality is higher in rural areas. If this is so we would overestimate male
out migration in poor areas, and equivalently we would overestimate the (positive) change in
the femininity ratio in poor areas, mainly in the adult cohort.
In Africa the picture is more complex, and it is hard to know a priori the sign of this
type of biases. On the one hand in some countries, in our sample Uganda, there are wars
which change the risk of death substantially, and the risk is correlated with regions, in our
case northern Uganda, not the level of rurality. Additionally, there is increased mortality
caused by AIDS, however, we do not know with certainty, if this type of mortality is higher
in urban or rural settings. The main source to study HIV-AIDS mortality, the Demographic
and Health Surveys, do not differentiate between urban and rural areas.
17 We obtained the mortality rates from life tables for Latin America as produced by CELADE (2001). In the case of Uganda and Kenya, we used Demographic and Health Surveys, in particular DHS (1996) and DHS (2001) for Uganda, and DHS (1994) and DHS (1999) for Kenya.
26
After calculating the predicted net migration rates, we explore how they vary by
income levels. We construct a welfare index, forming a principal components index of
different housing characteristics that are usually asked for in a population census, like floor,
ceiling, and walls construction materials; and access to water, electricity and sewage. These
are in a sense stock measures of wealth, as opposed to income or expenditure usually used in
household surveys which measure flow. We compare how the migration rates vary across the
different wealth quartiles, and also how the population characteristics change across these
wealth quartiles.
We also compare results by education quartiles and distance quartiles. Distance was
calculated as a simple linear distance from the center of the administrative unit (the centroid
of the polygon) to the closest city of at least 100,000 inhabitants at the time of the second
census. We use the threshold in the second census, because the fact that a city is crossing the
threshold says something about its migration attracting force. These three measures are
different indicators of marginality. We want to explore how marginality relates to population
movements. In Table 15, we report the correlation of the different indicators in the three
countries under review. We see that all the signs are as expected, education and housing
quality, two different types of assets, are positively correlated; this correlation is very high in
all countries with the exception of Ecuador. The correlation between assets and distance is as
expected negative, but these correlations are not as high.
The population census data we use comes from complete tabulations using the online
REDATAM facility in the cases of Ecuador and Panama18. For the rest of the countries we
used census samples. In the case of Mexico we use census samples (10% in 1990, and 10.6%
in 2000), prepared by INEGI (Mexico’s statistics office) and available at IPUMS (Minnesota
Population Center (2006)). The samples for Kenya and Uganda (5% and 10% respectively)
were also obtained from IPUMS, while the samples for Brazil (10%) were obtained from
IBGE (the Brazilian Statistics Office). The use of census sample data imposes certain limits,
these samples are weighed to produce the exact person count even in the smallest
administrative unit; however, they are not necessarily accurate for other indicators of the
population like age composition or housing quality for smaller municipalities which are as
small as 300 persons (as is the case of some Mexican municipalities). Therefore, to ensure
representativity of samples we merged together municipalities smaller than 3,500 inhabitants
by state, or districts.
18 See http://www.eclac.cl/celade/ for links to country level REDATAM access.
27
The Brazilian samples presented an additional challenge, as the country changed its
administrative units from roughly 4500 to 5400 municipalities in their inter-censal period
1991-2000. The scale of subdivisions and merging of administrative units, made it
impractical to individually identify each geopolitical rearrangement, as was done for example
with Ecuador. The task of identifying fission and fusions of municipalities was automated
with a GIS analysis of the areas of intersection of municipalities using geopolitical maps of
the census years, also available from the Brazilian statistical office.
B.- The Results 19
In Table 16 we present the results of tabulating imputed net migration and population
characteristics by wealth quartiles. All the following results are robust across the four Latin
American countries:
1. People out-migrate more from the poorer administrative units.
2. These are also the most rural communities.
3. Femininity, as reported above is lower in the poorer, more rural communities.
4. Indigenous populations form a larger share of poorer administrative units.
5. Assets are being accumulated faster in the poorest more marginal communities: both
education and housing quality indicators (we use floor quality as an example in the
tables) are growing faster in the poorest communities.
6. Indigenous populations are growing faster in the better off communities.
Result 1 is rather surprising. However, it does not negate the body of evidence in the
literature that shows that usually it is not the poorest individuals that out-migrate, but those
who are slightly better off, leaving the poorest behind. Result 1 indicates that it is the poorest
communities from where people out-migrate the most.
Result 5 is explained by three different effects. First, the asset base is lower in poorer
communities, therefore equivalent changes appear as higher, percentage wise in the poorer
communities. Then, as people are leaving, all these averages that are weighted implicitly over
persons, will rise if those who out-migrate, are below the community means in the assets
indicators. Finally, there are real improvements in education and housing quality in these
communities that raise the means. Anyhow, result 5 indicates that poorer communities are
catching up with their better off counter parts.
19 In this section we focus in the net migration results by welfare quartiles. Additional tables tabulating net-migration differences by distance and education quartiles are available in Appendix III.
28
The change in the relative size of indigenous population is given by:
1
1
t t
t
t
t
t
I IP P
IP
+
+
− (4),
where , t tI P are indigenous and total population respectively. Rearranging terms, it is easy to
show that the change will be positive iff 1 1/t t t / tI I P P+ +> , that is if indigenous populations
grow faster than the total population. Therefore, it is possible for this indicator to be positive
in all quartiles, as is the case of Ecuador20. However, consistently the change in ethnicity is
higher in wealthier communities. We interpret results 1, 4, and 6 together as indicating that
native populations are leaving the poorest communities and moving into the better-off
administrative units. This result is corroborated in Table 17 where we follow the
characteristics of the adult cohort alone. We interpret results 1 and 5 together as indicating
that population movements are helping to close the gap in Latin America between the
wealthy and marginal communities.
In East Africa, there is no such common thread, and the two countries analyzed
present two different stories in terms of net migration. In both countries poorer communities,
in terms of housing quality, also show lower levels of human capital accumulations.
Furthermore, in both countries, as opposed to Latin America, femininity is higher in the more
rural settings; again a result previously reported above. In terms of population movement the
countries diverge. In Kenya, it is the middle quartiles which have the highest level of out-
migration, while the poorest communities show a much lower rate of out-migration. Also,
welfare indicators (housing and educations) are growing much faster, precisely in the middle
quartiles. Hence, we can say that, like in Latin America, migration is working towards the
convergence of communities; however, in Kenya the poorest, least educated, and more
distant communities are being left behind. These communities show low out-migration, and
slower rate of growth of welfare indicators.
In Uganda, there is yet a different story. In-migration has been much higher in the
poorest regions, displaying what we can call a return migration, to the rural and poorer areas.
This return migration has caused the fastest improvement in welfare indicators in the poorest
regions of Uganda. Here, again, migration is acting towards (a downwards) convergence of 20 Unfortunately in Ecuador the question regarding spoken language, our ethnicity proxy, changed drastically between censuses, which is why probably we are overestimating the change in ethnicity in all parishes of the country. In spite of this bias, while we can not compare the evolution of ethnicity, we can still compare this biased measure between parishes as we are doing.
29
communities. However, it would be erroneous to attribute these trends to underlying socio-
economic characteristics. Uganda has been under a civil war since 1986, and a serious
internal displacement crisis has been growing since 1996, when the government started a
“protected villages” program. Today an independent organism linked to the United Nations
estimates at 1.9 million the number of internally displaced people, particularly in northern
Uganda (Internal Displacement Monitoring Centre (2005)). This means that a staggering
7.5% of the population is internally displaced. That is why Uganda shows net in-migration
rates in the higher quartile that are 4 times higher than any other country studied (see Table
17). Thus, most of what we are reading in our statistics as return urban to rural migration is
most likely internal displacement, a phenomenon that only indirectly is explained by socio-
economic differences.
In Table 17 we see that in Kenya out migrants have been mostly females, while no
such clear trend is evident in Uganda. Table 17 also shows that contrary to the evidence
displayed in Latin America, it is from communities with higher levels of education where
people out-migrate the most. This conclusion is further confirmed by regression analysis in
Table 18, where we estimate the determinants of the net-migration rate for the East African
countries. Although, results should be interpreted with care, as there is likely spatial
correlation in the equations not accounted for; both equations show that there is a strong
negative and quadratic relation between education levels and migration. These equations say
that at the higher tail of the education distribution, communities act as in-migration magnets,
but at the lower tail improvements in education promote higher out-migration. All the
evidence presented speaks quite clearly that education is the key asset that determines out-
migration, and in the case of Kenya, it is the lack of education that is leaving behind from the
migration channel the poorest communities.
