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Poverty-Environment Traps

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Abstract

Remote less-favored agricultural lands (LFAL) are regions in developing countries that face severe biophysical constraints on production and are in geographical locations that have limited market access. We estimate that, across developing countries, 130 million people with high infant mortality live in such areas, and the incidence is 40%. In low-income countries, the population in remote LFAL with high infant mortality increased 25% over 2000–2010 to 57 million, and the incidence is 94%. From case study evidence, we identify the key environmental and economic characteristics that influence the ability of rural households in remote LFAL to avoid poverty. We incorporate these characteristics in a model analyzing the behavior of a representative household, which illustrates conditions that enable the household to escape subsistence-level poverty. We also show empirically for 83 developing countries that the share of rural population on remote LFAL in 2000 affects the poverty-reducing impacts of per capita income growth over 2000–2012.

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Notes

  1. See, for example, Barbier (2010), Barbier and Hochard (2018b), Barrett (2008), Barrett and Bevis (2015), Battacharya and Innes (2013), Coomes et al. (2011), Coxhead et al. (2002), Delacote (2009), Emran and Hou (2013), Fan and Chan-Kang (2004), Fan and Hazell (2001), Gerber et al. (2014), Gollin and Rogerson (2014), Gonazález-Vega et al. (2004), Holden et al. (2004), Jalan and Ravallion (2002), Lade et al. (2017), Lang et al. (2013), Liu et al. (2017), and Zhang and Fan (2004).

  2. See, for example, Angelsen et al. (2014), Angelsen and Dokken (2018), Ansoms and McKay (2010), Barbier (2010), Barbier and Hochard (2018b), Battacharya and Innes (2013), Caviglia-Harris and Harris (2008), Debela et al. (2012), Delacote (2009), Gerber et al. (2014), López-Feldman (2014), Jansen et al. (2006), McSweeney (2005), Narain et al. (2008), Narloch and Bangalore (2018), Noack et al. (2019), Robinson (2016), Takasaki et al. (2004), Vedeld et al. (2007) and Wunder et al. (2014).

  3. See, for example, CAWMA (2008), CGIAR (1999), Fan and Chan-Kang (2004), Graw and Husmann (2014), Pender (2008), Pender and Hazell (2000), Pingali et al. (2014), World Bank (2003, 2008).

  4. The exceptions appear to be the Philippines, and possibly South Africa; see Table 2.

  5. As we noted in the Introduction, the partial-equilibrium modeling approach we take to investigating and illustrating the convergence to a long-run poverty trap outcome is similar to Ghatak (2015), although the latter does not consider the geographic characteristics of remoteness and low agricultural productivity in determining such an outcome, which are a major feature here.

  6. Also (5) implies that bringing more land into production requires increasing amounts of capital per person; e.g., rearranging the expression for x in (5) yields \(k = \frac{\uprho }{\upgamma \upbeta }\left( {\frac{x}{{x_{0} }}} \right)^{{\frac{1}{\upgamma }}} = \upkappa \left( x \right),\quad \upkappa^{\prime } > 0\).

  7. The latter condition requires \(B\left( {\upgamma \upvarepsilon } \right)^{\upgamma \upvarepsilon } \left( {\frac{1}{\upsigma }} \right)^{\upsigma } < A\left( {\upgamma \upbeta } \right)^{\upgamma \upbeta } \left( {\frac{1}{\uprho }} \right)^{\uprho }\), which is likely to be the case given the relative values of the respective parameters.

  8. See, for example, Angelsen et al. (2014), Angelsen and Dokken (2018), Barbier and Hochard (2018b), Battacharya and Innes (2013), Carter et al. (2007), Debela et al. (2012), Delacote (2009), Hallegatte et al. (2015, 2018), López-Feldman (2014), McSweeney (2005), Narain et al. (2008), Noack et al. (2019), Robinson (2016), Takasaki et al. (2004), Vedeld et al. (2007), Wunder et al (2014).

