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Are unemployment and out of the labor force behaviorally distinct labor force states?
Economics Letters North-Holland
113
36 (1991) 113-117
Are unemployment and out of the labor force behaviorally distinct labor force states? New evidence
from the gross change data
Doki IS. Tano * Universityof Received Accepted
Toledo, Toledo, OH 43606, USA
13 November 1990 7 January 1991
This paper uses the J-test developed by Mackinnon et al. (1983) and US Gross Change Data for six age-sex demographic groups to test the hypothesis that unemployment and out of the labor force are behaviorally meaningless distinctions. Results obtained with these data and test confirm those obtained by Flinn and Heckman (1983). We found that for young (ages 16-19) and young adult (ages 20-24) males and females, the two states are distinct; whereas for prime age males and females (ages 25-44), this distinction is meaningless.
1. Introduction
Unemployment according to the BLS is a state in which individuals are actively seeking employment but have not found a job, or they are temporary laid off and waiting to be recalled, or they are between jobs. Long spells of unemployment discourage some job seekers who leave the state of unemployment and exit the labor force. However, while out of the labor force, discouraged workers may still be hoping to find a job. Therefore some critics have argued that this category should be added to the number of unemployed. Also, because young people are frequently in transit between jobs, schools and other non-market activities, it has been difficult to ascertain whether they are unemployed or out of the labor force. Recently, there have been controversies about the distinction between these two labor market states. For example, using US Gross Change Data, Clark and Summers (1982) argued that the distinction between the two states arises as a result of measurement errors and that unemployment and out of the labor force represent the same labor market state of nonemployment. Flinn and Heckman (1983) charged that this finding is due to the fact that Clark and Summers’ analysis ignores job search theory. Based on the premise that this issue is particularly relevant in the study of the labor market dynamics for youths, they employed a sample of 122 high school graduates from the NLS of young men to test the hypothesis that unemployment * 1 wish to thank James the remaining errors. 0165-1765/91/$03.50
P. Lesage, Calman
Winegarden
0 1991 - Elsevier Science Publishers
and Frederick
E. Tank for helpful
B.V. (North-Holland)
comments.
I am responsible
for
114
D.K.
Tam
/
Unemployment
and out of the labor
force
and out of the labor force are meaningless distinctions. The test confirmed that the two states are behaviorally distinct. In this paper we test the same hypothesis but instead of the NLS data, we use the Gross Change Data 011 six age-sex demographic groups along with the J-test developed by Mackinnon et al. (1983). Distinct behavioral equations are written for the transition probability from unemployment to employment and for the transition probability from out of the labor force to employment. These transition models can be viewed as non-nested since the dependent variables differ across models. If the two states are not different, a J-test should not be able to isolate a single model as providing the best explanation of the transition to employment. On the other hand, if the two states are distinct, the J-test will determine a single model as best in describing the transition from nonemployment to employment. The advantage of using the J-test lies in its ability to isolate for each demographic group the most [productive state productive in the sense of Flinn and Heckman, (1983, p. 38) that is, the state which increases the rate at which job offers arrive]. Section 2 of the study presents the model and the J-test, followed by the data in section 3; section 4 reports the empirical results and their implications; finally, section 5 offers some concluding remarks.
2. The model and the J-test 2. I. The model In search of models for transition probabilities, the linear, log-linear, linear logit, log-binomial logit, and log-multinomial logit models of transition probabilities have been tested against each other [see Tano (1989)]. The test isolated the log-binomial logit and log-multinomial logit models as the best in describing variation in transition probabilities. In this study we chose the log-binomial logit model and examine transition probabilities from the unemployment state to employment and the out of the labor force state to employment. The binomial logit model assumes that each member of the six groups, at every period of time, faces two possibilities relative to the state of origin: either the individual changes state or does not. The logit is thus expressed as the ratio of the probability of changing states to the probability of not changing. The models of the two transition probabilities are expressed as follows:
(2) describing movement from unemployIn the two models, A,,, and A,, are transition probabilities ment to employment and out of the labor force to employment respectively. Denton (1973), Smith (1977), Smith et al. (1978), Ehrenber (1980), and Moser (1984) provide details for calculation of transition probabilities and estimation of transition probability models. We let v and E represent stochastic error terms and assumed that they are normal with mean 0 and constant variance IJ,’ and 0,’ respectively; V and U, are vacancy and total unemployment rates; b/w, is the replacement ratio, where b is weekly unemployment insurance and w, average hourly earnings; t and t2 are time trend variables incorporated to capture duration. If (Ye= (Ye= 0 and /& = & = 0, there is no duration dependence in the transitions from unemployment to employment and from out of the labor force to employment. If (Ye= 0, & = 0 and (Ye> 0, & > 0 ( (Ye< 0 and & < 0), it means that there exists a positive (negative) duration dependence in the transition probabilities. If (Ye# 0 and & # 0, then the
D.K. Tam / Unemployment
transition transition
and
out of the
need not be monotone. A, and B, are constant elasticities (Y; and p,, i = 1. _.6.
