Employment-At-Will Exceptions and Jobless Recovery

James DeNicco Drexel University Department of Economics JOB MARKET PAPER

Abstract

In this paper I study the effects on jobless recovery of diminishing the power of an employer to fire an employee through Employment-At-Will Exceptions (EWEs). I do so by using a dynamic panel with quarterly data ranging from 1976 to 2010 for the 50 states in the United States. I test both changes in state unemployment rates and state-weighted GDP growth in single variable regressions and VAR regressions. My contribution to the literature is threefold. First, I show two of the three EWEs contribute significantly to jobless recovery in the U.S. The statistical tests in this paper show that Implied Contract Exceptions slow decreases in the unemployment rate during recovery from recession by between 0.025 and 0.033 percentage points per quarter, and Covenant of Good Faith and Fair Dealing Exceptions do so by between 0.039 and 0.055 percentage points per quarter. Second, I lend support to the predictions of theory that increased firing costs decrease the rate of hiring during recoveries. Third, I resolve differences in the various sources documenting the three types of EWEs in different states.

JEL: E24, E32, J23, J32, J83, K12, K31

1. Introduction

In the United States, the legal relationship between employees and employers with regard to discharge has been historically guided by the Employment-At-Will doctrine. The Employment-At-Will doctrine is a common law doctrine, which gives complete discretion to the employer in discharging employees whose contracts do not expressly state the duration of the employment relationship.1 The precedent of Employment-At-Will goes back to 1884 in Payne v. Western & Atlantic Railroad, where the Supreme Court of Tennessee states that employees can be ﬁred for, “good cause, bad cause, or no cause at all.” Beginning with

1Common laws come from precedent setting court cases as opposed to passage through a legislature. 1 2

California in 1959 with Petermann v. Intl Brotherhood of Teamsters and spreading to the majority of states throughout the 1980’s and early 1990’s, state court systems diminished the power of U.S. firms to fire workers at-will as governed by the doctrine of Employment- At-Will. DeNicco and Laincz (2011) use the widely known Quandt-Andrews test for an unknown breakpoint and find significant structural breaks for increasingly jobless recoveries for the United States in 1959 and 1984. Here jobless recovery is a relative term comparing the relationship between GDP growth and changes in unemployment rates across states and time. The resulting structural breaks for jobless recoveries coinciding with changes in the legal framework governing employment relationships suggests a possible connection between Employment-At-Will Exceptions (EWEs) and slower recoveries in unemployment rates post recession. If EWEs increase the cost of firing a worker, DeNicco (2011) predicts they will in fact contribute to increasingly jobless recoveries through slower hiring. In this paper I examine the effects on jobless recovery of diminishing the power of an employer to fire an employee through EWEs using dynamic regression models on a state-level panel with quarterly data from 1976 to 2010. In order to do this I must test both changes in unemployment rates and GDP growth. The essence of jobless recovery is that an economy experiences growth in GDP without decreases in unemployment rates commensurate with past recoveries. First, however, I resolve differences in the various sources documenting the existence of the laws in each state. There are a number of sources that report on the presence of the EWEs and not all of them agree in their timing and classification. With the updated classification of the laws, I utilize panel regressions with recovery periods isolated by using interaction terms and by splitting the data into subsamples. There are three major categories of cases in which the courts have found it proper to limit the power of firms to fire workers. These three cases, referred to as Employment-At-Will Exceptions, are Public Policy Exceptions (PPEs), Implied Contract Exceptions (ICEs) and 3

Covenant of Good Faith and Fair Dealing Exceptions (CGFFDEs). Muhl (2001) describes the three main categories of EWEs. PPEs are the most clear cut. They apply to wrongful discharge “when the termination is against an explicit, well established public policy of the State.” Common applications of the PPEs include cases when workers have been fired due to discrimination, whistle-blowing or filing worker’s compensation claims. ICEs are “applied when an implied contract is formed between an employer and an employee, even though no express, written instrument regarding the employment relationship exists.” Common applications of the Implied-Contract exceptions are in cases when employers publish an employee handbook that leads to a “reasonable” interpretation of an altering of at-will status by the employee. Some states have gone further allowing oral statements to alter the at-will status of an employee. CGFFDEs are applied to all employee relationships regardless of the existence of implied or implicit contracts. They have been “interpreted to mean either that employer decisions are subject to a ‘just cause’ standard or that terminations made in bad faith or motivated by malice are prohibited.” Most states have applied the CGFFDE in cases when an employee has been terminated to prevent them from receiving compensation they would have otherwise been due, such as bonuses or merit pay. Court opinions explaining CGFFDEs are written loosely, leaving substantial room for legal interpretation and possible legal action against employers when an employee is discharged. All three EWEs could raise firing costs and contribute to jobless recovery. Mortensen and Pissarides (1999) suggest that higher firing costs result in slower job creation, which results in slower transitions back to steady state following the same negative shock. I provide evidence of slower recovery in unemployment rates while controlling for GDP growth, following a recession when court opinions set precedent in favor of EWEs. The statistical tests in this paper show ICEs slow decreases in unemployment rates during recovery between 0.025 and 0.033 percentage points per quarter, and CGFFDEs do so by between 4

0.039 and 0.055 percentage points per quarter. In other words if a state adopted both laws, negative movements in unemployment rates would be damped by an average of between 0.064 and 0.088 percentage points per quarter or between 0.256 and 0.352 percentage points over the course of a year. For example, let us take the case with the larger effects for both EWEs. Starting from the 9.9% unemployment rate in 2009Q4 and controlling for actual GDP growth, my model predicts the unemployment rate would fall to 7.7% in two years without the two EWEs. However, with both EWE’s in place the unemployment rate would only fall to 8.4%. With a labor force of 150 million people, that would leave an extra 1.1 million people unemployed and possibly collecting unemployment benefits, which cost on average about $300 per worker per week. I find no evidence of PPEs contributing to jobless recovery. Through their rulings on EWEs, state court systems have tried to protect those in the labor force who are already employed from wrongful discharge. However, they have made it more difficult for the unemployed to find jobs. By restricting firms in their ability to make decisions about how and why they fire their employees, my results robustly confirm the exceptions contribute to longer recovery periods in the labor market. State legislatures concerned with high unemployment should consider passing statutes affirming states’ ad- herence to the Employment-At-Will doctrine in regards to the two stronger exceptions. The rest of the paper is organized as follows: Section 2) a literature review; 3) the background of EWEs; 4) a summary look at the data and classification of case law; 5) results and analysis; 6) robustness tests; and 7) conclusions.

2. Literature Review

Theory predicts that imposing restrictions on the ability of firms to fire workers can contribute to an increased reluctance to create and fill vacancies during periods of economic recovery. Mortensen and Pissarides (1999) tell us that the effects of increased firing costs on steady state unemployment rates are ambiguous in a search and matching setting, 5 but the effects of increased firing costs clearly slow job creation. If EWEs increase firing costs, employers will be slower to hire new workers with increases in aggregate demand. Mortensen and Pissarides (1999) point out that firms have incentive to be more careful in their hiring practices and incur larger costs to avoid an employment match that becomes unsuitable in the future. There is also an increased marginal cost of employment due to defensive legal practices and administrative burdens, which can make firms more cautious in their screening process and slower to hire new workers. Hall (2005) and Shimer (2005) show there are stronger correlations between vacancies and unemployment than between separations and unemployment. Fujita and Ramey (2009) show statistical evidence dampening the findings of Shimer (2005) and Hall (2005) but still leave up to 60% of the fluctuations in the unemployment rate to vacancy creation and hiring rates. If so much of the variation in unemployment rates comes from these two factors, an increase in firing costs can result in persistence in unemployment rates through increased rigidity in the hiring process and a reluctance of firms to create vacancies. Faberman (2008) addresses jobless recovery using flow data from the relatively new Busi- ness Employment Dynamics (BED) data set from the Bureau of Labor Statistics (BLS) covering the period from 1990-2006. He investigates job creation and job destruction as defined by Davis, Haltiwanger and Schuh (1996), and in accordance with Hall (2005) and Shimer (2005), he attributes the jobless recovery from the 2001 recession to a persistent decline in the job creation rate.2 He links his findings to the reduction in volatility and increased persistence of job flows in the presence of aggregate shocks as seen in the Great Moderation period, first studied by Kim and Nelson (1999) and McConnell and Perez- Quiros (2000).

