Hiring Top Workers, Absorptive Capacity and Productivity Online Appendix

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1 Hiring Top Workers, Absorptive Capacity and Productivity Online Appendix Magnus Lodefalk Aili Tang January 16, 2017 Lodefalk: Örebro University, SE-70182, Örebro, Sweden, and Ratio Institute, Box-3203, SE-10364, Stockholm, Sweden ( Tang: Örebro University, SE-70182, Örebro, Sweden ( 1

2 Appendix A: Robustness check Table A1: Pearson correlation coefficients for variables included in the estimation Variables Managers t-1 Professionals t-1 Others 2 t-1 Firm size(log) t-2 Firm age Multinational enterprise (0,1) Managers t-1 1 Professionals t Others 2 t Firm size(log) t Firm age Multinational enterprise (0,1) Table A2: Robustness analysis: Controlling for lay-offs Absorptive capacity Managers Professionals Others Obs. Adj.R 2 Education High educ *** ( ) ( ) ( ) Knowledge-intensity Intensive ** ( ) ( ) ( ) R&D With expenditure ** ** ( ) ( ) ( ) Notes: Within-firm estimation including a dummy variable that indicates that the number of remaining leading personnel in the hiring year (t 1) is larger than the previous year. Dependent Variable: Total factor productivity (log). The legal form of the firm, industry fixed effect and year fixed effect are controlled throughout. Robust and firm-clustered standard errors in parentheses. Significance levels: * p<0.1, ** p<0.05, *** p<

3 Table A3: Robustness analysis: High educ. Municipality FE Industry*Year FE Non-linear link TFP t-1 TFP(log) t *** (0.0265) Managers and professionals t * * ** ** ( ) ( ) ( ) ( ) Managers and professionals 2 t ( ) Others 2 t ( ) ( ) ( ) ( ) Firm size(log) t *** *** *** *** ( ) ( ) ( ) (0.0130) Firm age * *** (0.0240) (0.0253) (0.0254) ( ) Multinational enterprise (0,1) * * * * (0.0120) (0.0119) (0.0119) (0.0137) Obs Adjusted R Notes: Within-firm estimation. Dependent Variable: Total factor productivity (log). The legal form of the firm, industry fixed effect and year fixed effect are controlled throughout. We also include municipality specific effects and industry*year specific effects separately. Robust and firm-clustered standard errors in parentheses. Significance levels: * p<0.1, ** p<0.05, *** p<

4 Table A4: Robustness analysis: Knowledge-intensive firms in service industry Municipality FE Industry*Year FE Non-linear link TFP t-1 TFP(log) t (0.0358) Managers and professionals t ** * ** *** ( ) ( ) ( ) ( ) Managers and professionals 2 t ( ) Others 2 t ( ) ( ) ( ) ( ) Firm size(log) t ** ** ** *** (0.0228) (0.0222) (0.0224) (0.0117) Firm age *** *** *** (0.0240) (0.0253) (0.0254) (0.0205) Multinational enterprise (0,1) (0.0316) (0.0317) (0.0315) (0.0227) Obs Adjusted R Notes: Within-firm estimation. Dependent Variable: Total factor productivity (log). The legal form of the firm, industry fixed effect and year fixed effect are controlled throughout. We also include municipality specific effects and industry*year specific effects separately. Robust and firm-clustered standard errors in parentheses. Significance levels: * p<0.1, ** p<0.05, *** p<0.01. Table A5: Robustness analysis: Firms with expenditure on R&D Municipality FE Industry*Year FE Non-linear link TFP t-1 TFP(log) t ** (0.0226) Managers and professionals t * * ** ** ( ) ( ) ( ) ( ) Managers and professionals 2 t ** ( ) Others 2 t * * ** * ( ) ( ) (0.0177) ( ) Firm size(log) t ** ** * (0.0170) (0.0178) (0.0177) (0.0199) Firm age (0.0380) (0.0164) (0.288) (0.0267) Multinational enterprise (0,1) (0.0375) (0.0382) (0.0366) (0.0369) Obs Adjusted R Notes: Within-firm estimation. Dependent Variable: Total factor productivity (log). The legal form of the firm, industry fixed effect and year fixed effect are controlled throughout. We also include municipality specific effects and industry*year specific effects separately. Robust and firm-clustered standard errors in parentheses. Significance levels: * p<0.1, ** p<0.05, *** p<

