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Poverty, productivity and production environment:
Food Policy 25 (2000) 463–478 www.elsevier.com/locate/foodpol
Poverty, productivity and production environment: a review of the evidence Mitch Renkow
*
Department of Agricultural and Resource Economics, North Carolina State University, Raleigh, NC 26795-8109, USA
Abstract This paper reviews the state of knowledge about the key issues needing to be understood to satisfactorily resolve a long-standing debate within the Consultative Group for International Agricultural Research (CGIAR) system. The debate revolves around the effects on various populations (particularly the poor) of different allocations of research effort between marginal and favoured production environments. This paper specifically focuses on what is known about the geographical distribution of the rural poor, across agro-ecological zones and over time. Variations in the income-generating activities—including non-agricultural activities—engaged in by the poor are examined and the ways in which specific technology packages affect the economic well-being of different types of households, both directly and indirectly. 2000 Elsevier Science Ltd. All rights reserved. Keywords: Poverty alleviation; Technological change; Agricultural research; Production environment
Introduction Determining the optimal mix of research activities to meet poverty alleviation goals is an enduring issue within the Consultative Group for International Agricultural Research (CGIAR). Most of the research conducted in CGIAR centres is unquestionably at least potentially pro-poor (Lipton and Longhurst, 1989). However, substantial uncertainty remains as to how the centres—individually and collectively—might best reduce poverty throughout the Third World. * Tel.: +1-919-515-5179; fax: +1-919-515-1824. E-mail address: [email protected] (M. Renkow). 0306-9192/00/$ - see front matter 2000 Elsevier Science Ltd. All rights reserved. PII: S 0 3 0 6 - 9 1 9 2 ( 0 0 ) 0 0 0 2 0 - 8
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A long-standing debate within the CGIAR system revolves around the effects on various populations (particularly the poor) of different allocations of research effort between marginal and favoured production environments.1 At the heart of this debate is the fact that most of the major ‘success stories’ produced by the CGIAR centres have occurred in irrigated or well-watered areas—most notably, the rapid increases in rice and wheat yields registered during the Green Revolution. Some argue that there has been systematic under-investment in marginal production environments— to the detriment of the many impoverished people within those areas. Others counter that investment in marginal areas historically has been low precisely because the returns to those investments are low and that diverting research resources away from favoured production environments would do more harm than good overall. Where the poor live, their types of income-generating activities and how new agricultural technologies alter the returns to household members’ resources must be carefully assessed to weigh the relative payoffs (in terms of poverty alleviation) of research in alternative breeding and crop management.
The geographic distribution of poverty Rural poverty is commonly presumed to be more pronounced in marginal than in favoured production environments, either in terms of the absolute number of poor people or the proportion of the population that is impoverished. However, the empirical evidence is mixed. Leonard et al. (1989) gives the most comprehensive accounting of the geographic distribution of poverty. Using data from the World Bank, the International Food Policy Research Institute (IFPRI) and the United Nations (UN) Centre for Human Settlements, he estimated that 47% of the world’s poor lived in marginal rural zones, 36% favoured rural zones and 17% in urban areas (Table 1). Table 1 Geographic distribution of poor people by production environment and regiona Location
Distribution (millions of people)b Rural areas Urban areas Favoured environments Marginal environments
Asiac Sub-Saharan Africa Latin America All developing countries
198 (36) 69 (44) 12 (15) 279 (36)
265 (49) 71 (46) 35 (45) 371 (47)
83 (15 16 (10) 31 (40) 130 (17)
Source: Leonard et al. (1989) a Poor people defined as the poorest 20% among the total population of all developing countries. b Figures in parentheses are percentages. c Includes China and the Middle East.
1 Throughout this paper, the categorisation of a production environment as ‘marginal’ or ‘favoured’ is based on the biophysical and agronomic characteristics affecting agricultural production.
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The regional breakdown of these estimates indicates that significantly more of the rural poor live in marginal areas in Asia and Latin America and that roughly equal numbers of poor people live in marginal and favoured rural areas of Sub-Saharan Africa. Authors weighing in on the favoured versus marginal lands debate (PinstrupAnderson and Pandya-Lorch, 1994; Conway, 1997) have widely cited these figures. However, they are dated and were constructed without the benefit of geographic information systems (GIS) technology and other recent advances in spatial mapping.2 Other studies of the poverty–environment relationship have tended to focus on specific crops or regions. Fan and Hazell (1999) present data from India on the incidence of poverty in irrigated, high- and low-potential rainfed areas. These indicate that the absolute number of poor people in India’s irrigated areas fell by nearly 20% (from 37 to 30 million) between 1972 and 1993, but remained roughly constant in both high- and low-potential rainfed areas (between 75 and 80 million). Over the same time period, the percentage of the total population below the poverty line fell in all areas, with the high-potential rainfed areas consistently having had the highest poverty rate (59% in 1972, 45% in 1993). In contrast, using different geographical definitions, Kelley and Parthasarathy Rao (1995) found that the absolute number of people living in poverty was significantly lower in the marginal production environments (vis-a`-vis favoured environments) of India. They also found no significant relationship between relative poverty and type of production environment. Similarly, Byerlee and Morris (1993) point out that the important favoured wheat zones of South Asia are characterised by very high poverty rates. They note that in most of the world’s wheat growing areas, production environments best described as marginal tend to be characterised by relatively less poverty than is found in more favoured environments. They primarily attribute this to the fact that most of the world’s marginal wheat production areas are concentrated in West Asia and North Africa, a region of relatively high income. Two other recent efforts in this area merit mention. Heisey and Edmeades (1999) found no significant correlation between poverty and production environment using a variety of poverty and land quality indicators.3 Rather, they conclude that other factors—including available technologies, policies, historical circumstances and institutional environment conditioning market relationships—are at least as important as agro-climatic differences in determining the spatial distribution of poverty.4 Conversely, preliminary evidence from a poverty mapping project for West Africa by the United Nations Environment Programme (UNEP) suggests a negative correlation 2 However, projects are currently underway that promise to significantly upgrade our knowledge of the spatial distribution of poverty through the application of GIS technology (Henninger, 1998; Bigman and Fofack, 2000). 3 The poverty indicators are GNP per capita, country poverty weights developed by the Technical Advisory Committee (TAC) of the CGIAR, and TAC estimates of the percentage of the rural population in absolute poverty. The production environment indicators are percentage area often subject to drought stress, aggregate maize yield and the (trend-corrected) coefficient of variation of maize yield. 4 This is in line with the contention of Reardon and Vosti (1995) that poverty–environment linkages depend as much on the socio-economic environment as they do on the biophysical environment within which households dwell.
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between agro-climatic potential and various human development indicators (UNEP/GRID, 1997). Two conclusions emerge from these studies regarding the targeting of efforts to alleviate poverty. First, clearly many poor people live in both marginal and favoured areas (although globally the largest number appear to live in the former). Thus, the global war on poverty must be fought on several fronts. Second, poverty distribution varies considerably geographically. Therefore, the targeting of poverty alleviation efforts will necessarily vary by region or country. In other words, strategies for poverty alleviation cannot be generalised—in particular, targeting certain geographical regions or one type of production environment to the exclusion of others. Nonetheless, the extent to which the benefits of alternative research investments reach poor households—either directly or through spillovers from one geographical area to another—remains an issue of concern to research managers and policymakers.
