Article Figures & Data
Figures
- Figure 1
Hawaii CNC Ban and Wage Variables from QWI
Notes: This figure presents period-specific means (controlling for industry fixed effects in the “Within-Hawaii, Cross-Industry” graphs and for state fixed effects in the “Cross-State, Within-Tech” graphs). Data are limited to the state of Hawaii in the “Within-Hawaii, Cross-Industry” graphs (top panel) and to “Tech” industries in the “Cross-State, Within-Tech” graphs (bottom panel). ‘Tech” is defined as QWI four-digit industry classifications that cover software design, development, and services, to concord with the definition of “technology business” in the Hawaii statute. Log Overall Average Monthly Earnings is the log of group average of overall Average Monthly Earnings (Full Quarter Employment) (that is, log EarnS). Log Hires Average Monthly Earnings is the log of Average Monthly Earnings of All Hires into Full Quarter Employment (that is, log EamHirAS). The group average means are weighted means, with industry–period Beginning of Quarter Employment (Emp) as (analytical) weights. Data are from the QWI, 2013Q2–2017Q1.
- Figure 2
Hawaii CNC Ban and Mobility Variables from QWI
Noes: This figure presents period-specific means (controlling for industry fixed effects in the “Within-Hawaii, Cross-Industry” graphs and for state fixed effects in the “Cross-State, Within-Tech” graphs). Data, are limited to the state of Hawaii in the “Within-Hawaii, Cross-Industry” graphs (top panel) and to ‘Tech” industries in the “Cross-State, Within-Tech” graphs (bottom panel). “Tech” is defined as QWI four-digit industry classifications that cover software design, development, and services, to concord with the definition of “technology business” in the Hawaii statute. The Overall Separation Rate is defined as All Separations (Sep) divided by Employment in the Reference Quarter (EmpTotal). The “Beginning-of-Quarter Separation rate” is beginning of quarter separation rate (SepBegr). The aggregated means are weighted means, with industry–period Beginning of Quarter Employment (Emp) as (analytical) weights. Data, are from the QWI, 2013Q2–2017Q1.
- Figure 3
Factor Analysis CNC Enforceability Index Scores for 2009 from Starr (2019)
- Figure 4
CNCs and High-Tech Workers’ Wage across Job Tenure (LEHD)
Notes: This figure plots the coefficient estimates of the differential relation of CNC enforceability with wage, for high-tech jobs relative to nontech jobs, translated to reflect the effect of applying the average enforceability score to a nonenforcing state (a difference of four standard deviations) and the associated 95 percent confidence intervals. Wage is the log of quarterly wage at 4th,…, 32nd quarter of the job spell. Data are from the LEHD (1991–2008).
- Figure 5
CNCs and High-Tech Workers’ Job Duration across Job Tenure (LEHD)
Notes: This figure plots the coefficient estimates and the 95 percent confidence intervals of the differential relation of CNC enforceability with job duration, for high-tech jobs relative to nontech jobs (from Table 6), translated to reflect the effect of applying the average enforceability score to a nonenforcing state (a difference of four standard deviations), normalized by the mean probability of surviving to the end of that quarter. For example, the point estimate of 0.0.0052 for the 32nd quarter (column 8 of Table 6) translated for the effect of a four standard deviation change yields 4 x 0.0052 = 0.0208; this is normalized by the mean probability of surviving to Quarter 32 of 0.124 (from Online Appendix Table OA2) to yield the plotted point of 0.0208/0.124 = 0.1677. Job duration is measured as the dummy variable for the spell surviving at 4th, ., 32nd quarter of the job spell. Data are from the QWI, 2013Q2–2017Q1.