5 Appendix B: Additional analysis Table B1: Descriptive statistics regarding firms education level Educ.(1) (24.35%) Educ.(2) (44.90%) Educ.(3) (20.67%) Educ.(4) (6.94%) Mean Median Sd Mean Median Sd Mean Median Sd Mean Median Sd No. of employee Managers and professionals (MaPs) Other workers Newly hired Managers Professionals Other workers Newly hired MaPs From enterprise group to stand-alone SMEs From MNE to non-mne affiliated firm From foreign trading firm to SMEs Other background Firm age Enterprise group status Multinational status Value added Physical capital stock Notes: Data refers to the year Total number of firm is 167,167. Money values are in 1,000 SEK. The percentages of education level applied do not sum to 100%, since Educ.(3) and Educ.(4) are clustered in Educ.(2) (See definition in Section managers2). 5

6 Table B2: Descriptive statistics regarding firms knowledge intensity Knowledge-intensive (51.23%) Less knowledge-intensive (48.77%) Mean Median Sd Mean Median Sd No. of employee Managers and professionals (MaPs) Other workers Newly hired Managers Professionals Other workers Newly hired MaPs From enterprise group to stand-alone SMEs From MNE to non-mne affiliated firm From foreign trading firm to SMEs Other background Firm age Enterprise group status Multinational status Value added Physical capital stock Notes: Data refers to service industry in year Total number of knowledge-intensive firms is 60,666 and total number of less knowledge-intensive firms is 50,745. Money values are in 1,000 SEK. 6

7 Table B3: Descriptive statistics regarding firms expenditure on R&D Without expenditure on R&D (90.94%) With expenditure on R&D (9.06%) Mean Median Sd Mean Median Sd No. of employee Managers and professionals (MaPs) Other workers Newly hired Managers Professionals Other workers Newly hired MaPs From enterprise group to stand-alone SMEs From MNE to non-mne affiliated firm From foreign trading firm to SMEs Other background Firm age Enterprise group status Multinational status Value added Physical capital stock Notes: Data refers to service industry in year Total number of firms with R&D expenditure is 2,131 and total number of firms without R&D expenditure is 22,216. Money values are in 1,000 SEK. 7

8 Table B4: Estimation results for managers and professionals, seperately: Absorptive Capacity and Distance to Frontier Some years of Post-Sec Educ. At least 3 years of Post-Sec Educ. Managers t ( ) ( ) Professionals t *** *** ( ) ( ) Others 2 t *** *** ( ) ( ) Share Post-Sec t ( ) Share 3-year Post-Sec t ( ) Technology gap t e-07*** 1.13e-07*** (Share Post-Sec * Tech-gap) t-1 (Share 3-year Post-Sec * Tech-gap) t-1 (1.85e-08) 4.18e-09* (1.93e-09) (1.75e-08) 6.39e-09 (3.28e-09) (Managers * Tech-gap) t e e-08 (2.75e-08) (2.66e-08) (Professionals * Tech-gap) t e-08* -2.63e-08* (1.15e-08) (1.22e-08) Firm size(log) t *** *** ( ) ( ) Firm age * * (0.0215) (0.0216) Multinational enterprise (0,1) *** *** (0.0103) (0.0103) Obs Adjusted R Notes: Within-firm estimation results. Dependent Variable: Total factor productivity (log). The legal form of the firm, industry fixed effect and year fixed effect are controlled throughout. Robust and firm-clustered standard errors in parentheses. Significance levels: * p<0.1, ** p<0.05, *** p<

