[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/p/ags/aaea04/20352.html
   My bibliography  Save this paper

Household Income Dynamics And Poverty Traps In Rural El Salvador

Author

Listed:
  • Rodriguez-Meza, Jorge
  • Gonzalez-Vega, Claudio
Abstract
A growing concern about effects of the economic strategies followed by developing countries along the lines of the Washington Consensus suggests that the recommended policy reforms are necessary but not sufficient conditions for poverty alleviation. Several authors have pointed out the possibility of growth with inequality and the fragile status of the poorest sectors of the population, which during the growth process may not be able to weather the effects of income shocks because of their isolation from markets and limited opportunities for diversification. Additional policy and institutional reforms, to address poverty and inequality, will require a better understanding of the dynamics of income generation. The rural areas of El Salvador offer an excellent laboratory to study income dynamics and poverty in the context of a growing economy. In the period between 1995 and 2001, rural households in this country faced major consecutive income shocks. In addition to drought, brought about by El Niño in 1997 and 2001, the rural economy endured the effects of Tropical Storm Mitch in early 1999, two major earthquakes in early 2001, and a steady fall, since 1997, in the international prices of coffee, a major export crop. Consequently, most rural households in El Salvador are expected to be out of their steady-state equilibrium. Longitudinal household income data would shed light about the dynamic path of returning to equilibrium. This paper takes advantage of a detailed data set of rural household characteristics and income generating activities, to test for nonlinearity in the process of movement toward equilibrium. Non-linearity would reveal the possible existence of low-income poverty traps. The data set contains information from a sample of representative households of the rural population of El Salvador interviewed four times about their activities during the years of 1995, 1997, 1999, and 2001. This information presents a unique opportunity for this type of analysis, as it provides consecutive observations of households over the period and as the data were collected with the purpose of computing household incomes. In El Salvador, as a consequence of systemic and idiosyncratic shocks, rural incomes exhibit great volatility. In spite of an overall growing trend in total income, inequality and poverty measures do not decline over the entire period. A non-decreasing trend in inequality and in the number of poor households between 1995 and 1997 is contrasted with a decreasing trend in both indicators between 1997 and 1999 and between 1999 and 2001. Around 20 percent of the extremely poor in 1995, those households unable to generate incomes equal to the cost of a basic basket of goods defined to maintain the nutritional status of an adult, still remained extremely poor 6 years later. This was the case even though some of them may have temporarily escaped extreme poverty in one or more of the intermediate years. Ten percent of the extremely poor in 1995 could not escape extreme poverty at all at any time. The persistent presence of some households in the extreme left-hand side of the income distribution seems to indicate that they may have fallen below a threshold level of income from which their asset composition and income generating capacity would make it difficult to escape. The presence of such a poverty trap can be econometrically tested by posing a non-linear income dynamic process that allows for multiple equilibria, one of which may be unstable. The appropriateness of such test is guaranteed by the use of a panel data set containing microeconomic data on household characteristics and a detailed enumeration of all income-generating activities, including those activities in which households engaged as a form of self-employment. The downward measurement error bias typical of income computations is well established in the literature. First-difference estimation takes care of this drawback while at the same time reducing the problem of correlation between lagged incomes and the error term. Attrition, another typical problem with longitudinal data, is shown not to be systematically related to the income process. Between 1995 and 2001, there was 28 percent attrition. However, attrition is not significant in both an income generation model, where income is regressed as a function of household characteristics, and in the non-linear dynamic income model that tests for the presence of multiple equilibria. The aparent existence of a low-income poverty trap in rural El Salvador may be explained by sizeable successive income shocks that may have depleted assets endowments for low-income households through the years and have eventually rendered them vulnerable and unable to recover. Government subsidies do not seem to be effective to solve this problem. The test of nonlinearity in the income process is as an indirect test of the effectiveness of the income-smoothing mechanisms available to rural households. In developing economies, income-smoothing mechanisms take the form of diversification of activities within the household, changes in labor market participation, migration, and access to formal credit as forms of preventing future fluctuations in income. The methodology used in this paper considers the success of these mechanisms by looking at the resulting income process instead of attempting to evaluate the effectiveness of each mechanism separately, thus capturing the whole spectrum of possibilities available to households in rural areas and avoiding the oversimplified analysis of individual mechanisms prevalent in the literature.

