Research

Publications: Selection

• Ramírez-Hassan, A. J., Rengifo-Castro, J., Manzur, M., Rueda-Ramírez, E. 2025. “Approximate Bayesian computation to estimate persistent and transient efficiency in stochastic frontier panel data models” Journal of Productivity Analysis. Online. 

We use approximate Bayesian computation (ABC) to estimate panel data stochastic frontier models, allowing for persistent and transient inefficiency, unobserved heterogeneity, and noise. We use ABC to estimate the generalized true random-effects (GTRE) specification. Simulation exercises for estimating technical efficiency show that our proposal has good finite-sample properties under different configurations of the variance parameters of the four random components, as well as on five well-known datasets. Our proposal is easy to implement in the half-normal case, and adaptable to different distributional assumptions regarding the one-sided error components. 

• Ramírez-Hassan, A. C. Mendez, Rueda-Ramírez, E. 2025. “Bayesian average of classical estimates for panel data: Can the puzzle of the shape of the regional Kuznets curve be solved?” Empirical Economics. Online. 

We evaluate the robustness of the regional Kuznets curve using the Bayesian average of classical estimates for panel data and identify the robust determinants of regional inequality. Our simulation exercise suggests that this method recovers the variables underlying the true data generation process. Our results indicate that in addition to real GDP per capita, linear and quadratic, the most robust determinants of regional inequality are natural resource rents, arable land, and ethnic inequality. We find an inverted U-shaped relationship between regional inequality and national development in the range of USD 189 to USD 71,682. Beyond this threshold, there is evidence suggesting inequality stabilization. 

• Puerta, A., Ramírez-Hassan, A. 2025. “A spatial one-sided error model to identify where unarrested criminals live” Economic Modelling. Online. 

The place of residence of unarrested criminals is mostly unknown. Existing research has not yet exploited that arrested criminals are a lower bound for criminals to enhance law enforcement and design structural policies. Based upon the stochastic frontier analysis, we propose a model to identify neighborhoods where unarrested criminals are likelier to live. We illustrate our approach empirically by considering Medellín, Colombia, a natural experimental field to analyze crime. We identify that unarrested murderers and drug dealers often reside in overlapping or neighboring areas with shared risk factors, reflecting the city’s history of drug-related violence. In addition, we find that employment policies targeting the young and unemployed living in the central-east and the north can mitigate homicides and motorcycle thefts. These findings illustrate how our proposal can be implemented to strengthen state capacities and design targeted, place-based policies for preventing and mitigating crime. 

• Ramírez-Hassan, A., Frazier, D. 2024. “Testing Model Specification in Approximate Bayesian Computation Using Asymptotic Properties” Journal of Computational and Graphical Statistics. Online. 

We present a novel procedure to diagnose model misspecification in situations where inference is performed using approximate Bayesian computation (ABC). Unlike previous procedures, our proposal is based on the asymptotic properties of ABC. We demonstrate theoretically, and empirically that our procedure can consistently detect the presence of model misspecification. The examples demonstrate that our proposal shows good finite-sample properties, outperforming existing approaches. An empirical application to modeling exchange rate log returns using a g-and-k distribution completes the article. Supplementary materials for this article are available online.

• Ramírez-Hassan, A., López-Vera, A. 2024. “Welfare implications of a tax on electricity: A semi-parametric specification of the incomplete EASI demand system” Energy Economics. 131, 1-13.

We perform a welfare analysis due to a tax on electricity consumption based on the incomplete exact affine Stone index (EASI) model using a novel data set in the Colombian economy. We provide a novel inferential framework based on a non-parametric specification of the stochastic errors using Dirichlet processes mixtures that allows handling non-normal errors, gaining efficiency, and taking into account, microeconomic restrictions, censoring, simultaneous endogeneity and non-linearity. We find that there is a 95% probability that the equivalent variation of the representative household is between US¢34.1 and US¢34.3, given an approximately 0.8% tariff increase (US¢0.12 per kWh). In addition, we observe that the welfare loss of the representative household of the lowest socioeconomic characteristics is approximately twice the loss of the representative household of the highest socioeconomic characteristics.   

• Ramírez-Hassan, A., García G., Saravia, E., Duque J. & Londoño, D. 2023. “What kind of schools parents choose when they have more options? Effects of school transport subsidies” Socio-Economic Palanning Sciences. 87 (Part A), 1-12. 

