RELATIONSHIP BETWEEN CHANGES IN AGRICULTURAL PRODUCTIVITY AND THE
INCIDENCE OF POVERTY IN DEVELOPING COUNTRIES
DFID Report No.7946 27/02/2001
Colin Thirtle1, Xavier Irz2, Lin Lin1, Victoria McKenzie-Hill1 and Steve Wiggins2
1
T.H.Huxley School of Environment, Earth Sciences and Engineering
Imperial College of Science, Technology and Medicine, London, SW 7.
2
Department of Agricultural and Food Economics, University of Reading.
OUTLINE
EXECUTIVE SUMMARY
1)
2)
THE DISTRIBUTION OF WORLD POVERTY
GROWTH, INEQUALITY AND POVERTY
2.1. Endogenous Growth
2.2 Agriculture’s Role in the Growth Process
2.3 Growth and Inequality
2.4 Growth and Poverty: Pro-Poor Growth?
2.5 Is Agricultural Growth Pro-Poor?
3)
LINKAGES: PRODUCTION, EMPLOYMENT AND INCOME
3.1 Linkages within the Overall Economy
3.2 Linkages within the Rural Economy
4)
NON-FARM INCOME AND LIVELIHOODS
4.1 Incomes from the Rural Non-farm Economy
4.2 How much of the Rural Non-farm Economy is Dependent on Agriculture?
4.3 The Rural Non-farm Economy, Poverty and Agricultural Productivity: some conclusions
5)
OUTPUT GROWTH AND PRODUCTIVITY GROWTH IN AGRICULTURE
5.1 Agricultural Productivity and Poverty
5.2 The Poverty Impact of Agricultural Technology
6)
EMPIRICAL RESULTS: EFFECTS OF AGRICULTURAL PRODUCTIVITY GROWTH ON
POVERTY AND NUTRITION
6.1 Regressions of the Cross Section of $1 per Day Poverty from WDR 2000
6.2
6.3
6.4
6.5
Regressions using Pooled Data on $1 per Day Poverty
Regressions using $1 per Day Poverty and agricultural TFP Indices for Asia
Regressions using the Human Development Index
Regressions using Nutrition Indicators
REFERENCES
RELATIONSHIP BETWEEN CHANGES IN AGRICULTURAL PRODUCTIVITY
1
AND THE INCIDENCE OF POVERTY IN DEVELOPING COUNTRIES
EXECUTIVE SUMMARY
There has long been a broad consensus amongst donors and developing country governments that
agricultural growth will directly benefit the rural poor and also improve the position of the urban poor by
reducing food prices. For the poorer developing countries, growth is dependent on increases in
agricultural productivity, which provides sufficient food for a growing non-agricultural population. As
this structural transformation proceeds, agriculture accounts for a falling proportion of employment and
income, but the growth process is driven by the development of the agricultural sector. Thus,
agricultural productivity improvements should be both pro-poor and pro-growth.
This conventional wisdom has not been overturned by an alternative paradigm, but the difficulties
of procuring the desired outcomes in the rural environment and the consequent failure of projects has led
to some pessimism. This study first assesses the evidence since there have been considerable recent
developments in several of the relevant literatures. It begins from the perspective of the “new growth
theories” that emphasise the dominant role of technology, infrastructure and education. The growth
literature shows that agricultural productivity growth is required for industrialisation to take place and
the empirical investigations show that agricultural growth is causally prior to growth in manufacturing
and services, but the reverse is not true. Although the effect of GDP growth on inequality is ambiguous,
the evidence suggests that agricultural growth is always pro-poor, but less so when the ownership of land
is as unequal as it is in much of Latin America. The effects of agricultural growth are spread to the nonfarm economy by linkages that result in rural diversification that increases the importance of non-farm
income sources.
Thus, improved technology produces agricultural productivity growth that drives a rural growth
process that can be inherently pro-poor. It can; benefit poor farmers directly by increasing their
production; benefit small farmers and landless labourers through greater employment; lead to lower food
prices for all consumers; increase migration opportunities for the poor; benefit the rural and urban poor
through growth in the rural and urban non-farm economy; lead to access to crops that are high in
nutrients; empower the poor by increasing their access to decision-making processes, increasing their
capacity for collective action, and reducing their vulnerability to shocks via asset accumulation.
Research-led technological change has propelled famine-plagued, food insecure Asian countries
into food self-sufficiency. A large supply of food keeps food prices down, which is critically important
to the poorest people who spend up to three-quarters of their income on food, but population growth has
masked many of the gains, keeping food prices up and rural wages down. Technology adoption has been
uneven, due to costs and unsuitability for resource-poor regions. So, alongside economic growth,
poverty alleviation requires special programs targeted at the poor. Technology alone is not enough,
without infrastructure and education, and will be ineffective in situations where inequalities in land
ownership are too great, which raises the land reform issue, in addition to the need for investment and
institutional change.
Still, the literature provides overwhelming theoretical and empirical evidence that agricultural
growth is essential, especially in the poorer developing countries. It identifies the diverse roles that
agriculture plays in the process of growth and development on the one hand, and the link between
economy-wide growth and poverty alleviation on the other. In this indirect way, this literature shows
how agricultural research generates productivity growth that improves the living conditions of the poor.
1
We thank Lawrence Haddad of IFPRI, Jonathan Kydd and Peter Dorward of Imperial College at Wye, for help and advice
and especially Gavin McGillivray of DFID for the original suggestion to investigate the relationship between productivity
and poverty in this manner.
2
The inter-relationships are shown in Figure 1. Agricultural research generates new technologies
that increase agricultural productivity. Agricultural productivity growth has an impact on GDP growth,
both directly and through agriculture’s linkages with the broader economy, that generate increases in
non-farm incomes. Both agricultural growth and GDP growth have impacts on inequality, poverty and
nutrition. Although there is a considerable literature on the link from agricultural research directly to
poverty, from R&D to productivity, on the effect of new technologies on the incomes of the poor and on
the relationship between productivity and growth, there are no estimates of the direct effect of
agricultural productivity growth and either poverty or nutrition. Thus, a major finding is that the
empirical estimates of this relationship appear to be robust. Regardless of differences in data and
formulation, the results show that a 1% increase in yields leads to a reduction in the percentage of people
living on less than $1 per day of between 0.6% and 1.2%. This is a very tangible result, since the R&D
cost of generating a 1% yield gain can be calculated. Our guess is that since agricultural R&D
expenditures are relatively small, this may be a cost effective means of poverty reduction.
Figure 1: Linkages Between R&D, Technology, Growth, Productivity and Poverty
Agricultural
Growth
Agricultural
R&D
Agricultural
Technology
Agricultural
Productivity
Linkages and NonFarm Income
GDP Growth
Inequality?
Poverty and Nutrition
INTRODUCTION
Recent influential publications such as the 1997 World Bank Report “Rural Development from Vision to
Action” and the 2000 World Bank, ABB and UNECA Report “Can Africa Claim the 21st Century?”
emphasise agricultural productivity increases, generated by research. In the same spirit, the Technical
Advisory Committee of the CGIAR (2000) states its new goal as; “To reduce poverty, hunger and
malnutrition by sustainably increasing the productivity of resources in agriculture, forestry and
fisheries”. To achieve this, the CGIAR will develop a two-pronged approach for the future support of
agricultural research, in favoured environments, to ensure food security and prevent future poverty,
while at the same time tackling the more complex problems of poverty in the marginal and hard areas.
This acknowledges the evidence from the green revolution, that producing sufficient food to feed a
growing population is best achieved by successful farmers in favoured regions, but that unless those who
farm in poor conditions are also a focus of attention, rural poverty will persist. The record shows that
research-led technical improvements contributed to a three-fold increase in food production in
developing countries, helping avert what some observers had predicted would be chronic, widespread
famine throughout the developing world. Thus, this paper reviews the literature on the subject of the
role of improved agricultural technology in alleviating poverty in developing countries.
3
1)
THE DISTRIBUTION OF WORLD POVERTY
The World Development Report (World Bank, 2000a) entitled Attacking Poverty, begins by reporting
that almost half of the world’s 6 billion people live on less than $2 per day and a fifth on less than $1 per
day. Overall, more than 90% of the poorest people live in South Asia, East Asia and Sub-Saharan
Africa.
Estimates of the proportion of the world’s poor that live in rural areas range from 62% (CGIAR,
2000) to 70% (ODI, 1999). In 31 out of 35 countries for which data is available, the percentage of the
rural population living in poverty is higher than for the urban population (World Bank (1999), Table 4).
The fact that agriculture’s share of GDP is far lower than its share of employment similarly indicates
that, on average, rural people are poorer than urban. Thus, for Africa, agriculture accounts for 35% of
GDP and 70% of employment, from which it can be inferred that output per capita in agriculture is only
half that for the rest of the economy (World Bank, 2000b). Often, the gap is larger: for example, in
Botswana, agriculture accounts for only 3.5% of GDP, but is the primary source of income for 50% of
the population. Thus, despite very expensive government programmes to support agriculture, the mean
urban income is still almost four times the rural level, which is about $0.5 per day (Thirtle et al, 2000).
The rural poor are closely correlated with “functionally vulnerable groups” studied by Jazairy et
al., (1992), who found that for a sample of 64 developing countries, 64% of the functionally vulnerable
were smallholders and 29% where landless. In SSA, smallholders accounted for 77% and the landless
only 11%, whereas for Asia 49% were smallholders and 26% landless (reported by Cox, Farrington and
Gilling, 1998). Lipton (2001) quotes IFPRI as noting that increasingly, the rural poor are concentrated
in arid, semi-arid and unreliably watered areas. From 1987-98, Chen and Ravallion (2000) show that
whereas poverty has been declining, especially in Asia, it has increased substantially in Sub-Saharan
Africa.