  9. Throughout this model, we assume that the natural resources in the surrounding environment are under community de jure or de facto ownership, so that efficient common-property management of the resources always occurs. Thus, the share of the commons allocated to the representative household is governed by this management regime to ensure sustainable use, even if the household fully exploits its share of these naturel resources. Although employing a different modeling approach, Delacote (2009) also examines cases where common property exploitation leads to a poverty trap. In addition, as shown by Takasaki (2007), different land and labor market governance and institutional conditions could influence significantly both the labor allocation and resource extraction decisions of a representative household. Similarly, López (1998) analyzes the situation where household agricultural and common-property activities compete for the household allocation of land and labor, and this allocation may be affected by changes in agricultural and trade policies, and Barbier et al. (2016) explore how household indebtedness can also influence natural resource degradation. Although we acknowledge that inefficiencies in and external disruptions to local land, labor, credit and common-property management institutions may have a significant role in influencing the welfare of households on remote less-favored agricultural lands, in this model we abstract from these possible influences in order to focus on how the interaction between the limited production potential of marginal agricultural land, dependence on exploiting the surrounding environment for natural resources and restricted opportunities for off-farm work may affect whether or not households are able to escape from subsistence-level poverty.

  10. From (6), it is clear that agricultural production uses only k, but resource-based activities might use both k and z.

  11. One consequence of these non-homothetic preferences is that the income elasticity of consumption is less than unity. In addition, the intertemporal elasticity of substitution (IES) is variable with respect to consumption, i.e. \(\upsigma \left( c \right) \equiv - \frac{{u^{\prime } \left( {c - \bar{c}} \right)}}{{u^{\prime \prime } \left( {c - \bar{c}} \right)c}} = \frac{{c - \bar{c}}}{\uptheta c}\). The properties of this variable IES make sense for a poor rural household. For example, as noted by Steger (2000), if income from all sources are insufficient so that consumption equals subsistence, then a household is unable to substitute consumption intertemporally and the IES equals zero. However, if aggregate household income allows consumption to exceed \(\bar{c}\), then the IES increases with the level of consumption. The IES also asymptotically converges to \(\uptheta^{ - 1}\) if consumption grows without bound.

  12. The solution for \(l = 0\) proceeds in the same way as for \(l = L\) shown in the “Appendix”, except with \(wL = 0\).

  13. See, for example, Adams and Page (2005), Barbier and Hochard (2016, 2018a), Dollar and Kraay (2002), Kraay (2006) and Ravallion (2012). The controls are inflation, government consumption as a share of GDP, arable land per capita, agricultural value added as a share of GDP and per worker, the GINI index, investment as a share of GDP, trade openness, primary school enrollment, and life expectancy. These variables were obtained from the World Development Indicators (https://databank.worldbank.org/data/reports.aspx?source=world-development-indicators#), and as far as possible, for 2000 and our sample of 83 countries. Other controls include a dummy for landlocked country, for small island developing, and distance from equator for each country. We also employ rule of law and democracy (voice and accountability) indices, from the Worldwide Governance Indicators (http://data.worldbank.org/data-catalog/worldwide-governance-indicators), which were averaged over 1996–2000 for each country. Finally, we use regional dummies for the six main developing country regions (see Table 1).

  14. For three of the countries, Fiji, Maldives and Serbia, insufficient spatial resolution or lack of data prevented constructing an estimate of the share of rural population on remote LFAL. This reduces the number of country observations in the regressions to 80. As a robustness check on our regression results, we follow Ravallion (2012) and replicate our analysis with a $1.25 per day poverty line, which we find gives similar results as the $2 a day poverty line.

  15. As a robustness check, (36) is also estimated with IV and SUR, with similar results and significance for the estimated parameters. We also employ the growth in private consumption per capita estimated nationally as an instrument for the growth in mean survey income, given that both the latter variable and the poverty headcount rate are based on the same household surveys and may share common measurement errors. This is a common approach in the poverty analysis literature (Barbier and Hochard 2018a; Ravallion 2001, 2012). The parameter estimates remain robust with the use of this instrument.

  16. Almost all the controls are individually and jointly insignificant in the 3SLS regressions, with the exception of agricultural value added per worker, investment as a share of GDP and the Europe and Central Asia dummy. This is also true when (36) is estimated IV and SUR. Although not shown in Table 3, these control variables have the expected signs. Investment share of GDP reduces overall poverty, and agricultural productivity increases poverty-adjusted growth. Poverty is lower, but poverty-adjusted growth generally higher, in Europe and Central Asia compared to other developing regions.

  17. See, for example, Angelsen et al. (2014), Angelsen and Dokken (2018), Barbier and Hochard (2018b), Battacharya and Innes (2013), Carter et al. (2007), Debela et al. (2012), Hallegatte et al. (2015, 2018), López-Feldman (2014), McSweeney (2005), Narain et al. (2008), Narloch and Bangalore (2018), Noack et al. (2019), Robinson (2016), Takasaki et al. (2004), Vedeld et al. (2007), Wunder et al. (2014).

