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Do past unemployment spells affect the duration of current unemployment?
Economics Letters 77 (2002) 157–161 www.elsevier.com / locate / econbase
Do past unemployment spells affect the duration of current unemployment? q HwaJung Choi a , Donggyun Shin b , * a
Division of Macroeconomics, Korea Development Institute, P.O. Box 113, Cheongryang, Seoul 130 -650, South Korea b Department of Economics, Hanyang University, Seongdong-Gu, Haengdang-Dong 17, Seoul 133 -791, South Korea Received 27 August 2001; accepted 19 February 2002
Abstract On the basis of monthly labor force histories of respondents constructed from the Panel Study of Income Dynamics, this paper finds no occurrence dependence in unemployment spells, positive occurrence dependence in unemployment insurance spells, and substantial heterogeneity in occurrence dependence. 2002 Elsevier Science B.V. All rights reserved. Keywords: Occurrence dependence; Unemployment duration; Unemployment insurance; Experienced workers; Heterogeneity JEL classification: J64
1. Introduction The question of whether or not the duration of unemployment depends on past unemployment experience has attracted great attention from labor economists and policymakers. As rigorously argued by Heckman and Borjas (1980) however, in order to understand how previous unemployment experience affects current unemployment behavior, it is desirable to distinguish different types of past unemployment experience, which leads to different policy implications. Heckman and Borjas (pp. 247–248) consider three types of state dependence, occurrence dependence, duration dependence, and lagged duration dependence—defined as the effects of the number of past unemployment spells, the elapsed time spent in the current spell, and the length of previous spells, respectively, on the probability of leaving current unemployment. They describe circumstances under which each of the q
This work was supported by the research fund of Hanyang University (HY-2001-I). * Corresponding author. Tel.: 182-2-2290-1036; fax: 182-2-2296-9587. E-mail address: [email protected] (D. Shin). 0165-1765 / 02 / $ – see front matter PII: S0165-1765( 02 )00091-5
2002 Elsevier Science B.V. All rights reserved.
158
H. Choi, D. Shin / Economics Letters 77 (2002) 157–161
three types of state dependence arises and argue that occurrence dependence may arise if employers use unemployment records in their hiring and firing decisions. They also describe the conditions under which each type of state dependence is identified and conclude that testing for occurrence dependence can be conducted under the least restrictive conditions. Most research on unemployment duration has dealt with duration dependence in light of the major econometrics issue of distinguishing pure duration-dependent effects from unobserved heterogeneity.1 However, despite the simplicity of test procedures and the relevance of policy implications, not much research has been conducted to test for the existence of occurrence dependence. A few exceptions are as follows. First, Heckman and Borjas (1980) developed a model that can distinguish between effects of different types of state dependence and found no occurrence dependence in the duration of unemployment from a sample of U.S. high school graduates. They noted, however, that their insignificant statistical results may be due to the small size of the sample (p. 279). Heckman and Borjas’s conclusion of no occurrence dependence is reconfirmed by Ellwood (1982, p. 350) and Ruhm (1991, p. 322). As is criticized by Willis (1982), however, because of data limitations that preclude the identification of separate spells of unemployment, Ellwood’s model is unable to distinguish between effects of duration in the current spell and effects of events that took place before the current spell began. A similar criticism can be applied to Ruhm’s study because methodologies adopted by Ellwood and Ruhm are virtually identical. Put together, all U.S. studies on this issue consistently find no occurrence dependence in the duration of unemployment spells. Unlike U.S. studies, by employing a large data set from Canadian administrative data and using the Heckman and Borjas’s method, Corak (1993) found strong evidence of mean occurrence dependence in the duration of unemployment insurance (UI) spells for young Canadian workers. On the basis of monthly labor force histories of respondents constructed from the Panel Study of Income Dynamics (PSID) data for the 1985–1995 period, this paper reinvestigates this issue for the U.S. labor market. First, Heckman and Borjas’s study is replicated by employing their methodology but using a large sample of more experienced U.S. workers. With the exception of Ruhm, all previous studies cited above use data on new entrants in the labor market who have earned, at most, high school diplomas and, therefore, focus on the secondary sector of the labor market. As conjectured by Heckman and Borjas, state dependence may be more evident among more experienced workers (p. 279). Second, the current study is the first that examines occurrence dependence in the duration of UI spells in the U.S. labor market, which bridges the gap between Corak’s result and those of U.S. studies.2 Third, this paper examines heterogeneity in occurrence dependence, which is motivated by Corak’s finding that positive occurrence-dependent effects are much greater for women than for men. We investigate this issue by race and educational attainment as well as by gender.
