The effect of immigration on innovation in Italy

Figures & data

Table 1. Ordinary least squares (OLS) and two-stage least squares (2SLS) estimates of the effect of immigrants on patent applications.

	OLS estimates			2SLS estimates
	(1)	(2)	(3)	(4)	(5)
share of immigrants (%)	0.072***	0.025**	0.004	0.020	–0.018
	(0.016)	(0.013)	(0.013)	(0.021)	(0.024)
% employed in R&D activity		–0.006	–0.004	–0.006	–0.002
		(0.011)	(0.012)	(0.011)	(0.012)
ln(population 2001)		0.251***	0.178***	0.256***	0.193***
		(0.043)	(0.044)	(0.044)	(0.045)
% active age population (2001)		0.055***	0.057***	0.054***	0.057***
		(0.021)	(0.022)	(0.021)	(0.021)
% of university graduates on pop. 18–64 (2001)		0.039***	0.108***	0.039***	0.109***
		(0.014)	(0.017)	(0.014)	(0.017)
ln(patent applications per capita in 1995)^a		0.316***	0.226***	0.316***	0.221***
		(0.030)	(0.033)	(0.029)	(0.033)
Share of employment (%)^b (science-based manufacturing)
agriculture, forestry and fishing			–0.027**		–0.026**
			(0.011)		(0.011)
mining and quarrying			–0.208***		–0.218***
			(0.052)		(0.052)
supplier-dominated manufacturing			–0.010		–0.010
			(0.010)		(0.010)
scale-intensive manufacturing			0.017		0.015
			(0.010)		(0.010)
specialized-suppliers manufacturing			0.007		0.008
			(0.011)		(0.011)
construction			–0.014		–0.015
			(0.013)		(0.012)
trade and accommodation			–0.011		–0.014
			(0.011)		(0.011)
transportation and communication			–0.049***		–0.052***
			(0.017)		(0.017)
financial and real-estate activities			0.005		0.005
			(0.020)		(0.019)
professional and technical activities			–0.030**		–0.032**
			(0.015)		(0.014)
public administration			–0.042***		–0.043***
			(0.013)		(0.012)
education and human health			–0.011		–0.014
			(0.012)		(0.012)
social work activities			–0.041***		–0.043***
			(0.016)		(0.015)
other services			–0.026		–0.030
			(0.027)		(0.027)
Weak instrument robust inference (p-value)^c				0.37	0.44
Observations	927	927	927	927	927
R^2	0.78	0.86	0.87	0.36	0.43

Notes. The dependent variable is the logarithm of patents’ applications per 1000 inhabitants at the province (NUTS-3) level for Italy, 2002–10. All models include year and region (NUTS-2) fixed effects. Standard errors are robust to heteroskedasticity.

^aPer 1000 inhabitants.

^bThe 15 sectors of economic activity are built by aggregating ATECO two-digit codes (the classification is available from the authors upon request). The omitted category (science-based manufacturing) is considered as the most innovative sector.

^cThe test reports the p-value on the coefficient(s) of the instrument(s) in the reduced form estimates.

***Significant at 1%; **significant at 5%; *significant at 10%.

Table 2. First stages of province-level estimates.

