_AccessFi,j_ : Access to financing ( _obstacle for the enterprise, regional average_ ) _EduMi,j_ : Level of education of the top manager ( _number of years_ ) _ExpMi,j_ : Experience of the top manager ( _number of years_ ) _Trainingi,j_ : Training of workers _RegLregi,j_ : Labor regulation (obstacle for the enterprise, regional average) _RegCorrupi,j_ : Corruption (obstacle for the enterprise, regional average) c _i:_ Country-Dummy variables c: Intercept _vi,j_ : Error terms _i / j:_ Enterprise and country index respectively The choice of the _IC_ variables has been based on their availability for as many firms/ industries/ countries as possible, as well as on capturing the different key dimensions of the investment climate. Our variables cover properly the four axes of the investment climate defined in the previous section. To address the problem linked to the endogeneity of the _IC_ variables when estimating the _TE_ frontier models, we have considered the city region-sector averages of Electricity delivery ( _RegElect_ ), Access to Internet ( _RegWeb_ ), Labor regulation ( _RegLreg_ ), and Corruption ( _RegCorrup_ ). The number of explanatory variables, however, has been limited by the multicolinearity between several _IC_ variables when estimating the _TE_ frontier models.

Other individual variables have consisted in: the percentage of sales exported by the firms ( _Export_ ), the percentage of foreign ownership of firms’ capital ( _Foreigni,j_ ), as well as the firm size ( _Sizej_ ). Export is a factor of productivity by confronting firms to international competition. Foreign ownership, as well, increases productivity if foreign investors bring new technologies and management techniques. As for the size, we intend to test the hypotheses of scales economies and increasing returns to scale in big enterprises<sup>38</sup> . It is worth noting that the expected sign for these variables is negative, due to the fact that the one step procedure explains firm-level inefficiency. The same precautions must be taken when interpreting the sign of the coefficients of the other variables. Country-dummy variables have also been introduced when estimating the production frontiers. There are good reasons to think that production may vary across countries for motives other than production factors. The country dummies can pick up the effect of countries specific factors, such as endowment in natural resources, national-level institutions, macro or political instability, trade policy, etc... Country-dummy variables are intentionally not included in the second part of the equation, when explaining Technical Efficiencies ( _TEs_ ), since they could reduce the impact of some _IC_ variables.

Equation ( _9_ ) has been estimated on unbalanced panels, going from 380 observations (in _Leather_ ) to 1601 observations (in _Garment_ ) depending on the industry. A Cobb-Douglass production function has been chosen to estimate the production frontiers. We have also maintained our previous assumption as regard the specification of the technology, as well as of the Technical Efficiency ( _TE_ ). Although the sample size modifies when incorporating the regressors explaining the firm distance to the frontier, the coefficients of the technology are marginally (but downward) affected. These modifications display the potential impact of the interactions and the limitation that we would face when estimating the Technical Efficiency ( _TE_ ) determinants through the two stage method, as previously discussed<sup>39</sup> . Sector-based estimates are presented in Table 6.

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