2Faberman also extends his data back to 1947 using the BED and previous estimates produced Davis, Haltiwanger, and Schuh (1996). He creates his own GMM predicted estimates of job creation and job destruction. Much in line with the structural breaks found in DeNicco and Laincz (2011 Working Paper) and the Great Moderation literature, Faberman observes the magnitude of job ﬂows began to steadily decline in the 1960’s and the volatility of job ﬂows dropped sharply in the mid 1980’s. He attributes the slow recovery from the 1990-91 recession to an increase in the job destruction rate. 6

Mortensen and Pissarides (1999) write that increased firing costs should also cause firms to be more reluctant to discharge employees, which will have a positive effect on steady state employment. This influence will cause ambiguity in the overall result for the level of the steady state unemployment rate. Gavrel and Lebon (2008) use the search and matching model and find that the overall effects of increased firing costs on unemployment rates depends on the type of firing cost. They find that if firing costs come in the form of layoff taxes that help finance unemployment benefits then unemployment will decrease due to the firm internalizing the tax on dismissals. This result is due to a decrease in payroll taxes used to finance unemployment benefits. If EWEs are not taxes on dismissal, but rather administrative and legal costs, there should be an increase in steady state unemployment rates by this theory. I investigate this theory in two different ways, using interaction terms and subsampling my data before statistical testing to account for different phases of the unemployment and GDP growth cycles. I find no statistically significant evidence of the EWEs altering the rate of change in the unemployment rate during a contraction, indicating EWEs only play a significant role in slowing changes in the unemployment rate during an expansion. There are of course numerous possible causes for jobless recovery leaving room for future work. Aaronson, Rissman and Sullivan (2004) summarize and critique a number of existing arguments for the causes of jobless recovery, including self-employment, just-in-time hiring, healthcare costs, participation rates and slack aggregate labor demand. They specifically note that the high cost involved in firing workers may push firms to use temporary workers. The one explanation they give for the high cost, is that it is perhaps due to the possibility of firms facing lawsuits. With large numbers of temporary workers desiring full time work, there will be persistently high unemployment rates during recoveries with many new jobs being taken by temporary workers shifting to full time. This shift leaves smaller numbers of workers moving from unemployment to employment. Workers who are part time for 7 economic reasons are one type of temporary worker. Ideally I would like to investigate whether EWEs contribute to increases in these type of workers, but due to data limitations at the BLS this is not possible. The data for states’ part time workers only goes back to 1997 and is only available in annual averages. These limitations preclude a thorough and conclusive investigation.

3. Employment-at-Will Background

Three empirical papers use macro data and explore the effects of EWEs with mixed results. Dertouzos and Karoly (DK) (1992) use a difference in difference approach and find a three percent decrease in aggregate employment with the adoption of the good faith and public policy exceptions. DK find this result to be equivalent to the effect of increasing employer taxes on wages by 10%. However, Autor, Donohue, and Schwab (ADS) (2004) argue that this result from DK may be biased due to the choice of instrumental variables. Miles (2000) revisits the effects of EWE laws on employment and finds no evidence of correlations between the laws and employment, but he does not investigate the discrepancies in the findings. ADS (2006) comprehensively reevaluate the issue and find statistically significant correlations between the adoption of the EWE laws and reductions in employment. The ADS (2006) results contrast with the DK results in the magnitude of the negative effects on employment. They find an average reduction of between 0.8% and 1.6% on the employment to population ratio. ADS (2004) explain the discrepancy between ADS (2006) and Miles (2000) as due to the classification of the timing of case law. ADS (2006) consider EWEs to be internalized by firms much earlier than Miles (2000) in most instances. ADS (2006) attempt to “locate the first case in a state that might trigger a client letter from attorneys warning about a change in law.” Miles (2000) does not consider the law implemented until it has worked its way through the states court system for validation. The ADS (2006) approach accounts for forward looking firms, as assumed in 8 the theoretical papers discussed here. Therefore I follow their method for classifying the timing of case law, which is discussed in the appendix. In this paper I move away from the short range difference in difference techniques in the three empirical papers described above. I use dynamic regression models on a panel with quarterly data ranging from 1976 to 2010 and isolate recovery periods through both interaction terms and split samples. While all three of the macro empirical papers studying EWEs focus on levels of employment and unemployment, I focus on changes in the unemployment rate and GDP growth. One empirical paper more closely aligned to my goals here is from Autor, Kerr and Kugler (AKK) (2007). They use establishment level data from 1976 to 1999 to explore the effects of the EWEs on overall employment fluctuations, firm entry rates and total factor productivity (TFP). In line with my results, they find the CGFFDEs significantly reduce overall employment fluctuations. They also find CGFFDEs reduce firm entry rates and TFP, but these results are not reflected in my findings for state- weighted GDP growth. I go beyond the previous papers using dynamic panel regression models to find the effects of the exceptions during recoveries on unemployment rates and GDP growth. Again, jobless recovery refers to an economy that experiences growth in GDP without decreases in unemployment rates commensurate with past recoveries. Therefore it is crucial to test the effects of EWEs on both GDP growth and changes in unemployment rates.

4. Data

The first tasks necessary for this study are updating the existing EWEs through 2010 and resolving differences in the existing literature. Here I compare four different publica- tions that independently document the existence of the exceptions in different states over time. For a further explanation of the details concerning my methodology and classifi- cations, please refer to the appendix. This task required finding and reading hundreds of different state court opinions through Lexis Nexis, state supreme court websites and on-line 9 databases. Documenting and categorizing differences in EWEs requires the interpretation of state court opinions and is dependent on the criteria set forth before documentation. If different sources use different criteria, discrepancies will result. In this paper I follow the criteria set forth in ADS (2006) for the timing of case law and I concur with most of their findings for the existence of precedent setting case law. I use state unemployment data from the Bureau of Labor and Statistics, and real national GDP data (in chained 2005 dollars) and state personal income data from the Bureau of Economic Analysis. In order to account for the asymmetry of both movements in unemployment rates and GDP growth over the business cycle, I separate the business cycle into the different phases of both unemployment dynamics and state-weighted GDP growth. I represent the different phases of the business cycle in my model by both using interaction terms and splitting the sample. For the unemployment cycle, I separate the data into periods of net Outflows and net Inflows. Outflows are characterized by periods of negative changes in unemployment rates. Inflows are defined as periods of positive changes in unemployment rates. For GDP growth cycles, I separate the data into two samples using changes in state-weighted real GDP. One subsample has positive or Expansion periods and the other has negative or Contraction periods. State Gross Product is only available annu- ally, therefore I use quarterly state personal income to weight national GDP and use the measure to find the effects of EWEs on state-weighted GDP. The results in the next section are robust to both the Inflows/Outflows and the Expansions/Contractions specifications. In Table 1, I show the average difference in unemployment rates and the average difference in state-weighted GDP growth conditional on the presence of the EWEs. The percent change measures the difference in the averages when the law is in existence versus when the law is not yet set in precedent. The t-tests provide summary evidence that suggests a slower recovery in unemployment rates during Outflows and Expansion periods due to 10 all three EWEs. The effects on unemployment rates are significantly larger than on state- weighted GDP growth. The presence of the EWEs coincides with a diminished recovery of unemployment rates during Outflows and Expansion periods, and less dramatic results with conflicting signs for changes in state-weighted GDP growth. This evidence suggests that EWEs may be affecting unemployment rates more than they are affecting state-weighted GDP growth during recovery periods. This result indicates EWEs contribute to jobless recovery in the United States beyond explanations of slower GDP growth. I fail to find the same evidence on the effects of EWEs during Inflows and Contraction periods. Comparing differences in unemployment rates between Table 1 and Table 2, I find the evidence supports the findings of Gavrel and Lebon (2008) that administrative type firing costs will have a slowing effect that is unambiguously larger on hiring than on firing.

