农村发展的外部性：中国的情况

PS as+< POLICY RESEARCH WORKING PAPER 2879 Externalities in Rural Development Evidence for China Martin Ravallion The World Bank Development Research Group m Poverty Team August 2002  I_POLICY RESEARCH WORKING PAPER 2879 Abstract Ravallion tests for external effects of local economic composition of local economic activity and private activity on consumption and income growth at the farm- returns to local human and physical infrastructure household level using panel data from four provinces of endowments. The results suggest an explanation for rural post-reform rural China. The tests allow for underdevelopment arising from underinvestment in nonstationary fixed effects in the consumption growth certain externality-generating activities, of which process. Evidence is found of geographic externalities, agricultural development emerges as the most important. stemming from spillover effects of the level and This paper-a product of the Poverty Team, Development Research Group-is part of a larger effort in the group to better understand the causes of poverty. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Catalina Cunanan, room MC-3-542, telephone 202-473-2301, fax 202-522-1151, email address ccunanan@worldbank.org. Policy Research Working Papers are also posted on the Web at http:H/ econ.worldbank.org. The author may be contacted at mravallion@worldbank.org. August 2002. (35 pages) The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Produced by the Research Advisory Staff  Externalities in Rural Development: Evidence for China Martin Ravallion' World Bank, 1818 H Street NW, Washington DC 20433, USA mravallion()worldbank.org Key words: Consumption growth, income growth, externalities, panel data, rural China JEL classification: D91, RI1, Q12 I The data used here were kindly provided by China's National Bureau of Statistics, and I am grateful for the assistance and advice provided by NBS staff in Beijing and at various provincial and county offices. For help with setting up the panel data set I am grateful to Shaohua Chen and Qinghua Zhao and for help with the calculations reported here I am grateful to Jyotsna Jalan. The support of the World Bank's Research Committee and a Dutch Trust Fund is also gratefully acknowledged. For their comments, I am grateful to Jyotsna Jalan, Peter Lanjouw, Forhad Shilpi, Dominique van de Walle and participants/discussants at the Cornell/LSE/WIDER Conference on Spatial Inequality and the World Bank's Economist's Forum 2002.   1. Introduction There is a long-standing view that externalities play an important causal role in economic development. Famously, Rosenstain-Rodan (1943) argued that the investment decisions made by one firm in a developing economy influenced the profitability of others, leading him to argue for international assistance for the industrialization of the lagging regions of Eastern and Southern Europe in the 1 940s. More recently, the hypothesis that there are extemalities through knowledge spillovers has been built into theoretical models of economic growth (notably Romer, 1986 and Lucas, 1993). In the context of rural development in poor countries, similar ideas have motivated policy arguments that getting one activity going locally stimulates others, in a "virtuous cycle" of growth; Mellor (1976) provided an influential statement of this hypothesis.2 Hazell and Haggblade (1990) tested the hypothesis using district and state level data for India, and reported seemingly strong effects of agricultural growth on rural nonfarn development.3 This paper explores the micro-empirical foundations of these arguments using household panel data for a developing rural economy. Some stylized facts about the setting will help motivate the subsequent analysis. One such fact is that in a poor rural economy, the income gains that are claimed to stem from linkage will be transmitted in large part through the farm- household economy, which accounts for the bulk of rural economic activity in most developing countries. No doubt, spillover effects will also involve rural-based firms. However, it is plausible in this setting that any external impacts of local economic activity on income growth would be evident at the farm-household level. A second stylized fact is that many farm- households engage in multiple activities simultaneously, including nonfarm activities. Casual 2 Building on Mellor and Lele (1972). Much earlier still, Clark (1940) had argued that higher agricultural productivity was a crucial precondition for industrialization. 3 Also see Haggblade et al. (1989) and Haggblade et al. (2002). Lanjouw and Lanjouw (2001) provide a useful review of the arguments and evidence on the rural nonfarm sector. 2  observations do not suggest that it is commonly the case that a rural household is fully specialized in either farm or nonfarm activities. Indeed, it has been argued that such income diversification is an important strategy by which rural households cope with uninsured risk (see, for example, Ellis, 1998). There is a large literature pointing to the problems of incomplete credit and risk markets in underdeveloped rural economies (for an overview see Besley, 1995). It is not implausible that there are extemalities in this setting. One way this happens is when farmers learn about new techniques of production from the experience of their neighbors; Feder and Slade (1985) provide survey evidence for northwest India that this is an important channel for knowledge diffusion amongst farmers. Foster and Rosenzweig (1995) find evidence of this type of leaming extemality in farm profitability from adopting new seed varieties in India. Network effects in the marketing of agricultural products can also generate externalities: a farmer can benefit from the infrastructure already in place locally. Another possible source of externalities is the presence of local nonfarm industries that encourage the acquisition of knowledge and skills that also benefit local farmers or non-farm enterprises at household level, possibly through knowledge sharing within households (Basu et al., 2002). In the case of China, it has been argued that higher output from the non-farm sector has brought