Suggested Citation

  • Rodriguez-Meza, Jorge & Gonzalez-Vega, Claudio, 2004. "Household Income Dynamics And Poverty Traps In Rural El Salvador," 2004 Annual meeting, August 1-4, Denver, CO 20352, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  • Handle: RePEc:ags:aaea04:20352
    DOI: 10.22004/ag.econ.20352
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/20352/files/sp04ro04.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.20352?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Alok Bhargava & J. D. Sargan, 2006. "Estimating Dynamic Random Effects Models From Panel Data Covering Short Time Periods," World Scientific Book Chapters, in: Econometrics, Statistics And Computational Approaches In Food And Health Sciences, chapter 1, pages 3-27, World Scientific Publishing Co. Pte. Ltd..
    2. Barrett, Christopher B. & Swallow, Brent M., 2006. "Fractal poverty traps," World Development, Elsevier, vol. 34(1), pages 1-15, January.
    3. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    4. Glewwe, P. & Hall, G., 1995. "Who is Most Vulnerable to Macroeconomic Shocks? Hypotheses Tests Using Panel Data from Peru," Papers 117, World Bank - Living Standards Measurement.
    5. Barrett, Christopher B. & Swallow, Brent M., 2006. "Fractal poverty traps," World Development, Elsevier, vol. 34(1), pages 1-15, January.
    6. Lokshin, Michael & Ravallion, Martin, 2000. "Short-lived shocks with long-lived impacts? - household income dynamics in a transition economy," Policy Research Working Paper Series 2459, The World Bank.
    7. Jalan, Jyotsna & Ravallion, Martin, 2001. "Household income dynamics in rural China," Policy Research Working Paper Series 2706, The World Bank.
    8. Azariadis, Costas, 1996. "The Economics of Poverty Traps: Part One: Complete Markets," Journal of Economic Growth, Springer, vol. 1(4), pages 449-496, December.
    9. Ravallion, Martin, 2001. "Growth, Inequality and Poverty: Looking Beyond Averages," World Development, Elsevier, vol. 29(11), pages 1803-1815, November.
    10. Nijman, Theo & Verbeek, Marno, 1992. "Nonresponse in Panel Data: The Impact on Estimates of a Life Cycle Consumption Function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(3), pages 243-257, July-Sept.
    11. Parke E. Wilde, 2000. "Deaton, Angus. The Analysis of Household Surveys: A Microeconometric Approach to Development Policy. Baltimore MD: Johns Hopkins University Press (Published for the World Bank), 1996, 479 pp., $39.95," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(3), pages 780-782.
    12. Ravallion, M., 1992. "Poverty Comparisons - A Guide to Concepts and Methods," Papers 88, World Bank - Living Standards Measurement.
    13. Jonathan Morduch, 1995. "Income Smoothing and Consumption Smoothing," Journal of Economic Perspectives, American Economic Association, vol. 9(3), pages 103-114, Summer.
    14. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    15. Timothy Besley & Robin Burgess, 2003. "Halving Global Poverty," Journal of Economic Perspectives, American Economic Association, vol. 17(3), pages 3-22, Summer.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. World Bank, 2005. "Shocks and Social Protection : Lessons from the Central American Coffee Crisis, Volume 1, Synthesis of Findings and Implications for Policy," World Bank Publications - Reports 8435, The World Bank Group.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yerrabati, Sridevi, 2022. "Does vulnerable employment alleviate poverty in developing countries?," Economic Modelling, Elsevier, vol. 116(C).
