Chunling Peng

HYSTERESIS: EVIDENCE FROM THE SOCIAL SECURITY DISABILITY

PROGRAM DURING THE JOBLESS RECOVERY

Chunling Peng

A dissertation submitted to the Faculty of the University of Delaware in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Economics

Spring 2020

HYSTERESIS: EVIDENCE FROM THE SOCIAL SECURITY DISABILITY

PROGRAM DURING THE JOBLESS RECOVERY

Chunling Peng

Approved: ______Michael A. Arnold, Ph.D. Chair of the Department of Economics

Approved: ______Bruce W. Weber, Ph.D. Dean of Lerner College of Business and Economics

Approved: ______Douglas J. Doren, Ph.D. Interim Vice Provost for Graduate & Professional Education and Dean of the Graduate College

I certify that I have read this dissertation and that in my opinion it meets the academic and professional standard required by the University as a dissertation for the degree of Doctor of Philosophy.

Signed: ______James L. Butkiewicz, Ph.D. Professor in charge of dissertation

Signed: ______Desmond Toohey, Ph.D. Member of dissertation committee

Signed: ______SeonYoung Park, Ph.D. Member of dissertation committee

Signed: ______Saul Hoffman, Ph.D. Member of dissertation committee

ACKNOWLEDGMENTS

I wish to thank the people, my adviser, James L. Butkiewicz; my committee member Desmond Toohey, SeonYoung Park, and Saul Hoffman. I must also thank my spouse, Cheng Liu, my parents and my two daughters, without whom none of this would ever been possible.

TABLE OF CONTENTS.

LIST OF TABLES……………………………………………………..….....viii LIST OF FIGURES………………………………………………..………….ix ABSTRACT………………………………………………………………….....xi

Chapter

1 INTRODUCTION………………………………………………………..1

1.1 The Jobless Recoveries and Hysteresis……..….……...……….1 1.2 Hysteresis and Disability………...………………...…………….2 1.3 Methodology & Identification………….………..……………….4 1.4 Implication…………………...……..……………….……………...6

2 BACKGROUND………………….………………………………………7

2.1 Introduction of Hysteresis Theory……..…………………….….7 2.2 The Relationship Between Hysteresis & Disability Programs.…………………………………………………………...8 2.3 Early Development of the DI Program..…………..………..…..9 2.4 Liberalization in 1984….……..….…...…………………..……..10 2.5 Hysteresis and the Jobless Recovery…...... ……12

3 LITERATURE REVIEW…………………...…………………………14

3.1 Hysteresis Literature…...…..………………………..…..……...14

3.1.1 Hysteresis Theory Research…………..….….…..…....14 3.1.2 Hysteresis Evidence: Unemployment Persistence....18 3.1.3 Hysteresis & Institutions……………………………...22 3.1.4 Recent Development…………...... ……………..………25

3.2 Disability Literature……….………..…………………………..26

3.2.1 DI Program and Labor Market Conditions.……..….26 3.2.2 The Liberalization in 1984….…………………..……..28 3.2.3 DI Program and Labor Participation……..……...…30

4 HYSTERESIS IN DISABILITY OF LOW-SKILLED WORKERS…………………………………………………………..….37

4.1 Introduction….…...…...………..……………...………………….37

4.2 Empirical Strategy….....……...……….…………………………39

4.2.1 Specification………….….…...…………..……………….39

4.2.1.1 The Difference-in-Difference Model………....40 4.2.1.2 Difference-in-Difference with Instrument….42 4.2.1.3 Difference-in-Difference with Instrument and FE………………………………………………...44

4.2.2 Data…….………..………….………..………...…………44 4.2.3 Pre-Treatment Comparison...…………….……………47

4.3 Empirical Results.…………....……….…………..….………..49

4.3.1 Hysteresis in Disability……………..…………………49

4.3.1.1 Positive Cyclical Shocks………….....…..……50 4.3.1.2 Existence of Insider-Outsider Mechanism: The Membership Rule and Benefit Premium………………………………………..52 4.3.1.3 Negative Structural Shocks…..……...…..…53

4.3.2 Comparisons with Existing Literature……..……….54

4.4 Conclusion………………….……...…….……………………….56

4.4.1 Robustness Check…………………………….………...56 4.4.2 Conclusion……...…….………..………...……………....57

5 HYSTERESIS IN DISABILITY OF OLDER WORKERS………..58

5.1 Introduction…………..….……………..…..…….…………….....58 5.2 Empirical Strategy…………...……………...…………………...61

5.2.1 Specifications……..………...………..…………………...61

5.2.1.1 The Difference-in-Difference Model……...... 62 5.2.1.2 Difference-in-Difference with Instrument.....64 5.2.1.3 Difference-in-Difference with Instrument and FE..………………………………………....……..65

5.2.2 Data…….………...…………….………..…….…...……...66 5.2.3 Pre-Treatment Comparison…….………..…..…….…...69

5.3 Empirical Results…….………….…………..……………………71

5.3.1 Hysteresis in Disability………….….…………...…………71

5.3.1.1 Positive Cyclical Shocks………….……………….72 5.3.1.2 Existence of Insider-Outsider Mechanism: The Membership Rule and Benefit Premium.……...74 5.3.1.3 Negative Structural Shocks…………..……….....75 5.3.1.4 Labor Institution……………..…..………..…...... 76

5.3.2 Comparisons with Existing Literature………………...... 76

5.4 Conclusion………………….…..………….……………..……….....79

5.4.1 Robustness Check……..………………………..……………79 5.4.2 Conclusion…………………………………………...…..……79

6 CONCLUSION…………………………….…..………...…………...…81

FIGURES & TABLES…………………………………………………………….....86

REFERENCES………………………...…………………………………...…….....148

Appendix

A BARTIK STATE-LEVEL LABOR DEMAND INSTRUMENT…...... 157

vii

LIST OF TABLES

Table 1: Variables & Description……...... ………………………………...... 100

Table 2: Summary of Variables………..…………………………………..…….103

Table 3: Disability Rate for Control & Treatment Group from 1980 to 2016……….…………………………...………………………………..106

Table 4: Summary Statistics for Control & Treatment Group.………………………………………………………………..…109

Table 5: Summary Statistics for Control & Treatment Group Pre- treatment…………...... ……………………………….111

Table 6: Hysteresis in Low Skilled Workers………..……………..……….....113

Table 7: Hysteresis in Low Skilled Workers (Robustness Check)….……...115

Table 8: Variables & Description...……...…….……………………………..…126

Table 9: Variables & Description……………...……………………………..…129

Table 10: Disability Rate for different age cohorts from 1980 to 2016…....132

Table 11: Disability Rate for Control and Treatment group from 1980 to 2016……………………………………………...……………………...134

Table 12: Summary Statistics for Control & Treatment Group…………………………..………………………………………..136

Table 13: Summary Statistics for Control & Treatment Group pre-treatment 1984 liberalization...…………………..……………………………138

Table 14: Summary Statistics for Control & Treatment Group pre-treatment (2000 NRA)……..…….…………………..…………………………….140

Table 15: Hysteresis in Older Workers……….……….……………………….142

Table 16: Hysteresis in Older Workers (Robustness Check)……..……..….145

viii

LIST OF FIGURES

Figure 1: Recovery Index of Real GDP. U.S. Post-War Recessions…..…...... 86

Figure 2: Recovery Index of Unemployment Rate. U.S. Post-War Recessions……….……………………………………………………...88

Figure 3: Recovery Index of Labor Force Participation Rate. U.S. Post-War Recessions………….…………………………………………………...90

Figure 4: Labor Participation Rate and Employment Population Ratio of United States from 1948 to 2016..……..……………………………....92

Figure 5: Timeline and Events of DI Program & New Benefit Awarded...... 93

Figure 6: Yearly Flow-in Disabled Population Ratio & Disability Employment Ratio……...…..………………………...... 94

Figure 7: Disabled population from year 1957 to 2014……………...……...... 95

Figure 8: Disabled Employment Ratio 1957 to 2014……….....………...... 96

Figure 9: Application Waiting Time (Months) of all Stages………...….…...... 97

Figure 10: Application at All Adjudicative Levels……….…….………..……….……..98

Figure 11: Application at Different Stages…………….………..…..…………………..99

Figure 12: Disability Rate for Control & Treatment Group from 1980 to 2016………………………….…………………………………………108

Figure 13: Unemployment Rate by State…….….………………..…………....116

Figure 14: Employment Population Ratio by State…….……...………….….118

Figure 15: Replacement Rate by State………...….………………………….…120

Figure 16: Employment in Agriculture by State………..…..………………...122

Figure 17: Employment in Manufacturing by State……………...... ………..124

Figure 18: Disability Rate for different age cohorts from 1980 to 2016…...133

Figure 19: Disability Rate for different age cohorts from 1980 to 2016

(Control & Treatment Groups)…………………...... ……………135

ABSTRACT

This paper studies the “hysteresis” in the SSDI (Social Security Disability

Insurance) program from 1980 to 2016 by comparing different models using CPS

(Current Population Survey) annual data with six million observations. The Social

Security Disability Benefits Reform Act of 1984 is used as an exogenous shock to create a unique natural experiment. Using the difference-in-differences with instrumental method and fixed effects, it is shown that demographics, education, marriage, number of children, benefit premium, membership rule and structural shocks are causal to the degree of hysteresis. My findings provide an important implication for the conduct of monetary and fiscal policy.

Chapter 1

INTRODUCTION

1.1 The Jobless Recoveries and Hysteresis

The three most recent U.S. cyclical recoveries (following the recessions of July 1990 to March 1991, March 2001 to November 2001 and December 2007 to June 2009) were labeled “jobless recoveries” which are characterized by higher unemployment in the job market although the economy has recovered from the recession (Gordon and Baily 1993; Groshen and Potter 2003;

Bernanke 2009). The 1949 recovery was the fastest one and 2009 recovery was the slowest (See Figure 1 to Figure 3 Recovery Index). The Real GDP was not fully recovered after two years during the recent three recoveries while the others reached an average of 104% of the pre-crisis level after two years. The unemployment rate fell fastest in the 1949 recovery and while in the recent three recoveries it kept increasing after the trough for 4 months. It reached the highest at 7.8% in June 1992, 6.3% in June 2003 and 10% in October 2009. The unemployment rate never fell to the pre-crisis level during 2001 recovery. It takes a very long time for the unemployment rate to go back to its pre-crisis level, 72 months for 1991 recovery and 96 months for 2009 recovery. On the other hand, the labor force participation recovery index had a downward trend in the 2009 recovery and was flat for the 1990 and 2001 recoveries.

The labor force participation rate (LFPR) broke through the 58% to 60% range in 1969 and ratcheted up to 67.3% in 1998, which was a 7.3% increment. 1

The labor participation rate lingered between 66% and 67% during the 1992 recovery, slightly dropped from 67% to 66% during the 2001 recovery and slumped from 66% to 62.4% in the Great Recession; this was the same as 1977’s level. The employment-population ratio is cyclical compared with LFPR. It reached a maximum 65% in 1999 and dropped to 62% during the 2001 recession. And it plummeted from 63% to 58.5% in 2009. The employmentpopulation ratio has never bounced back in the recent two recoveries. (See

Figure 4).

This is the salient indicator of hysteresis in economics. Hysteresis is the phenomenon in which persistently high unemployment fails to gravitate towards the natural rate of unemployment, arriving at a higher equilibrium level, and keeps that level for a protracted time (Blanchard and Summers

1986). That is how Blanchard and Summers (1986) described the unemployment dynamics from the 1960s to the 1980s in European countries.

Temporary or persistent shocks could affect workers’ long term employment or labor participation status, hence eroding their human capital. If these mechanisms are prevalent in the labor market, the hysteresis phenomenon exists in the economy. The shrinking labor force hinders the growth of the economy in the recovery.

1.2 Hysteresis and Disability

Since the 1980s, the increasingly generous Social Security Disability

Insurance (SSDI) and Supplemental Secuirty Income (SSI) programs in the

U.S. consistently attracted low-skilled workers to seek disability benefits or older workers (between 55 and 64 years old) to take early retirement due to the negative shocks. Having done this, these workers might not be able to return to the labor market (Zandweghe 2011; Hotchkiss, Pitts, and Rios-Avila

2012). However, whether these negative shocks are causal to workers’ decisions to enter and stay in the DI program is unclear.

Previous empirical studies on hysteresis did not address the issue of disability programs. The research has three categories and found the evidence or causality of hysteresis: (1) They use advanced time series forecasting models to test for a unit root of the unemployment rates (Mitchell 1993; Jaeger and

Parkinson 1994; Roed 1996; Leon-Ledesma 2002; Stanley 2004; Logeay and

Tober 2006). They report very mixed results regarding the hysteresis evidence of US labor market. (2) The second category was focused on whether fiscal policy and monetary policy have a substantial effect on the degree of hysteresis; evidence showed this to be true (Ball 1999; Stockhammer and Sturn 2012;

Schmitt-Grohé and Uribe 2012; Reifschneider, Wascher, and Wilcox 2015). (3)

The third category was to inquire whether local unemployment shock caused working-age individuals less likely to be employed, which was also found true

(Yagan 2017).

The previous empirical research on disability shows both that the

economic downturns are causal to disability application (Autor and Duggan

2003; Coe and Rutledge 2013; Lindner and Burdick 2013; Maestas, Mullen, and Strand 2013; Maestas, Mullen, and Strand 2015), and that the impact of rising disability claims influences labor participation (Parsons, 1980;

Leonard,1979; Halpern and Hausman, 1986; Haveman and Wolfe, 1984;

Bound 1989,1991; Bound and Waidmann, 1992; Parsons, 1991; Gruber and

Kubik, 1994; Gruber, 2000; Chen and van der Klaauw, 2008). Few studies examine the effects of temporary and persistent shocks on human capital accumulation over time.

1.3 Methodology & Identification

To answer this question, I take advantage of the disability programs as a window to observe the hysteresis phenomenom. Legislation in 1984 revised the disability criteria and allowed individuals with low mortality symptoms, musculoskeletal conditions and mental disorders to be eligible. The legislation extended the disability determination from objective to subjective criteria. This change provides me with a unique quasi-experiment. Low-skilled workers are more likely to be qualified for the benefit. In Chapter 4, I use a difference-in- difference model and find the existence of the insider-outsider mechanism and the effects of cyclical labor market shocks and structural shocks on the degree of hysteresis. The low-skilled workers are 5.2% more likely to be disabled and not working after the 1984 liberalization. An increase of 1% in the replacement rate was leading workers to a 0.15% higher probablity of being disabled and 4

not working. A decrease of 1% in the employment rate in agriculture and manufacturing industry increased by 0.1% the probability of workers being disabled and not working. An increase of 1% in the unemployment rate decrease by 0.3% the probability of workers being classified as disabled during the three recent recoveries.

In Chapter 5, I find almost the same result as that of Chapter 4 while I change the treatment group to older workers from 55 to 64 years old. I use the

1984 legislation liberalization and the Normal Retirement Age (NRA) policy change as two exogeneous shocks. In 1983, legislation passed adding two months per year to the retirement age until the year 2000 at which the retirement age would be 67, where it was to remain. The older workers are favored by the 1984 legislation change to qualify for the benefit. Meanwhile, the gradual reduction of the expected total pensions due to reaching the ceiling of the NRA in 2000 raised the incentive for older workers to seek early retirement in the SSDI program. I find older workers are 2.1% more likely to be disabled and not working than younger workers after the two legislative changes. In addition, the implementation of the NRA has led to an 8.7% to

11.9% higher probability of being disabled and not working. This is hysteresis due to labor institutions.

One of the major identification challenges is the endogeneity of the unemployment rate. The unemployment rate tends to decrease if health shocks

cause the disability population to rise. The 1984 legislation liberalization can act as an exogeneous shock to identify the impact of unemployment on participation in disability programs. This quasi-experiment assumes that the legislation liberalization was not driven by the individual’s disability condition and all the cohorts are on a parallel trend pre-treatment. Therefore, the difference-in-difference strategy is to compare the effect between the treatment group and a control group.

1.4 Implication

Research on hysteresis has been identified as a policy-significant research topic by the former Chair of the Board of Governors of the Federal

Reserve (Yellen 2016). She said, “Hysteresis effects--and the possibility they might be reversed--could have important implications for the conduct of monetary and fiscal policy.” This paper extends the hysteresis literature by the design of a quasi-experiment and finds the evidence driven by an insider- outsider mechanism (Blanchard and Summers 1986). The research shows the effects of 1) the membership rule, 2) the benefit premium, 3) positive cyclical labor market shocks, 4) negative structural positive shocks on the degree of hysteresis and 5) the labor institution. These findings may serve as a policy recommendation for human capital accumulation and also the conduct of monetary policy.

Chapter 2

BACKGROUND

2.1 Introduction to Hysteresis Theory

Blanchard and Summers (1986) described the hysteresis phenomenon in human capital accumulation. Temporary or persistent shocks could affect workers’ long term employment or labor participation status. They find the most promising mechanism for generating hysteresis to be the insider-outsider relationship. The insider-outsider approach places some labor market power into the hands of the employees. The wage rate is higher than that of competitive equilibrium and the position is quite sustainable. In the presence of a negative shock, which reduces employment, some workers lose their insider status and turn into outsiders. The new smaller insider group will redo the whole process so as to maintain the new lower level of employment

(Blanchard and Summers 1986). The outsiders lose the opportunity to maintain and update their skills and their attachment with the labor force, particularly for the long-term unemployed. Their skill deterioration leads to the inability to find a new job.

Galí (2015) revisits hysteresis theory and develops a new insider- outsider relationship model (Blanchard and Summers 1986) under the framework of a New Keynesian unemployment model (Galí, 2011; Galí, Smets, and Wouters, 2011). Following the previous theoretical literature (Blanchard

and Summers 1986), he finds that wage inflation depends on the change of employment instead of the gap between the unemployment rate and its natural counterpart. So wages increase as employment decreases in the economy. The employer has to keep fewer workers in order to maintain the wage premium for the insiders.