labor force
parameters
to be estimated
115
along with the
2.2. The J-test In the effort to design tests to choose between models, Mackinnon et al. (1983) developed the J-test for non-nested models. The J-test considers models in a pairwise comparison taking one model as the null hypothesis and the other as the alternative hypothesis. In the J-test, J stands for jointly since the coefficients of one hypothesis are in effect estimated jointly with a coefficient for the fitted value of the alternative hypothesis and it is this joint estimation which has given the test its name. In fact, an estimate of the model under the alternative hypothesis is obtained and the corresponding fitted value is computed. Thereafter, a model that combines the null hypothesis with the fitted value from the alternative hypothesis used as an explanatory variable is estimated. Then, the t-test is used to check the significance of the coefficient on the fitted value. A significant coefficient means that we are rejecting the model taken as the null hypothesis. On the other hand, if this coefficient is insignificant, the hypothesized model cannot be rejected in favor of the alternative. The test is implemented symmetrically, an estimate of the model under the null hypothesis and its fitted value is also obtained and used as an explanatory variable in the alternative model equation. The t-test is used as in the previous description. We should note that if the hypothesis test results from these symmetric experiments are conflicting, no model is selected and we conclude that the test failed to isolate one model as dominant. In the case of transition from unemployment to employment and out of the labor force to employment, if the two states are not distinct the J-test should not be able to isolate one transition model as better than the other transition model. In the event that the test is inconclusive we will conclude that there is no distinction between the states, whereas if the test isolates a model the inference drawn here is that the states are distinct.
3. The data Reliable data on job vacancy rate were difficult to obtain. The proxy commonly used for vacancy data is the Conference Board’s help wanted index [see Preston (1977) for detailed discussion of the data and the methodology used in the creation of the index]. Abraham and Katz (1986) have recently used a normalized help wanted index (national help wanted index divided by the non-agricultural payroll employment) and have also discussed the relevance and weaknesses of using this index as a proxy of the job vacancy rate for the United States. Because the normalized index is seen to capture a substantial amount of the cyclical fluctuations in the job vacancy rate, we use it as a proxy for the latter. The transition rates and groups’ unemployment are derived from the monthly tabulations of the Gross Change Data, a subsample of the Current Population Survey (CPS) Data, published by the Bureau of Labor Statistics. The problems (sample variability, misclassification and rotation group biases) of the Gross Change Data, their effects on estimates and the adjustments of the flows have been widely covered in the literature. See for example [Smith and Vanski (1978)], Hogue (1984) Solon (1984) [Stasny and Fienberg (1984)], [Abowd and Zellner (1984)], [Fuller and China (1984)], and [Poterba and Summers (1984)] for details on the history of the problems, their incidence on empirical estimates and adjustment techniques used to correct the data. The Gross Change Data used in this study are not seasonally adjusted. In fact, Clark and Summers (1982, p. 202) found that except for in-school youths, results obtained with transition probabilities for teenagers and mature adults do not appear seasonally aberrant. Weekly unemployment insurance and average hourly earnings are used to calculate the replacement ratio. The sample consists of 266 monthly observations, covering
D.K. Tam / Unemployment and out of the labor force
116
the period May, 1967 to July, 1989 of young (16-19) young adult (20-24) and prime-age (25-44) males and females. Because each group’s unemployment is used to calculate the group’s transition probability, aggregate unemployment rate instead of group unemployment is used as independent variable to eliminate potential simultaneity bias problems. The estimation of the model is based on these data.
