Knowledge that Transforms

The benefits of new technologies accrue not only to high‐skilled labor but also to owners of capital in the form of higher capital incomes. This increases inequality. To make this argument, we develop a tractable theory that links technology to the distribution of income and wealth—and not just that of wages—and use it to study the distributional effects of automation. We isolate a new theoretical mechanism: automation increases inequality by raising returns to wealth. The flip side of such return movements is that automation can lead to stagnant wages and, therefore, stagnant incomes at the bottom of the distribution. We use a multiasset model extension to confront differing empirical trends in returns to productive and safe assets and show that the relevant return measures have increased over time. Automation can account for part of the observed trends in income and wealth inequality.

We consider nonparametric estimation of a mixed discrete‐continuous distribution under anisotropic smoothness conditions and a possibly increasing number of support points for the discrete part of the distribution. For these settings, we derive lower bounds on the estimation rates. Next, we consider a nonparametric mixture of normals model that uses continuous latent variables for the discrete part of the observations. We show that the posterior in this model contracts at rates that are equal to the derived lower bounds up to a log factor. Thus, Bayesian mixture of normals models can be used for (up to a log factor) optimal adaptive estimation of mixed discrete‐continuous distributions. The proposed model demonstrates excellent performance in simulations mimicking the first stage in the estimation of structural discrete choice models.

This article contains new tools for studying the shape of the stationary distribution of sizes in a dynamic economic system in which units experience random multiplicative shocks and are occasionally reset. Each unit has a Markov‐switching type, which influences their growth rate and reset probability. We show that the size distribution has a Pareto upper tail, with exponent equal to the unique positive solution to an equation involving the spectral radius of a certain matrix‐valued function. Under a nonlattice condition on growth rates, an eigenvector associated with the Pareto exponent provides the distribution of types in the upper tail of the size distribution.

We study discrete panel data methods where unobserved heterogeneity is revealed in a first step, in environments where population heterogeneity is not discrete. We focus on two‐step grouped fixed‐effects (GFE) estimators, where individuals are first classified into groups using kmeans clustering, and the model is then estimated allowing for group‐specific heterogeneity. Our framework relies on two key properties: heterogeneity is a function—possibly nonlinear and time‐varying—of a low‐dimensional continuous latent type, and informative moments are available for classification. We illustrate the method in a model of wages and labor market participation, and in a probit model with time‐varying heterogeneity. We derive asymptotic expansions of two‐step GFE estimators as the number of groups grows with the two dimensions of the panel. We propose a data‐driven rule for the number of groups, and discuss bias reduction and inference.

This paper integrates the classic theory of firm boundaries, through span of control or taste for variety, into a model of the labor market with random matching and on‐the‐job search. Firms choose when to enter and exit, whether to create vacancies or destroy jobs in response to shocks, and Bertrand‐compete to hire and retain workers. Tractability is obtained by proving that, under a parsimonious set of assumptions, all worker and firm decisions are characterized by their joint surplus, which in turn only depends on firm productivity and size. The job ladder in marginal surplus that emerges in equilibrium determines net poaching patterns by firm characteristics that are in line with the data. As frictions vanish, the model converges to a standard competitive model of firm dynamics. The combination of firm dynamics and search frictions allows the model to: (i) quantify the misallocation cost of frictions; (ii) replicate elusive life‐cycle growth profiles of superstar firms; and (iii) make sense of the failure of the job ladder around the Great Recession as a result of the collapse of firm entry.

This paper studies optimal decision rules, including estimators and tests, for weakly identified GMM models. We derive the limit experiment for weakly identified GMM, and propose a theoretically‐motivated class of priors which give rise to quasi‐Bayes decision rules as a limiting case. Together with results in the previous literature, this establishes desirable properties for the quasi‐Bayes approach regardless of model identification status, and we recommend quasi‐Bayes for settings where identification is a concern. We further propose weighted average power‐optimal identification‐robust frequentist tests and confidence sets, and prove a Bernstein‐von Mises‐type result for the quasi‐Bayes posterior under weak identification.

Virtually all theories of economic growth predict a positive relationship between population size and productivity. In this paper, I study a particular historical episode to provide direct evidence for the empirical relevance of such scale effects. In the aftermath of the Second World War, 8 million ethnic Germans were expelled from their domiciles in Eastern Europe and transferred to West Germany. This inflow increased the German population by almost 20%. Using variation across counties, I show that the settlement of refugees had large and persistent effects on the size of the local population, manufacturing employment, and income per capita. These findings are quantitatively consistent with an idea‐based model of spatial growth if population mobility is subject to frictions and productivity spillovers occur locally. The estimated model implies that the refugee settlement increased aggregate income per capita by about 12% after 25 years and triggered a process of industrialization in rural areas.

Rust (1997b) discovered a class of dynamic programs that can be solved in polynomial time with a randomized algorithm. For these dynamic programs, the optimal values of a polynomially large sample of states are sufficient statistics for the (near) optimal values everywhere, and the values of this random sample can be bootstrapped from the sample itself. However, I show that this class is limited, as it requires all but a vanishingly small fraction of state variables to behave arbitrarily similarly to i.i.d. uniform random variables.

We construct an endogenous growth model with random interactions where firms are subject to distortions. The TFP distribution evolves endogenously as firms seek to upgrade their technology over time either by innovating or by imitating other firms. We use the model to quantify the effects of misallocation on TFP growth in emerging economies. We structurally estimate the stationary state of the dynamic model targeting moments of the empirical distribution of R&D and TFP growth in China during the period 2007–2012. The estimated model fits the Chinese data well. We compare the estimates with those obtained using data for Taiwan and perform counterfactuals to study the effect of alternative policies. R&D misallocation has a large effect on TFP growth.

This paper provides evidence that exposure to illegal labor markets during childhood leads to the formation of industry‐specific human capital at an early age, putting children on a criminal life path. Using the timing of U.S. antidrug policies, I show that when the return to illegal activities increases in coca suitable areas in Peru, parents increase the use of child labor for coca farming, putting children on a criminal life path. Using administrative records, I show that affected children are about 30% more likely to be incarcerated for violent and drug‐related crimes as adults. No effect in criminality is found for individuals that grow up working in places where the coca produced goes primarily to the legal sector, suggesting that it is the accumulation of human capital specific to the illegal industry that fosters criminal careers. However, the rollout of a conditional cash transfer program that encourages schooling mitigates the effects of exposure to illegal industries, providing further evidence on the mechanisms.