2.2 The Relationship Between Hysteresis and Disability Programs

The disability program could generate hysteresis through the insider- outsider mechanism due to the following two reasons. 1) The Social Security

Disability Benefits Reform Act of 1984 added musculoskeletal disorders and mental illness to the disability category and more people became eligible for the DI program. 2) The disability payment was inflated by the ratio of the average wage in the United States economy plus the rising value of medical benefits which increased the replacement rate of lower skilled workers. The relatively higher benefit payment and membership expansion created the incentive for unemployed and employed workers to seek DI benefits due to miscellaneous shocks and led a large number of workers to drop out of the labor force. Some of them applied for and were awarded DI benefits. Those workers have little chance of returning to the labor force. Hence, the social security disablity program is a window to observe the hysteresis phenomenon and estimate the effects of different factors on the erosion of human captial.

2.3 Early Development of the DI Program

The Social Security Disability Insurance program (SSDI) was enacted in 1956. Originally, the benefit was awarded to individuals who were older than 50 and have a chronic physical condition, a physical condition with high mortality rate, or a severe mental impairment. Figure 5 shows the timeline of the DI program. In 1960, individuals under the age of 50 were made eligible.

In 1965, the requirement of disability was expanded to include impairments expected to last at least a year, allowing those without permanent disabilities to qualify. In 1970, the Social Security Administration increased the ad hoc, across-the-board benefit by 15and by 10 in 1971, by 20% in 1972, and by

11% in 1974. Automatic cost-of-living adjustments (COLAs) began in

June 1975. In 1972, the waiting period from the onset of the disability to application was reduced from six to five months. In 1973, DI beneficiaries became eligible for Medicare after 24 months of disability. The reform from

1977 to 1982 was intended to refine the regulations and reduce the program size and award rates. First, the Social Security Administration decreased the disability benefits and replacement rates. As we can see figure 5 shows the legislative events of the DI program from 1957 to 2014. The 1977 Amendments

"decoupled" the effects of inflation on earnings and the adjustments of the consumer price index (CPI) in the benefit formula to reduce the unintended increase in disability benefits and replacement rates. The 1980 Amendments instituted a reduction of additional benefits by capping the family benefit

amount and reducing the number of dropout years in the benefit calculation.

In 1981, Congress eliminated the Social Security minimum benefit (minimum primary insurance amount) and placed a cap on the replacement rate from all public disability program benefits.

2.4 Liberalization in 1984

The 1984 liberalization has two major changes as follows: the extention of disability eligiblity criteria and the change of application process. At first, the legislation extended the criteria of disability to , musculoskeletal diseases and mental illnesses which are subjective rather than objective diagnoses.

“Objective” refers to information or analyses that are fact-based, measurable and observable, while “subjective” refers to information or analyses that are based on personal opinions, interpretations, points of view, emotions and judgment. Hence, the diagnosis process not only involves applicants’ personal feelings, but also doctors’ judgements. The program size kept expanding.

Figure 5 shows the newly awarded disabled workers per year. The total newly awarded disabled workers increased from 1.8 million in 1984 to 3.6 million in 2014, which is a 100% increase. The increase was particularly remarkable during the 2007 Great Recession, when the number of newly awarded disabled workers increased from 2.8 million to 3.6 million in just two years. In other words, 2% of all employed workers were awarded the disability benefit in 2009 (See Figure 5). Figure 6 shows a flatter upward trend since the

1980s, fluctuating around 2.2 million during 1990, and a steeper upward trend after 2000. The gradual increase in flow-in and the decrease in flow-out creates an upward trend of total DI workers. The total number of disabled workers increased from 40 million to 60 million within thirty years (See Figure 7) and resulted in the increase of the disability employment ratio from 22% to 31%

(See Figure 8). This means that among the working-age labor force, 31% are disabled.

Second, if the original Disability Insurance application is denied, applicants can pursue three levels of appeals, which is time consuming. Those who are denied first can request a reconsideration by a different team at the state office of Disability Determination Services. If the application is denied again, the applicant can then request a hearing with an Administrative Law

Judge. If that fails, the applicant can appeal at the highest levels: to the Social

Security’s Appeals Council, to the U.S. District Court and finally to the U.S.

Circuit Court of Appeals. The application in every stage is time consuming (See

Figure 9). The procedure takes an average of 82.3 months if the applicants exhaust all the appeal opportunities. The applications at all adjudicative levels

(Figure 11) started at 1.2 million in 1999 and ratcheted up to 3 million in the

Great Recession; between 1999 and 2014 the applications at every stage increased dramatically: they almost doubled, especially at the reconsideration and hearing levels (Figure 11). In 1986, about 54% of reconsideration denials were appealed to Administrative Law Judges. In 1997, 83% of all

reconsideration denials were appealed. In 2000, 98% of applicants whose claims were denied by an Administrative Law Judge appealed their claim to the Social Security Administration Appeals Council (Autor 2011).

2.5 Hysteresis and the Jobless Recovery

Hysteresis could be a possible explanation of the non-increasing labor participation rate phenomenon in the most recent three recoveries. The decline of the employment population ratio indicates the workers’ decision to drop out of the labor force, leading to human capital erosion, and to slow economic recoveries, accordingly.

The labor demand shift also explains hysteresis and jobless recoveries.

The U.S. labor market underwent rapid secular growth in the demand for occupations involving cognitive skills (Katz and Murphy 1992; Acemoglu 2002;

Beaudry and Green 2005) and for lower-paying manual-service jobs, and experienced a reduction in middle-wage routine occupations from 1960’s to

2000 (Juhn 1999; Autor and Murnane 2003; Autor, Katz, and Kearney 2006,

2008; Autor and Dorn 2013). The labor demand for cognitive tasks has been declining since 2000 (Beaudry, Green, and Sand 2016) which directly affects high-skilled workers. Moreover, the decline has substantial impact on both high-skilled workers and low-skilled workers, because high-skilled workers move down to perform jobs which were formerly the occupations of lower- skilled workers. The mechanism results in not only high-skilled workers, but 12

also low-skilled workers being pushed out of the labor force. This phenomenon was even worse in the great recession.

The drop of the aggregate of the LFPR is an indicator of hysteresis. Some prior literature addresses the issue by decomposing the aggregate LFPR by cyclical and demographic factors. Their findings are mixed. Aaronson et al.

(2006) decompose it into cyclical and structural factors. Aaronson, Davis, and

Hu (2012) show that only one-fourth of the decline in the LFPR since 2008 is attributable to demographic factors. Zandweghe (2011) finds cyclical factors account for 58% of the decline. Erceg and Levin (2013) and Hotchkiss and Rios-

Avila (2013) conclude that the decline in the LFPR since the Great Recession is fully explained by the deterioration of labor market conditions.

Chapter 3

LITERATURE REVIEW

3.1 Hysteresis Literature

3.1.1 Hysteresis Theory Research

“Hysteresis” is physics terminology that explains the behavior of magnetic materials. Hysteresis describes that the lag in time between the application of an external force and a change in the internal state of some thing.

The dynamics depends on the history path even after the external force dissipates, the recovery of the original state may be possible, although it may require a counter external force and a relatively extended period of time.

Blanchard and Summers (1986) incorporate hysteresis into labor economic theory by arguing that increasing unemployment has a direct impact on the nonaccelerating inflation rate of unemployment (NAIRU). The persistent high unemployment fails to gravitate towards the natural rate of unemployment and maintains the trend for a protracted time. Most previous hysteresis theoretical research focused on the insider-outsider mechanism. The human capital mechanism has never been discussed thoroughly.

Blanchard and Summers (1986) find the most promising mechanism for generating hysteresis is the insider-outsider mechanism. The employment context is changed due to negative shocks and leads to long-term persistent high unemployment. The employment context is changed through insider and outsider relationships (Lindbeck and Snower 1986).

From classical labor economic theory, a contract is determined through a screening and signaling processes in which the wage has a direct effect on productivity. The insider-outsider approach places some labor market power into the hands of employees (Lindbeck and Snower 1986). The wage rate is set by negotiation between employers and insiders; outsiders are unable to underbid insiders, or employers are unwilling to accept such bids due to the turnover cost: the cost of exchanging current, full-fledged employees for new employee. In this case, employers and insiders are highly incentive-compatible at a higher wage rate and long-term employment. Meanwhile, the insiders increase the reservation wages of the underbidders by harassing them. So the outsiders are unwilling to underbid the prevailing wage. As a result, the wage rate is higher than that of the competitive equilibrium and the position is quite sustainable. In the presence of a negative shock, which reduces employment, some workers lose their insider status and turn into outsiders. The new smaller insider group will redo the whole process so as to maintain the new lower level of employment(Blanchard and Summers 1986). The outsiders lose the opportunity to maintain and update their skills and their attachment with the labor force, particularly for the long-term unemployed. Their skill deterioration leads to the incapability of finding a new job.

If the mechanisms were a prevalent feature in the labor market, unemployment would show no tendency to go back to the pre-shock level accordingly. Moreover, the consistent negative shocks will repeatedly trigger

the mechanisms which will result in the ratchet-up of the unemployment rate.

Like what we can see in the unemployment dynamics in European countries, the failure of correction in the labor market results in an error-compound-error ascending in the unemployment rate. Hysteresis theory shows the mechanism explaining the increasing NAIRU based on the features of the labor market and the lag value of the unemployment rate.

The central debate of hysteresis theory is the causality of persistence. Is it due to the exogeneous shock or is it triggered by itself? Blanchard and

Summers (1986) point out that even the time series forecasting of AR (1) regarding the UK and US unemployment rates seems indeed highly persistent,

However, the cause of the changes cannot be identified. So they use OLS estimation of wage equations for the UK, France, Germany and the US from

1953 to 1984. They regress the rate of wage inflation on the expected price inflation, the change of one lag and two lags employment. They find that wage inflation strongly depends on the change in employment instead of employment itself, which tells a story of labor market distortion. The results indicate that a substantial degree of hysteresis exists in Germany, France, and the United Kingdom with higher persistency in Europe than in the United

States. When the same model is estimated separately for the 1952-1968 and

1969-1984 periods,the degree of hysteresis in the United States is much higher in the earlier period while lower in the latter period. The results suggest that bad times as well as unions account for hysteresis.

Galí (2015) revisits hysteresis theory and develops a new insider- outsider relationship (Blanchard and Summers 1986) under the framework of the New Keynesian unemployment model (Galí, 2011; Galí, Smets, and

Wouters, 2011). The contribution of this paper are the three hypotheses on the unit root in unemployment based on the the New Keynesian unemployment model framework: the natural rate hypothesis, the long-run tradeoff hypothesis and the hysteresis hypothesis. Following the previous theoretical literature (Blanchard and Summers 1986), hysteresis may result in wages being insufficiently responsive to unemployment, possibly explaining the persistence of the unemployment rate. By resetting the wage inflation equation consistent with the Calvo wage setting formalism, Galí finds that wage inflation depends on the change of employment instead of the gap between the unemployment rate and its natural counterpart. In other words, wages are set by the historical value of employment. The empirical research based on the derived New Keynesian unemployment model shows that none of the three hypotheses can account for the evidence on unemployment and wage inflation for the period 1970-2014 in the European area. The long-run tradeoff hypothesis could possibly explain the secular rise in unemployment in 1970s and 1980s as a consequence of the disinflation. The hysteresis hypothesis can account for the remarkable stability of wage inflation from 1994 to 2014.

3.1.2 Hysteresis Evidence: Unemployment Persistence

The following empirical research arises and shows controversial results of unemployment persistence by applying different methodologies using different dimensions and times. Most of them use an advanced time series forecasting model to test the unit root of unemployment rates. In a nutshell, there are four approaches provided in the previous literature. They have very mixed results regarding the US labor market.

One approach is the classical unit root test, basically the augmented

Dickey-Fuller (ADF) type. This approach finds hysteresis in OECD countries but mixed results for the US (Mitchell, 1993; Roed, 1996). Mitchell (1993) conducts unit root tests aiming at distinguishing whether the unemployment of OECD countries is a trend-stationary (TS) process or a difference-stationary

(DS) process. He argues that the Natural Rate Hypothesis (NRH) and the

Hysteresis Hypothesis (HH) can be represented as TS and DS processes, respectively. This is because TS is stationary after adding a structural break while DS is non-stationary which is stochastic. In other words, the TS process does not influence the long-term value while the DS process depends on the history of the value. The study uses quarterly data from the mid 1960s to 1991 and fails to reject the unit-root hypothesis generally. The unemployment rates in 15 OECD countries are a DS process except for Italy, and Finland’s status is questionable. Roed (1996) finds the similar results by studying 16 OECD countries from 1970 to 1994; only in the US is hysteresis rejected. The

difference from (Mitchell 1993) is Roed (1996) additionally uses the univariate

ARMA (Autoregressive-Moving-Average) on unemploymentra rates and

ARIMAX (Autoregressive Integrated Moving Average) by adding “unexpected” shocks in the world GDP growth rate and the change of oil prices into the MA process. This technique separates the exogeneous shocks from the dynamics of unemployment.

The usual procedure to test for a unit root is to apply the Augmented

Dickey-Fuller (ADF) or Phillips-Perron (PP) test to the unemployment series.

The low power of these tests against the stationary alternatives comes into question when the process is nearly integrated. So several recent studies focused on panel unit-root tests. They find similar results to the classical unit root tests.

Leon-Ledesma (2002) contributes an advanced time series Im-Pesaran-

Shin (IPS) test which can deal with cross-sectional dependence and a higher degree of heterogeneity to test the stationarity of unemployment rates, which is important given that OECD countries are highly correlated economies. He uses quarterly data of the unemployment rates of 51 US states and 12

European countries from 1985Q1 to 1999Q4. The results confirm a higher degree of persistence in European countries than in of the US. Stanley (2004) performs a meta-regression analysis of 24 publications with 99 regressions on the determinants of unemployment and finds a persistence coefficient of past unemployment of 0.864. The coefficient rises to 0.96 after weighting the small-

sample bias, which implies that unemployment has a unit root.

The third approach is structuralism. Structuralists argue that unemployment is stationary around a changing mean. The control of structural changes will change the results of a unit root test: the unit root hypothesis is often rejected by this methodology (Arestis and Mariscal, 1999; Papell, Murray, and Ghiblawi, 2002; Leon-Ledesma and McAdam, 2004; Camarero, Lluis, and

Tamarit, 2006). The earlier structuralists employ classical methodology while expanding the literature by adding panel unit-root tests later on. Arestis and

Mariscal (1999) apply the mean shift and trend shift tests to the unemployment rates of 26 OECD countries from the period 1960 to 1997 allowing for two endogenous breaks. The tests show the clear evidence of a unit root in unemployment rates for Austria, Canada, Japan and the US, but not for Australia, Belgium, Denmark, France, Germany, Greece, Iceland, Ireland,

Luxembourg, Netherlands, Portugal, Spain, Sweden, Switzerland and Turkey.

Papell, Murray, and Ghiblawi (2002) estimate additive outlier (AO) models in which the structural change is assumed to occur instantaneously.

The hypothesis of the unit root is rejected for ten of sixteen countries after incorporating one structural change. Camarero, Lluis, and Tamarit (2006) exploit the cross-sectional variations of the unemployment rate using panel data for 19 OECD countries covering the period from 1956 to 2001 allowing a different number of endogenous breakpoints. The methodology accounted for cross-correlation in the residuals, because not only the financial markets but

also the labor markets of these EU countries are highly correlated which seems unrealistic to explain the independence while the stationarity tests are performed. Hysteresis is rejected both in the panel and in the country-by- country individual tests.

Leon-Ledesma, and McAdam (2004) quantify the degree of persistence in the unemployment rates of transition countries. They take into account the existence of structural breaks and nonlinear dynamics in unemployment and observe the hysteresis in 12 Central and Eastern European Countries (CEECs).

They also employ the advanced time series IPS test with which can deal with cross-sectional dependence and a higher degree of heterogeneity to test the stationarity of unemployment rates given that CEEC countries are highly correlated in economy. The unit-root hypothesis is rejected after controlling for structural changes and business-cycle effects. Transition countries’ unemployment shows a faster speed of adjustment and larger changes in unemployment equilibria.

The last approach is the Kalman filter technique (Jaeger and Parkinson,

1994; Logeay and Tober, 2006). Jaeger and Parkinson (1994) apply an unobserved components model using a Kalman filter. The observed unemployment rate is decomposed into a natural rate component and a cyclical component. Hysteresis effects are introduced so that a one lag historical cyclical unemployment rate has an effect on the natural rate. The empirical analysis uses seasonally adjusted quarterly data for Canada, Germany, the UK,

and the US from 1961Q1 to 1991Q4. They find substantial hysteresis in the

Canadian, German and the UK unemployment rates and the magnitude of hysteresis. An increase in the cyclical unemployment rate of 1% leads to a permanent increase of the actual unemployment rate of about 0.20%. They do not find any hysteresis effects explaining movements in the US unemployment rate.

Logeay and Tober (2006) adopt the same methodology by estimating an additional equation, a Philips curve describing the interrelation between the unobserved unemployment gap and the observed inflation rate. They use quarterly data for the period from 1973 to 2002 in Euro-zone countries. The estimation shows a similar magnitude of hysteresis, between 0.22 and 0.18 for the United Kingdom and West Germany, respectively. In the two periods of increasing unemployment (1980-1984 and 1991-1993), hysteresis effects explain 45% to 65% of the NAIRU increase. Hysteresis effects explain 42% of the decrease of the NAIRU from 1997 to 2000.

3.1.3 Hysteresis & Institutions

Reifschneider, Wascher, and Wilcox (2015) introduce hysteresis to the

Federal Reserve suggesting that the cyclical downturn affects the long run trend. The shortfall of aggregate demand has diminished the productive capacity of the economy. In other words, deep recessions have significant negative effects on the supply side of the economy which result in some

structural damage in the labor market. So the endogeneity of aggregate supply leads to high persistent unemployment in the labor market. Two main policy scenarios, fiscal policy (Ball, Delong, and Summers 2014) and monetary policy

(Stockhammer and Sturn 2012; Schmitt-Grohé and Uribe 2012; Reifschneider,

Wascher, and Wilcox 2015), are widely debated in the political and economic aftermath of the Great Recession. Both are inclined to influence the supply side of the economy through the demand side to restore the potential output level.