Agricultural output growth should obviously have more of an impact on rural poverty than
changes in other sectors and there is the added advantage that it has a particularly strong impact on urban
poverty as well. This follows since increases in food output result in lower prices and the 30-40% of the
poor who are urban spend a larger proportion of their income on food than those who are better off.
2)
GROWTH, INEQUALITY AND POVERTY
2.1 Endogenous Growth
Differences in growth rates of output and productivity within and between countries have attracted renewed
interest since the area was revived by the development of endogenous growth models that allow a major
role for policy. The cornerstone of the new growth theory is that although all agents may be subject to
decreasing returns, the positive externalities associated with inputs like technology, education, health and
infrastructure, that have public good characteristics, can result in increasing returns at the aggregate level.
The new approach bridges the gap between development and growth by aiming to identify the exogenous
policy variables that are causally prior to growth in per capita output or TFP (Romer, 1989). If policies do
explain a significant proportion of the differences in growth and hence per capita incomes, the human
welfare consequences are staggering (Lucas, 1988). Thus, empirical growth models (associated especially
with Bill Easterly’s International Centre for Economic Growth at the World Bank) now provide substantial
support for growth strategies led by R&D and technology generation (but also education, infrastructure and
health).
2.2 Agriculture’s Role in the Growth Process
The development strategies adopted by newly independent countries in the post-war period were, with
few exceptions, characterized by an emphasis on the industrial sector, while agriculture was either
4
neglected or heavily taxed. Agriculture’s contributions to economic development were specified by
Johnson and Mellor (1961) as food and raw materials; labour and capital; foreign exchange and markets
for the outputs of the other sectors. The contributions were formalised in dualistic growth models, such
as Ranis and Fei (1961), which followed Lewis in stressing the labour contribution and the need to feed
a growing non-agricultural population. However, as Little (1982) noted, agriculture was treated as a sink
of under-utilised resources that was to be bullied into providing the means for industrialisation.
The neglect of agriculture in this era is odd, in that Ravallion and Datt (2000) When is growth
pro-poor) note that, “there is a long-standing view (though not, by and large, a dominant one it would
seem) that rural underdevelopment constrains prospects for industrialization; see for example Clarke
(1940).”2 The need for agricultural growth became part of the conventional wisdom with the Berg report
(World Bank, 1981) and the 1982 World Development, which stressed the proposition that agricultural
growth is causally prior to industrial development. Stern (1996) applies endogenous growth theory to the
role of agriculture in economic development and concludes that agriculture will continue to be of central
importance to the poor countries for some time to come. His empirical investigation found a statistically
significant relationship between the growth rate in non-agriculture and the growth rate in agriculture for
the 1965-1980 period. His cross-country regressions provide a first indication of the complementarity in
growth between sectors.
These sectoral inter-relationships have since been modelled with increasing degrees of
sophistication and supported with empirical investigations. Thus, two sector growth models, such as
Matsuyama (1992) established that subject to reasonable assumptions, accelerating economic growth
requires growth in agricultural productivity. However, once fertility and population growth are made
endogenous, Kogel and Furnkranz-Prskawetz (2000) show that industrialisation requires agricultural
productivity growth at an increasing rate.
Some recent research, however, has attempted to model the role of agriculture in the process of
development in the framework of a fully consistent theory. Irz and Roe (2000) develop a multi-sector
growth model that leads to two important results. First, in a largely agrarian economy, a minimum rate of
productivity growth in agriculture is necessary to counter population growth and avoid the Malthusian
trap. An empirical investigation suggests that the result is not a purely theoretical occurrence, since the
demographic and technological characteristics of several sub-Saharan countries are broadly consistent
with such a poverty trap. The second result states that a relatively small difference in agricultural
productivity can have a major impact on the speed of industrialization and the overall development
process, consistently with the results of Matsuyama (1992).
A separate literature applies recent time series techniques that can actually show that agricultural
growth is causally prior to growth in manufacturing. Mathew (1955) and Kanwar (2000) establish that
growth in agriculture was causally prior to growth to growth in manufacturing and construction, but the
reverse is not true, in the cases of Malaysia and India. Rangarajan (1982) quantifies this effect for India,
showing that 1% growth in agriculture generates 0.5% growth in manufacturing and 0.7% growth of the
overall economy. The issue of linkages is pursued further in the next section, which helps to explain
why agricultural growth has spillover effects.
There are implications for the definition of an appropriate approach to the relationship between
agricultural productivity and poverty alleviation. First, this relationship should be considered in the
context of the broader economy and address the direct as well as indirect effects of a rise in productivity
growth. Intuitive as it is, this point has not always been made in the past and Schuh (2000) considers that
by neglecting the general equilibrium effects of the introduction of new technologies in agriculture, and
by focusing excessively on poverty within the farm sector, the agricultural research community has
failed to take credit for a significant decrease in general poverty.
2
Clark was responsible for the early work on the “structural transformation”, which explains the declining share of
agriculture in GDP. This was a major reason for the neglect of the sector in early development plans.
5
Second, the recent growth literature points out to a number of dynamic effects through which
agricultural productivity can impact poverty in a major way. The model of Irz and Roe (2000) shows that
a decrease in the relative price of food resulting from technological progress in agriculture can lead to a
substantial rise in the saving rate of households and therefore provide an effective exit channel from
poverty. Another pathway involving feedbacks that create poverty traps corresponds to the positive
correlation between nutritional status and labour productivity of the poor, as emphasized by Dasgupta
(1998). In this context, Wichmann (1997) showed, based on a dynamic general equilibrium model, that
an increase in agricultural productivity can lead to significant increases in the household consumption of
the poor. However, this effect will be larger if nutritional status not only affects the productivity level of
workers but also their ability to improve their productivity via learning by doing.
This establishes the role of agricultural output and productivity growth in the overall growth
process. Agricultural productivity growth is required for industrialisation to take place and the
empirical investigations show that agricultural growth is causally prior to growth in
manufacturing and services, but the reverse is not true. The impact on rural poverty will be
established later and there is the added advantage that it has a particularly strong impact on urban
poverty as well, since more food results in lower prices and the urban poor spend a larger proportion of
their income on food than those who are better off.
2.3 Growth and Inequality
The World Bank (2000) devotes a chapter to this topic, beginning with graphical evidence illustrating
that in all cases growth rates and changes in the incidence of poverty are negatively related. However,
the broader relationship between growth and inequality is far more ambiguous, except that growth
reduces inequality twice as much in countries where the initial level of inequality is low. Thus, the
poverty elasticity – the percentage change in the poverty headcount that results from a one percent
change in GDP per capita – is positively related to inequality measures such as the Gini coefficient (this
proposition is investigated for productivity growth in the empirical section).
Many empirical investigations of growth and inequality centre largely on testing the Kuznets
(1955) curve hypothesis that, as incomes grow in the early stages of development, income distribution at
first worsens and then improves. Whilst this link between inequality and growth was recognized as a
stylised fact of economic development until the late 70s (Ahluwalia, 1976), recent evidence has raised
serious doubts on its validity. As a result, the consensus on the effect of growth on the distribution of
income has been shattered, and the views regarding the benefits of growth to lower income classes range
from the extreme optimism of Lal (1996), to far more circumspect evaluations. Hence, Barro (1998),
from panel regressions covering 100 countries over the 1965-1995 period, finds some support in favour
of the Kuznets curve but concludes, however, that it does not explain the bulk of variations in inequality
across countries or over time.
Ravallion (1995) used data from 36 developing countries, representing 78 percent of the
population of the developing world, to assess the growth-poverty link during the 1980s. Growth reduces
poverty, but has no systematic effect on inequality, suggesting that income gains were evenly spread.
But, the econometric analysis left “a sizable unexplained variation in country performance at reducing
poverty for a given rate of growth.” In other words, clearly other factors matter too.
Perhaps the most interesting aspect of the literature on growth and inequality lies with the
mechanisms suggested to explain a possible relationship. These mechanisms can often be used to think
about the distributional impact of the introduction of new technologies in agriculture. One strand of the
literature investigates the causal relationship from income inequality to growth.3 Benabou, (1996)
focuses on the imperfections of capital markets in developing countries: due to a wealth constraint on
3
Other theories explaining how income inequality affects growth hinge on the political economy of redistribution (Persson
and Tabellini, 1994) and on the idea that excessive inequalities result in socio-political unrest (Alesina and Pertti, 1996).
They are less relevant to our purpose here.
6
borrowing, the poor are unable to seize investment opportunities, in particular in education, and are
therefore excluded from the process of growth. This mechanism implies that a more unequal distribution
of income leads to lower investment and slower growth.
It can be related to the early criticisms of the green revolution, according to which only the
wealthier farmers have access to the credit necessary for the investment and the purchase of modern
inputs accompanying new agricultural technologies (Pearce, 1980; Sharma, 1997). These criticisms
have not survived scrutiny (Barker and Herdt, 1978; Blyn, 1983; Pinstrup-Andersen and Hazell, 1985;
Lipton and Longhurst, 1989; Hazell and Ramasamy, 1991; David and Otsuka, 1994). Although small
farmers did lag behind large farmers in adopting the green revolution technologies, most of them did
eventually adopt and benefit from increased production, as well as from greater employment
opportunities and higher wages in the agricultural and non-farm sectors. Moreover, most small farmers
were able to hold onto their land, and hence captured significant total production increases from their
holdings (Westley, 1986; Hazell and Ramasamy, 1991; Rosegrant and Hazell, 1999). In some cases,
small farmers and landless laborers actually ended up gaining proportionally more income than larger
farmers, with a net improvement in the distribution of village income (Hazell and Ramasamy, 1991).