2. Data and estimation technique For the years 1986–96, the PSID interviews collect monthly information on respondents’ labor force activities for the preceding calendar year. Therefore, the data from the 1986–96 interviews include monthly labor force variables for the January 1985 to December 1995 period. For the same 1
See, among others, Lancaster (1990) for a good survey of this issue. Many state UI programs effectively limit the eligibility for unemployment compensation of most youth (Flinn and Heckman, 1983, p. 28). Therefore, experienced workers are more relevant in examining UI spells. 2
H. Choi, D. Shin / Economics Letters 77 (2002) 157–161
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sample period, the PSID also reports whether or not each respondent received unemployment compensation in each month. These variables generate the completed duration of UI spells as well as unemployment spells. In each interview, the PSID also collects not only individual characteristics as of the interview date but also job characteristics of up to the two most recent jobs in the last year, from which various spell characteristics are derived.3 Our final sample includes both heads of households and wives who have completed schooling, have some previous work experience before the first interview in our sample, and are not self-employed. We follow the procedure suggested by Heckman and Borjas (1980) to test for occurrence dependence. Consider the following equation log Yin 5 b 9n Xin 1 ´in
(1)
where Yin represents the duration of the nth spell in our sample experienced by individual i, Xin is a set of spell characteristics, and ´in is an error term. As noted by Heckman and Borjas, this specification is valid if the sample contains only completed spell durations and there are no time-varying covariates (pp. 271–272). However, our sample covers a relatively long period running 132 months, which makes our results less subject to the bias that arises from using only completed spells. By subtracting Eq. (1) from the equation for the (n11)th spell, and by adding and subtracting b 9n11 Xin , we obtain log Yi,n 11 2 log Yin 5 b 9n 11 (Xi,n 11 2 Xin ) 1 ( bn11 2 bn )9Xin 1 (´i,n 11 2 ´in )
(2)
Mean occurrence dependence is said to exist if the coefficients on Xin are not jointly equal to zero, that is, the same characteristics have different effects on the duration of spells according to sequence number.4 The fact that some observations are differenced over a longer period than others has the following econometric implications, which are overlooked by previous studies. First, the variance of the error term in Eq. (2) varies across observations unless the correlation between two neighboring error terms in Eq. (1) is the same for all observations. Second, the length of difference itself should appear as a regressor in Eq. (2).
3. Empirical results For all samples to be examined below, a modified Breusch–Pagan test rejects the null hypothesis of homoskedasticity at any conventional significance level,5 and the length of time between spells is significant in explaining dependent variables in Eq. (2). For the sake of simplicity, we report only the 3
Details of data construction and quality are described in Shin and Shin (2000) and are also available upon request. In Eq. (2), marginal effects of changing variables, bn 11 , and unchanging variables themselves, Xin , are indexed at the (n11)th and nth spells, respectively. In principle, to check the robustness of results, we need to estimate the following equation that uses the opposite sequence-index. log Yi,n 11 2 log Yin 5 b n9 (Xi,n 11 2 Xin ) 1 ( bn 11 2 bn )9Xi,n 11 1 (´i,n 11 2 ´in ) This procedure does not change any of the results in Section 3. 5 To determine the appropriate weighting to correct for heteroskedasticity, we apply ordinary least squares (OLS) to the regression of squared residuals obtained from OLS estimation of Eq. (2) on a constant and the length of time between spells, and obtain the reciprocal of the square root of the predicted value. 4
H. Choi, D. Shin / Economics Letters 77 (2002) 157–161
