Nicholas Li

Welcome to my homepage!

I am a PhD candidate in economics at UC Berkeley, and I am on the job market this year. I am available for interviews at the 2019 ASSA Meetings in Atlanta.


  • Labor Economics
  • Urban Economics
  • Development Economics

e-mail: nicholasli at econ dot berkeley dot edu


1. Housing Market Channels of Segregation, Job Market Paper, November 2018.


Was the pattern of residential segregation that emerged in major US cities in the mid-twentieth century simply a reflection of white preferences? Or, was it due in part to constraints on the availability of homes to black families? In this paper, I draw on rich population data from the 1930 and 1940 censuses to answer these questions. I lay out a simple discrete choice model of residential choices by white and black families that depends on the local price of housing and on the fraction of black residents in each neighborhood. I show how the preferences of both race groups can be identified using information on the impacts of exogenous inflows of white and black residents to different neighborhoods. White and black rural inflows constituted a major source of inmigration to major cities during this time period; I construct a pair of novel instrumental variables for these inflows by connecting the distributions of white and black surnames in rural areas to earlier migrants living in different census tracts in 1930. The resulting structural estimates confirm that white families had a relatively high willingness to pay to avoid black neighbors, consistent with an important role for preferences in the evolution of neighborhood segregation. Combining white and black preferences, however, I also find strong evidence that black residents faced supply-side constraints on their neighborhood choices. I conclude that about one half of the overall degree of neighborhood segregation observed in 1940 was due to the different preferences of white and black families, while a comparable share was due to implicit or explicit constraints on which neighborhoods black families could move into.

2. Government Decentralization Under Changing State Capacity, with Ernesto Dal Bó, Frederico Finan, and Laura Schechter, July 2018.


Standard models of hierarchy assume that agents and middle managers are better informed than principals about how to implement a particular task. We estimate the value of the informational advantage held by supervisors (middle managers) when ministerial leadership (the principal) introduced a new monitoring technology aimed at improving the performance of agricultural extension agents (AEAs) in rural Paraguay. Our approach employs a novel experimental design that, before randomization of treatment, elicited from supervisors which AEAs they believed should be prioritized for treatment. We find that supervisors did have valuable information—they prioritized AEAs who would be more responsive to the monitoring treatment. We develop a model of monitoring under different allocation rules and rollout scales (i.e., the share of AEAs to receive treatment). We semi-parametrically estimate marginal treatment effects (MTEs) to demonstrate that the value of information and the benefits to decentralizing treatment decisions depend crucially on the sophistication of the principal and on the scale of rollout.

3. Reevaluating Agricultural Productivity Gaps with Longitudinal Microdata, with Joan Hamory Hicks, Marieke Kleemans, and Edward Miguel, July 2018. Non-technical summary in VoxDev. R&R, Journal of the European Economic Association.


Recent research has pointed to large gaps in labor productivity between the agricultural and non-agricultural sectors in low-income countries, as well as between workers in rural and urban areas. Most estimates are based on national accounts or repeated cross-sections of micro-survey data, and as a result typically struggle to account for individual selection between sectors. This paper uses long-run individual-level panel data from two low-income countries (Indonesia and Kenya). Accounting for individual fixed effects leads to much smaller estimated productivity gains from moving into the non-agricultural sector (or urban areas), reducing estimated gaps by over 80%. Per capita consumption gaps are also small once individual fixed effects are included. Estimated productivity gaps do not emerge up to five years after a move between sectors. We evaluate whether these findings imply a re-assessment of the conventional wisdom regarding sectoral gaps, discuss how to reconcile them with existing cross-sectional estimates, and consider implications for the desirability of sectoral reallocation of labor.

Teaching Materials and Notes

Coming soon.