Tables
- Table 1
QWI Wage Variables Analysis from the Hawaii Natural Experiment—Difference-in-Differences Results
Log Overall Average Monthly Earnings Log Hires Average Monthly Earnings (1) (2) (3) (4) (5) (6) (7) (8) Panel A: Cross-Industry, Within-Hawaii Post × Tech -0.000726 -0.00506 0.0389 0.0259*** (0.0245) (0.0147) (0.0348) (0.00973) SR_Post × Tech 0.0190 0.00269 0.0943*** 0.0440*** (0.0187) (0.0115) (0.0278) (0.0125) LR_Post × Tech -0.0414 -0.0212 -0.0751 -0.0115 (0.0381) (0.0216) (0.0683) (0.0139) Observations 453 453 3,428 3,428 423 423 3,335 3,335 R-squared 0.962 0.962 0.979 0.979 0.906 0.907 0.924 0.924 Sample Tech 2d Tech 2d All All Tech 2d Tech 2d All All Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Year–Quarter FE Yes Yes Yes Yes Yes Yes Yes Yes Panel B: Cross-State, Within-Tech Post × HI 0.0223*** 0.0178*** 0.0778*** 0.0711*** (0.00287) (0.00430) (0.00541) (0.00617) SR_Post × HI 0.0217*** 0.0180*** 0.0825*** 0.0776*** (0.00313) (0.00362) (0.00602) (0.00607) LR_Post × HI 0.0237*** 0.0175** 0.0681*** 0.0575*** (0.00548) (0.00700) (0.00584) (0.00739) Observations 3,721 3,721 4,753 4,753 3,668 3,668 4,690 4,690 R-squared 0.902 0.902 0.926 0.926 0.888 0.888 0.896 0.896 Sample 40 states 40 states All All 40 states 40 states All All Ind × Year-Qtr FE Yes Yes Yes Yes Yes Yes Yes Yes State × Ind FE Yes Yes Yes Yes Yes Yes Yes Yes Notes:
* p < 0.1,
↵** p < 0.05,
↵*** p < 0.01.
Robust standard errors in parentheses are clustered at the industry level in Panel A and state level in Panel B. Data are from the QWI, 2013Q2–2017Q1. Data are limited to the state of Hawaii in Panel A and to ‘Tech” industries in Panel B. “Tech” is defined as QWI four-digit industry classifications that cover software design, development, and services, to concord with the definition of “technology business” in the Hawaii statute. The dependent variable in Columns 1–4 is the log of overall Average Monthly Earnings (Full Quarter Employment) (that is, log EarnS). The dependent variable in Columns 5–8 is the log of the Average Monthly Earnings of All Hires into Full Quarter Employment (that is, log EamHirAS). “Post” is defined as July 2015 and afterwards; SR_Post is 2015Q3–2016Q2, and LR_Postis 2016Q3–2017Q1. In Panel A, Columns 1–2 and 5–6 are limited to four-digit industries within the two-digit industries that contain the tech industries, while other columns include all industries. In Panel B, Columns 1–2 and 5–6 are limited to the 40 states closest to Hawaii in the CNC score in absolute terms, while other columns include all states. All specifications use Beginning of Quarter Employment (Emp) as (analytical) weights. Number of observations adjusts for weights and singleton cells, that is, drops zero weights and singletoncells (when fixed effects are added). The mean (SD) for tech industries in the pre-July 2015 of Log Overall Average Monthly Earnings period is 8.788 (0.084) and of Log Hires Average Monthly Earnings is 8.640 (0.140).
- Table 2
QWI Wage and Mobility Analysis from the Hawaii Natural Experiment—Triple Difference Results
Log Overall Average Monthly Earnings Log Hires Average Monthly Earnings (1) (2) (3) (4) (5) (6) (7) (8) Panel A: QWI Wage Variables Post × HI × Tech 0.00964*** 0.00712** 0.0441*** 0.0424*** (0.00282) (0.00270) (0.00457) (0.00361) SR_Post × HI × Tech 0.0121*** 0.0100*** 0.0558*** 0.0548*** (0.00276) (0.00238) (0.00479) (0.00352) LR_Post × HI × Tech 0.00451 0.00104 0.0198*** 0.0166*** (0.00653) (0.00546) (0.00625) (0.00593) Observations 166,529 166,529 208,728 208,728 164,140 164,140 205,828 205,828 R-squared 0.992 0.992 0.993 0.993 0.975 0.975 0.975 0.975 Panel B: QWI Mobility Variables Post × HI × Tech 0.0104*** 0.00979*** 0.0104*** 0.00960*** (0.00180) (0.00130) (0.00108) (0.000912) SR_Post × HI × Tech 0.0114*** 0.0112*** 0.0129*** 0.0126*** (0.00177) (0.00125) (0.000840) (0.000644) LR_Post × HI × Tech 0.00836*** 0.00676*** 0.00533** 0.00337* (0.00214) (0.00173) (0.00198) (0.00174) Observations 163,965 163,965 205,608 205,608 166,450 166,450 208,632 208,632 R-squared 0.945 0.945 0.945 0.945 0.902 0.902 0.899 0.899 Sample 40 States 40 States All All 40 States 40 States All All Ind × Year–Qtr FE Yes Yes Yes Yes Yes Yes Yes Yes State × Ind FE Yes Yes Yes Yes Yes Yes Yes Yes State × Year–Qtr FE Yes Yes Yes Yes Yes Yes Yes Yes Notes:
↵* p < 0.1,
↵** p < 0.05,
↵*** p < 0.01.