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following two statistics.6 First, we present the F-statistics for tests of the null hypothesis that the coefficients of unchanging variables are jointly equal to zero (no mean occurrence dependence). Second, whenever the null is rejected, we report the relative length of two adjacent spells that is solely attributed to occurrence dependence. The latter measure, which is suggested by Corak (1993), is derived by setting the values of all of the changing variables to zero in estimated Eq. (2), predicting the dependent variables at sample means of unchanging variables, and exponentiating the predicted values. Numbers greater (less) than 1 represent positive (negative) occurrence dependence, meaning that, holding all characteristics constant between two adjacent spells, the following spell is longer (shorter) than the leading spell. Table 1 reports results for overall mean occurrence dependence. Entries in the first two columns are based on observations pooled over all spells of all individuals. In contrast, numbers in the last two columns are derived from using only the first and second spells. The latter is the more preferred sample because it is more representative of the population (Heckman and Borjas, p. 277) and because, especially in the case of UI spells, any structural changes occur early on rather than evolving continually with each spell (Corak, p. 66). For both samples, no occurrence dependence exists in the duration of unemployment spells, which reconfirms the results of all previous U.S. studies. For both samples of UI spells, however, the null is rejected even at the 1% level. For our preferred sample, the duration of UI spells increases by 33% due solely to the occurrence-dependent effects. This finding of positive occurrence dependence in UI spells is consistent with Corak’s finding. Table 2 reveals substantial heterogeneity in the mean occurrence dependence in the duration of UI spells. Estimates are based on the first and second spells. F-tests reject the null hypothesis of no mean occurrence dependence among males, the more-educated workers, and whites, and accept the null for the other groups. All these groups experience positive duration dependence. For these groups, the duration of UI spells increase by 35–45% due to the occurrence-dependent effects. Table 1 Mean occurrence dependence Spell types
All spells F-statistic
Unemployment
Unemployment Insurance
0.33 (0.98) [3141] 4.02 (,0.01) [1690]
First–second spells a
Relative length –
1.27
b
F-Statistic a 1.27 (0.24) [1842] 3.32 (,0.01) [988]
Relative b length –
1.33
P-values are in parentheses, and sample sizes are in brackets. a F-statistics for tests of no mean occurrence dependence. b The ratio of successive spell lengths that is solely attributed to occurrence dependence. 6
A similar set of explanatory variables is used as were used by Heckman and Borjas (1980) and Corak (1993). Overall, there are 12 unchanging variables including an intercept and five changing variables. Among them include variables on age, dependents, unemployment rate, industry dummies at former jobs, and the length of time between two successive spells. Full regression results are available upon request.
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161
Table 2 Heterogeneity in mean occurrence dependence between first and second UI spells Sample size
F-statistic a
Relative length b
Male
596
1.35
Female
390
At least some college At most high school graduation White
332
3.84 (,0.01) 0.87 (0.57) 2.06 (0.02) 1.37 (0.19) 3.47 (,0.01) 0.43 (0.93)
Non-white
642 894 82
– 1.45 – 1.34 –
P-values are in parentheses. a F-statistics for tests of no mean occurrence dependence. b The ratio of successive spell lengths that is solely attributed to occurrence dependence.
References Corak, M., 1993. Is unemployment insurance addictive? Evidence from the benefit durations of repeat users. Industrial and Labor Relations Review 47, 62–72. Ellwood, D.T., 1982. Teenage unemployment: permanent scars or temporary blemishes. In: Freeman, R.B., Wise, D.A. (Eds.), The Youth Labor Market Problem: Its Nature, Causes, and Consequences. University of Chicago Press, Chicago, pp. 349–385. Flinn, C.J., Heckman, J.J., 1983. Are unemployment and out of the labor force behaviorally distinct labor force states? Journal of Labor Economics 1, 28–42. Heckman, J.J., Borjas, G.J., 1980. Does unemployment cause future unemployment? Definitions, questions, and answers from a continuous time model of heterogeneity and state dependence. Economica 47, 247–283. Lancaster, T., 1990. The Econometric Analysis of Transition Data. Cambridge University Press, Cambridge. Ruhm, C.J., 1991. Are workers permanently scarred by job displacements? American Economic Review 81, 319–324. Shin, D., Shin, K., 2000. Inter- and intra-sectoral movement of labor and the unemployment rate. Unpublished manuscript. Willis, R.J., 1982. Comment. In: Freeman, R.B., Wise, D.A. (Eds.), The Youth Labor Market Problem: Its Nature, Causes, and Consequences. University of Chicago Press, Chicago, pp. 386–389.
