Flows to and from working part time for economic reasons and the labor market aggregates during and after the 2007-09 recession.
Part-time employment for economic reasons has become a concern since the 2007-09 recession because, even though the numbers of unemployed and marginally attached individuals have been decreasing since 2009, the number of individuals who are working part time for economic reasons has remained elevated. (3) During the 2014 Economic Symposium in Jackson Hole, Wyo., Fed Chair Janet Yellen noted that the elevated number of workers who are employed part time but desire full-time work might imply that the degree of resource underutilization in the labor market is greater than what is captured by the standard unemployment rate (Yellen 2014).
In this article, we first use cross-sectional data to evaluate whether part-time employment for economic reasons differs from full-time employment or part-time employment for noneconomic reasons such as childcare or other family reasons (henceforth, PTNER) along dimensions other than hours (i.e., observable characteristics of workers and wages). We then examine whether the changes in the labor market flows in and out of PTER during and in the aftermath of the 2007-09 recession can account for any of the changes in unemployment.
We find that PTER workers are typically less educated than fulltime or other part-time workers and are typically employed in middleor low-skill occupations. On average, PTER workers earn 19 percent less than full-time workers and 9 percent less (per hour) than PTNER workers, even after controlling for sociodemographic and occupational characteristics. The differences persist if we compare wages of PTER to wages of other workers within broad occupational categories. More research, however, is needed to understand whether PTER workers are workers who cannot find full-time jobs because of bad luck or because of structural reasons.
We now turn to the question of PTER and unemployment. Note that the number of PTER workers at any point in time (i.e., stock) is affected by the number of workers who worked PTER in the previous period and continue to do so, as well as the number of workers who transition (i.e., flow) into PTER from full-time employment, other part-time employment, unemployment, and out-of-the-labor-force (OLF) and the number of workers who transition from PTER into these other labor market statuses. Similarly, the flows into and out of PTER impact other labor market aggregates--full- and part-time employment, unemployment, and OLF. In this article, we decompose the changes in stocks of full-time employed, PTNER, unemployed, and OLF due to the changes in the flows of workers to and from PTER in the aftermath of the 2007-09 recession. Of course, the flows are in turn determined by fundamental factors affecting households' and firms' behavior. Nevertheless, it can be instructive to look at such decomposition. To this end, we perform a counterfactual exercise by fixing the transition probabilities between PTER and other labor force statuses at their respective sample means, and constructing the counterfactual time series of the labor market aggregates. The exercise is similar in spirit to the exercise presented by Shimer (2012) for the contribution of different labor market flows to changes in the unemployment rate.
The accounting exercise shows that changes in the transition probabilities to and from PTER after 2009 were associated with changes in stocks of full-time employed, PTER, and PTNER, but had almost no impact on the changes in stocks of unemployed or OLF. In the counterfactual exercise, the main drivers of the changes in the stocks of full-time employed, PTER, and PTNER were transition probabilities between PTER and full-time work and between PTER and PTNER. If the transition probabilities to PTER from either full-time or PTNER had remained at their sample means throughout 1994-2014, the population share of PTER in 2014 would have been 0.47 percentage points (pp) lower at the expense of full-time work and PTNER. If the transition probabilities from PTER to full-time work and to PTNER had remained at their sample means throughout 1994-2014, the population share of PTER in 2014 would have been 0.43 pp lower at the expense of full-time work and, to a lesser extent, of PTNER. In contrast, this same exercise yields counterfactual unemployment that is essentially identical to the one actually observed.
Thus, our results show that changes in the transition probabilities to and from PTER in the aftermath of the 2007-09 recession mainly impact the composition of employment (full versus part time, and the reasons for working part time) instead of the distribution of individuals between employment and non-employment. Consequently, policymakers' attention to PTER potentially implies a broader definition of resource underutilization in the labor market than the one captured by the standard unemployment rate. In particular, in addition to working fewer-than-desired hours, underutilization in the labor market can take the form of workers being overqualified for their jobs. For example, Abel, Deitz, and Su (2014) provide evidence of an upward trend in underemployment of recent college graduates whereby the graduates are employed in jobs that do not require a college degree. Importantly, the challenge for policymakers lies in determining how much of such changes in the quality of employment represent structural changes in the economy.
Finally, regarding the future of PTER, an examination of the series of PTER over time reveals that the ratio of the number of PTER workers to the number of unemployed workers typically increases during economic recoveries. The increase is fueled by PTER workers who cite an inability to find full-time work as a reason for part-time employment (the number of PTER workers who cite "slack work" declines during economic recoveries). PTER workers' share is highest in nonroutine manual (typically low-wage) occupations. Given the recent work on job polarization (Autor , among others), which shows that medium-wage jobs are disappearing but jobs on the high- and low-end of the wage distribution are growing, it thus becomes a challenging task to disentangle cyclical versus structural factors behind an increased number of PTER workers after the 2007-09 recession. Thus, the following questions might represent fertile ground for future research: (1) To what extent is PTER an important mechanism of labor market adjustment during recoveries from recessions? (2) What is the impact of trend-related developments like job polarization on such an adjustment, especially after deep recessions? (3) To what extent does the burden of adjustment fall more on certain demographic and socioeconomic groups than on the others?