The welfare effects of agricultural technologies Rural households in developing countries derive income from various sources. Consider, for example, a semi-subsistence agricultural household that earns income from three sources—profits from agricultural production (Y⌸), agricultural labour income (YL) and non-agricultural activities (YNA). Real income (Y*) is the sum of these, deflated by a household-specific price index (P*) that is the expenditure shareweighted average of prices paid for all commodities consumed by the household: Y∗⫽
Y⌸+YL+YNA P∗
(1)
Obviously, each of these categories of income-generating activities can be decomposed further. For example, agricultural profits (Y⌸) can be decomposed into crop income and income from livestock production, with the former being further subdivided into net returns from specific crops grown. Likewise, agricultural labour income (YL) includes both family labour used on-farm and labour sold to other farms, and non-agricultural income (YNA) can be decomposed into earnings from self-employment, wages received in rural non-farm labour markets, remittances from household members working in urban areas etc. Each of these components of overall household income varies widely in importance, depending on such aspects as differences in the household endowments of human and physical capital (including land), agronomic endowments of householdowned land, proximity to urban centres, and many other cultural, historical and institutional circumstances. These differences are important for understanding the distribution of the benefits from economic forces exogenous to households (e.g. government policies or the product of agricultural research) because they condition the degree to which specific actions or investments affect the well-being of specific types of households.
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This is most easily seen by totally differentiating Eq. (1) and re-expressing the income decomposition in terms of rates-of-change: Yˆ∗⫽l⌸Yˆ⌸⫹lLYˆL⫹lNAYˆNA⫺Pˆ∗
(2)
where ‘ˆ’ indicates a proportional rate of change (so that Xˆ=dlnX=dX/X) and li is the share of income component i in total income (i=⌸, L, NA). Eq. (2) clearly shows that gauging the impact of various exogenous shocks on the welfare of certain types of households will depend on the (household-specific) size of those shocks and the importance of the particular activity in the household’s portfolio of income-generating activities. All else equal, agricultural technologies that are better suited for a particular type of production environment will have a more profound direct effect on the welfare of households living in that production environment, i.e. Yˆ⌸ will be larger. Additionally, technologies that have comparable productivity impacts across production environments and/or household types will more profoundly impact on the well-being of households that depend heavily on crop income (i.e. households for whom l⌸ is large) than on households earning most of their income from non-agricultural enterprises. Eq. (2) provides a useful way of categorising past research on the distributional impacts of improved agricultural technologies across different household types located in different production environments. Direct effects Work on the direct effects of technical change focuses on Yˆ⌸—particularly changes in the net revenues resulting from downward shifts in marginal production costs. Most empirical work in this area has focused on socio-economic groups located in areas where the new technologies were adopted, examining the impacts of technological innovations on the incomes of various types of households and on returns to various factors of production owned by adopting households (e.g. Eckert, 1970; Barker et al., 1985). Historically, both adoption levels and productivity impacts have been markedly higher for favoured areas. However, especially in the post-Green Revolution period, marginal areas have witnessed steady advances in the adoption of high-yielding varieties (HYVs) and other yield-enhancing technologies for cereal crops and continued modest improvement can be expected (Byerlee, 1996). Despite these improvements for marginal areas—and despite widespread acknowledgement that cereal production on marginal lands will need to be significantly increased to ensure global food security over the medium term (Conway, 1997)— productivity increases are harder to achieve in marginal than in favoured environments. This is true even though the ‘easy’ yield gains in favoured areas that result from switching from traditional to modern varieties generally have been exhausted. Breeding for yield increases in marginal areas is inherently difficult, particularly when moisture stress underlies the ‘marginality’ of the agro-ecosystem (Byerlee and Morris, 1993). Progress in developing improved varieties for dry areas is likely to be slow even in countries with strong plant breeding programs that target such areas (Byerlee and Moya, 1993). In such locations, crop management efforts that improve
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moisture use efficiency and nutrient availability often tend to have more promise than varietal development as a means of enhancing productivity (Sanders et al., 1996). But this being so, the lower probability of crop management research in generating successful innovations (vis-a`-vis crop breeding research) represents another reason for tempering optimism over what can be accomplished in terms of boosting productivity in marginal areas (Traxler and Byerlee, 1992). Commodity market effects A large literature addresses the distributional impacts of various technological innovations caused by their effects on prices in commodity markets. Early work in this area was oriented toward ascertaining social returns to agricultural research (Ayer and Schuh, 1972; Akino and Hayami, 1975), emphasising the relative effects of technical change on producers and consumers using standard Marshallian surplus concepts. The basic message of these analyses was that in open economies producers reap the lion’s share of the benefits from technological change in the form of innovators’ rents, while in closed economies consumers benefit most via price effects in output markets. Expanding the analysis to include differential adoption patterns across production environments does not alter these qualitative conclusions. It merely shifts the focus onto whether or not farm households adopt the new technologies and (in the closed economy case) whether they are net consumers or net producers of the commodity in question (Renkow, 1994). Because output prices are unchanged in an open economy, all adopting farmers benefit from the new technology. If instead the economy is closed, aggregate supply shifts cause prices to fall. In this case, all net consumers benefit, non-adopting net producers suffer welfare losses and the impact on adopting net producers is ambiguous (because both unit costs and output prices fall). Both empirical and simulation analyses of the interaction between the adoption of Green Revolution technologies and food prices have validated these theoretical predictions. Where markets for staple foods are thought to be closed, the major beneficiaries of technological change have been consumers (Hayami and Herdt, 1977; Scobie and Posada, 1978; Renkow, 1993a). Regarding regional differences in the distributional effects of technological change, Scobie and Posada (1978) concluded that the introduction of HYV rice in Colombia mainly benefited consumers (particularly those with low incomes) and adopting producers, while the incomes of net producers of non-irrigated rice were negatively affected. Renkow (1993a) found that the adoption of HYV wheat in Pakistan had significant positive impact on the incomes of net consuming households and negative effects on net producers. Because food expenditure shares are typically higher for poorer households, it was often argued that the cheapening of food prices via technological progress was the most important ‘pro-poor’ effect of Green Revolution-type innovations (Ruttan, 1977; Lipton and Longhurst, 1989).5 Note, however, that this result depends on mar5 In the context of Eq. (2), this means that Pˆ∗ will be more negative for poor than for more well to do households.
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kets for the commodities involved being at least partially closed. In addition, analyses supporting this result have generally ignored the actions of government price and stabilisation policies. Such policies may have profound effects on interregional and intraregional income distribution, effects likely to extend to the transmission of benefits and costs arising from technological change. Multi-market models developed for Pakistan and the Philippines indicate that when commodity markets are open to international trade, these price effects essentially disappear (Coxhead and Warr, 1991; Renkow, 1993a). Neither will falling food prices inevitably benefit households initially not participating in local markets for reasons of high transactions costs (e.g. transport costs, liquidity constraints). This may be a particularly accurate characterisation of many poor households in geographically remote, marginal production environments, for whom the price bands defined by the transactions costs associated with market participation may be relatively wide (de Janvry et al., 1991). As long as the point of intersection of household demand and supply remains within these price bands these households will enjoy no welfare improvement from falling market prices. Labour market effects Considerable evidence indicates that adoption of HYV seed-fertiliser technologies of the Green Revolution have led to significant increases in labour demand (Ruttan, 1977; Jayasuriya and Shand, 1986; Lipton and Longhurst, 1989). Most commonly, these have been linked to increases in harvest and threshing labour associated with higher yields and increased cropping intensity facilitated by shorter duration varieties (Barker and Cordova, 1978). As long as labour supply is less than perfectly elastic, such changes in labour demand will put upward pressure on wage rates in local labour markets, thereby affecting the incomes of all households in adopting areas for whom agricultural labour is a source of household income. These include farm households that do not sell labour to other farms, but for whom the implicit return to their on-farm labour will have changed.6 The impact of a new labour-using technology may extend outside its area of adoption if labourers in non-adopting areas are sufficiently mobile. In this event, if increased labour demand caused wages to rise sufficiently to cover the cost of changing locations, labourers from non-adopting areas may migrate to take advantage of better employment opportunities. This will also put upward pressure on wage rates in non-adopting areas. The potential for rural–rural migration to transfer some of the benefits of technological change to agricultural labour households through higher wages in non-adopting areas has been widely recognised (Hazell and Anderson, 1984; Quizon and Binswanger, 1986). Evidence from India shows that the rapid diffusion of wheat HYVs
6
This abstracts from possible spillover effects from product markets. In a closed economy, for example, output price declines accompanying supply shifts may in fact lead to a net reduction in the derived demand for labour in adopting regions where product demand is relatively elastic.