Do past unemployment spells affect the duration of current unemployment? q HwaJung Choi a , Donggyun Shin b , * a
Division of Macroeconomics, Korea Development Institute, P.O. Box 113, Cheongryang, Seoul 130 -650, South Korea b Department of Economics, Hanyang University, Seongdong-Gu, Haengdang-Dong 17, Seoul 133 -791, South Korea Received 27 August 2001; accepted 19 February 2002
Abstract On the basis of monthly labor force histories of respondents constructed from the Panel Study of Income Dynamics, this paper finds no occurrence dependence in unemployment spells, positive occurrence dependence in unemployment insurance spells, and substantial heterogeneity in occurrence dependence. 2002 Elsevier Science B.V. All rights reserved. Keywords: Occurrence dependence; Unemployment duration; Unemployment insurance; Experienced workers; Heterogeneity JEL classification: J64
1. Introduction The question of whether or not the duration of unemployment depends on past unemployment experience has attracted great attention from labor economists and policymakers. As rigorously argued by Heckman and Borjas (1980) however, in order to understand how previous unemployment experience affects current unemployment behavior, it is desirable to distinguish different types of past unemployment experience, which leads to different policy implications. Heckman and Borjas (pp. 247–248) consider three types of state dependence, occurrence dependence, duration dependence, and lagged duration dependence—defined as the effects of the number of past unemployment spells, the elapsed time spent in the current spell, and the length of previous spells, respectively, on the probability of leaving current unemployment. They describe circumstances under which each of the q
This work was supported by the research fund of Hanyang University (HY-2001-I). * Corresponding author. Tel.: 182-2-2290-1036; fax: 182-2-2296-9587. E-mail address: [email protected] (D. Shin). 0165-1765 / 02 / $ – see front matter PII: S0165-1765( 02 )00091-5
2002 Elsevier Science B.V. All rights reserved.
158
H. Choi, D. Shin / Economics Letters 77 (2002) 157–161
three types of state dependence arises and argue that occurrence dependence may arise if employers use unemployment records in their hiring and firing decisions. They also describe the conditions under which each type of state dependence is identified and conclude that testing for occurrence dependence can be conducted under the least restrictive conditions. Most research on unemployment duration has dealt with duration dependence in light of the major econometrics issue of distinguishing pure duration-dependent effects from unobserved heterogeneity.1 However, despite the simplicity of test procedures and the relevance of policy implications, not much research has been conducted to test for the existence of occurrence dependence. A few exceptions are as follows. First, Heckman and Borjas (1980) developed a model that can distinguish between effects of different types of state dependence and found no occurrence dependence in the duration of unemployment from a sample of U.S. high school graduates. They noted, however, that their insignificant statistical results may be due to the small size of the sample (p. 279). Heckman and Borjas’s conclusion of no occurrence dependence is reconfirmed by Ellwood (1982, p. 350) and Ruhm (1991, p. 322). As is criticized by Willis (1982), however, because of data limitations that preclude the identification of separate spells of unemployment, Ellwood’s model is unable to distinguish between effects of duration in the current spell and effects of events that took place before the current spell began. A similar criticism can be applied to Ruhm’s study because methodologies adopted by Ellwood and Ruhm are virtually identical. Put together, all U.S. studies on this issue consistently find no occurrence dependence in the duration of unemployment spells. Unlike U.S. studies, by employing a large data set from Canadian administrative data and using the Heckman and Borjas’s method, Corak (1993) found strong evidence of mean occurrence dependence in the duration of unemployment insurance (UI) spells for young Canadian workers. On the basis of monthly labor force histories of respondents constructed from the Panel Study of Income Dynamics (PSID) data for the 1985–1995 period, this paper reinvestigates this issue for the U.S. labor market. First, Heckman and Borjas’s study is replicated by employing their methodology but using a large sample of more experienced U.S. workers. With the exception of Ruhm, all previous studies cited above use data on new entrants in the labor market who have earned, at most, high school diplomas and, therefore, focus on the secondary sector of the labor market. As conjectured by Heckman and Borjas, state dependence may be more evident among more experienced workers (p. 279). Second, the current study is the first that examines occurrence dependence in the duration of UI spells in the U.S. labor market, which bridges the gap between Corak’s result and those of U.S. studies.2 Third, this paper examines heterogeneity in occurrence dependence, which is motivated by Corak’s finding that positive occurrence-dependent effects are much greater for women than for men. We investigate this issue by race and educational attainment as well as by gender.