V.- Conclusions
This background paper has studied two different areas of the general characteristics of
rural populations. In the first part we explored the characteristics of farming operations at the
global level. Several important conclusions and policy implications can be drawn from that
analysis. Agricultural land is in expansion in Latin America and Africa, while the expansion
limits have been reached in South Asia. In this latter region as well, there is acute
landlessness, and fragmentation of the small farm. We estimate that roughly 90% of the
world’s farms are small, defined as smaller than 2 hectares. In general farms are smaller as
30
expected in high density areas; therefore small farms are much more prevalent in South and
East Asia. We found that small farms are more specialized in staple crop than their larger
counterparts. We also found encouraging signs that openness is correlated with
diversification away from staple crops; however, specialization in staples is also highly
correlated with population densities (or smaller farms as both go hand-in-hand). Thus, as we
look into future rounds of the WTO and the eventual liberalization of agricultural markets,
we can predict that the benefits of trade opening in high value crops will not be evenly
spread.
The revision of global rural demographic trends seems to debunk some notions in the
development field. With respect to feminization we find that such an unbalance only exists in
rural Sub- Saharan Africa. A superficial analysis of gender inequality showed that precisely
in this region inequality in terms of economic wellbeing and gender is also more acute. AIDS
however, is contributing to diminish femininity, not to intensify it. We found that ageing is
not really a concern for least developed countries, and actually the opposite is true. High
dependency ratios brought about by larger children shares of populations act to reduce rural
wellbeing in the developing world. Therefore, the supply of labor is not at risk even in
countries that are hard hit by the AIDS pandemic. This is confirmed by the macro
demographic indicators and the latest literature that studies the effects of AIDS at the
household level.
Finally, in our cross-country migration analysis we found that in Latin America,
migration is acting to help an upward convergence of rural communities. This is not true
however in East Africa, where we found that some communities are being left behind. We
also identified education as the main asset that enables migration. Communities that are not
endowed with sufficient levels of human capital are being left behind.
31
Tables
Table 1. Agricultural Censuses Considered in this Study Continent Country Name Census Year
Africa Algeria 1973 2001 Botswana 1982 1993 Egypt 1990 2000 Ethiopia 1977* 1990* 2002 Guinea 1989 1995* Malawi 1981* 1993* Tanzania 1971 1996* Togo 1982 1996 America Chile 1975 1997 Brazil 1970 1975 1980 1985 1996 Ecuador 1974 2000 Mexico 1970 1991 Panama 1980 1990 2001 Asia Bangladesh 1977 1996 India 1970 1975 1985 1990 1995 Pakistan 1980 1990 2000 Thailand 1978 1993
* Agricultural Sample Survey used instead. Table 2. Determinants of the Evolution of Farm Land
Total Land Change Agricultural Land Change Coefficient Std. Error Coefficient Std. Error
Available Land Proxy: Country Area / Total Pop. 54.8** 20.8 16.4 14.7 Agricultural Openness (t+s) 4.8** 1.8 -3.7 4.1 Land Holding Inequality (GINI) 9.0* 4.6 17.9* 9.8 4 Region Dummies Included R2 81 51 Observations 17 22 Note: White heteroscedasticity consistent standard errors reported. ** Coefficient significant at 95%, and * 10%.
32
Table 3. Evolution of Inequality Mean Farm Size and Population
Land Holdings GINI
Inequality
Land GINI Including Landless
Average Farm Size (ha)
Rural Landless
Households (%)
Country Years Start End Start End Start End Start End Thailand (T) 1978-93 43.5 46.7 52.9 53.1 3.6 2.9 38.1 44.1 Brazil (T) 1970-96 74.8 76.6 75.0 74.0 4.2 10.3 33.2 37.9 Chile (A) 1975-97 60.7 58.2 67.7 62.4 10.7 7.0 23.0 41.9 Ecuador (T) 1974-00 69.3 71.2 74.0 4.2 3.5 23.6 Panama (T) 1990-01 77.1 74.5 79.0 75.9 3.1 2.9 3.5 4.1 Algeria (A) 1973-01 64.9 60.2 5.8 8.3 Egypt (T) 1990-00 46.5 37.8 0.9 0.8 Bangladesh (T) 1977-96 43.1 48.3 54.2 58.9 1.3 0.6 86.8 31.7 India (T) 1970-75 52.6 50.4 57.9 53.6 2.2 1.9 9.9 6.6 Pakistan (T) 1980-90 50.3 53.5 52.3 53.3 4.4 3.6 53.2 51.8 Botswana (P) 1982-93 39.3 40.5 44.4 42.5 3.3 4.8 58.0 60.6 Ethiopia (T) 1977-90 61.7 41.5 1.1 0.9 Guinea (T) 1989-95 44.6 40.9 57.0 53.3 1.9 2.0 35.3 44.8 Malawi (P)† 1981-93 34.4 33.2 49.9 44.6 1.2 0.7 32.7 13.8 Tanzania (P) 1971-96 40.5 37.6 1.3 1.0 Togo (P) 1983-96 47.8 42.1 1.5 2.0 Notes: Author’s calculations using agricultural and population censuses and sample surveys. A, P, T denotes that the inequality measure was calculated using agricultural, planted, and total farm land, respectively. † The second distribution measure was approximated using the IHS2 2004/2005 household survey. Table 4. Changes in Farm Land Inequality (GINI)
%Δ GINI of Farm Land
Without Bangladesh
Coefficient Standard Error Coefficient Standard
Error %Δ Average Farm Size 0.445*** 0.105 0.415* 0.216 %Δ Farm Land -0.385*** 0.071 -0.358* 0.185 %Δ Number of Farms 0.336*** 0.051 0.307 0.218 Constant -1.795 1.081 -1.721 1.162 Mean Dependent Variable -1.73 -2.38 Standard Error of Regression 5.41 5.54 Observations 22 21 R2 31.3 12
Notes: *** Significant at the 99%, ** 95%, and * 90% level..
33
Table 5. Relative Prevalence of the Small Farm
Share of Small Farms
(%)
Country / Region Year Number Land Total Farms / Households
Threshold(ha)
East Asia & Pacific China 1997 97.9 n/a 193,446,000 2
Lao PDR 1999 73.5 42.8 668,000 2 Myanmar 1993 56.7 20.7 2,924,898 2.02
Philippines 1991 65.1 23.4 4,610,041 2 Thailand 1993 32.9 7.6 5,647,490 1.6 Vietnam 2001 94.8 n/a 10,689,753 2
Average 70.2 23.6 Weighted Average† 94.7 15.9
Europe & Central Asia
Albania 1998 90.0 17.3 466,809 2
Latin America & Caribbean Argentina 1988 15.1 0.1 378,357 5
Brazil 1996 20.2 1.6 4,859,865 2 Chile 1997 13.5 0.6 329,563 1
Colombia 2001 41.1 2.0 2,021,895 3 Ecuador 2000 43.4 2.0 842,882 2
Nicaragua 2001 19.8 0.6 199,549 1.8 Panama 2001 63.0 1.6 236,613 2
Uruguay 2000 11.0 0.1 57,131 4 Venezuela, RB 1997 22.6 0.3 500,979 2
Average 27.7 1.0 Weighted Average† 27.5 0.7
Middle East & North Africa
Algeria 2001 28.9 2.7 1,023,799 2 Egypt, Arab Rep. 2000 95.8 57.5 4,541,884 2.1
Jordan 1997 67.0 10.0 92,258 2 Lebanon 1998 86.8 34.9 194,829 2 Morocco 1996 55.3 12.3 1,496,349 3
Average 66.8 23.5 Weighted Average† 77.6 16.1
South Asia
Bangladesh 1996 95.5 68.8 17,828,187 2.02 India 1995 80.3 36.0 115,579,000 2
Nepal 2002 92.4 68.7 3,364,139 2 Pakistan 2000 57.6 15.5 6,620,224 2
Average 81.5 47.3 Weighted Average† 81.4 35.8
34
Table 5 (cont.)