Robust standard errors in parentheses are clustered at the state level. Data are from the QWI, 2013Q2–2017Q1. “Tech”is defined as QWI four-digit industry classifications that cover software design, development, and services, to concord with the definition of “technology business” in the Hawaii statute. In Panel A, the dependent variable in Columns 1–4 is the Overall Separation Rate defined as All Separations (that is, Sep) divided by Employment in the Reference Quarter (that is, EmpTotal), and in Columns 5–8 is the Beginning-of-Quarter separation rate (that is, SepBegR). In Panel B, the dependent variable in Columns 1–4 is the log of overall Average Monthly Earnings (Full Quarter Employment) (that is, log EarnS), and in Columns 5–8 is the log of the Average Monthly Earnings of All Hires into Full Quarter Employment (that is, log EamHirAS). “Post” is defined as July 2015 and afterwards; SR_Post is 2015Q3–2016Q2, and LR_Postis 2016Q3–2017Q1. Columns 1–2 and 5–6 are limited to the 40 states closest to Hawaii in the CNC score in absolute terms, while other columns include all states. All specifications use Beginning-of-Quarter Employment (Emp) as (analytical) weights. Number of observations adjusts for weights and singleton cells, that is, drops zero weights and singleton-cells (when fixed effects are added). The mean (SD) for tech industries in the pre-July 2015 of Log Overall Average Monthly Earnings period is 8.788 (0.084) and of Log Hires Average Monthly Earnings is 8.640 (0.140). The mean (SD) of the Overall Separation Rate for Tech industries in the pre-July 2015 period is 0.091 (0.020) and for Beginning-of-Quarter Separation Rate is 0.085 (0.025).
- Table 3
QWI Mobility Analysis from the Hawaii Natural Experiment—Dijference-in-Differences Results
Overall Separation Rate Quarter Beginning Separation Rate (1) (2) (3) (4) (5) (6) (7) (8) Panel A: Cross-Industry, Within-Hawaii Post × Tech 0.0125* 0.00272 0.0109 0.00373 (0.00701) (0.00219) (0.00737) (0.00282) SR_Post × Tech 0.0187*** 0.00753*** 0.0219*** 0.00974*** (0.00487) (0.00206) (0.00653) (0.00286) LR_Post × Tech -0.000164 -0.00728** -0.0118 -0.00876** (0.0185) (0.00369) (0.0211) (0.00417) Observations 413 413 3,321 3,321 452 452 3,423 3,423 R-squared 0.735 0.736 0.803 0.803 0.282 0.284 0.664 0.664 Sample Tech 2d Tech 2d All All Tech 2d Tech 2d All All Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Year-Quarter FE Yes Yes Yes Yes Yes Yes Yes Yes Panel B: Cross-State, Within-Tech Post × HI 0.0114*** 0.0115*** 0.0113*** 0.0114*** (0.00119) (0.000875) (0.000966) (0.000716) SR_Post × HI 0.0135*** 0.0136*** 0.0145*** 0.0144*** (0.00128) (0.000930) (0.000567) (0.000504) LR_Post × HI 0.00706*** 0.00718*** 0.00479* 0.00514*** (0.00237) (0.00170) (0.00241) (0.00173) Observations 3,651 3,651 4,653 4,653 3,720 3,720 4,752 4,752 R-squared 0.822 0.822 0.835 0.835 0.760 0.760 0.782 0.782 Sample 40 States 40 states All All 40 states 40 states All All Ind × Year-Qtr FE Yes Yes Yes Yes Yes Yes Yes Yes State × Ind FE Yes Yes Yes Yes Yes Yes Yes Yes Notes:
↵* p < 0.1,
↵** p < 0.05,
↵*** p < 0.01.