The rest of the article is structured as follows. Section 1 describes the construction of the PTER series in the CPS data. Section 2 presents basic facts about PTER. Section 3 presents the main results. Finally, section 4 concludes.
1. MEASUREMENT OF PTER IN THE CPS
The data in the analysis are from the Current Population Survey (CPS) monthly microdata files from January 1994 to August 2014. The survey features a rotating panel structure in which households are surveyed for four months, taken out of the sample for eight months, and then surveyed for another four months to complete their participation. The CPS allows us to classify each individual into one of five labor force statuses: employed full time, employed part time for economic reasons, employed part time for noneconomic reasons, unemployed, and OLF. (4)
The survey asks respondents about their hours worked during the reference week, their desire and availability for full-time work if they work part time, and their reason for working part time. The individuals who work fewer than 35 hours per week are considered part-time workers. (5) For the part-time work to be classified as "for economic reasons," the worker must desire full-time work and cite an economic reason as the primary reason for not working full time. Such economic reasons are "slack work or business conditions," "could only find part-time work," and seasonal work. Noneconomic reasons are child care problems, other family/personal obligations, health/medical limitations, school/training, retired/Social Security limit on earnings, full-time workweek is less than 35 hours, weather affected job, military/civic duty, labor dispute, holiday, own illness, vacations, and other (unspecified) reasons.
The 1994 CPS redesign affected the PTER series. Prior to 1994, the CPS did not specifically ask whether part-time workers wanted to or were available to work full time. (6) Additionally, the survey did not distinguish between respondents who usually worked full time and those who usually worked part time; it only asked about actual hours worked. The effect of the CPS redesign on the PTER series after 1994 is therefore twofold: (1) it decreased the number of part-time workers classified as PTER because it excludes those who do not want to work full time; and (2) it may have increased the total number of part-time workers because it includes those who usually, but not actually in the reference week, work fewer than 35 hours per week. (7) Consequently, caution needs to be exercised while constructing a longer series of PTER that begins prior to 1994. Another change the redesign introduced was "seasonal work" as an economic reason for working part time. (8) Prior to 1994, only slack work, not being able to find a full-time job, and a job starting or ending during the reference week were considered economic reasons for working part time. Therefore, our analysis focuses on the 20-year period following the 1994 CPS redesign so that we can use the BLS U-6 definition of PTER.
2. BASIC FACTS ABOUT WORKING PART TIME FOR ECONOMIC REASONS
Wages, Hours, and Occupations
Table 1 shows average weekly hours and real hourly wages over the 1994-2014 period for three different groups of the employed: full-time, PTER, and PTNER workers. (9) During 1994-2014, a full-time worker's average real hourly wage is $17.02 (in 2013 U.S. dollars), while it is $13.66 for a PTNER worker and $11.81 for a PTER worker.
PTER workers report working 23 hours per week on average as compared to 45 hours reported by those working full time. (10) They also work on average 1.5 hours more per week than PTNER workers. As can be seen from Figure 1, these gaps persist throughout 1994-2014.
Table 2 presents demographic characteristics of the three groups of employed workers, shedding some light on the difference in hourly wages between PTER workers and other employed persons. For example, fulltime workers are more likely to have finished high school or college than part-time workers; among part-time workers, PTNER workers tend to be more highly educated than PTER workers (41.4 percent of full-time workers, 33.0 percent of PTNER workers, and 22.0 percent of PTER workers have a college degree or higher). PTER workers tend to be younger, with a comparatively large share of 20-24 year olds.
To further understand the differences between wages of PTER workers and the rest of the employed population, we tabulate the shares of different types of workers across different occupations. Following Jaimovich and Siu (2012) (see also Autor, Levy, and Murnane  and Acemoglu and Autor ), we classify the occupations into four different groups: non-routine cognitive, routine cognitive, routine manual, and non-routine manual occupations. (11) Routine occupations are typically middle-skill occupations. (12) As discussed in Autor (2010) and Jaimovich and Siu (2012), the U.S. labor market is experiencing a job polarization phenomenon where employment in routine occupations is shrinking while employment in non-routine cognitive and non-routine manual occupations is growing.
