In this chapter we come to a core question of the thesis: what are the factors contributing to improvements in efficiency in the context of economic reform? Can we rigorously measure the contribution of each economic reform policy to improvements in technical efficiency?
The relationship between state enterprise productivity and economic reform has never been clearly identified. Empirical work on the productivity of state enterprises has generated differing and even contradictory results. The effectiveness of economic reform in state enterprises has become a controversial topic much discussed by researchers (chapter 5).
The reasons for this can be found in the way the argument is approached in the literature. Assessment of the impact of economic reform on productivity or efficiency has been based on indirect inference rather than rigorous tests. It is common practice for studies to carry out a two-stage exercise. Productivity or efficiency in pre-reform and post-reform periods is first estimated, and then the impact of economic reform is inferred on the basis of the differences between the two periods. This method is inadequate for two reasons. First, economic reform in state enterprises, as detailed in chapters 3 and 4, is an extremely complex and multi-dimensional process realised both in the external market environment and in the operational mechanisms of enterprises. Different reform packages were used in different enterprises and the intensity with which reform policies were pursued varied greatly. Moreover, as shown in chapters 3 and 4, certain reform policies were seriously flawed. It is inappropriate, then, to generalise about "economic reform" without further discriminating between policy packages and the intensity of reform.
The relationship between efficiency and economic reform is further complicated by the difference in response of state enterprises to reform policies. This was influenced by such factors as the soft budget constraint and the intervention of local govemments. Enterprise-specific and region-specific characteristics are important determinants of efficiency, as we saw earlier (chapter 4). Hence the estimates of efficiency themselves depend on the sample chosen for estimation. The estimation will show improvements in efficiency only if the sample contains enterprises that have appropriate reform policies in place and that are exhibiting a reasonable degree of rational response. Consistent measures for assessing the impact of specific reforms at the enterprise level are therefore needed (Jefferson 1990b; Kuan Chen et al. 1988a).
167 In this study, we are reluctant to conclude immediately that observed improvements in technical efficiency were due to economic reform. The analysis will be furthered by rigorously testing and quantifying the impact of reform in many areas on the efficiency index. The hypothesis underlying this exercise is that we will then be able to pinpoint quantitatively the impact of all reform policies on technical efficiency; the impact will be statistically significant if overall economic reform was successful. If reform was partially successful, we can expect to find a statistically significant correlation between the efficiency index and some reform measures, and improvements in efficiency wWl be attributable to reform in these areas. The implications for policy can then be assessed. If no reform measure is statistically significant, then improvements can only be attributed to autonomous changes in efficiency, an idea underlying time-variant models in the literature on the stochastic production frontier (Kumbhakar 1990).
MODEL SPECIFICATION
Three issues are dealt with in this section: methodology, representation of heterogeneity in intercepts and slopes, and model selection. The advantage of the stochastic production frontier approach is its ability to discriminate between observed output and potential maximum output and to provide a measure of the gap (technical efficiency) for each observation. These measures indicate loss of efficiency suffered by firms due to non-price and organisational factors, which are often called socio-economic variables. The determination of an appropriate method for quantitatively identifying the relationship between technical efficiency and socio-economic variables is again at issue. The availability of panel data gives economists the opportunity to analyse economic problems more consistently and efficiently (Hsiao 1989, pp. l-Sy than can be done with cross- sectional and time-series data. The challenge in using panel data is to detect and correct heterogeneity in intercepts and/or slopes of the function used in estimation. This will be discussed further in this section.
A Methodological Issue
In the literature on production efficiency, in general two methods are used to measure the impact of socio-economic or institutional variables on efficiency. The first is to treat socio-economic variables as independent inputs and incorporate them into the production function (equation 5.20 in chapter 5) for joint estimation. This approach can be used in the full production fi-ontier fi-amework (Reischneder and Stevenson 1991; Battese and Coelli 1993) and in the average production frontier (Lin 1992).
In the full production frontier framework, this approach has intrinsic problems of interpretation, both economically and econometrically, despite its merits. The argument
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for this approach is that the exclusion of inefficiency effects will affect the parameters of the core variables of the production function, and that it is therefore desirable to incorporate socio-economic variables explicidy into the production function for joint estimation. In the original framework of Aigner, Lovell and Schmidt (1977) and Meeusen and Van den Broeck (1977), the reason for attaching the additional truncated distribution to the production function was to capture the effects of all the socio- economic variables. Giving socio-economic variables the explicit form does not seem to add any superiority to the full frontier model.
The second problem is potentially more serious. Production theory in neoclassical economics requires a specific functional form to approximate the production process,^ which should determine the technical relationship between output and inputs only (Johnson 1967). Following the neoclassical approach it becomes necessary to impose an arbitrary relationship on output and socio-economic variables, and on the core inputs and socio-economic variables. The inclusion of these variables into the stochastic production frontier will most likely lead to biased estimates of coefficients of the production frontier and efficiency index. Moreover, since the socio-economic variables and core input factors are jointiy estimated in the production function, it is very difficult to use other econometric techniques to estimate the relationship between efficiency and socio- economic variables in a situation in which the relationship exhibits a high degree of complexity due either to data or institutional characteristics.
The second approach is usually associated with the full production frontier approach and has two steps. In the first step, the stochastic production frontier model is specified and its validity tested. In the second step, the estimates of technical efficiency obtained in the first step are used as a dependent variable for second-round estimation. In this step, socio-economic variables are used as explanatory variables to estimate their effect on firm-specific efficiency. This approach was first used by Kalirajan (1981, 1982, 1989 and 1990), and later by Kalirajan and Flinn (1983) and Kalirajan and Shand (1986) among others. By using this approach, the above-mentioned theoretical problems can be avoided. Moreover, sophisticated econometric methods can be applied in the second step of the estimation. Based on the preceding discussion, we choose to use the second approach. The technical efficiency index obtained in chapter 5 gives us our dependent variables, and a set of indices representing various reform measures are used as explanatory variables to estimate the impact of reform on technical efficiency.
^For a functional form to be able to represent a production process, it needs to satisfy certain required regularity conditions, such as monotonicity, convexity and differentiability. Hence the variables used in the production function should also be reasonably compatible with these conditions. Refer to Kreps (1990, pp. 235-9) for details of the regularity condition of the production function.
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