9 Table B5: Estimation results by education level, discerning the background of newly hired From enterprise group to stand-alone firm From a MNE Educ.(1) Educ.(2) Educ.(3) Educ.(4) Educ.(1) Educ.(2) Educ.(3) Educ.(4) Managers t * (0.0937) (0.0167) (0.0181) (0.0141) (0.148) (0.0219) (0.0328) (0.0105) Professionals t ** * (0.143) (0.0126) (0.0264) ( ) (0.228) (0.0141) (0.0197) ( ) Others 2 t *** ** *** *** *** *** (0.0183) ( ) ( ) ( ) (0.0176) ( ) ( ) ( ) Obs Adjusted R From a foreign-trading firm to a non-trader Others Educ.(1) Educ.(2) Educ.(3) Educ.(4) Educ.(1) Educ.(2) Educ.(3) Educ.(4) Managers t * (0.0665) (0.0151) (0.0302) ( ) (0.0700) (0.0135) (0.0177) ( ) Professionals t *** (0.107) ( ) (0.0271) ( ) (0.0510) (0.0104) (0.0167) ( ) Others 2 t *** *** *** ** *** *** * (0.0176) ( ) ( ) ( ) (0.0219) ( ) ( ) ( ) Obs Adjusted R Notes: Within-firm estimation. Dependent Variable: Total factor productivity (log). The legal form of the firm, industry fixed effect and year fixed effect are controlled throughout. Robust and firm-clustered standard errors in parentheses. For brevity, other covariate estimates are not reported. Significance levels: * p<0.1, ** p<0.05, *** p<

10 Table B6: Estimation results by knowledge intensity, discerning the background of newly hired From enterprise group to stand-alone firm From a MNE Knowledge-intensive Less knowledge-intensive Knowledge-intensive Less knowledge-intensive Managers t (0.0184) (0.0131) (0.0116) ( ) Professionals t * ( ) ( ) ( ) ( ) Others 2 t * ( ) ( ) ( ) ( ) Obs Adjusted R From foreign-trading to a non-trader Others Knowledge-intensive Less knowledge-intensive Knowledge-intensive Less knowledge-intensive Managers t ( ) ( ) ( ) ( ) Professionals t * ( ) ( ) ( ) ( ) Others 2 t * ( ) ( ) ( ) ( ) Obs Adjusted R Notes: Within-firm estimation of service industry. Dependent Variable: Total factor productivity (log). The legal form of the firm, industry fixed effect and year fixed effect are controlled throughout. Robust and firm-clustered standard errors in parentheses. For brevity, other covariate estimates are not reported. Significance levels: * p<0.1, ** p<0.05, *** p<

11 Table B7: Estimation results by expenditure on R&D, discerning the background of newly hired From enterprise group to stand-alone firm From a MNE Without expenditure on R&D With expenditure on R&D Without expenditure on R&D With expenditure on R&D Managers t ( ) (0.0294) ( ) (0.0198) Professionals t * ( ) ( ) ( ) ( ) Others 2 t *** ** *** ** ( ) ( ) ( ) ( ) Obs Adjusted R From foreign-trading to a non-trader Others Without expenditure on R&D With expenditure on R&D Without expenditure on R&D With expenditure on R&D Managers t ( ) ( ) ( ) ( ) Professionals t * * ( ) ( ) ( ) ( ) Others 2 t ** ** *** ** ( ) ( ) ( ) ( ) Obs Adjusted R Notes: Within-firm estimation. Dependent Variable: Total factor productivity (log). The legal form of the firm, industry fixed effect and year fixed effect are controlled throughout. Robust and firm-clustered standard errors in parentheses. For brevity, other covariate estimates are not reported. Significance levels: * p<0.1, ** p<0.05, *** p<

12 Table B8: Estimation results for managers and professionals, seperately: Level of education Educ.(1) Educ.(2) Educ.(3) Educ.(4) Managers t (0.0522) ( ) (0.0146) ( ) Professionals t *** (0.0597) ( ) (0.0146) ( ) Others 2 t *** *** *** (0.0179) ( ) ( ) ( ) Firm size(log) t *** *** *** (0.188) (0.0166) (0.0162) (0.0102) Firm age * ** *** * (0.0299) (0.0176) ( ) (0.0240) Multinational enterprise (0,1) ** * * (0.0782) (0.0496) (0.0725) (0.0120) Obs Adjusted R Notes: Within-firm estimation. Dependent Variable: Total factor productivity (log). The legal form of the firm, industry fixed effect and year fixed effect are controlled throughout. Robust and firm-clustered standard errors in parentheses. Significance levels: * p<0.1, ** p<0.05, *** p<

