Labor Laws and Innovation: Online Appendix Viral V. Acharya NYU-Stern, CEPR, ECGI and NBER [email protected]
Ramin P. Baghai Stockholm School of Economics [email protected]
Krishnamurthy V. Subramanian Indian School of Business krishnamurthy [email protected]
In the main paper, we provided evidence of the impact of labor laws on innovation in a crosscountry setting. In this Online Appendix, we present tests of our main hypothesis based on U.S. data alone. These tests exploit a discontinuity introduced by the passage of the federal U.S. Worker Adjustment and Retraining Notification (WARN) Act and do not rely on labor law index data. Our tests in this section are aimed at mitigating concerns about (i) potential measurement error arising from the use of U.S. patents to proxy innovation by international firms; and (ii) measurement error stemming from the use of law indices.
An overview of the WARN Act
The WARN Act is a federal law that was enacted by the U.S. Congress on August 4, 1988, and became effective on February 4, 1989.1 The WARN Act requires employers to give written notice 60 days before the date of a mass layoff or plant closing to: (i) affected workers; (ii) chief elected official of the local government where the employment site is located; and (iii) the State Rapid Response Dislocated Worker Unit. Subject to the law are private employers with 100 or more fulltime employees, or with 100 or more employees who work at least a combined 4,000 hours a week.2 In the case of non-compliance, employees, their representatives, and units of local government can bring lawsuits against employers. Employers who violate the WARN Act are liable for damages in the form of back pay and benefits to affected employees. To investigate whether innovative companies were affected by the WARN Act, we obtained WARN Act notices received by the Employment Development Department in California in 2009. These included several innovative companies, which illustrates that dismissal presents a distinct threat to employees in innovative firms as well.3 After discussing our data and test methodology, we will provide additional evidence of the importance of the WARN Act by showing the impact of its passage on employment fluctuations. The requirement of prior notification to local government together with penalties for noncompliance imply that the WARN passage increases the hurdles faced by employers when dismissing employees. This effect is in line with the effect of dismissal laws as discussed in our theoretical motivation. Therefore, we expect WARN to have the predicted positive effect on innovation. 1 The details on the WARN Act reported in this section are drawn from the following two sources, unless otherwise noted: United States Department of Labor – Employment & Training Administration (http : //www.doleta.gov/layof f /warn.cf m); and Levine (2007). 2 Only layoffs classified as “mass layoffs” or “plant closings,” or layoffs of 500 or more full-time workers at a single site of employment, are covered. A “plant closing” is defined as a closure of a facility within a single site of employment involving layoffs of at least 50 full-time workers. In the case of a “mass layoff,” an employer lays off either between 50 and 499 full-time workers at a single site of employment, or 33% of the number of full-time workers at a single site of employment. For further details, see Levine (2007). 3 This list includes Adobe Systems Incorporated, AT&T company, Comcast Cable, FOX Interactive Media, Inc., Genentech, Inc., Henkel Corporation, National Semiconductor Corporation, NEC Electronics America, Inc., Palm, Inc.; SAP America, Inc., Seagate Technology LLC, Siemens, Stanford University, Sun Microsystems, Inc., Symantec, The Boeing Company, Valeant Pharmaceuticals International, Virgin Mobile USA, Yahoo! Inc., and many others. Source: http : //www.edd.ca.gov/Jobs and T raining/warn/eddwarnlwia09.pdf
Data and sample
In order to examine the effect of the passage of the WARN Act on innovation, we match the NBER patents file to Compustat data.4 As before, to construct proxies for innovation, we employ patents filed with the USPTO and citations to these patents, compiled in the NBER Patents File (Hall et al., 2001). Since it is not possible to construct time varying measures of the standard deviation of citations at the firm level, we exclude this dependent variable from the WARN analysis. The summary statistics for the main variables used in these firm-level tests are displayed in Panel B of Table A-1.
