New Algorithm to Estimate Inequality Measures in Cross-Survey Imputation: An Attempt to Correct the Underestimation of Extreme Values

Gianni Betti, Vasco Molini () and Lorenzo Mori

No 10013, Policy Research Working Paper Series from The World Bank

Abstract: This paper contributes to the debate on ways to improve the calculation of inequality measures in developing countries experiencing severe budget constraints. Linear regression-based survey-to-survey imputation techniques are most frequently discussed in the literature. These are effective at estimating predictions of poverty indicators but are much less accurate with inequality indicators. To demonstrate this limited accuracy, the first part of the paper discusses several simulations using Moroccan Household Budget Surveys and Labor Force Surveys. The paper proposes a method for overcoming these limitations based on an algorithm that minimizes the sum of the squared difference between a certain number of direct estimates of an index and its empirical version obtained from the predicted values. Indeed, when comparing the estimated results with those directly estimated from the original sample, the bias is negligible. Furthermore, the inequality indices for the years for which there are only model estimates, rather than direct information on expenditures, seem to be consistent with Moroccan economic trends.

Date: 2022-04-20
New Economics Papers: this item is included in nep-ara
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://documents.worldbank.org/curated/en/09983330 ... 7ae0f41688c4262c.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wbk:wbrwps:10013

Access Statistics for this paper

More papers in Policy Research Working Paper Series from The World Bank 1818 H Street, N.W., Washington, DC 20433. Contact information at EDIRC.
Bibliographic data for series maintained by Roula I. Yazigi ().