Institutions, freedom, and technical efficiency.

• Author: Adkins, Lee C.

1. Introduction

   The impact of institutions on economic performance is currently the subject of much debate among economists and policy makers. In this paper we examine empirically a mechanism through which institutions and economic variables can affect a country's economic performance. It is generally accepted (see, e.g., World Bank 1993) that different countries operate at different distances from the production frontier. We postulate that such technical inefficiency, as measured by deviations from the production possibility frontier, is a function of certain measurable economic and institutional variables. An important contribution of this paper is that it provides statistically significant estimates of several determinants of these deviations.

   Although economists have demonstrated that institutions may have large effects on certain aspects of economic performance, these economists have concentrated more on growth and unemployment than on technical inefficiency. For instance, several recent studies have examined the role of institutions in promoting economic growth and productivity. Dawson (1998) estimated cross-country growth and investment regressions and found that economic growth is associated with economic freedom because of the latter's positive effect on investment and the level of total factor productivity (TEP). Aspects of political freedom are associated with higher investment, but there is no indication that they are associated with higher TFP.

   Using some of the same data used in our study, Edwards (1998) first estimated a production function for a panel of 93 developed and developing countries and calculated TFP growth. He then estimated a relationship between the degree of trade openness and TFP growth and found that the initial per capita GDP, the initial level of human capital, and the degree of openness are important determinants of TFP growth.

   Rodrik (1997) discusses political factors that can affect economic performance. He provides evidence that democracies are associated with (i) more stable long-run growth rates, (ii) greater short-run stability, (iii) better ability to deal with adverse shocks, and (iv) higher wages (Rodrik 1999). He proposes three explanations for the empirical regularities. First, democracies may have greater stability because the preferences of the median voter inhibit radical policy actions that would yield extreme results. Second, a voice in the political process for citizens reduces the amount of internal conflict. Finally, losers in political battles are more likely to avoid economic losses in a democracy than they are under other types of government.

   Rodrik (2000) extends his discussion of institutions, democracy, and economic performance by defining five types of institutions that permit markets to work adequately: the institution of property rights, regulatory institutions, institutions for macroeconomic stabilization, institutions for social insurance, and institutions of conflict management. He argues that the building of institutions can be thought of as a form of technology transfer that allows increased productivity. Participatory democracy is a meta-institution that helps build better institutions. Rodrik (2000) provides evidence that participatory democracy improves economic performance in terms of both higher long-run growth rates and increased short-term stability. We study the effect of participatory democracy on economic performance by testing whether political rights or civil liberties affect technical efficiency.

   Other research more closely related to ours indicates that planned economies are less efficient than unplanned ones. Bergson (1987, 1989, 1991), Marer (1981), Moroney and Lovell (1997), and others have compared the performances of centrally planned economies with those of western market economies. Bergson (1987, 1989) estimates a constant-returns-to-scale production function via ordinary least squares and a dummy variable identifying planned economies. He finds that planned economies tend to use capital and land less efficiently than market economies do. Moroney (1992) follows a similar approach and shows that planned economies used capital and energy less efficiently than Western European economies did during 1978-1980.

   Moroney and Lovell (1997) were the first to use stochastic production frontier panel data techniques to compare the productive performances of market and planned economies. Their goal was to quantify the extent to which market economies are more efficient than planned ones. They found that Western European market economies were much more productive than a group of seven Eastern European planned economies during 1978-1980. They attribute most of the difference to the use of better technology in market economies. The Eastern European economies were no more than 76% as efficient as the Western European economies were during this period.

   None of the aforementioned studies accounts for sources of technical inefficiency except with the use of dummy variables indicating planned or market economies. In addition, they focus on OECD countries versus the former USSR or Eastern European economies. In this study, we use panel data to estimate a production frontier and examine the sources of inefficiency among a much broader set of countries than has previously been considered. Our study also differs from previous research in that our concern is the extent to which economic and political institutions contribute to technical inefficiency or deviations from a stochastic frontier rather than how they influence output growth, TFP growth, or unemployment. Importantly, our study also uses a more flexible functional form and includes human capital as an input in the production function. Our results suggest that among a broad set of countries at various stages of economic development, variations in human capital, economic freedom, and development status are li nked to efficiency.