In these U.S. based tests, we exploit the discontinuity introduced by the WARN Act, which was applicable only to firms with 100 or more employees. Our identification strategy is based on comparing U.S. firms that were affected by the law change (firms with 100 or more employees) to U.S. firms that were not (firms with less than 100 employees). Figure A-1 illustrates this discontinuity in innovation caused by the passage of the WARN Act. Here, we plot the before-after difference in the number of patents filed by each firm against the number of employees in the firm in 1987, i.e. one year before the passage of the WARN Act. The figure also shows linear fits over the [50,99] and [100,150] employee ranges as well as a local polynomial smoothing fit over the same range. The figure illustrates clearly that the break in innovation occurs at the threshold of 100 employees. We first investigate the effect for the entire sample of firms using the following regression: yit = βi + βt + β1 ∗ (Over100)i,1987 ∗ (Af ter1988)t + β · Xit + it
where yit is a proxy for innovation by firm i in year t. (Over100)i,1987 is a dummy taking the value of one if a firm has ≥ 100 employees in the year 1987, i.e. two years before the passage of the WARN Act, and 0 otherwise. By using employment information from the year 1987 only, we avoid the endogeneity stemming from group classification due to the layoffs. This is a useful instrument for two reasons. First, it is unlikely that the WARN Act had an impact on employment two years prior to its passage. Second, whether a firm had more than 100 employees in 1987 is a good predictor for the other years. (Af ter1988)t is a dummy taking the value of one after the passage of the WARN Act (i.e. for the years 1989-1994). The firm dummies βi control for any residual time-invariant heterogeneity among firms. The year dummies βt account for general macro-economic factors. Xit represents the set of control variables which include Size and Market-to-Book ratio.5 The sample 4
Each assignee in the NBER dataset is given a unique and time-invariant identifier. We match the U.S. assignee names in the NBER patent dataset to the names of divisions and subsidiaries belonging to a corporate family from the Directory of Corporate Affiliations. We then match the name of the corporate parent to Compustat. Additionally, we augment our match of the U.S. assignee names to the Compustat parent with the recent gvkey-assignee match developed by NBER. See https://sites.google.com/site/patentdataproject/Home/downloads for the details about this new match. 5 Market-to-Book is the market value of assets to total book assets. Market value of assets is total assets (Compustat
covers twelve years around the passage of the WARN Act (from 1983-1994). In all the regressions, we cluster standard errors at the firm level. Table A-2 shows the results. In Columns 1–4, we employ logs of the number of patents and citations respectively as the dependent variables. While Columns 1 & 2 do not include additional variables, we control for firm size and the market-to-book ratio in Columns 3 & 4. In line with Hypothesis 1, we find that the WARN Act had a positive and significant impact on U.S. firm-level innovation. Based on estimates from Column 1, compared to the control group, annual patents increased by 18.4% for the treatment group of firms, with an even larger effect for citations. According to our theoretical motivation, the positive effect of dismissal laws on innovation results from the positive effect that these laws have on employee effort. Unlike our cross-country set-up, our sample here is constructed at the firm level. Therefore, we can investigate whether the passage of WARN had an effect on employee effort in innovative projects. For this purpose, we normalize our proxies for innovation using the number of employees in a firm. In Columns 5-8 of Table A-2, we report the results using ln(patents/employees) and ln(citations/employees) as the dependent variables. Here, we find that both patents and citations per employee increase after the passage of WARN for the “treatment” group of firms. This finding shows that the theoretical backdrop finds support not only in the result linking dismissal laws to innovation, but also in the specific mechanism we conjecture to be at play, i.e. that the positive effect of dismissal laws on innovation results from the positive effect that these laws have on employee effort.
To fully exploit the discontinuity due to the WARN Act and thereby provide the cleanest evidence of our hypotheses, we focus on firms in the range [90,110].6 To ascertain that results are not spurious, as placebo tests, we also test for any effects on innovation by using cutoffs of 50 and 150 employees and a sample of firms with employees in the range [40, 60] and [140, 160] respectively. We proceed by first showing that WARN indeed had a dampening effect on employee dismissals in affected firms. We then estimate the effect of WARN on innovation.
