Growth and shocks: evidence from rural Ethiopia

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Abstract

Using panel data from rural Ethiopia, the article discusses the determinants of consumption growth (1989–1997), based on a microgrowth model, controlling for heterogeneity. Consumption grew substantially, but with diverse experiences across villages and individuals. Rainfall shocks have a substantial impact on consumption growth, which persists for many years. There also is a persistent growth impact from the large-scale famine in the 1980s, as well as substantial externalities from road infrastructure. The persistent effects of rainfall shocks and the famine crisis imply that welfare losses due to the lack of insurance and protection measures are well beyond the welfare cost of short-term consumption fluctuations.

Introduction

The study of poor people's impediments to escape poverty remains at the core of development economics. This paper discusses the determinants of growth in living standards in a number of rural communities in Ethiopia between 1989 and 1997. The focus is on the role of shocks, such as drought and famine, on poverty persistence, as well as on identifying the correlates of welfare improvements.

Inspired by the standard growth literature, the paper uses household panel data covering 1989 to 1997 and six villages across the country to study rural consumption growth in this period using a linearised empirical growth model. The focus is on the impact of shocks, and more specifically on persistent effects of rainfall shocks on growth. The results suggest that idiosyncratic and common shocks had substantial contemporaneous impact. Especially better rainfall contributed to the observed growth. I also test for persistence of the effects of past shocks. I find that there is evidence of some persistence-lagged rainfall shocks matter for current growth. Furthermore, indicators of the severity of the famine in 1984–1985 are significant to explain growth in the 1990s, further suggesting persistence. Finally, road infrastructure is a source of divergence in growth experience across households and communities.

The study of growth in developing countries using micro-level household data is not common, largely because suitable panel data sets are missing to embark on such work. Deininger and Okidi (2003) and Gunning et al. (2000) look into the determinants of growth in Ugandan and Zimbabwean panel data. As part of a number of papers using data from rural China, Ravallion and Jalan (1996) use a framework inspired by both the Solow model and the endogenous growth literature to investigate sources of divergence and convergence between regions. In further work using the household level data from their panel (e.g. Jalan and Ravallion, 1997, Jalan and Ravallion, 1998, Jalan and Ravallion, 2002, Jalan and Ravallion, 2004, divergence due to spatial factors is explicitly tested for and discovered, suggesting spatial poverty traps. This paper draws inspiration from their approach by explicitly disentangling community and individual effects. It goes beyond their approach by focusing explicitly on the impact of uninsured risk on household outcomes.

It is well documented that households and individuals in developing countries use different strategies to cope with risk, including self-insurance via savings, informal insurance mechanisms or income portfolio adjustments towards lower overall risk in their activities. Literature surveys suggest that these mechanisms typically only succeed in partial insurance Morduch, 1995, Townsend, 1995. Given that households are generally ‘fluctuation averse’, the resulting fluctuations in consumption and other welfare outcomes imply a loss of welfare due to uninsured risk. However, beyond this transient impact on welfare, there may also be a ‘chronic’ impact from uninsured risk, i.e. persistent or even permanent effects on levels and growth rates of income linked to uninsured risk. In particular, one can distinguish two effects. First, an ex-ante or behavioural impact: uninsured risk implies that it is optimal to avoid profitable but risky opportunities. Households may diversify, enter into low risk but low return activities or invest in low risk assets, all at the expense of mean returns. Second, an ex-post impact, after a ‘bad’ state, has materialised: the lack of insurance against such a shock implies that human, physical or social capital may be lost reducing access to profitable opportunities. In short, uninsured risk may be a cause of poverty. Several theoretical models of poverty traps and persistence have been developed whereby temporary events affect long-term outcomes Banerjee and Newman, 1993, Acemoglu and Zilibotti, 1997. A number of empirical studies (e.g. Rosenzweig and Binswanger, 1993, Rosenzweig and Wolpin, 1993, Morduch, 1995) find evidence consistent with permanent effects linked to risk. There is also evidence from studies focusing on health and educational outcomes consistent with permanent impacts of shocks such as drought Alderman et al., 2001, Hoddinott and Kinsey, 2001. A few recent studies investigate the impact of risk on growth using household data. Jalan and Ravallion, 2004 and Lokshin and Ravallion (2000) test the idea of a shock-induced poverty trap, by testing for whether the transition dynamics after a shock are convex; they do not find evidence of a transition to a low-outcome equilibrium but the recovery after a shock in income is nevertheless slow. Elbers et al. (2003), using data from Zimbabwe, calibrate and simulate a household optimal growth model accounting for both ex-ante and ex-post responses to risk, allowing them to quantify the losses linked to uninsured risk, which proved substantial in their data set.