Share of Small Farms
(%)
Country / Region Year Number Land Total Farms / Households
Threshold(ha)
Sub-Saharan Africa Botswana 1993 25.0 5.5 56,214 2
Ethiopia 2002 87.1 70.9 10,758,597 2 Guinea 1995 65.2 32.2 442,168 2 Malawi 1993 95.0 70.3 1,561,420 2
Namibia 1997 38.9 15.8 102,357 2 Senegal 1999 37.5 8.1 437,037 2
Tanzania 1996 88.3 57.6 3,994,882 2 Togo 1996 59.0 29.3 429,534 2
Average 62.0 36.2 Weighted Average† 85.2 55.8
Global Average 58.1 23.1 Global Weighted Average† 87.6 14.9 Source: National Agricultural Censuses † The Share of small farms is weighted by the number of farms of the country, while the land under small farms is weighted by the total farm land of the country. Table 6. Main Crops and their Land Shares by Country
Country
Main Crops
Share of
Land Year
Type of
Land Algeria Cereals 47.3 2001 A Botswana Maize Sorghum Millet Beans/Pulses 98.0 1993 P Egypt Wheat Rice Maize 83.1 2000 A Ethiopia Teff Barley Wheat Maize Sorghum 77.3 2002 P Guinea Rice Maize Millet Groundnut Cassava 99.3 1995 T Malawi Maize 78.2 1993 P Tanzania Maize Paddy Sorghum Millet Beans 68.0 1996 P Togo Maize Sorghum Millet Yam Groundnut 83.6 1996 P Brazil Rice Sugarcane Soy Beans Manioc Wheat Maize 50.4 1996 A Chile Wheat Maize Potatoes Oats 27.9 1997 A Ecuador Rice Maize Potatoes 24.9 2000 A Mexico Beans Maize Sorghum Wheat 45.3 1991 A Panama Rice Maize Sorghum Beans Sugarcane Yucca 30.8 2001 A Bangladesh Aman Rice Aus Rice Boro Rice Wheat Pulses 133.3 1996 A India Rice Sorghum Millet Wheat Pulses 69.4 1995 T Pakistan Wheat Rice Maize Sorghum/Millet Pulses 88.8 2000 A Thailand Rice 55.4 1993 T
Notes: A, P, T indicates that the share of land is calculated over agricultural, planted, and total land respectively.
35
36
Table 7. Determinants of the Share of Land under Main Crops
Coefficient Standard Error
Agricultural Openness -29.5*** 9.82 Population Density 0.119*** .02 Constant 68.4*** 6.92 R2 59 Observations 36 Note: White heteroscedasticity consistent standard errors reported. *** Coefficient significant at 99%. Table 8. Population Age Composition and Development A.- National Shares Share old Population (>64) Share Children (<15)
Coefficient Standard
Error Coefficient Standard
Error log of Real GDP per Capita 2.81 0.18 -7.33 0.38 Constant -17.16 1.50 96.53 3.25 Mean Dep. Variable 6.70 34.14 Std. error of regression 2.50 5.45 Observations 190 193 R2 57.75 66.17 B.- Rural Shares Share old Population (>64) Share Children (<15)
Coefficient Standard
Error Coefficient Standard
Error log of Real GDP per Capita 2.91 0.22 -6.96 0.47 Constant -17.52 1.85 95.77 4.00 Mean Dep. Variable 7.17 36.47 Std. error of regression 3.08 6.70 Observations 193 196 R2 48.73 53.53 C.- Urban Shares Share old Population (>64) Share Children (<15)
Coefficient Standard
Error Coefficient Standard
Error log of Real GDP per Capita 3.03 0.17 -6.15 0.36 Constant -19.68 1.49 83.91 3.08 Mean Dep. Variable 6.12 31.47 Std. error of regression 2.40 5.15 Observations 191 194 R2 63.35 60.58
Table 9. Dependency Ratios and Dependency Ratio Changes by Global Regions and Condition of Rurality National
Dependency Ratio Urban
Dependency Ratio Rural
Dependency Ratio Urban –
Rural† Region Average Std.
Deviation Average Std.
Deviation Average Std.
Deviation Obs.
East Asia & Pacific 0.67 0.13 0.56 0.12 0.73 0.14 25 -- Europe & Central Asia 0.58 0.12 0.51 0.08 0.67 0.14 21 -- Latin America & Caribbean 0.70 0.11 0.63 0.07 0.82 0.16 36 -- Middle East & North Africa 0.87 0.14 0.77 0.13 0.99 0.15 15 -- South Asia 0.84 0.12 0.67 0.09 0.91 0.15 14 -- Sub-Saharan Africa 0.93 0.14 0.74 0.16 1.02 0.15 39 -- High income: non OECD 0.58 0.09 0.54 0.09 0.65 0.10 13 -- High income: OECD 0.52 0.06 0.49 0.06 0.57 0.06 35 -- Total 0.71 0.18 0.61 0.14 0.79 0.21 198 --
Mean Yearly % Change
National Dependency Ratio
Mean Yearly % Change Urban Dependency Ratio
Mean Yearly % Change Rural Dependency Ratio
Urban – Rural†
Region Average Std. Deviation
Average Std. Deviation
Average Std. Deviation
Obs.
East Asia & Pacific -2.01 0.73 -2.08 1.49 -1.78 0.92 9 - Europe & Central Asia -0.58 . -1.22 . 0.45 . 1 - Latin America & Caribbean -0.88 0.58 -0.71 0.62 -1.03 0.85 17 + Middle East & North Africa -1.59 0.92 -1.38 1.11 -1.48 0.63 5 + South Asia -0.43 0.47 -0.91 0.94 -0.26 0.65 7 - Sub-Saharan Africa -0.48 0.71 -0.69 0.46 -0.24 0.74 8 - High income: non OECD -1.04 0.98 -0.92 0.88 -1.03 1.22 5 + High income: OECD -0.83 1.08 -0.74 1.03 -0.89 1.16 18 + Total -0.98 0.91 -0.98 1.02 -0.93 1.02 70 -
Source: Author’s calculations from a Database of Census Sex/Age/Rurality Tables for the period 1980-2003. The Femininity Ratio refers to the number of females for every 100 males. † +/– Identifies the sign of the difference between urban and rural mean. ++/-- Indicates that the difference is statistically significant at 90% using a t-test.
37
38
Table 10. Femininity Ratios National
Femininity Ratio Urban
Femininity Ratio Rural
Femininity Ratio Urban –
Rural† Region Average Std.
Deviation Average Std.
Deviation Average Std.
Deviation Obs.
East Asia & Pacific 99.2 4.7 99.0 7.8 99.0 5.2 28 Europe & Central Asia 106.2 5.6 107.5 6.2 104.5 6.3 27 ++ Latin America & Caribbean 102.3 2.6 107.2 2.9 92.6 7.5 43 ++ Middle East & North Africa 95.7 3.2 94.7 4.3 96.4 3.7 17 - South Asia 94.8 2.8 87.6 5.0 96.8 3.0 14 -- Sub-Saharan Africa 104.2 4.3 99.4 7.0 106.0 5.3 45 -- High income: non OECD 99.1 6.8 100.8 7.7 95.0 6.3 14 ++ High income: OECD 103.1 2.6 105.6 3.4 97.3 5.7 41 ++ Total 101.8 5.2 101.9 7.8 99.0 7.5 229 ++
National
Working Age (15-49) Femininity Ratio
Urban Working Age (15-49)
Femininity Ratio
Rural Working Age(15-49)
Femininity Ratio
Urban – Rural†
Region Average Std. Deviation
Average Std. Deviation
Average Std. Deviation
Obs.
East Asia & Pacific 100.9 5.3 99.9 9.7 100.7 6.6 28 - Europe & Central Asia 100.3 2.9 103.2 5.5 95.6 6.7 27 ++ Latin America & Caribbean 103.6 3.1 109.2 4.2 91.4 9.5 42 ++ Middle East & North Africa 96.4 6.5 93.4 7.4 99.3 8.7 16 -- South Asia 97.8 4.8 84.3 7.9 103.3 7.3 14 -- Sub-Saharan Africa 110.3 6.7 98.1 10.4 116.3 10.3 42 -- High income: non OECD 97.8 9.1 100.1 10.0 92.0 8.9 14 ++ High income: OECD 98.2 2.3 100.7 2.8 92.2 4.6 40 ++ Total 101.9 6.7 100.4 9.5 99.2 12.1 223 +
Source: Author’s calculations from a Database of Census Sex/Age/Rurality Tables for the period 1980-2003. The Femininity Ratio refers to the number of females for every 100 males. † +/– Identifies the sign of the difference between urban and rural mean. ++/-- Indicates that the difference is statistically significant at 90% using a t-test.
