It is also interesting to note that, when focusing on small and medium domestic firms, we find that more _IC_ variables explain firm-level performances. This is due to the fact that big enterprises are less sensitive to _IC_ limitations and bias downward the estimated coefficients when dealing with the whole sample. Restricting the sample to small and medium firms better highlights the impact of _IC_ and firms characteristics on firm-level performances and competitiveness, thus drawing more substantial policy implications. This is well illustrated by the case of _Garment_ , for which very few _IC_ variables were previously significant.

**Table 8. Estimation Results: Common Model with Aggregated** **_IC_ Variables and Sample Differentiation (domestic firms with less than 150 employees)** _<u>(Dependant Variable: Value Added)</u>_

|**_Independent_**<br>**_Variables_**|**Textile**|**Leather**|**Garment**|**Agro**<br>**Industry**|**Metal &**<br>**Machinery**<br>**Products**|**Chemic**<br>**&Pharm**<br>**Products**|**Wood**<br>**&**<br>**Furniture**|**Non Metal**<br>**& Plastic**<br>**Materials**|
|---|---|---|---|---|---|---|---|---|
|**_ln(l )_**|0.547<br>_(9.01)***_|0.882<br>_(23.30)***_|0.975<br>_(15.3)***_|0.460<br>_(5.92)***_|0.834<br>_(15.85)***_|0.549<br>_(6.74)***_|0.779<br>_(11.39)***_|0.981<br>_(14.07)***_|
|**_ln(k)_**|0.319<br>_(12.38)***_|0.252<br>_(16.25)***_|0.177<br>_(6.06)***_|0.384<br>_(18.61)***_|0.251<br>_(10.87)***_|0.390<br>_(13.89)***_|0.223<br>_(10.01)***_|0.252<br>_(8.74)***_|
|**_Intercept_**|2.153<br>_(4.18)***_|1.732<br>_(5.00)***_|-0.309<br>_(0.93)_|2.105<br>_(2.33)**_|1.903<br>_(4.86)***_|2.426<br>_(3.11)***_|2.238<br>_(3.16)***_|1.024<br>_(2.42)**_|
|**_Size_**|-2.897<br>_(1.91)*_|0.045<br>_(0.27)_|0.186<br>_(0.84)_|-0.357<br>_(2.88)***_|-2.331<br>_(1.76)*_|-0.345<br>_(2.44)**_|-0.412<br>_(2.51)**_|0.678<br>_(1.37)_|
|**_Export_**|-0.417<br>_(0.98)_|-0.010<br>_(1.85)*_|-0.003<br>_(0.81)_|-0.005<br>_(1.79)*_|-0.475<br>_(0.99)_|-0.016<br>_(1.49)_|-0.013<br>_(1.62)_|-0.316<br>_(0.95)_|
|**_RegInfra_**|1.170<br>_(2.13)**_|-0.127<br>_(1.09)_|0.763<br>_(2.97)***_|0.007<br>_(0.09)_|0.869<br>_(2.01)**_|0.161<br>_(1.83)*_|0.157<br>_(1.60)_|0.472<br>_(1.72)*_|
|**_H_**|-1.352<br>_(2.04)**_|-0.133<br>_(0.86)_|-0.276<br>_(0.77)_|-0.201<br>_(2.82)***_|-1.103<br>_(1.35)_|-0.108<br>_(1.23)_|-0.263<br>_(2.50)**_|-1.444<br>_(2.09)**_|
|**_RegGov_**|-0.171<br>_(0.51)_|-0.105<br>_(1.17)_|1.552<br>_(2.88)***_|-0.045<br>_(1.05)_|0.424<br>_(0.94)_|-0.067<br>_(1.48)_|-0.063<br>_(0.81)_|0.154<br>_(0.54)_|
|**_,_**<sup>**_Fin_**</sup>|1.170<br>_(2.05)**_|0.222<br>_(1.97)**_|0.665<br>_(3.33)***_|0.093<br>_(1.96)**_|0.496<br>_(0.99)_|0.146<br>_(2.60)***_|0.178<br>_(2.42)**_|-0.520<br>_(1.57)_|
|**_Constant_**|-0.254<br>_(0.15)_|-0.348<br>_(0.44)_|-3.389<br>_(2.40)**_|1.894<br>_(2.58)***_|-1.468<br>_(0.73)_|1.509<br>_(3.33)***_|1.389<br>_(3.36)***_|-2.307<br>_(1.40)_|
|**Observations**|730|359|1093|1123|639|607|650|395|
|**sigma_u**|1.42|1.02|0.28|0.73|1.41|0.43|0.80|0.91|
|**sigma_v**|0.90|0.45|0.73|0.77|0.71|0.80|0.51|0.69|
|**Wald chi2**|663.77|1615.42|763.09|787.50|1175.83|479.31|576.56|796.86|
|**Prob> chi2**<br>_Notes_<br>:The o|0.00<br>ne step proce|0.00<br>dure explain|0.00<br>s firm-level|0.00<br>inefficiency|0.00<br>. The expecte|0.00<br>d sign of th|0.00<br>e_IC_ aggrega|0.00<br>ted variables i|

_<u>Notes</u>_ <u>: The one step procedure explains firm-level inefficiency. The expected sign of the</u> _IC_ aggregated variables is positive for _RegInfra_ , _RegGov_ and _Fin,_ and negative for H (see definition of variables in section 5). Variables _Size_ , _Foreign_ and _Export_ are also expected with a negative coefficient. All regressions contain country-dummy variables when estimating the production function. * Significant at 10%; ** significant at 5%; *** significant at 1%. Absolute value of z statistics are in parentheses. _<u>Source</u>_ <u>. Authors’ estimations</u> In addition, our estimations confirm once more that it is the small and medium firms more exposed to international competition that suffer the most of the deficiencies of their _IC_ . This is particularly true for _Textile_ and _Garment_ , but also to some extent for _Non Metal & Plastic Material_ . This finding still verifies that an improvement of the _IC_ would particularly benefit to this category of firms, which competitiveness and export capacity could be significantly boosted.

Finally, another result tends to confirm the importance of the size as a factor of productivity and efficiency. Actually, small and medium domestic firms appear to gain more from concentration than big and foreign ones (what looks like a reasonable outcome). This is the case in _Textile_ and _Metal & Machinery Products_ where the variable _Size_ is now significant, as 24