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Consumption Growth and Spatial Poverty Traps: An Analysis of the Effect of Social Services and Community Infrastructures on Living Standards in Rural Peru

Author

Listed:
  • Philippe de Vreyer

    (DIAL - Développement, institutions et analyses de long terme)

  • Javier Herrera

    (IRD [Guinée] - Institut de Recherche pour le Développement, DIAL - Développement, institutions et analyses de long terme)

  • Sandrine Mesplé-Somps

    (DIAL - Développement, institutions et analyses de long terme, IRD [Guinée] - Institut de Recherche pour le Développement)

Abstract

Why are there areas with persistenly low levels of income or consumption? This could result from the concentration of households with a low capital endowment or from variations in households' environment. Peru is a country with a very much fragmented topography and climate, that combines dry deserts, high mountains and rain forest. One important question is to assess the weight of the geographic endowment in the growth process. If differences in geographic endowment matter more than those in households' characteristics, then encouraging migration to better endowed regions might be a good development policy whereas, in the opposite, it might be better to invest in households' capital. Of course several factors, either geographic or not, can combine to explain persistent poverty in a given area. In this chapter we test the effect of local geographic endowment of capital on household growth in living standards in rural Peru, using a four years unbalanced panel data set. Our theoretical model of household consumption growth allows for the effect of community variables to modify the returns to augmented capital in the household production function. Three different sources of data are used: the ENAHO 1997-2000 households surveys, the population census of 1993 and the district infrastructure census of 1997. Altogether the addition of these different data sources makes an unusually rich data set, at least when considered with developing country standards. As in Jalan and Ravallion (2002), we use a quasi-differencing method to identify the impact of locally determined geographic and socioeconomic variables, while removing unobserved household and community level fixed effects. GMM are then used to estimate the model parameters. Several significant interesting results appear, showing that private consumption growth depends on local geographic variables, but more on local endowments of private and public assets than on pure geographic characteristics. This suggests to combine policies focused on private and public asset endowments that will reinforce local positive externalities, with infrastructure investments that will help poor households to take advantage of growth opportunities, offered by more dynamic markets across local communities.

Suggested Citation

  • Philippe de Vreyer & Javier Herrera & Sandrine Mesplé-Somps, 2009. "Consumption Growth and Spatial Poverty Traps: An Analysis of the Effect of Social Services and Community Infrastructures on Living Standards in Rural Peru," Post-Print hal-01377603, HAL.
    • Handle: RePEc:hal:journl:hal-01377603
    DOI: 10.7551/mitpress/9780262113243.003.0005
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    References listed on IDEAS

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    Cited by:

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    3. Michael Grimm, 2007. "Removing the anonymity axiom in assessing pro-poor growth," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 5(2), pages 179-197, August.
    4. Johannes Gräb & Michael Grimm, 2007. "Robust Multiperiod Poverty Comparisons," Ibero America Institute for Econ. Research (IAI) Discussion Papers 160, Ibero-America Institute for Economic Research.
    5. David Stifel & Bart Minten, 2008. "Isolation and agricultural productivity," Agricultural Economics, International Association of Agricultural Economists, vol. 39(1), pages 1-15, July.

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    More about this item

    Keywords

    Croissance; Trappes spatiales de pauvreté; Pérou; Services publics; Poverty; Geographic capital; Consumption; Community; Statistics;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs

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