Table 11. Rural Gender Inequality Female Headed Households (%) by per Capita Expenditure Quintiles
Country Year Q1 Q2 Q3 Q4 Q5 Total Gender
InequalityIndex
Malawi 1997 48.7 33.2 22.3 16.9 10.7 26.4 10 Malawi 2004 40.8 28.0 21.5 17.1 12.6 24.0 10 Indonesia 1993 33.3 16.1 11.6 10.8 8.2 16.0 8 Nepal 1996 26.9 14.0 9.7 7.8 5.7 12.8 8 Bangladesh 2000 19.7 6.3 4.9 6.6 5.6 8.7 7 Ecuador 1995 24.5 13.6 10.0 12.9 9.7 14.1 7 Vietnam 1998 38.8 21.9 16.6 16.2 14.3 21.6 7 Albania 2005 15.1 6.5 7.3 4.0 4.3 7.4 6 Bulgaria 2001 50.0 20.1 19.0 10.9 9.8 22.0 6 Ecuador 1998 20.4 15.6 14.5 11.7 12.4 14.9 6 Ghana 1992 35.2 33.0 29.6 22.1 24.2 28.8 5 Pakistan 2001 13.0 9.2 8.2 7.4 5.8 8.7 5 Vietnam 1992 36.2 21.3 20.9 19.4 15.5 22.7 5 Bulgaria 1995 54.3 30.1 29.3 30.1 28.2 34.4 4 Madagascar 1993 20.6 15.9 18.8 16.5 15.4 17.4 4 Nigeria 2004 16.1 14.2 14.6 13.7 13.2 14.4 4 Pakistan 1991 7.0 3.4 2.8 2.8 3.3 3.9 4 Albania 2001 16.3 14.7 9.1 7.8 6.1 10.8 3 Ghana 1998 36.3 36.4 34.7 27.0 22.1 31.3 3 Panama 2003 21.0 21.1 20.3 16.7 17.3 19.3 2 Chile† 1990 13.5 13.1 14.1 14.7 13.6 13.8 0 Chile† 2003 17.9 17.3 18.9 19.8 17.7 18.3 0 Guatemala 2000 10.1 11.7 14.7 15.6 18.6 14.1 0 Indonesia 2000 15.8 14.7 16.6 18.9 18.4 16.9 0 Madagascar 2001 18.4 18.1 17.6 23.6 19.0 19.3 0 Nicaragua 2001 19.3 20.4 18.3 16.0 16.9 18.2 0 Panama 1997 12.6 13.7 18.1 19.7 20.1 16.8 0 Notes: Author’s calculations using the RIGA database of Household Surveys. † Income per capita quintiles. See text for explanation on the calculation of the gender inequality index. Table 12. Femininity Ratio of the International Migrant Stock by Development Region
Region Average Std. Deviation Obs.
East Asia & Pacific 88.5 18.6 23 Europe & Central Asia 126.5 19.9 25 Latin America & Caribbean 102.9 22.7 31 Middle East & North Africa 78.4 28.3 14 South Asia 96.2 58.6 8 Sub-Saharan Africa 92.7 18.8 46 High income: non OECD 93.5 31.9 31 High income: OECD 107.7 9.4 24 Total 99.0 27.1 202
Author’s calculations using UNSTATS’ “Statistics and indicators on women and men”.
39
Table 13. Gender of International Migrants Leaving Latin American Countries
Urban Rural Total
Femininity 157.4 156.2 156.3Panama 2000 Migrants 17,867 2,458 20,325Femininity 98.9 65.0 88.5Ecuador 2001 Migrants 276,480 101,428 377,908Femininity 105.1 111.1 109.1Dominican
Republic 2002 Migrants 326,511 152,133 478,644Femininity 32.8 55.7 34.0Mexico 2000 Migrants 1,532,198 100,854 1,633,052
Notes: Author’s calculations using the REDATAM online consultation system for Panama, Ecuador, and the Dominican Republic, and official tabulations by INEGI, for Mexico. The number of migrants are not comparable, because in the Dominican Republic the question was open ended, in Ecuador it was asked for migrants in the last ten years, while in Panama and Mexico it asked for international migrants in the last 5 years. Table 14. Rural Household Head Age and Wealth Mean Age of Household Head by per Capita Expenditure Quintiles
Country Year Q1 Q2 Q3 Q4 Q5 Total % Rural Pop.
>60 and Year of Calculation
Albania 2001 50.3 50.6 50.2 49.6 52.3 50.6 10.6 2001 Albania 2005 54.3 52.8 52.0 50.0 51.1 52.0 10.6 2001 Bangladesh 2000 42.3 41.2 43.8 45.3 50.6 44.6 4.9 1988 Bulgaria 1995 70.3 64.4 57.8 55.8 52.0 60.1 31.2 1995 Bulgaria 2001 63.2 55.0 58.3 53.3 53.5 56.7 32.5 2003 Chile† 1990 42.8 45.3 49.1 53.6 51.5 48.4 10.8 1992 Chile† 2003 46.8 50.2 53.3 55.6 54.5 52.1 13.4 2002 Ecuador 1995 48.5 45.9 43.9 46.0 44.3 45.9 7.1 1990 Ecuador 1998 48.4 48.5 49.5 48.1 47.8 48.4 9.8 2001 Ghana 1992 47.0 44.6 43.8 44.0 46.3 45.1 6.4 1984 Ghana 1998 49.1 46.5 45.7 45.3 46.0 46.5 7.8 2000 Guatemala 2000 43.1 43.2 44.1 44.3 44.7 43.9 4.5 1989 Indonesia 1993 52.4 46.5 43.4 44.5 44.9 46.3 6.8 1990 Indonesia 2000 47.4 45.9 46.9 45.9 44.0 46.0 7.9 2000 Madagascar 1993 43.9 43.3 42.2 43.0 42.1 42.9 6.0 1975 Malawi 1997 43.0 40.2 41.0 41.6 43.4 41.8 6.1 1999 Malawi 2004 46.9 42.9 41.8 41.9 42.0 43.1 6.1 1998 Nepal 1996 43.4 42.1 44.9 44.8 48.2 44.7 5.9 1991 Nicaragua 2001 46.8 45.2 45.9 45.0 47.7 46.1 4.4 2003 Nigeria 2004 46.0 46.0 47.5 48.4 51.2 47.8 5.8 1991 Pakistan 1991 45.3 42.1 44.7 48.2 50.0 46.0 6.6 1995 Pakistan 2001 43.1 42.8 45.4 46.6 49.9 45.5 5.9 1998 Panama 1997 47.5 48.6 48.1 50.7 50.9 49.2 9.1 2000 Panama 2003 51.0 49.3 47.9 48.1 48.6 49.0 7.4 1990 Vietnam 1992 44.1 41.3 44.6 46.3 48.1 44.9 7.4 1989 Vietnam 1998 50.2 44.7 45.9 46.7 48.3 47.2 8.3 1999 Notes: Author’s calculations using the RIGA database of Household Surveys. † Income per capita quintiles.
40
41
Table 15. Correlation Matrices of Marginality Quartiles
Panama Housing Quality Quartiles
Average Education Quartiles
Housing Quality Quartiles 1 Average Education Quartiles 0.927 1 Distance to Main City Quartiles -0.249 -0.334 Mexico Housing Quality Quartiles 1 Average Education Quartiles 0.747 1 Distance to Main City Quartiles -0.214 -0.266 Ecuador Housing Quality Quartiles 1 Average Education Quartiles 0.527 1 Distance to Main City Quartiles -0.454 -0.095 Brazil Housing Quality Quartiles 1 Average Education Quartiles 0.833 1 Distance to Main City Quartiles -0.328 -0.336 Kenya Housing Quality Quartiles 1 Average Education Quartiles 0.828 1 Distance to Main City Quartiles -0.494 -0.686 Uganda Housing Quality Quartiles 1 Average Education Quartiles 0.708 1 Distance to Main City Quartiles -0.382 -0.327
Table 16. Population Movement, Initial Population Characteristic, and Changes by Housing Quality Quartiles
Ecuador 1990-2001
Mexico 1990-2000
Panama 1990-2000
Housing Quality Quartiles
Housing Quality Quartiles
Housing Quality Quartiles
1 2 3 4 1 2 3 4 1 2 3 4
Population Movement Mean* -22.13 -15.91 -7.63 4.86 -12.85 -8.33 -2.78 1.77 -42.06 -21.35 -15.49 3.86 Std. Error of the Mean 1.79 1.63 1.20 1.09 0.61 0.85 0.89 0.76 0.05 0.03 0.02 0.01 Coefficient of variation -1.22 -1.54 -2.38 3.37 -0.98 -2.10 -6.58 8.77 -0.43 -0.48 -0.51 1.20 Administrative Units 229 228 229 228 422 421 422 421 17 16 17 16 Parishes Municipalities Districts %Rural 99.56 95.77 92.40 72.00 81.88 61.22 48.53 27.02 98.23 91.53 85.87 54.85 Initial Population Characteristics Mean by Housing Quality Quartiles Education (Years) 3.60 4.13 4.36 5.32 3.32 4.47 5.32 6.39 3.84 5.46 6.37 8.17 Femininity (Ratio) 90.11 94.37 100.90 102.14 100.42 101.04 102.91 104.55 90.20 83.23 86.82 97.40 Age 21.76 23.55 24.79 25.25 22.29 23.48 23.93 24.53 22.44 25.84 26.68 28.22 Ethnicity (%) 16.52 6.06 6.07 3.94 0.45 0.12 0.04 0.01 40.68 2.33 4.95 1.34 Change in Population Characteristics % Change by Housing Quality Quartiles Education 15.13 14.69 13.62 6.98 55.00 30.56 22.03 18.79 42.43 18.22 14.44 12.27 Femininity 1.77 3.12 2.52 1.47 2.72 2.99 2.76 1.39 -2.94 3.58 2.32 0.41 Age 13.65 12.94 13.10 12.10 8.53 10.41 10.11 10.33 12.48 9.57 8.08 7.98 Floor Quality Index 8.40 7.24 5.37 4.39 56.78 18.34 10.66 6.10 77.11 35.26 11.54 5.47 Ethnicity** 33.42 49.16 41.07 54.57 -1.81 -11.54 1.28 26.04 -33.43 30.90 30.53 62.37
* Weighted by initial population of the administrative unit. ** Weighted by indigenous population in the administrative unit.
