Identifying distributional characteristics in random coefficients panel data models

We study the identification of panel models with linear individual-specific coefficients, when T is fixed. We show identification of the variance of the effects under conditional uncorrelatedness. Identification requires restricted dependence of errors, reflecting a trade-off between heterogeneity and error dynamics. We show identification of the density of individual effects when errors follow an ARMA process under conditional independence. We discuss GMM estimation of moments of effects and errors, and introduce a simple density estimator of a slope effect in a special case. As an application we estimate the effect that a mother smokes during pregnancy on child's birth weight.

Robust priors in nonlinear panel data models

Many approaches to estimation of panel models are based on an average or integrated likelihood that assigns weights to different values of the individual effects. Fixed effects, random effects, and Bayesian approaches all fall in this category. We provide a characterization of the class of weights (or priors) that produce estimators that are first-order unbiased. We show that such bias-reducing weights must depend on the data unless an orthogonal reparameterization or an essentially equivalent condition is available. Two intuitively appealing weighting schemes are discussed. We argue that asymptotically valid confidence intervals can be read from the posterior distribution of the common parameters when N and T grow at the same rate. Finally, we show that random effects estimators are not bias reducing in general and discuss important exceptions. Three examples and some Monte Carlo experiments illustrate the results.

The link between wage inequality and the housing market's boom and bust in Spain

Our study uses administrative data to document large cyclical variations in the evolution of earnings inequality in Spain. We find that the construction sector played a key role in this evolution

Functional Differencing in Networks

Economic interactions often occur in networks where heterogeneous agents (such as workers or firms) sort and produce. However, most existing estimation approaches either require the network to be dense, which is at odds with many empirical networks, or they require restricting the form of heterogeneity and the network formation process. We show how the functional differencing approach introduced by Bonhomme (2012) in the context of panel data, can be applied in network settings to derive moment restrictions on model parameters and average effects. Those restrictions are valid irrespective of the form of heterogeneity, and they hold in both dense and sparse networks. We illustrate the analysis with linear and nonlinear models of matched employer-employee data, in the spirit of the model introduced by Abowd, Kramarz, and Margolis (1999)

Posterior Average Effects

Economists are often interested in estimating averages with respect to distributions of unobservables, such as moments of individual fixed-effects, or average partial effects in discrete choice models. For such quantities, we propose and study posterior average effects (PAE), where the average is computed conditional on the sample, in the spirit of empirical Bayes and shrinkage methods. While the usefulness of shrinkage for prediction is well-understood, a justification of posterior conditioning to estimate population averages is currently lacking. We show that PAE have minimum worst-case specification error under various forms of misspecification of the parametric distribution of unobservables. In addition, we introduce a measure of informativeness of the posterior conditioning, which quantifies the worst-case specification error of PAE relative to parametric model-based estimators. As illustrations, we report PAE estimates of distributions of neighborhood effects in the US, and of permanent and transitory components in a model of income dynamics

Grouped Patterns of Heterogeneity in Panel Data

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 unrestricted. 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. Recent advances in the clustering literature allow for fast and efficient computation. We provide conditions under which our estimator is consistent as both dimensions of the panel tend to infinity, and we develop inference methods. Finally, we allow for grouped patterns of unobserved heterogeneity in the study of the link between income and democracy across countries

Generalized nonparametric deconvolution with an application to earnings dynamics - Published Review of Economic Studies, Vol. 77, Issue 2, pp. 491-533, 2010

In this paper,we construct a nonparametric estimator of the distributions of latent factors in linear independent multi-factor models under the assumption that factor loadings are known. Our approach allows to estimate the distributions of up to L(L+1)/2 factors given L measurements. The estimator works through empirical characteristic functions. We show that it is consistent, and derive asymptotic convergence rates. Monte-Carlo simulations show good finite-sample performance, less so if distributions are highly skewed or leptokurtic. We finally apply the generalized deconvolution procedure to decompose individual log earnings from the PSID into permanent and transitory components

The cycle of earnings inequality : evidence from Spanish Social Security data

We use detailed information on labor earnings and employment from Social Security records to document earnings inequality in Spain from 1988 to 2010. Male earnings inequality was strongly countercyclical: it increased around the 1993 recession, showed a substantial decrease during the 1997-2007 expansion and then a sharp increase during the recent recession. These developments were partly driven by the cyclicality of employment and earnings in the lower-middle part of the distribution. We emphasize the importance of the housing boom and subsequent housing bust, and show that demand shocks in the construction sector signifi cantly impacted aggregate labor market outcomesEn este trabajo se utiliza información detallada de ingresos salariales y empleo procedente de registros de la Seguridad Social para documentar la evolución de la desigualdad salarial en España desde 1988 a 2010. La desigualdad salarial para los hombres ha sido fuertemente contracíclica: aumentó alrededor de la recesión de 1993, mostró un descenso sustancial en el período de expansión de 1997 a 2007 y evidenció un marcado incremento durante la crisis reciente. Esta evolución se ha debido, en parte, a la evolución cíclica del empleo y de los salarios en la parte baja-media de la distribución salarial. Enfatizamos la importancia del boom de la construcción y su posterior caída, y mostramos que los shocks de demanda en este sector han tenido efectos signifi cativos sobre variables agregadas del mercado laboral españo

Consistent noisy independent component analysis

We study linear factor models under the assumptions that factors are mutually independent and independent of errors, and errors can be correlated to some extent. Under factor non-Gaussianity, second to fourth-order moments are shown to yield full identification of the matrix of factor loadings. We develop a simple algorithm to estimate the matrix of factor loadings from these moments. We run Monte Carlo simulations and apply our methodology to data on cognitive test scores, and financial data on stock returns

Assessing the Equalizing Force of Mobility Using Short Panels: France, 1990–2000

In this paper, we document whether and how much the equalizing force of earnings mobility has changed in France in the 1990’s. For this purpose, we use a representative three-year panel, the French Labour Force Survey. We develop a model of earnings dynamics that combines a flexible specification of marginal earnings distributions (to fit the large cross-sectional dimension of the data) with a tight parametric representation of the dynamics (adapted to the short time-series dimension). Log earnings are modelled as the sum of a deterministic component, an individual fixed effect and a transitory component which is assumed first-order Markov. The transition probability of the transitory component is modelled as a one-parameter Plackett copula. We estimate this model using a sequential expectation-maximization algorithm. We exploit the estimated model to study employment/earnings inequality in France over the 1990– 2002 period. We show that, in phase with business-cycle fluctuations (a recession in 1993 and two peaks in 1990 and 2000), earnings mobility decreases when cross-section inequality and unemployment risk increase. We simulate individual earnings trajectories and compute present values of lifetime earnings for various horizons. Inequality presents a hump-shaped evolution over the period, with a 9% increase between 1990 and 1995 and a decrease afterwards. Accounting for unemployment yields an increase of 11%. Moreover, this increase is persistent, as it translates into a 12% increase in the variance of log present values. The ratio of inequality in present values to inequality in one-year earnings, a natural measure of immobility or of the persistence of inequality, remains remarkably constant over the business cycle