Identifying the Good Jobs among the Lousy Ones: Job Quality and Short-term

Transcription

1 Identifying the Good Jobs among the Lousy Ones: Job Quality and Short-term Empirical Main Hau Chyi 1 Orgul Demet 2 1 Hanqing Institute, Renmin University of China, NORC, University of Chicago 2 University of South Carolina October 8, 2013

2 Less educated workers after welfare reform Empirical Main Due to their lack of educational training, less educated workers first jobs are generally on the lower end of the wage distribution. This is what Autor, Levy and Murnane (2003) called lousy jobs. Recidivism of welfare use and lack of upward career opportunities are common. See Duncan and Chase-Lansdale (2003), Acs and Loprest (2004), Autor and Houseman (2010)

3 Empirical Main What are the (short-run) labor market outcomes of workers who have different first jobs? Are all low-end jobs dead-end? Do different tasks performed in a job affect the probability and speed of making more than (state) poverty line? If so, how?

4 Empirical Main What are the (short-run) labor market outcomes of workers who have different first jobs? Are all low-end jobs dead-end? Do different tasks performed in a job affect the probability and speed of making more than (state) poverty line? If so, how? Several dimensions : Cognitive/manual and routine/nonroutie tasks Specialization/diversification in tasks

5 Empirical Main Although increasing over time, only 9% of workers can make a monthly earning above state poverty line by the end of the 12th month. Not all low-end jobs are dead-end. Confirmed that even for lousy jobs, those high in cognitive, manual tasks are better. However, total levels of skills are not all:

6 Empirical Main Although increasing over time, only 9% of workers can make a monthly earning above state poverty line by the end of the 12th month. Not all low-end jobs are dead-end. Confirmed that even for lousy jobs, those high in cognitive, manual tasks are better. However, total levels of skills are not all: Compared to repetitive skills, jobs that require more non-repetitive tasks are even better.

7 Empirical Main Although increasing over time, only 9% of workers can make a monthly earning above state poverty line by the end of the 12th month. Not all low-end jobs are dead-end. Confirmed that even for lousy jobs, those high in cognitive, manual tasks are better. However, total levels of skills are not all: Compared to repetitive skills, jobs that require more non-repetitive tasks are even better. In fact, levels of skills are not as important as whether a worker specialize or diversify in their skill learning.

8 Empirical Main Low-skilled single mothers; Worked for no more than twenty four months restriction Youngest child below 18 years old;

9 Defining Short-Term Empirical Main Similar to the definition used by Johnson and Corcoran (JPAM, 2003) A dummy variable defined as holding a job in a month that is: i. making at least state poverty line, or ii. making 80% of the poverty line with employer-related health insurance

10 wellbeing - some more information Empirical Main definitions - state minimum wage or full-time work. Using monthly information means we see people in and out of economic independence Also use holding a such job for four-consecutive month to define dummy

11 A Reduced-Form How does different dimensions of skills contribute to economic independence? Empirical Main y i = f (c ji, m ji, nr ji, r ji, X i ; θ) + ɛ i, where y i is a dummy indicating whether i makes more than state poverty line in a given month; c, m, nr and r represent measures of levels of cognitive, manual, non-repetitive and repetitive skills of the initial occupation j. X i are other explanatory variables.

12 y = f (c, m, nr, r, X; θ) Empirical Main (c, m) and (r, nr) are not mutually exclusive However, c + m = nr + r, where r is the level of repetitive skills. Only c, m and nr are included. If both c and m are fixed, an increase in nr implies a decrease in r. The coefficient of nr hence captures the net effect of non-repetitive skill over repetitive skill.

13 Empirical Main Accounting for into Treatment: Instrumental Variables Workers with better jobs may be fundamentally different from others. Our sample comes from all SIPP panels in the 1990s. An era of state waivers, welfare and other policy reforms targeting at low-skilled workers. Welfare parameters include: Cash guarantee, implicit tax rates for earning and other incomes; From Ziliak (2007) Widely used in studies as IVs (see review by Moffitt, 2002). As state and year fixed-effect are controlled, we are using the annual variation in the benefit rules within a state as excluded exogenous variation. Policy variables: Whether affected by 1993 EITC expansion; Welfare time limits and work requirement in the residing states (or after 1996); State unemployment rate.