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in the Punjab in the late 1960s induced a large influx of labourers from other provinces and that this migration tended to reduce interregional wage dispersion (Oberai and Singh, 1980; Acharya, 1989). David and Otsuka (1994) summarised research on labour market effects of HYV rice adoption in South-East Asia. They mostly corroborate the hypothesis that migration and subsequent wage equalisation have acted to transfer part of the benefits from technological change in favoured environments with high adoption levels to marginal environments with much lower adoption levels. Although rising real wages might appear to be the obvious result of increased labour demand caused by technological change, empirical confirmation for this is small. Rather, available evidence indicates stagnation or, at best, small increases in real wages occurred in most areas of rapid HYV adoption (Lipton and Longhurst, 1989). Possible explanations include high levels of unemployment or under-employment in adopting areas prior to adoption, high rates of population growth and migration of labourers into adopting areas. Importantly, none of these explanations contradicts the notion that remuneration of agricultural labourers was increased relative to what would have occurred without technological change.7 Income shares Most work on rural poverty in developing countries tends to focus on the agricultural sector. However, considerable evidence shows that non-farm income derived from local wage-paying activities, self-employment and external remittances is usually an important element in the portfolio of income-generating activities of both farm households and rural landless households. Reardon et al. (1998) confirm that average household income shares from off-farm activities and average shares of rural household labour devoted to non-farm employment are significant, ranging between 25 and 45% for different geographic regions (Table 2). Moreover, comparing these figures (drawn from studies in the 1980s and 1990s) with a similar review by Haggblade et al. (1989) of studies in the 1970s indicates that the rural non-farm sector appears to be growing in importance in most regions of the world. Reardon et al. (1992) point out that competing forces motivate rural households to participate in non-farm activities. Diversification may be related to household efforts to minimise exposure to production risks inherent in agriculture. This would tend to yield higher non-farm income shares in more risky, marginal agro-ecological zones. In contrast, the presence of significant forward and backward production and consumption linkages between the farm and non-farm sectors (Haggblade et al., 1989; Chuta and Liedholm, 1990) tends to promote greater non-farm income shares in more productive favoured areas. As regards household wealth status, better off households tend to be less risk averse and less liquidity constrained and therefore
7
Conversely, numerous examples exist of mechanisation occurring on the heels of adoption of seedfertiliser technologies (Conway, 1997). These have tended to erode welfare gains of poorer individuals, particularly the landless.
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Table 2 Non-farm income and employment shares of rural households by regiona Location
Non-farm income share Mean (%) CV
Non-farm employment shareb Mean (%) CV
Africa East and Southern Africa West Africa Asia East Asia South Asia Latin America
42 45 36 32 35 29 40
– – – 44 44 43 25
0.45 0.47 0.36 0.33 0.19 0.52 0.20
– – – 0.32 0.29 0.40 0.33
Source: Reardon et al. (1998) a All data are regional averages of country case studies. b Employment shares represent the share of households in the rural population for which non-farm activity is the primary occupation.
more likely to engage in alternative non-farm enterprises requiring initial capital investments (e.g. starting a small business). At the same time, the existence of wage earning opportunities that are less risky than agricultural production would give rise to a greater propensity toward diversification on the part of poorer, more riskaverse households. Thus, there is no compelling theoretical justification, a priori, for predicting either where or for whom rural non-farm income shares will be most important; rather, these are empirical questions. And, not surprisingly, where data exist that allow comparison of non-farm income shares across production environments, no apparent systematic differences emerge between favoured and marginal areas. This may be seen in Fig. 1, which summarises the findings of various household income studies for selected African and Asian countries.8 Empirical evidence on differences in the importance of non-farm income to different income classes across agro-ecological zones is likewise mixed (see Fig. 2). Studies conducted in South Asia indicate that poorer households in both favoured and marginal areas depend heavily on non-farm income. Additional evidence from Pakistan indicates that the importance of non-farm income in marginal areas—particularly remittances from abroad—grew dramatically between 1965 and 1987 (Renkow, 1993b). In contrast, evidence from West Africa indicates that the rural poor depend more on agriculture than do the rural non-poor (Reardon et al., 1992). Quibria and Srinivasan (1991), cited in Malik (1998), found a similar relationship in seven Asian developing countries in the late 1980s.
8
It is important to recognise that non-farm income considered in these studies is a broad categorisation of a variety of economic activities, ranging from menial wage labour to local self-employment to foreign remittances. Thus, in addition to reflecting the variety of household behavioural responses to external circumstances, the lack of any clear tendencies by location or wealth status is probably also an artefact of this high degree of aggregation.
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Fig. 1. Non-farm income shares in favoured vs. marginal production environments in selected African and Asian countries (averaged over all income classes). Sources used were Reardon (1997) for Africa, David et al. (1994) for the Philippines, Jatileksono (1994) for Indonesia, Isvilanonda and Wattanutchariya (1994) for Thailand, Hossain et al. (1994) for Bangladesh and Upadhyaya and Thapa (1994) for Nepal.
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Fig. 2. Non-farm income shares for selected countries by agro-ecological zone and income class. Sources used were Reardon et al. (2000) for Niger, Garcia and Alderman (1993) for Pakistan, Walker et al. (1983) for India and Renkow (1991) for Pakistan.
The considerable variability in the importance of non-agricultural sources of income directly translates into spatial variability in the welfare effects of technology adoption. This has important implications for the ability of research institutions like those of the CGIAR to achieve their goals of poverty alleviation. For locations where agricultural income is a relatively small share of total income for poor households, a ratcheting downward will occur of the potential for technical change as a tool of poverty alleviation. In these situations, other kinds of investments may well yield significantly larger dividends.
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Agricultural research vs infrastructure investment International agricultural research centres develop improved germplasm and crop management techniques that increase yields, cropping intensity and demand for labour where they are adopted. Some key variables and effects must be quantified to assess how an individual agricultural innovation or technology package will affect different types of households in different production environments. These include: 1. The innovation’s direct impacts on output, labour demand and input use on the part of different types of farm households; 2. Indirect spillover effects mediated through product and labour markets that change the relative prices faced by different types of households (including landless households); 3. The relative numbers of different types of households within a particular area or production environment; and 4. The share of agriculture-based income in total household income for different types of households. Once this information is known, the differential income effects can be assessed across locations and household types. Comprehensive studies in Pakistan (Renkow, 1993a) and South-East Asia (Coxhead and Warr, 1991; David and Otsuka, 1994) incorporating most or all of these elements into analysis of alternative technology adoption scenarios have usually decided in favour of continuing to strongly emphasise research strategies oriented toward favoured production environments. But the primary reason was different in each study. In Pakistan, the dominant influence was differences in agricultural income shares between marginal and favoured areas. In South-East Asia, the key factors were labour mobility and the responsiveness of agricultural workers to perturbations in labour markets. In contrast, Fan and Hazell (1999) find evidence that the marginal impacts on poverty reduction of many public investments in agricultural technology (specifically, HYV adoption), education and infrastructure are greater for India’s rainfed production environments than for (favoured) irrigated areas. They argue that these results suggest the “tantalising possibility that greater public investment in some low-potential areas could actually offer a ‘win–win’ strategy for addressing productivity and poverty problems” (p. 33). The Fan and Hazell study is important, partly because it figures prominently in recent debates over the desirability (on poverty alleviation grounds) of targeting a greater share of agricultural research expenditures to marginal production environments (e.g. Henninger, 1998) and partly because its conclusions regarding the distributional impacts of technical change depart rather strikingly from those of earlier work. However, radically re-orienting research priorities on the basis of their results is probably premature for at least two reasons. First, as the authors themselves quickly point out, their benefits measures do not include research costs. To the extent that achieving productivity increases in marginal areas is more costly (because of the inherent difficulties in working in these environments), net benefits will be lower.
