## **4.5- Firm-Level Productivity Measures: A High Correlation**

_Annex_ 6 displays, by industry, the Spearman coefficients of correlation of our three measures of firm-level productivity. All coefficients are highly significant and show a high degree of correlation between the different measures. This is the case in all industries, but more specifically in _Wood & Furniture, Non Metallic & Plastic Materials,_ and _Metal & Machinery Products_ (after _Agro-Processing, Chemicals & Pharmaceutical Products, Leather,_ and _Textile)_ . This result justifies our choice of Technical Efficiency ( _TE_ ) as a measure of firmlevel productivity to be explained by the countries/ industries investment climate (see section 6). It will also make our findings more general, because they can be extrapolated to the different indicators of firm-level performance.

# **5- Assessing the Investment Climate of the Manufacturing Industries**

Another step in our analysis has been to differentiate and categorize the different dimensions of the investment climate. The World Bank Investment Climate ( _ICA_ ) surveys provide information on a large number of investment climate ( _IC_ ) variables -- in addition to general information on firms’ status, productivity, sales and supplies. These _IC_ variables are classified into 6 broad categories: ( **_a_** ) _Infrastructures and Services_ , ( **_b_** ) _Finance_ , ( **_c_** ) _BusinessGovernment Relations_ , ( **_d_** ) _Conflict Resolution/Legal Environment_ , ( **_e_** ) _Crime_ , ( **_f_** ) _Capacity, Innovation, Learning_ , ( **_g_** ) _Labor Relations_ .

In the surveys, there are multiple indicators that cover a similar theme. Within the same theme, the correlation between indicators can be high. One solution consists to limit the number of indicators. This can however lead to a biased estimation, due to the omission of important explanatory variables. Also, it is not sure that the _IC_ variables retained are good proxy of investment climate _._ A solution to overcome these problems consists in generating a few composite indicators. Because we intend to determine which investment climate variables are more detrimental to firm performances, we tried to take into consideration an as large as possible set of _IC_ variables which are not typically used in the literature. Since these variables are likely to be correlated, we applied Principal Component Analysis ( _PCA_ ) to produce a limited number of composite indicators<sup>28</sup> .

Based on the _ICA_ surveys, we defined the investment climate by four broad categories: “Quality of Infrastructure” ( _Infra_ ), “Business-Government Relations” ( _Gov_ ), “Human Capacity” ( _H_ ), and “Financing Constraints” ( _Fin_ ). As seen in section 3, our choice of indicators has been restricted by important data limitations. This is also why we have not been able to cover all aspects initially developed in the surveys. Indicators have been selected on the bases of being available for the countries of our sample, as well as capturing the different key dimensions of the investment climate. Besides, we have tried to complete as much as possible the qualitative (perception-based) _IC_ indicators by quantitative information, in order to get a better picture of the investment climate in each industry/country.

The Quality of Infrastructure indicator ( _Infra_ ) has been defined by six variables: Obstacle for the operation of the enterprise<sup>29</sup> caused by deficiencies in ( **_a_** ) Telecommunications, ( **_b_** ) Electricity, and ( **_c_** ) Transport; ( **_d_** ) Does the Firm Own or Share a Generator, ( **_e_** ) if yes, which Percentage of Electricity Comes from that Source; Does the Enterprise have access to ( **_f_** ) E- mail or ( **_g_** ) Internet in its Interaction with Clients and Suppliers. Infrastructure deficiencies constitute an important constraint to private sector development in developing countries (see 14