5. Results and Analysis

In this section I run a benchmark specification for my empirical model and then perform robustness tests on it. My benchmark specification is modeled to test the predictions from DeNicco (2011) that increased firing costs result in slower recoveries in unemployment rates post recession. The basis for my specification comes from DeNicco and Laincz (2011), which finds structural breaks indicating increasingly jobless recoveries around the time that most of the EWEs became precedent. In subsection 5.1, I use interaction terms and split samples to test the relationship between EWEs and my independent variables in separate autocorrelation regressions. In subsection 5.2, I continue my analysis in split samples with panel VARs so that I can test changes in unemployment rates while controlling for state-weighted GDP growth. Then in section 6, I run robustness tests controlling for the presence of collective bargaining and a lagged implementation effect. In both sections the conclusions are the same; EWEs contribute to jobless recovery in the U.S. 11

Working with first differences in both unemployment rates and state-weighted GDP, I expect to reject the unit root for all variables. Using Schwarz information criteria (SIC) and looking at Table 3 for the full sample, we can reject the unit root in all cases for both a common and individual unit root process for both variables tested. All unit root tests are robust to Akaike information criteria (AIC) standards as well. Using split samples we can also reject the unit root at the 1% threshold by all tests performed for Outflows, Expansions and Inflows. Only differences in the unemployment rate during Contraction periods fail to be rejected for a unit root, and in only one test.

5.1. Single Variable Autocorrelation Regressions.

5.1.1. Differences in Unemployment Rates. Here I investigate the effects of the EWEs on differences in unemployment rates. The model is represented below:

4 X DURs,t = α+ βiDURs,t−i+β5PPEs,t+β6ICEs,t+β7CGF F DEs,t+β8EXPs,t+β9PPEs,t∗EXPs,t i=1

+β10ICEs,t ∗ EXPs,t + β11CGF F DEs,t ∗ EXPs,t + fs + qt + s,t where DUR represents differences in unemployment rates in a cross section of U.S. states over a quarterly time series. PPE, ICE, and CGF F DE are individual variables for the three different exceptions, and EXP represents Expansion periods. fs and qt represent fixed cross section and quarterly time series effects. s indexes states and t indexes time. I use a four-lag representation throughout the paper due to the quarterly data, but the results are generally robust from one to twelve lags. Arellano and Bover (2005) note there is a possible bias induced in the predetermined variables by the fixed effects, but Roodman (2006) shows us the cause for concern is greatest with a panel that has a large cross section, N, and a small number of time periods, T . 12

These concerns are greatest in samples with T less than 30. In my data I have 140 time periods for the full sample, 128 time periods with an average of 73 per state for the Outflows sample, 134 time periods with an average of 106 per state for the Expansion sample, 133 time periods with an average of 62 per state for the Inflows sample and 122 time periods with an average of 29 per state for the Contraction sample. I feel confident in all but the Contraction sample that my T is large enough that any bias will be as Roodman (2006) says, “insignificant and the problem will disappear.” In Table 4 we begin to see what emerges as a pattern throughout this statistical analysis. In the first regression for changes in the unemployment rate, the interaction terms for EXP with ICE and CGF F DE have statistically significant coefficients.3 The negative coefficient on the EXP variable alone reflects the countercyclical nature of unemployment rates. The positive coefficients on the two interactions terms tell us the ICEs and the CGFFDEs have significantly diminished the difference between Expansion and Contrac- tion periods by 0.034 and 0.043 percentage points per quarter. Adding the coefficient of -0.012 on ICE and the coefficient of 0.034 on ICE ∗ EXP yields a positive sum of 0.022, which tells us that the recovery of unemployment rates post recession will be slower during Expansion periods with the exception in place. Adding the coefficient of -0.006 on CGF F DE and the coefficient of 0.043 on CGF F DE∗EXP yields a positive sum of 0.037, which tells us that the recovery of unemployment rates post recession when CGFDEs are in place will be even slower than when ICEs are in place. Using a Wald test, I can reject the null hypothesis at the 5% threshold that combing the coefficients on the two interaction terms is equal to zero. This result tells us that the difference between ICEs and CGFFDEs in the effects of EWEs on unemployment is statistically significant.

3One reason for the insignificant coefficients on PPE may be that many of the instances covered under PPEs, such as firing employees for race or gender, are covered with federal legislation and apply to all states. If PPEs have already been covered under federal legislation, then the timing for the state’s adoption of the law is meaningless. Beyond the timing issue, there is also no real variation across states once the federal policy is enacted. Another reason may be that 5 states have the PPEs, before my sample period starts, which again detracts from time series variation. 13

However, these results are evidence of both the ICEs and CGFFDEs contributing to slower recoveries of unemployment rates during Expansion periods, controlling for lagged differences in unemployment rates and fixed state and time effects. The negative coefficients on ICE and CGF F DE tell us that as in Expansion periods there is a buffering in the movements (positive movements here) of the unemployment rate during Contraction periods. However, neither of the coefficients on the two EWEs are significant. EWEs have resulted in smaller positive movements in unemployment rates during Contraction periods, but their effects are much stronger during Expansion periods. Inter- preting the result through the lens of Gavrel and Lebon (2008), EWEs act like destructive administrative costs. They predict that if EWEs act like administrative costs, controlling for GDP growth, they will cause an increase in steady state unemployment rates by slowing job creation more than job destruction.

5.1.2. Log Differences In State Personal Income-Weighted Real GDP. In order to understand if the EWEs simply slowed down GDP growth causing a subsequent slow down in decreases in the unemployment rate, we need to examine the state specific measure of GDP growth. If these laws are only slowing down differences in unemployment rates during Out- flow periods due to their effects on GDP growth, then we do not have a jobless recovery story. The model is represented below:

4 X DGDPs,t = α+ βiDGDPs,t−i+β5PPEs,t+β6ICEs,t+β7CGF F DEs,t+β8OFs,t+β9PPEs,t∗OFs,t i=1

+β10ICEs,t ∗ OFs,t + β11CGF F DEs,t ∗ OFs,t + fs + qt + s,t where DGDP represents log differences in state personal income-weighted real GDP in a cross section of U.S. states over the quarterly time series. PPE, ICE, and CGF F DE represent indicator variables for the three different exceptions, and OF represents Outflows. 14 fs and qt represent fixed cross section and quarterly time series effects. s indexes states and t indexes time. Again, I use a four-lag representation throughout the paper due to the quarterly data, but the results are generally robust from one to twelve lags. Examining regression 2 in Table 4, the coefficient for OF is significant and positively related to changes in state personal income-weighted GDP, but none of the interaction terms are significant. This result is important because it tells us there are no significant effects on Outflows from the EWEs with respect to state-weighted GDP growth that could be driving the previous results for differences in unemployment rates. Furthermore the coefficient for the interaction term with CGF F DE is positive, indicating increased growth during Outflows with the adoption of CGFFDEs. One explanation for this result could be firing costs are causing firms to substitute away from hiring labor, towards investing in capital. The evidence here supports a story of jobless recovery as opposed to a story of diminished state-weighted GDP growth resulting in slower recovery from high unemployment rates. In order to test for consistency in these results, I rerun my regressions in split samples. I use the classification of Outflows versus Inflows and Expansion periods versus Contraction periods for the criteria in my sub-sampling. I test all four subsamples.

5.1.3. Outflows Subsample. The regression equations below examine the effects of EWEs on the recovery of unemployment rates and state-weighted GDP growth during the Outflows subsample. In 5.1.4 I use the Expansion periods subsample, but I put more weight on the Outflows subsample because it is more appropriate in capturing labor dynamics and unemployment cycles. The Outflows subsample better captures unemployment cycles such as the peak of unemployment rates and the subsequent recovery. However, the drawback is that the Outflows subsample ignores periods of rising unemployment rates that take place during periods of positive state-weighted GDP growth. In fact, 40% of Expansion periods 15 coincide with Inflows. While the Expansion periods characterize the recovery of GDP well, they miss 13.3% of Outflows. So while I put more weight on the Outflows subsample for this paper, these results are robust to either specification.4

4 (O) X DURs,t = α + βiDURs,t−i + β5PPEs,t + β6ICEs,t + β7CGF F DEs,t + fs + qt + s,t i=1

4 (O) X DGDPs,t = α + βiDGDPs,t−i + β5PPEs,t + β6ICEs,t + β7CGF F DEs,t + fs + qt + s,t i=1 where DUR(O) represents differences in the unemployment rate during Outflows and DGDP (O) represents log differences in state personal income-weighted real GDP during Outflows.