42
Table 16. (cont.)
Brazil 1991-2000
Kenya 1989-1999
Uganda 1991-2002
Housing Quality Quartiles
Housing Quality Quartiles
Housing Quality Quartiles
1 2 3 4 1 2 3 4 1 2 3 4
Population Movement Mean* -13.47 -11.76 -1.80 -0.15 -2.65 -8.33 -10.27 3.19 41.31 5.87 -2.78 2.08 Std. Error of the Mean 0.01 0.00 0.01 0.00 8.93 2.10 3.44 3.82 10.85 4.00 8.91 4.41 Coefficient of variation -1.77 -1.52 -15.46 -75.87 -11.17 -0.80 -1.06 3.79 1.70 4.32 -20.57 13.44 Administrative Units 732 732 732 731 11 10 10 10 42 40 41 40 Municipalities Districts Counties %Rural 57.53 39.35 26.49 6.37 86.41 93.27 90.70 65.94 97.56 98.24 94.86 60.86 Initial Population Characteristics Mean by Housing Quality Quartiles Education (Years) 1.48 2.25 3.28 4.64 1.71 3.37 3.84 4.95 2.18 2.66 3.05 4.12 Femininity (Ratio) 97.77 98.51 99.95 106.25 99.71 106.18 101.82 95.63 110.08 103.83 103.20 105.00 Age 23.56 23.91 25.47 27.03 20.88 20.86 21.23 22.66 21.17 21.18 21.48 20.94 Ethnicity (%) 5.90 5.26 5.55 5.91 - - - - - - - - Change in Population Characteristics % Change by Housing Quality Quartiles Education 75.41 50.81 33.56 22.50 15.52 24.22 23.09 18.68 326.88 75.65 60.23 45.10 Femininity -0.81 0.01 0.82 0.73 0.93 1.46 2.31 3.50 -7.02 -2.76 -3.75 -3.67 Age 10.01 10.18 10.18 8.91 3.81 -0.95 -2.28 1.26 4.93 4.26 5.69 3.77 Floor Quality Index *** 53.53 10.24 -1.31 -10.00 18.01 36.62 33.52 25.83 302.88 101.37 49.90 29.04 Ethnicity** 35.49 33.55 27.73 22.96 - - - - - - - -
* Weighted by initial population of the administrative unit. ** Black and Indigenous in Brazil. Weighted by indigenous population in the administrative unit. *** Sewage in Brazil.
43
Table 17. Characteristics of the Adult Cohort, and Changes by Population Movement Quartiles
Ecuador 1990-2001
Mexico 1990-2000
Panama 1990-2000
Population Movement Quartiles Population Movement Quartiles Population Movement Quartiles
1 2 3 4 1 2 3 4 1 2 3 4 Initial Characteristics of the Adult Cohort (15-49) Mean by Population Movement Quartiles Education (Years) 5.30 5.60 5.61 5.91 5.01 5.17 5.79 6.82 5.07 5.40 6.83 8.55 Femininity (Ratio) 86.69 97.65 107.12 109.55 101.21 106.54 109.80 110.66 82.51 80.25 87.07 99.65 Age 28.34 28.45 28.62 28.59 28.08 28.14 28.09 28.02 28.71 29.20 30.05 30.86 Ethnicity (%) 7.46 4.78 8.33 11.25 15.16 21.29 17.93 9.68 23.33 17.42 8.01 0.85 % Change in the Characteristics of the Adult Cohort (15-49) % Change by Population Movement Quartiles % Population Change* -42.76 -19.96 -8.18 8.98 -22.64 -13.13 -4.33 13.47 -49.14 -24.45 -12.31 4.08 Education -4.27 -1.86 -2.40 1.33 4.27 7.01 8.76 11.34 24.60 1.81 4.11 7.23 Femininity 11.78 4.71 1.94 -2.48 7.35 4.01 2.45 1.02 -0.03 2.03 4.68 -0.38 Ethnicity** 32.16 33.25 38.11 49.61 5.61 4.95 2.30 17.33 -62.11 9.68 5.66 37.02 % Rural 98.42 92.25 88.04 81.03 70.73 61.80 51.19 34.95 98.60 92.39 83.59 56.03
* Weighted by initial population of the administrative unit. ** Weighted by indigenous population in the administrative unit.
44
45
Table 17 (cont.)
Brazil 1991-2000
Kenya 1989-1999
Uganda 1991-2002
Population Movement Quartiles Population Movement Quartiles Population Movement Quartiles
1 2 3 4 1 2 3 4 1 2 3 4 Initial Characteristics of the Adult Cohort (15-49) Mean by Population Movement Quartiles Education (Years) 3.09 3.69 6.07 5.89 4.94 4.74 5.64 4.15 4.32 4.31 4.25 3.29 Femininity (Ratio) 98.95 101.23 106.95 102.20 112.04 110.95 102.40 96.64 108.49 110.33 109.99 121.29 Age 28.38 28.60 29.42 29.37 27.28 27.17 27.31 27.23 27.20 27.19 27.23 27.78 Ethnicity (%) 5.47 5.49 6.71 5.38 - - - - - - - - % Change in the Characteristics of the Adult Cohort (15-49) % Change by Population Movement Quartiles % Population Change* -11.81 6.56 12.00 28.73 -18.73 -9.16 -1.61 15.47 -19.47 -1.70 13.90 93.20 Education 63.28 47.21 26.39 25.13 4.30 4.56 10.51 -6.03 -17.43 -15.76 -14.28 135.48 Femininity -1.24 -1.74 -1.01 0.61 -8.64 -9.32 -0.10 5.61 -7.42 -8.91 -7.31 -12.10 Ethnicity** 33.50 33.58 27.63 27.30 - - - - - - - - % Rural 48.78 40.78 15.54 9.90 89.12 93.00 80.14 73.78 90.38 86.73 82.35 92.82
* Weighted by initial population of the administrative unit. ** Black and Indigenous in Brazil. Weighted by indigenous population in the administrative unit.