Blanchard and Summers (1986) devote quite a bit space to explaining the hysteresis problem saying that a high unemployment rate reflects a high natural unemployment rate caused by labor institutions. Aggregate demand expansion could possibly lead to higher wage inflation unless a necessary labor institution reform is pursued. In other words, monetary easing during recovery affects wage rates and unemployment rates under the circumstance that labor institution rigidity is, to some extent, removed and a flexible labor market is built.

Ball (1999) challenges the conventional theory that monetary policy has both short-term and long-term effects on unemployment. Moreover, he finds evidence that expansionary policy has a long-run effect of aggregate demand in an analysis which covered the recessions of 17 OECD countries in the early

1980s. The mechanism is as follows: the expansionary policy reacting in the recession period decreases NAIRU; when the NAIRU falls, the further

expansion starts. The expansion leads to a further downturn in NAIRU. So aggregate demand expansion does lead to the improvement of the labor market.

The contribution of Ball (1999) is that he exams hysteresis in aggregate demand. He finds that expansions can produce permanent decreases in unemployment. First, he regresses the change of NAIRU and degree of hysteresis on the maximum easing policy and the duration of unemployment benefits. The change of the NAIRU is defined as the value of the peak before the recession to five years after the peak. The degree of hysteresis is defined as a ratio with the change in the NAIRU from its peak to five years later as the numerator and greatest increase in actual unemployment within five years after the peak as the denominator. He finds that the maximum easing and the duration of unmeployment benefits have big effects on the degree of hysteresis.

The reactions of policy makers to the early 1980s recessions largely explain the differences between 17 OECD countries. In the countries which conducted strong countercyclical policies, unemployment rose temporarily while, in the countries which conducted tightening policies, the unemployment rate remained persistently high in the face of the 1980s recession. Second, he finds controversial inflation results for countries that dealt successfully with the recession and those that failed. The successful countries had a transitory high unemployment rate accompanied by lower inflation with demand expansion.

The countries that failed had a permanent high unemployment rate with inflation runups. Finally, he compares the average replacement rate for

unemployment benefits and the tax wedge and finds labor market policies are not important causes of the unemployment successes and failures since 1985.

Stockhammer and Sturn (2012) use an extensive sample to investigate whether the occurrence of hysteresis in the aftermath of recessions depends on monetary policy reactions. The data set includes all 40 recessions between

1980 and 2003 for 19 OECD countries with quarterly data for output, short- term interest rates, consumer price index, unemployment and Non-

Accelerating Wage Rate of Unemployment (NARWU). Also, a set of labor institution variables are controlled, such as labor market spending per unemployed to GDP per capita, employment protection legislation, product market regulation, tax wedge, union density, unemployment benefit duration in years, average unemployment benefit replacement rate, etc. The results of econometric analysis show that monetary easing has a substantial effect on the degree of hysteresis: a one standard deviation change in monetary easing reduces the degree of hysteresis by 0.27. Labor institutions were not found to have substantial effects.

3.1.4 Recent Development

Federal Reserve Chair Janet Yellen has recently emphasized that the

Federal Reserve is concerned about possible hysteresis effects. “Hysteresis effects--and the possibility they might be reversed–could have important implications for the conduct of monetary and fiscal policy (Yellen, 2016).”

Yagan (2017) uses local areas in the US to test whether the Great Recession depressed 2015 employment. He found that exposure from 2007 to 2009 to a 1% larger local unemployment shock caused working-age individuals to be 0.4 percentage points less likely to be employed at all in 2015. Also, disability enrollment explains a small amount of 2015’s depressed employment.

3.2 Disability Literature

3.2.1 DI Program and Labor Market Conditions

The mechanism of early retirement and disability (Blanchard and

Summers 1986) might explain the permanent decline of LFPR since the persons with welfare benefits were unlikely to return to the labor market (S.

Aaronson et al. 2014). What would happen to unemployed workers if they were facing a bleak job market future? They might decide to take early retirement or seek disability benefits with little chance of returning to work once the recession concluded (Zandweghe 2011; Hotchkiss, Pitts, and Rios-Avila 2012).

The DI entitlement payment program turns into their only income resource.

Therefore, the variation of employment has a casual effect on the variation of disability rates and application of disability welfare benefits.

Some recent research tries to explore and explain the relationship between DI applications and labor market conditions, individual characteristics, and the composition of DI applicants (Coe and Rutledge 2013;

Lindner and Burdick 2013; Maestas, Mullen, and Strand 2013; Maestas,

Mullen, and Strand 2015). Maestas, Mullen, and Strand (2015) use data for 26

claims filed from 1992 through 2012 and find that economic downturns are causal to disability applications. Lindner and Burdick (2013) explore the characteristics of DI applicants over the business cycle; they find that SSDI applicants during recessions have higher past earnings and more recent work experience. Also, the applications and allowances for SSDI due to a higher unemployment rate are from people whose applications are either initially rejected or determined by vocational factors. Coe and Rutledge (2013) find the composition of DI applicants’ individual characteristics changes across the business cycle: applicants during economic downturns are younger, better educated, have higher incomes, and are more likely to have recent work experience. Maestas, Mullen, and Strand (2013) study the disincentive effect of the SSDI program on the labor supply of applicants. They compare the labor supply of similar applicants who are allowed or denied benefits on the basis of random assignment to disability examiners and find that the SSDI caseload has become increasingly dominated by individuals with impairments that are particularly difficult to assess, such as mental and musculoskeletal impairments.

Black et al. (2002) use the local economic change as a exogeneous shock to identify the impact of earnings growth on disability program participation.

He takes advantage of variations in workers’ earning growth during the coal boom of the 1970's and the bust of the 1980's, and between counties which have different coal reserve levels. During the coal boom, the growth in average

county earnings was 5.8 percent for counties with large coal reserves, 3.4 percent for counties with moderate coal reserves, and 2.2 percent for counties without coal. During the bust, noncoal counties grew 2.7 percent annually while coal counties had essentially no earnings growth. He finds that for the

DI program, the elasticity of program payments with respect to local earnings is about -0.3 or -0.4. For SSI, the elasticity is somewhat larger, between -0.4 and -0.7. This analysis provides clear evidence that as the value of labor market participation increases, disability program participation falls.

3.2.2 The Liberlization in 1984

The liberalization in 1984 allowed individuals with musculoskeletal conditions and mental disorders to be eligible for SSDI. The definition of disability that Congress adopted in 1984 is so comprehensive that it increased the difficulty of the determination process. The new legislation in 1984 requires the Social Security Administration to evaluate an applicant’s workplace function and the extent of pain or mental illness. Moreover, if the original Disability Insurance application is denied, an applicant can pursue three levels of appeals.

Autor and Duggan (2003) study the impact of the supply and demand for DI benefits on the labor force behavior of low-skilled workers during the period of 1978-1998. They find that DI application rates for given demand

shocks rose secularly after the disability reforms of 1984, reaching two to three times their prereform levels by the late 1990s.

They use differential time variation in average benefits across geographical regions to identify the impact of DI on the LFP of low-skilled workers. Moreover, they not only explore the impacts of disability benefits on the level of labor participation in the United States, but also expand the existing literature by analyzing the impact of the supply of disability benefits on the responses of low-skilled workers to adverse labor market shocks. Their research suggest that less stringent DI screening and higher replacement rates coupled with declining labor market prospects for the low skilled are likely to have increased the propensity of job losers to exit the labor force to seek disability benefits.

They develop two sets of instrumental variables as proxies for demand and supply conditions. On the supply of disability benefits side, they exploit the progressivity of the DI benefits formula as the exogenous variation. On the demand side, they use a weighted sum of the national industry employment change projected onto the state industry composition. They find that DI application rates for given demand shocks rose secularly after the disability reforms of 1984, reaching two to three times their prereform levels by the late

1990s; the propensity of displaced high school dropouts to exit the labor force increased 60 percent, which is consistent with labor market conditions with respect to low-skilled workers. On the other hand, they find that the increasing

supply of DI benefits induced the labor force exit by low-skilled workers between 1984 and 1998. The state level reductions in the benefits supply induced relatively large increases in the labor force participation of male and female high school dropouts, but did not induce change in the labor force participation of more educated workers. Finally, they calculate the magnitude of impact of the labor force participation rate on the US unemployment rate.

Their findings suggest that the unemployment rate among adults ages 25 to

64 in 1998 would have been about 13 percent higher if it had not been for the liberalization of DI in 1984.

3.2.3 DI Program and Labor Participation

The majority of these works focused on the impact of the DI program’s screening and generosity on the behavior of older workers. Other works expanded to include all ages, female workers (Chen and van der Klaauw 2008) and low-skill workers (Autor and Duggan 2003). They found quite a wide range of elasticity of DI benefits on the labor force non-parcicipation. The central issue in this topic is the degree to which disability participation and labor-force participation are substitutes.

The challenge of identification is the endogeneity of the benefit denial rate and replacement rate. For example, workers with chronic health problems might have lower earnings that would qualify them to have a higher replacement rate. In this case, a high replacement rate may simply reflect a

worker’s chronic health problems and dropping out of the labor market is positively correlated with health problems, not the generosity of the DI benefit.

It is possible that the benefits formula produces a higher replacement rate for low income workers, so low income workers have a higher incentive to apply and participate in the DI program. In this case, a high replacement rate is positively correlated to the decline of lower skill workers’ wage rates. In order to solve this identification problem, most of the latter literature used advanced econometric techniques such as difference-in-difference, regression discountinuities, etc..

Parsons (1980) uses cross-sectional varation to identify the labor force non-participation of older males due to DI generosity. He uses 1969 data of males from 48 to 62. His estimate shows that DI generosity has a very large disincentive effect in the participation of older males. The estimated elasticity of non-participation is from 0.63 to 1.80. Subsequent studies find lower elasticities of non-participation with respect to DI benefits. The most significant difference in idenfitication is that Parson’s techniques separate the application for benefits from the awarding of benefits.

Leonard (1979) and Halpern and Hausman (1986) consider the application acceptance probablity of benefits, which is the level of benefits multiplied by the probability of acceptance. He argues that applicants anticipate the probablity of acceptance before application. So the application for DI is endogeneous of the previous denial rate. The data he uses is the 1972 31

Survey of Disabled and Non-disabled Adults (SDNA). The labor force non- participation elasticiy is in the range of 0.12 to 2.0. Haveman and Wolfe (1984) also consider the effects of DI on labor force non-participation without separating out the applications process from the labor force status outcome.

The estimated elasticities range from 0.06 to 0.21.

Bound (1989,1991) argues the identification problem in modeling the effect of DI benefits on workers’ participation decisions. Low income workers have a higher incentive to apply for the benefits because they receive a higher replacement rate from DI. An alternative methodology examining the behavior of workers who are not accepted for DI benefits should provide an upper boundary on the potential labor force participation of accepted workers, because those who are refused are healthier and more capbable of participating in the labor force than the people who get accepted. By using data from the

1972 survey of Disabled and Non-Disabled Adults (SDNA) and the 1978 Survey of Disability and Work (SDW), he finds that the DI program’s growth explains no more than 40% of the rise in non-participation among older males.

Bound and Waidmann (1992) argue that changing the availability of disability benefits is at least as important a determinant of behavior as was changing the generosity of benefits. They use data of the National Health

Interview Survey (NHIS) from 1957 to 1987. First, they find the fraction of older working-aged men (45 to 64 years old) identifying themselves as unable

to work to help answer the question of whether those currently receiving disability benefits would work if benefits were not available. The basis of the logic is their assumption that the actual health of the older working-aged population was not deteriorating during this period. Therefore, the change in labor force participation can be accounted for by changes in the fraction of men indicating that their health limits their ability to work. They find that starting in 1970, changes in the proportion of 45 to 54 year old men identified as disabled closely mirror changes in the proportion of this age group out of the labor force. For men 55 and above the drop in participation is substantially greater than the rise in the proportion of men identified as disabled. This evidence suggests to us that, for 45 to 54 year-old men but not for those 55 and above, a major part of the drop in labor force participation that occurred during the 1970s was associated with the movement of men onto the disability rolls.

Parsons (1991) also pursues the approach of the DI program’s availability instead of its generosity. He takes advantage of the DI denial rate increase in 1977 and presents a self-screening model that predicts the fall of applications as the denial rate rises or the delay in the benefits application process increases. A moral hazard problem is implied in the administration of the DI program because applicants may be unaware of appeal prospects or the eligibility decision delay is lengthened as denied applicants are forced to pursue their claims in the appeals process. Halpern and Hausman (1986) also find strong effects of denial rates on applications probabilities. The estimated

elasticity of applications with respect to changes in the denial rate is approximately 0.45.

Gruber and Kubik (1994) takes advantage of the increase of the DI application denial rate from 53.8 percent in 1977 to 69.3 percent in 1980 which is due to the response to a funding crisis in some states. They use the data from

National Health Interview Survey for 1976 through 1978 and examine the effect of changes in denial rates from 1977 to 1980 on the labor non- participation decisions of 45 to 64 year old male workers with probit model.

They find that a 10% increase in denial rates led to a 2.7% fall in non- participation among 45 to 64 year old males. Their finding is close to the findings of Leonard (1979), Haveman and Wolfe (1984) and Halpern and

Hausman (1986). It also implies the likelihood of returning to work in this era presented in Bound (1989) and Parsons (1991). Bound (1989) finds 45% of denied applicants have returned to work after 18 months following denial or more; Parsons (1991) finds 75% of denied applicants who are not receiving other types of government assistance are working after 5 years since application.

Gruber (2000) uses the change of benefit generosity and takes advantage of the natural experiment of the DI program in Canada, which operates two distinct disability insurance programs, one for Quebec (QPP) and the other for the rest of Canada (CPP). The latter program raised its benefits by 36 percent

in January 1987, whereas benefits in Quebec were constant. He uses difference-in-difference methodology, focuses on the labor supply effect of the large relative change in benefits in the rest of Canada relative to Quebec, and finds the difference between the two regions. The second is a more parameterized estimate that exploits the underlying variation in the impact of this policy change across workers within the CPP and QPP plans. He finds an elasticity of labor force nonparticipation with respect to disability insurance benefits in the range of 0.28 to 0.36. The finding is roughly in the midrange of previous literature of the benefits elasticity, it is much larger than estimates of Leonard (1979), Haveman and Wolfe (1984), Halpern and Hausman (1986) and Gruber and Kubik (1994), and is lower than Parsons (1980).

While the literature has focused mainly on the labor supply of men,

Chen and van der Klaauw (2008) extend the analysis to study the employment effects of the DI program on the labor supply of both men and women using regression-discontinuity (RD). They use matched survey administrative data on DI applicants from the 1990s and exploit the fact that the eligibility determination process is based in part on an individual’s age. The estimates indicate that the work disincentive effects associated with DI benefit receipt during the 1990s were relatively modest. The LFP rate of DI beneficiaries would have been at most 20 percentage points higher than of those who were denied benefits. By comparing the effects within the group, they find that

males and SSDI applicants generally show somewhat larger labor supply responses than females and SSI applicants.

Chapter 4

HYESTERESIS IN DISABILITY OF LOW-SKILLED WORKERS

4.1 Introduction

The 1984 liberalization generated hysteresis through the insider- outsider mechanism by the change in membership rules and the rising replacement rate. First of all, low-skilled workers were clustered in agriculture and manufacturing industries, where workers have a higher probability of disabilities from musculoskeletal conditions. More workers with musculoskeletal conditions from agriculture and manufacturing are qualified for disability after the legislation change. Second, low-skilled workers had a higher propensity to seek disability benefits due to the rising replacement rate.

Indeed, the benefit payment plus Medicare was potentially greater than some positions’ wage levels. Moreover, the deterioration of labor market conditions

(especially the decline in employment in the agriculture and manufacturing industries in the U.S.) resulted in a higher job loss hazard and a lower reemployment opportunity for low-skilled workers. In the face of a bleak labor market the DI program, on the contrary, ensured the certainty of cashflow payments. Therefore, the low-skilled workers preferred to claim disability and wait for the award than remain in the labor market after a job loss.

The legislation change separated individuals into treatment and control groups. In this chapter, I treat individuals with a college education as the control group, and individuals without a college education as the treatment 37

group. I solve the omitted variable bias problem of the linear probability model and identify the impacts of the unemployment rate (employment population ratio) on the probability of being disabled and not working, which is employment hysteresis. The level of the unemployment rate in response to the local industry shift (Bartik 1991) affects the disability propensity and choice of participating the labor market. The coefficient of the replacement rate explains the hysteresis due to the insider and outsider wage premium. I will interpret the coefficients of explanatory variables in Section 3 of this chapter.

Chapter 4 builds on the hysteresis theory in the human capital mechanism (Blanchard and Summers 1986), and finds and tests the promising mechanism of the hysteresis in disabled workers. Chapter 4 also builds on the empirical disability literature (Autor and Duggan 2003) which analyzes the impact of the supply of disability benefits on the responses of low-skilled workers to adverse labor market shocks. Chapter 4 refers to some other influential literature exploring the impacts of disability benefits on labor force participation (Leonard 1979; Parsons 1980; Haveman and Wolfe 1984; Halpern and Hausman 1986; Bound 1989; Bound 1991; Bound and Waidmann 1992;

Parsons 1991; Gruber and Kubik 1994; Gruber 2000; Chen and van der

Klaauw 2008; Lindner and Burdick 2013; Maestas, Mullen, and Strand 2013;

Maestas, Mullen, and Strand 2015; Hill, Maestas, and Mullen 2016; Aizawa,

Kim, and Rhee 2018; Armour 2018). Distinct from previous literature, Chapter

4 takes advantage of the 1984 legislation liberalization as an exogenous shock, 38

applies the hysteresis human capital theory and proves the existence of the insider-outsider mechanism during jobless recoveries.