Table 3 shows the distribution of full-time, PTER, and PTNER work across four broad occupational groups with cognitive-manual and routine/non-routine classifications. Part-time workers represent a significantly higher fraction of low-skill and medium-skill occupations than of high-skill occupations. Interestingly, among the highest skill occupations, classified as non-routine cognitive, the share of PTER workers is only 0.03 while the share of PTNER workers is 0.16. The share of PTER workers is highest among non-routine manual occupations (0.11), which are typically low-skill occupations.
To understand whether the differences in wages between full-time, PTNER, and PTER workers can be explained by the differences in their sociodemographic characteristics and/or their occupations, we estimate a linear regression of the logarithm of the real hourly wage on educational level, occupation, race, gender, year, and employment type dummy variables. The omitted category for employment type is PTER. The coefficients for the type of employment show the difference in the (log of the) real hourly wage between PTER and working full time or PTNER, after controlling for sociodemographic and occupational characteristics. The results of this regression are presented in Table 4. (13) On average, full-time workers earn 19 percent more and PTNER workers earn 9 percent more (per hour) than PTER workers, taking into account education, age, and broadly defined occupational categories.
To further understand the wage differences, instead of occupational and employment dummy variables, we include a full set of interactions between seven occupational categories and the three types of employment (full time, PTER, and PTNER). If, for example, better workers (either employed full or part time) are employed in higher-paying occupations, then one should compare the wages of full- and part-time workers in these occupations in order to estimate the differences in earnings between full- and part-time workers. Table 5 contains the results of the regression with the interaction terms. (14) We then perform a series of pairwise t-tests comparing the coefficient for the interaction term of full-time work (and similarly PTNER) to the coefficient for the interaction term of PTER with each of the seven occupational categories. In each of the seven occupational categories, we find that PTER workers receive lower wages than full-time or PTNER workers. For example, on average, PTER workers in service occupations are paid 18 percent less than full-time workers and 7 percent less than PTNER workers in service occupations. (15)
The regression results in Tables 4 and 5 also show that the year dummies are positive during the 2008-11 recession years and turn negative in the post-recession years, 2012-14, which points to a somewhat lagged response of wages during the 2007-09 recession. (16)
Working Part Time for Economic Reasons Over the Years
Figure 2 shows the population shares of full-time, PTNER, and PTER workers. As can be seen, there is a notable drop in the share of fulltime workers and an increase in PTER workers during the 2007-09 recession. Figure 3 shows a close-up of the PTER series. The PTER population share was higher in the 2007-09 recession than in the 2001 recession. In 2009, the series reached 3.8 percent. In 2014, the PTER population share stands at 3.0 percent. (17)
Figure 4 examines PTER by reason: slack work, could only find part-time work, and "other," which includes a job starting/ending during the reference week (such that hours add up to less than 35) and seasonal work. The first two reasons account for the majority of the PTER workers. Notably, during the 2007-09 recession the share of workers who reported slack work/business conditions increased to a much higher level than during the previous recession. While the share of workers reporting "slack work" has declined substantially since 2009, the share of workers who are working part time because they could only find part-time work has remained elevated since 2009. A similar cyclical pattern is observed during previous downturns. (18)
Figure 5 shows the ratio of PTER workers to the number of unemployed workers in the economy. Interestingly, the ratio was about 10 percentage points higher at the trough of the 2007-09 recession than at the trough of the 2001 recession. The ratio appears procyclical, indicating that during recessions PTER grows at a slower rate than unemployment. The most recent growth started in 2010, increasing from 0.60 in 2010 to 0.74 in 2014.
3. THE TRANSITION PROBABILITIES OF PTER FLOWS DURING 2007-09 AND EFFECTS ON EMPLOYMENT, UNEMPLOYMENT, AND OLF
In this section, we focus on the transition probabilities to and from the stock of PTER and other states of the labor market. We decompose changes in the stocks of the labor market aggregates--full- and part-time employment, unemployment, and out-of-the-labor-force (OLF)--into the changes in these transition probabilities during the 2007-09 recession. The counterfactual exercises show that these changes were not associated with the changes in the stocks of unemployment or OLF, but they were associated with the decrease of the stocks of full-time and PTNER employment.
Transition Probabilities to and from PTER
As mentioned above, each individual in the population can be classified into one of the following five labor force statuses: employed full time (FT), PTER, PTNER, unemployed (U), and OLF. The labor market is characterized by the flows of individuals among these statuses. The stocks and the transition probabilities among them are linked via the following equation
S (t) = P (t)S(t - 1), (1)
where S(t) is the vector of stocks (expressed in population shares), and P(t) is the matrix of discrete transition probabilities. (19)
The change in the stock of PTER can be decomposed into components representing changes in the probabilities of entering and exiting PTER as well as components representing changes in the transition probabilities between the remaining labor force statuses. Likewise, changes in entry and exit to/from PTER are associated with the changes in the stocks of FT, PTNER, U, and OLF.