Test design: WARN Act and employee layoffs
When examining the effect of the WARN Act on innovation, a key question that arises is whether the WARN Act indeed imposed a binding constraint for innovative firms. The anecdotal evidence on employee layoffs discussed above provided preliminary evidence of the same. To test this formally, we define employee layoffs to have occurred in firm i in year t if the number of employees in that year is lower than that in the previous year. We then estimate the following linear probability data item at) plus market value of equity minus book value of equity. The market value of equity is calculated as common shares outstanding (csho) times fiscal-year closing price (prccf). Book value of equity is defined as common equity (ceq) plus balance sheet deferred taxes (txdb). Size is the natural logarithm of sales (sale). 6 Since the number of firms in the [99,101] range is quite limited, we employ the expanded window [90,110].
model for the twelve years surrounding the passage of the WARN Act (1983-1994): Ind(Empi,t − Empi,t−1 < 0) = βt + β1 · (Over100)i,1987 ∗ (Af ter1988)t + β2 · (Over100)i,1987 + it (A-2) where Ind(Empi,t − Empi,t−1 < 0) is a binary variable taking on a value of one in case of a net employment reduction in firm i from year t − 1 to year t. The other variables are as defined in equation (A-1). Since employee layoffs due to the WARN Act do not exhibit much withinfirm variation, we do not include firm-fixed effects. However, to control for average differences in employee layoffs across years, we include the year fixed effects βt . Column 1 in Panel A of Table A-3 reports the results of the tests of equation (A − 2) for firms having employees in the range [90,110] in 1987. We find that the passage of WARN decreased the likelihood of layoffs in the affected firms. Compared to the control set of firms in the range [90,99], the before-after difference in the likelihood of employee layoffs decreased by 25% for the treated firms in the range [100,110]. Columns 2-5 in Panel A of Table A-3 show the results for the effect of WARN on innovation. First, in Columns 2-3 of Panel A, we report the results of tests for our proxies of aggregate innovation using the log of the number of patents and citations respectively as the dependent variables. In line with Hypothesis 1, we find that the strengthening of dismissal laws via the WARN Act had a positive and significant impact on U.S. firm-level innovation. To assess the economic magnitude of the effect of the WARN Act, note that the median firm with employees in the range [90, 110] files one patent per year in our sample; the median firm in this range also receives 17 cumulative citations. After passage of the WARN Act, affected firms file about one additional patent every two years after the passage of WARN. Furthermore, these firms receive 14 additional citations in all. Since citations are a stock measure, the 14 additional citations post WARN translate into two additional citations per year on average after the passage of the WARN Act. We test Hypothesis 2 in Columns 4-5 of Panel A by using ln(patents/ employees) and ln(citations/ employees) as the dependent variables. Here as well, we find that both patents and citations per employee increase after the passage of WARN for the “treatment” group of firms; however, the increase is only statistically significant for the citations-based measure. Panels B and C of Table A-3 show the results for the placebo tests using only firms with employment in the range [40, 60] and [140, 160] respectively in 1987. In each of these panels, Column 1 shows the effect on employee layoffs, while Columns 2-3 show the results for the log of the number of patents and citations, respectively. Finally, Columns 4-5 report the results using log of the number of patents and citations per employee respectively. In both these panels, we can infer that there was no differential effect at the corresponding placebo cutoffs. This provides reassurance that the positive effect of WARN on innovation documented in Panel A is not spurious.