4. Empirical estimation
and results
The estimation of logit models by least squares methods is possible but under certain circumstances however, the estimation of the logit models by OLS may be inappropriate. Difficulty arises when the dependent variable, say p,, is a binary variable that takes on the values zero and one and when p, is equal to zero or one, the ratio becomes infinity and the dependent variable will not be defined. In this study the problem does not arise because the transition rates are directly generated from the Gross Change Data and follow a general class of logit models described by Pindyck and Rubinfeld (1981, ch. IO), for which simple OLS can be used in estimation of the different transition probabilities. The J-test results based on these estimates are reported in table 1 below. The t-statistics of the coefficients on the fitted value of the alternative hypothesis used as an additional variable in the test of the model taken as the null hypothesis are reported. For young males, the coefficient on the fitted value A,,, is significant at the 1 percent level, whereas the coefficient on the fitted value i ue is insignificant. This result suggests that out of the labor force is the most productive state (productive still in the sense of Flinn and Heckman). For young females, this result is reversed; A,,, is significant at the 1 percent level, whereas fi,, is insignificant suggesting that unemployment is the most productive state for this group. The same pattern is found for young adults (20-24) males and females. These empirical results confirm the findings by Flinn and Heckman, that out of the labor force and unemployment are distinct. When the J-test is applied to prime-age workers, both males and females, the coefficients on both fitted values have approximately the same level of significance, with a slight advantage given to the unemployment to employment transition model for females. Since both coefficients on the fitted values are significant, the test is inconclusive leading to the conclusion that for prime-age workers, unemployment and out of the labor force are not distinct. For prime-age females, the results suggest that though the two states cannot be distinguished, the most productive state seems to be the state of unemployment. To summarize, we conclude that the J-test applied to the Gross Change Data, leads us to conclude that, for young (16-19) and young adults (20-24) males and females, the states of out
Table 1 Summary
of the J-test
results for the different
A,,,b
-2.14
***
H&h,,):
A,,”
-1.10
-2.90
a
20-24 Male female
16-19 Male female
H,(X,,):
age-sex groups.
-0.63 ***
25-44 Male female
-4.55
***
-1.88
* -7.11
-2.19 ***
**
- 4.61 * * * - 2.91 * * * -4.73
***
-9.43
***
a * indicates 10 percent level significance: * * indicates 5 percent level significance; * * * indicates 1 percent level significance. ’ The first row in table 1 depicts the case where the transition from unemployment to employment is the null hypothesis. The transition from out of the labor force to employment is the alternative hypothesis here. ’ The second row shows the case where the null hypothesis is the transition equation from out of the labor force to employment and the alternative hypothesis is the transition from unemployment to employment.
D.K. Tam
of the labor force and unemployment states are basically the same. 5. Concluding
/ Unemployment
are definitely
and out of the labor force
distinct,
whereas
117
for prime-age
workers
these
remarks
This study has applied the J-test using the Gross Change Data, to test the hypothesis that unemployment and out of the labor force are artificial distinctions. Prior studies that employed the Gross Change Data concluded that the states of unemployment and out of the labor force are not distinct for youths. The J-test model selection criteria contradicts previous conclusions based on an empirical inspection of the nature of the Gross Change Data. In fact, the test reveals that, notwithtanding the nature of the Gross Change Data, unemployment and out of the labor force are distinct labor market states for young people. This conclusion reinforces the findings by Flinn and Heckman in 1983, with the NLS data. The test also reveals that the distinction between the two states is immaterial for prime age males and females. These results suggest that aggregation of the two states for older demographic groups may be appropriate in the study of labor market dynamics. References Abowd, John M., and Arnold Zellner, 1984, Application of adjustment techniques to U.S. gross flow data, Proceedings of the Conference on Gross Flows in Labor Force Statistics (Washington, DC) July 19-20. Abraham, Katharine G. and Lawrence F. Katz, 1986, Cyclical unemployment: Sectoral shifts or aggregate disturbances?, Journal of Political Economy 94, no. 3. Clark, Kim B., M. James and Lawrence H. Summer, 1982. The dynamics of youth unemployment, in: B. Freeman and David A. Wise, eds., The youth labor market problem: Its nature, causes, and consequences (NBER, Cambridge, MA). Denton, Frank T., 1973, A simulation model of month-to-month labor force movement in Canada, International EconomicReview 14. no. 2. Eihrenberg, Ronald G., 1980, The demographic structure of unemployment rates and labor market transition probabilities, Research in Labor Economics 3, 241-293. Flinn, C. and J. Heckman. 