A second strand of the literature focuses on the reverse causal relationship, from economic
growth to inequality. Kuznets (1955) justified his hypothesis on the fact that economic growth is
accompanied by the continuous reallocation of workers from the rural sector, featuring low per capita
income, to the urban sector, characterized by higher per capita income. At early stages of development,
the rural sector dominates the economy and growth results in the expansion in size of the small and
relatively rich group of persons: it increases inequalities. However, as the size of the agricultural sector
diminishes, the dominant effect of continued mobility on inequality is that more of the poor agricultural
workers are enabled to join the relatively rich industrial sector, and growth eventually reduces
inequalities. Barro (1998) estimates that the turning point (the maximum of the Kuznets curve) is
reached at a level of per capita income of $2000.
More recently, this reasoning has been extended to understand the distributional consequences
of the introduction of new technologies (Helpman, 1997). The adoption of a new technology, or the
mobility from a sector using an old technology to a sector using the new technology, requires
familiarization and re-education. Therefore, due to a mechanism similar to the one described for the
Kuznets curve, many technological innovations tend, in a first stage, to raise inequality, since few
persons get to share initially in the relatively high income generated by the new technology. But
subsequently, as more people take advantage of the superior techniques, inequality tends to fall. In this
context, the implications for innovation in agriculture are twofold: first, the full effect of a new
technology on inequality can only be assessed with a significant time lag. And second, in order to limit
the negative effect of innovation on income distribution, policies intended to help the adoption of the
new technology by the poorest should be introduced. Such policies include in particular the development
of educational programs and the provision of extension services.
Bruno, Ravallion and Squire (1998) review the recent evidence and find that while income
inequality differs significantly across countries, there is no discernable systematic impact over time of
growth on inequality. Though there are exceptions, as a general rule sustainable economic growth
benefits all layers of society roughly in proportion to their initial levels of living. However, countries
that give priority to schooling, health and nutrition are more likely to see improving income distributions
and higher average incomes over the longer term. A more equitable distribution of physical assets,
notably land, can also help greatly.
The sectoral composition of economic growth has also been emphasized as an important factor.
Sectoral biases against the rural sector in pricing, exchange rates, and public investment are not in the
interests of either higher growth or better distribution. And sound macroeconomic policies appear to be
essential for sustained growth, and either have no systematic effect on distribution, or have potentially
adverse short-term impacts but which are typically not strong enough to outweigh the gains to the poor
from growth. Paying attention to the composition of public expenditures in the adjustment program and
to the inclusion of effective safety nets for the poor will help improve the distributional outcome in the
7
transition to a pro-poor growth recovery. While these policies should be pursued in all countries, we now
suspect that these will be less effective and/or less well implemented in high-inequality countries. Thus
reducing inequality is good because it will benefit the poor both immediately and in the longer term
through higher growth.
2.4 Growth and Poverty: Pro-Poor Growth?
The question as to how growth impacts poverty has not been studied as thoroughly as the relationship
between inequality and growth, probably due to the fact that most economists take it for granted. It is
true that the conditions necessary for growth to result in an increase in poverty seem a priori implausible
considering the results of the previous section: growth would need to increase inequalities considerably
to have a negative impact on the incidence of poverty. Further, an early study by Fields (1991) showed
that increases in poverty and economic growth form an exceptional combination. More recent research,
made possible by the new database compiled at the World Bank, also supports the view that growth
tends to reduce poverty (Roemer and Gugerty, 1997; Bruno et al, 1996).
Yet, a number of reasonable mechanisms whereby growth could lead to a worsening in the
conditions of the poor exist, and macro-level studies might simply be too general to identify these
relationships. Dasgupta (1998) makes a convincing case for the existence of poverty traps that might be
reinforced by growth. Among other mechanisms, he points out that economic growth in developing
countries, in the context of a relatively high income elasticity of demand for cereals, can induce grain
prices to rise, further impoverishing the poor. He also suggests that technological change in agriculture
can result in an erosion of the local commons that often form an essential source of livelihood of the
poor. The distributional consequences of different growth processes therefore need to be examined rather
than assuming that growth will automatically trickle down to the poor. The extent to which agricultural
growth contributes to poverty alleviation is now addressed.
The 1990 World Development Report took the promotion of broad-based growth as the first
prong of its strategy to reduce poverty, but the meaning of broad based growth was never defined, so it
was interpreted to refer to the labour-intensity of growth, its geographical or distributional impact, or the
sectoral pattern of growth. While the terminology shifted to pro-poor growth, the definition remains
vague (White and Anderson, 2000).
Though it is to be expected that growth will tend to reduce poverty, the extent to which growth
alone can be expected to alleviate the problem is open to question. Critics such as Lal (1996), Demery
and Squire (1996) and Dollar and Kraay (2000) have argued that distributional changes are both too
small and too slow to be relied upon to bring about substantial reductions in poverty, so the main driving
force for eliminating poverty is growth.
This may be the case, but there remains the situation where growth associated with progressive
distributional changes will have a greater impact in reducing poverty than growth which leaves
distribution unchanged. The key question in this study is the extent to which agricultural growth can be
expected to be pro-poor, a question that is now addressed.
2.5 Is Agricultural Growth Pro-Poor?
Thus, the distribution consequences of different sectoral growth patterns need to be examined. It is
frequently argued that the agricultural sector is particularly important as a source of broad based growth
as it often possesses the characteristics that can stimulate the sort of growth that reduces poverty rapidly
(Lipton, 1977; Stewart 1978 and Ranis 1979 for an early statement of these views and Mellor 1999 for a
recent one).
8
Since the majority of the poor live in rural areas and derive most of their income from their
labour, it has often been argued that agricultural growth based on the introduction of labour-intensive
technologies is particularly suited to fight poverty in developing countries (Lipton, 1977). Further, in an
economy where food is only partially tradable, productivity increases in agriculture should result in a
decrease in food prices that primarily benefit the poor.
Recent studies of poverty reduction in India support this view (Datt and Ravallion 1990, 1996
and 1998). Poverty reduction is the result of growth within agriculture and not to the shift of inputs from
low to high productivity sectors, associated with Kuznet’s inverted “U” trajectory of inequality rising
then falling in the course of development. Hence growth of agricultural output and small-scale ancillary
industries gives reduction of poverty whereas manufacturing growth does not.
Datt and Ravallion (1996) show that the sectoral composition of economic growth matters to the
poor in India. First, rural growth reduces poverty both in rural and urban areas, but urban growth does
not alleviate poverty in rural areas. Second, a decomposition of growth in terms of output sectors
establishes that growth in the primary sector benefits the poor in both urban and rural areas, while
growth in manufacturing has no impact on poverty in either. Woden (1999) produces similar findings for
Bangladesh, where simulations show that rural grow reduces poverty 3% more than urban growth, by
2008, and the differential effect on the poverty gap is greater. Throbecke and Jung (1996) come to
similar conclusions for Indonesia using simulations from a SAM. The significance of agriculture for
poverty reduction is also confirmed by results from cross section data sets.
A cross-country examination of the relationship between growth and poverty by Gallup et al.
(1997) establishes that a one percent increase in agricultural GDP leads to a 1.61% increase in income of
the poorest quintile, while the corresponding values for the manufacturing and services sectors are only
1.16% and 0.79%. Other cross-country studies (Timmer, 1997; Bourguignon and Morrisson (1998))
provide further evidence of the pro-poor bias of agricultural growth, with only the results of White and
Anderson contradicting this view4. Timmer (1997) finds that manufacturing reduces poverty directly due
to an increase in the income of employed workers, but it also worsens the distribution of income,
reducing the effect on the poor, in contrast agricultural growth, which is not associated with worsening
income distribution. Bourgignon and Morrison (1998), using a sample 38 small and medium size
developing countries find that growth in agriculture and in basic services reduced poverty more than
expanding industrial output.
The conclusion that agricultural growth is best for poverty reduction is however conditional on
equitable land distribution. Mellor (1999) argues that agricultural growth reduces poverty so effectively,
as in addition to generating income for poor farmers, it generates demand for goods and services that can
easily be produced by the poor (non-durable consumer goods sold by small shops, market trading
services, hoes, ploughs and other capital goods etc). However, if land and income distribution is highly
skewed, as is common in Latin America and some SSA countries, consumptions patterns of landowners
are skewed toward imported or capital intensive consumer goods rather than the products of the smallscale labour intensive domestic manufacturing and services, which dampens the effects.
Hanmer and Naschold (2000) report de Janvry and Sadoulet’s (1996) analysis of poverty
reduction in eleven countries in Latin America and the Caribbean as confirming Mellor’s conclusion. De
Janvry and Sadoulet (2000) report that poverty has declined in Latin America over the last three decades.
However, due to the level of inequality in land ownership, a main escape route for the rural poor has
been migration to urban areas, so urban poverty has grown as rural poverty has fallen. Thus, they
4
White and Anderson (2000) find a negative effect of agricultural growth on poverty. Their data is from the same source as
Timmer (1997) and Gallup et al. (1997),but they retain only “high quality” data, thus dropping many LDCs, and all of SSA
except Zambia. Thus their result seems to come from the fact that half the sample is developed countries, with agricultural
sectors that are to small to affect poverty. This is of little interest for the current study, where the emphasis is on the poorer
LDCs.
