Manual An Assessment of the Investment Climate in South Africa (Directions in Development)

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The objective of this paper is to help progress in that direction, especially in the MENA region. Drawing on World Bank firm surveys, we analyze the relationship between investment climate and firm productivity for the eight most significant manufacturing industries in 22 countries. By broadening the sample to a larger number of countries, we compare MENA performance to that of emerging economies that are major competitors on the world market, especially China and India.

Section II sheds light on different measures of productive performance and discusses their respective advantages and limits. We begin with simple measures of firm partial productivity levels and then move to stochastic production frontier analyses SFA. SFA provides technical efficiencies equivalent in our context to relative total factor productivity TFP levels where labor and capital are considered together.

In Section VI, these deficiencies are linked to productive performance.

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The SFA model incorporating inefficiency determinants is adopted, allowing a simultaneous estimation of both the production technology and the explanatory factors of inefficiencies. Econometric impacts are explored by considering factors on an individual basis and through composite indicators reflecting various dimensions of the IC. Many options are available to appraise firm productivity, all of them having their own strengths and weaknesses.

Partial labor productivity LP , as defined by the ratio of the value added Y to the number of employees L , is a common indicator. In the formula below i and j denote the enterprise and country indices, respectively. Compared to alternative partial productivity measures, such as capital productivity, this ratio is less affected by the error in measurement of the denominator.

Indeed, the capital stock refers to the value of machinery and equipment bought in different periods. Each transaction is accounted at the historical value. Counterbalancing these advantages, the LP ratio suffers some deficiencies. First, as with any partial productivity index, this indicator considers only one input and ignores the others. For a static analysis, the all things being equal principle looks embarrassing.

Use of these partial indicators in the formulation of management and policy advice can be misleading, potentially resulting in an excessive use of those inputs not included in the efficiency measure. Second, the indicator can be biased by the choice of the exchange rate when converting production into US dollars. This is important in our framework where calculations are proposed for international comparison.

Following the previous remarks, all relevant inputs might be considered together. This objective can be achieved through parametric total factor productivity TFP analyses or by referring to the technical efficiency TE concept. In a dynamic analysis, TFP growth can be the result of a technical change or the consequence of a TE improvement.

The former channel represents an upward shift of the production frontier, while the latter depicts a move within the feasible production set toward the frontier, the technology being unchanged. Indeed, TE is no more than a relative productivity level, all sample firms being benchmarked by those operating on the frontier e.

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To determine how MENA organizations perform compared with their counterparts, the parametric technical efficiency concept looks particularly attractive; it accounts for random noise and then does not consider the whole residual as a TFP measure, which is the case in the Solow approach. The Cobb—Douglas technology is the most commonly used functional form, with properties on the production structure e. The translog technology is more flexible but generally suffers from a collinearity problem among the regressors.

an assessment of the investment climate in south africa directions in development Manual

The first component v is the classical random noise, which may reflect unpredictable variations in machine or labor performance. Such random noise potentially occurs in any firm, although certain industries are more prone to stochastic fluctuations than others. For example, the production of steel is highly dependent on the quality of power provision. It can be a systematic problem or a random one if power production is related to the impact of rains on dam levels.

The second term captures the technical inefficiency —u that may follow different statistical distributions.

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  • Any choice can be criticized and is not deprived of any arbitrariness. This statistical law complies with the analysis of the inefficiency determinants, when using the model of Battese and Coelli , which can be written as: 2. For convenience, we do not keep the country index we used earlier for the partial productivity of labor j. In the literature, one way to do this is to estimate the stochastic production frontier and to regress, in a second run, the obtained TE on a vector of explanatory factors z.

    When any of the production frontier input X is influenced by common causes affecting efficiency, there is a simultaneity problem owing to omission of explanatory variables in the first stage of the estimation. Following this method the stochastic frontier model can be rewritten as: 3 Y i is the output for the i th firm and X i the vector of inputs K, L. On average, South African and Brazilian firms perform best. Moroccan firms also are among the best performers of the sample, along with Saudi Arabia in the three industries covered by the survey, both ahead of the two Asian giants China and India.

    As far as the other MENA countries are concerned, the ranking also remains rather stable across industries. The partial labor productivity of some MENA countries does not mean, however, that the labor cost of this region is not competitive and does not support the integration of manufacturing sectors into the world economy.

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    The story is more complicated, as average wages e. This is particularly true in Algeria and Egypt—countries where firm labor productivity is among the lowest—but also in Morocco, Saudi Arabia, and, to some extent, Lebanon. This situation constitutes a serious handicap for MENA competiveness, which suffers from both faster technological innovations and lower wages in Asia. On average, TE results are close to the ones obtained for productivity of labor. These countries are again followed by Morocco and Saudi Arabia. As far as other MENA countries are concerned, Egypt and Lebanon still rank at the bottom of the sample with a very limited number of enterprises for the latter country , and Algeria is at a low—intermediate position.

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    This fragility is all the more damageable for the MENA region, given the high specialization of some MENA countries Morocco and Egypt in particular and exposure to international competition of firms in these industries. But the heterogeneity of MENA economies, as well as the small size of the control group, call for cautious interpretations of these results. Recent developments in the economic literature have put the investment climate at the center of economic performance.

    Will WEF yield investment returns for SA?

    The investment climate is defined by the World Bank as the policy, institutional, and regulatory environment in which firms operate. A main hypothesis in the literature is that IC affects particular activity through the incentive to invest. Improving the IC reduces the cost of doing business and leads to higher and more certain returns on investment. It also creates new opportunities for example, through trade or access to new technology and puts competitive pressure on firms. The World Bank reports, as well, that a better investment climate contributes to the effective delivery of public goods necessary for productive business.

    The deficiencies of the investment climate are also seen as barriers to entry, exit, and competition. A short review, in Appendix I , presents the main justifications and findings of the literature for different dimensions of IC. Appendix II is a detailed list of variables in this classification. Although most of the empirical literature relies on individual variables to capture the different dimensions of the investment climate, few authors have shown interest in substituting aggregate measures for individual variables.

    This option also poses the question of whether the selected variables provide a representative description of the investment climate or not. The solution of using composite indicators has the advantage of obtaining more accurate estimates, in addition to including more dimensions of the IC.

    In our empirical analysis, both individual variables and aggregated indicators have been considered Section VI.

    Although different methods of aggregation exist, the principal component analysis PCA aggregates basic indicators in a more rigorous way than a subjective scoring system does. Each component corresponds to a virtual axis on which the data are projected. The earlier components explain more of the variance of the series than do the later components. The number of components is proportional to the number of initial variables that are used in the PCA. Usually, only the first components are retained, because they explain most of the variance in the dataset.

    The cumulative R 2 gives the explanatory power of the cumulative components. This has produced 32 aggregated indicators four indicators for each of the eight industries. Our initial indicators were selected because they are available for as many countries as possible and because they capture the different key dimensions of the IC. The analysis usually treats the IC indicators as exogenous determinants of firm performance. However, this is not always the case.