My paper proceeds as follows. In Section 2, I discuss the empirical strategy, compare the specifications in detail and explain data and list variables. In Section 3 I discuss the results and compare my results with the existing literature. In Section 4 I conclude the chapter.

4.2 Empirical Strategy

I use the difference-in-difference strategy and LPM as the primary specification for the following four reasons. First, y is a binary variable taking on the value zero and one. Y takes a value of 1 if individual receives DI benefits and is not working, and 0 otherwise. Second, the difference-in-difference methodology solves the omitted variables bias problem. Third, the key assumption of difference-in-difference methodology lies on the linear parameter. Last, the health shocks which are causal to the probablility of being disabled and uncorrleated with unemployment will result in the inconsistent parameter estimates in Probit and Logit models. Therefore, the

LPM will identify the consistent linear parameter even though the residual is heteroskedastic.

4.2.1 Specifications

To examine employment hysteresis, I use the difference-in-difference

(DID) model. I also introduce DID with IV to identify the effect of exogenous variation of the unemployment rate to the probability of being disabled and not working. Finally, I use DID with IV and FE to deal with geographical heterogeneity and a time trend.

4.2.1.1 The Difference-in-Difference Model

I conduct the difference-in-difference LPM to identify the effect of regional variables on an individual’s probability of being disabled and not working. The models are as follows:

1984 P ( Yirt = 1 ) = ∂ Educationi × Postit + βUnemploymentRatert + Policyt

µ + R’rt !+ X’irt " + εirt (1)

1984 P ( Yirt = 1 ) = ∂ Educationi × Postit + β EmployPopulationrt + Policyt

µ + R’rt !+ X’irt " + εirt (2)

My primary interest is the probablity of an individual being disabled and not working (see Table 4.1). Yirt is the disability status of individual i in region r in year t. Yirt takes a value of 1 if individual i in state r receives DI benefits and is not working, and 0 otherwise. Educationi is a dummy variable that takes value of 1 if individual i is below a college degree and 0 otherwise.

1984 Postit is a time dummy variable that takes a value of 1 if year t is after 1984,

and 0 before 1984. Unemploymentrt is the state-level unemployment rate.

EmployPopulationrt is the state-level employment population ratio. X’irt is the vector of an individual’s specific characteristics such as age, age2, gender, race, education, etc. Policyt are the dummy variables which represent the public policies related to disability. Rrt are the other regional economic variables at state r and time t, for example, the state-level replacement rate, and the employment rate in manufacturing by state. The replacement rates are calculated by the average disability payment of each state divided by the mean annual earnings of each state from the March CPS ( Current Population

Survey). εirt is the idiosyncratic error term (See Table 1: Variables &

Descriptions).

The key design of this unique quasi-experiment lies on three points.

First is an exogenous shock. The Social Security Disability Benefits Reform Act of 1984 was not an endogenous shock due to the onset of other health and economic shocks. It was randomly assigned. Second, the treatment group trends parallel to the control group pre-treatment; Third, the disability rate of the treatment group follows a different trend from their control group counterparts in the post-policy years.

Figure 12 shows the control and treatment groups are on a parallel trend pre-treatment. The treatment group has had a higher disability rate and experienced a steeper trend since the 1990s in the post-policy years (See Table

3 and Figure 12).

The difference-in-difference specification lies in solving the omitted variable bias problem. The health shocks which are causal to unemployment or the employment population ratio also affect the probability of being disabled. The estimated coefficient of unemployment or the employment population ratio is biased and inconsistent. The difference-in-difference specification eliminates the omitted variables which are extremely hard to collect.

The variation of unemployment or the employment population ratio identifies the cyclical effect of local labor market conditions on the probability of enrollment in the DI program, which is the evidence of employment hysteresis. The replacement rate identifies the hysteresis due to the wage premium. The difference-in-difference between treatment and control groups identifies the hysteresis due to the expansion of membership in the disability program. The employment in the manufacturing and agriculture industries explains the hysteresis due to the structural changes in the economy.

4.2.1.2 Difference-in-Difference with Instrument

My primary interest is to find the evidence of hysteresis. The temporary or persistent labor supply and labor demand shocks could affect workers’ labor participation status and their enrollment in DI. The variation of

unemployment in the local industry shift (Bartik 1991) identifies the exogenous effect of the unemployment rate on human capital erosion.

Appendix 1 shows the calculation and graphs of the Bartik State-level

Industry Shift Index. It is a valid instrument and satisfies the assumptions as follows: 1) It is exogenous to local labor market growth. The national average industry growth rates are exogenous to local economic conditions, which avoid endogeneity associated with the local employment growth rate. The potential violation of the exogeneity assumption could be that the predominant industries of the local economy also represent the main portion of the industries in the national economy. 2) It is exclusive to disability. The local industry shift does not affect the individual’s probability of being disabled and not working.

Combining the difference-in-difference estimation with instrument specification, the model identifies the local average treatment effect of the industry shift index on the unemployment rate and hence on human capital accumulation (the probability of being disabled and not working). The instrument specification distinguishes the exogenous variation of the unemployment rate in response to the local industry shift, and identifies the effect of the unemployment rate on the probability of being disabled and not working.

4.2.1.3 Difference-in-Difference with Instrument and FE

Moreover, the geographical characteristics result in heterogeneity at the state level, for example in the states along the border of Mexico and states with more metropolitan areas . On the other hand, the other macro or national health trends also affect DI enrollment aross the states. Therefore, the study is conducted adding state and time fixed effects. The models are as follows:

1984 P ( Yirt = 1 ) = ∂ Educationi × Postit + β Unemploymentrt + Policyt µ

+ R’rt ! + X’irt " + γr + γt + εirt (3)

1984 P ( Yirt = 1 ) = ∂ Educationi × Postit + β EmployPopulationrt + Policyt

µ + R’rt !+ X’irt " + γr + γt + εirt (4)

1984 where Yirt, Educationi, Postit , Policyt, R’rt and X’irt are the same variables as in Equation (1) and (2). γr is the state fixed effect and γt is the time fixed effect. I expect that the DID with IV and FE is the best specification. In the following sections, I will discuss the data, and then the results and the conclusions.

4.2.2 Data

In this section, I introduce the data sources and structure, define the variables I use, and explain why I chose these variables. The data is collected from the Current Population Survey (CPS) March Annual Supplement from

1980 to 2016, Bureau of Labor Statistics (BLS), and Social Security

Administration (SSA). The variables and descriptions are listed in Table 1.

I compiled individual characteristic data, calculated the state-level data and created policy variables by year level. I deleted the observations where age is below 25 or above 64 years old and constructed pooled cross-sectional data to find the existence of the insider-outsider mechanism and analyze the different shocks on workers’ decisions to be in the DI program and not participate in the labor market. In the remaining part of Section 2, I discuss the sources, calculations and reasons I chose those variables.

Disability is the dependent variable which takes a value of 1 if individual i in state r received DI benefits and is not working, and 0 otherwise

(Table 1). Therefore, individuals who are employed with a disabling condition are not counted as hysteresis. Unemployment is the state-level unemployment rate, which is the unemployment population divided by labor participation from the BLS. EmployPopulation is the state-level employment population ratio, Replacement Rate is calculated by the average disability payment in each state from the SSA divided by the mean annual earnings of each state from the March CPS. Employment Rate in Agriculture by State is calculated by the employment population in agriculture divided by total employment from the March CPS. Employment Rate in Manufacturing by State is calculated by the employment population in manufacturing divided by total employment from the March CPS.

The individual’s specific characteristics include age, age2, gender, race, number of children in the family, and marital status. Three policy variables are included in the regression which are the public policies related to disability from 1980 to 2016. ADA represents The American with Disabilities Act, which was signed on July 26, 1990. The legislation requires employers to offer job amenities to disabled workers. ADAAA represents The ADA Amendments Act of 2008 which was enacted on September 25, 2008. The ADA and its amendment increase the accommodations made for disabled workers, decrease the probability of claiming DI during the onset of disability, decrease the probability of being categorized as disabled and enable disabled workers to stay longer in a work position. WOTC represents The Work Opportunity Tax

Credit for the disabled and was signed on March 2nd, 2004. It is calculated by

$9600 which is the fixed minimal tax credit to businesses when they employ disabled workers adjusted by inflator.

Table 2 summarizes the mean and standard deviation of each variable.

The average probability of being disabled and not working from 1980 to 2016 is 5.1% and the standard deviation is very low at 0.22. The Liberalization dummy has a mean of 0.874 due to the duration of treatment being relatively long. The mean of Education is 0.478, which indicates that the control and treatment group are equally divided in this quasi-experimental design. For the regional economic cycle variables, the mean of Unemployment Rate is 6.3% and Employment Population Ratio is 47%. The Replacement Rate is calculated

by the average disability payment of each state from the SSA divided by the mean annual earnings of each state from the March CPS. The mean of the

Replacement Rate is 23.5%. The actual replacement rate should be higher because disability beneficiaries automatically qualify for Medicare or

Medicaid. Another two structural economic variables are Employment Rate in

Agriculture by State and Employment Rate in Manufacturing by State. The means are 2.5% and 22.9% respectively, and the standard deviation is big considering the geographical heterogeneity and time trend in these two industries.

Some personal characteristic variables are also added into the regression including age, gender, race, number of children in the family and marital status. The sample average age is 42.593, and the standard deviation is very big, at 10.985, which implies the data is spread out over a large range of values. The sample includes half males and half females; there are 0.96 children on average in each family; 68.2% have been married before; 10% are black, 13.5% are Hispanic, 5.8% are other race. There are 33.7% who are high school graduates, 25.2% have some college, 17.8% are college graduates and

9.2% have a graduate degree.

4.2.3 Pre-Treatment Comparison

The disability rate of both groups increased from 1980 to 2016. The disabled population in the control group increased from 1% in 1980 to 3% in

2016, while it increased from 6% to 14% in the treatment group (See Table 3).

Figure 12 shows the trend of the disablity rate between the control group and treatment group. The disablity rate of the control group is 1.2% and 6.8% for the treatment group. And the two lines are very stationary and parallel from

1980 to 1984. The disability rate of both control and treatment cohorts increased since 1992. The disabled workers who do not have any college education increased especially dramatically since the 1990s; the slope is deeper than that of the treatment cohort (See Figure 12). The control group increased from 1.2% to 2.1% from 1992 to 2001 which is a 0.9% increase, and from 7.9% to 10% for the treatment group which is a 2.1% increase. The disability rate of the treatment group increased from 10.57% to 14.07% from 2002 to 2016 which is a 3.5% increase. Compared to the treatment group, the control group increased from 2.1% to 3.4% which is a 1.3% increase. Overall, the trend of

Figure 12 shows that the disability rate seems to be negatively correlated with the labor participation rate (See Figure 4). However, the decline in the labor participation rate may be attributed to cyclical, structural or demographic factors. Whether those factors are causal to people’s decision to exit the labor market and enroll in the DI program requires further examination.

I present Table 4 which compares the characteristics between the control group and treatment group. The descriptive statistics show no significant difference between them in regional variables and individual charateristics. To check whether the control and treatment groups are on a 48

parallel trend pre-treatment, I also present Table 5 and use the difference of mean between the two groups. The results indicate a slight difference from

Table 4. The only problem that should be addressed here is the balance of the treatment group and control group. There are no big differences between the control group and treatment group for the whole sample. If we look at total population pre-treatement, the total number of the treatment population is

89,272 bigger than that of the control, pre-treatment, and 141,652 less for the whole sample. The proportion of observations in the control group increased while it decreased in the treatment group due to the upward trend of average education level.

4.3 Empirical Results

4.3.1 Hysteresis in Disability

From the background analysis in Section 1 and the descriptive analysis in Section 2, I found the cohort without college education had a higher disability rate. In this section, I will examine the existence of the insider- outsider mechanism, and whether the positive cyclical shocks (unemployment rate & employment population ratio) and negative structural shocks

(employment rate of manufacturing by state and employment rate of agricultrue by state) affect workers’ decision to be in the DI program without participating the labor market. I interpret the results from the specifications discussed in Section 3.

4.3.1.1 Positive Cyclical Shocks

The Social Security Disability Benefits Reform Act of 1984 extended the criteria of disability to musculoskeletal and mental illnesses which are subjective, compared to objective. I estimate equations (1) and (2), which are based on the difference-in-difference approach. The reults are showed on

Columns (1) and (4) of Table 6. The coefficient of unemployment is 0.03 and statistically significant. Equations (1) and (2) considered the omitted variable bias problem. The unobserved health shocks increase the probability of being disabled and also affect the unemployment rate. However, the endogeneity of unemployment is not identified. The coefficient of unemployment is biased.

To solve endogeneity problem of unemployment, I added the industries shift (Bartik 1991), using the Bartik State-level Labor demand shift to identify the effect of local labor demand shift on the the degree of hysteresis. Table 6

Columns (2) and (5) presents the result of the difference-in-difference with the

Bartik instrument estimation. The unemployment rate negatively affects the probability of being disabled. However, the coefficent is statistically insignificant.

Next, the study is conducted adding state and time fixed effects. The coefficient is statistically significant and negatively affects the probability of being disabled ( See Table 6 Columns (3) and (6)). Given state-level, individual- level and other characteristics being controlled, the 1% decrease in

unemployment led workers to a 0.3% higher probability of being disabled and not working; the 1% increase in employment population ratio led workers to a

0.4% higher probablity of being disabled and not working.

During recent three jobless recoveries, the unemployment rate kept rising as the real GDP already recovered to its pre-crisis level. Figure 13 shows the state level unemployment rate. After 1990 recession, the real GDP recovered to the pre-crisis level in October 1991, while unemployment did not recover till May 1996. The unemployment rate post the 2001 recession never recovered. It began going back to its precrisis level in October 2015, after the

2007 Great Recession. During the three recent recoveries, the 1% exogenous decrease of the unemployment rate increased 0.3% probability for working age population to be disabled. The positive exogenous labor demand shift decreased the unemployment. However, the positive cyclical shock alone increase the degree of hysteresis from 1980 to 2016 and the magnitude is very small. The possible explaination is the total number of disabled workers kept increasing from 1980 to 2016, the coefficient of unemployment rate has a negative sign due the accumulated stock of flow-in disabled workers. The further research or discussion will be needed to address since more disabled workers came back to the labor market since 2016 and the total number of disabled workers dropped dramatically.

4.3.1.2 Existence of Insider-Outsider Mechanism: The Membership Rule and

Benefit Premium

When the Social Security Disability Benefits Reform Act of 1984 added musculoskeletal disorder conditions, more low-skilled workers qualified for the disability program. The difference-in-difference dummies are all statistically significant from column (1) to column (6), which identify the effect of the membership rule on the degree of hysteresis. After the 1984 liberalization, low- skilled workers became 5.2% more likely to be disabled and not working than high-skilled workers due to the new disability category. The legislation change increased the degree of hysteresis.

The replacement rate is the average disability payment of each state from the SSA divided by the mean annual earnings of each state from the

March CPS. Figure 15 shows the replacement rate by state, which. has an upward trend for all states. The rate keeps moving upward from around 10% to 35%. The replacement rate positively affects the probabilty of being disabled and not working. An increase of 1% in the replacement rate was leading workers to a 0.15% higher probability of being disabled and not working (Table

6 Column (3)). The coefficient in Table 6 Column (6) shows a 0.27% higher probability. The further explanation is that the increase of the benefit premium on the state mean earnings was leading more workers to stay in or enroll in the DI program. The benifit premium is causal to the human capital erosion. This is the evidence of the insider-outsider mechanism of hysteresis. 52

From the discussion above, the 1984 disability legislation change broadened the membership qualifications and low-skilled workers are 5.2% more likely to be disabled and not working than high-skilled workers. A 1% rise in the replacement rate leads to a 0.15% ( 0.27% in column(6)) higher probablity of being disabled and not working. To sum up, the Social Security

Disability Benefits Reform Act of 1984 changed the membership rules and more low-skilled workers became disability insiders. With the rising replacement rate, they are more likely to stay in the DI program than return to the labor market.

4.3.1.3 Negative Structural Shocks

I constructed Employment Rate in Agriculture by State and

Employment Rate in Manufacturing by State to find the effects of structural shocks to the degree of hysteresis. Table 6 columns (3) and (6) presents the results of equations (3) and (4). The coefficients are statistically significant.

Figure 16 shows the Employment Rate in Agriculture by State and Figure 17 shows the Employment Rate in Manufacturing by State. The Employment

Rate in Agriculture by State seems to fluctuate around the mean and does not have a trend; the Employment Rate in Manufacturing by State has a downward trend in most of the states in the U.S.. The decrease of 1% in

Employment Rate in Agriculture by State leads to a 0.1% higher probability of being disabled and not working; the decrease of 1% in Employment Rate in

Manufacturing by State leads to a 0.1% higher probability of being disabled 53

and not working. Therefore, the negative structural shocks between 1980 and

2016 intensified the degree of hysteresis.

4.3.2 Comparisons with Existing Literature

My research is complementary to the hysteresis theory which proves the existence of the human capital mechanism (Blanchard and Summers

1986). Yagan (2017) also found hysteresis by analyzing local employment data.

He found the exposure to a 1% larger 2007-2009 local unemployment shock caused working-age individuals to be 0.4 percentage points less likely to be employed at all in 2015. Compared with his research, my research shows the existence of the insider-outsider mechanism, the effects of miscellaneous shocks on the degree of hysteresis, and whether the positive cyclical shock may reverse the hysteresis. The decrease of 1% in Employment Rate in Agriculture by State leads to a 0.1% higher probability of being disabled and not working;

The decrease of 1% in Employment Rate in Manufacturing by State leads to a

0.1% higher probability of being disabled and not working. So the negative structural shocks intensify the degree of hysteresis. Also, the positive one can reverse the hysteresis effect. The effect of cyclical shock is negative. An 1% exogenous decrease of the unemployment rate in response to a positive labor demand shift causes working-age individuals 0.3% more likely to be disabled.

