. | Long difference of total employment, OLS . | |||||||
---|---|---|---|---|---|---|---|---|
. | (1) . | (2) . | (3) . | (4) . | (5) . | (6) . | (7) . | (8) . |
Denmark . | Finland . | Germany . | Italy . | Norway . | Spain . | Sweden . | United Kingdom . | |
Panel A. Automotive industry | ||||||||
Exposure to robots | −0.243 | 1.038 | −0.082 | −0.476*** | 0.704 | 0.117 | −0.204 | −0.217 |
(0.667) | (0.666) | (0.065) | (0.154) | (1.244) | (0.116) | (0.190) | (0.175) | |
R-squared | 0.174 | 0.586 | 0.181 | 0.649 | 0.290 | 0.864 | 0.310 | 0.435 |
Panel B. All Industries excluding automotive | ||||||||
Exposure to robots | 1.308 | −2.791*** | −0.286 | −0.734 | −3.642*** | −0.916 | −0.442 | −1.028* |
(1.285) | (0.784) | (0.178) | (0.639) | (0.941) | (0.626) | (0.612) | (0.524) | |
R-squared | 0.189 | 0.631 | 0.177 | 0.626 | 0.447 | 0.867 | 0.305 | 0.440 |
Panel C. Highly exposed manufacturing industries | ||||||||
Exposure to robots | 0.456 | −0.245 | −0.099 | −0.524*** | −1.904*** | 0.090 | −0.296 | −0.361* |
(0.751) | (0.734) | (0.066) | (0.151) | (0.708) | (0.119) | (0.189) | (0.184) | |
R-squared | 0.177 | 0.571 | 0.185 | 0.655 | 0.348 | 0.863 | 0.322 | 0.439 |
Observations | 99 | 70 | 319 | 110 | 74 | 49 | 100 | 352 |
Regional FE | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Baseline controls | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
. | Long difference of total employment, OLS . | |||||||
---|---|---|---|---|---|---|---|---|
. | (1) . | (2) . | (3) . | (4) . | (5) . | (6) . | (7) . | (8) . |
Denmark . | Finland . | Germany . | Italy . | Norway . | Spain . | Sweden . | United Kingdom . | |
Panel A. Automotive industry | ||||||||
Exposure to robots | −0.243 | 1.038 | −0.082 | −0.476*** | 0.704 | 0.117 | −0.204 | −0.217 |
(0.667) | (0.666) | (0.065) | (0.154) | (1.244) | (0.116) | (0.190) | (0.175) | |
R-squared | 0.174 | 0.586 | 0.181 | 0.649 | 0.290 | 0.864 | 0.310 | 0.435 |
Panel B. All Industries excluding automotive | ||||||||
Exposure to robots | 1.308 | −2.791*** | −0.286 | −0.734 | −3.642*** | −0.916 | −0.442 | −1.028* |
(1.285) | (0.784) | (0.178) | (0.639) | (0.941) | (0.626) | (0.612) | (0.524) | |
R-squared | 0.189 | 0.631 | 0.177 | 0.626 | 0.447 | 0.867 | 0.305 | 0.440 |
Panel C. Highly exposed manufacturing industries | ||||||||
Exposure to robots | 0.456 | −0.245 | −0.099 | −0.524*** | −1.904*** | 0.090 | −0.296 | −0.361* |
(0.751) | (0.734) | (0.066) | (0.151) | (0.708) | (0.119) | (0.189) | (0.184) | |
R-squared | 0.177 | 0.571 | 0.185 | 0.655 | 0.348 | 0.863 | 0.322 | 0.439 |
Observations | 99 | 70 | 319 | 110 | 74 | 49 | 100 | 352 |
Regional FE | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Baseline controls | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
This table presents OLS estimates of the impact of the exposure to robots in automotive industries and others. The measurement of robot exposure is an instrumental variable. The outcome variables are the long difference in employment-to-population ratio. Regressions are weighted by population in the start-of-period. The list of covariates is documented in Appendix A. The missing geographic and industry values in Germany are due to confidentiality-related data limitations. Statistical significance based on robust standard errors (reported in parentheses) is denoted by ***P < 0.01, **P < 0.05, and *P < 0.10.