9
confirm that agricultural growth is still pro-poor and the best option, but when there is extreme
inequality the positive effects will be reduced.
3)
LINKAGES: PRODUCTION, EMPLOYMENT AND INCOME
Whereas the early work on linkages stressed the upstream and downstream outputs generated by
industrial investments, such as steelworks, but recent work shows that the secondary effects of
agricultural output growth are also substantial. Although diversifying agriculture and movement of
labour to other sectors is central to long-run growth (Delgado, 1995), agricultural growth is important to
reach this stage. Higher real incomes in the agricultural sector stimulate demand for the products of other
sectors and labour within the sector. Higher agricultural outputs stimulate the creation of non-farm rural
and urban employment opportunities, through backward and forward linkages to service and
manufacturing sector activities (Hanmer and Naschold (2000).
3.1 Linkages within the Overall Economy
‘In poorer countries, agriculture typically accounts for the lion’s share of national income,
employment and export earnings. Under these conditions, even a modest growth rate for
agriculture can have significant leverage on the national economy. Rapid agricultural growth
contributes to the economic transformation in a number of important ways. It supplies basic
foods, raw materials for agro-industry, and exports, and frees up foreign exchange for the
importation of strategic industrial and capital goods. It releases labour and capital (in the form of
rural savings and taxes) to the non-farm sector. It generates purchasing power among the rural
population for non-food consumer goods and services and therefore supports growth in services
and trade and provides a nascent market for an emerging manufacturing sector. It reduces
poverty by increasing labour productivity and employment, and by lowering food prices for all.’
(Hazell & Haddad 2000, 11)
These effects have led some (see, for example, Mellor & Adams 1986, Adelman & Fetini 1991,
De Franco & Godoy 1993) to argue for ‘agricultural development led industrialisation’ (ADLI). There is
some empirical evidence to support these arguments. Rangarajan (1982, reported in Hazell & Haddad
2000) estimated that a 1% addition to the agricultural growth rate in India stimulated a 0.5% addition to
the growth rate of industrial output, and a 0.7% addition to the growth rate of national income. For
Kenya, Block & Timmer (Timmer 1995), obtained a multiplier from an exogenous shift in farm output
of 1.64 for the rest of the economya higher value than that arising from industrial growth where the
multiplier was estimated at 1.23.
This literature is supported by modelling such as Social Accounting Matrices (SAMs) and
Computable General Equilibrium (CGE) models. Examples, such as Townsend et al (1998) and McDonald
and Townsend (1998) show that for the Botswana and the Western Cape Province of South Africa, the
employment and output effects of agricultural output growth are larger than those for manufacturing. Thus,
at the income levels relevant for most of the countries of Africa and Asia, the agricultural linkages can be
well established.
Coxhead & Warr (1991) used a computable general equilibrium (CGE) model loosely styled on
the case of the Philippines, to show how, in a small open economy, technical improvements in farming
are likely to benefit labour, especially if the technical change is labour-using, land-saving. Taking the
case of a 10% improvement in farm output, with technology that saves land but uses labour, applicable
to irrigated areas, their work predicts almost an 8% increase in the incomes of landless labourers.
For Bolivia, de Franco & Godoy (1993) also built a CGE model to show that technical
improvements to crops generate all round benefits in the economy, stimulating growth and employment.
But improvements to the main non-tradable, potatoes, have the greater effects than improvements to
traded crops such as wheat or soybeansin large part since the price of potatoes falls, raising real
10
incomes in a country where the poor spend large fractions of their household budgets on foodstuffs.
These studies clearly suggest that improved farm productivity has widespread benefit to society,
especially by reducing the real prices of staples that cannot easily be traded.
3.2 Linkages within the Rural Economy
The specific linkages from farming to the rest of the rural economy can be disaggregated as follows:
o
o
o
Production links, both ‘upstream’ from the farm in demand for inputs and services for
agriculture, as well as ‘downstream’ from the farm in the demand for processing, storage, and
transport of produce;
Consumption links as farmers spend their increased incomes on goods and services; and,
Gains to rural human capital as increased food production allows better nutrition of rural
workers and investment in education (Timmer 1995).
The precise degree and form of these linkages are likely to be affected by factors such as the
amount of rural infrastructure, rural population density, the need for immediate and local processing of
farm produce, the nature of technical change in farming, and the tradability of both farm output and the
goods and services demanded by farming communities.
Table 1: Multipliers from increases in farm output to other sectors
Study location and time
Muda Valley, Malaysia, 1972
Multiplier estimated
Source
1.83
Haggblade, Hazell & Brown 1989
[1.71]
[Haggblade, Hammer & Hazell 1991]
1.87
Hazell & Ramasamy 1991
Tami
North Arcot District,
Nadu, India, 1982–83
Sierra Leone, Rural, 1974–75
Burkina 1984–85
Niger 1989–90
Senegal 1989–90
Zambia 1985–86
1.35
Ranging from
1.31 to 4.62
Haggblade, Hammer & Hazell 1991
Delgado et al. 1994
Delgado, Hopkins & Kelly 1994
Since the early 1980s models have been built to estimate the production and consumption
multipliers for specific regions.5 These studies have shown high multipliers, as Table 1 shows. The
Muda Valley figure of 1.83 means that a 1% increase in agricultural output also gives a 0.83% increase
in non-agricultural output. Most of these studies show that the bulk75% or moreof the effects arise
through consumption linkages.
Some studies show greater effects in areas with more infrastructure and well-developed ruralurban links, and correspondingly lower multipliers for cases from Africa where these conditions do not
usually apply. On the other hand, Delgado and colleagues (1998) have produced equally high if not
higher linkages for African cases, arguing that isolation in rural Africa means that any exogenous
increase in farm earnings will be spent disproportionately on locally-produced goods and services. These
are amended in alter work, but Delgado at al. (1998) still have large African multipliers: 2.88 for
Burkino Faso, 1.96 for Niger, 2.3 for Senegal and 2.57 for Zambia.
These findings have, however, been disputed. As with most models, the assumptions built in
may be questioned. In particular the strong multipliers for rural Africa reported by Delgado have been
5
See Haggblade, Hammer & Hazell (1991) for a discussion of these models. They favour using semi-input-output models.
11
criticised by de Janvry (1994) as being based on unlikely assumptions about the perfect elasticity of
supply of non-tradables.
Hart (1989) comments that the Muda Valley study underplayed the extent to which increased
spending went on goods that were imported into the region. She also focuses on how capital has been
exported from the Muda Valley. In similar fashion, Harriss-White’s detailed studies (1997) of a market
town and its hinterland in North Arcot suggests that capital is being siphoned out of the rural areas to the
urban economy. She also casts doubt on the extent to which the farm economy stimulates the non-farm
economy. Her work suggests that the growth of rural industries in the town of Arni owes more to links to
the urban hierarchy of India as a whole, than to the fortunes of the surrounding rural areas.
Delgado (1999) suggests vertical integration as a means of overcoming the high transaction costs in
African agriculture. Holloway et al. (2000) similarly follow a New Institutional Economics approach in
considering how milk cooperatives may aid agro-industrialisation in the East African highlands and Staal et
al. (1997) apply similar reasoning to dairying in Kenya and Ethiopia. Thus, Eicher (1989) noted that
To meet the (African) crisis one must turn to agricultural led growth. But, based on historical
experience, an agricultural-led strategy must be framed in no less than a twenty-year horizon and
must entail a combination of technological innovation, policy reform, and institutional
restructuring because each, by itself, is limited.
The linkages issue raises the question of the role of the rural non-farm economy, to which the
argument now turns.
4)
NON-FARM INCOME AND LIVELIHOODS
The linkages to activities such as processing and transportation mean that, in the early stages, non-farm
incomes should be considered as secondary effects of farm output growth. The growing importance of
non-farm income to rural families has emerged from the livelihood studies supported by DFID and from
much of the work of Reardon and his associates. For instance, work by Wiggins, for DFID, on Mexican
villages, showed that non-farm income was actually greater than income that came directly from
farming.
Bryceson (2000) attributes diversification out of agriculture in Sub-Saharan Africa to the pressures
induced by structural adjustment programmes and the demise of marketing boards. However, Ellis
(2000) is more positive, suggesting that diversification is driven by changes in income-earning
opportunities that are promoted by structural adjustment and market liberalisation policies. For the very
poor, this can provide the opportunity for a more viable livelihood, although the role of diversification at
the lower end of the income distribution does not mean it has an equalising effect on rural incomes
overall, especially when inequality of land ownership is taken into account.
Smallholder households are diversifying their livelihood strategies and increasing the shares of
non-farm income that they earn (Reardon et al, 1998, Carney, 1998). Smallholders and landless workers
typically earn more than half their total household income from nonagricultural sources. Such
diversification could reflect worsening impoverishment and desperation as land becomes increasingly
scarce, or it could reflect increasing prosperity, as rural workers are attracted into higher paying nonfarm jobs. The South East Asian experience has been largely of the latter variety, with rapid growth in
rural non-farm employment and income as a result of dynamic national economies (Rosegrant and
Hazell, 1999). Many South East Asian countries seem to be following the Japanese experience, and are
retaining large numbers of smallholder farms that are becoming little more than part time enterprises.
Diversification is more likely to be associated with greater impoverishment when increasing land
scarcity occurs in conjunction with slow agricultural growth and stagnant national and regional
12
economies, and wages are falling (Haggblade, Hazell and Reardon, 2000). Such situations are not
uncommon in many of the poorer countries in South Asia and Sub-Saharan Africa.
