Fabian Eckert

PhD Candidate in Economics at Yale University

Research Interest: Trade, Economic Geography, and Macroeconomics

papers

The Return to Big City Experience w/ Mads Hejlesen & Conor Walsh

In this paper, we provide evidence for higher returns to experience in big cities. We exploit a government policy of quasi-random settlement of political refugees in Denmark between 1986 and 1998, which generated plausibly exogenous variation in workers' initial local labor market. Detailed matched employer-employee datasets allow us to track workers' location and labor market experience, including their employers. We show that the slope of a refugee's lifetime wage path depends strongly and positively on initial placement in the country's capital, Copenhagen. Conditional on observables, settled refugees initially earn similar hourly wages across regions, but those placed in Copenhagen see their wages grow 0.81% faster than others with each year of experience they accumulate. We further show that this premium is driven by the greater acquisition of experience at high-wage establishments and by differential sorting across occupations. Estimating a statistical spatial model of earnings dynamics reveals that sorting on unobserved ability within cities plays an important role in explaining observed patterns.

Saving the American Dream? w/ Tatjana Kleineberg

Neighborhoods in the United States differ substantially in the educational and economic opportunities they offer to children. General equilibrium effects complicate the analysis of policies that equalize school quality across localities: local wages, rents, and school funding respond to parents' residential choices. This paper proposes a dynamic spatial model to evaluate the long-run effects of education policies in spatial equilibrium. The framework incorporates costly migration across labor markets and dynastic human capital formation as a function of childhood location and parental background. Differential labor market access and endogenous and exogenous components of local school quality generate persistent effects of childhood neighborhoods on adult labor market outcomes. We estimate the model parameters on the realistic geography of the US. We show that a counterfactual policy equalizing government school funding across all students would have only moderate effects on intergenerational mobility in general equilibrium. Partial equilibrium analysis is shown to overestimate such effects.

Spatial Structural Change w/ Michael Peters

This paper studies the spatial implications of structural change. The secular decline in spending on agricultural goods hurts workers in rural locations and increases the return to moving towards non-agricultural labor markets. We combine detailed spatial data for the U.S. between 1880 and 2000 with a novel quantitative theory to analyze this process and to quantify its macroeconomic implications. We show that spatial reallocation across labor markets accounts for almost none of the aggregate decline in agricultural employment. The reason is that population flows, while large in the aggregate, were only weakly correlated with agricultural specialization. Labor mobility nevertheless had important aggregate effects. Without migration income per capita would have been 15% lower. Moreover, spatial welfare inequality would have been substantially higher, especially among low-skilled, agricultural workers, which were particularly exposed to the structural transformation.

Combinatorial Discrete Choice [Python Codes] w/ Costas Arkolakis

Discrete choice problems with complementarities are prevalent in economics but the large dimensionality of potential solutions substantially limits the scope of their application. We define and characterize a general class that we term combinatorial discrete choice problems and show that it covers many existing problems in economics and engineering. We propose single crossing differences (SCD) as the sufficient condition to guarantee that simple recursive procedures can find the global maximum. We introduce an algorithm motivated by this condition and show how it can be used to revisit problems whose computation was deemed infeasible before. We finally discuss results for a class of games characterized by these sufficient conditions.

Imputing Missing Values in the US Census Bureau's County Business Patterns [Draft on request] w/ Peter Schott & Natalie Yang

The County Business Pattern data published by the US Census Bureau track employment and establishments by county and industry from 1946 to the present. While these data are extremely useful in many areas of research, they have two limitations: data for many cells are suppressed due to the Census Bureaus' need to protect confidentiality; and both industry and county classifications change over time. This paper addresses both issues. Building on techniques proposed by Isserman and Westervelt (2006), we develop a method for imputing missing observations using hierarchical adding-up constraints, and construct concordances that allow industries and counties to be compared over long periods of time.

A Consistent County-Level Crosswalk for US Spatial Data since 1790 [Materials] w/ Andres Gvirtz & Michael Peters

Both the number and the geographic boundaries of the counties covering the territory of the United States changed frequently and significantly since 1790. In order to study the spatial aspects of the United States' economic history it is frequently necessary to create a consistent panel of spatial units, which are consistent over time. We provide such a crosswalk, that enables researchers to aggregate historical US county data for every decade since 1790 to current US counties and commuting zones. This note describes the details of our data construction and how to use this crosswalk in practice. A data package containing the decadal crosswalks since 1790 and the accompanying GIS files is available on our websites.