. | Long difference of total employment, OLS . | |||||||
---|---|---|---|---|---|---|---|---|
. | (1) . | (2) . | (3) . | (4) . | (5) . | (6) . | (7) . | (8) . |
Denmark . | Finland . | Germany . | Italy . | Norway . | Spain . | Sweden . | United Kingdom . | |
Panel A. Automotive industry | ||||||||
Exposure to robots | −0.243 | 1.038 | −0.082 | −0.476*** | 0.704 | 0.117 | −0.204 | −0.217 |
(0.667) | (0.666) | (0.065) | (0.154) | (1.244) | (0.116) | (0.190) | (0.175) | |
R-squared | 0.174 | 0.586 | 0.181 | 0.649 | 0.290 | 0.864 | 0.310 | 0.435 |
Panel B. All Industries excluding automotive | ||||||||
Exposure to robots | 1.308 | −2.791*** | −0.286 | −0.734 | −3.642*** | −0.916 | −0.442 | −1.028* |
(1.285) | (0.784) | (0.178) | (0.639) | (0.941) | (0.626) | (0.612) | (0.524) | |
R-squared | 0.189 | 0.631 | 0.177 | 0.626 | 0.447 | 0.867 | 0.305 | 0.440 |
Panel C. Highly exposed manufacturing industries | ||||||||
Exposure to robots | 0.456 | −0.245 | −0.099 | −0.524*** | −1.904*** | 0.090 | −0.296 | −0.361* |
(0.751) | (0.734) | (0.066) | (0.151) | (0.708) | (0.119) | (0.189) | (0.184) | |
R-squared | 0.177 | 0.571 | 0.185 | 0.655 | 0.348 | 0.863 | 0.322 | 0.439 |
Observations | 99 | 70 | 319 | 110 | 74 | 49 | 100 | 352 |
Regional FE | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Baseline controls | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
. | Long difference of total employment, OLS . | |||||||
---|---|---|---|---|---|---|---|---|
. | (1) . | (2) . | (3) . | (4) . | (5) . | (6) . | (7) . | (8) . |
Denmark . | Finland . | Germany . | Italy . | Norway . | Spain . | Sweden . | United Kingdom . | |
Panel A. Automotive industry | ||||||||
Exposure to robots | −0.243 | 1.038 | −0.082 | −0.476*** | 0.704 | 0.117 | −0.204 | −0.217 |
(0.667) | (0.666) | (0.065) | (0.154) | (1.244) | (0.116) | (0.190) | (0.175) | |
R-squared | 0.174 | 0.586 | 0.181 | 0.649 | 0.290 | 0.864 | 0.310 | 0.435 |
Panel B. All Industries excluding automotive | ||||||||
Exposure to robots | 1.308 | −2.791*** | −0.286 | −0.734 | −3.642*** | −0.916 | −0.442 | −1.028* |
(1.285) | (0.784) | (0.178) | (0.639) | (0.941) | (0.626) | (0.612) | (0.524) | |
R-squared | 0.189 | 0.631 | 0.177 | 0.626 | 0.447 | 0.867 | 0.305 | 0.440 |
Panel C. Highly exposed manufacturing industries | ||||||||
Exposure to robots | 0.456 | −0.245 | −0.099 | −0.524*** | −1.904*** | 0.090 | −0.296 | −0.361* |
(0.751) | (0.734) | (0.066) | (0.151) | (0.708) | (0.119) | (0.189) | (0.184) | |
R-squared | 0.177 | 0.571 | 0.185 | 0.655 | 0.348 | 0.863 | 0.322 | 0.439 |
Observations | 99 | 70 | 319 | 110 | 74 | 49 | 100 | 352 |
Regional FE | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Baseline controls | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
This table presents OLS estimates of the impact of the exposure to robots in automotive industries and others. The measurement of robot exposure is an instrumental variable. The outcome variables are the long difference in employment-to-population ratio. Regressions are weighted by population in the start-of-period. The list of covariates is documented in Appendix A. The missing geographic and industry values in Germany are due to confidentiality-related data limitations. Statistical significance based on robust standard errors (reported in parentheses) is denoted by ***P < 0.01, **P < 0.05, and *P < 0.10.
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