4.1
Incomes from the Rural Non-farm Economy
Thus, the amount of income earned by rural households from the non-farm economy constitutes an
important part of their total income.6 For Latin America and the Caribbean, Berdegué, Reardon and
Escobar (2000) report the results of twelve studies showing a range from 38% to 68%, with a median of
46%. For Africa, a review of 35 studies gives a range of 15% to 80%, with a median value of 43%
(Barrett & Reardon 2000). For Asia, a review of 42 household surveys gave a mean share of non-farm
income as 31% (Baliascan & Reardon 1998, reported in Reardon et al. 1998). Of the many household
surveys that report the share of income coming from non-farm sources, few report shares lying outside of
a range of 25% to 60%, with most estimates clustering in the 40–50% range.
The share of rural income contributed by non-farm activities seems to be increasing (Bryceson
1998), although the lack of comparable surveys through time makes this difficult to establish beyond
doubt (Hunt 1995 is an exception). It also seems that non-farm income may be disproportionately
important for the rural poor (Adams 1994), unsurprising, since they usually have less access to land than
better-off rural groups, and hence depend more on waged work and petty enterprises. But this does not
mean that the poor earn more from the non-farm economy than the better-off. Indeed, it seems that in
some cases the rich earn more from non-farm activities than the poor, even though they may not have
such a high share of this in their total incomes, and even if they spend less time working in this sector.
As Berdegué, Reardon and Escobar (2000) report for Latin America and the Caribbean, the nonfarm economy can be divided into two groups of activities. On the one hand stand those formal jobs paid
by a salary, often public service occupation such as teaching, and substantial7 businesses (for example,
maize milling, welding, large shops, coffee processing, motorised transport, etc.). These occupations
require either formal qualifications or more-than-petty amounts of working capital. They may also
depend on having social and political contacts. By and large, only the better-off households have the
qualifications and resources to access these occupations. The rewards are worthwhile: earnings in this
part of the non-farm economy are relatively good compared to farming.
On the other hand, however, there are many non-farm occupations that require few qualifications
and minimal capital to participatemanual labouring, petty trading, food preparation and brewing,
gathering wood, water, and fodder for sale, tailoring, clothes-mending and knitting, some artisan crafts,
charcoal-making, etc. It is in these occupations that the poor tend to be involved. Unfortunately, many of
these activities yield lower returns per hour worked than farming. Low returns are partly a reflection of
the low physical productivity of activities that involve few tools or facilities, and partly a reflection of
the strong competition for such jobs. Tellegen (1998) reports how between 1983 and 1993 in two
Districts of Malawi, hard times in farming and rising population saw the number of rural non-farm
enterprises rise by almost four times, but with declining real returns for most occupations.
This division between the well-paid activities that the rich can access and the badly-rewarded
occupations left for the poor explains why in several African, specifically Kenyan, studiessee Murton
1999 on Machakos, Kenya; Hunt 1995 on Mbeere, Kenya; Evans & Ngau 1991 on Kutus, Kenya;
Reardon 1997 on Africa in generalnon-farm income is skewed in favour of the better-off households.
In southern Mali it is the households with greater wealth and landholdings that participate most in
6
Estimates of income accruing from the rural non-farm economy are bedevilled by different definitions. Many surveys
report ‘off-farm earnings’. These include not just earnings from the rural non-farm economy, but also include wages from
labouring on the fields of others and remittances and commuter salaries from household members working in the urban
economy. ‘Non-farm’ itself is a term that is imprecise: at what point does the processing and marketing of a crop
downstream of the field cease to be a ‘farm activity’ and become a ‘non-farm’ activity?
7
Defined in rural Mexico as those businesses that involved renting premises or employing workers others than household
and family members (Wiggins & Proctor 2000).
13
livestock rearing and non-farm activities.8 Piesse et al (1999) found that in a communal area of
Zimbabwe close to Harare, non-farm income particularly benefited those with lower farm incomes,
whereas in a more remote region the opposite was true. This indicates the importance of markets and
infrastructure in enhancing the incomes of the rural poor. Hence, it should not be thought that the rural
non-farm economy is necessarily something that reduces income inequality.
4.2
How much of the Rural Non-farm Economy is Dependent on Agriculture?
How closely is the non-farm economy linked to agriculture? To the extent that the rural non-farm
economy consists of occupations that serve farming (input supply, tractor services, veterinary assistance,
etc.), or that process and market outputs (refineries, mills, warehousing and haulage), or that depend on
farmers spending their incomes (shops, cafes, bars), it can be closely linked.
But what fraction of the non-farm economy is so constituted? There are plenty of reports of
village occupations that have no linkage to farmingfor example, crafts, carpentry, textiles, etc. In some
cases, these occupations use raw materials supplied from towns and sell their output back to the urban
areas. Other than employing farm labour in the off-season, these are independent of the farm economy.
To these sources of rural income may be added remittances and transfers from household members and
family with jobs in urban areas. This has led some observers to cast doubt on the extent to which
agriculture is the core of the rural economy (Ellis 1998, Bryceson 1998). Indeed, in these cases it may be
farming that receives transfers of capital from non-farm sectorsin complete contrast to dual-sector
models where agriculture is the source of surplus factors of production.
Unfortunately, there are few if any studies that record the relative magnitudes of non-farm
income coming from occupations that are not linked to farming. But although there may be doubts on the
extent to which rural economies may depend on farming, there is the following empirical observation for
Latin America (from Berdegué, Reardon and Escobar 2000). The rural non-farm economy tends to be
most dynamic and productive in areas where farming thrives. Where farming does poorly, non-farm jobs
may be important, but they are often jobs of last resort offering poverty wages. The association between
the farm and non-farm economy may be direct, as when prosperous farming allows investment in the
non-farm economy. Or it may be indirect, through the effects of demands from nearby cities. Urban
centres constitute good markets for farm produce, especially perishables. But the same cities also
demand services in their peri-urban hinterlandsrecreation, commuting homes. Some city-base
enterprises may decamp from the city proper to rural areas from which there are good communications
with the city. Consequently, in addition to the rural-urban links noted above, there can also be feedback
effects whereby urban economic growth acts as a motor for both agriculture and the rural non-farm
economy. Sao Paulo and its rural surroundings is cited as an example of these effects.
Similarly in Asia, those rural areas that have become well known for their levels of
industrialisationTaiwan, coastal south-east China, some parts of Javahave also seen considerable
growth of agriculture, usually prior to industrialisation. The linkages here do not seem to be directly
from farming to rural industry. Nor are they solely driven by the dynamism of metropolitan economies.
Rather, in these cases it seems that agricultural development leads to conditions conducive to investing
in rural areasthrough the creation of a road networks, power supplies, communications; and the
formation of a healthy and educated rural work force.9 Note that these linkages are not part of the formal
models (see the previous section) through which multipliers have been estimated.
Conversely, in the many parts of rural Africa where agricultural development has been slow or
faltering, there are few if any examplesother than the singular case of mining10of a dynamic nonfarm sector providing rural growth.
8
This is not attributed as it is under review: a forthcoming issue of Food Policy will be devoted to this subject area.
Some of which education comes from temporary migration to urban areas where they learn factory skills and disciplines
and make business contacts (see Murphy 1999, Otsuka & Reardon 1998).
10
Even with mining, the effects on the surrounding rural areas are often pathetic. Mining towns exist as enclaves par
excellence. If the hinterlands of mines prosper, it is usually through public investments and subsidies provided by a state that
9
14
4.3 The Rural Non-farm Economy, Poverty and Productivity: Some Conclusions
It may be the case that for many rural households, 40% or more of their incomes come from non-farm
occupations. For the rural poor, the share may typically be higher, 50% or more. To those who see the
countryside as identical to farms and fields, this may be remarkable. But it should not blind us to the fact
that this still leaves substantial fractions of income directly arising from farming. Moreover, of the
remaining non-farm income, some at very least will be linked to the success of agriculture. With few
exceptions, the more successful is farming, the more non-farm jobs and the higher the returns to them.
It would be difficult, then, to dismiss agriculture as a motor of the rural economy. Even for the
poorer groups in rural areas who lack access to land, in most cases at least 40% of their livelihoods will
be linked closely to farming. No other activity consistently offers the same degree of importance to the
rural poor as does agriculture.
Moreover, there remains the empirical observation that thriving farm economies are closely
associated with prosperous non-farm economies. This supports the long-standing arguments for the
linkages in production and consumption, but also suggests that there may be wider linkagesin terms of
investment in physical infrastructure and human capitalbetween farming and other sectors.
Indeed, there may be links in social capital as well. A vigorous farm economy generates
immediate links in the production chain that establish the basis of trust between owners of enterprises,
and between them and those that service such enterprise, such as bank managers. To the extent to which
this generates trust and information about markets and techniques, it lowers transactions costs in rural
areas and makes it that much easier to launch businesses in activities unrelated to farming.11 That said,
these relations remain at the level of hypotheses, since there are no studies (known to these authors) to
confirm them at present.
In conclusion, then, the evidence suggests that whilst agriculture and linked activities may not be
the only source of livelihoods in the rural economy, it would be perverse to set aside the importance of
farming in most rural areas. Stimulating agriculture is likely to stimulate the non-farm rural economy.
That said, there are clearly investments that can be made publicly or encouraged by governmentin
infrastructure, education, healththat benefit the rural areas in their entirety.