To construct the transition probabilities matrix we match individuals between consecutive months in the CPS following the matching procedure described in Shimer (2012). Because the unit of observation is the physical address, we use sex, age, and race in addition to the household identification number to produce matches. The transition probability from state i in month t-1 to state j in month t is the flow of individuals moving from state i to state j divided by the total number of individuals in state i in month t-1 (out of those that can be matched). We call this the "exit probability from" state i to state j, or the "entry probability to" state j from state i.
Table 6 shows the mean of annual average transition probabilities among the five labor market statuses during 1994-2014. A PTER worker has probability 0.31 of transitioning to full-time employment next month. This probability is 0.30 for a PTNER worker and 0.13 for an average unemployed worker. Thus, in their propensity to join full-time work, PTER workers are closer to PTNER than to unemployed workers. The data reveal the substantial flows between PTER and PTNER. An unemployed worker and a PTNER worker have similar probabilities of transitioning into PTER, 0.048 and 0.044, respectively.
Panels A and B of Figure 6 show the transition probabilities from and to PTER, respectively, by labor force status. The observations from the figure can be summarized as follows. First, the transition probability from PTER to FT declined during 2007-09 and has remained low since then. Second, the transition probability from PTER to PTNER declined during 2007-09 and has only slightly increased since then. Third, the transition probability from FT to PTER increased during 2007-09 and has decreased since then. Fourth, the transition probability from PTNER to PTER increased during 2007-09 and has remained elevated since 2009.
Counterfactual Exercises with the Transition Probabilities to and from PTER
To separately examine the effects of exit and entry, we perform a series of counterfactual exercises using equation (1). The exercises are as follows:
1. fix all transition probabilities from PTER (to FT, to PTNER, to U, and to OLF);
2. fix transition probabilities from PTER to FT;
3. fix transition probabilities from PTER to PTNER;
4. fix transition probabilities from PTER to U;
5. fix transition probabilities from PTER to OLF;
6. fix all transition probabilities to PTER (from FT, from PTNER, from U, and from OLF);
7. fix transition probability from FT to PTER;
8. fix transition probability from PTNER to PTER;
9. fix transition probability from U to PTER; and
10. fix transition probability from OLF to PTER.
To perform these exercises, we fix the respective probabilities at their 1994-2014 sample means and construct the monthly counterfactual time series of the fixed labor force status stocks using equation (1) recursively, setting [t.sub.0] = 1994. (20)
We start in 1994 because of the changes in the series after the 1994 CPS redesign (mainly PTNER and FT). In the exercises, the diagonal elements of the transition matrix (i.e., the probability of remaining in the same status) are adjusted accordingly so that the column elements add to 1. Figures 7 and 8 show the resulting counterfactuals using annual averages of monthly series. (21) Figure 7 shows the counterfactuals with fixed exit rates from PTER. All stocks are expressed as shares of the population. The effect of the counterfactual transition probabilities of exiting PTER on the aggregate stocks is as follows:
1. PTER (Figure 7, Panel C): If all exits from PTER are fixed at their sample means, the counterfactual share of PTER in 2014 is 0.43 pp lower than the actual share. PTER is primarily affected by exits from PTER to FT and from PTER to PTNER.
2. FT (Figure 7, Panel A): If the exit from PTER to FT is fixed at its 1994-2014 sample mean, the population share of FT in 2014 increases by 0.69 pp (as compared to its 44.4 pp level in 2014). Other exits from PTER do not have a substantial impact on the share of FT workers.
3. PTNER, U, and OLF (Figure 7, Panels B, D, and E): The relative magnitudes of the effect of the fixed exits on PTNER, U, and OLF are much smaller than the effect of the counterfactual exits on the share of FT workers.
Figure 8 shows the counterfactuals with fixed transition probabilities to PTER. The effect of the counterfactual transition probabilities of entering PTER on the aggregate stocks is as follows:
1. PTER (Figure 8, Panel C): We observe that if all transition probabilities to PTER are fixed at their sample means, PTER in 2014 is 0.47 pp lower than the actual population share observed. As with the case of fixed exit rates, PTER is primarily affected by transition probabilities from FT and from PTNER.
2. FT (Figure 8, Panel A): If the transition probability from FT to PTER remains at its 1994-2014 sample mean, the population share of FT in 2014 increases by 0.39 pp (as compared to its 44.4 pp level in 2014). Other entries to PTER do not have a substantial impact on the share of FT workers.
3. PTNER (Figure 8, Panel B): If the transition probability from PTNER to PTER remains at its 1994-2014 sample mean, the population share of PTNER in 2014 is 0.27 pp higher than the actual share observed. Other entries to PTER do not have a substantial impact on the share of PTNER workers.