13 Table B9: Benchmark estimation results by level of education Educ(1) Educ (2) Educ(3) Educ (4) Managers and professionals t * (0.0430) ( ) (0.0104) ( ) Others 2 t *** *** *** (0.0177) ( ) ( ) ( ) Firm size(log) t *** *** *** (0.185) (0.0166) (0.0162) (0.0102) Firm age * ** *** * (0.0300) (0.0176) ( ) (0.0241) Multinational enterprise (0,1) * * * (0.0691) (0.0497) (0.0725) (0.0120) Obs Adjusted R Notes: Within-firm estimation results of Manufacturing industry. Dependent Variable: Total factor productivity (log). The legal form of the firm, industry fixed effect and year fixed effect are controlled throughout. Robust and firm-clustered standard errors in parentheses. Significance levels: * p<0.1, ** p<0.05, *** p<0.01. Table B10: Benchmark estimation results by knowledge intensity Knowledge-intensive Less knowledge-intensive Managers and professionals t ** ( ) ( ) Others 2 t ( ) ( ) Firm size(log) t *** *** ( ) ( ) Firm age ** (0.0291) (0.0187) Multinational enterprise (0,1) ** (0.0199) (0.0179) Obs Adjusted R Notes: Within-firm estimation results of service industry. Dependent Variable: Total factor productivity (log). The legal form of the firm, industry fixed effect and year fixed effect are controlled throughout. Robust and firm-clustered standard errors in parentheses. Significance levels: * p<0.1, ** p<0.05, *** p<

14 Table B11: Benchmark estimation results by expenditure on R&D Without R&D With R&D Managers and professionals t * ( ) ( ) Others 2 t ** * ( ) ( ) Firm size(log) t *** ** ( ) (0.0177) Firm age (0.0125) (0.288) Multinational enterprise (0,1) *** (0.0105) (0.0366) Obs Adjusted R Notes: Within-firm estimation results. Dependent Variable: Total factor productivity (log). The legal form of the firm, industry fixed effect and year fixed effect are controlled throughout. Robust and firm-clustered standard errors in parentheses. Significance levels: * p<0.1, ** p<0.05, *** p<0.01. Appendix C: Difference-in-difference matching method To test the robustness of the results from within-firm estimation of Eq.(1), we adopt a quasiexperiment model and perform a difference-in-difference (DiD) estimation to investigate the differences in growth impact from hiring top workers and from hiring other workers in the postrecruitment periods. Put it differently, the growth of production should result from the recruitment of managers and professionals, rather than the reverse. In the first step of the DiD analysis, valid control firms are generated to represent counter-factual cases to the treated firms. We divide firms into those only recruit top workers/managers and professional (treated firm) and those instead hire other workers (candidates of control firms). To address the underlying heterogeneity between these two types of firms, propensity score matching is used to match pre-treatment characteristics. The central idea of the matching method is that the bias that arises due to differences in the pre-hiring condition of the firms are reduced. Thereafter, the comparison of outcomes is performed form the firms of similar pre-hiring condition with the same likelihood in hiring either leading personnels or other workers, whereby treatment is as if randomly assigned. 14

15 Formally, we follow the suggestions in Angrist and Pischke (2009, p. 83), using a model containing continuous covariates with a few polynomial terms to estimate the propensity scores on which the selection of the control firms will be based. E(D i = 1 a it, x it ) = P (D i = 1 x it ) = f(x it ) + IND it + T REND t (0.1) 1, MaP i > 0 O i = 0 where D i = ; The selected covariates stand for the pre-hiring characteristics of the firm, including lagged firm size, value-added, firm age, share of skilled workers 0, MaP i = 0 O i > 0, share of managers and professionals, the average age of workers, as well as the squared values of the selected variables. IND it is a two-digit industry indicator variable; and T REND t is a common trend variable. Based on the resulting propensity score, each treated firm is assigned with a control using nearest neighbour (one-to-one) matching, allowing replacement. 15

16 Table C1: Probit regression results for prepensity score matching: High educ. Firm size t *** (0.0637) Firm size 2 t * ( ) Value added t ( ) Value added t e-10 (7.16e-10) Average worker age t *** (0.0476) Average worker age 2 t *** ( ) Firm age t *** (0.0332) Firm age 2 t *** ( ) Share of managers and professionals t Obs. 934 Pseudo R Industry Year Yes Yes Notes: Response variable is treatment indicator. Underlying Probit regression for prepensity score matching with firms at education level (4). Robust and firm-clustered standard errors in parentheses. Significance levels: * p<0.1, ** p<0.05, *** p<