			By skill
	All immigrants		High skilled	Low skilled	High skilled	Low skilled
	(1)	(2)	(3)	(4)	(5)	(6)
predicted share of immigrants (%)	0.453***	0.403***
	(0.026)	(0.027)
predicted share of immigrants: HS (%)^a			0.233***	0.072	0.191**	–0.001
			(0.073)	(0.110)	(0.076)	(0.112)
predicted share of immigrants: LS (%)^a			0.063	0.383***	0.054	0.392***
			(0.061)	(0.099)	(0.065)	(0.103)
% employed in R&D activity	0.013	0.048*	0.007	–0.026	0.030	–0.009
	(0.024)	(0.028)	(0.018)	(0.028)	(0.021)	(0.030)
ln(population 2001)	0.172*	0.172*	–0.003	0.176***	–0.006	0.145**
	(0.094)	(0.096)	(0.067)	(0.068)	(0.071)	(0.073)
% active age population (2001)	0.157***	0.181***	0.055*	–0.014	0.081**	–0.027
	(0.055)	(0.053)	(0.032)	(0.039)	(0.032)	(0.041)
% of university graduates on pop. 18–64 (2001)	–0.225***	–0.098***	0.033	–0.198***	0.059**	–0.149***
	(0.031)	(0.033)	(0.024)	(0.031)	(0.027)	(0.044)
ln(patent applications per capita in 1995)^b	0.020	–0.107**	0.011	0.052	–0.041	–0.039
	(0.053)	(0.047)	(0.036)	(0.049)	(0.037)	0.051
Share of employment (%)^c (science-based manufacturing)
agriculture, forestry and fishing		–0.001			–0.019	–0.026
		(0.017)			(0.013)	(0.017)
mining and quarrying		–0.250**			–0.058	–0.108
		(0.105)			(0.077)	(0.092)
supplier-dominated manufacturing		0.008			–0.026**	–0.008
		(0.020)			(0.012)	(0.016)
scale-intensive manufacturing		–0.051***			–0.051***	–0.038**
		(0.019)			(0.014)	(0.016)
specialized-suppliers manufacturing		–0.008			–0.023	–0.006
		(0.022)			(0.014)	(0.020)
construction		–0.053**			–0.060***	–0.041
		(0.024)			(0.018)	(0.027)
trade and accommodation		–0.072***			–0.063***	–0.054***
		(0.019)			(0.014)	(0.016)
transportation and communication		–0.067**			–0.032	–0.054**
		(0.033)			(0.022)	(0.027)
financial and real-estate activities		–0.057			0.014	0.001
		(0.044)			(0.044)	(0.063)
professional and technical activities		–0.053*			–0.077***	–0.046
		(0.030)			(0.024)	(0.033)
public administration		–0.018			–0.047***	–0.055**
		(0.021)			(0.014)	(0.026)
education and human health		–0.100***			–0.067***	–0.068***
		(0.020)			(0.015)	(0.020)
social work activities		–0.073**			–0.017	0.026
		(0.030)			(0.027)	(0.031)
other services		–0.216***			–0.095**	–0.091
		(0.065)			(0.048)	(0.080)
F-statistic-excluded instruments	307.49	220.04	40.13	57.98	23.18	39.26
Observations	927	927	927	927	927	927

Notes: The dependent variables are the total share of immigrants in columns (1) and (2), the share of high-skilled immigrants in columns (3) and (5), and the share of low-skilled immigrants in columns (4) and (6). All models include year and region (NUTS-2) fixed effects. Standard errors are robust to heteroskedasticity.

^aHS and LS= high- and low-skilled respectively. The former are defined as those with at least secondary education.

^bPer 1000 inhabitants.

^cThe 15 sectors of economic activity are built by aggregating ATECO two-digit codes (the classification is available from the authors upon request). The omitted category (science-based manufacturing) is considered as the most innovative sector.

***Significant at 1%; **significant at 5%; *significant at 10%.

Table 3. Ordinary least squares (OLS) and two-stage least squares (2SLS) estimates of the effect of high- (HS) and low-skilled (LS) immigrants on patent applications.

	OLS estimates			2SLS estimates
	(1)	(2)	(3)	(4)	(5)
share of immigrants: HS (%)^a	0.126***	0.017	0.012	–0.065	–0.034
	(0.031)	(0.022)	(0.022)	(0.271)	(0.260)
share of immigrants: LS (%)^a	0.045**	0.061***	0.043***	0.077	–0.017
	(0.021)	(0.017)	(0.016)	(0.156)	(0.140)
% employed in R&D activity		–0.004	–0.004	–0.003	–0.002
		(0.011)	(0.012)	(0.013)	(0.015)
ln(population 2001)		0.237***	0.159***	0.243***	0.192***
		(0.042)	(0.043)	(0.054)	(0.051)
% active age population (2001)		0.062***	0.063***	0.062**	0.056*
		(0.021)	(0.022)	(0.027)	(0.032)
% of university graduates on pop. 18–64 (2001)		0.044***	0.110***	0.053	0.110***
		(0.014)	(0.017)	(0.045)	(0.041)
ln(patent applications per capita in 1995)^b		0.313***	0.231***	0.312***	0.221***
		(0.029)	(0.033)	(0.030)	(0.033)
Share of employment (%)^c (science-based manufacturing)
agriculture, forestry and fishing			–0.026**		–0.027**
			(0.011)		(0.011)
mining and quarrying			–0.199***		–0.217***
			(0.051)		(0.052)
supplier-dominated manufacturing			–0.009		–0.011
			(0.010)		(0.011)
scale-intensive manufacturing			0.019*		0.014
			(0.011)		(0.013)
specialized-suppliers manufacturing			0.007		0.007
			(0.011)		(0.012)
construction			–0.012		–0.017
			(0.013)		(0.017)
trade and accommodation			–0.007		–0.016
			(0.011)		(0.015)
transportation and communication			–0.046***		–0.053***
			(0.017)		(0.017)
financial and real-estate activities			0.004		0.006
			(0.020)		(0.020)
professional and technical activities			–0.027*		–0.034*
			(0.015)		(0.019)
public administration			–0.039***		–0.045***
			(0.013)		(0.014)
education and human health			–0.008		–0.016
			(0.012)		(0.014)
social work activities			–0.042***		–0.041**
			(0.015)		(0.016)
other services			–0.022		–0.031
			(0.027)		(0.030)
Weak instrument robust inference (p-value)^d				0.56	0.73
Observations	927	927	927	927	927
R^2	0.79	0.86	0.88	0.36	0.42