It seems that facilitating access to a higher spectrum of schools implies that students will attend higher quality schools, as measured by students’ end-of-class test scores. We test this hypothesis showing new evidence for the effects of school transport subsidies targeting low-income students on school choice in the context of a developing country (Colombia) using a unique panel dataset involving a public-school population with approximately 15 million records. We built a creative instrument deducing unobserved optimal commute decisions, which seems to satisfy the exclusion and relevance conditions, and we found by means of two-stage least squares that metro and bus subsidy beneficiaries choose statistically and economically significantly better schools, approximately a 33% and 37% improvement in the quality school index, respectively. In addition, we found using endogenous ordered probit models that these subsidies increase the probability of attending very high-quality schools by 59% and 94% for the representative beneficiary, respectively. These results suggest that the reduction of costs of transport not only increases accessibility and the set of school choices among low-income students, but also targets students enrolled in better quality schools. Therefore, the local government should increase efforts to get more subsidies targeting uncovered areas. 

• Jacobi, L., Kwok, C. & Ramírez-Hassan, A. 2023. “Posterior Manifolds over Prior Parameter Regions: Beyond Pointwise Sensitivity Assessments for Posterior Statistics from MCMC Inference” Studies in Nonlinear Dynamcis & Econometrics. 28 (2), 403-434. 

Increases in the use of Bayesian inference in applied analysis, the complexity of estimated models, and the popularity of efficient Markov chain Monte Carlo (MCMC) inference under conjugate priors have led to more scrutiny regarding the specification of the parameters in prior distributions. Impact of prior parameter assumptions on posterior statistics is commonly investigated in terms of local or pointwise assessments, in the form of derivatives or more often multiple evaluations under a set of alternative prior parameter specifications. This paper expands upon these localized strategies and introduces a new approach based on the graph of posterior statistics over prior parameter regions (sensitivity manifolds) that offers additional measures and graphical assessments of prior parameter dependence. Estimation is based on multiple point evaluations with Gaussian processes, with efficient selection of evaluation points via active learning, and is further complemented with derivative information. The application introduces a strategy to assess prior parameter dependence in a multivariate demand model with a high dimensional prior parameter space, where complex prior-posterior dependence arises from model parameter constraints. The new measures uncover a considerable prior dependence beyond parameters suggested by theory, and reveal novel interactions between the prior parameters and the elasticities. 

• Ramírez-Hassan, A., Estefanía Rúa-Ledesma & Jhon García. 2023. “Estimation of the X-Factor in the Colombian Electric Power Distribution Sector: A Competition with Just Three Winners” The Journal of Energy and Development. 48 (1), 109-128. 

Regulatory councils should estimate the X-factor, that is, the total factor productivity (TFP) change, in industries where the market structure is a monopoly, and consequently, firms do not have incentives to translate productivity gains to consumers, and yardstick competition must be used for setting prices under incentive regulation. Thus, we estimate the X-factor of the Colombian electric power distribution sector in the period 2010-2019 by means of stochastic frontier analysis (SFA). Our estimates suggest an overall average X-factor equal to -1.6%, where just 3 out of 23 network operators (NOs) have average positive X-factors. This worrisome performance is explained by efficiency and scale losses during this period, -2.6% and -1.1%, respectively. On the other hand, there is an overall average technical change equal to 2.1%. In addition, we found that the average efficiency in the period is equal to 32.8% in this sector. This suggests that there is gap for improvements in the sector because the average efficiency level is too low, and just three NOs had technical changes higher than the efficiency and scale negative changes. In particular, the prospects of new regulatory frameworks should put more pressure in the sector, not only in terms of technical efficiency, but also in term of quality, competition, and the remuneration mechanism.

• Jetter, M. Mahmood, R., Parmeter, C. & Ramírez-Hassan, A.. 2022. “Post-Cold War civil conflict and the role of history and religion: A stochastic search variable selection approach” Economic Modelling. 114, 1–14. 

Despite colossal economic and human losses caused by conflict and violence, designing effective policies to avoid conflict remains challenging. While the literature has proposed a voluminous set of candidate predictors, their robustness is questionable and model uncertainty masks the true drivers of conflicts and wars. Considering a comprehensive set of 34 potential determinants in 175 post-Cold-War countries, we employ stochastic search variable selection (SSVS) to sort through all 234 possible models to address model uncertainty. We find past conflict constitutes the most powerful predictor of current conflict: Path dependency matters. Also, larger shares of Jewish, Muslim, or Christian citizens are associated with increased conflict, while economic and political factors remain less relevant than colonial origin and religion. Our results help future researchers and policymakers by inching towards causality and providing a standard set of covariates that need to be accounted for in designing any relevant policies.