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Second, it is unclear to what degree the research culminating in the development of the HYVs that were adopted in low-potential areas was actually targeted to those areas. This is particularly important for wheat and rice, crops for which the bulk of improved germplasm adopted in India’s marginal environments was developed for more favoured agro-ecological zones (Traxler et al., 1996).9 Fan and Hazell’s findings regarding the relatively larger impact of infrastructure investments on poverty reduction in low-potential areas are highly significant. Such investments can work in several ways to enhance the welfare of the rural poor via the agricultural economy. They can lead directly to greater availability (at lower cost) of necessary agricultural inputs such as fertilisers and chemicals and thus enhance welfare by directly increasing agricultural productivity. Perhaps more importantly, improved transportation and communications infrastructure facilitate spatial integration of product and factor markets, thereby augmenting the potential for inter-regional spillover effects of new technologies. Infrastructure investments also serve as stimulus to rural non-farm economic activity and in many circumstances this may hold higher potential than agricultural research for alleviating poverty in marginal areas. Moreover, these kinds of investments also tend to facilitate opportunities to take advantage of non-local employment opportunities via migration of some household members.
Concluding remarks I have sought to clarify the key issues regarding the debate over the optimal allocation of agricultural research between marginal and favoured production environments. Weighing the impact on poverty alleviation of alternative breeding and crop management research activities requires a careful assessment of where the poor live, what types of income-generating activities they engage in and the ways in which new agricultural technologies alter the returns to resources owned by household members. Available evidence does not support easy generalisations about the best means of improving the welfare of the poor in marginal areas. Instead, it reinforces the need for continual examination of alternative policies and investment strategies on a case by case basis. The bulk of research in this area usually supports maintaining a strong emphasis on research strategies oriented toward favoured production environments on both efficiency and (global) equity grounds. Almost by definition, the ultimate productivity impacts of improved agricultural technologies will be lower in marginal areas than in favoured areas. And where ‘marginality’ is correlated with physical remoteness, inferior infrastructure or institutional inadequacies, the poorer availability and higher cost of complementary inputs tends to even further widen inter9 Nor is it clear that there has been systematic under-investment in agricultural research targeting marginal areas, at least for cereals that are grown in both favoured and marginal agro-ecological zones. Worldwide, rice and wheat research targeted to marginal environments is generally high relative to production emanating from these areas (Byerlee, 1996).
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regional disparities in the direct effects of new technologies. Furthermore, because the direct effects of new technologies are usually larger in favoured areas, spillover effects operating through factor and product markets also tend to be larger when they emanate from favoured areas. Undoubtedly agricultural research that specifically targets difficult production environments may represent the most pro-poor public investment available for some marginal areas. This is especially likely to be true for locations in which agricultural income shares of the poor are high, agronomic circumstances limit the adoption of technologies developed for other, more favourable production environments and prospects for research successes are reasonably high. However, in many situations, government investments in infrastructure and institutional reform may well yield significantly larger and more rapid benefits to the poor in marginal areas than will investments in agricultural research targeted to those areas—especially where nonagricultural sources of income are relatively important.
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Eckert, J., 1970. The impact of dwarf wheats on resource productivity in West Pakistan’s Punjab. Ph.D. thesis. Michigan State University, East Lansing, MI. Fan, S., Hazell, P.B.R., 1999. Are returns to public investment lower in less-favoured rural areas? An empirical analysis of India. EPTD Discussion Paper No. 43, International Food Policy Research Institute, Washington, DC. Garcia, M., Alderman, H., 1993. Poverty, household food security and nutrition in rural Pakistan. Research Report No. 96, International Food Policy Research Institute, Washington, DC. Haggblade, S., Hazell, P.B.R., Brown, J., 1989. Farm–non-farm linkages in rural Sub-Saharan Africa. World Development 17 (8), 1173–1201. Hayami, Y., Herdt, R.W., 1977. Market price effects of technical change on income distribution in semisubsistence agriculture. American Journal of Agricultural Economics 59 (2), 245–256. Hazell, P.B.R., Anderson, J., 1984. Public policy toward technical change in agriculture. Greek Economic Review 6, 453–482. Heisey, P.W., Edmeades, G.O., 1999. Maize Production in Drought-Stressed Environments. 1997/98 World Maize Facts and Trends. Centro Internacional de Mejoramiento de Maı´z y Trigo (CIMMYT), Mexico, DF. Henninger, N., 1998. Mapping and geographic analysis of human welfare and poverty—review and assessment. World Resources Institute, Washington DC (mimeo). Hossain, M., Quasem, M.A., Jabbar, M.A., Akash, M.M., 1994. Production environments, modern variety adoption and income distribution in Bangladesh. In: David, C.C., Otsuka, K. (Eds.), Modern Rice Technology and Income Distribution in Asia. Lynne Rienner Publishers, Boulder, CO, pp. 221–280. Isvilanonda, S., Wattanutchariya, S., 1994. Modern variety adoption, factor-price differential and income distribution in Thailand. In: David, C.C., Otsuka, K. (Eds.), Modern Rice Technology and Income Distribution in Asia. Lynne Rienner Publishers, Boulder, CO, pp. 173–220. Jatileksono, T., 1994. Varietal improvements, productivity change and income distribution: the case of Lampung, Indonesia. In: David, C.C., Otsuka, K. (Eds.), Modern Rice Technology and Income Distribution in Asia. Lynne Rienner Publishers, Boulder, CO, pp. 129–172. Jayasuriya, S., Shand, R., 1986. Technical change and labour absorption in Asian agriculture: some emerging trends. World Development 14, 415–428. Kelley, T.G., Parthasarathy Rao, P., 1995. Marginal environments and the poor: evidence from India. Economic and Political Weekly 30 (4), 2494–2495. Leonard, H.J. et al., 1989. Environment and the Poor: Development Strategies for a Common Agenda. US–Third World Policy Perspectives No. 11, Overseas Development Council. Transactions Books, New Brunswick, NJ. Lipton, M., Longhurst, R., 1989. New Seeds and Poor People. Johns Hopkins University Press, Baltimore, MD. Malik, S.J., 1998. Rural poverty and land degradation: a reality check for the CGIAR. Report to the Technical Advisory Committee to the CGIAR. FAO, Rome. Oberai, A., Singh, H., 1980. Migration flows in Punjab’s green revolution belt. Economic and Political Weekly 15, A2–A12. Pinstrup-Anderson, P., Pandya-Lorch, R., 1994. Alleviating poverty, intensifying agriculture and effectively managing natural resources. Food Agriculture and the Environment Discussion Paper No. 1, International Food Policy Research Institute, Washington, DC. Quibria, M.G., Srinivasan, T.N., 1991. Mimeo. Rural poverty in Asia: priority issues and policy options. Asian Development Bank, Manila. Quizon, J., Binswanger, H.P., 1986. Modeling the impact of agricultural growth and government policy on income distribution in India. The World Bank Economic Review 1, 103–148. Reardon, T., 1997. Using evidence of household income diversification to inform study of the rural nonfarm labour market in Africa. World Development 25 (5), 735–748. Reardon, T., Vosti, S.A., 1995. Links between rural poverty and the environment in developing countries: Asset categories and investment poverty. World Development 23 (9), 1495–1506. Reardon, T., Delgado, C., Matlon, P., 1992. Determinants and effects of income diversification amongst farm households in Burkino Faso. Journal of Development Studies 28 (2), 264–296. Reardon, T., Stamoulis, K., Balisacan, A., Cruz, M.E., Berdegue, J., Banks, B., 1998. Rural non-farm