Robustness Tests for the Effect of the WARN Act on Innovation
We present additional robustness tests for the effect of the WARN Act on innovation in Table A-4. In these tests, we examine the piecewise linear effect of the passage of the WARN Act on 4
innovation across firms with different numbers of employees. We employ the whole sample of firms in these tests. In Columns 1 and 2, we confirm what we observed in Figure A-1: consistent with the 100 employee threshold imposed by the WARN Act, we find that the positive break in innovation occurs for firms with more than 100 employees when compared to firms with fewer than 100 employees. These tests also further underscore that the effect occurs at the level of hundred employees and not below. Inference from a regression-discontinuity design can be invalid if the assignment variable – in our setting the number of employees per firm – can be precisely manipulated. One could argue that to avail themselves of the positive innovation incentive effects that the credible commitment to a more stable employment policy in the form of stronger dismissal laws brings, firms may choose their employment figures so as to fall within the scope of application of WARN. However, inference is valid as long as there is not exact manipulation. As Lee and Lemieux (2010, p.283) point out: “If individuals – even while having some influence – are unable to precisely manipulate the assignment variable, a consequence of this is that the variation in treatment near the threshold is randomized as though from a randomized experiment.” We argue that this is the case in our tests for three reasons. First, employers cannot unilaterally decide on the number of employees - employees are free to quit anytime, and there are well-documented search frictions in the labor market which can prevent employers from going on a hiring spree. Second, we use the employee count in 1987, one year before the passage of WARN, to classify firms into treatment and control group, which reduces the possibility of such manipulation driving our results. To the extent that some of the firms that we classify as control firms increase employment so as to fall under WARN and then innovate more, this actually biases against finding the hypothesized result. Finally, it is likely that firms below 100 employees would switch to the treatment group after WARN only if it helps them to create greater incentives for innovation. Thus, if it were the case that such endogenous switching from control to treatment group is accounting for our results, in Columns 3 & 4 of Table A-4, we would observe the greatest and lowest magnitudes respectively in the (100 ≤ Emp < 105)i,1987 and (125 ≤ Emp)i,1987 ranges with the coefficient for the (105 ≤ Emp < 125)i,1987 range between these two extremes. This is because the effect of endogenous switching is likely to be largest in the [100, 105) group and be lowest in the ≥ 125 group. However, in Columns 3-4 we do not find such differences in the coefficients. In fact, we find that the F-test for the equality of the coefficients corresponding to each range cannot be rejected at the 95% level. In sum, we are able to alleviate concerns that our results are driven by the endogenous switching of firms from control to treatment groups or vice-versa.
Apart from not suffering from concerns relating to unobserved factors at the country level, the above tests based on WARN offer other attractive advantages. First, since our sample for the WARN tests ended in 1994, they enable us to conclude that our results on the positive effect of dismissal laws on innovation are not driven by any spurious effects that patent reforms motivated
by the General Agreement on Tariffs and Trade (GATT) may have had.7 Second, the tests based on the WARN Act mitigate effects of any other contemporaneous factors that may confound our results. This strength of the WARN based tests stems from a combination of three factors. First and foremost, since the firms are separated into treatment and control groups based on the number of employees, any unobserved factor that affects all firms uniformly (i.e. irrespective of employment figures) cannot be driving our results. Nevertheless, as a second line of defense, we have used firm-fixed effects to account for time-invariant effects of unobserved factors, in general, and firm size, in particular. Second, we have performed regression-discontinuity tests to focus on firms just above and below the employment cut-off relevant for WARN. Third, in the difference-in-difference tests, we have included firm size to account for any time-varying correlation of any unobserved factors with firm size. Therefore, laws or policy changes or any other unobserved factors that may influence innovation cannot affect the results unless they resemble WARN in discriminating based on the size of the workforce. Related to the above, the WARN tests also alleviate concerns that our results may be affected by the coinciding of the post WARN period with the recession in the early 1990s. To the extent that this recession slowed down the average pace of innovation, the application year fixed effects should capture this effect. Finally, since firms of similar sizes should have felt the effect of the recession similarly, the regression-discontinuity specification provides confirmation that our results are not affected by the recession in the 1990s. Finally, the WARN Act was not intended to specifically encourage innovation or economic growth.8 Therefore, our tests above can reasonably be interpreted as a truly causal effect of the WARN Act passage on innovation.
Under the GATT changes, an unexpired issued patent or a patent application pending on June 8, 1995, has a term of protection that is the longer of 17 years from the date of issuance of the patent or 20 years from the filing date of the patent application. For applications filed on or after June 8, 1995, the patent life is now twenty years, measured from the earliest patent application. However, since our sample for the WARN tests ended in 1994, our results are not driven by GATT related changes. 8 Br¨ ugemann (2007) examines various articles in the business press that document the events preceding and following the WARN Act. He does not find any evidence arguing that the Act was aimed at improving a specific aspect of the U.S. economy.
References  Br¨ ugemann, B., 2007, “Employment Protection: Tough to Scrap or Tough to Get?” Economic Journal, 117, 386–415.  Hall, B., A. Jaffe, and M. Trajtenberg, 2001, “The NBER Patent Citations Data File: Lessons, Insights and Methodological Tools,” Working Paper, NBER.  Lee, D., and T. Lemieux, 2010, “Regression Discontinuity Designs in Economics,” Journal of Economic Literature, 48, 281–355.  Levine, L., 2007, “The Worker Adjustment and Retraining Notification Act (WARN),” Working Paper, Congressional Research Service.
