Approximating Income Distribution Dynamics Using Aggregate Data
This paper proposes a methodology to approximate individual income distribution dynamics using only time series data on aggregate moments of the income distribution. Under the assumption that individual incomes follow a lognormal autoregressive pro...
| Main Authors: | , |
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| Format: | Working Paper |
| Language: | English en_US |
| Published: |
World Bank, Washington, DC
2017
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/807641498574886507/Approximating-income-distribution-dynamics-using-aggregate-data http://hdl.handle.net/10986/27626 |
| Summary: | This paper proposes a methodology to
approximate individual income distribution dynamics using
only time series data on aggregate moments of the income
distribution. Under the assumption that individual incomes
follow a lognormal autoregressive process, this paper shows
that the evolution over time of the mean and standard
deviation of log income across individuals provides
sufficient information to place upper and lower bounds on
the degree of mobility in the income distribution. The paper
demonstrates that these bounds are reasonably informative,
using the U.S. Panel Study of Income Dynamics where the
panel structure of the data allows us to compare measures of
mobility directly estimated from the micro data with
approximations based only on aggregate data. Bounds on
mobility are estimated for a large cross-section of
countries, using data on aggregate moments of the income
distribution available in the World Wealth and Income
Database and the World Bank's PovcalNet database. The
estimated bounds on mobility imply that conventional
anonymous growth rates of the bottom 40 percent (top 10
percent) that do not account for mobility substantially
understate (overstate) the expected growth performance of
those initially in the bottom 40 percent (top 10 percent). |
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