New Algorithm to Estimate Inequality Measures in Cross-Survey Imputation : An Attempt to Correct the Underestimation of Extreme Values
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 discus...
| Main Authors: | , , |
|---|---|
| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank,Washington, DC
2022
|
| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/099833304202232000/IDU04786da1d0b37c04df60b7ae0f41688c4262c http://hdl.handle.net/10986/37329 |
| Summary: | 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. |
|---|