5) OUTPUT GROWTH AND PRODUCTIVITY GROWTH IN AGRICULTURE
Much of the discussion in previous sections is in terms of output growth, rather than productivity
growth, which is the growth in output per unit of input(s). Binswanger (1989, 1994) has long argued
that in the long run agricultural supply response at the sector level is not possible without productivity
growth. There are partial productivity measures, most commonly, yield (land productivity) or labour
productivity, and total factor productivity, which is outputs (aggregated with appropriate weights) per
unit of all inputs (again aggregated with appropriate weights). Inputs and outputs may be in physical
terms or value terms at constant prices, which is essentially the same thing. Each serves its purpose but
the measures will usually differ.
The distinction between the output and productivity growth is not important for some of the
effects, but for others it can be crucial. The two can also have opposite signs; for Hungary, during the
transition of the early 1990s, liberalisation led to a 25% increase in productivity while output fell by
redistributes mineral royalties. This is not just an African phenomenon, Roberts (1995) records the extent to which the
Carajás mining towns of Brazil are provisioned from distant metropolis, ignoring the potential for local farms to supply them
with foodstuffs,
11
Taiwan may provide examples of this. Before WWII rural industry included pineapple canning. After 1949, the sheds were
used to can mushrooms produced as side-business to farming. The skills of running this business were transferred from the
1980s onwards to rural engineering plants, sub-contracted to urban light industry. See Benziger 1996.
15
15%. Productivity gains that come from input reduction may in fact reduce output and this has indeed
happened in SSA as liberalisation and subsidy reduction has reduced the use of modern inputs in some
areas.12 A new crop technology may reduce input use, raise yields, raise labour productivity, or in the
case of say a short-season maize variety, allow the cultivated area to be expanded. The first will increase
profit, but not output and may reduce employment; the second will increase output and probably
employment, but not necessarily profits; the third will raise the remuneration of labour, but probably at
the expense of employment and the output effect is indeterminate. The last may raise output,
employment and profits, but could well lower yields. A physical increase in productivity may vanish, if
output is in value terms, due to the fall in the output price.
The relationship between labour and land productivity can be stated as an identity:
Y/L = (Y/A)(A/L)
where Y is output, L is labour, and A is land. Thus, labour productivity can be decomposed into the
product of land productivity and the inverse of labour per unit of land. Land area per worker (A/L), can be
increased by animal power and technical improvements in machinery and equipment, which allow power to
be substituted for labour. This process may be called mechanical technical change. Similarly, biological
advances, such as high-yielding, fertiliser-responsive seed varieties, raise the average product of land (Y/A)
and may be referred to as biological/chemical technical change
However, in the investigation which follows, the ratio of land to labour, taken from the
FAO/World Bank statistics, is better viewed as an indicator of the land endowment of the countries in
the sample, as these differences can otherwise dominate any measurement of productivity change.
Regardless of the cause, increases in food output result in gains to consumers. Subsistence producers
have greater output and only the marketed surplus is subject to the lower prices that result from the
increased supply. For commercial production, early adopters earn a quasi-rent, but as supply increases,
the gains are passed on to consumers (both rural and urban) by way of lower prices. Since the poor spend
a greater proportion of their incomes on food, they benefit most.
This demand side argument is unambiguous, but the effect on producers, including labourers,
varies according to the cause of the output or productivity change, as was noted at the beginning of this
section. Thus, the literature on the distributional effects of agricultural productivity growth, driven by
new technologies, is much more mixed. During the green revolution, feeding the urban poor took
precedence over rural poverty alleviation and the poorest farmers were usually the last to obtain the new
technologies. Thus, there is a literature (noted in Hazell and Haddad, 2001), which looks at the early
part of the diffusion curve and finds that the new technologies worsened the lot of the resource poor
farmers. However, Hayami and Ruttan (1986) show that the consequences of population growth
without the green revolution technology would have had dire consequences for the poor. Hayami
(1981) concluded that the growing inequality in the rural sector of developing countries was the result of
the green revolution not making sufficient progress to counteract the growing population pressure on the
land. However, Byerlee (1987) and others have noted that post green revolution, yield increases are more
difficult and require skills.
Public sector agricultural R&D is tends to be yield-increasing. The conditions under which yield
enhancing technologies are likely to have equitable on-farm benefits are now reasonably well
understood. These include a) a scale-neutral technology package that can be profitably adopted on farms
of all size; b) an equitable distribution of land with secure ownership or tenancy rights; c) efficient input,
credit and product markets so that farms of all sizes have access to needed modern farm inputs and
information and are able to receive similar prices for their products; and d) policies that do not
discriminate against small farms and landless labourers (for example, no subsidies on mechanization, or
scale-biases in agricultural research and extension). These conditions are not easily met, and it typically
12
This has occurred in the areas of Kenya studied by Nyariki and Thirtle (2000).
16
requires a concerted effort by government to ensure that small farmers do have fair access to land and
needed knowledge and modern inputs so as not to be left behind.
Whilst this is useful, the literature on R&D and technology has always pointed out that
agricultural research is a very blunt instrument for alleviating poverty. R&D is essential to achieve the
necessary output growth, but it unlikely to solve the distribution problems. However, it is more likely to
achieve this if the suggestions above are given serious consideration.
5.1 Agricultural Productivity and Poverty
Hanmer and Naschold (2000) show that the higher ratio of agricultural to modern sector labour
productivity, the greater is the reduction in poverty headcounts, but only for sub-Saharan Africa and
South Asia. This suggests that it is at the lower levels of development that agricultural productivity
growth has the greatest effect on poverty.
Ravallion and Datt (2000) study the poverty impact of growth in India for 1960-1994. The
sectoral composition of economic growth and initial conditions interact to influence how much economic
growth reduced consumption poverty. The elasticities of poverty to (urban and rural) non-farm output
varied appreciably, and the differences were quantitatively important to the overall rate of poverty
reduction. States with initially lower farm productivity, lower rural living standards relative to urban
areas, and lower literacy experienced a less pro-poor growth process.
Datt and Ravallion (1998) take the productivity connection further, by estimating the effects of
yield growth on poverty, the relative price of food and real wages in rural India, from 1958-94. Both
higher agricultural wages and higher yields reduce poverty to a very similar degree. In food markets, the
poor gain in absolute terms, and there is strong evidence that important indirect channels linking average
farm productivity to living standards of the rural poor do exist.
Fan, Hazell and Thorat (1999) also use Indian data to estimate the returns to alternative
investments in rural India on agricultural total factor productivity and poverty reduction.13 The greatest
impacts on both variables came from investments in roads and in agricultural R&D and extension, which
apart from increasing incomes, had much of their effect through wage increases and lower food prices.
This is described as a win-win situation, as there appears to be no trade-off between TFP growth and
poverty reduction.
Thus, a combination of infrastructure and research investment is called for, which is not an
unusual finding. Thirtle et al. (1993) found that in post independence Zimbabwe, where the technologies
were already in use by the settlers, the key explanatory variables in spreading the green revolution to the
communal lands were depots for the distribution of inputs (and collection of outputs) and rural credit.
Technology does not work without infrastructure, but this raises the issue of the allocation of effort
between marginal and favoured environments. Fan, Hazell and Haque (2000) find that in the Indian
case, marginal returns in the favoured areas have been driven down so far that technology and
infrastructure investments in the marginal rain-fed areas appear to be the option with the highest return.
This may well be true in some African countries, such as Kenya, where the allocation of
resources in KARI was extremely biased in favour of the highlands while the lower potential semi-arid
and coastal regions attracted very little funding (Thirtle, 1989). Recent evidence on the impact of
agricultural research in Africa is reviewed by Maredia, Byerlee and Pee (2000) while Byerlee (2000)
considers targeting poverty directly in priority setting. Renkow (2000) tackles this problem and also the
share of non-farm income, and although he concludes that R&D for marginal areas may be especially
pro-poor, the more so when there is less scope for NFI, the conclusion is still that infrastructure
13
The World Bank (1991) explained that TFP is the appropriate measure as it correlates with cost reduction whereas yield
increases may come from intensification
17
investments and institutional changes are likely to have higher payoffs than investing in R&D and
technology.
5.2 The Poverty Impact of Agricultural Technology
Does technology-led agricultural productivity growth lead to widespread economic growth and poverty
reduction? There is a considerable recent literature on agricultural research and poverty alleviation,
which is relevant here since it assumes that R&D produces new technologies and looks at the poverty
impact.14 Thus, Hazell and Haddad (2001) list the potential poverty impacts of improved technology:
1) It can benefit poor farmers directly through an increase in their level of own-farm production. This
may involve production of more food and nutrients for their own consumption, and increasing the
output of marketed products for increased farm income;
2) It can benefit small farmers and landless labourers through greater agricultural employment
opportunities and higher wages within the adopting regions;
3) It can increase migration opportunities for the poor to other agricultural regions;
4) It can benefit a wide range of rural and urban poor through growth in the rural and urban nonfarm
economy;
5) It can lead to lower food prices for all consumers, whether from rural or urban areas
6) It can lead to greater physical and economic access to crops that are high in nutrients that are crucial
to the well-being of the poor and to poor women in general
7) It can empower the poor by increasing their access to decision-making processes, increasing their
capacity for collective action, and reducing their vulnerability to shocks via asset accumulation .
Many of these benefits do not necessarily materialize for the poor; there are many conditioning
factors that determine who benefits from technological change. Nor do they all necessarily work in the
same direction. For example, while many of the poor may benefit from the indirect benefits of
technological change, the direct impacts may be disappointing or even perverse. The net outcomes, both
for individual poor people and for the poor in total, can be difficult to determine a priori.