Table 18. Intercensal Net Migration Rate Kenya 1989 - 1999 Uganda 1991 - 2002 Age -0.973 1.664 9.029*** 2.280 Education -33.809*** 9.656 -56.562*** 13.670 Education2 3.418*** 0.989 6.694*** 1.7513 Standard Deviation of Education 24.381** 11.060 -3.848 15.703 Housing Quality -1.774 3.226 -19.706*** 6.732 Share of Employment in Primary Sector -21.453 17.738 -22.388 34.415 Rurality (%) 16.428 23.101 -93.826* 48.081 Distance to City -0.021 0.040 -62.072* 33.505 R2 0.261 0.542 Obs. 41 163
46
Figures
Figure 1. Farm Land
Forests Grasslands
Natural
PlantedUnusable Land: Hills, Rocks, Desert, Buildings
Agricultural Land Planted Land Unused
Fallow Harvested Land
Figure 2. Evolution of Total Farm Land
PAK 90-00PAK 80-90
IND 90-95
IND 85-90IND 75-85
IND 70-75BGD 77-96
EGY 90-00PAN 90-01
PAN 80-90MEX 70-91
CHL 75-97BRA 85-96
BRA 80-85BRA 75-80
BRA 70-75THA 78-93
-10 0 10 20 30Intercensal % Change of Total Farm Land
47
Figure 3. Evolution of Agricultural Land
TGO 83-96TZA 71-96
MWI 81-93ETH 90-02
ETH 77-90BWA 82-93
PAK 90-00PAK 80-90
IND 90-95IND 85-90
IND 75-85IND 70-75
BGD 77-96EGY 90-00
DZA 73-01PAN 90-01
PAN 80-90MEX 70-91
ECU 74-00CHL 75-97
BRA 85-96BRA 80-85
BRA 75-80BRA 70-75
THA 78-93
-50 0 50 100 150Intercensal % Change of Agricultural Land
48
Figure 4. Land Distributions in Sub-Saharan Africa and Latin America a) Botswana
Planted Land Distribution 1982
05
10152025
0 5 10 15 20
Land Ha
% fa
rms
% la
nd
Farms
Land
Planted Land Distribution 1993
05
101520
0 5 10 15 20
Land Ha
% fa
rms
% la
nd
Farms land
b) Ethiopia
Crop Land Distribution 1989
0
10
20
30
40
0 5 10 15 20 25 30
Land Ha
% fa
rms
% la
nd
farmsland
Total Land Distribution 2001/02
0
10
20
30
40
0 5 10 15 20
Land Ha%
farm
s %
land
farms
land
c) Brazil
Total Land Distribution 1970
0
5
10
15
20
1 10 100 1000 10000 100000 1000000
Land Ha
% fa
rms
% la
nd
farmsland
Total Land Distribution 1995
0.00
5.00
10.00
15.00
20.00
1 10 100 1000 10000 100000 1000000
Land Ha
% fa
rms
% la
nd
farmsland
d) Chile
Agricultural Land Distribution 1975
0
5
10
15
20
25
30
35
1 10 100 1,000 10,000
Land Ha
% fa
rms
% la
nd
Farms
Land
Agricultural Land Distribution 1997
0
5
10
15
20
25
30
1 10 100 1,000 10,000
Land Ha
% fa
rms
% la
nd
farms
land
49
Figure 5. Land Distributions in Bangladesh 1977 Total Land Distribution
0
20
40
60
0 1 2 3 4 5
Land Ha
% fa
rms
% la
nd
farms
land
1996 Total Land Distribution
01020304050
0 1 2 3 4 5
Land Ha
% fa
rms
% la
nd
farms
land
Figure 6. Change in Land Under Small Holders
BGD 77-96PAK 80-90
PAK 90-00THA 78-93
IND 75-85TZA 71-96
EGY 90-00MWI 81-93IND 70-75
IND 85-90PAN 90-01IND 90-95ETH 77-90
PAN 80-90ETH 90-02
GIN 89-95ECU 74-00
DZA 73-01TGO 83-96
BRA 80-96CHL 75-97
BRA 70-80BWA 82-93
-50 0 50 100 150Change in the Share of Land Held by Small Farms
50
Figure 7. Small Farm Fragmentation
DZA 73-01
BWA 82-93BRA 85-96CHL 75-97
ETH 90-02
GIN 89-95
TGO 83-96
BGD 77-96
ECU 74-00
EGY 90-00
IND 90-95MWI 81-93
PAK 90-00
PAN 90-01
TZA 71-96THA 78-93
Fragmentation
Expansion
-50
050
100
% C
hang
e of
Lan
d H
eld
by S
mal
l Far
ms
-40 -20 0 20 40 60% Change of Mean Size of Small Farms
Figure 8. Specialization in Main Crops Small Farmers vs. Larger Farms
020
4060
80S
hare
of L
and
Sm
all F
arm
s (li
ne)
.51
1.5
22.
5M
ain
Cro
p S
hare
: Sm
all/L
arge
Far
ms
MEX70MEX91
BRA70BRA80
ECU74ECU00
CHL75CHL97
PAK80PAK90
PAK00BGD77
BGD96THA78
THA93TZA71
TZA96EGY00
IND70IND75
IND85IND90
IND95ETH77
ETH90ETH02
DZA73MWI81
51
Figure 9. Correlation between Specialization in Main Crops and Openness
050
100
150
Sha
re o
f Lan
d un
der M
ain
Cro
ps
0 .5 1Agricultural Trade Openness
Figure 10. Bean Yields in Chile
Beans Yields by Farm Area
0
5
10
15
20
1 10 100 1000 10000
Land
Mea
n Yi
eld
100k
g/H
a
19971976
Authors’ calculations base on National Agricultural Censuses
52
Figure 11. Sorghum Yields in Botswana
Sorghum Yields in Botswana by Planted Area
0100200300400500600700
0 5 10 15
ha
Kg/
ha 19821992
Authors’ calculations base on National Agricultural Censuses Figure 12. Maize Yields in Brazil
Maize Yields by Harvested Area
00.5
11.5
22.5
33.5
4
1 10 100 1000Farm Size (Ha)
Yiel
d To
n/H
arve
sted
Ha
19801996
Authors’ calculations based on National Agricultural Censuses
53
Figure 13 Rice Yields in Vietnam (Kg/Ha)
Rice Yields in Vietnam
2000
2500
3000
3500
4000
4500
5000
0 2 4 6 8
Farm Size (Ha)
Yiel
d K
g/H
a
19921998
Note: Author’s calculations using LSMS household surveys. Figure 14. Economically Dependent Population and Income
010
2030
4050
Popu
latio
n Sh
are
(%)
500 1000 5000 10000 30000 60000Real (PPP) GDP per Capita (US$ 2000)
95% CI Rural Share >6495% CI Urban Share >64Rural Share <15 Urban Share <15
54
55
Figure 15. Rural Dependency Ratio and Income
ALB
DZA ARGARGARG
BGD
ARM
BOLBOL
BWA
BWABRA
BRA
BRA
KHM
CPV
CAF
LKACHL
CHL
CHL
CHN
CHN
CHNCHN
COLCOL
COL
COM
HRV
BENBEN
DOMDOM
ECU
ECU
ECU
SLV
EST
FJIFJI
GHA GHA GTM
HND
HUNHUN
INDIND IND
IDNIDN
IDN
IDN
IRNIRN
IRN
CIV
JAM
JAMJOR
KEN
KGZ
LSO
LVA
MWI
MWI
MYS
MYS
MLI
MUS
MUS
MEX
MEXMEX
MNG
MNG
MDA
MAR
MAR
MOZ
NAMNAM
NPLNPL
NPL
VUT
NER
NGA PAKPAK
PAK PAN
PAN PANPNG
PNG
PRYPRY
PRY
PER
PHL
PHL
ROMRUS
RWA KNA
SEN
VNM
VNMVNM
ZAF
ZWE
ZWE
ZWE
SDN
SWZSWZ
SWZSYR
SYRTJK
THA
THA
TONTUN
TUN
TKM
UGA
UGA
UKRMKD
EGY
TZATZA
BFA
URY URY
VEN
VEN
VEN
YEM
ZMB
ZMBZMB
.4.6
.81
1.2
1.4
Rur
al D
epen
denc
y R
atio
5000 10000 15000Real (PPP) GDP per Capita (US$ 2000)
Figure 16. Changes in the Femininity Ratio by Initial Femininity A. Rural Femininity
DZAARG ARM
BOL BWA
BRA
CHL
CHN
COL
BEN
DOM
ECU
FJIIND
IDN
IRN
JAMMWIMYS
MDVMEX
MAR NAMNPL
PAKPAN
PRY
VNM
ZAF
ZWE
SYR
TUN
TUR
UGAEGY
TZA
URY
VEN
ZMB
-10
12
3M
ean
year
ly ra
te o
f cha
nge
(%) o
f Rur
al F
emin
inity
70 80 90 100 110Femininity Ratio Rural
High HIV incidence countries
B. Adult Rural Femininity
ARG ARM
BOL
BWA
BRA
CHL
CHN
COL
BEN
DOM
ECU
FJIIND
IDN
IRN
JAMMWIMYS
MDVMEX
MAR NAMNPL
PAKPAN
PRY
VNM
ZWE
SYRTUN
TUR
UGAEGYTZA
URY
VEN
ZMB
-20
24
Mea
n ye
arly
rate
of c
hang
e (%
) of A
dult
Rur
al F
emin
inity
60 80 100 120 140Rural Adult (15-49) Femininity Ratio
Figure 17. Rural Ageing Developing Countries
PAK98-01
-20
24
Mea
n ye
arly
rate
of c
hang
e (%
) of t
he R
ural
Old
(>60
) Sha
re
5000 10000 15000Real (PPP) GDP per Capita (US$ 2000)
56
Figure 18. Rural Ageing in Sub Saharan Africa
BWA81-91
BWA91-01
BEN92-02
NAM91-01
ZWE82-92
ZWE92-97
UGA91-02
TZA88-02ZMB90-00
-2-1
01
2M
ean
year
ly ra
te o
f cha
nge
(%) o
f the
Rur
al O
ld (>
60) S
hare
5000 10000 15000Real (PPP) GDP per Capita (US$ 2000)
57