4. U and OLF (Figure 8, Panels D, and E): The fixed transition probabilities into PTER have essentially no effect on U or OLF.
As can be seen from Figures 7 and 8, the transition probabilities to PTER contribute substantially to the cyclical behavior of the share of PTER workers, while the exit rates do not drive much of the cyclical fluctuations.
The elevated number of PTER workers in the aftermath of the 200709 recession has raised a concern of whether the extent of resource underutilization in the labor market is greater than that captured by the standard unemployment rate.
In this article, we find that the changes in the transition probabilities to and from PTER in the aftermath of the 2007-09 recession have been mainly associated with the composition of employment (full versus part time, and part time for economic versus for noneconomic reasons) instead of with the distribution of individuals between employment and non-employment.
We also find that, in general, part-time workers represent a significantly higher fraction of low-skill and medium-skill occupations than of high-skill occupations. Among the highest skill occupations, classified as non-routine cognitive, the share of PTER workers is only 0.03 while the share of PTNER workers is 0.16. The share of PTER workers is highest among non-routine manual occupations, which are typically low-skill occupations. The educational achievement of PTER workers is typically lower than of those working full time or part time for noneconomic reasons. PTER workers typically earn less per hour than full-time or PTNER workers, even after controlling for age, education, and broadly defined occupational groups. Given the recent work on job polarization (Autor 2010), it thus becomes a challenging exercise to disentangle the effect of cyclical versus structural factors on driving up the number of PTER following the deep recession of 2007-09.
Canon is an economist at the Federal Reserve Bank of St. Louis. Luo is an assistant professor at the City University of Hong Kong. Kudlyak is an economist and Reed is a research associate at the Federal Reserve Bank of Richmond. The authors are very grateful to Alex Wolman, Felipe Schwartzman, Christian Matthes, and Peter Debbaut for useful comments and suggestions. The views expressed here are those of the authors and do not reflect those of the Federal Reserve Bank of Richmond, the Federal Reserve Bank of St. Louis, the Federal Reserve System, or any other institution with which the authors are affiliated. Email: Marianna.Kudlyak@rich.frb.org.
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(1) This is the term ("slack work/business conditions") used in the CPS questionnaire as opposed to the term "slack" used in recent policy discussions that typically describes a degree of labor utilization below a level of full resource utilization.
(2) The data in this paragraph are from HAVER.
(3) See, for example, Kearns and Smialek (2014) for a summary of policy discussions about individuals working PTER. For research on working PTER, see Valetta and Bengali (2013) and Cajner et al. (2014). For alternative measures of resource utilization in the labor market that incorporate PTER, see Hornstein, Kudlyak, and Lange (2014).
(4) We restrict the analysis to the civilian noninstitutionalized population 16 years and older (henceforth, population).
(5) We use actual hours worked in the reference week to differentiate full-time and part-time workers. We count those workers who are absent from work (and thus whose actual hours are not available in the survey) as full-time workers if they report that they usually work full-time hours. Workers who are absent from work and report that they usually work part-time hours are excluded from our analysis (for example, 0.62 percent of the population in 2013) because they are not asked to provide a reason for why they work part time.
(6) That is, after the 1994 redesign, if the respondents do not desire full-time work, they are asked to choose from only noneconomic reasons. If the respondents desire fulltime work, they are asked for the primary reason for working part time, with the option to provide an economic or noneconomic reason. Therefore, in order to be considered working part time for economic reasons after 1994, workers must desire full-time work in addition to citing economic reasons. Prior to 1994, the survey does not separate those who do and those who do not want full-time jobs.
(7) See Polivka and Rothgeb (1993) for a thorough treatment of the effect of the redesign on part-time work calculations and for an explanation of how to adjust the series to be consistent over time.
(8) Seasonal work, however, does not constitute a large portion of PTER.
(9) To construct hourly wages, we use hourly earnings (if they are reported) or construct the wage by dividing weekly earnings by usual weekly hours. The reported wage statistics are based on non-imputed data. We also calculated the statistics incorporating imputed data and the results do not differ significantly. In the calculations we use outgoing rotation group weights.
(10) We take the mean of each year's average actual hours worked at all jobs in the reference week from 1994 to 2014. Usual hours, which are used to construct hourly earnings for non-hourly workers, are lower for full-time workers and higher for both voluntary and involuntary part-time workers.
(11) Non-routine cognitive occupations include management, business, and financial occupations and professional related. Routine cognitive occupations include sales and office occupations and office and administrative support occupations. Routine manual occupations include construction and extraction; installation, maintenance, and repair; production; and transportation and moving material occupations. Non-routine manual occupations include service occupations.