The further research is needed to clarify the effect of cyclical shock to the stock of disabled workers. 54

Stockhammer and Sturn (2012) show that monetary easing has a substantial effect on the degree of hysteresis; a one standard deviation change in monetary easing reduces the degree of hysteresis by 0.27. But the labor institution does not have any effect on the degree of hysteresis. Chapter 4 investigated the exogenous legislation shock and finds that the Social Security

Disability Benefits Reform Act of 1984 did not increase the degree of hysteresis which is consistent with the previous hysteresis empirical literature.

Black et al. (2002) use the local economic change as an exogenous shock to identify the impact of earnings growth on disability program participation.

This analysis provides clear evidence that as the value of labor market participation increases, disability program participation falls. Instead of observing the flow-out of disability participation, my analysis also shows that the increased value of the disability benefit increases the flow-in of disabled workers. The negative structural shocks increased the degree of hysteresis.

Several recent studies of disability estimate the effect of unemployment on the number of DI applications. Autor and Duggan (2003) found that a 1% projected log state employment contraction yielded 1.3 additional DI applications per 1000 nonelderly adults. The propensity of displaced high school dropouts to exit the labor force increased by 60 percent, which is consistent with labor market conditions with respect to low-skilled workers. I found that the Social Security Disability Benefits Reform Act of 1984 increased low-skilled workers’ probability of being disabled and not working by 5.2%

compared with high-skilled workers. Maestas, Mullen, and Strand (2015) found that a 1 percentage point increase in the national unemployment rate is associated with a 3.1 percent increase in monthly SSDI applications over the entire 1992–2012 period. The increase from October 2006 to December 2012 was 1.3%. My research shows an 1% exogenous decrease of the unemployment rate is associated with 0.3% higher probablity of being disabled. The result is contradict to the effect of business cycle to DI application.

Some earlier disability research was conducted to estimate the effects of labor force non-participation elasticity on DI application. The estimated coefficient is larger considering the endogeneity problem of DI application.

Leonard (1979) and Halpern and Hausman (1986) estimated the coefficient to be in the range of 0.12 to 2.0. Haveman and Wolfe (1984) also consider the effects of DI on labor force non-participation without separating out the applications process from the labor force status outcome. The estimated elasticities range from 0.06 to 0.21.

4.4 Conclusion

4.4.1 Robustness Check

In this section, I limit the samples from 1980 to 2012, re-estimate model

(3) to (6) and present the results in Table 7. The estimations of coefficients in the full sample and subsample are the same. It indicates that the estimation

is very consistent and robust.

4.4.2 Conclusion

In Chapter 4, I find the empirical evidence of hysteresis through the insider-outsider mechanism in the U.S. I use The Social Security Disability

Benefits Reform Act of 1984 as an exogenous shock to design the quasi- experiment. The change in membership rules and benefit premiums does increase the degree of hysteresis in the economy. The presence of negative structural shocks, such as the decline of manufacturing and agricultural employment, result in the increase of the disability population. The laid-off low-skilled workers found it difficult to find a job and lost the opportunity to update their skills and maintain their attachment with the labor force.

However, the expansion of membership and inflated payment of disability benefits and welfare benefits seems to be very competitive to the insider wage for low-skilled workers. Even though there was a huge shortage of labor in the recoveries, most of the disabled workers decided to stay in the DI program.

Chapter 5

HYSTERESIS IN DISABILITY OF OLDER WORKERS

5.1 Introduction

The 1984 liberalization and the implementation of Normal Retirement

Age (NRA) generated hysteresis through the insider-outsider mechanism by the change of membership rules and rising replacement rate. First of all, the

1984 liberalization extended the criteria of disability to musculoskelatal and mental illnesses in which the diagnoses are “subjective” as opposed to

“objective”. Older workers are more likely to have musculoskeletal conditions compared with other cohorts because of aging. And they are more likely to qualify for DI due to the passage of the 1984 legislation. The NRA increased the normal age of retirement for all workers by 2 months per year for cohorts born in 1938 and after. Older workers who are still younger than the full retirement age cannot retire and receive the full amount of retirement benefits. However, early retirement is still available for people between the age of 62 and the full retirement age; this cohort began reaching early retirement age in 2000. To sum up, the 1984 liberalization offered older workers the possibility of qualifying for the DI program and the Normal Retirement Age

Act created the incentive for older workers from 55 to 64 to apply or stay in the

DI program.

Moreover, the deterioration of labor market conditions result in a higher

job loss hazard and a lower reemployment opportunity for older workers. From the employers’ perspective, the Age Discrimination in Employment Act

(ADEA) actually increased the cost of hiring older workers making them less likely to be reemployed after a job loss. Even if employed after a job loss, their wage level could be much lower. The DI program, on the contrary, ensures the certainty of a cashflow payment which is a substitute for a pension, and the benefit payment plus Medicare could be higher than some positions’ wage level.

Therefore, the older workers who quality for DI preferred to claim disability and wait to be awarded than remain in the labor market after a job loss.

There is some evidence that DI benefits seem became more attractive to older workers since the 2000 Normal Retirement Age policy, especially during the Great Recession. The disability rate between workers aged 55 to 64 increased from 10% to 14% ( Table 10 and Figure 18). Figure 18 shows 18% of male workers and 22% of female workers between the ages of 55 to 64 are disabled. The DI recipient rate per 1,000 labor participants in the 55 to 64 year old cohort is the highest among all the working age groups.

In this chapter, I treat individuals between 25 to 54 as the control group, and individuals between 55 to 64 as the treatment group. I solve the omitted bias problem of unemployment and identify the impacts of unemployment

(employment population ratio) on the probability of being disabled and not working which, is employment hysteresis. The variation of unemployment in

response to the local industry shift (Bartik 1991) identifies the exogenous effect of unemployment on the degree of hysteresis in the labor market. The coefficient of the replacement rate explains the hysteresis due to insider and outsider benefit premiums. I will interpret the estimated coefficients of the variables in Section 4.

Chapter 5 builds on the hysteresis theory in the human capital mechanism that Blanchard and Summers 1986 finds and tests the promising mechanism of hysteresis in disabled workers. Chapter 5 also builds on the empirical disability literature (Autor and Duggan 2003) which analyzes the impact of the supply of disability benefits on the responses of low-skilled workers to adverse labor market shocks. Chapter 5 refers to some other influential literature exploring the impacts of disability benefits on labor participation (Leonard 1979; Parsons 1980; Haveman and Wolfe 1984; Halpern and Hausman 1986; Bound 1989; Bound 1991; Bound and Waidmann 1992;

Parsons 1991; Gruber and Kubik 1994; Gruber 2000; Chen and van der

Klaauw 2008; Lindner and Burdick 2013; Maestas, Mullen, and Strand 2013;

Maestas, Mullen, and Strand 2015; Hill, Maestas, and Mullen 2016; Aizawa,

Kim, and Rhee 2018; Armour 2018). Distinct from previous literature, Chapter

5 takes advantage of the 1984 legislation liberalization and the Normal

Retirement Age as two exogenous shocks, applies the hysteresis human capital theory and proves the existence of the insider-outsider mechanism during jobless recoveries. 60

Chapter 5 proceeds as follows. In Section 2 I discuss the empirical strategy, compare the specifications in detail and explain data and list variables. In Section 3 I discuss the results and compare my results with the existing literature. In Section 4 I conclude the paper.

5.2 Empirical Strategy

I use the difference-in-difference strategy and LPM as the primary specification for the following four reasons: 1) y is a binary variable taking on the values zero and one, 2) the difference-in-difference methodology solves the endogeneity problem of unemployment, 3) the key assumption of the difference-in-difference methodology lies on the linear parameter and 4) the health shocks which are causal to probablility of being disabled and uncorrleated with unemployment will result in the inconsistent parameter estimates in Probit and Logit models. Therefore, LPM will identify the consistent linear parameter even though the residual is heteroskedastic.

5.2.1 Specifications

In this section, I start with the baseline model 1) difference-in-difference.

After discussing the endogeneity problem of unemployment, I present 2) DID with IV to identify the exogenous variation of labor demand shift to the probability of being disabled and not working. 3) DID with IV and FE deal with the geographical heterogeneity and time trend.

5.2.1.1 Difference-in-Difference

I estimate the difference-in-difference LPM to identify the effect of the state unemployment rate to an individual’s probability of being disabled and not working. The models are as follows:

1984 P ( Yirt = 1 ) = ∂1 Olderworkersi × Postit + ∂2 Olderworkersi ×

1999 Postit + β Unemploymentrt + Policyt µ + R’rt !+ X’irt " + εirt (1)

1984 P ( Yirt = 1 ) = ∂1 Olderworkersi × Postit + ∂2 Olderworkersi ×

1999 Postit + β EmployPopulationrt + Policyt µ + R’rt !+ X’irt " + εirt (2)

My primary outcome of interest is the individual level probablity of being disabled and not working (See Table 5.3). Yirt is the disability status of individual i in region r in year t. Yirt takes a value of 1 if individual i in state r received DI benefits and is not working, and 0 otherwise. Olderworkersi is a dummy variable that takes value of 1 if individual i’s age is between 55 to 64

1984 and 0 otherwise. Postit is a time dummy variable that takes a value of 1 if

1999 year t is after 1984, and 0 before 1984. Postit is a time dummy variable that takes a value of 1 if year t is after 1999, and 0 before 1984. Unemploymentrt is the state-level unemployment rate. EmployPopulationrt is the state-level employment population ratio. Xirt is a vector of an individual’s specific

2 characteristics such as age, age , gender, race, education, etc.. Policyt are the 62

dummy variables which represent the public policies related to disability. R’rt are the other regional economic variables at state r and time t, for example, the state-level replacement rate and the employment rate in manufacturing by state. The replacement rates are calculated by the average disability payment of each state divided by the mean annual earnings of each state from the March

CPS. εst is the idiosyncratic error term (See Table 8: Variables & Descriptions).

The key design of this unique quasi-experiment lies on three points.

First are the exogenous shocks. The Social Security Disability Benefits Reform

Act of 1984 and 2000 NRA were not endogenous shocks due to the onset of other health and economic shocks. It was randomly assigned. Second, the treatment group is on a trend parallel to the control group pre-treatment; 3)

The disability rate of the treatment group follows a different trend from their control group counterparts in the post-policy years.

Table 10 and Figure 18 show the disability rate of three different cohorts. The three cohorts seem to be on a parallel trend, pre-treatment. The treatment group, the workers who are between 55 to 64 years old, has a higher disability rate and steeper trend since the 1990s, and experience a jump since the 2000s. (Table 11 and Figure 19)

The difference-in-difference specification lies in solving the omitted bias problem of unemployment. The health shocks which are causal to unemployment also affect the probability of being disabled. The estimated

coefficient of unemployment is biased and inconsistent. The difference-in- difference specification eliminates the omitted variables which are extremely hard to collect.

The variation of unemployment or employment population ratio identifies the cyclical effect of local labor market conditions on the probability of enrollment in the DI program, which is the evidence of employment hysteresis . The replacement rate identifies the hysteresis due to the wage premium. The difference-in-difference between treatment and control group identifies the hysteresis due to the expansion of the disability program(membership). The employment in the manufacturing and agriculture industries explain the hysteresis due to the structural changes of economy.

5.2.1.2 Difference-in-Difference with Instrument

My primary interest is to find the evidence of hysteresis caused by the human capital mechanism (Blanchard and Summers 1986). The temporary or persistent labor supply and labor demand shocks could affect workers’ labor participation status and the enrollment in DI. The variation of unemployment on local industry shift (Bartik 1991) identifies the effect of local industry shift on human capital erosion.

Appendix 1 shows the calculation and graphs of the Bartik State-level

Labor Demand Index. It is a valid instrument and satisfies the assumptions as follows: 1) It is exogenous to local labor market growth. The national average

industry growth rates are exogenous to local economic conditions, which avoid endogeneity associated with the local employment growth rate. The potential violation of the exogeneity assumption could be that the predominant industries of the local economy also represent the main portion of the industries in the national economy. 2) It is exclusive to disability. The local industry shift does not affect the individual’s probability of being disabled.

Combining the difference-in-difference with instrument specification, the LPM identifies the effect of local labor market conditions on human captial accumulation (the probablity of being disabled and not working). The instrument specification distinguishes exogenous variation of unemployment in response to the labor demand shift, and identifies the effect of the unemployment rate on the probability of being in the DI program and not working from 1980 to 2016.

5.2.1.3 Difference-in-Difference with Instrument and FE

Moreover, the geographical characteristics result in the heterogeneity at the state level, for example the states along the border of Mexico and the states with more metropolitan areas. On the other hand, the other macro or national health trends also affect the DI roll aross states. Therefore, the study is conducted adding state and time fixed effects.

1984 P ( Yirt = 1 ) = ∂1 Olderworkersi × Postit + ∂2 Olderworkersi ×

1999 Postit + β Unemploymentrt + Policyt µ + R’rt ! + X’irt " + γr + γt + εirt

(3)

1984 P ( Yirt = 1 ) = ∂1 Olderworkersi × Postit + ∂2 Olderworkersi ×

1999 Postit + β EmployPopulationrt + Policyt µ + R’rt ! + X’irt " + γr + γt +

εirt (4)

1984 1999 Where Yirt, Olderworkersi, Postit , Postit , Policyt, R’rt and X’irt are the same variables as in Equation (1) and (2). γr is the state fixed effect and γt is the time fixed effect. I expect that the DID with IV and FE is the best specification. In the following sections, I will discuss the data, and then the results and the conclusions.

5.2.2 Data

In this section, I introduce the data sources and structure, define the variables I use, and explain why I chose these variables. The data is collected from Current Population Survey March Annual Supplement from 1980 to 2016

(March CPS), the Bureau of Labor Statistics (BLS), and the Social Security

Administration(SSA). The variables and descriptions are listed in Table 8.

I compiled individual characteristic data, calculated the state-level data and created policy variables by year level. I deleted the observations where age is below 25 or above 64 years old and constructed pooled cross-sectional data to find the existence of the insider-outsider mechanism, and analyze the effect of the local shocks on workers’ decision to participate in the DI program and not in the labor market. In the remaining part of Section 2, I discuss the sources, calculations and reasons to choose those variables.

Disability is the dependent variable which takes a value of 1 if individual i in state r received DI benefits and is not working, and 0 otherwise

(Table 8). Therefore, individuals with a disabling condition who are employed are not counted as hysteresis. Unemploymentrt is the state-level unemployment rate, which is the unemployment population divided by labor participation from the BLS. EmployPopulationrt is the state-level employment population ratio. Replacement Rate is calculated by each state’s average disability payment from the SSA divided by the mean annual earnings of each state from the March CPS. Employment Rate in Agriculture by State is calculated by the employment population in agriculture divided by total employment from the March CPS. Employment Rate in Manufacturing by

State is calculated by the employment population in manufacturing divided by total employment from the March CPS.

The individual’s specific characteristics include Age, Age2, Gender, Race,

Number of Children in the Family, Marriage Status, High School, Some

College, College and Advanced. Three policy variables are included in the regression, which are the public policies related to disability from 1980 to 2016.

ADA represents The American with Disabilities Act, which was signed on July

26, 1990. The legislation requires employers to offer job amenities to disabled workers. ADAAA represents The ADA Amendments Act of 2008, which was enacted on September 25, 2008. The ADAAA increased the accommodations to disabled workers; decreased the probability of claiming DI during the onset of disability; decreased the probability of being disabled and enabled disabled workers to stay longer in the work position. WOTC represents The Work

Opportunity Tax Credit for the disabled and was signed on March 2nd, 2004. It is calculated by $9600 which is the fixed minimal tax credit to businesses when they employ disabled workers adjusted by inflator.

Table 9 summarizes the mean and standard deviation of each variable.

The average probability of being disabled and not working from 1980 to 2016 is 5.1% and the standard deviation is very low at 0.22. Liberalization dummy has a mean of 0.874 due to the relatively long treatment duration. The mean of older is 0.181, which indicates that the unbalanced control and treatment groups are in the quasi-experiment design. For the regional economic cycle variables, the mean of Unemployment Rate is 6.3% and Employment

Population Ratio is 47%. The Replacement Rate is calculated by the average disability payment in each state from the SSA divided by the mean annual earnings of each state from the March CPS. The mean of the Replacement Rate

is 23.5%. The actual replacement rate should be higher because the disability beneficiaries are automatically qualified for Medicare and Medicaid. Another two structural economic variables are Employment Rate in Agriculture by

State and Employment Rate in Manufacturing by State. The means are 2.5% and 22.9% respectively, and the standard deviation is big considering the geographical heterogeneity and time trend in these two industries.

Some personal characteristic variables are also added into the regression including age, gender, race, number of children in the family, marriage status and level of education. The sample’s average age is 42.593, and the standard deviation is very big at 10.985, which implies the data is spread out over a large range of values. The sample is half male and half female; there is an average of 0.96 children in each family; 68.2% have married before; 10% are black, 13.5% are Hispanic, and 5.8% are other race. There are

33.7% high school graduates; 25.2% have some college, 17.8% are college graduates and 9.2% have a graduate degree.

5.2.3 Pre-Treatment Comparison

The disability rate of both groups increased from 1980 to 2016. The disabled population in the control group increased from 3% in 1980 to 5% in

2016, while it increased from 10% to 14% in the treatment group (See Table

11). The disability rate of older workers increased dramatically since the 1990s

(See Figure 18). Figure 19 shows the trend of disablity rate between the control 69

group and treatment group. The disablity rate of the control group and treatment group are very stationary and parallel from 1980 to 1984. The disablity rate of the control group is around 2.9%, and approximately 10% for the treatment group from 1980 to 1984. The disability rate of both control and treatment cohorts increases since 1993. This increase is dramatic among the disabled workers who are between 55 to 64 years old; the slope is flatter than that of the treatment cohort (See Figure 19). The control group increased from

3.4% to 5.4% from 1993 to 2016 which is a 2.0% increase, and 8.9% to 13.7% for the treatment group which is a 4.8% increase.