Figure A-1: WARN Act and Innovation by U.S. Firms. This figure shows the effect of the passage of the WARN Act on innovation by treatment group (firms with ≥ 100 employees) and control group (firms with < 100 employees). Specifically, we plot the before-after difference (with respect to the WARN passage in 1989; the sample spans the years 1983–1994) in patents for each firm against its number of employees in 1987. The solid curves indicate a polynomial smoothing fit of this before-after difference in patenting for treated and control firms, respectively; the dashed lines show a linear fit for the same data.
Table A-1: Summary Statistics. This table presents summary statistics for the main variables used in the U.S. WARN tests. Market-to-Book ratio is the market value of assets to total book assets. Market value of assets is total assets plus market value of equity minus book value of equity. The market value of equity is calculated as common shares outstanding times fiscal-year closing price. Book value of equity is defined as common equity plus balance sheet deferred taxes. Size is the natural logarithm of sales. The sample spans 1983–1994. Patent data is from the NBER Patents File (Hall, Jaffe and Trajtenberg, 2001). Firm-level data is from Compustat.
Number of patents Number of citations Number of employees Market-to-Book Size
13099 13099 11704 10389 11655
17.117 173.697 16147 2.097 5.565
2 24 2552 1.337 5.763
61.041 706.825 45635 4.270 2.583
1 0 0 0.304 -6.215
1622 21164 876800 260.180 11.933
Firm and Application Year FE Observations Adjusted R-squared
(Over100)i,1987 ∗ (Af ter1988)t
Dependent variable is ln of
X 13,067 0.851
X 13,067 0.736
(2) Citations 0.216*** (0.052) 0.179*** (0.025) -0.002 (0.004) X 10,297 0.868
(3) Patents 0.477*** (0.103) 0.141*** (0.038) -0.001 (0.007) X 10,297 0.754
X 11,693 0.904
(5) Patents / Employees
X 11,693 0.824
(6) Citations / Employees
0.323*** (0.092) -0.394*** (0.027) -0.005 (0.006) X 10,229 0.924
(7) Patents / Employees
0.580*** (0.114) -0.437*** (0.038) -0.003 (0.008) X 10,229 0.829
(8) Citations / Employees
The regressions below implement the following model: yit = βi + βt + β1 ∗ (Over100)i,1987 ∗ (Af ter1988)t + βXit + it βi and βt are firm and year fixed effects, respectively. (Over100)i,1987 is a dummy variable taking the value of one in each year if a firm has ≥ 100 employees in 1987, and zero otherwise; as, for a given firm, this variable does not vary over time, its effect is subsumed in the firm dummies. (Af ter1988)t is a dummy taking the value of one after the passage of the WARN Act (i.e. the years 1989-1994); this coefficient is subsumed by the year dummies. In Columns 1–4, the dependent variables are the natural logarithm of patents, as well as the natural logarithm of citations. In Columns 5–8, the patents and citations are scaled by the number of employees before taking the log. Patent data is from the NBER Patents File (Hall, Jaffe and Trajtenberg, 2001). Firm-level data is from Compustat. The sample period is 1983–1994. Robust standard errors (clustered at the firm level) are given in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
Table A-2: Within-country evidence: Difference-in-difference tests.