• Ramírez-Hassan, A. & Graciano-Londoño, M. 2021. “A GUIded tour of Bayesian regression” R Journal. 13 (2), 135–152. 

This paper presents a Graphical User Interface (GUI) to carry out a Bayesian regression analysis in a very friendly environment without any programming skills (drag and drop). This paper is designed for teaching and applied purposes at an introductory level. Our GUI is based on an interactive web application using shiny and libraries from R. We carry out some applications to highlight the potential of our GUI for applied researchers and practitioners. In addition, the Help option in the main tap panel has an extended version of this paper, where we present the basic theory underlying all regression models that we developed in our GUI and more applications associated with each model.

• Jacobi, L. Nghiem, N., Ramírez-Hassan, A. & Blakely, T. 2021. “Food Price Elasticities for Policy Interventions: Estimates from a Virtual Supermarket Experiment in a Multistage Demand Analysis with (Expert) Prior Information” Economic Record. 97 (319), 457–490. 

Food price elasticities (PEs) are essential for evaluating the impacts of food pricing interventions to improve dietary and health outcomes. This paper innovates the use of experimental purchasing data from a recent New Zealand virtual supermarket experiment to estimate PEs for a large set of disaggregated foods across major food groups relevant for food policies in a Bayesian multistage demand framework. We propose the use of available prior information to elicit prior demand parameter assumptions that are consistent with published PEs and economic assumptions and are weighted according to expert knowledge, increasing precision in PE inference and policy predictions, and yielding somewhat stronger price effects.

• Martin, G., Loaiza-Maya, R., Frazier, D., Maneesoonthorn, W. & Ramírez-Hassan, A. 2021. “Optimal probabilistic forecasts: When do they work?” International Journal of Forecasting. 38 (1), 384–406. 

Proper scoring rules are used to assess the out-of-sample accuracy of probabilistic forecasts, with different scoring rules rewarding distinct aspects of forecast performance. Herein, we re-investigate the practice of using proper scoring rules to produce probabilistic forecasts that are ‘optimal’ according to a given score and assess when their out-of-sample accuracy is superior to alternative forecasts, according to that score. Particular attention is paid to relative predictive performance under misspecification of the predictive model. Using numerical illustrations, we document several novel findings within this paradigm that highlight the important interplay between the true data generating process, the assumed predictive model and the scoring rule. Notably, we show that only when a predictive model is sufficiently compatible with the true process to allow a particular score criterion to reward what it is designed to reward, will this approach to forecasting reap benefits. Subject to this compatibility, however, the superiority of the optimal forecast will be greater, the greater is the degree of misspecification. We explore these issues under a range of different scenarios and using both artificially simulated and empirical data.

• Puerta, A. & Ramírez-Hassan, A. 2021. “Promoting academic honesty: a Bayesian causal analysis of an integrity pilot campaign?” Education Economics. Online. 

We examine the effect of an integrity pilot campaign on undergraduates' behavior. As with many costly small-scale experiments and pilot programs, our statistical inference has to rely on small sample size. To tackle this issue, we perform a Bayesian retrospective power analysis. In our setup, a lecturer intentionally makes mistakes that favors students' grades, who decide whether to disclose them or not. We find evidence that at least in the short term, the pilot campaign has a positive impact on the students' disclosure probability.

• Ramírez-Hassan, A. 2021. “Bayesian estimation of the EASI demand system: Replicating the Lewbel and Pendakur (2009) results” Journal of Applied Econometrics, 36 (4), 484–491. 

This paper proposes a Bayesian approach to perform inference in the exact affine Stone index (EASI) demand system that was proposed by Lewbel and Pendakur (2009), while taking into account nonlinearity and endogeneity. A Bayesian approach enables us to easily handle censored data, test and impose inequality restrictions (strict cost monotonicity) and concavity of the cost function, and perform inference of nonlinear functions of the parameter estimates as by-product of the posterior chains. We compare our proposal with Lewbel and Pendakur (2009)'s results, based on iterative linear three-stage least squares (3SLS). Although we found no statistically significant differences in point estimates between these two approaches, it seems that ignoring censoring overestimates precision.