Especially after 2001, the slope of the control group is much flatter than that of the treatment group. The treatment group increased from 10% to 13.7% which is a 3.7% increase compared with a 1.3% increase in the control group.

Overall, the trend of Figure 18 shows that the disability rate seems to be negatively correlated with the labor participation rate (See Figure 4). However, the decline in the labor participation rate may be attributed to cyclical, structural or demographic factors. Whether those factors are causal to people’s decision to exit the labor market and enroll in the DI program requires further examination.

I present Tables 5.5, 5.6 and 5.7 which compare the characteristics between the control group and treatment group. The descriptive statistics show no significant difference between regional variables and individual

characteristics. There is some difference between the variables Age, Number of Children in the Family and Ever Married (Table 12). To check whether the control and treatment groups are on a parallel trend pre-treatment, I also present Tables 5.6 and 5.7, using the difference of the mean between the two groups. The results indicate a slight difference from Table 12. Two problems should be addressed here: (1) There are some differences between variables

Age, Number of Children in the Family and Ever Married, which are due to the control and treatment group selection. The workers between 55 and 64 years are more likely married and their children are less likely to live with them. (2) There are unbalanced observations between the treatment group and control group. The sample size of the treatment group is much smaller than that of the control group.

5.3 Empirical Results

5.3.1 Hysteresis in Disability

From the background analysis in Chapter 1 and the descriptive analysis in Section 2, I found 55 to 64 year old workers had a higher disability rate. In this section, I will examine the existence of the insider-outsider mechanism, and whether the negative cyclical labor market shocks and negative structural shocks (employment rate of manufacturing by state and employment rate of agricultrue by state) affect workers’ decisions to be in the SSDI program without participating the labor market. I interpret the results from the specifications discussed in Section 3. 71

5.3.1.1 Positive Cyclical Shocks

The Social Security Disability Benefits Reform Act of 1984 extended the criteria of disability to musculoskeletal and mental illnesses which are

“subjective” rather than “objective”. I estimate Equations (1) and (2) which are based on the difference-in-difference approach and the results are showed on

Columns (1) and (4) of Table 15. The coefficient of unemployment in Equation

(1) is statistically insignificant. Equations (1) and (2) considered the omitted variable bias problem. The unobserved health shocks increase the probability of being disabled and also affect the unemployment rate. However, the endogeneity of unemployment is not identified. The estimated coefficient is biased.

To solve the endogeneity problem of unemployment, I added industries shift (Bartik 1991), using the Bartik State-level Labor demand shift to identify the effect of the exogenous variation of the unemployment rate to the degree of hysteresis. Table 15 Columns (2) and (5) present the results of the difference- in-difference with the Bartik instrument estimation. The unemployment rate negatively affects the probabilty of being disabled and the coefficient is -0.074.

Eventually, the study adds state and time fixed effects. The coefficient is statistically significant and negatively affects the probability of being disabled

( See Table 15 Columns (3) and (6)). Given state-level, individual-level and other characteristics controlled, the decrease of 1% in unemployment was

leading workers to a 0.3% higher probability of being disabled and not working.

The increase of 1% in the employment population ratio is leading workers to a

0.4% higher probablity of being disabled and not working. The estimation is consistent with Chapter 4.

During the recent three jobless recoveries, the unemployment rate kept rising as the real GDP had already recovered to its pre-crisis level. Figure 13 shows the state-level unemployment rate. After the 1990 recession, the real

GDP recovered to the pre-crisis level in October 1991, while unemployment did not recover until May 1996. The unemployment rate post the 2001 recession has never recovered, but it went back to its precrisis level in October 2015 after the 2007 Great Recession. During the three recent recoveries, the 1% exogenous decrease of the unemployment rate increased 0.3% probability for working age population to be disabled. The positive exogenous labor demand shift decreased the unemployment. However, the positive cyclical shock alone increase the degree of hysteresis from 1980 to 2016 and the magnitude is very small.

The possible explaination is the total number of disabled workers kept increasing from 1980 to 2016, the coefficient of unemployment rate has a negative sign due to the accumulated stock of flow-in disabled workers. The further research or discussion will be needed to address since more disabled workers came back to the labor market since 2016 and the total number of

disabled workers dropped dramatically.

5.3.1.2 Existence of Insider-Outsider Mechanism: Membership Rule & Benefit Premium

The Social Security Disability Benefits Reform Act of 1984 added musculoskeletal disorder conditions and more older workers are qualified for the disability program. Early retirement in the disability program is still available in spite of the increase of Normal Retirement Age since 2000. The difference-in-difference dummies identify the effect of the membership rule to the degree of hysteresis. The coefficients are all statistically significant except the Normal Retirement Age dummy (Table 15 column (3)). After the 1984 liberalization, older workers are 0.1% less likely to be disabled and not working than the younger workers. The Social Security Disability Benefits Reform Act of 1984 does not increase the degree of hysteresis. The Normal Retirement Age

Act increases the older workers’ probability of being disabled and not working by 2.1% compared with younger workers. Model (6) (Table 15 column (6)) shows a 14% higher probability of being disabled and not working than younger workers after 2000. Therefore, the two legislations together increased the older workers’ degree of hysteresis.

The replacement rate is the average disability payment of each state from the SSA divided by the mean annual earnings of each state from the

March CPS. Figure 15 shows the replacement by state. The replacement rate by state has an upward trend for all states in the US. It kept moving upward 74

from around 10% to 35%. The replacement rate positively affects the probabilty of being disabled and not working. An 1% increase in the replacement rate leads workers to a 0.15% higher probablity of being disabled and not working

(Table 15 Column (3)). The coefficient in column (6) shows a 0.26% higher probability of being disabled and not working. The estimation is very consistent with Chapter 4. The further explanation is the increase in the benefit premium to the state mean earnings leads more workers to stay or enroll in the DI program. The benefit premiums are causal to the human capital erosion. This is the evidence of hysteresis due to the insider-outsider mechanism.

5.3.1.3 Negative Structural Shocks

I constructed Employment Rate in Agriculture by State and

Employment Rate in Manufacturing by State to find the effect of structural shocks to the degree of hysteresis. Figure 16 shows the Employment Rate in

Agriculture by State and Figure 17 shows the Employment Rate in

Manufacturing by State. The Employment Rate in Agricultrue by State seems to fluctuate around the mean and does not have a trend; The Employment Rate in Manufacturing by State has a downward trend in most of the states in the

U.S.. Table 15 Columns (3) and (6) present the results of equations (3) and (4).

The coefficient of Employment Rate in Agriculture by State is statistically significant in column (3) and insignificant in column (6). The decrease of 1% in

Employment Rate in Agriculture by State leads to a 0.1% higher probability of 75

being disabled and not working. The coefficients of Employment Rate in

Manufacturing by State are all significant. The decrease of 1% in Employment

Rate in Manufacturing by State leads to a 0.1% higher probability of being disabled and not working. Therefore, the negative structural shocks during

1980 to 2016 intensified the degree of hysteresis.

5.3.1.4 Labor Institution

The NRA increased the Normal Retirement Age by 2 months per year for cohorts born in 1938 and after. Early retirement is still available for people in the SSDI program aged 62, and this cohort began reaching early retirement age in 2000. Table 15 Columns (3) (6) presents the results of equation (3) and

(4). The NRA increases the degree of hysteresis. The implement of NRA leads to a 8.7% to 11.9% higher probability of being disabled and not working. This is the hysteresis due to labor institution.

5.3.2 Comparing with Existing Literature

My research is a complementary to the hysteresis theory which proves the existence of human capital mechanism (Blanchard and Summers 1986).

Yagan (2017) also found hysteresis by analyzing local employment data. He found the exposure to a 1% larger 2007-2009 local unemployment shock caused working-age individuals to be 0.4 percentage points less likely to be employed at all in 2015. Compared with his research, my research shows the existence

of the insider-outsider mechanism and the effects of miscellaneous shocks on the degree of “ hysteresis”. So the negative structural shocks intensify the degree of hysteresis. Also, the positive one can reverse the hysteresis effect.

The effect of cyclical shock is negative. An 1% exogenous decrease of the unemployment rate in response to a positive labor demand shift causes working-age individuals 0.3% more likely to be disabled. The further research is needed to clarify the effect of cyclical shock to the stock of disabled workers.

Stockhammer and Sturn (2012) show that monetary easing has a substantial effect on the degree of hysteresis. A one standard deviation change in monetary easing reduces the degree of hysteresis by 0.27. But the labor institution does not affect the degree of hysteresis. Chapter 5 investigated two exogenous legislation shocks and found the Normal Retirement Age Act increased the degree of hysteresis which raised the controversies in the previous hysteresis empirical literature.

Black et al. (2002) uses the local economic change as an exogenous shock to identify the impact of earnings growth on disability program participation.

Recent literature of disability estimates the effect of unemployment on the DI application. Autor and Duggan (2003) found that a 1% projected log state employment contraction yielded 1.3 additional DI applications per 1000 nonelderly adults. The propensity of displaced high school dropouts to exit the labor force increased by 60 % which is consistent with labor market conditions with respect to low-skilled workers. I found that the Normal Retirement Age

Act increased the older workers’ probability of being disabled and not working by 2.1% compared with younger workers. Maestas, Mullen, and Strand (2015) found that a 1 percentage point increase in the national unemployment rate is associated with a 3.1% increase in monthly SSDI applications over the entire

1992–2012 period. The increase from October 2006 to December 2012 period was 1.3 percent. My research shows an 1% exogenous decrease of the unemployment rate is associated with 0.3% higher probablity of being disabled.

The result is contradict to the effect of business cycle to DI application.

Some earlier disability research estimated the effects of labor force non- participation elasticity on DI applications. The estimated coefficient is larger considering the endogeneity problem of DI applications. Leonard (1979) and

Halpern and Hausman (1986) estimated elasticity is in the range of 0.12 to 2.0.

Haveman and Wolfe (1984) also consider the effects of DI on labor force non- participation without separating out the applications process from the labor force status outcome. The estimated elasticities range from 0.06 to 0.21.

5.4 Conclusion

5.4.1 Robustness Check

In this section, I limit the samples from 1980 to 2012, re-estimate the equations and present the results in Table 5.9. The reason of selecting this particular sample in the robustness check is due to this experimental design.

There exists two exogenous shocks, The 1984 liberalization and the implementation of Normal Retirement Age (NRA).The results show the same estimation of coefficients as in the full sample and subsample. It indicates that the estimation is very consistent and robust.

5.4.2 Conclusion

In chapter 5, I find the empirical results of hysteresis through the insider-outsider mechanism in the U.S.. I use the Social Security Disability

Benefits Reform Act of 1984 and the 2000 Normal Retirement Act as two exogenous shocks to design the quasi-experiment. The changing of the membership rule and benefit premium does increase the degree of hysteresis in the economy. The presence of negative structural shocks, such as the decline of manufacturing and agricultural employment, result in the increase of the disability population. Laid-off older workers find it extremely difficult to find a job and lose the opportunity to update their skills and maintain their attachment within the labor force. However, the expansion of membership and

inflated payment of disability benefits and other welfare benefits seem a very competitive insider wage for them. Even though there is a huge shortage of labor supply in the recoveries, most of the disabled older workers decided to stay in the SSDI program.

Chapter 6

CONCLUSION

In this dissertation, I applied hysteresis theory to the SSDI program and found the the existence of the insider-outsider mechanism and the cyclical labor market shocks, structural shocks and labor institution are causal to the persistence of hysteresis. The empirical evidence provided the answer to the central debate in the hysteresis theory which is concerned about the causality of the persistence. My models underscore how the insider-outsider mechanism is applied to the SSDI program and the effects of cyclical and structural shocks to the degree of hysteresis. The difference-in-difference model solves the omitted variables bias problem and identifies hysteresis due to labor institution.

Blanchard and Summers (1986) describe the hysteresis phenomenon from observing the unemployment dynamics from the 1960s to the 1980s in

European countries and find the insider-outsider mechanism promising as a driver of the dynamics. The employment context is changed through insider and outsider relationships (Lindbeck and Snower 1986). The insider-outsider approach places some labor market power into the hands of the employees. The wage rate is higher than that of the competitive equilibrium and the position is quite sustainable. In the presence of a negative shock, which reduces employment, some workers lose their insider status and turn into outsiders.

The outsiders lose the opportunity to maintain and update their skills and their attachment with the labor force, particularly among the long-term unemployed. Their skill deterioration leads to the inability to find a new job.

Like what we can see in the unemployment dynamics in European countries, the failure of a correction in the labor market results in an error-compounding- error rise in the unemployment rate.

The central debate of hysteresis theory is whether the mechanism is prevalent, persistent and reversible. The SSDI program satisfies all the characteristics; however, the causality of the persistence is unknown. Disabled workers are a prevalent phenomenon in the working-age population. The total number of disabled workers has increased from 10 million to 60 million since

1957 (See Figure 7) and results in the increase of the disability employment ratio from 11% to 31% (See Figure ). The total disabled population seems highly persistent from 1957 to 2014. The total of newly awarded disabled workers per year increased from 1.8 million in 1984 to 3.6 million in 2014, which is a 100% increase, and the total disabled worker population has an upward trend. Disabled workers may return to the labor market through a different path. Previous literature proves that the increased value of labor market participation leads the drop in disability program participation (Black et al. 2002). So it is reversible.

Using individual data from the Current Population Survey paired to state-level SSA administrative disability payments and the labor market data

from the Bureau of Labor Statistics, I design the quasi-experiment, and use a difference-in-difference model to find the existence of the insider-outsider mechanism and how the effects of cyclical labor market shocks and structural shocks are causal to the degree of hysteresis. I use low-skilled workers as treatment group in Chapter 4 and older workers as treatment group in Chapter

5. The major identification challenge is the endogeneity of the unemployment rate. The 1984 legislation liberalization can act as an exogenous shock to identify the impact of unemployment on disability program participation. The variation of unemployment on local industry shifts (Bartik 1991) identifies the exogenous effect of unemployment rate on human capital erosion. Adding state and time fixed effects controls the heterogeneity at the state level and in macro trends.

The insider-outsider mechanism is generated through the expansion of the qualifications and the inflated benefit payments in the Social Security

Disability Benefits Reform Act of 1984. In Chapter 4, my model shows the low- skilled workers are 5.2% more likely to be disabled and not working than the high-skilled workers after 1984 as insiders. Compared with low-skilled workers, the 1984 liberalization offered older workers the opportunity to quality for the DI program, and the Normal Retirement Age Act created the incentive for older workers from 55 to 64 to apply for or stay in the DI program.

The Social Security Disability Benefits Reform Act of 1984 does not increase the degree of hysteresis. The Normal Retirement Age Act increases the older

workers’ probability of being disabled and not working by 2.1% compared with younger workers. On the other hand, a 1% rise of the replacment rate leads to a 0.15% ( 0.27% in Model(6)) higher probablity of being disabled and not working.

Chapter 4 and Chapter 5 find the causality of cyclical shocks and structural shocks to human capital erosion. The unemployment rate decreases in reponse to the positive labor demand shift, but increases the degree of hysteresis in the SSDI program during the three jobless recoveries. One percent decrease of unemployment led workers to a 0.3% higher probability of being disabled and not working. The increase of 1% in the employment population ratio leads workers to a 0.4% higher probablity of being disabled and not working. The negative structural shocks intensify the degree of hysteresis. The decrease of 1% in Employment Rate in Agriculture by State leads to a 0.1% higher probability of being disabled and not working. The decrease of 1% in Employment Rate in Manufacturing by State leads to a 0.1% higher probability of being disabled and not working. Chapter 5 also finds the

Normal Retirement Age Act increases the degree of hysteresis. The implementation of the NRA leads to between an 8.7% to an 11.9% higher probability of being disabled and not working. This is hysteresis due to labor institution.

The most controversial results in this paper are the effects of the cyclical

shock and structural shocks to the degree of hysteresis. We found that the positive cyclical shock increased the degree of hysteresis, and positive structural shocks decreased the degree of hysteresis. The further research is needed to clarify the effect of cyclical shock to the stock of disabled workers.

TABLES AND FIGURES

Figure 1: Recovery Index of Real GDP. U.S. Post-War Recessions.

Recovery Index Real GDP: Peak to Peak

160

150

140

130

120

110

100

90 1 5 9 13 17 21 25 29 33 37 41

1948 1953 1957 1960 1969 1973 1981 1990 2001 2007

$%&&'() *'+, -./ Source: U.S. Bureau of Labor Statistics; The Real GDP Recovery Index (Peak to Peak)= *'+, -./ +) /'+0

*100

Recovery Index Real GDP: Trough to Peak

160

150

140

130

120

110

100

90 1 5 9 13 17 21 25 29 33 37 41

1949 1954 1958 1961 1970 1975 1982 1991 2001 2009

$%&&'() *'+, -./ Source: U.S. Bureau of Labor Statistics; The Real GDP Recovery Index (Trough to Peak)= *'+, -./ +) 1&2%34

*100

Figure 2: Recovery Index of Unemployment Rate. U.S. Post-War Recessions

Recovery Index Unemployment Rate: Peak to Peak

250

230 1948 210 1953 190 1957

170 1960

150 1969

130 1973 Recover Index 1981 110 1990 90 2001 70 2007 50 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121 126

Source: U.S. Bureau of Labor Statistics; Unemployment Rate Recovery Index (Peak to Peak)=$%&&'() 5('67,286'() *+)' 5('67,286'() *+)' +) /'+0

*100

Recovery Index Unemployment Rate: Trough to Peak 120

110

100 1949 90 1954 1958 80 1961 1970 70 1975 1982

Recover Index 60 1991 2001 50 2009

20 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117 121

Source: U.S. Bureau of Labor Statistics; Unemployment Rate Recovery Index (Trough to

Peak)= $%&&'() 5('67,286'() *+)' *100 5('67,286'() *+)' +) 1&2%34

Figure 3: Recovery Index of Labor Force Participation Rate. U.S. Post-War

Recessions

Recovery Index Labor Force Participation Rate: Peak to Peak

106

104

102

100

94 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117 121 125 129

1948 1953 1957 1960 1969 1973 1981 1990 2001 2007

$%&&'() 9:/* Source: U.S. Bureau of Labor Statistics; Labor Force Participation Recovery Index (Peak to Peak) == 9:/* +) /'+0

*100

Recovery Index Labor Force Participation Rate (Trough to Peak)

106

104

102

100 Labor Participation Index 98

94 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 109 113 117 121

1949 1954 1958 1961 1970 1975 1982 1991 2001 2009

Source : U.S. Bureau of Labor Statistics; Labor Force Participation Recovery Index (Trough to Peak)

$%&&'() 9:/* = *100 9:/* +) 1&2%34

Figure 4: Labor Participation Rate and Employment Population Ratio of the

United States from 1948 to 2016

Source: U.S. Bureau of Labor Statistics

Figure 5: Timeline and Events of the DI Program and New Benefits Awarded

Source: Social Security Bulletin: Annual Statistical Supplement 1957 to 2014

Figure 6: Yearly Flow-in of Disabled Population Ratio and Disability

Employment Ratio

Source: Social Security Bulletin: Annual Statistical Supplement 1957 to 2014; U.S. Bureau of Census.