extemal benefits to the traditional farm sector, through improved technologies and management (Sengupta and Lin, 1995). Or a higher density of commercial enterprises may enhance the local tax base, allowirig better local public goods, and so promoting higher growth for those not actually engaged in those enterprises. Alternatively, negative externalities might result when the expansion of one activity creates congestion, or otherwise crowds out, another activity. The most obvious way this could happen is though the existence of local-level fixed factors of production (including environmental assets) that are shared across activities. For example, with imperfect credit 3  markets leading to rationing of the available credit, an expansion in one activity may crowd out growth prospects in another. With restricted migration and wage stickiness, the same could happen with regard to labor. If the patterns found in aggregate data reflect such externalities this would provide an important insight into the causal processes creating rural underdevelopment. That depends crucially on whether markets exist for the externalities.4 That cannot be judged on a priori grounds. However, a complete set of such markets is not inherently plausible for the sorts of extemalities discussed above. Knowledge spillovers or network effects do not lend themselves to the excludability properties needed for a market. (It would clearly be difficult to define and enforce property rights for such externalities.) So there must be a reasonable presumption that private decision-makers will not typically take account of the extemal costs and benefits of their allocative decisions and so one will expect to see under-investment in the activities that generate positive externalities, and over-investment in those that have negative externalities. The externalities then impede or distort rural development. On the other hand, if the underlying linkage effects are purely intemal at the farm- household level then their welfare and policy significance is greatly diminished.5 Given the stylized facts summarized above, the averaging of purely intemal effects within diversified farm- household units could readily generate the appearance of extemalities in economic activity in aggregate data when in fact none exist at the micro level. For example, given capital market imperfections, higher farm income for a given household may create the resources needed to 4 On the economic theory of markets for externalities, see Dasgupta and Heal (1979, Chapter 3). s It is often argued that the same is true if the externalities are "pecuniary," meaning that they are transmitted through prices. However, it is known that with incomplete markets, pecuniary externalities can still be a source of inefficiency (Greenwald and Stiglitz, 1986; Hoff, 1998, 2000). The externality transmitted through prices could exacerbate the pre-existing inefficiency. 4  finance a new nonfarm activity. Farm and nonfarm incomes may then co-move in a process that one might identify as inter-sectoral linkage in aggregate data even though there is no genuine externality involved. The causal connection is of course unclear, nor is it obvious that there would be any believable identification strategy. The concern with geographic externalities goes beyond economic efficiency. It also raises concerns about horizontal equity. In particular, if the micro growth process involves such externalities then the economy will reward otherwise identical individuals differently depending on where they live. This may also help understand geographic dimensions of social unrest, as has been evident in China in the 1990s.6 Motivated by these observations, the central question addressed in this paper is whether the signs of linkage amongst economic activities found in geographic data stem from externalities. From what we know about the features of a developing rural economy it is clear that one cannot conclude from the existing literature on linkages in rural development that externalities are present to any significant extent. The signs of linkage in geographically aggregated data could easily stem from a process in which there is in fact no interdependence amongst individual farm-household units. Testing for externalities poses a problem, even with micro panel data. Correlations between individual outcomes and geographic variables have been widely reported in the literature. However, as is well-recognized, one cannot assume that the geographic placement of 6 For example, an article in the New York Times (Dec. 27, 1995, p.1) wrote that: "As China's economic miracle continues to leave millions behind, more and more Chinese are expressing anger over the economic disparities between the flourishing provinces of China's coastal plain and the impoverished inland, where 70 million to 80 million people cannot feed or clothe themselves and hundreds of millions of others are only spectators to China's economic transformation." 5  economic activity is exogenous at the micro level.7 Placement in a given locality cannot be expected to be independent of the characteristics of the households that live there - no doubt including characteristics that are unobserved by the analyst. Persistent spatial concentrations of individuals with personal attributes that inhibit growth in their living standards, and lead to a worse assignment of geographic assets, can readily entail that the cross-sectional correlations often found in the data are entirely non-causal, with little or no bearing on development policy. All one is really picking up in the data is the fact that households who are poor in terms of some latent characteristic tend to be grouped together spatially and are less able to attract infrastructure and other geographically assigned resources. To make this argument more concrete, consider t. ,..v,... - ... economy, the quality of farmland is likely to be important to the productivity of current and past investments and hence economic growth. Land quality tends to be spatially correlated; the quality of one farmer's land is positively correlated with the quality of his neighbor's. However, land quality is rarely captured well even in quite comprehensive