PPE, ICE, and CGF F DE are individual variables for the three different exceptions. fs and qt represent fixed cross section and quarterly time series effects. s indexes states and t indexes time. In regression 1 in Table 5, the results for the coefficient on ICE is significant at the 5% threshold and close to the 1% threshold for differences in the unemployment rate, DUR. The coefficient on CGF F DE is significant at the 1% threshold. These results are robust up to 11 lags at the 1% threshold and at the 5% threshold for the 12th lag. Consistent with the specification using interaction terms, the results indicate a slowdown in negative movements in unemployment rates with the adoption of these two exceptions. On average per quarter, changes towards lower unemployment rates experience a 0.033 percentage point slowdown with the adoption of ICEs and a 0.055 percentage point slowdown with the adoption of CGFFDEs. In regression 2 in Table 5, the coefficients indicate that EWEs play much less of a role in affecting state personal income-weighted GDP growth, DGDP , during Outflows. None of the coefficients on the EWEs are significant and the signs for the adoption of all three

4My results are strengthened even further when I run tests with only quarters that are part of both the Outﬂows and Expansion subsamples. 16

EWEs is positive. The results from using the Outflows subsample clearly support the findings of my specifications using interaction terms. Regression 1 and 2 in Table 5 provide evidence that despite state-weighted GDP growth, there have been slower recoveries in unemployment with the adoption of the EWEs. Both ICEs and CGFFDEs contribute to jobless recovery. Moreover, my findings do not just indicate that EWEs lead to slower GDP growth which in turn causes slower recoveries in unemployment. I show that EWEs act as destructive administrative costs leading to slower recovery by an amount that is over and above that of lacking aggregate demand.

5.1.4. Expansion Periods Subsample: Even with a significant slowdown in Outflows, there are enough Inflows that take place during recovery from a recession that the overall effect on changes in unemployment rates could be insignificant. Therefore, it is imperative to also test the Expansion subsample to see if the results are robust to this specification allowing for periods of rising unemployment rates during recovery from recession.5

4 (E) X DURs,t = α + βiDURs,t−i + β5PPEs,t + β6ICEs,t + β7CGF F DEs,t + fs + qt + s,t i=1

4 (E) X DGDPs,t = α + βiDGDPs,t−i + β5PPEs,t + β6ICEs,t + β7CGF F DEs,t + fs + qt + s,t i=1 where DUR(E) represents differences in the unemployment rate during Expansion periods, DGDP (E) represents log differences in state personal income-weighted real GDP during Expansion periods. PPE, ICE, and CGF F DE are individual variables for the three different exceptions. fs and qt represent fixed cross section and quarterly time series effects. s indexes states and t indexes time.

5For the same reasons, I also must test both Inﬂows and Contraction periods to understand the eﬀect of EWEs during recessionary times. 17

In regression 3 in Table 5 there are differences with respect to the Outflows subsample with slightly smaller coefficients on ICE and CGF F DE. The coefficient on ICE is consistently significant close to and sometimes breaking the 5% threshold up to 12 lags. The coefficient on CGF F DE is consistently significant to the 5% threshold and sometimes to the 1% threshold up to 12 lags. Thus while using the Expansion subsample slightly dampens the degree to which EWEs affect jobless recovery, they are still contributing significantly. In regression 4 in Table 5 the most noticeable difference with respect to the Outflow subsample is the consistently negative and significant coefficient on ICE for state personal income-weighted GDP. Again however, when looking at magnitude, the coefficient on ICE in the regression for state-weighted GDP growth is about 12.2% of the average for the Expansion subsample, while the coefficient on ICE in the regression for differences in the unemployment rate is about 44.6% of the average for the subsample. The results for both dependent variables in the Expansion subsample tell the same gen- eral story as the results from the interaction terms and the Outflow subsample. The PPEs have played very little or no role in shaping the behavior of unemployment and an inconclu- sive role in affecting GDP growth. ICEs and CGFFDEs in this subsample show evidence of slowing down reductions in unemployment rates during recoveries while having little or no effect on state-weighted GDP growth. The consistency of the evidence strongly suggests, and the robustness tests support, that ICEs and CGFFDEs contribute to jobless recoveries in the United States.

5.1.5. Inflows and Contraction Periods Subsamples: The results in Table 6 from re-running the previous tests with subsamples from Inflows and Contraction periods again shed light on the ambiguity discussed in Mortensen and Pissarides (1999) for EWEs. For differences in unemployment rates, DUR, in regressions 1 and 3, there are no significant results for 18 any EWEs. This result clearly shows there is much less of a buffering effect on periods of increases than on periods of decreases in unemployment rates.6 EWEs seem to affect job creation more than job destruction. The results from both using interaction terms and subsample analysis lead me to interpret EWEs as destructive administrative costs along the lines of the theory proposed by Gavrel and Lebon (2008).

5.2. Panel VARs in Split Samples. This subsection continues the investigation of the impact of EWEs on jobless recovery in split samples using a two variable panel vector autoregression (panel VAR). The reason for this further analysis is to directly address the inextricable linkages between changes in unemployment rates and GDP growth with their recursive eﬀects on one another. Jobless recovery refers to the movements of both variables, so I investigate the eﬀects of EWEs on a system of both variables simultaneously in the model below:

4 4 X X DURs,t = α+ γiDURs,t−i+ βiDGDPs,t−i+β5PPEs,t+β6ICEs,t+β7CGF F DEs,t+fs+qt+s,t i=1 i=1

4 4 X X DGDPs,t = ψ+ θiDURs,t−i+ κiDGDPs,t−i+κ5PPEs,t+κ6ICEs,t+κ7CGF F DEs,t+fs+qt+s,t i=1 i=1 where again, from Roodman (2006), I am confident because of the large T in my panel regressions that any bias induced in the predetermined variables by the fixed effects will be insignificant.7 The results of the panel VARs for the Outflows and Expansion subsamples are presented

6The results are robust up to at least 12 lags. 7Ideally I would like to run the panel VAR using the method from Love and Zicchino (2002) to remove any possible bias from fixed effects, but this is not possible with my model. The first issue is the method requires all variables to be treated as endogenous and transformed through a forward mean differencing process.The forward mean differencing method is known as the Helmert Process. My EWE laws are exogenous and they are binary variables, which leave a difficult interpretation when they are forward mean differenced. 19 in Table 7. The results look very similar to the single variable analysis results. In VAR 1 for the Outflows subsample, the coefficients on PPE and CGF F DE are positive and significant for differences in unemployment rates, DUR.8 In fact, the coefficients change very little with respect to the case of the single variable analysis. This result shows us that while fixed effects in this panel VAR may introduce some bias, the results are very consistent. The results for state-weighted GDP growth, DGDP , for the Outflows subsample in VAR 1 are also consistent with the single variable analysis. Again, I find that while both ICEs and CGFFDEs significantly dampen the changes in unemployment rates during Outflows, they have a minimal effect on state-weighted GDP growth. The coefficient on CGF F D for state-weighted GDP growth actually indicate an increase in growth. With these results we see that running regressions on the system as a whole, as opposed to the individual parts, reinforces the evidence of EWEs contribution to jobless recovery.9 In VAR 2 in Table 7, the results also reinforce the results from the single variable analysis. Using the Expansion subsample slightly weakens the results from the Outflows subsample, but the results still indicate that EWEs contribute significantly to jobless recovery. Table 8 for Inflows and Contraction subsamples again shows no significant impact of the EWEs on changes in unemployment rates. It does show positive and significant effects for state-weighted GDP growth from both PPEs and ICEs. This result indicates that once the EWEs are in place, negative periods of growth are less severe. Again, this could be the result of EWEs influencing firms to substitute away from hiring labor towards investing in capital, which may be contributing to recessions being less deep. To illustrate the economic significance of my results, the quarterly peak for the unemployment rate was 9.9% in 2009Q4. Using VAR 1 in Table 7 for the Outflows subsample

8This result is robust to all lag specifications tested, which is from 4 to 8. 9Again, when rerunning these VAR’s including only quarters that are in both the Expansion and Outflows subsamples, the results are robust and even stronger. 20 without either ICEs or CGFFDEs, the model predicts an unemployment rate two years later of 7.7%. With the ICEs and CGFFDEs the model predicts an unemployment rate of 8.4%.10 With a workforce of about 150 million that would imply an additional 1.1 million unemployed by 2011Q4. With workers receiving on average $300 per week in unemployment benefits, these results predict that if all states had adopted these EWEs they would have cost the U.S. an extra $13.3 billion in 2011.