Robust standard errors in parentheses are clustered at the industry level in Panel A and state level in Panel B. Data are from the QWI, 2013Q2–2017Q1. Data are limited to the state of Hawaii in Panel A, and to “Tech” industries in Panel B. “Tech” is defined as QWI four-digit industry classifications that cover software design, development and services, to concord with the definition of “technology business” in the Hawaii statute. The dependent variable in Columns 1–4 is the Overall Separation Rate defined as All Separations (that is, Sep) divided by Employment in the Reference Quarter (that is, EmpTotal). The dependent variable in Columns 5–8 is the Beginning-of-Quarter separation rate (that is, SepBegR). “Post” is defined as July 2015 and afterwards; SR_Post is 2015Q3–2016Q2, and LR_Postis 2016Q3–2016Q4 (latest date for which separations data was available). In Panel A, Columns 1–2 and 5–6 are limited to four-digit industries within the two-digit industries that contain the tech industries, while other columns include all industries. In Panel B, Columns 1–2 and 5–6 are limited to the 40 states closest to Hawaii in the CNC score in absolute terms, while other columns include all states. All specifications use Beginning-of-Quarter Employment (Emp) as (analytical) weights. Number of observations adjusts for weights and singleton cells, that is, drops zero weights and singleton-cells (when fixed effects are added). The mean (SD) of the Overall Separation Rate for Tech industries in the pre-July 2015 period is 0.091 (0.020) and for Beginning-of-Quarter Separation Rate is 0.085 (0.025).
Dependent Variable: Log of Wage at xth Quarter Initial Wage
(1)4th Qtr.
(2)8th Qtr.
(3)12th Qtr.
(4)16th Qtr.
(5)20th Qtr.
(6)24th Qtr.
(7)28th Qtr.
(8)32nd Qtr.
(9)Tech × CNC score -0.0259*** -0.0057*** -0.0066*** -0.0068*** -0.0069*** -0.0061*** -0.0054*** -0.0061*** -0.0057*** (0.0019) (0.0006) (0.0006) (0.0007) (0.0008) (0.0008) (0.0009) (0.0012) (0.0016) Observations 13,205,400 10,904,200 7,397,200 5,399,500 4,048,400 3,145,300 2,478,900 1,858,400 ,1412,600 R-squared 0.1853 0.6726 0.6090 0.5764 0.5570 0.5429 0.5323 0.5237 0.5114 Fixed effects State + [Industry – Starting Year – Firm Size – Starting Age – Sex] State + [Industry – Starting Year – Firm Size – Starting Wage – Starting Age – Sex] Sample All continuing jobs in the quarter Notes: This table reports the differential effect of CNC enforceability on wage across job tenure by industry (high-tech jobs vs. nontech jobs). The dependent variables are the log of monthly earnings at 4th,…, 32nd quarter of the job spell. CNC score is measured as the 2009 CNC enforceability index scores. Data are from the LEHD (1991–2008). All standard errors are clustered by state.
* p < 0.10,
** p < 0.05,
↵*** p < 0.01.
- Table 5
CNCs and High-Tech Workers’ Cumulative Wage and Wage Growth across Job Tenure (LEHD)
Dependent Variable: Log of Cumulative Wage at 4th Qtr.
(1)8th Qtr.
(2)12th Qtr.
(3)16th Qtr.
(4)20th Qtr.
(5)24th Qtr.
(6)28th Qtr.
(7)32nd Qtr.
(8)Panel A: Cumulative Wage Tech × CNC score -0.0060*** -0.0072*** -0.0077*** -0.0079*** -0.0080*** -0.0084*** -0.0081*** -0.0094*** (0.0008) (0.0005) (0.0006) (0.0006) (0.0007) (0.0009) (0.0012) (0.0015) Observations 10,904,000 7,397,000 5,399,000 4,048,000 3,145,000 2,479,000 1,858,000 1,413,000 R-squared 0.5902 0.6437 0.6708 0.6838 0.6891 0.6894 0.6887 0.6814 Fixed effects State + [Industry – Starting Year – Firm Size – Starting Wage – Starting Age – Sex] Sample All continuing jobs in the quarter Dependent Variable: Log of Wage at xth Quarter – Log of Initial Wage 4th Qtr.
(1)8th Qtr.
(2)12th Qtr.
(3)16th Qtr.
(4)20th Qtr.
(5)24th Qtr.
(6)28th Qtr.
(7)32nd Qtr.