2. The Stochastic Frontier Model

   A number of studies have estimated a production frontier and used the difference from the frontier (a measure of the predicted efficiencies) in a second-stage regression to determine reasons for differing efficiencies. In the first stage, the predicted inefficiencies are estimated under the assumption that they are independently and identically distributed. The regression of other variables on the inefficiencies in a second stage is a clear violation of the independence assumption. According to Kumbhakar, Ghosh, and McGuckin (1991), there are at least two problems with such a procedure. First, inefficiency may be correlated with the inputs; if so, the inefficiencies and the parameters of the second-stage regression are inconsistently estimated. Second, the use of ordinary least squares (OLS) in the second stage ignores the fact that the dependent variable (inefficiency) takes on values over the positive domain. Therefore, OLS may yield predictions that are inconsistent with this fact and are therefore not app ropriate.

   Kumbhakar, Ghosh, and McGuckin (1991) and Reifschneider and Stevenson (1991) have proposed models of technical inefficiency in the context of stochastic frontier models. In these cross-sectional models, the parameters of the stochastic frontier and the determinants of inefficiency are estimated simultaneously given appropriate distributional assumptions about the model's errors. Battese and Coelli (1995) proposed a stochastic frontier model in which inefficiencies are expressed as specific functions of explanatory variables. The panel specification of this model can be expressed as follows:

   [y.sub.it] = [x.sub.it][beta] + ([V.sub.it] - [U.sub.it]); i = 1,...,N t = 1,...,T, (1)

   where [y.sub.it] is the logarithm of the output of country i in period t, [x.sub.it] is a k X 1 vector of inputs, [beta] is a vector of unknown parameters, and [V.sub.it] are random variables that are assumed to be independently and identically N(0, [[sigma].sup.2.sub.V]) distributed and independent of [U.sub.it]. [U.sub.it] are nonnegative random variables that account for technical inefficiency in production; they are assumed to be independently distributed as truncations of the N([m.sub.it], [[sigma].sup.2.sub.U]) distribution at zero. The mean inefficiency is a deterministic function of p explanatory variables:

   [m.sub.it] = [z.sub.it][delta], (2)

   where [delta] is a p X 1 vector of parameters to be estimated. Following Battese and Corra (1977), we let [[sigma].sup.2] = [[sigma].sup.2.sub.V] + [[sigma].sup.2.sub.U] and [gamma] = [[sigma].sup.2.sub.U]/([[sigma].sup.2.sub.V] + [[sigma].sup.2.sub.U]).

   The inefficiencies ([U.sub.it]) in Equation 1 can be specified as

   [U.sub.it] = [z.sub.it][delta] + [W.sub.it]. (3)

   where [W.sub.it] is defined by the truncation of the normal distribution with mean zero and variance [[sigma].sup.2].

   Using this parameterization, a test can be constructed to determine whether the estimated frontier is actually stochastic; [gamma] = 0 implies that the variance associated with the one-sided (efficiency) errors, [[sigma].sup.2.sub.U], is zero, meaning that these deviations from the frontier are better represented as fixed effects in the production function. Therefore, a test of the null hypothesis that [gamma] = 0 against the alternative that [gamma] is positive is used to test whether deviations from the frontier are stochastic and whether one should proceed with the estimation of parameters related to the sources of inefficiency within the context of a stochastic production frontier. Failure to reject the null hypothesis suggests that the determinants of inefficiency, [z.sub.ip] should be included in the production function. (1)

   The parameters of the model ([beta], [delta], [[sigma].sup.2], and [gamma]) are estimated using the maximum-likelihood estimator; the likelihood function can be found in the Appendix. Then, the technical inefficiency of the ith country at time t is

   [TE.sub.it] = exp(-[U.sub.it]) =...