# **Table 6. Estimation Results: Common Model with Individual** **_IC_ Variables**

_(Dependant Variable: Value Added)_

|**_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 ) _**|0.657<br>_(16.14)***_|0.789<br>_(28.82)***_|0.735<br>_(7.12)***_|0.560<br>_(13.32)***_|0.871<br>_(21.75)***_|0.540<br>_(11.09)***_|0.883<br>_(18.78)***_|0.860<br>_(10.18)***_|
|**_ln(k)_**|0.321<br>_(14.61)***_|0.255<br>_(14.93)***_|0.242<br>_(7.18)***_|0.395<br>_(24.64)***_|0.268<br>_(13.21)***_|0.444<br>_(20.01)***_|0.235<br>_(11.28)***_|0.249<br>_(8.81)***_|
|**_Intercept_**|0.720<br>_(1.55)_|1.597<br>_(4.21)***_|1.993<br>_(2.25)**_|3.780<br>_(5.79)***_|1.654<br>_(4.88)***_|2.985<br>_(6.08)***_|0.157<br>_(0.55)_|1.251<br>_(2.22)**_|
|**_Size_**|0.018|-0.105|-0.092|-0.195|0.600|-0.193|-0.316|0.014|
||_(0.11)_|_(0.21)_|_(0.48)_|_(2.57)**_|_(0.96)_|_(1.92)*_|_(1.29)_|_(0.07)_|
|**_Foreign_**|-0.242|-0.384|-0.011|-0.005|-0.397|-0.005|-0.000|-0.007|
||_(0.53)_|_(0.43)_|_(1.30)_|_(3.36)***_|_(1.16)_|_(1.88)*_|_(0.01)_|_(1.07)_|
|**_Export_**|-0.006|-0.183|-0.007|-0.001|-0.107|-0.005|-0.019|-0.009|
||_(1.06)_|_(1.43)_|_(2.87)***_|_(1.06)_|_(0.97)_|_(1.64)_|_(1.22)_|_(1.32)_|
|**_RegElect_**|0.077|0.323|0.228|0.042|1.006|0.053|-0.025|0.068|
||_(0.54)_|_(0.60)_|_(1.94)*_|_(0.83)_|_(1.92)*_|_(0.86)_|_(0.16)_|_(0.60)_|
|**_RegWeb_**|-2.641|2.138|0.329|-0.426|0.768|-0.757|-1.542|-0.847|
||_(2.43)**_|_(1.26)_|_(0.94)_|_(2.07)**_|_(0.50)_|_(3.39)***_|_(1.77)*_|_(1.57)_|
|**_Cred_**|-1.011|-2.421|-0.403|-0.144|-1.842|-0.085|-0.304|-0.554|
||_(2.08)**_|_(2.42)**_|_(2.74)***_|_(2.38)**_|_(2.07)**_|_(1.02)_|_(1.25)_|_(2.26)**_|
|**_AccessF_**|0.006<br>(0.11)|0.118<br>(0.65)|0.059<br>(1.41)|0.044<br>(2.34)**|-0.022<br>(0.11)|0.068<br>(2.43)**|0.126<br>(1.74)*|-0.051<br>(1.22)|
|**_Training_**|-0.135|0.234|-0.142|-0.217|0.428|-0.123|-0.400|-0.103|
||_(0.43)_|_(0.33)_|_(0.93)_|_(3.23)***_|_(0.56)_|_(1.22)_|_(1.34)_|_(0.59)_|
|**_EduM_**|-0.148|-0.282|-0.076|-0.064|-0.673|-0.073|-0.096|-0.158|
||(2.02)**|(1.53)|(2.08)**|(3.03)***|(2.61)***|(1.96)*|(1.46)|(2.84)***|
|**_ExpM_**|-0.037|0.045|-0.000|-0.003|0.014|-0.002|-0.006|-0.000|
||_(2.26)**_|_(1.50)_|_(0.05)_|_(0.90)_|_(0.48)_|_(0.38)_|_(0.56)_|_(0.04)_|
|**_RegLregul_**|0.024|-0.827|-0.069|0.007|0.362|0.020|-0.112|-0.006|
||_(0.13)_|_(1.52)_|_(0.50)_|_(0.10)_|_(0.70)_|_(0.20)_|_(0.53)_|_(0.05)_|
|**_RegCorrup_**|0.081|0.074|0.168|-0.054|-0.272|-0.008|0.073|0.124|
||_(0.51)_|_(0.17)_|_(1.53)_|_(0.96)_|_(0.59)_|_(0.11)_|_(0.52)_|_(1.40)_|
|**_Constant_**|1.460|-2.422|1.493|3.388|-2.612|2.358|1.279|1.568|
||_(2.87)***_|_(1.25)_|_(2.00)**_|_(5.45)***_|_(1.34)_|_(4.94)***_|_(1.91)*_|_(2.66)***_|
|**Observations**|942|380|1601|1494|838|695|774|480|
|**sigma_u**|0.75|1.69|0.77|0.90|1.46|0.75|1.10|0.64|
|<br>**sigma_v**|0.86|0.81|0.54|0.43|0.76|0.46|0.57|0.67|
|**Wald chi2**|1351.45|2787.67|241.01|1306.40|2484.52|1060.30|1321.23|300.67|
|**Prob> chi2**<br>_Notes_<br>:<br>|0.00<br>The one step p|0.00<br>rocedure exp|0.00<br>lains firm-lev|0.00<br>el inefficienc|0.00<br>y. Variables_Siz_|0.00<br>_e_,_Foreign_an|0.00<br>d_Export_are|0.00<br>expected with|

_<u>Notes</u>_ <u>:</u> The one step procedure explains firm-level inefficiency. Variables _Size_ , _Foreign_ and _Export_ are 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> A first set of conclusions concerns the production frontiers. Our regressions confirm the choice to estimate a production frontier by industry. Elasticites of capital and labor reveal to be different from one industry to another. Impact of capital is strong in _Chemicals & Pharmaceutical Products_ , _Agro-Processing_ and, to a lower extend, _Textile_ . On the opposite, elasticity of labor is higher in _Metal & Machinery, Non Metal & Plastic Materials, Wood & Furniture, Leather_ and _Garment_ . These industries look like being more intensive in labor, although two of them: _Metal & Machinery_ and _Non Metal & Plastic Materials_ , are usually considered as applying more capitalistic technologies in developed economies. This result is confirmed by the computation of the ratio of the two elasticities (capital/ labor). All coefficients are highly significant (at 1% level), what stresses the robustness of our results 19