(8)Panel B: Wage Growth Tech × CNC score -0.0054*** -0.0063*** -0.0065*** -0.0066*** -0.0057*** -0.0050*** -0.0057*** -0.0056*** (0.0005) (0.0006) (0.0007) (0.0008) (0.0008) (0.0009) (0.0012) (0.0015) Observations 10,904,000 7,397,000 5,399,000 4,048,000 3,145,000 2,479,000 1,858,000 1,413,000 R-squared 0.1455 0.1779 0.2047 0.2281 0.2504 0.2721 0.2946 0.3129 Fixed effects State + [Industry – Starting Year – Firm Size – Starting Wage – Starting Age – Sex] Sample All continuing jobs in the quarter Notes: This table reports the differential effector CNC enforceability on cumulative wage and on wage growth from initial wage, across job tenure, by industry (high-tech jobs vs. nontech jobs). The dependent variables are the log of cumulative wage at 4th, 8th, …, 32nd quarter of the job spell for Panel A, and the difference between the log of monthly wages at 4th, 8th,…, 32nd quarter of the job spell and the log of initial wage for Panel B. CNC score is measured as the 2009 CNC enforceability index scores. Data are from the LEHD (1991–2008). All standard errors are clustered by state.
* p < 0.10,
** p < 0.05,
↵*** p < 0.01.
Dependent Variable: Job-Spell Survival at 4th Qtr.
(1)8th Qtr.
(2)12th Qtr.
(3)16th Qtr.
(4)20th Qtr.
(5)24th Qtr.
(6)28th Qtr.
(7)32nd Qtr.
(8)Ln(job-spell)
(9)Tech × CNC score -0.0002 0.0033*** 0.0040*** 0.0046*** 0.0051*** 0.0057*** 0.0046*** 0.0052*** 0.0152*** (0.0008) (0.0011) (0.0009) (0.0012) (0.0009) (0.0009) (0.0008) (0.0007) (0.0027) Observations 12,984,300 12,425,700 11,971,100 11,602,500 11,334,900 11,127,400 10,861,700 10,661,700 6,492,100 R-squared 0.2108 0.1741 0.1731 0.1768 0.1817 0.1836 0.1831 0.1885 0.2113 Fixed effects State + [Industry – Starting Year – Firm Size – Starting Wage – Starting Age – Sex] Sample All jobs that are not right-censored by the quarter Spell started 2000 or earlier Notes: This table reports the differential effect of CNC enforceability on job duration by industry (high-tech jobs vs. nontech jobs). The dependent variables are dummy variables for the job spell surviving at 4th, …, 32nd quarter of the job spell for Columns 1–8, and the log of length of job spells in number of quarters for Column 9. CNC score is measured as the 2009 CNC enforceability index scores. Estimation samples are all jobs that are not right-censored by the quarter for Columns 1–8 and all jobs that started its spell in year 2000 or earlier for Column 9. Data are from the LEHD (1991–2008). All standard errors are clustered by state.
* p < 0.10,
** p < 0.05,
↵*** p < 0.01.
Dependent Variable: Log of Cumulative Earnings at 4th Qtr.
(1)8th Qtr.
(2)12th Qtr.
(3)16 th Qtr.
(4)20th Qtr.
(5)24th Qtr.
(6)28th Qtr.
(7)32nd Qtr.
(8)Panel A: Cumulative Earnings across Employment History Tech × CNC score -0.0112*** -0.0118*** -0.0123*** -0.0128*** -0.0126*** -0.0125*** -0.0121*** -0.0115*** (0.0028) (0.0025) (0.0022) (0.0020) (0.0017) (0.0015) (0.0012) (0.0012) Observations 7,517,000 6,389,000 5,594,000 4,973,000 4,485,000 4,057,000 3,671,000 3,229,000 R-squared 0.6245 0.6121 0.5951 0.5778 0.5603 0.5448 0.5291 0.5143 Dependent Variable: Log of Cumulative Number of Jobs at 4th Qtr.
(1)8th Qtr.
(2)12th Qtr.
(3)16 th Qtr.
(4)20th Qtr.
(5)24th Qtr.
(6)28th Qtr.
(7)32nd Qtr.
