Different continuous-time models for interest rates coexist in the literature. We test parametric models by comparing their implied parametric density to the same density estimated nonparametrically. We do not replace the continuous-time model by discrete approximations, even though the data are recorded at discrete intervals. The principal source of rejection of existing models is the strong nonlinearity of the drift. Around its mean, where the drift is essentially zero, the spot rate behaves like a random walk. The drift then mean-reverts strongly when far away from the mean. The volatility is higher when away from the mean.

Testing Continuous-Time Models of the Spot Interest Rate

We provide evidence that democracy has a significant and robust positive effect on GDP per capita. Our empirical strategy controls for country fixed effects and the rich dynamics of GDP, which otherwise confound the effect of democracy on economic growth. To reduce measurement error, we introduce a new dichotomous measure of democracy that consolidates the information from several sources. Our baseline results use a dynamic panel model for GDP, and show that democratizations increase GDP per capita by about 20% in the long run. We find similar effects of democratizations on annual GDP when we control for the estimated propensity of a country to democratize based on past GDP dynamics. We obtain comparable estimates when we instrument democracy using regional waves of democratizations and reversals. Our results suggest that democracy increases GDP by encouraging investment, increasing schooling, inducing economic reforms, improving the provision of public goods, and reducing social unrest. We find little support for the view that democracy is a constraint on economic growth for less developed economies.

Democracy Does Cause Growth

Finite Mixture Distributions

We synthesize two related literatures on firm-level drivers of wage inequality. Studies of rent sharing that use matched worker-firm data find elasticities of wages with respect to value added per worker in the range of 0.05–0.15. Studies of wage determination with worker and firm fixed effects typically find that firm-specific premiums explain 20% of overall wage variation. To interpret these findings, we develop a model of wage setting in which workers have idiosyncratic tastes for different workplaces. Simple versions of this model can rationalize standard fixed effects specifications and also match the typical rent-sharing elasticities in the literature.

/pdf/firms-and-labor-market-inequality-evidence-and-some-theory-10f93d0u3s.pdf

Firms and Labor Market Inequality: Evidence and Some Theory

Panel Data in Econometrics

This paper introduces time-varying grouped patterns of heterogeneity in linear panel data models. A distinctive feature of our approach is that group membership is left unspecified. We estimate the parameters of the model using a “grouped fixed-effects” estimator that minimizes a least-squares criterion with respect to all possible groupings of the cross-sectional units. We rely on recent advances in the clustering literature for fast and efficient computation. Our estimator is higher-order unbiased as both dimensions of the panel tend to infinity, under conditions that we characterize. As a result, statistical inference is not affected by the fact that group membership is estimated. We apply our approach to study the link between income and democracy across countries, while allowing for grouped patterns of unobserved heterogeneity. The results shed new light on the evolution of political and economic outcomes of countries.

/pdf/grouped-patterns-of-heterogeneity-in-panel-data-45chpksxjc.pdf

Grouped Patterns of Heterogeneity in Panel Data

We develop a discrete heterogeneity framework for matched employer employee data. The framework allows for unrestricted interactions between worker and rm unobserved characteristics in the wage function, as well as unrestricted sorting based on these unobservables. Pooling cross-sectional observations together with information from the joint distribution of wages of job movers, we establish a series of nonparametric identication results in short panels. We evaluate our method on data simulated from a theoretical model under both positive and negative sorting. We apply our method to Swedish matched employer employee panel data and report estimated wage functions and sorting patterns.

/pdf/a-distributional-framework-for-matched-employer-employee-4atscfi8u7.pdf

A Distributional Framework for Matched Employer Employee Data

I consider settings where outcomes depend on own characteristics and on the characteristics of other individuals in the sample. I propose a method to identify individuals generating spillovers and their strength using panel data on outcomes and characteristics. This is in contrast to existing approaches, which require a priori knowledge of the structure of interactions. The method is suitable when the structure of interactions is stable over time and few individuals generate spillovers distinct in magnitude from the rest. To estimate the model, I introduce the Pooled Lasso estimator, a paneldata counterpart of the Lasso estimator and develop an iterative algorithm for computation that alternates between Lasso estimation and pooled panel regression. Average marginal spillover effects across individuals enjoy gains in the rate of convergence under independence of model selection noise. I apply this methodology to study technological spillovers in productivity in a panel of US firms. I find evidence that spillovers are asymmetric across firms, arising mostly from small, highly productive firms.

/pdf/estimating-the-structure-of-social-interactions-using-panel-373vfctz5j.pdf

Estimating the Structure of Social Interactions Using Panel Data

We study panel data estimators based on a discretization of unobserved heterogeneity when individual heterogeneity is not necessarily discrete in the population. We focus on two-step grouped-fixed effects estimators, where individuals are classified into groups in a first step using kmeans clustering, and the model is estimated in a second step allowing for group-specific heterogeneity. We analyze the asymptotic properties of these discrete estimators as the number of groups grows with the sample size, and we show that bias reduction techniques can improve their performance. In addition to reducing the number of parameters, grouped fixed-effects methods provide effective regularization. When allowing for the presence of time-varying unobserved heterogeneity, we show they enjoy fast rates of convergence depending of the underlying dimension of heterogeneity. Finally, we document the nite sample properties of two-step grouped fixed-effects estimators in two applications: a structural dynamic discrete choice model of migration, and a model of wages with worker and firm heterogeneity.

https://www.ifs.org.uk/uploads/publications/wps/WP201703.pdf

Discretizing unobserved heterogeneity

Many studies use matched employer-employee data to estimate a statistical model of earnings determination where log-earnings are expressed as the sum of worker effects, firm effects, covariates, and idiosyncratic error terms. Estimates based on this model have produced two influential yet controversial conclusions. First, firm effects typically explain around 20% of the variance of log-earnings, pointing to the importance of firm-specific wage-setting for earnings inequality. Second, the correlation between firm and worker effects is often small and sometimes negative, indicating little if any sorting of high-wage workers to high-paying firms. The objective of this paper is to assess the sensitivity of these conclusions to the biases that arise because of limited mobility of workers across firms. We use employer-employee data from the US and several European countries while taking advantage of both fixed-effects and random-effects methods for bias-correction. We find that limited mobility bias is severe and that bias-correction is important. Once one corrects for limited mobility bias, firm effects dispersion matters less for earnings inequality and worker sorting becomes always positive and typically strong.

/pdf/how-much-should-we-trust-estimates-of-firm-effects-and-wsdtoi23sp.pdf

Elena Manresa

Papers

Grouped Patterns of Heterogeneity in Panel Data

A Distributional Framework for Matched Employer Employee Data

Estimating the Structure of Social Interactions Using Panel Data

Discretizing unobserved heterogeneity

How Much Should we Trust Estimates of Firm Effects and Worker Sorting