1983, Are unemployment and out of the the labor force behaviorally distinct labor force states?, Journal of Labor Economics 1, no. 1, 28-42. Fuller, Wayne A. and Tin Chiu Chua, 1984, Gross change estimation in the presence of response error, Proceedings of the Conference on Gross Flows in Labor Force Statistics (Washington, DC) July 19-20. Elogue, Carma, 1984, History of the problems encountered in estimating gross flows, Proceedings of the Conference on Gross Flows in Labor Force Statistics (Washington, DC) July 19-20. Mackinnon, James G., H. White and R. Davidson, 1983, Tests for model specification in the presence of alternative hypotheses, Journal of Econometrics 21, 53-70. Moser. James W.. 1984, A principal component analysis of labor market indicators, Working paper (Miami University, OH). Pmdyck, R. and Daniel Rubinfeld, 1981. Econometric models and economic forecasts (McGraw-Hill, New York) Ch. 10, 287-300. Poterba, James M. and Lawrence H. Summers, 1984, Adjusting the gross changes data: Implications for labor market dynamics”. Working paper no. 1436, August (NBER, Cambridge, MA). Preston. Noreen L., 1977, The help wanted index: Technical description and behavioral trends, Report no. 716 (Conf. Bd., New York). Smith Ralph E., 1977, A simulation model of the demographic composition of employment unemployment, and labor force participation, in: Ronald G. Ehrenberg, ed., Research in Labor Economics, Vol. 1, 259-303. Smith, Ralph E., Jean E. Vansky, 1978. Gross change data: The neglected data base, Background Paper no. 11 (National Commission on Employment and Unemployment Statistics. Government Printing Office, Washington, DC) 131-49. Solon, Gary, 1984, Effects of rotation group bias on estimation of unemployment,” Proceedings of the Conference on Gross Flows in Labor Force Statistics (Washington, DC) July 19-20. Srasny, Elizabeth A., Stephen E. Fienberg, 1984, Some stochastic models for estimating gross flows in the presence of nonrandon nonresponse, Proceedings of the Conference on Gross Flows in Labor Force Statistics (Washington, DC) July 19-20. Tano, Doki K., 1989, A J-test for selection of models to analyze transition rates in the labor market. Bowling Green State University and The University of Toledo Departments of Economics Working Papers, no. UT89-22.
113
36 (1991) 113-117
Are unemployment and out of the labor force behaviorally distinct labor force states? New evidence
from the gross change data
Doki IS. Tano * Universityof Received Accepted
Toledo, Toledo, OH 43606, USA
13 November 1990 7 January 1991
This paper uses the J-test developed by Mackinnon et al. (1983) and US Gross Change Data for six age-sex demographic groups to test the hypothesis that unemployment and out of the labor force are behaviorally meaningless distinctions. Results obtained with these data and test confirm those obtained by Flinn and Heckman (1983). We found that for young (ages 16-19) and young adult (ages 20-24) males and females, the two states are distinct; whereas for prime age males and females (ages 25-44), this distinction is meaningless.
1. Introduction
Unemployment according to the BLS is a state in which individuals are actively seeking employment but have not found a job, or they are temporary laid off and waiting to be recalled, or they are between jobs. Long spells of unemployment discourage some job seekers who leave the state of unemployment and exit the labor force. However, while out of the labor force, discouraged workers may still be hoping to find a job. Therefore some critics have argued that this category should be added to the number of unemployed. Also, because young people are frequently in transit between jobs, schools and other non-market activities, it has been difficult to ascertain whether they are unemployed or out of the labor force. Recently, there have been controversies about the distinction between these two labor market states. For example, using US Gross Change Data, Clark and Summers (1982) argued that the distinction between the two states arises as a result of measurement errors and that unemployment and out of the labor force represent the same labor market state of nonemployment. Flinn and Heckman (1983) charged that this finding is due to the fact that Clark and Summers’ analysis ignores job search theory. Based on the premise that this issue is particularly relevant in the study of the labor market dynamics for youths, they employed a sample of 122 high school graduates from the NLS of young men to test the hypothesis that unemployment * 1 wish to thank James the remaining errors. 0165-1765/91/$03.50
P. Lesage, Calman
Winegarden
0 1991 - Elsevier Science Publishers
and Frederick
E. Tank for helpful
B.V. (North-Holland)
comments.
I am responsible
for
114
D.K.