(8)Panel B: Number of Jobs across Employment History Tech × CNC score -0.0085 -0.0121* -0.0142** -0.0136* -0.0156** -0.0185** -0.0197** -0.0215** (0.0057) (0.0062) (0.0065) (0.0073) (0.0076) (0.0074) (0.0080) (0.0079) Observations 7,517,000 6,389,000 5,594,000 4,973,000 4,485,000 4,057,000 3,671,000 3,229,000 R-squared 0.3325 0.2892 0.2626 0.2477 0.2368 0.2330 0.2332 0.2352 Fixed effects State + [Industry – Starting Year – Firm Size – Starting Wage – Starting Age – Sex] Sample All employed workers in the quarter Notes: This table reports the differential effect of CNC enforceability on cumulative number of jobs taken across workers’ employment history in Panel B and on cumulative earnings across workers’ employment history in Panel A by industry (high-tech jobs vs. nontech jobs) of the worker’s first job. The dependent variables are the log of cumulative number of jobs taken at 4th, …, 32nd quarter of the workers’ employment history in Panel B and the log of cumulative earnings at 4th, …, 32nd quarter of the workers’ employment history in Panel A. The high-tech job dummy variable is that of the first job of the worker. CNC score is measured as the 2009 CNC enforceability index scores of the state where the first job of the worker is geographically located. The job-level fixed effects controls for the job characteristics of the first job of the worker. Data are from the LEHD (1991–2008). All standard errors are clustered by state.
↵* p < 0.10,
↵** p < 0.05,
↵*** p < 0.01.
Dependent Variable: Ln(l + Cumulative # of State Switch) at 4th Qtr.
(1)8th Qtr.
(2)12th Qtr.
(3)16th Qtr.
(4)20th Qtr.
(5)24th Qtr.
(6)28th Qtr.
(7)32nd Qtr.
(8)Panel A: Switch States Tech × CNC score 0.0003* 0.0008*** 0.0012*** 0.0014*** 0.0012*** 0.0013*** 0.0013*** 0.0013** (0.0001) (0.0003) (0.0003) (0.0004) (0.0003) (0.0004) (0.0005) (0.0006) R-]squared 0.0746 0.0774 0.0855 0.0926 0.0987 0.104 0.1085 0.1138 Dependent Variable: Ln(l + Cumulative # of Industry Switch) at 4th Qtr.
(1)8th Qtr.
(2)12th Qtr.
(3)16th Qtr.
(4)20th Qtr.
(5)24th Qtr.
(6)28th Qtr.
(7)32nd Qtr.
(8)Panel B: Switch Industry Tech × CNC score -0.0018*** -0.0044*** -0.0067*** -0.0094*** -0.0119*** -0.0135*** -0.0162*** -0.0186*** (0.0006) (0.0012) (0.0021) (0.0027) (0.0033) (0.0038) (0.0038) (0.0037) R-squared 0.1305 0.1394 0.1502 0.158 0.1633 0.1674 0.1722 0.1749 Observations 7,517,000 6,389,000 5,594,000 4,973,000 4,485,000 4,057,000 3,671,000 3,229,000 Fixed effects State + [Industry – Starting Year – Firm Size – Starting Wage – Starting Age – Sex] Sample All employed workers in the quarter Dependent Variable: Ln(1 + Cumulative # of State Switch without Industry Switch) at 4th Qtr.
(1)8th Qtr.
(2)12th Qtr.
(3)16th Qtr.
(4)20th Qtr.
(5)24th Qtr.
(6)28th Qtr.
(7)32nd Qtr.
(8)Panel C: Switch State but not Industry Tech × CNC score 0.0001*** 0.0003*** 0.0006*** 0.0007*** 0.0007*** 0.0008*** 0.0008*** 0.0009*** (0) (0.0001) (0.0001) (0.0001) (0.0002) (0.0002) (0.0002) (0.0003) R-squared 0.043 0.0525 0.0611 0.0685 0.074 0.0779 0.0814 0.0858 Dependent Variable: Ln(1 + Cumulative # of Industry Switch without State Switch) at 4th Qtr.
(1)8th Qtr.
(2)12th Qtr.
(3)16th Qtr.
(4)20th Qtr.
(5)24th Qtr.
(6)28th Qtr.
(7)32nd Qtr.