17 Table C2: Probit regression results for prepensity score matching: Knowledge-intensive firms Firm size t *** (0.0686) Firm size 2 t ** ( ) Value added t ( ) Value added t e-10 (5.71e-10) Average worker age t *** (0.0208) Average worker age 2 t *** ( ) Firm age t *** (0.0261) Firm age 2 t * ( ) Share of skilled workers t ** (0.2568) Share of skilled workers 2 t ** (0.112) Share of managers and professionals t (0.449) Share of managers and professionals 2 t (0.450) Obs Pseudo R Industry Year Yes Yes Notes: Response variable is treatment indicator. Underlying Probit regression for prepensity score matching with knowledge-intensive firms in service industry. Robust and firm-clustered standard errors in parentheses. Significance levels: * p<0.1, ** p<0.05, *** p<

18 Table C3: Probit regression results for prepensity score matching: Firms with R&D expenditure Firm size t *** (0.0523) Firm size 2 t ** ( ) Value added t ( ) Value added t e-10 (6.23e-10) Average worker age t *** (0.0302) Average worker age 2 t *** ( ) Firm age t *** (0.0623) Firm age 2 t * ( ) Share of skilled workers t ** (0.2146) Share of skilled workers 2 t ** (0.136) Share of managers and professionals t (0.216) Share of managers and professionals 2 t (0.457) Obs Pseudo R Industry Year Yes Yes Notes: Response variable is treatment indicator. Underlying Probit regression for prepensity score matching with knowledge-intensive firms in service industry. Robust and firm-clustered standard errors in parentheses. Significance levels: * p<0.1, ** p<0.05, *** p<

19 Table C4: Results of balancing test: High educ. Mean t-test of the equality of the means Treated firms Control firms t-value p-value Reduced bias(%) Firm size t Firm size 2 t Value added t Value added t e e Average worker age t Average worker age 2 t Firm age t Firm age 2 t Share of managers and professionals t Notes: The treated firms are firms that only hire managers and professionals and no other workers. The control firms only hire other workers. The average and median bias per variable before matching is 28.6% and 25.7%, respectively, and after matching 15.9% and 12.4%, respectively. Table C5: Results of balancing test: Knowledge-intensive firms Mean t-test of the equality of the means Treated firms Control firms t-value p-value Reduced bias(%) Firm size t Firm size 2 t Value added t Value added t e e Average worker age t Average worker age 2 t Firm age t Firm age 2 t Share of skilled workers t Share of skilled workers 2 t Share of managers and professionals t Share of managers and professionals 2 t Notes: The treated firms are firms that only hire managers and professionals and no other workers. The control firms only hire other workers. The average and median bias per variable before matching is 35.6% and 27.8%, respectively, and after matching 13.4% and 10.1%, respectively. Table C6: Results of balancing test: Firms with R&D expenditure Mean t-test of the equality of the means Treated firms Control firms t-value p-value Reduced bias(%) Firm size t Firm size 2 t Value added t Value added t e Average worker age t Average worker age 2 t Firm age t Firm age4 2 t Share of skilled workers t Share of skilled workers 2 t Share of managers and professionals t Share of managers and professionals 2 t Notes: The treated firms are firms that only hire managers and professionals and no other workers. The control firms only hire other workers. The average and median bias per variable before matching is 42.6% and 36.1%, respectively, and after matching 12.9% and 16.4%, respectively. 19

20 Figure C1: DiD matching estimator results of high educ. - Average treatment on the treated Year Year Treated: Average ln(tfp) Control: Average ln(tfp) Treated: Average ln(tfp) Control: Average ln(tfp) (a) Years (b) Years Figure C2: DiD matching estimator results of knowledge-intensive firms in service industry - Average treatment on the treated Year Year Treated: Average ln(tfp) Control: Average ln(tfp) Treated: Average ln(tfp) Control: Average ln(tfp) (a) Years (b) Years Figure C3: DiD matching estimator results of firms with R&D expenditure - Average treatment on the treated Year Year Treated: Average ln(tfp) Control: Average ln(tfp) Treated: Average ln(tfp) Control: Average ln(tfp) (a) Years (b) Years