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. Framing the estimators within a GMM setup, we characterize the implications of weak identification for estimation and inference—most notably, inconsistent estimators and unreliable Wald-type inference. We then discuss and implement identification- robust procedures to conduct inference for control-function estimators. Thereby we provide a guideline for applied work to assess identification and to report inference that remains valid when identification is weak.
This paper studies identification and estimation of production function parameters when the proxy used in control-function methods is noisy and productivity is only partially predictable from observables. We allow intermediate-input demand to contain an unobserved error term, so that the canonical scalar-unobservable condition fails and productivity cannot be perfectly recovered from the proxy. Identification of the structural coefficients nonetheless goes through under two weaker requirements: conditional mean independence of innovation terms and a forecast-sufficiency condition under which the conditional expectation of future productivity depends on observables only through a one-dimensional index. Casting the problem as a semiparametric conditional moment model, we characterize local identification via the full rank of an orthogonalized Jacobian matrix and discuss corresponding GMM estimators.
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) across European countries using MDI data from CompNet. The analysis focuses on productivity growth, the role of misallocation, and systematic cross-country differences. This provides a comparative view of efficiency dynamics across Europe.
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.