Table A-3: Within-country evidence: Regression discontinuity tests. The regressions below implement the following model: yit = βi + βt + β1 ∗ (Over100)i,1987 ∗ (Af ter1988)t + βXit + it βi and βt are firm and year fixed effects, respectively. Across all three panels, the dependent variables are (the log of) patents and citations, as well as (the log of) patents and citations scaled by the number of employees. In addition, we also employ as dependent variable Ind(Empi,t − Empi,t−1 < 0), a binary variable taking on a value of one in case of a net employment reduction in firm i from year t − 1 to year t. (Over100)i,1987 is a dummy variable taking the value of one in each year if a given firm has ≥ 100 employees in 1987, and zero otherwise; as, for a given firm, this variable does not vary over time, its effect is subsumed in the firm dummies. (Af ter1988)t is a dummy taking the value of one after the passage of the WARN Act (i.e. the years 1989-1994); this coefficient is subsumed by the year dummies. In Panel A, the sample is restricted to firms whose 1987 employment is just below or just above the relevant WARN cutoff, i.e. firms with employment between 90 and 110 employees. In Panel B, the sample is restricted to firms whose 1987 employment is between 40 and 60 employees. Finally, in Panel C, only firms whose employment in the year 1987 is between 140 and 160 are included in the sample. The sample period is 1983–1994. Robust standard errors (clustered at the firm level) are given in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. Panel A: 90 ≤ Employmenti,1987 < 110 Dependent Variable is (Over100)i,1987 ∗ (Af ter1988)t (Over100)i,1987 Firm FE Application Year FE Number of Firms Observations Adjusted R-squared
(1) Ind(Empi,t − Empi,t−1 < 0)
(4) Ln(Patents / Employees)
(5) Ln(Citations / Employees)
-0.293** (0.114) 0.283*** (0.083)
X X 239 916 0.828
X X 239 916 0.693
X X 219 765 0.649
X X 219 765 0.681
X 117 435 0.027
Panel B: 40 ≤ Employmenti,1987 < 60 Dependent Variable is (Over50)i,1987 ∗ (Af ter1988)t (Over50)i,1987 Firm FE Application Year FE Number of Firms Observations Adjusted R-squared
(1) Ind(Empi,t − Empi,t−1 < 0)
(4) Ln(Patents / Employees)
(5) Ln(Citations / Employees)
-0.076 (0.209) -0.030 (0.147)
X X 77 259 0.188
X X 77 259 0.348
X X 70 230 0.633
X X 70 230 0.492
X 39 115 0.039
Panel C: 140 ≤ Employmenti,1987 < 160 Dependent Variable is (Over150)i,1987 ∗ (Af ter1988)t (Over150)i,1987 Firm FE Application Year FE Number of Firms Observations Adjusted R-squared
(1) Ind(Empi,t − Empi,t−1 < 0)
(4) Ln(Patents / Employees)
(5) Ln(Citations / Employees)
0.104 (0.159) -0.124 (0.181)
X X 40 170 0.490
X X 40 170 0.633
X X 37 155 0.447
X X 37 155 0.516
X 21 88 0.073
Table A-4: Within-country evidence: Robustness of regression discontinuity results. The regressions below implement the following model: yit = βi + βt + β1 ∗ (X ≤ Emp < Y )i,1987 ∗ (Af ter1988)t + βXit + it βi and βt are firm and year fixed effects, respectively. The dependent variables are (the log of) patents and citations. (X ≤ Emp < Y )i,1987 is a dummy variable taking the value of one in each year if a given firm has between X and Y employees in 1987, and zero otherwise; as, for a given firm, this variable does not vary over time, its effect is subsumed in the firm dummies. (Af ter1988)t is a dummy taking the value of one after the passage of the WARN Act (i.e. the years 1989-1994); this coefficient is subsumed by the year dummies. Patent data is from the NBER Patents File (Hall, Jaffe and Trajtenberg, 2001). Firm-level data is from Compustat. The sample period is 1983–1994. Robust standard errors (clustered at the firm level) are given in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
Dependent Variable is LN of (60 ≤ Emp < 80)i,1987 ∗ (Af ter1988)t (80 ≤ Emp < 100)i,1987 ∗ (Af ter1988)t (100 ≤ Emp < 120)i,1987 ∗ (Af ter1988)t (120 ≤ Emp)i,1987 ∗ (Af ter1988)t
-0.212* (0.118) -0.154* (0.090) 0.179** (0.086) 0.076 (0.048)
-0.208 (0.230) -0.137 (0.161) 0.402** (0.184) 0.364*** (0.121)
(100 ≤ Emp < 105)i,1987 ∗ (Af ter1988)t (105 ≤ Emp < 125)i,1987 ∗ (Af ter1988)t (125 ≤ Emp)i,1987 ∗ (Af ter1988)t Firm and Application Year FE Observations Adjusted R-squared
X 13,067 0.851
X 13,067 0.736
0.230 (0.150) 0.255*** (0.092) 0.166*** (0.047) X 13,067 0.851
0.441** (0.193) 0.437** (0.196) 0.448*** (0.082) X 13,067 0.736