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income in developing countries: importance and policy implications. In: FAO, The State of Food and Agriculture. FAO, Rome, pp. 283–356. Reardon, T., Taylor, J.E., Stamoulis, K., Lanjouw, P., Balisacam, A., 2000. Effects of non-farm employment on rural income inequality in developing countries: an investment perspective. Journal of Agricultural Economics 50(3) in press. Renkow, M., 1991. Modeling the aggregate effects of technological change on income distribution in Pakistan’s favoured and marginal production environments. Economics Paper No. 4, CIMMYT, Mexico, DF. Renkow, M., 1993a. Differential technology adoption and income distribution in Pakistan: implications for research resource allocation. American Journal of Agricultural Economics 75 (1), 33–43. Renkow, M., 1993b. Technological change in wheat production in Pakistan: markets behavior and income dynamics. In: Dvorak, K. (Ed.), Social Science Research for Agricultural Technology Development: Spatial and Temporal Dimensions. CAB International, London, pp. 65–87. Renkow, M., 1994. Technology, production environment and household income: assessing the regional impacts of technological change. Agricultural Economics 10 (3), 219–232. Ruttan, V., 1977. The green revolution: seven generalizations. International Development Review 19 (1), 16–23. Sanders, J.H., Shapiro, B.I., Ramaswamy, S., 1996. The Economics of Agricultural Technology in Semiarid Sub-Saharan Africa. Johns Hopkins University Press, Baltimore, MD. Scobie, G.M., Posada, T.R., 1978. The impact of technical change on income distribution: the case of rice in Colombia. American Journal of Agricultural Economics 60 (1), 85–92. Traxler, G., Byerlee, D., 1992. Economic returns to crop management research in the post-green revolution setting. American Journal of Agricultural Economics 74 (3), 573–582. Traxler, G., Byerlee, D., Jain, K.B.L., 1996. Linking technical change to research effort: an examination of aggregation and spillover effects. Mimeo. Department of Agricultural Economics and Rural Sociology, Auburn University. UNEP/GRID (United Nations Environment Programme/Global Resource Inventory Database), 1997. Mapping Indicators of Poverty in West Africa. UNEP/DEIA Technical Report No. 97-8. FAO, Rome. Upadhyaya, H.K., Thapa, G.B., 1994. Modern variety adoption, wage differentials and income distribution in Nepal. In: David, C.C., Otsuka, K. (Eds.), Modern Rice Technology and Income Distribution in Asia. Lynne Rienner Publishers, Boulder, CO, pp. 281–322. Walker, T.S., Singh, R.P., Asokan, M., Binswanger, H.P., 1983. Fluctuations in income in three villages of India’s semi-arid tropics. Economics Program Progress Report No. 57, International Crops Research Institute for the Semi-Arid Tropics, Hyderabad.
Poverty, productivity and production environment: a review of the evidence Mitch Renkow
*
Department of Agricultural and Resource Economics, North Carolina State University, Raleigh, NC 26795-8109, USA
Abstract This paper reviews the state of knowledge about the key issues needing to be understood to satisfactorily resolve a long-standing debate within the Consultative Group for International Agricultural Research (CGIAR) system. The debate revolves around the effects on various populations (particularly the poor) of different allocations of research effort between marginal and favoured production environments. This paper specifically focuses on what is known about the geographical distribution of the rural poor, across agro-ecological zones and over time. Variations in the income-generating activities—including non-agricultural activities—engaged in by the poor are examined and the ways in which specific technology packages affect the economic well-being of different types of households, both directly and indirectly. 2000 Elsevier Science Ltd. All rights reserved. Keywords: Poverty alleviation; Technological change; Agricultural research; Production environment
Introduction Determining the optimal mix of research activities to meet poverty alleviation goals is an enduring issue within the Consultative Group for International Agricultural Research (CGIAR). Most of the research conducted in CGIAR centres is unquestionably at least potentially pro-poor (Lipton and Longhurst, 1989). However, substantial uncertainty remains as to how the centres—individually and collectively—might best reduce poverty throughout the Third World. * Tel.: +1-919-515-5179; fax: +1-919-515-1824. E-mail address: [email protected] (M. Renkow). 0306-9192/00/$ - see front matter 2000 Elsevier Science Ltd. All rights reserved. PII: S 0 3 0 6 - 9 1 9 2 ( 0 0 ) 0 0 0 2 0 - 8
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A long-standing debate within the CGIAR system revolves around the effects on various populations (particularly the poor) of different allocations of research effort between marginal and favoured production environments.1 At the heart of this debate is the fact that most of the major ‘success stories’ produced by the CGIAR centres have occurred in irrigated or well-watered areas—most notably, the rapid increases in rice and wheat yields registered during the Green Revolution. Some argue that there has been systematic under-investment in marginal production environments— to the detriment of the many impoverished people within those areas. Others counter that investment in marginal areas historically has been low precisely because the returns to those investments are low and that diverting research resources away from favoured production environments would do more harm than good overall. Where the poor live, their types of income-generating activities and how new agricultural technologies alter the returns to household members’ resources must be carefully assessed to weigh the relative payoffs (in terms of poverty alleviation) of research in alternative breeding and crop management.
The geographic distribution of poverty Rural poverty is commonly presumed to be more pronounced in marginal than in favoured production environments, either in terms of the absolute number of poor people or the proportion of the population that is impoverished. However, the empirical evidence is mixed. Leonard et al. (1989) gives the most comprehensive accounting of the geographic distribution of poverty. Using data from the World Bank, the International Food Policy Research Institute (IFPRI) and the United Nations (UN) Centre for Human Settlements, he estimated that 47% of the world’s poor lived in marginal rural zones, 36% favoured rural zones and 17% in urban areas (Table 1). Table 1 Geographic distribution of poor people by production environment and regiona Location
Distribution (millions of people)b Rural areas Urban areas Favoured environments Marginal environments
Asiac Sub-Saharan Africa Latin America All developing countries
198 (36) 69 (44) 12 (15) 279 (36)
265 (49) 71 (46) 35 (45) 371 (47)
83 (15 16 (10) 31 (40) 130 (17)
Source: Leonard et al. (1989) a Poor people defined as the poorest 20% among the total population of all developing countries. b Figures in parentheses are percentages. c Includes China and the Middle East.
1 Throughout this paper, the categorisation of a production environment as ‘marginal’ or ‘favoured’ is based on the biophysical and agronomic characteristics affecting agricultural production.