Notes: The dependent variable is the logarithm of patents’ applications per 1000 inhabitants at the province (NUTS-3) level for Italy, 2002–10. All models include year and region (NUTS-2) fixed effects. Standard errors are robust to heteroskedasticity.

^aHS and LS = high- and low-skilled respectively. The former immigrants are defined as those with at least secondary education.

^bPer 1000 inhabitants.

^cThe 15 sectors of economic activity are built by aggregating ATECO two-digit codes (the classification is available from the authors upon request). The omitted category (science-based manufacturing) is considered as the most innovative sector.

^dThe test reports the p-value on the coefficient(s) of the instrument(s) in the reduced form estimates.

***Significant at 1%; **significant at 5%; *significant at 10%.

Table 4. Two-stage least squares (2SLS) estimates of the effect of immigrants on firms’ self-reported innovation.

	Second stage			First stage
	Product	Process	Organization
	(1)	(2)	(3)	(4)
firm-level variables capital intensity	–0.000	0.001***	–0.000	–0.000
	(0.000)	(0.000)	(0.000)	(0.000)
ln(firm size)	0.066***	0.069***	0.059***	0.017
	(0.005)	(0.007)	(0.005)	(0.016)
college share	0.003***	0.001*	0.003***	–0.000
	(0.001)	(0.001)	(0.001)	(0.000)
R&D intensity	0.018***	0.010***	0.009***	0.000**
	(0.002)	(0.001)	(0.001)	(0.000)
province-level variables share of immigrants (%)	0.009	–0.002	0.000
	(0.008)	(0.007)	(0.006)
predicted share of immigrants (%)				0.424***
				(0.066)
ln(population 2001)	–0.030*	–0.004	0.007	0.417
	(0.018)	(0.014)	(0.015)	(0.321)
% active age population (2001)	0.005	0.003	–0.014*	0.161
	(0.009)	(0.007)	(0.008)	(0.187)
% of university graduates on pop. 18–64 (2001)	0.006	–0.001	–0.005	–0.296***
	(0.005)	(0.004)	(0.004)	(0.088)
ln(patent applications per capita in 1995)	0.015	–0.002	0.011	–0.053
	(0.012)	(0.009)	(0.008)	(0.189)
F-statistic-excluded instruments
Predicted share of immigrants	41.13	41.13	41.13
Weak instrument robust inference (p-value)^a	0.26	0.80	0.96
Observations	11,214	11,214	11,214	11,214

Notes: The dependent variables in columns (1)–(3) are dichotomous indicators for having introduced product, process and organizational innovations respectively. The dependent variable in column (4) is the total share of immigrants. All outcomes are modelled using linear probability models (LPMs), and estimated using 2SLS. All regressions control for year, two-digit industry, and (NUTS-2) region fixed effects. Heteroskedasticity-robust standard errors are clustered at the province level.

^aAnderson–Rubin Wald test. The test reports the p-value on the coefficient(s) of the instrument(s) in the reduced form estimates.

***Significant at 1%; **significant at 5%; *significant at 10%.