We thank seminar participants at Northwestern, the World Bank, Chicago GSB, University of Wisconsin–Madison, Minneapolis Federal Reserve, Princeton University, European University in Florence, University of Southern California, New York University, Boston University, Bocconi, Universitè Pompeu Fabra, Ente Einaudi, Boston Federal Reserve, and Harvard University and conference participants at the 2009 winter NBER EF&G Meetings, 2008 AEA, SITE, the 2007 NBER Summer Institute, the SED Conference, LAEF Households, Gender and Fertility Conference, the NBER Group on Macroeconomics across Time and Space, Midwest Macro Meetings, the NY/Philadelphia Workshop on Quantitative Macro, IZA/SOLE, and Ammersee. We especially thank Stefania Marcassa for excellent research assistance and Stefania Albanesi, Roland Benabou, Raquel Bernal, Jason Faberman, Jeremy Greenwood, Luigi Guiso, Larry Jones, Patrick Kehoe, Narayana Kocherlakota, Ellen McGrattan, Fabrizio Perri, Harald Uhlig, and the anonymous referees for comments and suggestions. Laura Veldkamp thanks Princeton University for their hospitality and financial support through the Kenen fellowship.
Nature or Nurture? Learning and the Geography of Female Labor Force Participation
Article first published online: 1 JUL 2011
© 2011 The Econometric Society
Volume 79, Issue 4, pages 1103–1138, July 2011
How to Cite
Fogli, A. and Veldkamp, L. (2011), Nature or Nurture? Learning and the Geography of Female Labor Force Participation. Econometrica, 79: 1103–1138. doi: 10.3982/ECTA7767
- Issue published online: 1 JUL 2011
- Article first published online: 1 JUL 2011
- Manuscript received March, 2008; final revision received March, 2010.
- Female labor force participation;
- information diffusion;
- economic geography
One of the most dramatic economic transformations of the past century has been the entry of women into the labor force. While many theories explain why this change took place, we investigate the process of transition itself. We argue that local information transmission generates changes in participation that are geographically heterogeneous, locally correlated, and smooth in the aggregate, just like those observed in our data. In our model, women learn about the effects of maternal employment on children by observing nearby employed women. When few women participate in the labor force, data are scarce and participation rises slowly. As information accumulates in some regions, the effects of maternal employment become less uncertain and more women in that region participate. Learning accelerates, labor force participation rises faster, and regional participation rates diverge. Eventually, information diffuses throughout the economy, beliefs converge to the truth, participation flattens out, and regions become more similar again. To investigate the empirical relevance of our theory, we use a new county-level data set to compare our calibrated model to the time series and geographic patterns of participation.