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The regional breakdown of these estimates indicates that significantly more of the rural poor live in marginal areas in Asia and Latin America and that roughly equal numbers of poor people live in marginal and favoured rural areas of Sub-Saharan Africa. Authors weighing in on the favoured versus marginal lands debate (PinstrupAnderson and Pandya-Lorch, 1994; Conway, 1997) have widely cited these figures. However, they are dated and were constructed without the benefit of geographic information systems (GIS) technology and other recent advances in spatial mapping.2 Other studies of the poverty–environment relationship have tended to focus on specific crops or regions. Fan and Hazell (1999) present data from India on the incidence of poverty in irrigated, high- and low-potential rainfed areas. These indicate that the absolute number of poor people in India’s irrigated areas fell by nearly 20% (from 37 to 30 million) between 1972 and 1993, but remained roughly constant in both high- and low-potential rainfed areas (between 75 and 80 million). Over the same time period, the percentage of the total population below the poverty line fell in all areas, with the high-potential rainfed areas consistently having had the highest poverty rate (59% in 1972, 45% in 1993). In contrast, using different geographical definitions, Kelley and Parthasarathy Rao (1995) found that the absolute number of people living in poverty was significantly lower in the marginal production environments (vis-a`-vis favoured environments) of India. They also found no significant relationship between relative poverty and type of production environment. Similarly, Byerlee and Morris (1993) point out that the important favoured wheat zones of South Asia are characterised by very high poverty rates. They note that in most of the world’s wheat growing areas, production environments best described as marginal tend to be characterised by relatively less poverty than is found in more favoured environments. They primarily attribute this to the fact that most of the world’s marginal wheat production areas are concentrated in West Asia and North Africa, a region of relatively high income. Two other recent efforts in this area merit mention. Heisey and Edmeades (1999) found no significant correlation between poverty and production environment using a variety of poverty and land quality indicators.3 Rather, they conclude that other factors—including available technologies, policies, historical circumstances and institutional environment conditioning market relationships—are at least as important as agro-climatic differences in determining the spatial distribution of poverty.4 Conversely, preliminary evidence from a poverty mapping project for West Africa by the United Nations Environment Programme (UNEP) suggests a negative correlation 2 However, projects are currently underway that promise to significantly upgrade our knowledge of the spatial distribution of poverty through the application of GIS technology (Henninger, 1998; Bigman and Fofack, 2000). 3 The poverty indicators are GNP per capita, country poverty weights developed by the Technical Advisory Committee (TAC) of the CGIAR, and TAC estimates of the percentage of the rural population in absolute poverty. The production environment indicators are percentage area often subject to drought stress, aggregate maize yield and the (trend-corrected) coefficient of variation of maize yield. 4 This is in line with the contention of Reardon and Vosti (1995) that poverty–environment linkages depend as much on the socio-economic environment as they do on the biophysical environment within which households dwell.
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between agro-climatic potential and various human development indicators (UNEP/GRID, 1997). Two conclusions emerge from these studies regarding the targeting of efforts to alleviate poverty. First, clearly many poor people live in both marginal and favoured areas (although globally the largest number appear to live in the former). Thus, the global war on poverty must be fought on several fronts. Second, poverty distribution varies considerably geographically. Therefore, the targeting of poverty alleviation efforts will necessarily vary by region or country. In other words, strategies for poverty alleviation cannot be generalised—in particular, targeting certain geographical regions or one type of production environment to the exclusion of others. Nonetheless, the extent to which the benefits of alternative research investments reach poor households—either directly or through spillovers from one geographical area to another—remains an issue of concern to research managers and policymakers.
The welfare effects of agricultural technologies Rural households in developing countries derive income from various sources. Consider, for example, a semi-subsistence agricultural household that earns income from three sources—profits from agricultural production (Y⌸), agricultural labour income (YL) and non-agricultural activities (YNA). Real income (Y*) is the sum of these, deflated by a household-specific price index (P*) that is the expenditure shareweighted average of prices paid for all commodities consumed by the household: Y∗⫽
Y⌸+YL+YNA P∗
(1)
Obviously, each of these categories of income-generating activities can be decomposed further. For example, agricultural profits (Y⌸) can be decomposed into crop income and income from livestock production, with the former being further subdivided into net returns from specific crops grown. Likewise, agricultural labour income (YL) includes both family labour used on-farm and labour sold to other farms, and non-agricultural income (YNA) can be decomposed into earnings from self-employment, wages received in rural non-farm labour markets, remittances from household members working in urban areas etc. Each of these components of overall household income varies widely in importance, depending on such aspects as differences in the household endowments of human and physical capital (including land), agronomic endowments of householdowned land, proximity to urban centres, and many other cultural, historical and institutional circumstances. These differences are important for understanding the distribution of the benefits from economic forces exogenous to households (e.g. government policies or the product of agricultural research) because they condition the degree to which specific actions or investments affect the well-being of specific types of households.
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467
This is most easily seen by totally differentiating Eq. (1) and re-expressing the income decomposition in terms of rates-of-change: Yˆ∗⫽l⌸Yˆ⌸⫹lLYˆL⫹lNAYˆNA⫺Pˆ∗
(2)
where ‘ˆ’ indicates a proportional rate of change (so that Xˆ=dlnX=dX/X) and li is the share of income component i in total income (i=⌸, L, NA). Eq. (2) clearly shows that gauging the impact of various exogenous shocks on the welfare of certain types of households will depend on the (household-specific) size of those shocks and the importance of the particular activity in the household’s portfolio of income-generating activities. All else equal, agricultural technologies that are better suited for a particular type of production environment will have a more profound direct effect on the welfare of households living in that production environment, i.e. Yˆ⌸ will be larger. Additionally, technologies that have comparable productivity impacts across production environments and/or household types will more profoundly impact on the well-being of households that depend heavily on crop income (i.e. households for whom l⌸ is large) than on households earning most of their income from non-agricultural enterprises. Eq. (2) provides a useful way of categorising past research on the distributional impacts of improved agricultural technologies across different household types located in different production environments. Direct effects Work on the direct effects of technical change focuses on Yˆ⌸—particularly changes in the net revenues resulting from downward shifts in marginal production costs. Most empirical work in this area has focused on socio-economic groups located in areas where the new technologies were adopted, examining the impacts of technological innovations on the incomes of various types of households and on returns to various factors of production owned by adopting households (e.g. Eckert, 1970; Barker et al., 1985). Historically, both adoption levels and productivity impacts have been markedly higher for favoured areas. However, especially in the post-Green Revolution period, marginal areas have witnessed steady advances in the adoption of high-yielding varieties (HYVs) and other yield-enhancing technologies for cereal crops and continued modest improvement can be expected (Byerlee, 1996). Despite these improvements for marginal areas—and despite widespread acknowledgement that cereal production on marginal lands will need to be significantly increased to ensure global food security over the medium term (Conway, 1997)— productivity increases are harder to achieve in marginal than in favoured environments. This is true even though the ‘easy’ yield gains in favoured areas that result from switching from traditional to modern varieties generally have been exhausted. Breeding for yield increases in marginal areas is inherently difficult, particularly when moisture stress underlies the ‘marginality’ of the agro-ecosystem (Byerlee and Morris, 1993). Progress in developing improved varieties for dry areas is likely to be slow even in countries with strong plant breeding programs that target such areas (Byerlee and Moya, 1993). In such locations, crop management efforts that improve
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moisture use efficiency and nutrient availability often tend to have more promise than varietal development as a means of enhancing productivity (Sanders et al., 1996). But this being so, the lower probability of crop management research in generating successful innovations (vis-a`-vis crop breeding research) represents another reason for tempering optimism over what can be accomplished in terms of boosting productivity in marginal areas (Traxler and Byerlee, 1992). Commodity market effects A large literature addresses the distributional impacts of various technological innovations caused by their effects on prices in commodity markets. Early work in this area was oriented toward ascertaining social returns to agricultural research (Ayer and Schuh, 1972; Akino and Hayami, 1975), emphasising the relative effects of technical change on producers and consumers using standard Marshallian surplus concepts. The basic message of these analyses was that in open economies producers reap the lion’s share of the benefits from technological change in the form of innovators’ rents, while in closed economies consumers benefit most via price effects in output markets. Expanding the analysis to include differential adoption patterns across production environments does not alter these qualitative conclusions. It merely shifts the focus onto whether or not farm households adopt the new technologies and (in the closed economy case) whether they are net consumers or net producers of the commodity in question (Renkow, 1994). Because output prices are unchanged in an open economy, all adopting farmers benefit from the new technology. If instead the economy is closed, aggregate supply shifts cause prices to fall. In this case, all net consumers benefit, non-adopting net producers suffer welfare losses and the impact on adopting net producers is ambiguous (because both unit costs and output prices fall). Both empirical and simulation analyses of the interaction between the adoption of Green Revolution technologies and food prices have validated these theoretical predictions. Where markets for staple foods are thought to be closed, the major beneficiaries of technological change have been consumers (Hayami and Herdt, 1977; Scobie and Posada, 1978; Renkow, 1993a). Regarding regional differences in the distributional effects of technological change, Scobie and Posada (1978) concluded that the introduction of HYV rice in Colombia mainly benefited consumers (particularly those with low incomes) and adopting producers, while the incomes of net producers of non-irrigated rice were negatively affected. Renkow (1993a) found that the adoption of HYV wheat in Pakistan had significant positive impact on the incomes of net consuming households and negative effects on net producers. Because food expenditure shares are typically higher for poorer households, it was often argued that the cheapening of food prices via technological progress was the most important ‘pro-poor’ effect of Green Revolution-type innovations (Ruttan, 1977; Lipton and Longhurst, 1989).5 Note, however, that this result depends on mar5 In the context of Eq. (2), this means that Pˆ∗ will be more negative for poor than for more well to do households.