Tam
/
Unemployment
and out of the labor
force
and out of the labor force are meaningless distinctions. The test confirmed that the two states are behaviorally distinct. In this paper we test the same hypothesis but instead of the NLS data, we use the Gross Change Data 011 six age-sex demographic groups along with the J-test developed by Mackinnon et al. (1983). Distinct behavioral equations are written for the transition probability from unemployment to employment and for the transition probability from out of the labor force to employment. These transition models can be viewed as non-nested since the dependent variables differ across models. If the two states are not different, a J-test should not be able to isolate a single model as providing the best explanation of the transition to employment. On the other hand, if the two states are distinct, the J-test will determine a single model as best in describing the transition from nonemployment to employment. The advantage of using the J-test lies in its ability to isolate for each demographic group the most [productive state productive in the sense of Flinn and Heckman, (1983, p. 38) that is, the state which increases the rate at which job offers arrive]. Section 2 of the study presents the model and the J-test, followed by the data in section 3; section 4 reports the empirical results and their implications; finally, section 5 offers some concluding remarks.
2. The model and the J-test 2. I. The model In search of models for transition probabilities, the linear, log-linear, linear logit, log-binomial logit, and log-multinomial logit models of transition probabilities have been tested against each other [see Tano (1989)]. The test isolated the log-binomial logit and log-multinomial logit models as the best in describing variation in transition probabilities. In this study we chose the log-binomial logit model and examine transition probabilities from the unemployment state to employment and the out of the labor force state to employment. The binomial logit model assumes that each member of the six groups, at every period of time, faces two possibilities relative to the state of origin: either the individual changes state or does not. The logit is thus expressed as the ratio of the probability of changing states to the probability of not changing. The models of the two transition probabilities are expressed as follows:
(2) describing movement from unemployIn the two models, A,,, and A,, are transition probabilities ment to employment and out of the labor force to employment respectively. Denton (1973), Smith (1977), Smith et al. (1978), Ehrenber (1980), and Moser (1984) provide details for calculation of transition probabilities and estimation of transition probability models. We let v and E represent stochastic error terms and assumed that they are normal with mean 0 and constant variance IJ,’ and 0,’ respectively; V and U, are vacancy and total unemployment rates; b/w, is the replacement ratio, where b is weekly unemployment insurance and w, average hourly earnings; t and t2 are time trend variables incorporated to capture duration. If (Ye= (Ye= 0 and /& = & = 0, there is no duration dependence in the transitions from unemployment to employment and from out of the labor force to employment. If (Ye= 0, & = 0 and (Ye> 0, & > 0 ( (Ye< 0 and & < 0), it means that there exists a positive (negative) duration dependence in the transition probabilities. If (Ye# 0 and & # 0, then the
D.K. Tam / Unemployment
transition transition
and
out of the
need not be monotone. A, and B, are constant elasticities (Y; and p,, i = 1. _.6.
labor force
parameters
to be estimated
115
along with the
2.2. The J-test In the effort to design tests to choose between models, Mackinnon et al. (1983) developed the J-test for non-nested models. The J-test considers models in a pairwise comparison taking one model as the null hypothesis and the other as the alternative hypothesis. In the J-test, J stands for jointly since the coefficients of one hypothesis are in effect estimated jointly with a coefficient for the fitted value of the alternative hypothesis and it is this joint estimation which has given the test its name. In fact, an estimate of the model under the alternative hypothesis is obtained and the corresponding fitted value is computed. Thereafter, a model that combines the null hypothesis with the fitted value from the alternative hypothesis used as an explanatory variable is estimated. Then, the t-test is used to check the significance of the coefficient on the fitted value. A significant coefficient means that we are rejecting the model taken as the null hypothesis. On the other hand, if this coefficient is insignificant, the hypothesized model cannot be rejected in favor of the alternative. The test is implemented symmetrically, an estimate of the model under the null hypothesis and its fitted value is also obtained and used as an explanatory variable in the alternative model equation. The t-test is used as in the previous description. We should note that if the hypothesis test results from these symmetric experiments are conflicting, no model is selected and we conclude that the test failed to isolate one model as dominant. In the case of transition from unemployment to employment and out of the labor force to employment, if the two states are not distinct the J-test should not be able to isolate one transition model as better than the other transition model. In the event that the test is inconclusive we will conclude that there is no distinction between the states, whereas if the test isolates a model the inference drawn here is that the states are distinct.