    2. Tahir Andrabi & Jishnu Das & Asim Ijaz Khwaja & Tristan Zajonc, 2011. "Do Value-Added Estimates Add Value? Accounting for Learning Dynamics," American Economic Journal: Applied Economics, American Economic Association, vol. 3(3), pages 29-54, July.
    3. Seung C. Ahn & Gareth M. Thomas, 2023. "Likelihood-based inference for dynamic panel data models," Empirical Economics, Springer, vol. 64(6), pages 2859-2909, June.
    4. Giorgio Calzolari & Laura Magazzini, 2014. "Improving GMM efficiency in dynamic models for panel data with mean stationarity," Working Papers 12/2014, University of Verona, Department of Economics.
    5. Nicholas Bamegne Nambie & Philomena Dadzie & Dorcas Oye Haywood-Dadzie, 2023. "Measuring the Effect of Income Inequality, Financial Inclusion, Investment, and Unemployment, on Economic Growth in Africa: A Moderating Role of Digital Financial Technology," International Journal of Economics and Financial Issues, Econjournals, vol. 13(4), pages 111-124, July.
    6. Sowmya Dhanaraj & Christy Mariya Paul & Smit Gade, 2019. "Household income dynamics and investment in children: Evidence from India," Education Economics, Taylor & Francis Journals, vol. 27(5), pages 507-520, September.
    7. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.
    8. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    9. Andrés Rodríguez-Pose, 2012. "Trade and Regional Inequality," Economic Geography, Taylor & Francis Journals, vol. 88(2), pages 109-136, April.
    10. Muinelo-Gallo, Leonel, 2022. "Business cycles and redistribution: The role of government quality," Economic Systems, Elsevier, vol. 46(4).
    11. Martin Ravallion, 2012. "Why Don't We See Poverty Convergence?," American Economic Review, American Economic Association, vol. 102(1), pages 504-523, February.
    12. Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
    13. Moral-Benito, Enrique & Bartolucci, Cristian, 2012. "Income and democracy: Revisiting the evidence," Economics Letters, Elsevier, vol. 117(3), pages 844-847.
    14. Binder, Michael & Hsiao, Cheng & Pesaran, M. Hashem, 2005. "Estimation And Inference In Short Panel Vector Autoregressions With Unit Roots And Cointegration," Econometric Theory, Cambridge University Press, vol. 21(4), pages 795-837, August.
    15. Pinaki Chakraborty & Bharatee Bhusana Dash, 2017. "Fiscal Reforms, Fiscal Rule, and Development Spending: How Indian States Have Performed?," Public Budgeting & Finance, Wiley Blackwell, vol. 37(4), pages 111-133, December.
    16. Laptieva, Nataliia, 2016. "Information sharing and the volume of private credit in transition: Evidence from Ukrainian bank-level panel dataAuthor-Name: Grajzl, Peter," Journal of Comparative Economics, Elsevier, vol. 44(2), pages 434-449.
    17. Hak Yeung & Jürgen Huber, 2022. "Further Evidence on China’s B&R Impact on Host Countries’ Quality of Institutions," Sustainability, MDPI, vol. 14(9), pages 1-17, May.
    18. Gayle, George-Levi & Viauroux, Christelle, 2007. "Root-N consistent semiparametric estimators of a dynamic panel-sample-selection model," Journal of Econometrics, Elsevier, vol. 141(1), pages 179-212, November.
    19. Dercon, Stefan, 2004. "Growth and shocks: evidence from rural Ethiopia," Journal of Development Economics, Elsevier, vol. 74(2), pages 309-329, August.
    20. Frédéric Docquier & Elisabetta Lodigiani, 2010. "Skilled Migration and Business Networks," Open Economies Review, Springer, vol. 21(4), pages 565-588, September.

    More about this item

    Keywords

    Consumer/Household Economics;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aaea04:20352. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.