"#$ %$&'(#( )*+&,-#( ./01-&2*/3 Yearly Flow-in Disabled Population Ratio = *100; Yearly Flow-in Disability 4/2&- ./01-&2*/3

"#$ &$&'(#( )*+&,-#( ./01-&2*/3 Employment Ratio = *100. 4/2&- 560-/76#32 ./01-&2*/3

Figure 7: Disabled Population from 1957 to 2014

60 Millions 50

0 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 2012

Retired Worker Disabled Non-retired Worker Disabled Total Disabled Workders

Source: Social Security Bulletin: Annual Statistical Supplement 1957 to 2014; U.S. Bureau of Census

Figure 8: Disabled Employment Ratio 1957 to 2014

EmploymentPopulation Ratio Disability Employment Ratio

61.00%

51.00%

41.00%

31.00%

21.00%

11.00%

1957 1960 1965 1966 1970 1975 1980 1985 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Source: Social Security Bulletin: Annual Statistical Supplement 1957 to 2014; U.S. census of Bureau;

U.S. Bureau of Labor Statistics.

4/2&- 560-/76#32 ./01-&2*/3 Employment Population Ratio= *100; 4/2&- ./01-&2*/3

4/2&- )*+&,*-*27 ./01-&2*/3 Disability Employment Ratio= *100. 560-/76#32 ./01-&2*/3

Figure 9: Application Waiting Time (in Months) of all Stages 97

Source: Social Security Administration, Disability Research file

Figure 10: Applications at All Adjudicative Levels

Data Source: Social Security Administration, Disability Research file

Figure 11: Applications at Different Stages

Data Source: Social Security Administration, Disability Research file

Table 1: Variables and Description

Variables Description Type

Disability Disability Status. disability= 1 if disabled Binary

and not working

Liberalization Liberalization=1 if year>=1985 Dummy

Education education=1 if education is less than Dummy

college

Liberalization*Education =education*liberalization Dummy

ADA ADA=1 if year >=1991 Dummy

ADAAA ADAAA=1 if year>=2009 Dummy

WOTC The real term of Work Opportunity Tax Numerical

Credit (WOTC) for Disabled

Unemployment Rate State Unemployment Rate by Year Numerical

Numerical

100

Employment Population State Employment Population Ratio by

Ratio Year

Replacement Rate Replacement of State Average Income to Numerical

State Average Disability Payment

Employment Rate in Percent of Employment in Agriculture by Numerical

Agriculture by State State

Employment Rate in Percent of Employment in Manufacturing Numerical

Manufacturing by State by State

Age 24

Age2 age^2 Numerical

Female Gender (Male=0) Dummy

Number of Children in the Number of Children in the Family Numerical

Family

Ever Married Ever Married=1 (Divorced, Separated, Dummy

Married, Widowed), Never Married=0

101

Black Race (Black=1, other=0) Dummy

Hispanic Race (Hispanic=1, other=0) Dummy

Other Race Race (Asian or others=1, other=0) Dummy

High School High School=1 if education is high school Dummy

graduate

Some College Some College=1 if education is college Dummy

dropout

College College=1 if education is college graduate Dummy

Advanced Advanced=1 if education is beyond college Dummy

(graduate school)

Obs. 3,233,064

102

Table 2: Summary of Variables

Variables Mean Standard Deviation

Disability 0.051 0.220

Liberalization 0.874 0.332

Education 0.478 0.500

ADA 0.730 0.444

ADAAA 0.248 0.432

WOTC 3921.76 4680.51

Unemployment Rate 0.063 0.021

Employment Population 0.470 0.033

Ratio

Replacement Rate 0.235 0.067

103

Employment Rate in 0.025 0.022

Agriculture by State

Employment Rate in 0.229 0.063

Manufacturing by State

Age 42.593 10.985

Female 0.520 0.500

Number of Children in the 0.963 1.200

Family

Ever Married 0.682 0.466

Black 0.100 0.300

Hispanic 0.135 0.341

Other Race 0.058 0.234

High School 0.337 0.473

104

Some College 0.252 0.434

College 0.178 0.382

Advanced 0.092 0.288

Obs. 3,233,064

105

Table 3: Disability Rate for Control and Treatment Groups from 1980 to 2016

Year Control Treatment

1980 0.0128 0.0671

1981 0.0129 0.0641

1982 0.0119 0.0690

1983 0.0121 0.0679

1984 0.0115 0.0682

1985 0.0120 0.0653

1986 0.0114 0.0685

1987 0.0111 0.0684

1988 0.0121 0.0690

1989 0.0121 0.0705

1990 0.0114 0.0728

1991 0.0136 0.0724

1992 0.0118 0.0791

1993 0.0138 0.0827

1994 0.0154 0.0971

1995 0.0166 0.1024

1996 0.0174 0.1002

1997 0.0185 0.1010

1998 0.0186 0.1022

1999 0.0203 0.1034

2000 0.0213 0.0999

2001 0.0212 0.0975

2002 0.0208 0.1057

106

2003 0.0223 0.1056

2004 0.0252 0.1125

2005 0.0237 0.1152

2006 0.0241 0.1119

2007 0.0253 0.1126

2008 0.0252 0.1162

2009 0.0262 0.1227

2010 0.0286 0.1225

2011 0.0296 0.1206

2012 0.0297 0.1321

2013 0.0306 0.1309

2014 0.0307 0.1284

2015 0.0330 0.1360

2016 0.0339 0.1407

Source: CPS. Disability Rate is calculated using the annual data from the 1980-2016 March CPS supplement.

!"#$%"&"'( *+*,&$'"+- Disability Rate is calculated by in control and treatment groups. Control Group: Education &$%+. /+.01 is above college including Some College, College and Advanced Degree. Treatment Group: Education is below college including High School and Less Than High School.

107

Figure 12: Disability Rate for Control and Treatment Groups from 1980 to 2016

108

Table 4: Summary Statistics for Control and Treatment Groups

Explanatory Variables Control Group Treatment Group Treatment-Control

Mean Std. Dev Mean Std. Dev

Unemployment Rate 0.062 0.021 0.064 0.021 0.002

Employment 0.473 0.032 0.467 0.033 0.006

Population Ratio

Replacement Rate 0.242 0.067 0.228 0.067 -0.014

Employment Rate in 0.029 0.034 0.035 0.040 0.006

Agriculture by State

Employment Rate in 0.221 0.060 0.237 0.065 0.016

Manufacturing by State

Age 41.904 10.643 43.346 11.300 1.442

Female 0.517 0.500 0.523 0.500 0.006

Number of Children in 0.947 1.150 0.980 1.252 0.033

the Family

109

Ever Married 0.836 0.371 0.848 0.359 0.012

Black 0.086 0.281 0.115 0.319 0.029

Hispanic 0.082 0.275 0.192 0.394 0.11

Other Race 0.068 0.252 0.047 0.212 -0.021

Obs. 1,687,358 1,545,706

Notes: Control group includes individuals who received education below high school, those who did not complete high school and high school graduates; the treatment group includes individuals who received some college, college and graduate school.

110

Table 5: Summary Statistics for Control and Treatment Groups Pre-treatment

Explanatory Variables Control Group Treatment Group Treatment-Control

Mean Std. Dev Mean Std. Dev

Unemployment Rate 0.080 0.022 0.082 0.023 0.002

Employment 0.441 0.028 0.437 0.029 -0.004

Population Ratio

Replacement Rate 0.149 0.022 0.151 0.023 0.002

Employment Rate in 0.095 0.060 0.096 0.059 0.001

Agriculture by State

Employment Rate in 0.289 0.067 0.299 0.066 0.010

Manufacturing by State

Age 39.091 10.815 43.504 11.850 4.413

Female 0.463 0.499 0.556 0.497 0.093

Number of Children in 0.991 1.190 1.073 1.322 0.082

the Family

111

Ever Married 0.858 0.349 0.915 0.278 0.057

Black 0.059 0.235 0.101 0.301 0.042

Hispanic 0.049 0.216 0.113 0.316 0.064

Other Race 0.034 0.181 0.028 0.165 -0.006

Obs. 158,535 247,807

1980 to 1984.

112

Table 6: Hysteresis in Low-Skilled Workers Dependent Variable: Probability of Being Disabled and Not Working Controls (1) (2) (3) (4) (5) (6)

Liberalization Dummy -0.019*** -0.020*** -0.032*** -0.014*** -0.021*** -0.051*** (0.0007) (0.0008) (0.0020) (0.0007) (0.0013) (0.0066)

Education Dummy 0.031*** 0.031*** 0.030*** 0.031*** 0.031*** 0.031*** (0.0007) (0.0007) (0.0007) (0.0007) (0.0007) (0.0007)

Liberalization*Education 0.022*** 0.022*** 0.022*** 0.022*** 0.022*** 0.021*** (0.0007) (0.0007) (0.0008) (0.0007) (0.0007) (0.0008)

ADA 0.0016*** 0.0013** -0.069 0.0069*** -0.0001 0.0965* (0.0004) (0.0005) (0.0536) (0.0005) (0.0012) (0.0052)

ADAAA -0.0085 -0.007*** -0.010* -0.006*** -0.0084*** -0.015** 113 (0.0005) (0.0007) (0.0061) (0.0005) (0.0006) (0.0061)

WOTC -0.0000003*** -0.0000003*** -0.000007 -0.0000001** -0.0000004*** -0.000012** (4.247e-08) (4.483e-08) (0.000005) (4.457e-08) (9.437e-08) (5.312e-06)

Unemployment Rate 0.0313*** -0.0351 -0.302*** (0.0068) (0.0244) (0.0780)

Employment Population - - -0.142*** 0.041 0.426*** Ratio (0.0048) (0.028) (0.110)

Replacement Rate 0.242*** 0.243*** 0.151*** 0.174*** 0.262*** 0.271*** (0.0040) (0.0041) (0.0113) (0.0047) (0.0144) (0.0393)

Employment in -0.065*** -0.074*** -0.081*** -0.030*** -0.080*** -0.105 Agriculture by State (0.0060) (0.0068) (0.0157) (0.0061) (0.0100) (0.0267)

0.037*** 0.039*** -0.086*** 0.040*** 0.037*** -0.104***

Employment in (0.0026) (0.0027) (0.0068) (0.0025) (0.0026) (0.0098) Manufacturing by State 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** Age (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001)

0.00002*** 0.00002*** 0.00003*** 0.00002*** 0.00002*** 0.00003*** Age2 0.000001 0.000001 (0.000001) (0.000001 (0.000001) (0.000001)

0.0002 0.0002 0.00007 0.0002 0.0002 0.00007 Female (0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002)

-0.004*** -0.004*** -0.004*** -0.004*** -0.004*** -0.004*** Number of Children in (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) the Family -0.049*** -0.050*** -0.050*** -0.050*** -0.049*** -0.050*** Ever Married (0.0004) (0.0004) (0.0004) (0.0004) (0.0004) (0.0004)

114 0.047*** 0.048*** 0.047*** 0.047*** 0.048*** 0.047*** Black (0.0004) (0.0004) (0.0004) (0.0004) (0.0005) (0.0004)

-0.0002 0.00005 0.003*** -0.0013*** 0.0003 0.003*** Hispanic (0.0004) (0.0004) (0.0004) (0.0004) (0.0004) (0.0004)

0.004*** 0.004*** 0.007*** 0.003*** 0.005*** 0.007*** Other Race (0.0005) (0.0005) (0.0006) (0.0005) (0.0006) (0.0006)

Adjusted R-squared 0.0506 0.0506 0.0525 0.0509 0.0505 0.0525 Observations 3,233,064 3,233,064 3,233,064 3,233,064 3,233,064 3,233,064 Sources: CPS March Supplement; BLS; SSA. All regressions are based on the full sample of the study. Columns (1) and (4) are estimated by difference-in-difference; (2) and (5) are estimated by difference-in-difference with Bartik instrument. Columns (3) and (6) are estimated by difference-in-difference with Bartik instrument, including the full set of state fixed effects and year fixed effects. The replacement rate is calculated based on CPS and BLS, Employment Rate in Agriculture by State and in Manufacturing by State are calculated based on CPS. In addition, all the specifications are controlled for the individual level characteristics, including age, age2, female, marriage, number of children in the family, race and education. ***, **, * indicate significance at the 1%, 5% and 10% levels, respectively. Standard errors are shown in parentheses.

Table 7: Hysteresis in Low-Skilled Workers (Robustness Check) Dependent Variable: Probability of Being Disabled and Not Working Model: Difference-in-Difference with Bartik Instrument & Fixed Effect

Full Sample Subsample (3) (6) (3) (6) Unemployment -0.302*** -0.244*** Rate (0.0780) (0.0007) 0.426*** 0.319*** Employment (0.110) (0.0979) Population Ratio 0.151*** 0.271*** 0.147*** 0.230*** Replacement Rate (0.0113) (0.0393) (0.0142) (0.0366)

Adjusted R-squared 0.0525 0.0525 0.0500 0.0499 Observations 3,233,064 3,233,064 2,858,468 2,858,468 Sources: CPS March Supplement; BLS; SSA. All regressions are based on estimates by difference-in-difference with Bartik instrument, including the full set of state fixed effects and year fixed effects. The Robustness Check is conducted with a subsample from 1980 to 2012. The replacement rate is calculated based on CPS and BLS, Employment Rate in Agriculture by State and in Manufacturing by State are calculated based on CPS. In addition, all the specifications are controlled for the individual level characteristics, including age, age2, female, marriage, number of children in the family, race and education. ***, **, * indicate significance at the 1%, 5% and 10% levels, respectively. Standard errors are shown in parenthesis.

115

Figure 13: Unemployment Rate by State

116

117

Figure 14: Employment Population Ratio by State

118

119

Figure 15: Replacement Rate by State

120

121

Figure 16: Employment in Agriculture by State

122

123

Figure 17: Employment in Manufacturing by State

124

125

Table 8: Variables and Description

Variables Description Type

Disability Disability Status. disability= 1 if Binary

disabled and not working

Liberalization Liberalization=1 if year>=1985 Dummy

Older Worker Older Worker=1 if age>=54 Dummy

Liberalization*Older =Older Worker*liberalization Dummy

Worker

NRA NRA=1 if year>=2000 Dummy

NRA*Older Worker =Older Worker*liberalization Dummy

ADA ADA=1 if year >=1991 Dummy

ADAAA ADAAA=1 if year>=2009 Dummy

WOTC The real term of Work Opportunity Tax Numerical

Credit (WOTC) for Disabled

126

Unemployment Rate (%) State Unemployment Rate by Year Numerical

Replacement Rate (%) Replacement of State Average Income to Numerical

State Average Disability Payment

Employment Rate in Percent of Employment in Agriculture Numerical

Agriculture by State (%) by State

Employment Rate in Percent of Employment in Numerical

Manufacturing by State Manufacturing by State

(%)

Age 24

Age2 age^2 Numerical

Female Gender (Male=0) Dummy

Number of Children in the Number of Children in the Family Numerical

Family

Ever Married Ever Married=1 (Divorced, Separated, Dummy

Married, Widowed), Never Married=0

127

Black Race (Black=1, other=0) Dummy

Hispanic Race (Hispanic=1, other=0) Dummy

Other Race Race (Asian or others=1, other=0) Dummy

High School High School=1 if education is high Dummy

school graduate

Some College Some College=1 if education is college Dummy

dropout

College College=1 if education is college Dummy

graduate

Advanced Dummy

Advanced=1 if education is beyond

college (graduate school)