Appendix I. Countries and Censuses or Sample Surveys Included in the Global Population Database
Regions Country Code Years
American Samoa ASM 1990 Cambodia KHM 1998 China CHN 2000 China [inc. Taiwan province] CHN 1982 1987 1990 Fiji FJI 1986 1996 Indonesia IDN 1980 1985 1990 2000 Korea, Democratic People's Republic of PRK 1993 Malaysia MYS 1991 2000 Mongolia MNG 2000 Myanmar MMR 1983 Papua New Guinea PNG 1980 2000 Philippines PHL 1980 1990 Thailand THA 1980 1990 Tonga TON 1986 Vanuatu VUT 1989
East Asia & Pacific
Viet Nam VNM 1989 1999 1999 Armenia ARM 1989 2001 Azerbaijan AZE 1989 Belarus BLR 1989 Croatia HRV 1991 Estonia EST 1989 Georgia GEO 1989 Hungary HUN 1980 1990 Kazakhstan KAZ 1989 Kyrgyzstan KGZ 1989 Latvia LVA 1989 Lithuania LTU 1989 Poland POL 1988 Republic of Moldova MDA 1989 Romania ROM 1992 Russian Federation RUS 1989 Serbia and Montenegro YUG 1991 Tajikistan TJK 1989 The former Yugoslav Republic of Macedonia MKD 1994 Turkey TUR 1980 1985 1990 Turkmenistan TKM 1989 Ukraine UKR 1989 Uzbekistan UZB 1989
Europe & Central Asia
Yugoslavia [former Socialist Federal Republ YUG 1981 Argentina ARG 1980 1991 2001 Bolivia BOL 1992 2001 Brazil BRA 1980 1991 2000 Chile CHL 1982 1992 2002 Colombia COL 1985 1993 2006 Cuba CUB 1981
Latin America & Caribbean
Dominican Republic DOM 1993 2002
58
Ecuador ECU 1982 1990 2001 El Salvador SLV 1992 Guatemala GTM 1981 Honduras HND 1988 Jamaica JAM 1982 1991 Mexico MEX 1980 1990 1995 2000 Panama PAN 1980 1990 2000 Paraguay PRY 1982 1992 2002 Peru PER 1981 Saint Kitts and Nevis KNA 1980 Saint Lucia LCA 1991 Uruguay URY 1985 1996
Venezuela VEN 1981 1990 2001 Algeria DZA 1987 1998 Egypt EGY 1986 1996 Iran (Islamic Republic of) IRN 1986 1991 1996 Iraq IRQ 1987 Jordan JOR 1994 Libyan Arab Jamahiriya LBY 1984 Morocco MAR 1982 1994 Syrian Arab Republic SYR 1981 1994 Tunisia TUN 1984 1994
Middle East & North Africa
Yemen YEM 1994 Bangladesh BGD 1981 India IND 1981 1991 2001 Maldives MDV 1985 1990 1995 Nepal NPL 1981 1991 2001 Pakistan PAK 1981 1998 2001
South Asia
Sri Lanka LKA 1981 Benin BEN 1992 2002 Botswana BWA 1981 1991 2001 Burkina Faso BFA 1985 Cape Verde CPV 1990 Central African Republic CAF 1988 Comoros COM 1980 Cote d'Ivoire CIV 1988 Equatorial Guinea GNQ 1983 Ghana GHA 1984 Kenya KEN 1989 Lesotho LSO 2001 Liberia LBR 1984 Malawi MWI 1982 1987 1998 Mali MLI 1987 Mauritius MUS 1990 Mauritius Island MUS 1983 Mozambique MOZ 1997 Namibia NAM 1991 2001 Niger NER 1988 Nigeria NGA 1991 Rwanda RWA 2002 Senegal SEN 1988
Sub-Saharan Africa Somalia SOM 2002
59
60
South Africa ZAF 1980 1985 1991 1996 Sudan SDN 1983 Swaziland SWZ 1986 Uganda UGA 1991 2002 United Republic of Tanzania TZA 1988 2002 Zambia ZMB 1980 1990 2000
Zimbabwe ZWE 1982 1992 1997 Brunei Darussalam BRN 1981 1991 2001 China, Hong Kong Special Administrative Re HKG 1981 1986 Cyprus CYP 1982 1992 Israel ISR 1983 1995 New Caledonia NCL 1989 Puerto Rico PRI 1980 1990 Slovenia SVN 1991
High income: nonOECD
United States Virgin Islands VIR 1980 Australia AUS 1981 1986 Austria AUT 1981 1991 Canada CAN 1981 1986 1991 Denmark DNK 1981 Finland FIN 1990 France FRA 1982 1990 Greece GRC 1981 1991 Ireland IRL 1981 1986 1996 2002 Japan JPN 1980 1985 1990 1995 2000 Korea, Republic of KOR 1980 1985 1990 1995 New Zealand NZL 1981 1986 1991 1996 Norway NOR 1980 1990 Portugal PRT 1981 1991 Sweden SWE 1980 1990 Switzerland CHE 1980 1990
High income: OECD
United States USA 1980 1990 2000
Equator
Tropic of Cancer
Tropic of Capricorn
Antarctic Circle
Arctic Circle
Pacific Ocean
Atlantic Ocean
Indian Ocean
Pacific Ocean
-180°
-180°
-160°
-160° -140°
-140°
-120°
-120°
-100°
-100° -80°
-80° -60°
-60° -40°
-40° -20°
-20°
0°
0°
20°
20°
40°
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60°
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80°
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0° 0°
-20° -20°
20° 20°
-40° -40°
40° 40°
-60° -60°
60° 60°
-80° -80°
80° 80°
Rural Femininity RatioWorking Age (15-49)( Census Data OnlyLatest Available )
N/A
70 - 80
80 - 90
90 - 95
95 - 100
100 - 105
105 - 110
110 - 120
120 - 140
Rural Femininity Ratio - Working Age (15-49)
Appendix II. Maps
Equator
Tropic of Cancer
Tropic of Capricorn
Antarctic Circle
Arctic Circle
Pacific Ocean
Atlantic Ocean
Indian Ocean
Pacific Ocean
-180°
-180°
-160°
-160° -140°
-140°
-120°
-120°
-100°
-100° -80°
-80° -60°
-60° -40°
-40° -20°
-20°
0°
0°
20°
20°
40°
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60°
60°
80°
80°
100°
100°
120°
120°
140°
140°
160°
160°
180°
180°
0° 0°
-20° -20°
20° 20°
-40° -40°
40° 40°
-60° -60°
60° 60°
-80° -80°
80° 80°
Rural Share of Old Population > 60( Census Data OnlyLatest Available )
(%)
N/A
< 4
4 - 6
6 - 8
8 - 10
10 - 12.5
12.5 - 15
15 - 20
> 20
Rural Ageing - Share of Rural Population > 60
Appendix III. Additional Net-Migration Tables
Table 19. Population Movement, Initial Population Characteristic, and Changes by Average Education Quartiles
Ecuador 1990-2001
Mexico 1990-2000
Panama 1990-2000
Education Quartiles Education Quartiles Education Quartiles
1 2 3 4 1 2 3 4 1 2 3 4 Population Movement Mean * -13.57 -16.18 -13.88 4.39 -12.67 -10.37 -7.15 2.17 -41.60 -25.19 -13.19 3.52 Std. Error of the Mean 1.86 1.44 1.35 1.14 0.51 0.50 0.82 0.82 0.05 0.02 0.02 0.01 Coefficient of variation -2.07 -1.34 -1.47 3.91 -0.83 -0.99 -2.35 7.78 -0.44 -0.32 -0.58 1.44 Administrative Units 229 228 229 228 422 421 422 421 17 16 17 16 Parishes Municipalities Districts %Rural 99.46 97.88 93.70 68.68 79.53 66.95 50.33 21.84 98.23 90.22 87.55 54.38 Initial Population Characteristics Mean by Housing Quality Quartiles Education (Years) 3.02 4.01 4.58 5.81 3.06 4.35 5.25 6.84 3.81 5.30 6.52 8.19 Femininity (Ratio) 99.60 97.39 95.06 95.44 101.38 102.24 102.15 103.14 90.22 82.16 88.11 97.08 Age 23.10 23.73 23.99 24.54 22.28 23.57 24.12 24.26 22.61 25.28 27.31 27.94 Ethnicity (%) 15.81 8.45 5.10 3.25 0.43 0.12 0.06 0.02 42.15 4.21 1.48 1.59 Change in Population Characteristics % Change by Housing Quality Quartiles Education 22.61 14.58 12.84 6.64 57.89 29.09 21.61 17.79 42.53 17.28 15.53 11.95 Femininity 2.12 1.57 2.54 2.65 2.51 3.10 2.94 1.30 -2.35 3.58 1.76 0.39 Age 12.46 13.55 13.24 12.53 7.90 11.21 10.40 9.87 12.70 9.14 8.30 7.95 Floor Quality Index 8.62 12.02 3.94 0.80 52.06 21.56 11.80 6.46 76.39 32.95 14.35 5.54 Ethnicity** 40.89 53.25 43.54 51.11 -1.94 -7.90 -2.04 11.90 -32.33 25.21 55.51 62.17
* Weighted by initial population of the administrative unit. ** Weighted by indigenous population in the administrative unit.