6. Robustness Tests

In this section, I present further robustness tests of the results in section 5. These tests account for the percentage of workers covered by collective bargaining agreements and the inclusion of a lagged implementation effect for firms. For both robustness checks, I rerun all of the regressions from the previous section. In both cases I find that my results are robust to these alternative specifications.11 The first robustness check addresses a potentially omitted variable by incorporating the percentage of workers covered by collective bargaining. If collective bargaining agreements make firing workers more difficult and costly, then according to Mortensen and Pissarides (1999) these agreements would have the same effect as EWEs in slowing job creation. If there is a strong correlation between states with large percentages of workers covered by collective bargaining agreements and states with EWEs, then my regression coefficients may be showing firms’ reluctance to hire new workers due to the presence of collective bargaining agreements rather than due to the EWEs. As a measure of the percentage of workers covered by collective bargaining, called union density here, I use estimated annual data of each state’s nonagricultural wage and salary employees from the Union

10I use BEA estimates of GDP growth in these calculations. 11For brevity sake I only show the tables for my OLS panel VARs, but any and all of the robustness test results are available upon request. 21

Membership and Coverage Database.12 The percentages are estimates from 1977 to 2010 formulated using BLS methods and based on the 1983-2011 Current Population Survey (CPS), Outgoing Rotation Group (ORG) earnings files and the 1977-81 May CPS earnings files.13 I apply the annual data to all four quarters of each year. Annual variations in percentages of workers covered by collective bargaining agreements are small, making me confident that missing quarterly variations should not have a significant impact on my results. I run the following VAR for both Outflows and Expansion subsamples:

4 4 X X DURs,t = α+ γiDURs,t−i+ βiDGDPs,t−i+β5PPEs,t+β6ICEs,t+β7CGF F DEs,t+β8UDs,ty +fs+qt+s,t i=1 i=1

4 4 X X DGDPs,t = ψ+ θiDURs,t−i+ κiDGDPs,t−i+κ5PPEs,t+κ6ICEs,t+κ7CGF F DEs,t+κ8UDs,ty +fs+qt+s,t i=1 i=1 where UDs,ty stands for union density and represents the annual average of the percentage of workers under collective bargaining agreements in each state, applied to each quarter of the year. Table 9 shows some evidence in the Expansion subsample of the percentage of workers covered by collective bargaining contributing to jobless recovery. For the EWEs however, the signs, magnitudes and degrees of significance show little change when I account for union influence. These results support the previous findings that EWEs contribute to jobless recovery. The second of these two robustness tests changes the timing of the EWEs effect on jobless recovery. This check is important of two reasons. First, ADS (2006) use this method to allow for a learning effect for the firm and since I am extending their work, I want to check for consistency with their results. The second reason is that I use quarterly data, which can result in an EWE that gets credit for having taken effect for a whole quarter even if the

12www.unionstats.com 13Hirsch, Barry and David A. Macpherson, and Wayne G. Vroman (2001) 22 precedent setting court case is not settled until the second or third month of the quarter. Again, I rerun all of the tests from the previous section using a lagged implementation effect of one through four quarters. I only present the results for the Outflows subsample for brevity sake, but the results are generally robust to all specifications. Looking at Table 10, we see the Panel VAR’s for the Outflows subsample with a one through four quarter lagged implementation effect. The coefficient on CGF F DE is positive and significant to at least the 10% threshold for changes in the unemployment rate all the way through a four quarter lagged implementation effect. The coefficient on ICE is positive and significant up to a three quarter lagged effect for changes in the unemployment rate. The coefficient on CGF F DE is positive and significant for changes in state-weighted personal income all the way through a four quarter lagged implementation effect as well. Even with a lagged effect, we are seeing a slow down in changes in the unemployment rate with the adoption of these two EWEs. In the case of CGFFDEs this is occurring even with an increase in state-weighted GDP growth. These results bolster my confidence in the fact that ICEs and CGFFDEs are significantly contributing to jobless recovery. Controlling for state personal income-weighted GDP growth, there is a definitive slowdown in the recovery of unemployment rates within a year of these laws becoming precedent.

7. Conclusions

After updating and correcting the existing sources relating to Employment-At-Will Ex- ceptions in the U.S., I use panel data from across the states in single autocorrellation and panel VAR testing to ﬁnd these laws signiﬁcantly contribute to jobless recovery. My results indicate that Implied Contract Exceptions slow decreases in the unemployment rate during economic expansion by between 0.025 and 0.033 percentage points per quarter and Covenant of Good Faith and Fair Dealing Exceptions do so by between 0.039 and 0.055 percentage points per quarter. 23

Mortensen and Pissarides (1999) predict that increased firing costs will cause employers to be slower in their hiring, controlling for GDP growth. Firms become more careful in their hiring practices with the possibility of incurring larger costs if they deem a current employment match is unsuitable in the future. EWEs create a new administrative burden and a need for defensive legal practices. According to theory, that makes firms more cautious and stringent in their screening process. My results provide evidence that this effect is indeed occurring; resulting in an increased joblessness of economic recoveries. Mortensen and Pissarides (1999) also expect that increased firing costs should cause firms to be more reluctant to discharge employees, following a negative shock. However, while I find significant slowdowns in the recovery of unemployment rates, I find no significant effects on changes in the unemployment rate during either Inflows or Contraction periods as a result of Employment-At-Will Exceptions. As opposed to previous work by DK (1992) and ADS(2006), this paper focuses on changes to the unemployment rate during transition periods rather than on the steady state levels of labor market variables. Gavrel and Lebon (2008) predict that if Employment-At-Will Exceptions are administrative and legal costs, they will cause an increase in steady state unemployment rates controlling for GDP growth. The Employment-At-Will Exceptions would affect higher steady state unemployment rates by slowing job creation more than they are slowing job destruction. I leave the examination of Employment-At-Will Excep- tions on steady state unemployment rates to future research. There is substantial room for future work on jobless recovery examining both the demand side factors mentioned in Aaronson, Rissman and Sullivan (2004) and supply side factors such as dramatic increases seen in entitlements, which may reduce job search intensity and increase reservation wages. In this paper, the results suggest that despite the courts’ intent to aid workers, the precedent setting rulings in favor of the Implied Contract Exceptions and Covenant of Good Faith and Fair Dealing Exceptions have negatively affected some of the more vulnerable 24 parts of the labor force. While the work system may protect some who are employed from termination, the unemployed have a more difficult time finding a job. If state legislatures want to undo the effects of these rulings, they should pass statutes that explicitly state workers have no legal recourse against their employers in regards to the Implied Contract Exceptions and Covenant of Good Faith and Fair Dealing Exceptions. 25

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Appendix: Classification of Case Law