Obs. 3,233,064

128

Table 9: Variables and Description

Variables Mean Standard Deviation

Disability 0.051 0.220

Liberalization 0.874 0.332

Older Worker 0.181 0.385

NRA 0.525 0.499

ADA 0.730 0.444

ADAAA 0.248 0.432

WOTC 3921.76 4680.51

Unemployment Rate 0.063 0.021

Employment Population 0.470 0.033

Ratio

129

Replacement Rate 0.235 0.067

Employment Rate in 0.025 0.022

Agriculture by State

Employment Rate in 0.229 0.063

Manufacturing by State

Age 42.593 10.985

Female 0.520 0.500

Number of Children in the 0.963 1.200

Family

Ever Married 0.682 0.466

Black 0.100 0.300

Hispanic 0.135 0.341

Other Race 0.058 0.234

130

High School 0.337 0.473

Some College 0.252 0.434

College 0.178 0.382

Advanced 0.092 0.288

Obs. 3,233,064

131

Table 10: Disability Rate for Different Age Cohorts from 1980 to 2016

Year 25≤Age≤39 40≤Age≤54 54≤Age≤64

1980 0.0164 0.0437 0.1066 1981 0.0166 0.0429 0.0965 1982 0.0166 0.0449 0.1016 1983 0.0151 0.0426 0.1014 1984 0.0161 0.0408 0.0995 1985 0.0167 0.0410 0.0889 1986 0.0174 0.0403 0.0925 1987 0.0179 0.0393 0.0901 1988 0.0183 0.0397 0.0905 1989 0.0169 0.0419 0.0887 1990 0.0172 0.0422 0.0935 1991 0.0193 0.0429 0.0902 1992 0.0200 0.0439 0.0910 1993 0.0223 0.0458 0.0888 1994 0.0248 0.0509 0.1032 1995 0.0247 0.0557 0.1046 1996 0.0257 0.0556 0.1075 1997 0.0262 0.0565 0.1074 1998 0.0268 0.0561 0.1044 1999 0.0255 0.0579 0.1065 2000 0.0247 0.0568 0.1073 2001 0.0242 0.0581 0.0997 2002 0.0251 0.0557 0.1099 2003 0.0267 0.0562 0.1053 2004 0.0287 0.0606 0.1129 2005 0.0282 0.0614 0.1122 2006 0.0272 0.0608 0.1111 2007 0.0263 0.0612 0.1159 2008 0.0252 0.0624 0.1136 2009 0.0278 0.0641 0.1155 2010 0.0290 0.0673 0.1195 2011 0.0301 0.0660 0.1196 2012 0.0315 0.0698 0.1255 2013 0.0322 0.0690 0.1219 2014 0.0322 0.0693 0.1285 2015 0.0326 0.0737 0.1311 2016 0.0339 0.0740 0.1373 Source: CPS. Disability Rate is calculated using the annual data from the 1980-2016 March CPS !"#$%"&"'( *+*,&$'"+- supplement. Disability rate is calculated by in each cohort. &$%+. /+.01

132

Figure 18: Disability Rate for Different Age Cohorts from 1980 to 2016

133

Table 11: Disability Rate for Control and Treatment Groups from 1980 to 2016

Year Control Treatment

1980 0.0301 0.1066 1981 0.0298 0.0965 1982 0.0307 0.1016 1983 0.0289 0.1014 1984 0.0285 0.0995 1985 0.0288 0.0889 1986 0.0288 0.0925 1987 0.0286 0.0901 1988 0.0290 0.0905 1989 0.0294 0.0887 1990 0.0297 0.0935 1991 0.0311 0.0902 1992 0.0320 0.0910 1993 0.0341 0.0888 1994 0.0378 0.1032 1995 0.0402 0.1046 1996 0.0406 0.1075 1997 0.0413 0.1074 1998 0.0415 0.1044 1999 0.0417 0.1065 2000 0.0407 0.1073 2001 0.0412 0.0997 2002 0.0404 0.1099 2003 0.0414 0.1053 2004 0.0446 0.1129 2005 0.0448 0.1122 2006 0.0440 0.1111 2007 0.0437 0.1159 2008 0.0438 0.1136 2009 0.0459 0.1155 2010 0.0481 0.1195 2011 0.0480 0.1196 2012 0.0506 0.1255 2013 0.0506 0.1219 2014 0.0507 0.1285 2015 0.0531 0.1311 2016 0.0539 0.1373 Source: CPS. Disability Rate is calculated using the annual data from the 1980-2016 March CPS supplement. !"#$%"&"'( *+*,&$'"+- Disability rate is calculated by in control and treatment groups. &$%+. /+.01 Control Group: Cohort 25≤Age≤39 and 40≤Age≤54. Treatment Group: Cohort 55≤Age≤64. 134

Figure 19: Disability Rate for Different Age Cohorts from 1980 to 2016

135

Table 12: Summary Statistics for Control and Treatment Groups

Explanatory Variables Control Group Treatment Group Treatment-Control

Mean Std. Dev Mean Std. Dev

Unemployment Rate 0.063 0.021 0.064 0.021 0.001

Employment 0.470 0.033 0.468 0.033 -0.002

Population Ratio

Replacement Rate 0.234 0.067 0.239 0.070 0.005

Employment Rate in 0.032 0.037 0.032 0.038 0

Agriculture by State

Employment Rate in 0.229 0.062 0.228 0.064 -0.001

Manufacturing by

State

Age 38.908 8.409 59.228 2.862 20.32

Female 0.519 0.500 0.523 0.500 0.004

136

Number of Children in 1.127 1.232 0.222 0.646 -0.905

the Family

Ever Married 0.821 0.384 0.937 0.243 0.116

Black 0.099 0.299 0.103 0.304 0.004

Hispanic 0.143 0.350 0.095 0.294 -0.048

Other Race 0.059 0.236 0.054 0.225 -0.005

High School 0.334 0.472 0.351 0.478 0.017

Some College 0.261 0.439 0.215 0.411 -0.046

College 0.187 0.390 0.136 0.343 -0.051

Advanced 0.091 0.288 0.093 0.291 0.002

Obs. 2,646,622 586,442

Notes: Control group includes individuals who are between 25 and 54 years old; the treatment group includes individuals who are between 55 and 64 years old.

137

Table 13: Summary Statistics for Control and Treatment Groups Pre- treatment1984 Liberalization

Explanatory Variables Control Group Treatment Group Treatment-Control

Mean Std. Dev Mean Std. Dev

Unemployment Rate 0.081 2.237 0.081 2.255 0.000

Employment 0.438 0.029 0.438 0.029 0.000

Population Ratio

Replacement Rate 0.150 0.022 0.150 0.022 0.000

Employment Rate in 0.096 0.059 0.095 0.059 -0.001

Agriculture by State

Employment Rate in 0.294 0.067 0.298 0.066 0.004

Manufacturing by

State

Age 37.541 8.634 59.302 2.843 21.761

Female 0.517 0.500 0.531 0.500 0.014

138

Number of Children in 1.243 1.306 0.206 0.632 -1.037

the Family

Ever Married 0.878 0.327 0.954 0.209 0.076

Black 0.086 0.280 0.078 0.268 -0.008

Hispanic 0.095 0.293 0.060 0.237 -0.035

Other Race 0.032 0.177 0.022 0.146 -0.010

High School 0.377 0.485 0.365 0.482 -0.012

Some College 0.211 0.408 0.131 0.338 -0.008

College 0.142 0.349 0.082 0.275 -0.060

Advanced 0.069 0.254 0.042 0.200 -0.027

Obs. 327,145 79,791

Notes: Control group includes individuals who are between 25 and 54 years old; the treatment group includes individuals who are between 55 and 64 years old. Pre-treatment is the time period from 1980 to 1984. 139

Table 14: Summary Statistics for Control and Treatment Groups Pre- treatment (2000 NRA)

Explanatory Variables Control Group Treatment Group Treatment-Control

Mean Std. Dev Mean Std. Dev

Unemployment Rate 0.065 2.073 0.066 2.133 0.001

Employment 0.466 0.033 0.463 0.034 0.001

Population Ratio

Replacement Rate 0.181 0.032 0.180 0.032 -0.001

Employment Rate in 0.046 0.046 0.049 0.048 0.003

Agriculture by State

Employment Rate in 0.264 0.061 0.268 0.063 0.004

manufacturing State

Age 37.981 8.353 59.367 2.869 21.386

Female 0.517 0.500 0.531 0.499 0.014

140

Number of Children in 1.126 1.250 0.190 0.613 -1.037

the Family

Ever Married 0.846 0.361 0.954 0.211 0.108

Black 0.089 0.285 0.078 0.272 -0.011

Hispanic 0.115 0.319 0.076 0.265 -0.039

Other Race 0.039 0.194 0.028 0.166 -0.011

High School 0.364 0.481 0.379 0.485 0.015

Some College 0.240 0.427 0.159 0.366 -0.081

College 0.162 0.368 0.097 0.295 -0.065

Advanced 0.078 0.268 0.061 0.239 -0.017

Obs. 1,267,247 268,231

141

Table 15: Hysteresis in Older Workers Dependent Variable: Probability of Being Disabled and Not Working

Controls (1) (2) (3) (4) (5) (6)

Liberalization Dummy -0.004*** -0.006*** -0.015*** 0.001** -0.007** -0.033*** (0.0005) (0.0007) (0.0020) (0.0005) (0.0012) (0.0066)

Older Worker Dummy 0.002* 0.002* 0.002** 0.002* 0.002* 0.002** (0.0010) (0.0010) (0.0010) (0.0010) (0.0010) (0.0010)

Liberalization*Older -0.002** -0.002** -0.003*** -0.002** -0.002** -0.003*** Worker (0.0010) (0.0010) (0.0010) (0.0010) (0.0010) (0.0010)

NRA -0.003*** -0.003*** 0.087 -0.0006 -0.004*** 0.119** (0.0005) (0.0005) (0.0529) (0.0005) (0.0006) (0.0515)

142 NRA*Older Worker 0.022*** 0.022*** 0.022*** 0.022*** 0.022*** 0.022*** (0.0007) (0.0007) (0.0007) (0.0007) (0.0007) (0.0007)

ADA 0.0035*** 0.0032*** 0.005*** 0.0076*** 0.0007 -0.0022 (0.0004) (0.0005) (0.0017) (0.0005) (0.0010) (0.0036)

ADAAA -0.0067 -0.0051*** -0.013** -0.0048*** -0.0077*** -0.0165*** (0.0005) (0.0006) (0.0061) (0.0005) (0.0006) (0.0061)

WOTC -2.272e-07*** -2.420e-07*** -9.815e-06* 2.564e-08 -4.025e-07*** -1.369-05*** (4.749e-08) (4.766e-08) (5.441e-06) (4.857e-08) (7.185e-08) (5.279e-06)

Unemployment Rate 0.0071 -0.074*** -0.275*** (0.0068) (0.0230) (0.0774)

Employment Population - - - -0.119*** 0.081** 0.389*** Ratio (0.0048) (0.025) (0.110)

Replacement Rate 0.228*** 0.231*** 0.153*** 0.165*** 0.271*** 0.263*** (0.0042) (0.0043) (0.0119) (0.0049) (0.0140) (0.0390)

Employment in -0.101*** -0.113*** -0.095*** -0.065*** -0.127*** -0.030 Agriculture by State (0.0061) (0.0069) (0.0156) (0.0062) (0.0100) (0.0265)

Employment in 0.012*** 0.014*** -0.114*** 0.016*** 0.010*** -0.131*** Manufacturing by State (0.0026) (0.0027) (0.0067) (0.0026) (0.0027) (0.0097)

Age 0.003*** 0.003*** 0.003*** 0.003*** 0.003*** 0.003*** (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001)

Age2 -0.000007*** -0.000007*** -0.000006*** -0.000007*** -0.000007*** -0.000006** 0.000002 0.000002 0.000002 (0.000002) (0.000002) (0.000002)

Female 0.0001 0.0001 -0.00002 0.0001 0.0001 -0.00002 (0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002)

143 Number of Children in -0.005*** -0.005*** -0.005*** -0.005*** -0.005*** -0.005***

the Family (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001)

Ever Married -0.047*** -0.047*** -0.047*** -0.047*** -0.047*** -0.047*** (0.0004) (0.0004) (0.0004) (0.0004) (0.0004) (0.0004)

Black 0.041*** 0.041*** 0.041*** 0.040*** 0.041*** 0.041*** (0.0004) (0.0004) (0.0004) (0.0004) (0.0004) (0.0004)

Hispanic -0.0181*** -0.0178*** -0.0149*** -0.0190*** -0.0174*** -0.0146*** (0.0004) (0.0004) (0.0004) (0.0004) (0.0004) (0.0004)

Other Race 0.0014*** 0.0015*** 0.0043*** 0.0005 0.0021*** 0.0045*** (0.0005) (0.0005) (0.0006) (0.0005) (0.0006) (0.0006)

High School -0.073*** -0.073*** -0.073*** -0.073*** -0.073*** -0.073*** (0.0004) (0.0004) (0.0004) (0.0004) (0.0004) (0.0004)

Some College -0.092*** -0.092*** -0.091*** -0.092*** -0.092*** -0.092*** (0.0004) (0.0004) (0.0004) (0.0004) (0.0004) (0.0004)

College -0.115*** -0.115*** -0.115*** -0.115*** -0.115*** -0.115*** (0.0004) (0.0005) (0.0005) (0.0004) (0.0005) (0.0005)

Advanced -0.125*** -0.126*** -0.126*** -0.125*** -0.126*** -0.125*** (0.0005) (0.0005) (0.0005) (0.0005) (0.0005) (0.0005)

Adjusted R-squared 0.0627 0.0629 0.0644 0.0627 0.0624 0.0642 Observations 3,233,064 3,233,064 3,233,064 3,233,064 3,233,064 3,233,064 Sources: CPS March Supplement; BLS; SSA. All regressions are based on the full sample of the study. Columns (1) and (3) are estimated by difference-in-difference; (2) and (4) are estimated by difference-in-difference with Bartik instrument. The replacement rate is calculated based on CPS and BLS, Employment Rate in Agriculture by State and in Manufacturing by State are calculated based on CPS. In addition, all the specifications are controlled for the individual level characteristics, including age, age2, female, marriage, number of children in the family, race and education. ***, **, * indicate significance at the 1%, 5% and 10% levels, respectively. Standard errors are shown in parentheses. 144

Table 16: Hysteresis in Older Workers (Robustness Check) Dependent Variable: Probability of Being Disabled and Not Working Model: Difference-in-Difference with Bartik Instrument & Fixed Effect

Full Sample Subsample (3) (6) (3) (6) Unemployment -0.275*** -0.236** Rate (0.0774) (0.0774) 0.389*** 0.308*** Employment (0.110) (0.097) Population Ratio 0.153*** 0.263*** 0.152*** 0.232*** Replacement Rate (0.0119) (0.0390) (0.0141) (0.0363)

Adjusted R-squared 0.0644 0.0642 0.0625 0.0624 Observations 3,233,064 3,233,064 2,858,468 2,858,468 Sources: CPS March Supplement; BLS; SSA. All regressions are based on estimated with difference-in-difference with Bartik instrument, including the full set of state fixed effects and year fixed effects. The Robustness Check is conducted with subsample from 1980 to 2012. The replacement rate is calculated based on CPS and BLS, Employment Rate in Agriculture by State and in Manufacturing by State are calculated based on CPS. In addition, all the specifications are controlled for the individual level characteristics, including age, age2, female, marriage, number of children in the family, race and education. ***, **, * indicate significance at the 1%, 5% and 10% levels, respectively. Standard errors are shown in parentheses.

145

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polarization in local labor markets. Unlike Metropolitan Statistical Areas (MSAs), CZs cover the entire U.S. and unlike counties, they do not constrain local labor markets to fall within state boundaries.

3. Empirical Strategy

The level of employed labor in an MSA or CZ is a function of both labor demand and labor supply. We only observe the equilibrium and using observed changes in labor confounds whether the results are driven by supply or demand. Instead, we use labor demand shocks as our identifying source of variation. We employ the Bartik instrument to act as our exogenous change in local labor demand. The Bartik instrument is constructed by using national changes in employment by industry interacted polarizationAppendix in local labor A markets. Unlike Metropolitan Statistical Areas (MSAs), CZs cover the entire U.S. andwith unlike a geographic counties, area’sthey do initial not constrain baseline localindustry labor composition. markets to fall The within first state step boundaries.is to predict next period’s employmentBARTIK STATEin the geographic-LEVEL area LABOR using the followingDEMAND formula: INSTRUMENT 3. Empirical Strategy N a t i o n a l Employment in I n d u s t r y a t t i m e tk ˆ The level of employed labor in an MSA or CZ is a function of both labor demand and labor L jt Ar e a j Employment in In d u s t r y a t t i m e tk 1 k N a t i o n a l Employment in In d u s t r y a t t i m e tk 1 supply. We only observe the equilibrium and using observed changes in labor confounds whether the where j indexes geographic area and t indexes time. Predicted employment in the next period relies results are driven by supply or demand. Instead, we use labor demand shocks as our identifying source only on initialThe industry data composition is collected and fromnational the growth CPS rates. March An area’s Supplement own growth is not 1980 used- 2016.in this j of variation. We employ the Bartik instrument to act as our exogenous change in local labor demand. formula. The Bartik Instrument, then, is simply the predicted employment growth rate: The Bartikindexes instrument 52 states is constructed in the by usingU.S. national and t changesindexes in employment time, and by k industry indexes interacted industries from with a geographic area’s initial baseline industryˆ composition. The first step is to predict next period’s jt LL tj 1, employment1980 toinBartik the 1982, geographic Instrument 1983 area tojt 2002using the and following 2003. formula: to 2016. Predicted employment in the (1) next L tj 1, period relies only on initial industry composition in every state and national ˆ N a t i o n a l Employment in I n d u s t r y a t t i m e tk L jt Equivalently, we can write this equation as Ar e a j Employment in In d u s t r y a t t i m e tk 1 k N a t i o n a l Employment in In d u s t r y a t t i m e tk 1 wheregrowth j indexes geographic rates. area The and t Bartik indexes time. Instrument, Predicted employment then, in the is next simply period relies the predicted L kt only on initial industry composition and nationalL jk t growth1, rates.1 An area’s own growth is not used in this L employment growth rate:k tk 1, formula. The BartikBartik Instrument, Instrument then,jt is simply the predicted employment. growth rate: L jk t1, k ˆ jt LL tj 1, Bartik Instrument . (1) jt L L This equation illustrates how thetj 1, national industry-specific employment growth ( kt ) interacts with L tk 1, Equivalently, we can write this equation as theGeographical geographic-level Graphs initial distribution of Bartik for variation.State-level The Bartik Labor instrument Demand does Instrument not use industry-

specific variation in growth at the geographic-level L because this source of variation is potentially L kt 1 jk t1, L problematic and driven by klabor supply tk shocks.1, Note that the total change in employment in geographic Bartik Instrument jt . L jk t1, k

7 Lkt This equation illustrates how the national industry-specific employment growth ( ) interacts with L tk 1, the geographic-level initial distribution for variation. The Bartik instrument does not use industry- specific variation in growth at the geographic-level because this source of variation is potentially problematic and driven by labor supply shocks. Note that the total change in employment in geographic

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