3. The data Reliable data on job vacancy rate were difficult to obtain. The proxy commonly used for vacancy data is the Conference Board’s help wanted index [see Preston (1977) for detailed discussion of the data and the methodology used in the creation of the index]. Abraham and Katz (1986) have recently used a normalized help wanted index (national help wanted index divided by the non-agricultural payroll employment) and have also discussed the relevance and weaknesses of using this index as a proxy of the job vacancy rate for the United States. Because the normalized index is seen to capture a substantial amount of the cyclical fluctuations in the job vacancy rate, we use it as a proxy for the latter. The transition rates and groups’ unemployment are derived from the monthly tabulations of the Gross Change Data, a subsample of the Current Population Survey (CPS) Data, published by the Bureau of Labor Statistics. The problems (sample variability, misclassification and rotation group biases) of the Gross Change Data, their effects on estimates and the adjustments of the flows have been widely covered in the literature. See for example [Smith and Vanski (1978)], Hogue (1984) Solon (1984) [Stasny and Fienberg (1984)], [Abowd and Zellner (1984)], [Fuller and China (1984)], and [Poterba and Summers (1984)] for details on the history of the problems, their incidence on empirical estimates and adjustment techniques used to correct the data. The Gross Change Data used in this study are not seasonally adjusted. In fact, Clark and Summers (1982, p. 202) found that except for in-school youths, results obtained with transition probabilities for teenagers and mature adults do not appear seasonally aberrant. Weekly unemployment insurance and average hourly earnings are used to calculate the replacement ratio. The sample consists of 266 monthly observations, covering
D.K. Tam / Unemployment and out of the labor force
116
the period May, 1967 to July, 1989 of young (16-19) young adult (20-24) and prime-age (25-44) males and females. Because each group’s unemployment is used to calculate the group’s transition probability, aggregate unemployment rate instead of group unemployment is used as independent variable to eliminate potential simultaneity bias problems. The estimation of the model is based on these data.
4. Empirical estimation
and results
The estimation of logit models by least squares methods is possible but under certain circumstances however, the estimation of the logit models by OLS may be inappropriate. Difficulty arises when the dependent variable, say p,, is a binary variable that takes on the values zero and one and when p, is equal to zero or one, the ratio becomes infinity and the dependent variable will not be defined. In this study the problem does not arise because the transition rates are directly generated from the Gross Change Data and follow a general class of logit models described by Pindyck and Rubinfeld (1981, ch. IO), for which simple OLS can be used in estimation of the different transition probabilities. The J-test results based on these estimates are reported in table 1 below. The t-statistics of the coefficients on the fitted value of the alternative hypothesis used as an additional variable in the test of the model taken as the null hypothesis are reported. For young males, the coefficient on the fitted value A,,, is significant at the 1 percent level, whereas the coefficient on the fitted value i ue is insignificant. This result suggests that out of the labor force is the most productive state (productive still in the sense of Flinn and Heckman). For young females, this result is reversed; A,,, is significant at the 1 percent level, whereas fi,, is insignificant suggesting that unemployment is the most productive state for this group. The same pattern is found for young adults (20-24) males and females. These empirical results confirm the findings by Flinn and Heckman, that out of the labor force and unemployment are distinct. When the J-test is applied to prime-age workers, both males and females, the coefficients on both fitted values have approximately the same level of significance, with a slight advantage given to the unemployment to employment transition model for females. Since both coefficients on the fitted values are significant, the test is inconclusive leading to the conclusion that for prime-age workers, unemployment and out of the labor force are not distinct. For prime-age females, the results suggest that though the two states cannot be distinguished, the most productive state seems to be the state of unemployment. To summarize, we conclude that the J-test applied to the Gross Change Data, leads us to conclude that, for young (16-19) and young adults (20-24) males and females, the states of out
Table 1 Summary
of the J-test
results for the different
A,,,b
-2.14
***
H&h,,):
A,,”
-1.10
-2.90
a
20-24 Male female
16-19 Male female
H,(X,,):
age-sex groups.
-0.63 ***
25-44 Male female
-4.55
***
-1.88
* -7.11
-2.19 ***
**
- 4.61 * * * - 2.91 * * * -4.73
***
-9.43
***
a * indicates 10 percent level significance: * * indicates 5 percent level significance; * * * indicates 1 percent level significance. ’ The first row in table 1 depicts the case where the transition from unemployment to employment is the null hypothesis. The transition from out of the labor force to employment is the alternative hypothesis here. ’ The second row shows the case where the null hypothesis is the transition equation from out of the labor force to employment and the alternative hypothesis is the transition from unemployment to employment.