In this paper I follow the criteria set forth in ADS (2006) for the timing of case law and I concur with most of their findings for the existence of precedent setting case law. I explain and cite case law for any differences in classification between this paper and ADS (2006), as well as run robustness tests for discrepancies. ADS (2006) is the first source used in this paper and covers the period of 1970 to 1999. The second source is from Muhl (2001), which describes the evolution of Employment-At-Will Exceptions over time. Muhl (2001) updates the existence of the exceptions through 2000. The third source is from Dau-Schmidt and Haley (2007), which looks more generally at the history of employment contracts in the U.S. The results from Dau-Schmidt and Haley (2007) are used in a report from the 2008 National Conference of State Legislators. The fourth source is a comprehensive 50 state survey from the 2009/2010 edition of The National Employer, which uses case law in a state by state explanation of the existence and history of Employment-At-Will Exceptions in each state. The most agreed upon category for Employment-At-Will Exceptions is the Public Pol- icy Exception with only two states in question. Muhl (2001) included Nebraska and Dau- Schmidt and Haley (2007) included Maryland as states without the Public Policy Excep- tion. ADS (2006) and the National Employer (2009/2010) both disagree and include the same 43 states as having the exception. I find the case law cited in ADS (2006) is clear that both states do have the Public Policy Exception.14 The discrepancies under the Implied Contract Exception are more numerous and com- plicated, requiring more rigorous analysis to settle. Under my criteria, I find Muhl (2001) differs with six states in my final list.15 Dau-Schmidt and Haley (2007) are closer to my findings, differing with only two states.16 My analysis agrees with the National Employer (2009/2010) and disagrees with ADS (2006) findings of Implied Contract Exceptions in two states. I find evidence in both Arizona and Pennsylvania of case law that would “trigger a client letter warning about a law change,” and “that signaled the sustained adoption of the particular at-will exception.” Dau-Schmidt and Haley (2007) and the National Employer (2009/2010) both agree with my analysis of Pennsylvania and all three other sources agree with my analysis of Arizona. In Pennsylvania I find the opinion in Martin v. Capital Cities Media, Inc., while rejecting the wrongful discharge claim in appeal, includes language showing the willingness of the

14Adler v. American Standard Corp., 432 A.2d 464 (Md. 1981 July).; Ambroz v. Cornhusker Square, 416 N.W.2d 510 (Neb. 1987 November). 15Indiana: Romak v. Public Service Co., 511 N.E.2d 1024 (Ind. 1987 August)., Massachusettes: Hobson v. McLean Hospital Corp., 522 N.E.2d 975 (Mass. 1988 May)., Montanta: Montana Wrongful Discharge from Employment Act, Mont. Code Ann. 39-2-901 to 914 (1987 June)., Pennsylvania: Martin v. Capital Cities Media, Inc., 511 A.2d 830 (Pa. Super. 1986), Texas: Johnson v. Ford Motor Co., 690 S.W.2d 90 (Tex. Civ. App. 1985 April). and Virginia: Frazier v. Colonial Williamsburg Foundation, 574 F. Supp. 318 (E.D. Va. 1983 September). 16Indiana: Romak v. Public Service Co., 511 N.E.2d 1024 (Ind. 1987 August).; Tennessee: Hamby v. Genesco Inc., 627 S.W.2d 373 (Tenn. Ct. App. 1981 November). 29 court to enforce Implied Contract Exceptions with regard to employee handbooks.17 The court rejects the claim but shows its willingness to apply the Implied Contract Excpetion stating, “We do not believe a reasonable person in the appellant’s position would have read this handbook provision as converting her from an at-will employee to an employee with an indefinite contract who could never be discharged without objective just cause.” Furthermore, later cases such as Baur v. Pottsville Area Emergency Med. Servs., Inc,18 cite Martin v. Capital Cities Media, Inc. when applying the reasonable person test and finding in favor of the plaintiff’s wrongful discharge claim. In Arizona, ADS (2006) rightly show that the precedent setting case for Implied Con- tract Exceptions was vacated by the Arizona Supreme Court a year after the lower court opinion was rendered. However, in vacating the decision the court also stated, “We agree with Leikvold that personnel manuals can become part of employment contracts. Whether any particular personnel manual modifies any particular employment-at-will relationship and becomes part of the particular employment contract is a question of fact. Evidence relevant to this factual decision includes the language used in the personnel manual as well as the employer’s course of conduct and oral representations regarding it.”19 The decision of the lower court was overturned because as the Supreme Court writes, “Summary judgment is inappropriate where a genuine dispute exists as to material facts.”20 In other words the lower court decision was not vacated because it found employee handbooks can alter the Employment-At-Will relationship, but because they found it did alter the relationship in this case instead of letting a jury decide the question of fact. As well, in the precedent setting opinion expanding Arizona to include both Public Policy and Covenant of Good Faith and Fair Dealing Exceptions, the court expressly states its view that, “Arizona is among the jurisdictions that have recognized the implied-in-fact contract term as an exception to the at-will rule.”21 Even more than in Pennsylvania, the existence of the Implied Contract Exception in Arizona appears to be clearly settled. The Covenant of Good Faith and Fair Dealing Exception is the category with the largest number of discrepancies across the literature. My findings, which look for a Covenant of Good Faith and Fair Dealing Exception in every employment relationship, completely cor- roborate the findings in ADS (2006). Like ADS (2006), under my criteria I exclude the three states of New Hampshire, Vermont and Utah included in the National Employer (2009/2010) as having the exception. In New Hampshire, the court in Harper v. Health- source N.H., Inc. finds that, “An employer violates an implied term of a contract for employment at-will by firing an employee out of malice or bad faith in retaliation for action taken or refused by the employee in consonance with public policy.” The opinion

17Martin v. Capital Cities Media, Inc., 511 A.2d 830 (Pa. Super. 1986) 18Baur v. Pottsville Area Emergency Med. Servs., Inc, 758 A.2d 1265 (Pa. Super. 2000) 19Leikvold v. Valley View Community Hosp., 688 P.2d 201 (Ariz. App. 1983 June), vacated, 688 P.2d 170 (Ariz. 1984). 20Washington National Trust Co. v. W.M. Dary Co., 116 Ariz. 171, 568 P.2d 1069 (1977). 21Wagenseller v. Scottsdale Memorial Hosp., 710 P.2d 1025 (Ariz. 1985 June). 30 restricts the use of the exception to cases where the decision to fire the employee is, “con- trary to public policy.”22 The Covenant of Good Faith and Fair Dealing Exception is not a separate cause for action and is not read into every employment relationship. In Vermont, the decision cited by the National Employer (2009/2010) requires that the at will relationship has already been altered for the Covenant of Good Faith and Fair Dealing Exception to apply.23 Lastly in Utah I disagree with the National Employer (2009/2010) assertion of the existence of the exception as evidenced in Berube v. Fashion Centre, Ltd.24 Barbaresi (1990) summarizes the Berube case well in the Brigham Young University Law Review stating the opinion of Justice Durham, “allowing a breach of the implied covenant of good faith and fair dealing to serve as evidence of a wrongful discharge,” could not persuade the, “majority of the court to agree. The court in fact refused to allow the use of the Covenant of Good Faith and Fair dealing in the context of wrongful discharge.25 Dau-Schmidt and Haley (2007) by far find the largest number of states with the Covenant of Good Faith and Fair Dealing Exception at twenty-one, as compared with my final findings of twelve. The main reason for the discrepancies is that there are court findings which, using Alabama as an example, often state something along the lines of, “a covenant of good faith and fair dealing is implied in every contract.”26 However, many of these states either limit the findings to certain contracts (often insurance contracts), expressly exclude employment relationships or do not address employment relationships at all.27 Since all states generally adhere to the Employment-At-Will Doctrine (besides the exceptions discussed in this paper), a state must expressly apply the exception to employment relationships. All disagreements with Muhl (2001) have already been covered in the discussion above regarding discrepancies.

22Harper v. Healthsource N.H., Inc., 674 A.2d 962, 965 (N.H. 1996). 23Ross v. Times Mirror, 665 A.2d 580, 585 (Vt. 1995). 24Berube v. Fashion Centre, Ltd., 771 P.2d 1033, 1044 (Utah 1989). 25The results of this paper are robust to including New Hampshire, Vermont and Utah individually and collectively as having the Covenant of Good Faith and Fair Dealing Exception from the dates of the cited cases. 26Grant v. Butler, 590 So. 2d 254, 256 (Ala. 1991) 27Alabama, Arkansas, Illinois, Indiana, New Hampshire, New Jersey, New York, Oklahoma, Pennsylvania, and South Carolina. 31

8. Tables & Figures

Table 1. Average Changes in Unemployment Rates and State-Weighted GDP Growth, Conditional on the Presence of EWEs: Outﬂows and Expan- sion Periods.

Subsample Outﬂows Expansion EWE Presence of EWE DUR DGDP DUR DGDP No -0.2970 0.0112 -0.0750 0.0135 Public Policy Yes -0.2082 0.0091 -0.0482 0.0107 % Change -30%*** -19%*** -36%*** -21%*** No -0.3025 0.0117 -0.0761 0.0137 Implied Contract Yes -0.2058 0.0089 -0.0474 0.0106 % Change -32%*** -24%*** -38%*** -23%*** No -0.2411 0.0096 -0.0607 0.0115 Good Faith Yes -0.2056 0.0107 -0.0341 0.0119 % Change -15%*** 11%** -44%** 3% ***,**,* denote signiﬁcance at the 1%, 5%, and 10% threshold respectively for a two tailed t-test.