Kerr and Kolavalli (1999) summarise the findings of their extensive survey of this issue in the
following manner, which is quoted here in a shortened form. Agricultural research has had many
unconditionally positive effects, of which its effect in stimulating the supply of food is perhaps the most
important. Increased agricultural production resulting from research-led technological change has
propelled famine-plagued, food insecure Asian countries into food self-sufficiency. A large supply of
food keeps food prices down, which is critically important to the poorest people who spend up to threequarters of their income on food. Nutrition status has improved in many but not all countries.
Population growth has masked many of the gains provided by agricultural research. Food
production has increased tremendously in developing countries, but food production per capita has
grown only very slowly if at all. Also, agricultural productivity growth has created many jobs, but each
year there are millions of additional unskilled workers who need jobs. The proportion of people who are
poor has fallen significantly but, with population growth, the absolute number of poor people has not. It
is difficult to eliminate poverty when most babies are born into households headed by parents who are
very short on income, education or job skills.
Technological change has had ambiguous effects on income distribution across different
categories of rural households. Cash-intensive technologies, for example, may be difficult for the poorest
farmers to adopt. If other farmers widely adopt such a technology, higher aggregate output will result
and output prices will fall. Non-adopting farmers will face lower returns under such conditions and they
may become absolutely worse off as a result. A critical issue here is that effects of technical change on
14
It is not possible to do justice to this topic in the space available. Apart from the substantial documents mentioned this is
the topic of a special issue of Food Policy, published in August 2000, and there is a web site reporting on a conference at
www.ciat.cgiar.org.poverty/workshop.htm.
18
producers and workers are integrally linked to other institutions and policies. Technology is more likely
to have widespread benefits if assets are equitably distributed and infrastructure and social services are
well developed. Well-functioning, easily accessible markets for credit, for example, help farmers
purchase productivity-enhancing inputs. Unfavorable social outcomes are more likely when these
conditions are not in place.
Uneven adoption across regions is another source of concern about inequitable income
distribution. Uneven adoption can lead to higher farm profits and wages in adopting regions but lower
returns in less favorable, non-adopting regions. Evidence from Asia suggests that over time, some
workers migrate from the non-adopting to the adopting areas where labor demand has risen and this
reduces regional wage differences. Non-adopting regions shift to other crops in which they have a
comparative advantage. Inequitable impact across regions is a reality, but there is a certain inevitability
about this due to the importance of agro-climatic conditions in agricultural production.
It is important to note that while adoption of early green revolution varieties was confined to
favorable irrigated areas and required major inputs of pesticides and fertilizer, subsequent research led to
the development of modern varieties that perform well in un-irrigated conditions and without chemical
inputs. As a result, rain-fed areas of India that adopted improved varieties in the 1980s exceeded the
original irrigated area covered by the green revolution in the 1960s. Similarly, the latest improved wheat
varieties do not require fungicides and the latest improved rice varieties do not require pesticide.
Virtually all modern varieties respond to fertilizer, but they can also give respectable yields without
fertilizer. Also, successive generations of improved wheat have become increasingly responsive to
fertilizer, so that smaller applications have larger effects on yields. Performance will always be superior
with better use of inputs, but recent technical improvements give cash-constrained farmers in rain-fed
areas better opportunities to raise their production greatly. In this sense agricultural technology has made
steady progress in responding to the needs of poor people.
Evidence on changes in employment and wages resulting from technical change is complicated.
Improved varieties raise employment, though this effect has weakened considerably since the initial
introduction of green revolution varieties in the 1960s. Changes in real wages resulting from increased
demand are difficult to track for at least three reasons. First, wages in the nonagricultural sector play a
role in determining agricultural wages; second, economic policies influence wages; and third, steady
growth in the population of unskilled job-seekers and migrants counteracts the demand effect. The
agricultural sector has absorbed huge numbers of new workers since the 1960s, but raising their wages is
difficult when labor supply has also grown by so much.
Agricultural productivity growth can stimulate wider growth in the non-farm rural economy,
which in turn can contribute to poverty alleviation. However, poverty alleviation through economic
growth takes time and depends on favorable conditions such as relatively equitable initial division of
assets, widespread access to infrastructure and government services, and promotion of labor-intensive
enterprises. While economic growth is not sufficient to alleviate poverty, evidence suggests that it is
necessary. Alongside economic growth, poverty alleviation requires special programs targeted to poor
people to provide safety nets and give them opportunities.
6)
EMPIRICAL RESULTS: THE EFFECT OF AGRICULTURAL PRODUCTIVITY GROWTH ON
POVERTY AND NUTRITION
In this section we examine the proposition that agricultural productivity has a direct impact on
poverty headcounts and other poverty measures.15 This relationship does not appear to have been
investigated and is similar to the notion of estimating poverty elasticities for growth. Fan and Hazell, in
15
The poverty measures are not discussed here, but they can differ considerably. See Woden (1997) for some comparisons
of poverty indicators in Bangladesh that give inconsistent results.
19
their investigation into linkages between government spending and poverty alleviation, explored the
productivity-rural poverty links in India by modelling the relationship of the $1 a day poverty indicator
against Total Factor Productivity (TFP), but their simultaneous equation approach does not lead to a
poverty elasticity.
Following the relationship that appeared in the text, the link between labour and land productivity
can be stated in value added terms as an identity:
VALUE ADDED
VALUE ADDED
LAND
≡
×
PER UNIT LABOUR
LAND
PER UNIT LABOUR
Value added is net of the costs of intermediate inputs, which would remove the cost effects of
intensification using increasing amounts of modern inputs. This should make it closer to TFP than the total
output-based measure suggested in the text. In this way labour productivity can be decomposed into the
product of two components: land productivity, or yield, and the land labour ratio, which can be viewed as
an indicator of a country’s resource endowment. Thus, the yield contribution to labour productivity can be
separated from the relative scarcity of land, which it is not possible to change. This is important because a
country such as the USA has several hundred times as much land per unit of labour as a land scarce
country, such as Bangladesh, and will have far higher labour productivity as a result.
6.1
Regressions of the Cross Section of $1 per Day Poverty from WDR 2000
First, the $1 per day poverty indicator from the World Development Report is explained by the
productivity indices for land and for labour, by means of cross sectional regressions. Then, the
decomposition is used, making the land labour ratio and land productivity the independent variables. All
the variables are expressed in logarithms, so that the coefficients can be interpreted as elasticties.
MODEL 1: ln POVERTY COUNT = α + β ln [VALUE ADDED / LAND ] + ε
MODEL 2 : ln POVERTY COUNT = α + β ln [VALUE ADDED / LABOUR ] + ε
MODEL 3 : ln POVERTY COUNT = α + β ln [VA / LAND ] + δ ln [ LAND / LABOUR ] + ε
Data
The poverty indicator used is the headcount index (% living in households that consume less that the
poverty line) with data taken from the World Development Report 2000/2001. Data for the independent
variables, Value Added, Land and Labour were obtained from the World Development Indicators (2000)
data set. The minimum set of 40 countries (models 1 and 3) is: Algeria, Botswana, Bulgaria, Burkina
Faso, Central African Republic, Chile, Cote d'Ivoire, Ecuador, Egypt, Estonia, Guatemala, Kenya,
Korea, Lesotho, Madagascar, Mali, Mauritania, Mexico, Mongolia, Morocco, Namibia, Nepal, Niger,
Paraguay, Poland, Portugal, Romania, Rwanda, Senegal, Sierra Leone, Slovenia, South Africa, Sri
Lanka, Tanzania, Tunisia, Turkey, Uganda, Uruguay, Uzbekistan and Zimbabwe.
Results
The results are reported in Table 2. Model 1 has only 40 observations because the yield data ends at
1995 and many of the 72 poverty estimates are for later dates. However, the 40 countries are a
reasonable sample of the developing world, as the list above shows. The adjusted R2 of 0.20 means that
yields explain only 20% of the variance in poverty, which is not satisfactory, since it suggests that other,
omitted variables explain the majority of the differences, but the productivity measure is significantly
20
different from zero at a high level of confidence. The poverty elasticity of – 0.37 means firstly that
higher yields result in lower percentages of the population living in poverty and secondly that a 1%
increase in yields reduces the percentage of the populations living on less than $1 per day by 0.37%.
Table 2: Dependent Variable is % of Population with Less than $1 per Day
Explanatory
Variables
VA/LAND
VA/LABOUR
LAND/LABOUR
Constant
R square
F Test
Sample Size
Expected Sign
Estimated Coefficients
Model 1
-0.37**
Negative
Negative
Negative
Model 2
Model 3
-0.91**
-0.83**
**
**
4.26
0.20
13.35**
40
8.06
0.506
13.35**
66
-0.819**
8.48**
0.625
53.42**
40
** significant at the 1% level, two-tailed test.
Model 2 explains just over 50% of the variance, which is far better and the poverty elasticity,
which is again highly significant, rises to – 0.83, so a 1% improvement in labour productivity reduces
the poverty count by 0.83%. The sample increases to 66, but the problem with this model is that the
effect could all be coming from the land-labour ratio component of the labour productivity index.
Thus, following these preliminary tests, Model 3 separates the two terms. The model explains
62% of the variance in poverty and the large increase in the F statistic indicates that it is statistically
preferred to the two previous attempts. A 1% increase in the land labour ratio reduces poverty by
0.82%, which is surprisingly low relative to the effect of the land productivity term, which indicates that
a 1% improvement in yields decreases the percentage of the population living on less than $1 per
day by 0.91%. Again, the variables are highly significant and this is the preferred model. The result
can be developed further if an elasticity can be calculated to link R&D expenditure to yield gains.