• Ramírez Hassan, A. & Carvajal, D. 2021. “Regressor selection uncertainty in Internet adoption: a developing city case”. Utilities Policy, 70. 

Internet adoption fosters economic growth and development. Specifying policy control drivers is particularly relevant for developing countries. However, there is no consensus on the most relevant variables. We explored 33.6 million potential models to identify the most important determinants of household internet adoption using stochastic search variable selection and socioeconomic data from Medellín, Colombia. We found that monthly income, the head of household education and voting, and having a computer and cable television at home are the most relevant variables.

• Ramírez Hassan, A. & Guerra, R. 2021. “Treatment effects due to a subsidized health insurance program: A Bayesian ordered potential outcome analysis”. Empirical Economics, 60, 1477-1506. 

We analyze the treatment effects due to patients’ status, covered or uncovered by the subsidized health program in Medellín (Colombia), on the number of preventive health care visits to physicians. We use a Bayesian endogenous switching model that allows interaction effects as well as endogeneity due to patients’ status. This framework allows the calculation of the posterior distributions of heterogeneous treatment effects and presents Bayesian learning of the covariance between the two potential outcomes, a relevant policy-maker parameter, even though we do not observe individuals in both states at the same time. We found that there are self-selection effects as well as moral hazard, which may imply adverse consequences for the health care system in Colombia.

• Sanchez, J., Restrepo, D. & Ramírez Hassan, A. 2021. “Inefficiency and Bank Failures: A Joint Bayesian Estimation of a Stochastic Frontier Model and a Hazards Model”. Economic Modelling. 95, 344–360. 

We propose a Bayesian one-stage approach to estimate the effect of inefficiency on the time to failure (bankruptcy) of U.S. commercial banks. We do so combining stochastic frontier and proportional hazards settings. Most of the existing literature use two-stage methods which may yield inefficient, biased, and inconsistent estimates. Our proposal overcomes these issues, allows computing the marginal distribution of inefficiencies for each observational unit, and facilitates statistical inference of non-linear functions of parameters such as returns to scale. Simulation exercises show that our proposal outperforms the two-stage maximum likelihood approach traditionally used in the literature. In addition, empirical evidence suggests that inefficiency of U.S. commercial banks during the global financial crisis in 2008–2009 played a statistically and economically significant role determining the time to failure.

• Ramírez Hassan, A. & Guerra, R. 2020. ”Optimal portfolio choice: A minimum expected loss approach”. Mathematics and Financial Economics, 14, 97-120.

The mainstream in finance tackles portfolio selection based on a plug-in approach without consideration of the main objective of the inferential situation. We propose minimum expected loss (MELO) estimators for portfolio selection that explicitly consider the trading rule of interest. The asymptotic properties of our MELO proposal are similar to the plug-in approach. Nevertheless, simulation exercises show that our proposal exhibits better finite sample properties when compared to the competing alternatives, especially when the tangency portfolio is taken as the asset allocation strategy. We have also developed a graphical user interface to help practitioners to use our MELO proposal.

• Ramírez Hassan, A. & Blandón Montoya, S. 2020. “Forecasting from others’ experience: Bayesian Estimation of the Generalized Bass Model”. International Journal of Forecasting, 36 (2), 442-465. 

We propose a Bayesian estimation procedure for the generalized Bass model that is used in product diffusion models. Our method forecasts product sales early based on previous similar markets; that is, we obtain pre-launch forecasts by analogy. We compare our forecasting proposal to traditional estimation approaches, and alternative new product diffusion specifications. We perform several simulation exercises, and use our method to forecast the sales of room air conditioners, BlackBerry handheld devices, and compressed natural gas. The results show that our Bayesian proposal provides better predictive performances than competing alternatives when little or no historical data are available, which is when sales projections are the most useful.

• Ramírez Hassan, A. 2019. “Dynamic variable selection in dynamic logistic regression: an application to Internet subscription” Empirical Economics, 59, 909-932. 

We extend the dynamic model averaging framework for dynamic logistic regression proposed by McCormick et al. (Biometrics 68(1):23–30, 2012) to incorporate variable selection. This method of accommodating uncertainty regarding predictors is particularly appealing in scenarios where relevant predictors change through time, and there are potentially many of them, as a consequence, the computational burden is high. Simulation experiments demonstrate that our greedy variable selection strategy works well in identifying the relevant regressors. We apply our algorithm to uncover the determinants of Internet subscription in Medellín (Colombia) among 18 potential factors, and thus 262,144 potential models. Our results suggest that subscription to pay TV, household members studying, years of education and number of household members are positively associated with Internet subscription.