(12) Following Autor (2010), high-skill occupations include managers, professionals, and technicians. Middle-skill occupations include sales; office and administration; production, craft, and repair; and operators, fabricators, and laborers. Finally, low-skill occupations include protective services; food preparation; building and grounds cleaning; and personal care and personal services.
(13) In Table 4, the dependent variable is the log of real hourly wage as described in the note to Table 1. The explanatory variables are type of employment dummies, occupation, education, age, race, gender, and a set of annual time dummies. The omitted categories are working part time for economic reasons, less than high school education, service occupations, male, and white. *** denotes statistical significance at the 1 percent level for a two-sided test. All data are from 1994 to August 2014 and include employed working age persons in months four and eight of the CPS sample except for those in the armed forces or farming, fishing, and forestry occupations. The regression is estimated by OLS with heteroscedasticity robust standard errors, with the CPS outgoing rotation group sampling weights. See footnote 14 for the details about the occupational classification.
(14) In Table 5, the dependent variable is the log of real hourly wage as described in the note to Table 1. The explanatory variables are type of employment interacted with occupation, education, age, race, gender, and a set of annual time dummies. The omitted categories are working part time for economic reasons interacted with service occupations, less than high school education, male, and white. See footnote 13. The occupational classification used in the regression is as follows (accounting for the change in coding in 2002): (1) Healthcare support occupations; protective service; food preparation and serving related; building and grounds cleaning and maintenance; personal care and service occupations (post-2002) and service occupations (pre-2002); (2) Construction trades, extraction workers; transportation and material moving occupations (post-2002); and operators, fabricators, and laborers (pre-2002); (3) Installation, maintenance, and repair workers and production occupations (post-2002) and production occupations (pre-2002); (4) Sales and related occupations (post-2002) and sales occupations (pre-2002); (5) Office and administrative support occupations (post-2002) and administrative support occupations, including clerical (pre-2002); (6) Computer and mathematical; architecture and engineering; life, physical, and social science; community and social services; legal occupations; education, training, and library; arts, design, entertainment, sports, and media; healthcare practitioners and technical occupations (post-2002); and professional specialty and technicians and related support occupations (pre-2002); (7) Management; business and financial operations occupations (post-2002); and executive, administrative, and managerial occupations (pre-2002). Occupation 1 is non-routine manual; occupations 2-3 are routine manual; occupations 4-5 are routine cognitive; and occupations 6-7 are non-routine cognitive.
(15) However, more analysis is needed to examine how much of the wage difference can be attributed to worker fixed effect. Such analysis is beyond the scope of the article.
(16) See Elsby, Shin, and Solon (2014) for a detailed exploration of wage adjustment in the 2007-09 recession.
(17) This figure is calculated using January 1994-August 2014 data.
(18) However, due to the changes to the CPS described in section 1, most of these observed downturns are not strictly comparable.
(19) In the analysis, we also include inflows and outflows into the population.
(20) Due to the rotating panel structure of the CPS, at most 75 percent of the observations may be matched to the following month when we exclude individuals in months four and eight in the survey, and thus the labor force stocks tabulation from unmatched monthly CPS data may differ from the labor force stocks tabulation from the matched month-to-month files (see, for example, Frazis et al. ). We therefore employ a procedure that ensures that in every period the recursion delivers the distribution of the labor force stocks consistent with the one observed in the unmatched CPS monthly files.
(21) Additionally, we impute missing data in unmatchable months, i.e., we take the average of each stock and probability of the adjacent months for June-September 1995 and May 2004. We employ the same procedure for September 1998 and September 2009 for FT and PTNER to remove the effect of full-time workers being classified as part time for noneconomic reasons due to Labor Day--this is the only national holiday occurring in any reference week after 1994 and would constitute a significant spike in the series if not adjusted.