D.K. Tam
of the labor force and unemployment states are basically the same. 5. Concluding
/ Unemployment
are definitely
and out of the labor force
distinct,
whereas
117
for prime-age
workers
these
remarks
This study has applied the J-test using the Gross Change Data, to test the hypothesis that unemployment and out of the labor force are artificial distinctions. Prior studies that employed the Gross Change Data concluded that the states of unemployment and out of the labor force are not distinct for youths. The J-test model selection criteria contradicts previous conclusions based on an empirical inspection of the nature of the Gross Change Data. In fact, the test reveals that, notwithtanding the nature of the Gross Change Data, unemployment and out of the labor force are distinct labor market states for young people. This conclusion reinforces the findings by Flinn and Heckman in 1983, with the NLS data. The test also reveals that the distinction between the two states is immaterial for prime age males and females. These results suggest that aggregation of the two states for older demographic groups may be appropriate in the study of labor market dynamics. References Abowd, John M., and Arnold Zellner, 1984, Application of adjustment techniques to U.S. gross flow data, Proceedings of the Conference on Gross Flows in Labor Force Statistics (Washington, DC) July 19-20. Abraham, Katharine G. and Lawrence F. Katz, 1986, Cyclical unemployment: Sectoral shifts or aggregate disturbances?, Journal of Political Economy 94, no. 3. Clark, Kim B., M. James and Lawrence H. Summer, 1982. The dynamics of youth unemployment, in: B. Freeman and David A. Wise, eds., The youth labor market problem: Its nature, causes, and consequences (NBER, Cambridge, MA). Denton, Frank T., 1973, A simulation model of month-to-month labor force movement in Canada, International EconomicReview 14. no. 2. Eihrenberg, Ronald G., 1980, The demographic structure of unemployment rates and labor market transition probabilities, Research in Labor Economics 3, 241-293. Flinn, C. and J. Heckman. 1983, Are unemployment and out of the the labor force behaviorally distinct labor force states?, Journal of Labor Economics 1, no. 1, 28-42. Fuller, Wayne A. and Tin Chiu Chua, 1984, Gross change estimation in the presence of response error, Proceedings of the Conference on Gross Flows in Labor Force Statistics (Washington, DC) July 19-20. Elogue, Carma, 1984, History of the problems encountered in estimating gross flows, Proceedings of the Conference on Gross Flows in Labor Force Statistics (Washington, DC) July 19-20. Mackinnon, James G., H. White and R. Davidson, 1983, Tests for model specification in the presence of alternative hypotheses, Journal of Econometrics 21, 53-70. Moser. James W.. 1984, A principal component analysis of labor market indicators, Working paper (Miami University, OH). Pmdyck, R. and Daniel Rubinfeld, 1981. Econometric models and economic forecasts (McGraw-Hill, New York) Ch. 10, 287-300. Poterba, James M. and Lawrence H. Summers, 1984, Adjusting the gross changes data: Implications for labor market dynamics”. Working paper no. 1436, August (NBER, Cambridge, MA). Preston. Noreen L., 1977, The help wanted index: Technical description and behavioral trends, Report no. 716 (Conf. Bd., New York). Smith Ralph E., 1977, A simulation model of the demographic composition of employment unemployment, and labor force participation, in: Ronald G. Ehrenberg, ed., Research in Labor Economics, Vol. 1, 259-303. Smith, Ralph E., Jean E. Vansky, 1978. Gross change data: The neglected data base, Background Paper no. 11 (National Commission on Employment and Unemployment Statistics. Government Printing Office, Washington, DC) 131-49. Solon, Gary, 1984, Effects of rotation group bias on estimation of unemployment,” Proceedings of the Conference on Gross Flows in Labor Force Statistics (Washington, DC) July 19-20. Srasny, Elizabeth A., Stephen E. Fienberg, 1984, Some stochastic models for estimating gross flows in the presence of nonrandon nonresponse, Proceedings of the Conference on Gross Flows in Labor Force Statistics (Washington, DC) July 19-20. Tano, Doki K., 1989, A J-test for selection of models to analyze transition rates in the labor market. Bowling Green State University and The University of Toledo Departments of Economics Working Papers, no. UT89-22.