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Socioeconomic Index for Income and Poverty Prediction: A Sufficient Dimension Reduction Approach

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Listed:
  • Sabrina Duarte
  • Liliana Forzani
  • Pamela Llop
  • Rodrigo García Arancibia
  • Diego Tomassi

Abstract

The present paper introduces a novel method for the construction of Socioeconomic Status (SES) indices that are specific to a target variable of interest. It is based on the Sufficient Dimension Reduction (SDR) paradigm and uses a factorized model‐based approach to simultaneously deal with predictor variables of mixed nature (i.e. quantitative, binary, and ordinal), which are usual in microeconomic data. These SES indices also identify relevant predictor variables using a two‐step regularized matrix factorization approach. Using data from household surveys for Argentina (Encuesta Permanente de Hogares‐EPH), the proposed method is compared with other existing dimension reduction algorithms such as standard Principal Component Analysis (PCA) and its version for mixed variables, regression on the full set of variables and Least Absolute Shrinkage and Selection Operator regression (LASSO).

Suggested Citation

  • Sabrina Duarte & Liliana Forzani & Pamela Llop & Rodrigo García Arancibia & Diego Tomassi, 2023. "Socioeconomic Index for Income and Poverty Prediction: A Sufficient Dimension Reduction Approach," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(2), pages 318-346, June.
    • Handle: RePEc:bla:revinw:v:69:y:2023:i:2:p:318-346
    DOI: 10.1111/roiw.12529
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    References listed on IDEAS

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    1. Han, Xuehui & Khan, Haider & Zhuang, Juzhong, 2014. "Do Governance Indicators Explain Development Performance? A Cross-Country Analysis," ADB Economics Working Paper Series 417, Asian Development Bank.
    2. Cook, R. Dennis & Forzani, Liliana, 2009. "Likelihood-Based Sufficient Dimension Reduction," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 197-208.
    3. Rema Hanna & Benjamin A. Olken, 2018. "Universal Basic Incomes versus Targeted Transfers: Anti-Poverty Programs in Developing Countries," Journal of Economic Perspectives, American Economic Association, vol. 32(4), pages 201-226, Fall.
    4. Giovanni Maria Merola & Bob Baulch, 2019. "Using sparse categorical principal components to estimate asset indices: new methods with an application to rural southeast asia," Review of Development Economics, Wiley Blackwell, vol. 23(2), pages 640-662, May.
    5. Mathieu J. P. Poirier & Karen A. Grépin & Michel Grignon, 2020. "Approaches and Alternatives to the Wealth Index to Measure Socioeconomic Status Using Survey Data: A Critical Interpretive Synthesis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 148(1), pages 1-46, February.
    6. Efstathia Bura & Sabrina Duarte & Liliana Forzani, 2016. "Sufficient Reductions in Regressions With Exponential Family Inverse Predictors," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1313-1329, July.
    7. Noha EMARA & I-Ming CHIU, 2016. "The Impact of Governance Environment on Economic Growth: The Case of Middle Eastern and North African Countries," Journal of Economics Library, KSP Journals, vol. 3(1), pages 24-37, March.
    8. Zhu, Yu & Zeng, Peng, 2006. "Fourier Methods for Estimating the Central Subspace and the Central Mean Subspace in Regression," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1638-1651, December.
    9. Vivi Alatas & Abhijit Banerjee & Rema Hanna & Benjamin A. Olken & Julia Tobias, 2012. "Targeting the Poor: Evidence from a Field Experiment in Indonesia," American Economic Review, American Economic Association, vol. 102(4), pages 1206-1240, June.
    10. Li, Bing & Wang, Shaoli, 2007. "On Directional Regression for Dimension Reduction," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 997-1008, September.
    11. Stanislav Kolenikov & Gustavo Angeles, 2009. "Socioeconomic Status Measurement With Discrete Proxy Variables: Is Principal Component Analysis A Reliable Answer?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 55(1), pages 128-165, March.
    12. Michael E. Tipping & Christopher M. Bishop, 1999. "Probabilistic Principal Component Analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 611-622.
    13. Tohari, Achmad & Parsons, Christopher & Rammohan, Anu, 2019. "Targeting poverty under complementarities: Evidence from Indonesia's unified targeting system," Journal of Development Economics, Elsevier, vol. 140(C), pages 127-144.
    14. Efstathia Bura & R. Dennis Cook, 2001. "Estimating the structural dimension of regressions via parametric inverse regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 393-410.
    15. Forzani, Liliana & García Arancibia, Rodrigo & Llop, Pamela & Tomassi, Diego, 2018. "Supervised dimension reduction for ordinal predictors," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 136-155.
    16. Juan M Villa, 2016. "A harmonised proxy means test for Kenya’s National Safety Net programme," Global Development Institute Working Paper Series 032016, GDI, The University of Manchester.
    17. Cook, R. Dennis & Ni, Liqiang, 2005. "Sufficient Dimension Reduction via Inverse Regression: A Minimum Discrepancy Approach," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 410-428, June.
    18. Yin, Xiangrong & Li, Bing & Cook, R. Dennis, 2008. "Successive direction extraction for estimating the central subspace in a multiple-index regression," Journal of Multivariate Analysis, Elsevier, vol. 99(8), pages 1733-1757, September.
    19. Cook, R. Dennis & Forzani, Liliana M. & Tomassi, Diego R., 2011. "LDR: A Package for Likelihood-Based Sufficient Dimension Reduction," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i03).
    20. Efstathia Bura & Liliana Forzani, 2015. "Sufficient Reductions in Regressions With Elliptically Contoured Inverse Predictors," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 420-434, March.
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