Table 2. Average Changes in Unemployment Rates and State-Weighted GDP Growth, Conditional on the Presence of EWEs: Inﬂows and Contrac- tion Periods.

Subsample Inﬂows Contraction EWE Presence of EWE DUR DGDP DUR DGDP No 0.3416 0.0031 0.2486 -0.0127 Public Policy Yes 0.2865 0.0041 0.2626 -0.0081 % Change -16%*** 32% 06% -36%*** No 0.3495 0.0028 0.2890 -0.0131 Implied Contract Yes 0.2809 0.0043 0.2404 -0.0078 % Change -20%*** 54%** -17%* -41%*** No 0.3074 0.0036 0.2578 -0.0099 Good Faith Yes 0.2876 0.0045 0.2575 -0.0082 % Change -6% 25% 0% -17% ** ***,**,* denote signiﬁcance at the 1%, 5%, and 10% threshold respectively for a two tailed t-test. 32

Table 3. Panel unit root tests: Summary For Diﬀerences in Unemployment Rates and State Personal Income-Weighted GDP, includes individual eﬀects and a linear trend.

Test Statistics Null: Common Unit Root Null: Individual Unit Root Sample Levin, Breitung Im, Pesaran & ADF-Fisher PP-Fisher Lin Chu t* t-stat Shin W-stat χ2 χ2 -23.9*** -19.9*** -30.2*** 1036.6*** 1040.3*** DUR, Full Sample (6794) (6744) (6794) (6794) (6900) -64.8*** -4.8*** -76.2*** 1459.6*** 1633.4*** DUR, Outflows (3726) (3676) (3726) (3726) (3741) -20.0*** -4.9*** -35.0*** 1221.7*** 1245.9*** DUR, Expansion (5351) (5301) (5351) (5351) (5426) -25.9*** -10.8*** -32.0*** 981.4*** 970.7*** DUR, Inflows (3136) (3086) (3136) (3136) (3159) -1.8** -2.7*** -0.7 194.6*** 160.4*** DUR, Contraction (1448) (1399) (1448) (1448) (1448) -61.7*** -40.3*** -61.6*** 2493.1*** 3141.1*** DGDP, Full Sample (6875) (6825) (6875) (6875) (6900) -47.6*** -8.8*** -59.7*** 1641.8*** 1626.7*** DGDP, Outflows (3741) (3691) (3741) (3741) (3741) -70.1*** -2.9*** -95.9*** 3248.2*** 3468.6*** DGDP, Expansion (5417) (5367) (5417) (5417) (5426) -26.0*** -9.3*** -34.6*** 1037.8*** 1271.1*** DGDP, Inflows (3149) (3099) (3149) (3149) (3159) -36.7*** -7.9*** -49.7*** 2246.3*** 2295.3*** DGDP, Contraction (1448) (1399) (1448) (1448) (1448) ***,**,* denote significance rejecting the unit root at the 1%, 5%, and 10% threshold respectively. ( ) contains the number of observations in the test. Cross sections = 50 for all samples except the Contraction subsample, which has 49 due to a lack of data points in one state. 33

Table 4. Single Variables Analysis with Interaction Terms.

Regression 1 2 Dependent Variable DUR DGDP Statistics Coefficient Stand. Error Coefficient Stand. Error C 0.052*** (0.018) 0.0042*** (0.0007) DUR(-1) 0.404*** (0.012) DUR(-2) -0.129*** (0.013) DUR(-3) 0.030** (0.013) DUR(-4) -0.002 (0.012) DGDP(-1) -0.0595*** (0.0122) DGDP(-2) 0.0358*** (0.0122) DGDP(-3) 0.0158 (0.0121) DGDP(-4) 0.0821*** (0.0121) PPE 0.005 (0.020) 0.0014** (0.0007) ICE -0.012 (0.021) 0.0002 (0.0007) CGFFDE -0.006 (0.024) 0.0006 (0.0008) EXP -0.060*** (0.017) PPE*EXP -0.025 (0.020) ICE*EXP 0.034* (0.020) CGFFDE*EXP 0.043** (0.022) OF 0.0027*** (0.0006) PPE*OF -0.001 (0.0007) ICE*OF -0.0003 (0.0007) CGFFDE*OF 0.0006 (0.0008) R2 0.613 0.613 Adj. R2 0.602 0.602 F-Stat 53.545*** 53.545*** AIC 0.112 0.112 DW 2.017 2.017 ***,**,* denote significance at the 1%, 5%, and 10% threshold respectively. ( ) contains standard errors. 34

Table 5. Single Variable Analysis with Outﬂows and Expansion Subsamples.

Sample Outﬂows Expansion Regression 1 2 3 4 Dependent Variable DUR DGDP DUR DGDP -0.2591*** 0.0099*** -0.0466*** 0.0122*** C (0.0136) (0.0008) (0.0129) (0.0005) 0.0868*** 0.5892*** DUR(-1) (0.0125) (0.0152) -0.0663*** -0.264*** DUR(-2) (0.012) (0.0146) 0.0022 0.0462*** DUR(-3) (0.0131) (0.0130) -0.0264* -0.0036 DUR(-4) (0.0137) (0.0119) -0.0973*** 0.0032 DGDP(-1) (0.0165) (0.0120) -0.0315*** -0.0148 DGDP(-2) (0.0152) (0.0106) 0.0449** -0.0035 DGDP(-3) (0.0155) (0.0104) 0.0097 0.0511*** DGDP(-4) (0.0163) (0.0113) 0.0028 0.0003 -0.0133 0 PPE (0.0136) (0.0007) (0.0133) (0.0005) 0.0328** 0.0003 0.0251** -0.0014*** ICE (0.0133) (0.0007) (0.0129) (0.0005) 0.0551*** 0.0015 0.0386** 0.0004 CGFFDE (0.0182) (0.001) (0.0166) (0.006) T 128 128 134 134 N 50 50 50 50 Obser 3669 3669 5304 5304 R2 0.373 0.333 0.564 0.363 Adj. R2 0.3402 0.298 0.5482 0.339 F-Stat 11.33*** 9.52*** 35.04*** 15.39*** AIC -0.4137 -6.242 -0.173 -6.75 DW 1.4141 1.784 1.836 1.466 ***,**,* denote signiﬁcance at the 1%, 5%, and 10% threshold respectively. ( ) contains standard errors. 35

Table 6. Single Variable Analysis with Inﬂows and Contraction Subsamples.

Sample Inﬂows Contraction Regression 1 2 3 4 Dependent Variable DUR DGDP DUR DGDP 0.2436*** 0.0011 0.2437*** -0.0137*** C (0.0175 ) ( 0.0009 ) (0.0339) (0.0012) 0.5238*** 0.1620*** DUR(-1) (0.0219 ) (0.0246) -0.2595*** -0.0128 DUR(-2) (0.0261 ) (0.0318) 0.1459*** 0.1161*** DUR(-3) (0.0220 ) (0.0404) -0.0132 -0.0304 DUR(-4) (0.0158 ) (0.0372) -0.0402** -0.1381*** DGDP(-1) (0.0185 ) (0.0209) 0.0967*** 0.1154*** DGDP(-2) (0.0207 ) (0.0260) -0.0345* 0.0889*** DGDP(-3) (0.0197 ) (0.0268) 0.1469*** 0.0791*** DGDP(-4) (0.0185 ) (0.0209) -0.0222 0.0022** 0.0047 0.0024** PPE (0.0178 ) (0.0009 ) (0.0344) (0.0012) -0.0242 -0.0002 -0.0266 0.0029** ICE (0.0186) (0.0009 ) ( 0.0380) (0.0013) 0.0138 0.0003 -0.0543 -0.0010 CGFFDE (0.0225 ) (0.0011) (0.0487) (0.0017) T 133 133 122 122 N 50 50 50 50 Obser 3081 3081 1446 1146 R2 0.615 0.438 0.682 0.45 Adj. R2 0.59 0.401 0.638 0.33 F-Stat 24.59*** 11.97*** 15.37*** 5.076*** AIC -0.08 -6.05 0.659 -6.08 DW 1.43 2.25 1.94 1.797 ***,**,* denote signiﬁcance at the 1%, 5%, and 10% threshold respectively. ( ) contains standard errors. 36

Table 7. OLS Panel VAR with Fixed State and Time Eﬀects for Outﬂows and Expansion Subsamples.

