Experience, Innovation and Productivity - Empirical Evidence from Italy's Slowdown

Table 6. Second stage results for system (9’), OLS and 2SLS.

Dependent	OLS		2SLS			OLS		2SLS
K ∆₂ ln — ² L Lit	Coeff	Robust St.E.	Coeff	Robust St.E.		Coeff	Robust St.E.	Coeff	Robust St.E.
	Innovative firms					Non Innovative firms
α₁ (K/L ratio)	0.256***	0.0265	0.199***	0.0302	«2	0.087***	0.0212	0.085***	0.0239
β₁ (constant)	20.164***	3.204	20.46***	3.146	β2	-6.68	7.755	-6.67	7.698
Yi (age)	-0.307***	0.0465	-0.313***	0.0453	Y2	0.061	0.1375	0.061	0.1366
μ₁ (temporary workers)	-0.114***	0.023	-0.123***	0.0234	μ2	-0.273**	0.120	-0.272**	0.1194
-^σεiω	2.597*	1.526	3.659**	1.528	^σε2ω	-1.557	3.152	-1.545	3.1343

R²	0.215		0.099		R²	0.208		0.099
Sargan test			3.429 [0.064]					.413 [0.520]
N	5859		5859		N	1914		1914

Note. All regressions include size, sector, area and group dummies. The first stage refers to column 1 and 2 of
Table 5. The 2SLS method instruments the growth rate of the capital stock per worker with the lagged K/L in
levels and the lagged investment intensity.

As a robustness check we replicate the estimates of equation (4) for the sub-sample of managers
who are supposed to take decisions within the board. We select those firms whose board contains
competence-specific managers, who are supposed to influence the decision to introduce innovations
in the firm, eventually. The sign and size of the main coefficients are confirmed. We omit reporting
them here for brevity.

We finally implement the Maximum Likelihood Endogenous Switching model which allows us to
obtain consistent and efficient estimates.

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Experience, Innovation and Productivity - Empirical Evidence from Italy's Slowdown

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