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kets for the commodities involved being at least partially closed. In addition, analyses supporting this result have generally ignored the actions of government price and stabilisation policies. Such policies may have profound effects on interregional and intraregional income distribution, effects likely to extend to the transmission of benefits and costs arising from technological change. Multi-market models developed for Pakistan and the Philippines indicate that when commodity markets are open to international trade, these price effects essentially disappear (Coxhead and Warr, 1991; Renkow, 1993a). Neither will falling food prices inevitably benefit households initially not participating in local markets for reasons of high transactions costs (e.g. transport costs, liquidity constraints). This may be a particularly accurate characterisation of many poor households in geographically remote, marginal production environments, for whom the price bands defined by the transactions costs associated with market participation may be relatively wide (de Janvry et al., 1991). As long as the point of intersection of household demand and supply remains within these price bands these households will enjoy no welfare improvement from falling market prices. Labour market effects Considerable evidence indicates that adoption of HYV seed-fertiliser technologies of the Green Revolution have led to significant increases in labour demand (Ruttan, 1977; Jayasuriya and Shand, 1986; Lipton and Longhurst, 1989). Most commonly, these have been linked to increases in harvest and threshing labour associated with higher yields and increased cropping intensity facilitated by shorter duration varieties (Barker and Cordova, 1978). As long as labour supply is less than perfectly elastic, such changes in labour demand will put upward pressure on wage rates in local labour markets, thereby affecting the incomes of all households in adopting areas for whom agricultural labour is a source of household income. These include farm households that do not sell labour to other farms, but for whom the implicit return to their on-farm labour will have changed.6 The impact of a new labour-using technology may extend outside its area of adoption if labourers in non-adopting areas are sufficiently mobile. In this event, if increased labour demand caused wages to rise sufficiently to cover the cost of changing locations, labourers from non-adopting areas may migrate to take advantage of better employment opportunities. This will also put upward pressure on wage rates in non-adopting areas. The potential for rural–rural migration to transfer some of the benefits of technological change to agricultural labour households through higher wages in non-adopting areas has been widely recognised (Hazell and Anderson, 1984; Quizon and Binswanger, 1986). Evidence from India shows that the rapid diffusion of wheat HYVs
6
This abstracts from possible spillover effects from product markets. In a closed economy, for example, output price declines accompanying supply shifts may in fact lead to a net reduction in the derived demand for labour in adopting regions where product demand is relatively elastic.
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in the Punjab in the late 1960s induced a large influx of labourers from other provinces and that this migration tended to reduce interregional wage dispersion (Oberai and Singh, 1980; Acharya, 1989). David and Otsuka (1994) summarised research on labour market effects of HYV rice adoption in South-East Asia. They mostly corroborate the hypothesis that migration and subsequent wage equalisation have acted to transfer part of the benefits from technological change in favoured environments with high adoption levels to marginal environments with much lower adoption levels. Although rising real wages might appear to be the obvious result of increased labour demand caused by technological change, empirical confirmation for this is small. Rather, available evidence indicates stagnation or, at best, small increases in real wages occurred in most areas of rapid HYV adoption (Lipton and Longhurst, 1989). Possible explanations include high levels of unemployment or under-employment in adopting areas prior to adoption, high rates of population growth and migration of labourers into adopting areas. Importantly, none of these explanations contradicts the notion that remuneration of agricultural labourers was increased relative to what would have occurred without technological change.7 Income shares Most work on rural poverty in developing countries tends to focus on the agricultural sector. However, considerable evidence shows that non-farm income derived from local wage-paying activities, self-employment and external remittances is usually an important element in the portfolio of income-generating activities of both farm households and rural landless households. Reardon et al. (1998) confirm that average household income shares from off-farm activities and average shares of rural household labour devoted to non-farm employment are significant, ranging between 25 and 45% for different geographic regions (Table 2). Moreover, comparing these figures (drawn from studies in the 1980s and 1990s) with a similar review by Haggblade et al. (1989) of studies in the 1970s indicates that the rural non-farm sector appears to be growing in importance in most regions of the world. Reardon et al. (1992) point out that competing forces motivate rural households to participate in non-farm activities. Diversification may be related to household efforts to minimise exposure to production risks inherent in agriculture. This would tend to yield higher non-farm income shares in more risky, marginal agro-ecological zones. In contrast, the presence of significant forward and backward production and consumption linkages between the farm and non-farm sectors (Haggblade et al., 1989; Chuta and Liedholm, 1990) tends to promote greater non-farm income shares in more productive favoured areas. As regards household wealth status, better off households tend to be less risk averse and less liquidity constrained and therefore
7
Conversely, numerous examples exist of mechanisation occurring on the heels of adoption of seedfertiliser technologies (Conway, 1997). These have tended to erode welfare gains of poorer individuals, particularly the landless.
