This paper revisits control-function estimation of production functions. We show that, in empirically relevant environments, the structural parameters can be weakly identified even when they are formally point identified. Casting control-function estimators in a GMM framework, we characterize the consequences of weak identification for estimation and inference, including non-standard asymptotic behavior and unreliable Wald inference. We then develop and implement identification-robust inference procedures for control-function estimators and provide practical guidance for diagnosing weak identification and reporting inference that remains valid when identification is weak. Finally, we illustrate how weak identification propagates to economically relevant objects constructed from production-function estimates by studying firm-level markups under the production approach.
This paper studies identification and estimation of production-function parameters via the control-function approach when the control function is noisy, so invertibility fails and productivity is unobserved. We show that exact recovery of productivity is not needed: under conditional mean restrictions and a forecast-sufficiency condition, the production function is locally identified. We also propose a novel one-step estimator that avoids separate first-stage estimation and delivers a more parsimonious specification.
This paper develops a decision-theoretic framework for optimal policy design when structural parameters are weakly identified. We combine impulse-response function matching with quasi-Bayesian methods to characterize the optimal policy under parameter uncertainty. Using a local asymptotic framework, we establish that the quasi-Bayes rule dominates the plug-in approach in terms of expected welfare loss. We illustrate the framework through an empirical application to U.S. monetary policy in the post-war period.
This paper develops a dynamic general equilibrium model with heterogeneous firms, capital accumulation, and endogenous entry and exit. Aggregate outcomes depend on the entire distribution of firm states rather than on a representative firm. The framework extends the stylized entry–exit model of Hopenhayn (1992) by incorporating investment dynamics and capital adjustment costs, creating a richer firm block. This structure moves beyond static wedge approaches by providing a structural dynamic mechanism through which misallocation arises and by linking the analysis to micro-econometric estimates. This allows to quantify how frictions generate dispersion in marginal products and translate into aggregate TFP losses using empirical data.
We estimate total factor productivity (TFP) distributions across European countries using firm-level microdata from the Microdata Infrastructure (MDI), a secure platform developed by CompNet and supported by the IWH-CBPD. The MDI provides researchers and policymakers with remote access to confidential cross-country firm-level data, enabling rigorous comparative analysis of productivity, competitiveness, and firm dynamics. This rich microdata resource allows us to characterize the complete distribution of TFP across firms, regions, and sectors, moving beyond aggregate statistics to capture heterogeneity in firm performance. Our analysis reveals productivity growth dynamics, quantifies the role of factor misallocation in explaining cross-country income differences, and identifies systematic sources of variation in firm efficiency.
Despite the growing importance of digital trade in services, its measurement remains limited, particularly in developing countries. This study builds a database of over 2,600 websites from Argentina, Brazil, Chile, Colombia, Mexico, and Peru, analyzing their business models and functional attributes. Using clustering techniques, it identifies common patterns and typologies of online presence. Results reveal wide heterogeneity within sectors, with most sites generating value through direct sales or intermediation, and little variation across major regional markets. The findings provide insights to inform policies promoting digital trade in services.