Enrique Moral-Benito

Is infrastructure capital productive ? a dynamic heterogeneous approach

This paper offers an empirical evaluation of the output contribution of infrastructure. Drawing from a large data set on infrastructure stocks covering 88 countries and spanning the years 1960-2000, and using a panel time-series approach, the paper estimates a long-run aggregate production function relating GDP to human capital, physical capital, and a synthetic measure of infrastructure given by the first principal component of infrastructure endowments in transport, power, and telecommunications. Tests of the cointegration rank allowing it to vary across countries reveal a common rank with a single cointegrating vector, which is taken to represent the long-run production function. Estimation of its parameters is performed using the pooled mean group estimator, which allows for unrestricted short-run parameter heterogeneity across countries while imposing the (testable) restriction of long-run parameter homogeneity. The long-run elasticity of output with respect to the synthetic infrastructure index ranges between 0.07 and 0.10. The estimates are highly significant, both statistically and economically, and robust to alternative dynamic specifications and infrastructure measures. There is little evidence of long-run parameter heterogeneity across countries, whether heterogeneity is unconditional, or conditional on their level of development, population size, or infrastructure endowments.

Model Averaging in Economics

Fragility of regression analysis to arbitrary assumptions and decisions about choice of control variables is an important concern for applied econometricians (e.g. Leamer (1983)). Sensitivity analysis in the form of model averaging represents an (agnostic) approach that formally addresses this problem of model uncertainty. This paper presents an overview of model averaging methods with emphasis on recent developments in the combination of model averaging with IV and panel data settings.

Model Averaging in Economics: An Overview

Standard practice in empirical research is based on two steps: first, researchers select a model from the space of all possible models; second, they proceed as if the selected model had generated the data. Therefore, uncertainty in the model selection step is typically ignored. Alternatively, model averaging accounts for this model uncertainty. In this paper, I review the literature on model averaging with special emphasis on its applications to economics. Finally, as an empirical illustration, I consider model averaging to examine the deterrent effect of capital punishment across states in the USA.

Determinants of Economic Growth : A Bayesian Panel Data Approach

Model uncertainty hampers consensus on the key determinants of economic growth. Some recent cross-country, cross-sectional analyses have employed Bayesian Model Averaging to address the issue of model uncertainty. This paper extends that approach to panel data models with country-specific fixed effects. The empirical results show that the most robust growth determinants are the price of investment goods, distance to major world cities, and political rights. This suggests that growth-promoting policy strategies should aim to reduce taxes and distortions that raise the prices of investment goods; improve access to international markets; and promote democracy-enhancing institutional reforms. Moreover, the empirical results are robust to different prior assumptions on expected model size.

Likelihood-Based Estimation of Dynamic Panels With Predetermined Regressors

This article discusses the likelihood-based estimation of panel data models with individual-specific effects and both lagged dependent variable regressors and additional predetermined explanatory variables. The resulting new estimator, labeled as subsystem limited information maximum likelihood (ssLIML), is asymptotically equivalent to standard panel generalized method of moment as N → ∞ for fixed T but tends to present smaller biases in finite samples as illustrated in simulation experiments. Simulation results also indicate that the estimator is preferred to other alternatives available in the literature in terms of finite-sample performance. Finally, to provide an empirical illustration, I revisit the evidence on the relationship between income and democracy in a panel of countries using the proposed estimator.

Income and democracy: Revisiting the evidence

In an influential paper, Acemoglu et al. (2008) find that the positive correlation between income per capita and the level of democracy across countries vanishes once country-specific effects are accounted for. In this paper, we find evidence of a non-linear effect from income to democracy even after controlling for country-specific effects. In particular, our findings point to the existence of a positive effect only in low-income countries.

Determinants of health-system efficiency: evidence from OECD countries

This paper analyzes the most important determinants of healthcare efficiency across OECD countries. As previously documented in the literature, we first provide evidence of significant differences in the cross-country level of efficiency in healthcare provision. We then investigate how improvements in efficiency can be achieved by considering alternative efficiency indices (parametric and non-parametric) and a novel dataset with information on the characteristics of healthcare systems across OECD countries. Our empirical findings suggest a positive correlation between policies such as increasing the regulation of prices billed by providers and reducing the degree of gate keeping and the efficiency of national healthcare systems.

Business Cycles and Investment in Productivity-Enhancing Activities: Evidence from Spanish Firms

This paper tests the opportunity-cost theory on the long-run effects of business cycles using a panel of Spanish firms during the period 1991–2010. Under this theory, the share of productivity-enhancing activities (PEAs), such as R&D investment or on-the-job training, relative to production activities should increase during downturns because of the fall in their relative cost — in terms of forgone output. This would imply that business cycles may have a (positive) long-term impact on firms productivity growth. In the spirit of Aghion et al. (2008), we allow the impact of the cycle on PEA to vary between firms with different access to external funding. We find that, in accordance with the opportunity-cost approach, the share of R&D investment and training expenditures on total investment outlays follow a countercyclical pattern, which in the case of R&D may be reversed by the presence of credit constraints. However, the share of investment in other non-R&D-related intangible investments is found to be acyclical, which could suggest some kind of substitution across different PEAs over the cycle.

What drives a successful fiscal consolidation

Fiscal consolidations are currently in the agenda of fiscal authorities in many countries. Using Bayesian Model Averaging to overcome the problem of model uncertainty, we find that growth-enhancing policies and cuts in public wages are the most appropriate ingredients for successfully reducing debt levels and budget deficits.

Determinants of corporate default: a BMA approach

In this article, we aim to identify the main determinants of corporate default by considering Bayesian Model Averaging (BMA) techniques. Our empirical findings suggest that the most robust determinants of firm default are firm-specific variables such as the ratio of working capital to total assets and the SD of the firm stock return. In contrast, aggregate variables do not seem to play a relevant role once firm-specific characteristics (observable and unobservable) and model uncertainty are taken into consideration.