Research Economist
Marina Gindelsky
Education
Do transfers lower inequality between households? Demographic evidence from Dis…
Marina Gindelsky
Killer cities and industrious cities? New data and evidence on 250 years of urb…
Marina Gindelsky and Remi Jedwab
The Feasibility of a Quarterly Distribution of Personal Income (PDF)
Dennis J. Fixler , Marina Gindelsky , and Robert Kornfeld
Accounting for Land in the U.S.: Integrating Physical Land Cover, Land Use, and…
Scott Wentland , Zachary Ancona , Kenneth J Bagstad , James W Boyd , Julie L Hass , Marina Gindelsky , and Jeremy G. Moulton
Measuring Inequality in the National Accounts (PDF)
Dennis J. Fixler , Marina Gindelsky , and David Johnson
Distributing Personal Income: Trends Over Time
Dennis J. Fixler , Marina Gindelsky , and David Johnson
Valuing Housing Services in the Era of Big Data: A User Cost Approach Leveragin…
Marina Gindelsky , Jeremy G. Moulton , and Scott Wentland
Improving the Measure of the Distribution of Personal Income
Dennis J. Fixler , Marina Gindelsky , and David Johnson
Developing a national account-based measure of the distribution of income from the commonly used Census based concept of money income has been the subject of earlier research. We use publicly available survey and administrative data to construct a distribution of personal income after enhancing the top income distribution in the Current Population Survey (2007 and 2012). We show that inequality measures are fairly sensitive to the definition of income contemporaneously and across time. This work helps bridge the gap between micro data and macro statistics and informs about results from other studies, such as Piketty et al. (2018).
Dennis J. Fixler , Marina Gindelsky , and David Johnson
Recently, an idea has emerged that “the rich are getting richer and the poor are getting poorer”. Using tax data from Piketty, Saez, and Zucman (2017) (updated in the World Wealth& Income Database) and internal microdata from the Current Population Survey (1975-2015),this paper models inequality and performs pseudo-out-of-sample (2012-2015) and true out-of-sample (2016-2018) forecasts for 5 income inequality measures. The lowest forecast errors from the best models are found for distributional metrics, as compared to top income shares. While macroeconomic indicators, human capital, and labor force metrics often enhance models, measures of skill biased technological change are not found to be robust predictors of inequality trends. Näıve approaches often outperform more complex models and forecasts differ between models by <4% for all variables.
Marina Gindelsky
Towards a Distribution of Household Income: Linking Survey Data to Administrati…
Dennis J. Fixler , Marina Gindelsky , and David Johnson
Testing the acculturation of the 1.5 generation in the United States: Is there …
Marina Gindelsky
Demography, urbanization and development: Rural push, urban pull and…urban push?
Marina Gindelsky
Determinants of Bilingualism Among Children
Marina Gindelsky
Poverty and Shared Prosperity in Uruguay
Marina Gindelsky