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Authors and Affiliations

  1. Department of Economics, Colorado State University, Fort Collins, CO, 80253-1771, USA

    Edward B. Barbier

  2. Department of Economics and Institute for Coastal Science and Policy, East Carolina University, Greenville, NC, 27858, USA

    Jacob P. Hochard

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Appendix

Appendix

Derivation of the optimal solution (24) and (25). Solution of the differential Eq. (22) leads directly to (24), i.e. \(c\left( t \right) = \bar{c} + \left[ {c\left( 0 \right) - \bar{c}} \right]e^{\upchi t} ,\,\,\,\upchi = \uptheta^{ - 1} \left[ {\left( {1 - \uptau } \right)\upvarphi - \updelta - \uprho } \right]\). Define \(\tilde{k} = k\left( t \right) - \bar{k}\) and \(\,\uppsi = \left( {1 - \uptau } \right)\upvarphi - \updelta\). Thus (23) can be rewritten as \(\dot{\tilde{k}} = \uppsi \tilde{k} + w\tilde{L} - \left[ {c\left( 0 \right) - \bar{c}} \right]e^{\upchi t}\). Solution of this differential equation is \(b_{0} + e^{ - \uppsi t} \tilde{k}\left( t \right) = b_{1} - \frac{{w\tilde{L}}}{\uppsi }e^{ - \uppsi t} + b_{2} + \frac{{\left[ {c\left( 0 \right) - \bar{c}} \right]}}{\uppsi - \upchi }e^{{\left( {\upchi - \uppsi } \right)t}}\), which re-arranged becomes

$$\tilde{k}\left( t \right) = \upzeta e^{\uppsi t} - \frac{{w\tilde{L}}}{\uppsi } + \frac{{\left[ {c\left( 0 \right) - \bar{c}} \right]}}{\uppsi - \upchi }e^{\upchi t} ,\;\; \upzeta = b_{1} + b_{2} - b_{0} .$$
(37)

The transversality condition can be written as \(\mathop {\lim }\limits_{t \to \infty } \uplambda \left( t \right)k\left( t \right) = \mathop {\lim }\limits_{t \to \infty } \uplambda \left( t \right)\tilde{k}\left( t \right) = 0\) and (19) for the corner solution \(\tilde{L} = l\) is \(\dot{\uplambda } = \uplambda \left[ {\updelta - \left( {1 - \uptau } \right)\upvarphi } \right]\). The latter differential equation has the solution \(\uplambda \left( t \right) = \uplambda \left( 0 \right)e^{ - \uppsi t}\) and the transversality condition is \(\mathop {\lim }\limits_{t \to \infty } \uplambda \left( 0 \right)e^{ - \uppsi t} \tilde{k}\left( t \right) = 0\). Using (37) in the latter condition yields

$$\mathop {\lim }\limits_{t \to \infty } \uplambda \left( 0 \right)\varsigma - \frac{{w\tilde{L}}}{\uppsi }e^{ - \uppsi t} + \frac{{c\left( 0 \right) - \bar{c}}}{\uppsi - \upchi }e^{{ - \left( {\uppsi - \upchi } \right)t}} = 0.$$
(38)

As \(\uppsi - \upchi = \left( {1 - \uptau } \right)\upvarphi - \updelta - \uptheta^{ - 1} \left[ {\left( {1 - \uptau } \right)\upvarphi - \updelta - \uprho } \right] > 0\), the third term on the left-hand side of (38) converges to zero as time approaches infinity. The second term also converges to zero. Therefore \(\upzeta = b_{1} + b_{2} - b_{0} = 0\) and (37) becomes

$$\tilde{k}\left( t \right) = k\left( t \right) - \bar{k} = \frac{{\left[ {c\left( 0 \right) - \bar{c}} \right]}}{\uppsi - \upchi }e^{\upchi t} - \frac{{w\tilde{L}}}{\uppsi },$$
(39)

which at \(t = 0\) is \(\frac{{c\left( 0 \right) - \bar{c}}}{\uppsi - \upchi } = k\left( 0 \right) - \bar{k} + \frac{{w\tilde{L}}}{\uppsi }\). Substituting the latter expression into (39) leads to the solution (25) for k(t).

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Barbier, E.B., Hochard, J.P. Poverty-Environment Traps. Environ Resource Econ 74, 1239–1271 (2019). https://doi.org/10.1007/s10640-019-00366-3

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