This paper uses a reduced form econometric approach to test for the impact of uninsured risk. Measured recent and past shocks are directly introduced in the regressions, and their cumulative impact is quantified. This is similar to the study of persistence in macroeconomic series. Campbell and Mankiw (1987) investigate persistence in the log of GNP, i.e. whether shocks continue to have an effect ‘for a long time into the future’. Formally, they estimate the growth in GNP as a stationary autoregressive moving average process. Their persistence measure is based on the cumulative impact of past shocks on the level of GNP. This is not the same as testing for the existence of a ‘poverty trap’ in the sense of the investigation of the threshold, below which there is a tendency to be trapped in permanently low income, from which no escape is possible except for by large positive shocks. Persistence within the time period of the data does not exclude permanent effects, but does not imply them either.

Ethiopia is an obvious setting to study the impact of uninsured risk. About 85% of the population lives in rural areas and virtually all rural households are dependent on rainfed agriculture as the basis for their livelihoods. Drought is a recurrent event, while high incidence of pests as well as animal and human disease affect their livelihoods as well. Insurance and asset markets are functioning relatively poorly, while safety nets, even though present and widespread, are not able to credibly guarantee support when needed Jayne et al., 2002, Dercon and Krishnan, 2003. The data set used is relatively small—only 342 households with complete information for the core parts of the analysis. It implies that some care will have to be taken to interpret the findings; the paper may however give insights and suggestions on how to study these issues in other contexts and on larger data sets. Furthermore, the information available is relatively comprehensive: there are data on events, shocks and experiences over the survey period as well data collected using longer-term recall—including on experiences during the (by far largest recent) famine in the mid-1980s.

The sample is not a random sample of rural communities in Ethiopia, but they were initially selected since they had suffered from the drought in the mid-1980s, which had developed into a large scale famine due to the civil war and other political factors. During the 1990s, growth rates in GDP picked up considerably, with GDP per capita growing by about 14% between 1990 and 1997 (the study period). While the economic reform taking place in this period is likely to have been a necessary condition for this growth experience, it begs the question whether these growth rates should not be largely viewed as recoveries from earlier shocks. Indeed, it took until about 1996 for GDP per capita to surpass levels reached in the early 1980s, before the war, famine and repressive politics plunged Ethiopia into the crisis of the late 1980s. Furthermore, growth rates fluctuated considerably as well in the 1990s. In the survey villages, the issue of recovery and weather induced growth may even be more important. Consumption growth was well beyond national levels in the 1990s, implying impressive poverty reductions (Dercon and Krishnan, 2002). However, since the villages were chosen because the famine had strong effects, the question of recovery and differential effects across households and villages during this recovery becomes crucial to the understanding of the long-term impact of this type of crisis.

In the next section, I present the theoretical and empirical framework used. It is based on the standard ‘informal’ empirical growth model, drawing inspiration from both Mankiw et al. (1992) and endogenous growth theory, e.g. Romer (1986), and introduce into this framework an approach to the study of persistence. A number of testable hypotheses are derived. In Section 3, the context and data are presented. In Section 4, the econometric specifications are discussed and the estimates are presented are presented in Section 5. Section 6 concludes.

Section snippets

Theoretical and empirical framework

The framework used is a standard empirical growth model, allowing for transitional dynamics, inspired by Mankiw et al. (1992). In this model, growth rates are negatively related to initial levels of income, as well as related to a number of variables determining initial efficiency and the steady state, including investment rates in human and physical capital. In the context of panel data on per worker incomes of N households i (i=1,…N) across periods t, yit, this empirical model can be written

Data

The data used in this paper are from six communities in rural Ethiopia. In each village, a random sample was selected, yielding information on about 350 households (the attrition rate between 1989 and 1994 was about 3%, between 1994 and 1997 only about 2%).2

Econometric model

In this section, the framework and equations developed in Section 2 will be specified in more detail as an econometric model to take to the data. The left hand side variable used is the annualised growth rate in real food consumption per adult between 1989 and 1994, and between 1994 and 1997, with data carefully matched so that the data 1989 and 1997 (for which only one observation is available) are from the same period in the year as the respective data used from the 1994–1995 survey rounds,

Estimation results

Table 5, Table 6 present the results from testing the hypotheses against the data. Table 5 first focuses on the basic specification, presenting a fixed effects estimator of the growth in food consumption on initial levels of household and village consumption, and a set of common and idiosyncratic shock variables. Note that the regressions control for changes in demographic variables. The first column points to higher growth rates in richer villages, but lower growth rates for richer individual

Conclusions

In this paper, I analysed the growth experience in a number of villages in rural Ethiopia using a household panel data set covering 1989 to 1997. The focus was on the persistent impact of shocks and the famine of the 1980s on growth rates in the 1990s. Using a concept of persistence as used in macroeconomic analysis, the evidence suggests that rainfall shocks are not just strongly affecting food consumption in the current period, but its impact lingers on for many years: the evidence suggests

Acknowledgements

Paper prepared for the Conference on Macroeconomic Policies and Poverty Reduction, Washington, DC, March 14–15, 2002, organised by the International Monetary Fund. I am grateful for encouragement and useful comments from Jan Willem Gunning, Martin Ravallion, Cathy Pattillo and seminar participants at Oxford, Bristol, WIDER/UNU and the World Bank, as well as from two anonymous referees of this journal. All errors are mine.

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