• Serna Rodríguez, M., Ramírez Hassan, A. & Coad, A. 2019. “Uncovering value-drivers of high performance soccer players” Journal of Sports Economics, 20 (6), 819-849.

This article tries to uncover the drivers of soccer players’ market value in the five major European soccer leagues taking into account model uncertainty (variable selection) in a framework with 35 billion potential models. For this purpose, we use a hedonic regression framework and implement Bayesian model averaging (BMA) through Markov chain Monte Carlo model composition (MC3). To deal with endogeneity issues, instrumental variable Bayesian model averaging (IVBMA) is implemented as well. We find very strong, and robust evidence, that the most important value drivers are player’s performance, participation in the national team (senior and under-21), age, and age squared.

• Ramírez Hassan, A. & Correa Giraldo, M. 2019. “Focused econometric estimation for noisy and small datasets: A Bayesian Minimum Expected Loss estimator approach”. Australian and New Zealand Journal of Statistics, 61 (3), 360-379. 

Central to many inferential situations is the estimation of rational functions of parameters. The mainstream in statistics and econometrics estimates these quantities based on the plug-in approach without consideration of the main objective of the inferential situation. We propose the Bayesian Minimum Expected Loss (MELO) approach focusing explicitly on the function of interest, and calculating its frequentist variability. Asymptotic properties of the MELO estimator are similar to the plug-in approach. Nevertheless, simulation exercises show that our proposal is better in situations characterized by small sample sizes and noisy models. In addition, we observe in the applications that our approach gives lower standard errors than frequently used alternatives when datasets are not very informative.

• Ramírez Hassan, A. & Blandón Montoya, S. 2019. “Welfare gains of the poor: An endogenous Bayesian approach with spatial random effects” Econometric Reviews, 38 (3), 301-318. 

We introduce a Bayesian instrumental variable procedure with spatial random effects that handles endogeneity, and spatial dependence with unobserved heterogeneity. We find through a limited Monte Carlo experiment that our proposal works well in terms of point estimates and prediction. We apply our method to analyze the welfare effects generated by a process of electricity tariff unification on the poorest households. In particular, we deduce an Equivalent Variation measure where there is a budget constraint for a two-tiered pricing scheme, and find that 10% of the poorest municipalities attained welfare gains above 2% of their initial income.

• Ramírez Hassan, A. & Pantoja, J. 2018. “Co-movements between Latin American and U.S. stock markets: convergence after the financial crisis?” Latin American Business Review, 19 (2), 157–172 

Since 1980, the world has been undergoing a continuous process of integration in different aspects, and financial markets are no exception to this development. Even though global integration is gradual, specific events can accelerate this trend. This article shows that after the financial crisis of 2008, which was especially acute in the United States, the Latin American stock markets have exhibited a higher level of convergence, as measured by the correlation between the annual returns of their stock market indices. Additionally, we find convergence in the coefficient of co-movements between Latin American and the US stock markets using dynamic linear models at the regional level. In particular, we uncover consistent movements between the daily annual returns of the Latin American indices and the S&P index after the financial crisis. This kind of convergence might signal an acceleration of the virtual integration process in Latin America stock markets located in different countries, which has seen slow development since its beginning a few years ago.

• Ramírez Hassan, A. & Pericchi, R. 2018. “Effects of prior distributions: An application to piped water demand” Brazilian Journal of Probability and Statistics, 32 (1), 1–19. 

In this paper, we analyze the effect on posterior parameter distributions of four possible alternative prior distributions, namely Normal-Inverse Gamma, Normal-Scaled Beta two, Student’s t-Inverse Gamma and Student’s t-Scaled Beta two. We show the effects of these prior distributions when there is apparently conflict between the sample information and the elicited hyperparameters. In particular, we show that there is not systematic differences of posterior parameter distributions associated with these four priors using data of piped water demand in a linear model with autoregressive errors. To test the hypothesis that this result is due to using a moderate sample size and a relatively high level of expert’s uncertainty, we perform some simulation exercises assuming smaller sample sizes and lower expert’s uncertainty. We obtain the general same pattern, although Student’s t models are slightly less affected by prior information when there is a high level of expert’s certainty, and Scaled Beta two models exhibit a higher level of posterior dispersion of the variance parameter.