2. Data and estimation technique For the years 1986–96, the PSID interviews collect monthly information on respondents’ labor force activities for the preceding calendar year. Therefore, the data from the 1986–96 interviews include monthly labor force variables for the January 1985 to December 1995 period. For the same 1
See, among others, Lancaster (1990) for a good survey of this issue. Many state UI programs effectively limit the eligibility for unemployment compensation of most youth (Flinn and Heckman, 1983, p. 28). Therefore, experienced workers are more relevant in examining UI spells. 2
H. Choi, D. Shin / Economics Letters 77 (2002) 157–161
159
sample period, the PSID also reports whether or not each respondent received unemployment compensation in each month. These variables generate the completed duration of UI spells as well as unemployment spells. In each interview, the PSID also collects not only individual characteristics as of the interview date but also job characteristics of up to the two most recent jobs in the last year, from which various spell characteristics are derived.3 Our final sample includes both heads of households and wives who have completed schooling, have some previous work experience before the first interview in our sample, and are not self-employed. We follow the procedure suggested by Heckman and Borjas (1980) to test for occurrence dependence. Consider the following equation log Yin 5 b 9n Xin 1 ´in
(1)
where Yin represents the duration of the nth spell in our sample experienced by individual i, Xin is a set of spell characteristics, and ´in is an error term. As noted by Heckman and Borjas, this specification is valid if the sample contains only completed spell durations and there are no time-varying covariates (pp. 271–272). However, our sample covers a relatively long period running 132 months, which makes our results less subject to the bias that arises from using only completed spells. By subtracting Eq. (1) from the equation for the (n11)th spell, and by adding and subtracting b 9n11 Xin , we obtain log Yi,n 11 2 log Yin 5 b 9n 11 (Xi,n 11 2 Xin ) 1 ( bn11 2 bn )9Xin 1 (´i,n 11 2 ´in )
(2)
Mean occurrence dependence is said to exist if the coefficients on Xin are not jointly equal to zero, that is, the same characteristics have different effects on the duration of spells according to sequence number.4 The fact that some observations are differenced over a longer period than others has the following econometric implications, which are overlooked by previous studies. First, the variance of the error term in Eq. (2) varies across observations unless the correlation between two neighboring error terms in Eq. (1) is the same for all observations. Second, the length of difference itself should appear as a regressor in Eq. (2).
3. Empirical results For all samples to be examined below, a modified Breusch–Pagan test rejects the null hypothesis of homoskedasticity at any conventional significance level,5 and the length of time between spells is significant in explaining dependent variables in Eq. (2). For the sake of simplicity, we report only the 3
Details of data construction and quality are described in Shin and Shin (2000) and are also available upon request. In Eq. (2), marginal effects of changing variables, bn 11 , and unchanging variables themselves, Xin , are indexed at the (n11)th and nth spells, respectively. In principle, to check the robustness of results, we need to estimate the following equation that uses the opposite sequence-index. log Yi,n 11 2 log Yin 5 b n9 (Xi,n 11 2 Xin ) 1 ( bn 11 2 bn )9Xi,n 11 1 (´i,n 11 2 ´in ) This procedure does not change any of the results in Section 3. 5 To determine the appropriate weighting to correct for heteroskedasticity, we apply ordinary least squares (OLS) to the regression of squared residuals obtained from OLS estimation of Eq. (2) on a constant and the length of time between spells, and obtain the reciprocal of the square root of the predicted value. 4
H. Choi, D. Shin / Economics Letters 77 (2002) 157–161
160