(8)Panel D: Switch Industry but not State Tech × CNC score -0.0020** -0.0050*** -0.0074*** -0.0101*** -0.0125*** -0.0141*** -0.0168*** -0.0193*** (0.0007) (0.0014) (0.0022) (0.0028) (0.0033) (0.0037) (0.0038) (0.0038) R-squared 0.143 0.148 0.156 0.162 0.166 0.170 0.174 0.177 Observations 7,517,000 6,389,000 5,594,000 4,973,000 4,485,000 4,057,000 3,671,000 3,229,000 Fixed effects State + [Industry – Starting Year – Firm Size – Starting Wage – Starting Age – Sex] Sample All employed workers in the quarter Notes: This table reports the differential effect of CNC enforceability on cumulative number of state switches in Panel A, on cumulative number of industry switches in Panel B, on cumulative number of state-but-not-industry switches in Panel C, and on cumulative number of industry-but-not-state switches in Panel D, across workers’ employment history, by industry (high-tech jobs vs. nontech jobs) of the first job. The dependent variables are log (1 + cumulative number of state switches) in Panel A, log (1 + cumulative number of three-digit NAICS code switches) in Panel B, log (1 + cumulative number of state-but-not-industry-switches) in Panel C, and log (1 + cumulative number of industry- but-not-state-switches) in Panel D, at 4th,…, 32nd quarter of the workers’ employment history. CNC score is measured as the 2009 CNC enforceability index scores of the state in which the first job of the worker is geographically located in. The job-level fixed effects controls for the job characteristics of the first job of the worker. Data are from the LEHD (1991–2008). All standard errors are clustered by state.
↵* p < 0.10,
↵** p < 0.05,
↵*** p < 0.01.
- Appendix Table 1
QWI Data—Average Total Number of Employees, Hires, and Separations per Quarter
Cross-Industry, Within-Hawaii Cross-State, Within-Tech Nontech
(1)Tech
(2)Total
(3)Non-HI
(4)HI
(5)Total
(6)Beginning of quarter employment Pre-July 2015 ban 494,614 5,470 500,084 2,985,438 5,470 2,990,909 Post-July 2015 ban 514,361 5,574 519,935 3,194,901 5,574 3,200,475 New hires Pre-July 2015 ban 67,351 508 67,859 293,362 508 293,869 Post-July 2015 ban 75,997 550 76,547 313,636 550 314,187 Separations (all) Pre-July 2015 ban 74,218 553 74,770 288,647 553 289,200 Post-July 2015 ban 74,073 569 74,642 281,760 569 282,329 Notes: This table presents the average of the total number of beginning of quarter employment, new hires, and separations in the pre- and post-ban period in the QWI data (2013Q2-2017Q1). For example, Column 2, Row 1 indicates that there were on average 5,470 employees in “tech” industries in Hawaii in the beginning of the quarter, in the period 2013Q2-2015Q2, where “Tech” is defined as QWI four-digit industry classifications that cover software design, development, and services, to concord with the definition of “technology business” in the Hawaii statute.
Question Scoring Bishara Weight Starr Weight Statute of enforceability: “Question 1: Is there a state statute of general application that governs the enforceability of covenants not to compete?” Score of 10 to a state that has a statute that favors strong enforcement, 5 to a state that either did not have a statute or had a statute that was neutral in its approach to enforcement and 0 was given to a state that has a statute that disfavors enforcement. 0.10 0.09 Protectable interest: “Question 2: What is an employer’s protectable interest and how is that defined?” Score of 10 to a state that has a broadly defined protectable interest, 5 to a state that has a balanced approach to defining a protectable interest, and 0 to a state that has a strictly defined limited protectable interest for the employer. 0.10 0.12 Plaintiff’s burden of proof: “Question 3: What must plaintiff be able to show to prove the existence of an enforceable covenant not to compete?” Score of 10 to a state that places a weak burden of proof on the plaintiff employer, 5 to a state that has a balanced approach to the burden placed on the employer, and 0 to a state that places a strong burden of proof on the employer. 0.10 0.10 Consideration at inception: “Question 3a: Does the signing of a covenant not to compete at the inception of the employment relationship provide sufficient consideration to support the covenant?” Score of 10 to a state where the start of employment is always sufficient to support a covenant not to compete, around 5 to a state where the start of employment is sometimes sufficient, and 0 to a state where the start of employment is never sufficient. 0.05 0.13 Consideration post-inception: “Question 3b & 3c: Will a change in the terms and conditions of employment provide sufficient consideration to support a covenant not to compete entered into after the employment relationship has begun? Will continued employment provide sufficient consideration to support a covenant not to compete entered into after the employment relationship has begun?” Score of 10 or near 10 to a state where continued employment is always sufficient to support a covenant not to compete, around 5 to a state where only a beneficial change in terms was sufficient, and 0 to a state where neither continued employment nor a beneficial change in terms would be sufficient consideration. 