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Table 2 Non-farm income and employment shares of rural households by regiona Location
Non-farm income share Mean (%) CV
Non-farm employment shareb Mean (%) CV
Africa East and Southern Africa West Africa Asia East Asia South Asia Latin America
42 45 36 32 35 29 40
– – – 44 44 43 25
0.45 0.47 0.36 0.33 0.19 0.52 0.20
– – – 0.32 0.29 0.40 0.33
Source: Reardon et al. (1998) a All data are regional averages of country case studies. b Employment shares represent the share of households in the rural population for which non-farm activity is the primary occupation.
more likely to engage in alternative non-farm enterprises requiring initial capital investments (e.g. starting a small business). At the same time, the existence of wage earning opportunities that are less risky than agricultural production would give rise to a greater propensity toward diversification on the part of poorer, more riskaverse households. Thus, there is no compelling theoretical justification, a priori, for predicting either where or for whom rural non-farm income shares will be most important; rather, these are empirical questions. And, not surprisingly, where data exist that allow comparison of non-farm income shares across production environments, no apparent systematic differences emerge between favoured and marginal areas. This may be seen in Fig. 1, which summarises the findings of various household income studies for selected African and Asian countries.8 Empirical evidence on differences in the importance of non-farm income to different income classes across agro-ecological zones is likewise mixed (see Fig. 2). Studies conducted in South Asia indicate that poorer households in both favoured and marginal areas depend heavily on non-farm income. Additional evidence from Pakistan indicates that the importance of non-farm income in marginal areas—particularly remittances from abroad—grew dramatically between 1965 and 1987 (Renkow, 1993b). In contrast, evidence from West Africa indicates that the rural poor depend more on agriculture than do the rural non-poor (Reardon et al., 1992). Quibria and Srinivasan (1991), cited in Malik (1998), found a similar relationship in seven Asian developing countries in the late 1980s.
8
It is important to recognise that non-farm income considered in these studies is a broad categorisation of a variety of economic activities, ranging from menial wage labour to local self-employment to foreign remittances. Thus, in addition to reflecting the variety of household behavioural responses to external circumstances, the lack of any clear tendencies by location or wealth status is probably also an artefact of this high degree of aggregation.
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Fig. 1. Non-farm income shares in favoured vs. marginal production environments in selected African and Asian countries (averaged over all income classes). Sources used were Reardon (1997) for Africa, David et al. (1994) for the Philippines, Jatileksono (1994) for Indonesia, Isvilanonda and Wattanutchariya (1994) for Thailand, Hossain et al. (1994) for Bangladesh and Upadhyaya and Thapa (1994) for Nepal.
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Fig. 2. Non-farm income shares for selected countries by agro-ecological zone and income class. Sources used were Reardon et al. (2000) for Niger, Garcia and Alderman (1993) for Pakistan, Walker et al. (1983) for India and Renkow (1991) for Pakistan.
The considerable variability in the importance of non-agricultural sources of income directly translates into spatial variability in the welfare effects of technology adoption. This has important implications for the ability of research institutions like those of the CGIAR to achieve their goals of poverty alleviation. For locations where agricultural income is a relatively small share of total income for poor households, a ratcheting downward will occur of the potential for technical change as a tool of poverty alleviation. In these situations, other kinds of investments may well yield significantly larger dividends.
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Agricultural research vs infrastructure investment International agricultural research centres develop improved germplasm and crop management techniques that increase yields, cropping intensity and demand for labour where they are adopted. Some key variables and effects must be quantified to assess how an individual agricultural innovation or technology package will affect different types of households in different production environments. These include: 1. The innovation’s direct impacts on output, labour demand and input use on the part of different types of farm households; 2. Indirect spillover effects mediated through product and labour markets that change the relative prices faced by different types of households (including landless households); 3. The relative numbers of different types of households within a particular area or production environment; and 4. The share of agriculture-based income in total household income for different types of households. Once this information is known, the differential income effects can be assessed across locations and household types. Comprehensive studies in Pakistan (Renkow, 1993a) and South-East Asia (Coxhead and Warr, 1991; David and Otsuka, 1994) incorporating most or all of these elements into analysis of alternative technology adoption scenarios have usually decided in favour of continuing to strongly emphasise research strategies oriented toward favoured production environments. But the primary reason was different in each study. In Pakistan, the dominant influence was differences in agricultural income shares between marginal and favoured areas. In South-East Asia, the key factors were labour mobility and the responsiveness of agricultural workers to perturbations in labour markets. In contrast, Fan and Hazell (1999) find evidence that the marginal impacts on poverty reduction of many public investments in agricultural technology (specifically, HYV adoption), education and infrastructure are greater for India’s rainfed production environments than for (favoured) irrigated areas. They argue that these results suggest the “tantalising possibility that greater public investment in some low-potential areas could actually offer a ‘win–win’ strategy for addressing productivity and poverty problems” (p. 33). The Fan and Hazell study is important, partly because it figures prominently in recent debates over the desirability (on poverty alleviation grounds) of targeting a greater share of agricultural research expenditures to marginal production environments (e.g. Henninger, 1998) and partly because its conclusions regarding the distributional impacts of technical change depart rather strikingly from those of earlier work. However, radically re-orienting research priorities on the basis of their results is probably premature for at least two reasons. First, as the authors themselves quickly point out, their benefits measures do not include research costs. To the extent that achieving productivity increases in marginal areas is more costly (because of the inherent difficulties in working in these environments), net benefits will be lower.
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Second, it is unclear to what degree the research culminating in the development of the HYVs that were adopted in low-potential areas was actually targeted to those areas. This is particularly important for wheat and rice, crops for which the bulk of improved germplasm adopted in India’s marginal environments was developed for more favoured agro-ecological zones (Traxler et al., 1996).9 Fan and Hazell’s findings regarding the relatively larger impact of infrastructure investments on poverty reduction in low-potential areas are highly significant. Such investments can work in several ways to enhance the welfare of the rural poor via the agricultural economy. They can lead directly to greater availability (at lower cost) of necessary agricultural inputs such as fertilisers and chemicals and thus enhance welfare by directly increasing agricultural productivity. Perhaps more importantly, improved transportation and communications infrastructure facilitate spatial integration of product and factor markets, thereby augmenting the potential for inter-regional spillover effects of new technologies. Infrastructure investments also serve as stimulus to rural non-farm economic activity and in many circumstances this may hold higher potential than agricultural research for alleviating poverty in marginal areas. Moreover, these kinds of investments also tend to facilitate opportunities to take advantage of non-local employment opportunities via migration of some household members.
Concluding remarks I have sought to clarify the key issues regarding the debate over the optimal allocation of agricultural research between marginal and favoured production environments. Weighing the impact on poverty alleviation of alternative breeding and crop management research activities requires a careful assessment of where the poor live, what types of income-generating activities they engage in and the ways in which new agricultural technologies alter the returns to resources owned by household members. Available evidence does not support easy generalisations about the best means of improving the welfare of the poor in marginal areas. Instead, it reinforces the need for continual examination of alternative policies and investment strategies on a case by case basis. The bulk of research in this area usually supports maintaining a strong emphasis on research strategies oriented toward favoured production environments on both efficiency and (global) equity grounds. Almost by definition, the ultimate productivity impacts of improved agricultural technologies will be lower in marginal areas than in favoured areas. And where ‘marginality’ is correlated with physical remoteness, inferior infrastructure or institutional inadequacies, the poorer availability and higher cost of complementary inputs tends to even further widen inter9 Nor is it clear that there has been systematic under-investment in agricultural research targeting marginal areas, at least for cereals that are grown in both favoured and marginal agro-ecological zones. Worldwide, rice and wheat research targeted to marginal environments is generally high relative to production emanating from these areas (Byerlee, 1996).
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regional disparities in the direct effects of new technologies. Furthermore, because the direct effects of new technologies are usually larger in favoured areas, spillover effects operating through factor and product markets also tend to be larger when they emanate from favoured areas. Undoubtedly agricultural research that specifically targets difficult production environments may represent the most pro-poor public investment available for some marginal areas. This is especially likely to be true for locations in which agricultural income shares of the poor are high, agronomic circumstances limit the adoption of technologies developed for other, more favourable production environments and prospects for research successes are reasonably high. However, in many situations, government investments in infrastructure and institutional reform may well yield significantly larger and more rapid benefits to the poor in marginal areas than will investments in agricultural research targeted to those areas—especially where nonagricultural sources of income are relatively important.
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