surveys. At the same time, one can expect that the composition of economic activity and the placement of rural infrastructure (irrigation, roads and so on) will be influenced by land quality. In such seemingly plausible circumstances, one can expect to find correlations between one fanner's income growth rate over time and the attributes of the area in which he lives, even controlling for observable characteristics of the farmer, such as his capital stock. That correlation might look like an externality, but it may simply be picking up the geographically associated latent heterogeneity in land quality. For example, Foster and Rosenzweig (1996) report a significant coefficient on village placement of agricultural extension services in regressions for the adoption of high yielding varieties in micro data for India. As they point out, this cannot be considered a causal effect since the placement of extension services may depend on geographically-associated latent factors influencing adoption. 6  The paper presents results of a test for geographic externalities through the composition of economic activity that is robust to such latent heterogeneity. Both household panel data and geographic data are clearly called for to have any hope of identifying geographic extemalities in the growth process at the micro level. In modeling such data, one might turn to a standard panel data model with a time-invariant error component, as in (for example) the regressions for farm profits in Foster and Rosenzweig (1995). Allowing for latent heterogeneity in the household- level growth process will protect against spurious geographic effects due to time-invariant omitted variables. However, standard panel-data methods of eliminating the household-specific effect wipe out the time-invariant geographic variables of interest in this context, namely the initial composition of economic activity in the locality. Nor is it plausible that the latent heterogeneity in growth rates is time invariant; macroeconomic and geo-climatic conditions might well entail that the impact of these individual effects varies from year to year. However, by simply relaxing the assumption that the fixed effect has a time-invariant impact one can estimate the effect of geographic differences in the observed initial level of economic activity on the micro growth process robustly to the latent heterogeneity. In particular, .he analysis in this paper allows for nonstationary individual effects in the micro growth process, following Holtz-Eakin, Newey and Rosen (1988) and Jalan and Ravallion (2002). The analysis combines geographic data on the composition of economic activity and infrastructure endowments with longitudinal micro observations of consumption and income growth by sector. The growth rate of household consumption is decomposed by income source to explore the income effects of geographic differences in the composition of economic activity and other geographic characteristics. This allows a reasonably flexible description of the patterns of externalities within and between sectors of the economy, as they affect the growth process. 7  The following section outlines the econometric model. Section 3 describes the setting and data while section 4 presents the results. Section 5 summarizes the conclusions. 2. Econometric model The starting point is the following model of consumption growth for N households observed over Tperiods: A ln C1, = a +/, Xi, + ( Zi + i, (i=l,..,N; t2,..,7) (1) where C,1 is consumption by household i at date t, A In Ct is the growth rate of consumption, Xi, is a vector of time-varying explanatory variables, and Zi is a vector of exogenous time-invariant explanatory variables including measures of the initial economic activity in the locality in which household i lives. (The properties of the error term, ej,, are discussed below.) An economic model motivating equation (1) can be derived from a version of the Ramsey (1928) model of consumption growth with capital immobility (Jalan and Ravallion, 2002). In this model, output of the farm household is a concave function of the household's own-capital, but output also depends non-separably on characteristics of the area of residence, including the composition of economic activity. Given the constraints on access to credit, marginal products of own-capital are not equalized across farm-households. Households maximize the standard inter-temporally additive utility integral, with common preferences. The optimal rate of consumption growth is then directly proportional to the marginal product of own capital, which in turn depends on both the farm-household's capital stock and its geographic characteristics. The key feature of this model for the present purpose is that geographic externalities can influence consumption growth rates at the farm-household level, through their effects on the productivity of private investment, given capital market imperfections. (The extreme case in 8  which markets worked perfectly would imply that we had no power to explain the growth in consumption at the farm-household level.) Equation (1) is then obtained by assuming that the marginal product of own capital at the farm-household level is a linear function of Xi, and Zi. The assumptions made about the error term in (1) are of course critical. One naturally wants to include a fixed error component that may well be correlated with the regressors of interest, as discussed in the introduction. The potential endogeneity of the explanatory variables in (1) is assumed to be fully captured by non-zero correlations with this error component. However, it is not assumed that the impact of the heterogeneity is necessarily constant over time. For example, some farmers are more productive than others in ways that cannot be captured in the data and this matters more in a bad agricultural year than a good one. Following Holtz-Eakin et al., (1988), the specification of the error term allows for nonstationarity in the impacts of the individual effects: ui, = 0,1)i + pi/ (2) where aui, is the i.i.d. random variable, with zero mean and variance ao , and co is a time- invariant effect that is not orthogonal to the regressors, i.e., E(cojXj,)