VAR 1 2 Sample Outﬂows Expansion Dependent Variable DUR DGDP DUR DGDP -0.257*** 0.0098*** -0.049*** 0.0122*** C (0.014) (0.0007) (0.014) (0.0005) 0.087*** -0.0009 0.590*** -0.0025*** DUR(-1) (0.012) (0.0007) (0.0152) (0.0006) -0.065*** -0.0015** -0.264*** -0.0002 DUR(-2) (0.012) (0.0007) (0.0147) (0.0005) 0.001 -0.0005 0.0452*** -0.0005 DUR(-3) (0.013) (0.0007) (0.013) (0.0005) -0.024* -0.0024*** -0.003 -0.0015*** DUR(-4) (0.014) (0.0007) (0.012) (0.0004) -0.487 -0.1018*** 0.146 -0.0037 DGDP(-1) (0.304) (0.0165) (0.315) (0.0117) -0.398 -0.0382** -0.779*** -0.0205* DGDP(-2) (0.281) (0.0152) (0.286) (0.0106) 0.198 0.0356** 0.124 -0.0108 DGDP(-3) (0.287) (0.0155) (0.282) (0.0105) 0.707** 0.0019 0.858*** 0.0469*** DGDP(-4) (0.302) (0.0163) (0.304) (0.0113) 0.001 0.0003 -0.0137 -0.0001 PPE (0.014) (0.0007) (0.013) (0.0005) 0.033** 0.0004 0.0256** -0.0013*** ICE (0.013) (0.0007) (0.013) (0.0005) 0.055*** 0.0017* 0.0386** 0.0006 CGFFDE (0.018) (0.0010) (0.017) (0.0006) T 128 128 134 134 N 50 50 50 50 Obser 3669 3669 5304 5304 R2 0.375 0.338 0.566 0.368 Adj. R2 0.341 0.302 0.549 0.344 F-Stat 11.16*** 9.504*** 34.472*** 15.430*** AIC -0.414 -6.134 -0.174 -6.761 DW 1.419 1.787 1.834 1.473 ***,**,* denote signiﬁcance at the 1%, 5%, and 10% threshold respectively. ( ) contains standard errors. 37

Table 8. OLS Panel VAR with Fixed State and Time Eﬀects for Inﬂows and Contraction Subsamples.

VAR 1 2 Sample Intﬂows Contraction Dependent Variable DUR DGDP DUR DGDP 0.259*** 0.0034*** 0.263*** -0.0125*** C (0.018) (0.0009) (0.035) (0.0012) 0.516*** -0.006*** 0.154*** -0.0019** DUR(-1) (0.022) (0.0011) (0.025) (0.0008) -0.268*** -0.0018 -0.023 -0.0036*** DUR(-2) (0.026) (0.0013) (0.032) (0.0011) 0.141*** -0.0003 0.101** 0.0008 DUR(-3) (0.022) (0.0011) (0.041) (0.0014) -0.02 -0.0026*** -0.041 -0.0021 DUR(-4) (0.016) (0.0008) (0.038) (0.0013) -1.022*** -0.074*** -1.999*** -0.1535*** DGDP(-1) (0.375) (0.0187) (0.618) (0.0212) -0.917** 0.0709*** -1.808** 0.0978*** DGDP(-2) (0.415) (0.0207) (0.769) (0.0264) -0.403 -0.0521*** 0.082 0.0759*** DGDP(-3) (0.393) (0.0196) (0.792) (0.0272) -0.199 0.1348*** 0.271 0.0724*** DGDP(-4) (0.368) (0.0184) (0.608) (0.0209) -0.022 0.0019** 0.004 0.0021* PPE (0.018) (0.0009) (0.034) (0.0012) -0.025 -0.0002 -0.029 0.0031** ICE (0.019) (0.0009) (0.038) (0.0013) 0.015 0.0004 -0.047 -0.0006 CGFFDE (0.022) (0.0011) (0.049) (0.0017) T 133 133 122 122 N 50 50 50 50 Obser 3081 3081 1446 1446 R2 0.617 0.453 0.686 0.424 Adj. R2 0.591 0.417 0.641 0.342 F-Stat 24.227*** 12.471*** 15.263*** 5.148*** AIC -0.082 -6.078 0.652 -6.092 DW 1.423 2.237 1.945 1.824 ***,**,* denote signiﬁcance at the 1%, 5%, and 10% threshold respectively. ( ) contains standard errors. 38

Table 9. OLS Panel VAR with Fixed State and Time Eﬀects for Outﬂows and Expansion Subsamples with Union Density.

VAR 1 2 Sample Outﬂows Expansion Dependent Variable DUR DGDP DUR DGDP -0.238*** 0.0099*** -0.122** 0.0180*** C (0.035) (0.0019) (0.032) (0.0012) 0.087*** -0.0009 0.590*** -0.0025*** DUR(-1) (0.012) (0.0007) (0.015) (0.0006) -0.065*** -0.0015** -0.264*** -0.0002 DUR(-2) (0.012) (0.0007) (0.015) (0.0005) 0.001 -0.0005 0.0449*** -0.0005 DUR(-3) (0.013) (0.0007) (0.013) (0.0005) -0.024* -0.0024*** -0.003 -0.00150*** DUR(-4) (0.014) (0.0007) (0.012) (0.0004) -0.491 -0.1019*** 0.144 -0.00372 DGDP(-1) (0.305) (0.0165) (0.315) (0.0117) -0.400 -0.0383** -0.757*** -0.0203* DGDP(-2) (0.281) (0.0152) (0.286) (0.0106) 0.197 0.0356** 0.148 -0.0106 DGDP(-3) (0.287) (0.0155) (0.282) (0.0105) 0.707** 0.0019 0.878*** 0.0471*** DGDP(-4) (0.302) (0.0163) (0.304) (0.0113) -0.001 -0.0000 0.004** 0.0000 UD (0.002) (0.0001) (0.002) (0.0001) 0.002 0.0003 -0.0161 -0.0001 PPE (0.014) (0.0007) (0.013) (0.0005) 0.033** 0.0004 0.026** -0.0013*** ICE (0.013) (0.0007) (0.013) (0.0005) 0.054*** 0.0017* 0.043** 0.0007 CGFFDE (0.018) (0.0010) (0.017) (0.0006) T 128 128 134 134 N 50 50 50 50 Obser 3669 3669 5304 5304 R2 0.375 0.338 0.566 0.368 Adj. R2 0.341 0.302 0.55 0.344 F-Stat 11.104*** 9.451*** 34.361*** 15.351*** AIC -0.414 -6.247 -0.175 -6.76 DW 1.419 1.787 1.836 1.474 ***,**,* denote signiﬁcance at the 1%, 5%, and 10% threshold respectively. ( ) contains standard errors. 39

Table 10. Outflows: OLS Panel VAR with Fixed State and Time Effects and a Lagged Implementation Effect

Variable/Learning Eﬀect PPE ICE GFFDE AIC SIC DUR/1Q 0.0008 0.0349*** 0.0516*** -0.4144 -0.0963 DGDP/1Q 0.0007 0.0002 0.0017* -6.2477 -5.9296 DUR/2Q 0.0003 0.0284** 0.0451** -0.4132 -0.0951 DGDP/2Q 0.0008 0.0004 0.0018* -6.2481 -5.9300 DUR/3Q 0.000 0.0229* 0.0411** -0.4124 -0.0944 DGDP/3Q 0.0009 0.0005 0.0019** -6.2484 -5.9304 DUR/4Q -0.0007 0.0137 0.0385** -0.4117 -0.0937 DGDP/4Q 0.0006 0.0003 0.0022** -6.2484 -5.9303 ***,**,* denote signiﬁcance at the 1%, 5%, and 10% threshold respectively.