The name is absent

Table 6b Relationship between Export and Performance

	(1)	(2)	Dependent (3)	variable : (4)	TFP_index (5)	(6)	(7) importers
Export Dummy	0.085 [0.039]**	0.063 [0.047]
Export share			0.124 [0.070]*	0.210 [0.385]	0.113 [0.068]	0.158 [0.102]	0.271 [0.147]*
Age of the firm		0.004 [0.004]	0.004 [0.004]	0.004 [0.004]	-0.001 [0.002]	0.001 [0.002]
Share of public ownership		0.005 [0.002]*	0.005 [0.002]**	0.005 [0.002]**	0,031 [0.024]**	0.002 [0.002]
Share of foreign ownership		1.206 [0.363]***	1.194 [0.393]***	1.200 [0.407]***	1.205 [0.406]***	0.923 [0.281]***
Export share squared				-0.090 [0.433]
Age of Machineries						0.003 [0.004]
R&D_spending						0.000 [0.000]**
Export experience					0.004 [0.005]
Skill Intensity						0.007 [0.004]*
Imported new investments					-0.306 [0.188]
Constant	0.940 [0.076]***	1.101 [0.074]***	1.102 [0.079]***	1.105 [0.081]***	1.285 [0.088]***	1.068 [0.120]***	1.133 [0.612]*
Observations	573	563	563	563	359	334	146
R-squared	0.05	0.09	0.09	0.09	0.18	0.19	0.20

Notes: see Table 6a

The findings from this preliminary analysis substantiate further the importance of
investigating the combined role of import and export.

This is developed with the estimations reported in Table 7. Here, equation (8) is
estimated by substituting to X, first the dummy variable indicating the fact that a firm is
both an importer and an exporter, then the IE index as presented in the previous section.
As expected, both the interacted dummy and the IE index (5) display positive and
significant coefficient. This indicates that firms involved in foreign networks are more
productive and the higher is the degree of such involvement, the higher is productivity.
This results holds to the inclusion in the regressions of controls such as import and

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