following two statistics.6 First, we present the F-statistics for tests of the null hypothesis that the coefficients of unchanging variables are jointly equal to zero (no mean occurrence dependence). Second, whenever the null is rejected, we report the relative length of two adjacent spells that is solely attributed to occurrence dependence. The latter measure, which is suggested by Corak (1993), is derived by setting the values of all of the changing variables to zero in estimated Eq. (2), predicting the dependent variables at sample means of unchanging variables, and exponentiating the predicted values. Numbers greater (less) than 1 represent positive (negative) occurrence dependence, meaning that, holding all characteristics constant between two adjacent spells, the following spell is longer (shorter) than the leading spell. Table 1 reports results for overall mean occurrence dependence. Entries in the first two columns are based on observations pooled over all spells of all individuals. In contrast, numbers in the last two columns are derived from using only the first and second spells. The latter is the more preferred sample because it is more representative of the population (Heckman and Borjas, p. 277) and because, especially in the case of UI spells, any structural changes occur early on rather than evolving continually with each spell (Corak, p. 66). For both samples, no occurrence dependence exists in the duration of unemployment spells, which reconfirms the results of all previous U.S. studies. For both samples of UI spells, however, the null is rejected even at the 1% level. For our preferred sample, the duration of UI spells increases by 33% due solely to the occurrence-dependent effects. This finding of positive occurrence dependence in UI spells is consistent with Corak’s finding. Table 2 reveals substantial heterogeneity in the mean occurrence dependence in the duration of UI spells. Estimates are based on the first and second spells. F-tests reject the null hypothesis of no mean occurrence dependence among males, the more-educated workers, and whites, and accept the null for the other groups. All these groups experience positive duration dependence. For these groups, the duration of UI spells increase by 35–45% due to the occurrence-dependent effects. Table 1 Mean occurrence dependence Spell types
All spells F-statistic
Unemployment
Unemployment Insurance
0.33 (0.98) [3141] 4.02 (,0.01) [1690]
First–second spells a
Relative length –
1.27
b
F-Statistic a 1.27 (0.24) [1842] 3.32 (,0.01) [988]
Relative b length –
1.33
P-values are in parentheses, and sample sizes are in brackets. a F-statistics for tests of no mean occurrence dependence. b The ratio of successive spell lengths that is solely attributed to occurrence dependence. 6
A similar set of explanatory variables is used as were used by Heckman and Borjas (1980) and Corak (1993). Overall, there are 12 unchanging variables including an intercept and five changing variables. Among them include variables on age, dependents, unemployment rate, industry dummies at former jobs, and the length of time between two successive spells. Full regression results are available upon request.
H. Choi, D. Shin / Economics Letters 77 (2002) 157–161
161
Table 2 Heterogeneity in mean occurrence dependence between first and second UI spells Sample size
F-statistic a
Relative length b
Male
596
1.35
Female
390
At least some college At most high school graduation White
332
3.84 (,0.01) 0.87 (0.57) 2.06 (0.02) 1.37 (0.19) 3.47 (,0.01) 0.43 (0.93)
Non-white
642 894 82
– 1.45 – 1.34 –
P-values are in parentheses. a F-statistics for tests of no mean occurrence dependence. b The ratio of successive spell lengths that is solely attributed to occurrence dependence.
References Corak, M., 1993. Is unemployment insurance addictive? Evidence from the benefit durations of repeat users. Industrial and Labor Relations Review 47, 62–72. Ellwood, D.T., 1982. Teenage unemployment: permanent scars or temporary blemishes. In: Freeman, R.B., Wise, D.A. (Eds.), The Youth Labor Market Problem: Its Nature, Causes, and Consequences. University of Chicago Press, Chicago, pp. 349–385. Flinn, C.J., Heckman, J.J., 1983. Are unemployment and out of the labor force behaviorally distinct labor force states? Journal of Labor Economics 1, 28–42. Heckman, J.J., Borjas, G.J., 1980. Does unemployment cause future unemployment? Definitions, questions, and answers from a continuous time model of heterogeneity and state dependence. Economica 47, 247–283. Lancaster, T., 1990. The Econometric Analysis of Transition Data. Cambridge University Press, Cambridge. Ruhm, C.J., 1991. Are workers permanently scarred by job displacements? American Economic Review 81, 319–324. Shin, D., Shin, K., 2000. Inter- and intra-sectoral movement of labor and the unemployment rate. Unpublished manuscript. Willis, R.J., 1982. Comment. In: Freeman, R.B., Wise, D.A. (Eds.), The Youth Labor Market Problem: Its Nature, Causes, and Consequences. University of Chicago Press, Chicago, pp. 386–389.