Table A3.

Robustness test using different independent variables for urbanization and specialization economies

 (1)(2)(3)
Aging0.41***0.56***0.64***
(0.05)(0.15)(0.16)
SE0.22***0.08
(0.07)(0.07)
Aging × SE−0.07***−0.02
(0.02)(0.02)
UE0.21
(0.14)
Aging × UE−0.06
(0.04)
Observations39,17639,17639,176
No. of clusters128128128
Year FE
Industry FE
Controls
First-stage F-stat21.1620.9512.00
Hansen J-stat0.010.050.46
 (1)(2)(3)
Aging0.41***0.56***0.64***
(0.05)(0.15)(0.16)
SE0.22***0.08
(0.07)(0.07)
Aging × SE−0.07***−0.02
(0.02)(0.02)
UE0.21
(0.14)
Aging × UE−0.06
(0.04)
Observations39,17639,17639,176
No. of clusters128128128
Year FE
Industry FE
Controls
First-stage F-stat21.1620.9512.00
Hansen J-stat0.010.050.46

Notes: Columns (1) and (2) re-estimate the specification in column (9) of Table 1, while column (3) re-estimates the specification in column (9) of Table 2. Data are aggregate at the three-digit industry (s) and the NUTS-2 level (r). The dependent variable is |${\eta _{rst}}$| that is the time-varying regional industrial FE estimated from the production function. All columns use OLS and include industry and year FE. All columns adopt instrumental variables as explained in Section 3. 1. Excluded instruments are regional Log(live birth) in 1960, 1965, and 1970. Column (1) uses the log ratio between the s’s sector and all regional manufacturing sectors as the specialization index. Column (2) exploits the log transformation of the total number of employees as the specialization index. Column (3) uses the employment (in all sectors of the economy) as an index of urbanization economies. All columns include the interaction between the indexes of specialization/urbanization and the three Log(live birth) instruments as additional instruments and include the interaction between the index of specialization and aging as endogenous regressor. Standard errors are clustered at the regional level (NUTS-2). All specifications include industry and year FE.

***

P < 0.01,

**

P < 0.05, and

*

P < 0.1.

Table A3.

Robustness test using different independent variables for urbanization and specialization economies

 (1)(2)(3)
Aging0.41***0.56***0.64***
(0.05)(0.15)(0.16)
SE0.22***0.08
(0.07)(0.07)
Aging × SE−0.07***−0.02
(0.02)(0.02)
UE0.21
(0.14)
Aging × UE−0.06
(0.04)
Observations39,17639,17639,176
No. of clusters128128128
Year FE
Industry FE
Controls
First-stage F-stat21.1620.9512.00
Hansen J-stat0.010.050.46
 (1)(2)(3)
Aging0.41***0.56***0.64***
(0.05)(0.15)(0.16)
SE0.22***0.08
(0.07)(0.07)
Aging × SE−0.07***−0.02
(0.02)(0.02)
UE0.21
(0.14)
Aging × UE−0.06
(0.04)
Observations39,17639,17639,176
No. of clusters128128128
Year FE
Industry FE
Controls
First-stage F-stat21.1620.9512.00
Hansen J-stat0.010.050.46

Notes: Columns (1) and (2) re-estimate the specification in column (9) of Table 1, while column (3) re-estimates the specification in column (9) of Table 2. Data are aggregate at the three-digit industry (s) and the NUTS-2 level (r). The dependent variable is |${\eta _{rst}}$| that is the time-varying regional industrial FE estimated from the production function. All columns use OLS and include industry and year FE. All columns adopt instrumental variables as explained in Section 3. 1. Excluded instruments are regional Log(live birth) in 1960, 1965, and 1970. Column (1) uses the log ratio between the s’s sector and all regional manufacturing sectors as the specialization index. Column (2) exploits the log transformation of the total number of employees as the specialization index. Column (3) uses the employment (in all sectors of the economy) as an index of urbanization economies. All columns include the interaction between the indexes of specialization/urbanization and the three Log(live birth) instruments as additional instruments and include the interaction between the index of specialization and aging as endogenous regressor. Standard errors are clustered at the regional level (NUTS-2). All specifications include industry and year FE.

***

P < 0.01,

**

P < 0.05, and

*

P < 0.1.

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