Table 1 Average Weekly Hours and Real Hourly Wages, Full- and Part-Time Employment, 1994-2014 Full Time PTER PTNER Weekly hours 44.49 23.31 21.80 Hourly wage, $2013 17.02 11.81 13.66 Notes: The table shows mean of annual averages, 1994-2014. For 2014, the average is taken over the first eight months for which the data are available at the time of publication. To calculate hourly wage, we use hourly wages for hourly workers and compute hourly wages for salaried workers by dividing usual weekly earnings by usual weekly hours. Zero wages are dropped. All calculations employ the CPS outgoing rotation group sampling weights. Hourly wages are in 2013 dollars. Calculations are based on the CPS microdata basic files. Table 2 Education and Demographic Characteristics of Full- and Part-Time Employment, 1994-2014 Group's Share In Group Full Time PTER PTNER Female 40.24 49.84 63.21 High school degree 91.71 79.88 85.14 Associate's, 41.44 21.95 33.01 Bachelor's or higher degree Master's, 10.91 4.06 8.11 professional, or Doctorate degree Average age 40.82 36.62 39.12 Under 20 y.o. 1.54 8.05 13.82 20-24 y.o. 8.18 17.52 13.26 25-34 y.o. 24.19 23.32 16.90 35-44 y.o. 26.80 21.15 18.54 45-54 y.o. 24.26 17.69 16.39 Over 55 y.o. 15.03 12.28 21.08 Notes: The table shows mean of annual averages, 1994-2014. For 2014, the average is taken over the first eight months for which the data are available at the time of publication. Authors' calculations using the CPS microdata basic files. Table 3 Full-Time and Part-Time Employment Shares, by Occupation 1994-2014 Non-routine cognitive Routine cognitive Year Full time PTNER PTER Full time PTNER PTER 1994 0.781 0.196 0.024 0.713 0.251 0.036 1995 0.787 0.190 0.023 0.717 0.248 0.035 1996 0.786 0.192 0.022 0.715 0.251 0.033 1997 0.794 0.187 0.020 0.723 0.247 0.030 1998 0.775 0.207 0.018 0.706 0.267 0.027 1999 0.796 0.188 0.016 0.728 0.248 0.024 2000 0.811 0.175 0.014 0.742 0.236 0.022 2001 0.797 0.187 0.016 0.733 0.241 0.025 2002 0.802 0.180 0.018 0.735 0.236 0.029 2003 0.807 0.176 0.017 0.724 0.243 0.033 2004 0.805 0.179 0.017 0.719 0.249 0.032 2005 0.808 0.176 0.016 0.723 0.246 0.031 2006 0.809 0.176 0.015 0.727 0.245 0.028 2007 0.815 0.170 0.015 0.730 0.241 0.030 2008 0.815 0.167 0.019 0.726 0.235 0.040 2009 0.782 0.187 0.031 0.689 0.247 0.064 2010 0.799 0.169 0.031 0.701 0.229 0.070 2011 0.803 0.168 0.030 0.703 0.229 0.068 2012 0.809 0.162 0.029 0.707 0.228 0.065 2013 0.817 0.155 0.028 0.715 0.221 0.064 2014 0.814 0.160 0.025 0.710 0.230 0.061 Routine manual Non-routine manual Year Full time PTNER PTER Full time PTNER PTER 1994 0.811 0.142 0.047 0.606 0.321 0.073 1995 0.812 0.142 0.046 0.616 0.316 0.068 1996 0.814 0.145 0.042 0.624 0.311 0.065 1997 0.824 0.137 0.039 0.629 0.309 0.062 1998 0.806 0.159 0.036 0.628 0.318 0.054 1999 0.828 0.139 0.033 0.643 0.309 0.048 2000 0.840 0.129 0.032 0.654 0.300 0.045 2001 0.825 0.137 0.038 0.644 0.305 0.051 2002 0.827 0.130 0.042 0.641 0.301 0.057 2003 0.830 0.122 0.047 0.624 0.307 0.068 2004 0.834 0.121 0.045 0.625 0.308 0.067 2005 0.838 0.121 0.041 0.632 0.305 0.063 2006 0.839 0.120 0.040 0.642 0.301 0.057 2007 0.840 0.118 0.042 0.638 0.303 0.058 2008 0.820 0.119 0.061 0.631 0.294 0.075 2009 0.765 0.136 0.099 0.593 0.293 0.114 2010 0.793 0.119 0.088 0.600 0.280 0.119 2011 0.798 0.121 0.081 0.600 0.281 0.118 2012 0.814 0.117 0.069 0.606 0.280 0.114 2013 0.822 0.115 0.064 0.609 0.279 0.112 2014 0.824 0.118 0.058 0.607 0.288 0.106 Notes: The table shows shares of FT, PTER, and PTNER in each of the four occupational groups, annual averages of monthly series. Non-routine cognitive occupations include management, business, and financial occupations and professional related. Routine cognitive occupations include sales and office occupations and office and administrative support occupations. Routine manual occupations include construction and extraction; installation, maintenance, and repair; production; and transportation and moving material occupations. Non-routine manual occupations include service occupations. Authors' calculations using the CPS microdata basic files. Table 4 Hourly Wage, Demographic and Socioeconomic Characteristics, 1994-2014 Variable Coefficient Variable Coefficient Fulltime .1915106 *** Dummy 1996 -.0123845 *** (.000) (.000) Part time (PTNER) .0914258 *** Dummy 1997 -.0019732 (.000) (.348) Construction/ .2215752 *** Dummy 1998 .0278192 *** transportation (.000) (.000) Production and repair .3131558 *** Dummy 1999 .0382212 *** (.000) (.000) Sales and related .0678285 *** Dummy 2000 .0424516 *** (.000) (.000) Office/administrative .2269783 *** Dummy 2001 .0544279 *** support (.000) (.000) Professional specialty .4906422 *** Dummy 2002 .0624557 *** (.000) (.000) Management/executive .3944512 *** Dummy 2003 .0585759 *** (.000) (.000) High school .1300564 *** Dummy 2004 .0471874 *** (.000) (.000) Some college .1753108 *** Dummy 2005 .0319903 *** (.000) (.000) College .3062498 *** Dummy 2006 .0284528 *** (.000) (.000) Graduate degree .2087941 *** Dummy 2007 .0293244 *** (.000) (.000) Age .0413028 *** Dummy 2008 .0228746 *** (.000) (.000) [Age.sup.2] -.0004105 *** Dummy 2009 .0470142 *** (.000) (.000) Black -.057996 *** Dummy 2010 .0310914 *** (.000) (.000) American Indian/ -.0468621 *** Dummy 2011 .0099154 *** Alaskan Native (.000) (.000) Asian -.0150465 *** Dummy 2012 -.0020422 (.000) (.372) Other race -.0095699 *** Dummy 2013 -.0093538 *** (.003) (.000) Female -.1564769 *** Dummy 2014 -.0136535 *** (.000) (.000) Dummy 1995 -.0093544 *** Constant 1.260337 *** (.000) (.000) Mean(log real 2.630377 wages) N 1,483,262 [R.sup.2] .4109 Notes: See footnote 13. Table 5 Hourly Wage, Demographic and Socioeconomic Characteristics with Occupation-Employment Type Interactions, 1994-2014 Variable Coefficient Variable Coefficient Full time X .1823325 *** Black -.0578995 *** Service (.000) (.003) Full time X .398573 *** American -.046481 *** Constmction/ (.000) Indian/Alaskan (.000) transportation Native Full time X .4920935 *** Asian -.01469 *** Production (.000) (.000) and repair Full time X .2403244 *** Other race -.0098147 *** Sales and (.000) (.000) related Full time X .410917 *** Female -.1569275 *** Office/ (.000) (.000) administrative support Full time X .6591924 *** Dummy 1995 -.0092787 *** Professional (.000) (.000) specialty Full time X .5672558 *** Dummy 1996 -.0123659 *** Management/ (.000) (.000) executive Part time .0716929 *** Dummy 1997 -.0019273 non-economic (.000) (.359) (PTNER) X Service PTNERX .2472299 *** Dummy 1998 .0279902 *** Constmction/ (.000) (.000) transportation PTNER X .4085932 *** Dummy 1999 .0381384 *** Production (.000) (.000) and repair PTNER X Sales .1543721 *** Dummy 2000 .0425298 *** and related (.000) (.000) PTNERX .3011755 *** Dummy 2001 .0545344 *** Office/ (.000) (.000) administrative support PTNER X .5966031 *** Dummy 2002 .0623247 *** Professional (.000) (.000) specialty PTNERX .5108225 *** Dummy 2003 .0584042 *** Management/ (.000) (.000) executive PTERX .2226177 *** Dummy 2004 .0470143 *** Constmction/ (.000) (.000) transportation PTER X .2740908 *** Dummy 2005 .0318157 *** Production (.000) (.000) and repair PTER X Sales .060803 *** Dummy 2006 028199 *** and related (.000) (.000) PTER X Office/ .1890605 *** Dummy 2007 .0291946 *** administrative (.000) (.000) support PTER X .4600883 *** Dummy 2008 .0227143 *** Professional (.000) (.000) specialty PTERX .3030132 *** Dummy 2009 .0469685 *** Management/ (.000) (.000) executive High school .1296502 *** Dummy 2010 .0310504 *** (.000) (.000) Some college .1751928 *** Dummy 2011 009801 *** (.000) (.000) College .3063701 *** Dummy 2012 -.0020417 (.000) (.371) Graduate .2079177 *** Dummy 2013 -.0093253 *** degree (.000) (.000) Age .0409877 *** Dummy 2014 -.0135823 *** (.000) (.000) [Age.sup.2] -.0004068 *** Constant 1.278611 *** (.000) (.000) Mean(log 2.630377 real wages) N 1,483,262 [R.sup.2] .4115 Notes: See footnote 14. Table 6 Average Transition Probabilities, 1994-2014 To PTER To PTNER To FT To U To OLF From PTER .3702 .2146 .3092 .0614 .0447 From PTNER .0437 .5744 .2995 .0179 .0645 From FT .0156 .0797 .8795 .0094 .0158 From U .0482 .0705 .1263 .5185 .2364 From OLF .0033 .0218 .0175 .0260 .9314 Notes: We take the mean of each yearly average, 1994-2014.
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|Author:||Canon, Maria E.; Kudlyak, Marianna; Luo, Guannan; Reed, Marisa|
|Date:||Mar 22, 2014|
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