Family Inequality network member Arthur Lewbel is Professor of Economics at Boston College, where he holds the inaugural Barbara A. and Patrick E. Roche chair. His research is mainly in the areas of micro econometrics and in consumer demand analysis. He is a co-editor of Econometric Theory, a former co-editor of The Journal of Business and Economic Statistics and of Economics Letters, and has also served on the editorial boards of The Journal of Econometrics and The Journal of Applied Econometrics. Lewbel has a B.S. in Mathematics from the Massachusetts Institute of Technology and a Ph.D. in Management Applied Economics from the MIT Sloan School of Management.


Describe your area of study and how it relates to current policy discussions surrounding inequality.

I do microeconometrics and consumer demand. My work includes identifying the allocation of resources among family members within households, through a combination of data collection and structural modeling of household behavior. My colleagues and I have found that the intra-household allocation of resources has profound implications for the measurement of poverty and inequality. 

For example, due to within household inequality in the allocation of resources, children in Malawi and older women in India have substantially higher poverty rates than are indicated by standard household level poverty measures. Moreover, this previously unmeasured poverty is causally correlated with poorer health and mortality outcomes.

What areas in the study of inequality are most in need of new research?

More accurate measurement. Sen emphasized policies to empower women, but individual level measurement is needed to know which policies succeed. I've focused on intra-household (individual level) consumption issues, but there are many other examples of important findings stemming from improved measurement. Piketty and coauthors showed the value of accurately measuring wealth and income distributions. Through careful measurement, Deaton identified extreme poverty in rich countries like the U.S.

And note that measurement does not just mean data collection. Much of what economists measure is difficult or impossible to directly observe, like utility, ability, risk aversion, bargaining power, outside options, and joint consumption. Accurately measuring these and other inequality related concepts requires a combination of data collection, structural models of behavior, and econometric identification and estimation.

What advice do you have for emerging scholars in your field?

Don't take sides in the structural vs randomization vs machine learning debates. The best empirical work applies and combines all these tools: economic theory and econometrics and experiments and big data. Inequality spans a broad range of issues; don't confine yourself to a narrow range of methodologies. Merging these approaches is the future.