0.05 0.08 Overbroad contracts: “Question 4: If the restrictions in the covenant not to compete are unenforceable because they are overbroad, are the courts permitted to modify the covenant to make the restrictions more narrow and to make the covenant enforceable? If so, under what circumstances will the courts allow reduction and what form of reduction will the courts permit?” Score of 10 to a state where judicial modification is allowed and there are broad circumstances where revision can be made and limited restrictions on maximum enforcement, score of 5 to a state where so-called “blue pencil” modifications were allowed as a way to reform the contract instead of disallowing it outright, showing a balanced approach to the allowable scope of restrictions and to accommodating the plaintiff’s enforcement request, and low score, possibly 0, awarded to a state where neither “blue pencil” nor judicial modification was allowed. 0.05 0.04 Quit vs. Fire: “Question 8: If the employer terminates the employment relationship, is the covenant enforceable?” Score of 10 to a state where a covenant is always enforceable if the employer terminates, 5 to a state where a covenant is enforceable only in some circumstances, and 0 where a covenant is not enforceable if the employer terminates. 0.10 0.09 Notes: The Starr (2019) index used in this paper is constructed using factor analysis to reweight the seven dimensions of CNC enforceability initially scored in Bishara (2011). Bishara selected seven questions from periodic, comprehensive, state-by-state surveys of noncompete enforcement policies undertaken by Brian Malsberger.40 Bishara notes that these questions “were chosen because they directly address the legal issues relevant to measuring a given jurisdiction’s intensity of noncompete enforcement” and adds that “these questions, in the aggregate, can flesh out a full picture of a state’s policy on noncompetes, including if the state has contemplated its policy to the extent that it has enacted legislation on the topic.” The seven questions and how they were treated for the construction of the CNC index by Bishara (2011) are described in the table below; raw scores on each of the seven dimensions for each of states are provided in Online Appendix Table OA12. See Bishara (2011) for a detailed discussion of the scoring.
Percentile of CNC Enforceability Index (2009) Full Sample of 50 States (Ordered by Rank of Weakest to Strongest Enforceability within and across Categories) LEHD Sample of 30 States (Ordered by Rank of Weakest to Strongest Enforceability within and across Categories) Illustrative Samples of Enforceability Policies (from Bishara 2011 and Other Sources Indicated) Mean of Stuart and Sorenson (2003) Dummy for Low Enforceability (LEHD Sample) Bottom quintile (low enforceability) North Dakota, California, New York, Alaska, Oklahoma, West Virginia, Montana, Arkansas, Rhode Island, Virginia California, Oklahoma, West Virginia, Montana, Arkansas, Rhode Island, Virginia California (and North Dakota) have anti-noncompete enforcement statutes without exceptions for any postemployment restrictions. 0.57 Middle 60% (moderate enforceability) Texas, South Carolina, Hawaii, Nebraska, Wisconsin, Minnesota, Georgia, Nevada, Mississippi, Ohio, Oregon, Pennsylvania, Arizona, North Carolina, Wyoming, New Hampshire, Washington, Alabama, Colorado, Maine, Tennessee, Michigan, Massachusetts, Louisiana, Delaware, Maryland, Vermont, Indiana, New Mexico, Idaho Texas, South Carolina, Hawaii, Wisconsin, Georgia, Nevada, Oregon, North Carolina, Washington, Colorado, Maine, Tennessee, Louisiana, Maryland, Vermont, Indiana, New Mexico, Idaho Most have some legislation discussing noncompetes. For example, Colorado allows labor contracts that require an employee to repay training costs for employment that lasts less than two years, and recognizes noncompetes specifically for “e]xecutive and management personnel and officers and employees who constitute professional staff to executive and management personnel.” 0.18 Top quintile (high enforceability) Kentucky, New Jersey, Illinois, Utah, Iowa, South Dakota, Missouri, Kansas, Connecticut, Florida New Jersey, Illinois, Utah, Iowa, Florida Florida’s noncompete law is viewed as strongly proemployer; key provisions include one that prevents consideration of harm to the employee and another that encourages courts not to construe a restrictive covenant narrowly. 0 Notes: Stuart and Sorenson (2003) use a dummy indicator for whether the state has a statute that “precludes or severely limits” an employer’s ability to enforce CNCs (drawing on the Malsberger treatise edition of 1996).
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