Then, the cost of generating a 1% decrease in poverty could be calculated. Since R&D expenditures are
quite modest, our expectation is that this could look like a very cost effective means of reducing poverty.
6.2 Regressions using Pooled Data on $1 per Day Poverty
Whereas the WDR data has only single observations for each country, the data used by Hanmer and
Naschold (2000) has scattered observations from 1985 to 1995 for 58 countries, which increased sample
size to 109 observations.16 Models 1 to 3 are the same as in the previous section and the results are
reported in Table 3.
Table 3: Dependent Variable is % of population with less than $1 per day
Variables
VA/LAND
VA/LABOUR
LAND/LABOUR
GINI
YEAR DUMMY
Constant
R square
16
Expected Sign
Negative
Negative
Negative
Positive
Estimated Coefficients
Model 2
Model 3
-0.72**
**
-0.629
-0.605**
Model 1
-0.299**
-0.014
4.498**
0.088
0.117
7.177**
0.3095
We thank them for making these data available.
21
0.1066
7.616**
0.328
Model 4
-0.621**
-0.742**
2.153**
0.185
-0.776
0.50
F-statistic
Sample Size
6.44**
109
20.2**
113
14.79**
109
15.66**
109
** significant at the 5% level, two tailed test.
In model 1, the 1$ a day poverty indicator is regressed on land productivity and only 9% of the
variance of poverty is explained. Dummy variables were included to allow for the different time periods,
but the coefficients were not significant. Again, the productivity measure is significantly different from
zero at a high level of confidence. From the poverty elasticity we can infer that an increase of 1% in
labour productivity would bring about a 0.3% decrease in the poverty headcount index
In model 2, the poverty indicator is regressed on labour productivity, which explains 30% of
the variance and gives a highly significant elasticity of 0.63. Model 4, where the $1 per day poverty
indicator is regressed against both land productivity and the labour land ratio, explains over 32% of the
variance in poverty. Both components, land productivity and the land labour ration are significant at the
5% level. The poverty elasticities indicate that if land productivity were to increase by 1% there would
expect a 0.72% reduction in the poverty headcount index, whilst if the land labour ratio were to increase
by 1% this would bring about 0.6% decrease in the percentage of people living on less than $1 per day.
Model 4 adds the Gini coefficient, which is an index of inequality, varying from 0, which is
perfect equality to unity, which would be complete inequality. Throughout the literature review, it was
suggested that greater inequality prevented growth from reducing poverty. The adjusted R2 in Model 4
rises to 50% and all three variables are statistically significant at the 5% level. The results infer that a
1% increase in land productivity would reduce the poverty headcount index by 0.62 and that a 1%
increase in the land/labour ratio would reduce poverty the poverty headcount index by 0.62%. However,
the most striking effect is that if there were a 1% decrease in the Gini index, there would be a 219%
decrease in the poverty headcount index. The larger sample gives a very similar result. The poverty
reduction from a 1% increase in yields still appears to be about 0.7% and the relationship is again highly
significant.
6.3
Regressions using $1 per Day Poverty and agricultural TFP Indices for Asia
The two investigations of the relationship between poverty and agricultural productivity that are reported
above use partial measures of productivity: Value added per unit of land and per unit of labour. The
alternative, preferred on theoretical grounds is total factor productivity (TFP), which is used increasingly
in the recent literature. The few studies that have produced comparable TFP indices for range of
developing countries are reported in Table 4.
Table 4: Total factor productivity studies for a cross section of countries
Arnade (1998), Worldwide sample of 70 countries, with a developed country bias, from 1961-93.
Fulginiti and Perrin (1997), Disparate group of 18 LDCs, from 1961-85.
Lusigi et al. (2001), African sample of 47 countries, from 1961-91. Asian sample of 18 countries, 1961-96.
Piesse et al. (1998), Turkey with Asia & the Middle East. 2Turkey with the EU & potential entrants, 1961-96.
Thirtle et al. (1995), 22 countries in SSA, from 1971-86
Trueblood (1996), Worldwide sample of 78 countries, with a developed country bias, from 1961- 1993.
Data
To test that the TFP approach gives similar results, the Asia sample of Lusigi et al. (2001) is used.17 The
sample of 23 observations is as shown in Table 5 and the dependent variable is again the percentage of
the population living on less than $1 per day.
17
Further work is required to incorporate the data available.
22
Table 5: Asia sample for TFP and Poverty
Country
BANGLADESH
CHINA
INDIA
INDONESIA
MALAYSIA
NEPAL
PAKISTAN
SRI LANKA
THAILAND
Years of Data
1985, 1990
1985, 1990, 1995
1985, 1990, 1992
1985, 1990, 1995
1985, 1990
1985, 1990, 1995
1985, 1990, 1991
1985, 1990
1985, 1990
Results
The first model is now the effect of TFP on the $1 per day poverty count and the elasticity of 1.288
suggests that a 1% increase in TFP, for this Asian sample, decreases the percentage of the population
living on less than $1 per day by 1.3%, which is about double the lowest result in the previous set of
estimates. TFP can be decomposed into technical progress (model 2), which is not significant and
efficiency change (model 3), which has a coefficient of 2.04. However, it is not clear how these results
should be interpreted. For this sample R&D is providing new technologies and shifting the frontier
outwards, but this has an insignificant effect on poverty. This may well be because the technological
frontier is defined by the more successful counties like Malaysia and Thailand. On the other hand, it is
the poorer counties of South Asia that are being left behind and hence have negative efficiency change.
Hence, the significant elasticity on efficiency could be interpreted as meaning that is technology transfer
can produce efficiency change (getting South Asia closer to the frontier), a 1% gain in efficiency would
reduce the poverty count by over 2%. However, at this stage, this is speculation; the relationships may
not hold up when tested more extensively.
Table 6: Dependent Variable is % of population with less than $1 per day
Variables
Expected
Estimate Coefficients
Sign
Model 1
Model 2
TFP Index
Negative
-1.288**
Technical Progress
Negative
-0.186
Efficiency Change
Negative
Constant
3.033**
3.42**
R square
0.26
0.28
F-statistic
8.79**
5.16
Sample Size
23
23
Model 3
-2.04**
2.61**
0.11
23.98**
23
** significant at the 5% level, two tailed test.
Summary
The result that can be taken fairly seriously is that agricultural productivity growth,
however it is measured, does appear to have a consistent, robust and substantial impact on
poverty. The poverty reduction elasticity was always between 0.62 and 1.3.
6.4
Regressions using the Human Development Index
23
The earliest observations for the $1 per day poverty index are for 1985. The available index that does go
back further, at least to the final stages of the green revolution, is the Human Development Index (HDI)
The HDI is a composite index of development. The three most crucial components of the HDI are
measures of longevity, education and income and it may serve as a reasonable poverty index.
Educational attainment, income, and life expectancy are all associated with poverty position of an
economy. Thus, the HDI was used in the place of the $1 per day poverty measure.
Data
The HDI, from UNDP dataset, covers 174 countries for 1975, 1980, 1985, 1990, 1998, giving a total
number of observations without missing data of 280.
Results
Table 7: Explaining the Human Development Index
Variables
Expected Sign
VA/LAND
LAND/LABOUR
Constant
R square
F-statistic
Sample Size
Positive
Positive
Estimated Coefficients
Model 3
0.1226**
0.1011**
-2.39**
0.759
328.48**
280
** significant at the 5% level, two tailed test.
Only the preferred model 3 is reported as the preliminary tests have been repeated several times.
The two variables alone explain 76% of the variance in the HDI and both are highly significant. Thus,
this regression confirms the apparently solid link between agricultural productivity growth and poverty
reduction. Raising yields by 1% increases the HDI by 0.12%, which is the right direction, but
improving the value of a composite index does not have the obvious and appealing meaning of reducing
the $1 per day measure.
6.5
Regressions using Nutrition Indicators
Here the data are from Lawrence Haddad of IFPRI. There are 131 mixed cross section and time series
observations, but again this reduces to 109 observations in model 3, which is again the most successful
regression. Dummy variables were again used to deal with the time difference. Two variables D8089
and D9096 were generated to cover data during 1980-1989 and 1990-1996 respectively.
In the first case, the dependent variable is per capita dietary energy supply and 22% of the
variance is explained by land productivity and the land labour ratio. The elasticities are highly
significant and a 1% increase in land productivity increases the energy supply by 5.3%. This seems
somewhat low, especially relative to the results for the second nutrition variable.
Table 8: Explaining Per Capita Dietary Energy Supply
Variables
Expected Sign
VA/LAND
LAND/LABOUR
DUMMY 8089
DUMMY 9096
Constant
R square
Positive
Positive
24
Estimated Coefficients
Model 3
0.053**
0.060**
0.02
0.016
7.38**
0.22
9.89**
109
F-statistic
Sample Size
**significant at the 5% level, two tailed test.
Table 9 shows 30% of the variance in under-weight five year old children is explained by the
two variables and that a 1% increase in yields decreases the count by 0.42%.
Table 8: Explaining the % of Under-weigh Children below 5 years old
Variables
Expected Sign
Estimated Coefficients
Model 4
VA/LAND
Negative
-0.42**
LAND/LABOUR
Negative
-0.25**
D8089
-0.21
D9096
-0.3
Constant
5.04**
R square
0.31
F-statistic
7.63**
Sample Size
109
** significant at the 5% level, two tailed test.
Summary
The agricultural productivity variables all have significant effects on all the poverty and
nutrition measures. Although there is more work needed before the actual magnitudes can be
believed, there does seem to be a strong relationship between agricultural productivity and
poverty.
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