• Mejia, J. & Ramírez Hassan, A. 2017. “Proposing a new measure of distance in the gravity setting: evidence from Latin America” Economia Aplicada, 21 (1), 135–148. 

The gravity model is a workhorse tool that has been widely used in international trade. However, one empirical question that frequently arises is related to the conceptualization and measurement of an economic distance index. Our study proposes an index based on Multiple Factor Analysis. This technique summarizes information related to the geographical, cultural, political and economic variables that might affect international trade between countries. Estimates indicate that the signs of the load factors in the Multiple Factor Analysis are intuitively plausible, and panel data exercises give sensible robust outcomes.

• Mejía, S. & Ramírez Hassan, A. 2016. “Determining the Optimal Selling Time of Cattle: A Stochastic Dynamic Programming Approach” Agricultural Economics, 62 (11), 517–527. 

The world meat market demands competitiveness, and optimal livestock replacement decisions can help to achieve this goal. In the article, there is introduced a novel discrete stochastic dynamic programming framework to support a manager’s decision-making process of whether to sell or to keep fattening animals in the beef sector. In particular, the presented proposal uses a non-convex value function, combining both economic and biological variables, and involving uncertainty with regard to price fluctuations. The methodology is very general, so the practitioners can apply it in different regions around the world. There is illustrated the model convenience with an empirical application, finding that the methodology generates better results than actions based on the empirical experience.

• Jetter, M. & Ramírez Hassan, A. 2015. “Want export diversification? Educate the kids first” Economic Inquiry, 53 (4), 1765–1782. 

This paper uses Bayesian model averaging to uncover the true determinants of export diversification among 36 potential factors, and thus 2^36 potential models. Using data from 2001 to 2010, our results reveal two strong predictors: Primary school enrollment (99.7% posterior inclusion probability in the true model) raises export diversification, whereas the share of natural resources in gross domestic product (98.6%) lowers diversification levels. The importance of basic education coverage offers policymakers an opening toward diversifying exports, at least in the long run. This result is robust to accounting for the endogeneity of income levels by applying an instrumental variable BMA method.

• Ramírez Hassan, A. 2013. “A Multi-Stage Almost Ideal Demand System: The Case of Beef Demand in Colombia” Revista Colombiana de Estadística, 36 (1), 23–42. 

The main objective in this paper is to obtain reliable long-term and shortterm elasticities estimates of the beef demand in Colombia using quarterly data since 1998 until 2007. However, complexity on the decision process of consumption should be taken into account, since expenditure on a particular good is sequential. In the case of beef demand in Colombia, a Multi-Stage process is proposed based on an Almost Ideal Demand System (AIDS). The econometric novelty in this paper is to estimate simultaneously all the stages by the Generalized Method of Moments to obtain a joint covariance matrix of parameter estimates in order to use the Delta Method for calculating the standard deviation of the long-term elasticities estimates. Additionally, this approach allows us to get elasticity estimates in each stage, but also, total elasticities which incorporate interaction between stages. On the other hand, the short-term dynamic is handled by a simultaneous estimation of the Error Correction version of the model; therefore, Monte Carlo simulation exercises are performed to analyse the impact on beef demand because of shocks at different levels of the decision making process of consumers. The results indicate that, although the total expenditure elasticity estimate of demand for beef is 1.78 in the long-term and the expenditure elasticity estimate within the meat group is 1.07, the total short-term expenditure elasticity is merely 0.03. The smaller short-term reaction of consumers is also evidenced on price shocks; while the total own price elasticity of beef is -0.24 in the short-term, the total and within meat group long-term elasticities are −1.95 and −1.17, respectively.

• Ramírez Hassan, A. & Cadavid Montoya, R. & Garcia Pelaez, S. 2011. “Desempeño de las Empresas y Factores Institucionales en Colombia, 2002-2007 (Performance of Companies and Institutional Arrangements in Colombia, 2002-2007)” Revista de Economía Institucional, 13 (25), 179–198. 

En este trabajo se elabora y se estima un modelo de datos de panel dinámico para determinar el efecto de algunos factores institucionales sobre el desempeño de una muestra de empresas colombianas no financieras durante el periodo 2002-2007. Los resultados indican que las empresas analizadas son sensibles a estos factores. Las instituciones relacionadas con los derechos de propiedad y el cumplimiento de los contratos tienen efectos significativos sobre el crecimiento de estas empresas. En cambio, la profundización financiera no tiene un efecto significativo.