IDEAS home Printed from https://ideas.repec.org/a/udc/esteco/v32y2005i1p97-117.html
   My bibliography  Save this article

Characterizing income distribution for poverty and inequality analysis

Author

Listed:
  • Rómulo A.Chumacero
  • Ricardo D.Paredes

Abstract

This paper presents a systematic empirical characterization of income distribution in Chile. Such characterization helps us to understand the apparent paradox regarding the coexistence of a successful economic performance and persistently high inequality in income distribution and to assess the impact of different social policies dealing with poverty. Segmented sectors seem to be a crucial feature that is generally overlooked in the traditional analysis of income distribution and poverty. As an example, we conduct an exercises to evaluate two types of policies used to alleviate poverty: one focused on increasing coverage of education (wrongly assuming two populations).

Suggested Citation

  • Rómulo A.Chumacero & Ricardo D.Paredes, 2005. "Characterizing income distribution for poverty and inequality analysis," Estudios de Economia, University of Chile, Department of Economics, vol. 32(1 Year 20), pages 97-117, June.
    • Handle: RePEc:udc:esteco:v:32:y:2005:i:1:p:97-117
    as

    Download full text from publisher

    File URL: http://www.econ.uchile.cl/uploads/publicacion/c5e5ba93-86d3-489e-9e46-80854989bfab.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dickens, William T & Lang, Kevin, 1985. "A Test of Dual Labor Market Theory," American Economic Review, American Economic Association, vol. 75(4), pages 792-805, September.
    2. Jalan, Jyotsna & Ravallion, Martin, 1998. "Determinants of transient and chronic poverty : evidence from rural China," Policy Research Working Paper Series 1936, The World Bank.
    3. Basch, Michael & Paredes-Molina, Ricardo D., 1996. "Are there dual labor markets in Chile?: empirical evidence," Journal of Development Economics, Elsevier, vol. 50(2), pages 297-312, August.
    4. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643, January.
    5. Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-390, March.
    6. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
    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. Tejada, Mauricio M., 2016. "Lifetime inequality measures for an emerging economy: The case of Chile," Labour Economics, Elsevier, vol. 42(C), pages 1-15.
    2. Jaime Ruiz-Tagle, 2007. "Forecasting wage inequality," Estudios de Economia, University of Chile, Department of Economics, vol. 34(2 Year 20), pages 141-162, December.
    3. Claudio A. Agostini & Philip H. Brown, 2010. "Inequality at Low Levels of Aggregation in Chile," Review of Development Economics, Wiley Blackwell, vol. 14(2), pages 213-226, May.
    4. Andrés Solimano & Arístides Torche, 2008. "Income Distribution In Chile, 1987-2006: Analysis And Policy Considerations," Working Papers Central Bank of Chile 480, Central Bank of Chile.

    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. Dennis Kristensen, 2009. "Semiparametric Modelling and Estimation: A Selective Overview," CREATES Research Papers 2009-44, Department of Economics and Business Economics, Aarhus University.
    2. Giuseppe De Luca, 2008. "SNP and SML estimation of univariate and bivariate binary-choice models," Stata Journal, StataCorp LP, vol. 8(2), pages 190-220, June.
    3. Fosgerau, Mogens & Bierlaire, Michel, 2007. "A practical test for the choice of mixing distribution in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 41(7), pages 784-794, August.
    4. María Laura Alzúa, 2008. "Are Informal Workers Secondary Workers?: Evidence for Argentina," CEDLAS, Working Papers 0073, CEDLAS, Universidad Nacional de La Plata.
    5. Liu, Ruixuan & Yu, Zhengfei, 2022. "Sample selection models with monotone control functions," Journal of Econometrics, Elsevier, vol. 226(2), pages 321-342.
    6. Alzua María Laura, 2009. "Are Secondary Workers Informal Workers? Evidence for Argentina," Revista Desarrollo y Sociedad, Universidad de los Andes,Facultad de Economía, CEDE, September.
    7. Sharma, Anurag & Siciliani, Luigi & Harris, Anthony, 2013. "Waiting times and socioeconomic status: Does sample selection matter?," Economic Modelling, Elsevier, vol. 33(C), pages 659-667.
    8. Chen, Xiaohong, 2007. "Large Sample Sieve Estimation of Semi-Nonparametric Models," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 76, Elsevier.
    9. Zhewen Pan & Yifan Zhang, 2024. "Locally robust semiparametric estimation of sample selection models without exclusion restrictions," Papers 2412.01208, arXiv.org.
    10. Stewart, Mark B., 2005. "A comparison of semiparametric estimators for the ordered response model," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 555-573, April.
    11. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    12. repec:spo:wpmain:info:hdl:2441/7182 is not listed on IDEAS
    13. Harding, Don & Pagan, Adrian, 2011. "An Econometric Analysis of Some Models for Constructed Binary Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 86-95.
    14. Pierre‐Richard Agénor, 2004. "Macroeconomic Adjustment and the Poor: Analytical Issues and Cross‐Country Evidence," Journal of Economic Surveys, Wiley Blackwell, vol. 18(3), pages 351-408, July.
    15. Anton Muscatelli & Patrizio Tirelli & Carmine Trecroci, 2001. "Monetary and Fiscal Policy Interactions over the Cycle: Some Empirical Evidence," Working Papers 2002_13, Business School - Economics, University of Glasgow, revised Oct 2002.
    16. Niko Hauzenberger & Florian Huber, 2020. "Model instability in predictive exchange rate regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 168-186, March.
    17. Francesco Bianchi, 2013. "Regime Switches, Agents' Beliefs, and Post-World War II U.S. Macroeconomic Dynamics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 80(2), pages 463-490.
    18. Billio, Monica & Casarin, Roberto & Osuntuyi, Anthony, 2016. "Efficient Gibbs sampling for Markov switching GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 37-57.
    19. David Fairris & Gurleen Popli & Eduardo Zepeda, 2008. "Minimum Wages and the Wage Structure in Mexico," Review of Social Economy, Taylor & Francis Journals, vol. 66(2), pages 181-208.
    20. He, Hui & Yang, Jiawen, 2011. "Regime-switching analysis of ADR home market pass-through," Journal of Banking & Finance, Elsevier, vol. 35(1), pages 204-214, January.
    21. Čížek, Pavel, 2008. "General Trimmed Estimation: Robust Approach To Nonlinear And Limited Dependent Variable Models," Econometric Theory, Cambridge University Press, vol. 24(6), pages 1500-1529, December.

    More about this item

    Keywords

    Income Distribution; Poverty; Nonparametric Estimation.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination

    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:udc:esteco:v:32:y:2005:i:1:p:97-117. 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: Verónica Kunze (email available below). General contact details of provider: https://edirc.repec.org/data/deuclcl.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.