63
Table 19. (cont.)
Brazil 1991-2000
Kenya 1989-1999
Uganda 1991-2002
Education Quartiles Education Quartiles Education Quartiles
1 2 3 4 1 2 3 4 1 2 3 4 Population Movement Mean * -15.74 -11.44 -4.21 1.39 4.45 -9.40 -8.23 -0.34 41.31 5.87 -2.78 2.08 Std. Error of the Mean 0.00 0.01 0.00 0.00 7.77 3.38 2.28 4.94 10.85 4.00 8.91 4.41 Coefficient of variation -1.31 -2.54 -5.19 9.60 5.80 -1.14 -0.88 -46.12 1.70 4.32 -20.57 13.44 Administrative Units 962 962 962 961 11 10 10 10 42 40 41 40 Municipalities Districts Counties %Rural 55.81 41.59 30.24 5.88 84.47 91.64 92.34 68.05 97.47 97.62 95.79 60.61 Initial Population Characteristics Mean by Housing Quality Quartiles Education (Years) 1.30 2.18 3.16 4.91 1.53 3.22 4.02 5.12 1.87 2.66 3.15 4.32 Femininity (Ratio) 98.85 98.84 99.07 105.20 98.48 104.86 103.18 96.94 111.65 104.25 103.89 102.40 Age 23.54 24.07 25.31 26.97 20.87 21.78 20.51 22.48 20.86 21.29 21.52 21.09 Ethnicity (%) 9.94 10.55 8.96 8.52 - - - - - - - - Change in Population Characteristics % Change by Housing Quality Quartiles Education 80.46 48.73 32.80 16.71 15.29 27.44 21.53 17.29 237.29 67.48 53.48 37.78 Femininity -0.90 -0.11 0.81 0.88 1.64 1.50 1.88 3.10 -7.88 -2.79 -2.74 -3.89 Age 9.62 10.33 10.57 8.91 3.64 1.69 -3.18 -0.30 3.91 5.45 5.76 3.54 Floor Quality Index 47.17 13.01 0.84 -8.19 14.30 42.36 35.05 22.63 289.48 125.02 52.40 21.01 Ethnicity** 40.45 31.32 23.24 23.88 - - - - - - - -
* Weighted by initial population of the administrative unit. ** Black and Indigenous in Brazil. Weighted by indigenous population in the administrative unit. *** Sewage in Brazil.
64
Table 20. Population Movement, Initial Population Characteristic, and Changes by Distance Quartiles
Ecuador 1990-2001
Mexico 1990-2000
Panama 1990-2000
Distance Quartiles Distance Quartiles Distance Quartiles
1 2 3 4 1 2 3 4 1 2 3 4 Population Movement Mean * 4.77 -3.98 -13.45 -6.54 0.69 1.19 -6.78 -10.38 1.56 -6.82 -22.71 -19.81 Std. Error of the Mean 1.32 1.07 1.24 2.14 0.86 0.80 0.55 0.73 0.01 0.04 0.05 0.05 Coefficient of variation 4.27 -4.09 -1.38 -4.80 25.59 13.72 -1.67 -1.45 4.94 -2.10 -0.77 -1.16 Administrative Units 240 232 225 215 422 421 422 421 17 16 17 16 Parishes Municipalities Districts %Rural 88.52 90.71 90.16 90.40 36.36 54.28 61.46 66.62 76.59 79.99 89.80 85.23 Initial Population Characteristics Mean by Housing Quality Quartiles Education (Years) 6.77 6.30 4.50 4.80 5.66 4.81 4.59 4.44 7.01 6.21 4.95 5.55 Femininity (Ratio) 103.45 98.10 95.76 89.47 102.85 101.65 102.81 101.60 88.27 92.78 85.85 90.93 Age 25.12 24.53 23.71 21.82 23.52 23.34 23.78 23.59 25.83 28.17 25.04 24.04 Ethnicity (%) ** 7.26 4.42 5.35 16.20 0.11 0.14 0.15 0.23 2.28 6.72 15.86 26.15 Change in Population Characteristics % Change by Housing Quality Quartiles Education 10.14 7.87 13.84 11.66 27.42 31.30 32.50 35.21 12.42 15.69 23.77 36.77 Femininity 0.66 0.86 1.33 2.34 2.33 3.11 2.60 1.82 0.87 1.40 1.17 -0.24 Age 11.68 13.00 13.31 13.91 9.45 10.14 9.83 9.95 7.24 8.21 10.24 12.61 Floor Quality Index 5.38 4.60 6.74 8.91 14.97 20.33 21.13 35.47 11.83 16.70 44.14 58.75 Ethnicity** 43.14 47.45 64.55 37.08 9.18 -4.69 -7.62 -0.74 53.97 6.24 -5.54 -26.65
* Weighted by initial population of the administrative unit. ** Weighted by indigenous population in the administrative unit.
65
66
Table 20. (cont.)
Brazil 1991-2000
Kenya 1989-1999
Uganda 1991-2002
Education Quartiles Education Quartiles Education Quartiles
1 2 3 4 1 2 3 4 1 2 3 4 Population Movement Mean * 0.69 1.19 -6.78 -10.38 0.54 -10.95 -9.20 10.92 1.77 5.45 23.94 3.41 Std. Error of the Mean 0.00 0.01 0.00 0.01 3.64 3.05 3.61 9.49 5.35 8.78 8.86 6.40 Coefficient of variation 25.59 13.72 -1.67 -1.45 22.44 -0.88 -1.24 2.75 19.36 10.31 2.37 11.88 Administrative Units 422 421 422 421 11 10 10 10 41 41 41 40 Municipalities Districts Counties %Rural 9.45 31.03 38.45 41.98 71.03 94.05 92.36 80.41 83.63 89.32 91.37 87.84 Initial Population Characteristics Mean by Housing Quality Quartiles Education (Years) 4.62 2.99 2.70 2.21 4.72 3.76 3.37 1.72 3.68 2.81 2.83 2.64 Femininity (Ratio) 105.31 99.83 98.86 97.83 96.59 105.71 104.67 96.68 101.35 105.49 105.96 109.57 Age 26.49 25.75 25.35 23.85 21.96 20.81 21.58 21.17 21.42 21.19 21.74 20.40 Ethnicity (%) 9.00 9.31 8.31 10.05 - - - - - - - - Change in Population Characteristics % Change by Housing Quality Quartiles Education 20.84 40.11 46.92 54.74 19.43 23.13 23.44 15.12 47.92 72.39 85.01 163.70 Femininity 0.79 0.32 0.12 -0.14 3.06 1.46 1.45 2.03 -3.09 -3.86 -4.43 -5.97 Age 8.93 9.56 10.22 10.70 0.65 -2.10 1.40 2.19 6.06 5.95 7.27 -0.73 Floor Quality Index*** -6.18 7.73 14.01 27.09 23.88 36.34 37.79 15.37 48.11 88.00 61.51 297.02 Ethnicity** 25.76 20.40 25.28 39.68 - - - - - - - -
* Weighted by initial population of the administrative unit. ** Black and Indigenous in Brazil. Weighted by indigenous population in the administrative unit. *** Sewage in Brazil.
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