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



Table 7. Second stage results for system (9’), Maximum Likelihood efficient method.

Dependent

ML

/V

ML (predict k )

ML

/V

ML (predict k )

K

7 ln —

2 L

Lit

Coeff

Robust

St. E.

Coeff

Robust

Std.

Err.

Coeff

Robust

St. E.

Coeff

Robust

St. E.

Innovative firms

Non Innovative firms

αι

.2588***

.0244

.1967***

.0382

α2

.0199

.0195

-.0004

.0252

21______

17.00***

3.343

18.17***

3.381

β2_______

-6.658

10.85

-6.414

10.023

Yi

-.4028***

.056

-.3862***

.0591

Y2

-.0105

.1473

-.0051

.1462

μι

-.1878***

.0288

-.2172***

.0308

μ2

-.242**

.1174

-.2369**

.1167

σι

28.719***

.8895

30.51***

1.096

σ2

29.941***

.2622

29.93***

.1986

Pi

.6917

.0936

.6421

.1211

P2

.1126

.1385

.1039

.1179

Mills ratio

.4439**

.2767

.444**

.2821

1.364***

.5181

1.368***

.5282

Condition

Exogenous K

Endogenous K

Wald

χ2(i)

22.86

[0.000]

13.95
[0.000]

P(Inno)

.7356

.1785

.735

.1816

N

7773

7773

Note. All regressions include size, area and sector dummies. Columns 3 and 4 show ML with predicted value of the
growth rate of capital stock for innovative firms. Columns 7 and 8 replicate ML with predicted value for the non
innovative cluster. Wald Chi-square test is testing for the independence of the residuals in system (8). P(Inno) is the
predicted probability of being in the innovative regime in the first stage.

8. Conclusions

In this paper we exploited data from a sample of some eight thousands innovative and non-
innovative firm-observations to describe the pattern of correlation between experience, innovation
and productivity growth during the recent period of serious productivity slowdown in the Italian

24



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