Estimating Quarterly Poverty Rates Using Labor Force Surveys : A Primer
The paper shows how Labor Force Surveys can be used effectively to estimate poverty rates using Household Expenditure Surveys and cross-survey imputation methods. With only two rounds of Household Expenditure Survey data for Morocco (2001 and 2007), the paper estimates quarterly poverty rates for the period 2001-2010 by imputing household expenditures into the Labor Force Surveys. The results are encouraging. The methodology is able to accurately reproduce official poverty statistics by combining current Labor Force Surveys with previous period Household Expenditure Surveys, and vice versa. Although the focus is on head-count poverty, the method can be applied to any welfare indicator that is a function of household income or expenditure, such as the poverty gap or the Gini index of inequality. The newly produced time-series of poverty rates can help researchers and policy makers to: (a) study the determinants of poverty reduction or use poverty as an explanatory factor in cross-section and panel models; (b) forecast poverty rates based on a time-series model fitted to the data; and (c) explore the linkages between labor market conditions and poverty and simulate the effects of policy reforms or economic shocks. This is a promising research agenda that can expand significantly the tool-kit of the welfare economist.
Summary: | The paper shows how Labor Force Surveys
can be used effectively to estimate poverty rates using
Household Expenditure Surveys and cross-survey imputation
methods. With only two rounds of Household Expenditure
Survey data for Morocco (2001 and 2007), the paper estimates
quarterly poverty rates for the period 2001-2010 by imputing
household expenditures into the Labor Force Surveys. The
results are encouraging. The methodology is able to
accurately reproduce official poverty statistics by
combining current Labor Force Surveys with previous period
Household Expenditure Surveys, and vice versa. Although the
focus is on head-count poverty, the method can be applied to
any welfare indicator that is a function of household income
or expenditure, such as the poverty gap or the Gini index of
inequality. The newly produced time-series of poverty rates
can help researchers and policy makers to: (a) study the
determinants of poverty reduction or use poverty as an
explanatory factor in cross-section and panel models; (b)
forecast poverty rates based on a time-series model fitted
to the data; and (c) explore the linkages between labor
market conditions and poverty and simulate the effects of
policy reforms or economic shocks. This is a promising
research agenda that can expand significantly the tool-kit
of the welfare economist. |
---|