14 IV - Cont. Empirical Main Furthermore, we use occupation-specific, demand-side information that include: Projected employment growth Ratio of part-time workers Come from the (biannual) Occupational Projection and Training base (OPTD);

15 Why would these IVs work? Empirical Main These IVs are widely used to solve the endogeneity of labor force participation. What about selection into different jobs? We argue that the labor demand side factors affect availability of jobs with similar task training. A multinomial probit model of occupational choices shows that these IVs are significant determinants of occupational choices

16 Initial Occupations and IVs Managerial Service Farming, Precision, Operators, Transportation and Forestry and Production, Fabricators and and Material Professional Fishing Craft and Repair Laborers Moving work requirement? (9.409) (.630) (8.603) (5.205) (3.420) (5.842) time limit? * (12.144) (1.417) (14.634).846 (6.622) (6.105) (10.854) state unemployment (3.365) (.159) (2.411).295 (.979) (1.438) (1.336) children below 5 Empirical above *** (14.467) (12.177).197 (.482) (.771) ** (5.752) ** (7.448) (2.483) (3.833) (2.803) (6.794) (3.779) (10.409) below time limit (13.579) (.843) (17.698) (7.650) (8.919) (16.770) above13 time limit ** (14.501) (1.576) (18.147) (9.188) (13.421) (21.289) median *** *** earning (25.021) (2.302) (28.505) (9.191) (29.544) (55.830) whether ** Main degree? (20.383) (12.006) (22.745) (10.948) (24.292) (28.129) unemployment rate part-time proportion *** (3.207) (.798).429 (.425) * (.139) *** (4.614) *** (.749) * (1.388) *** (.234) *** (3.140) *** (.430) *** (3.405) *** (.940) job growth.759 (.602).205 ** (.091) *** (.556) (.201) (.245) (.422) tax on other income.524 *** (.188) (.010).248 * (.150) (.065) (.081).026 (.088) tax on earning *** (.359) (.017) *** (.290).192 (.123).047 (.104) (.169) guarantee (28.359).947 (1.145) *** (25.493) (8.071) (10.104) (17.569) eitc? *** (18.187).560 (.538) (8.645) (3.982) (5.018) (4.995) constant *** ( ) (22.134) * ( ) ( ) ( ) *** ( )

17 Endogenous Explanatory Variables Empirical We treat the following explanatory variables as endogenous: Main c ji, m ji, nr ji, r ji and interactions. Initial wage and work experiences Proportion not working since first observed

18 Explanatory Variables Empirical Main Initial job characteristics; Work intensity/experience: Initial wage rate; Work experience prior to each panel started Depreciation of human capital; Real income other than labor and welfare income; Demographic variables; number of children, race, age, age squared, education level Year and state fixed effects. Robust standard errors.

19 -SIPP Empirical Main Survey of Income and Program Participation (SIPP) 1990, 1991, 1992, 1993, and 1996 panels Advantages: Monthly information, More mothers experienced welfare reform, Surveyed every four month; fewer recall errors.

20 - Tasks/Skills Empirical Main Dictionary of Occupational Titles (DOT) edition Tasks information coming from Autor, Levy and Murnane (2003) Include four dimensions: Cognitive (C) / Manual (M) Tasks Routine (R) /Nonroutine (NR) Tasks

21 DOT tasks Empirical Main Initial DOT variables do not have a natural scale; ALM (2003) convert these variables into percentile values according to its rank; We convert percentile values into standardized values using normal distribution; Implicit assumption is that skill acquiring has the same shape of the (Gaussian) CDF of the task performed. Tasks of the same occupation performed by men/women can be different. We use gender-specific tasks profile

22 Empirical Main work variables instruments making above poverty line?.098 (.298) part time proportion (11.578) prior work exp (6.034) percentage change in employment (9.396) initial wage (7.838) work requirement?.289 (.453) work?.587 (.492) time limit?.306 (.461) full-time.348 (.476) state unemployment (1.593) hours of work (13.557) children below (.499) hourly rate (8.941) above (.328) personal earning ( ) below 5 above (.349) skill variables cognitive tax on (1.407) other income (18.711) manual tax on (.575) earning (13.509) non-repetitive (.777) guarantee.391 (.165) demographic variables Single (.000) EITC expansion?.378 (.485) on welfare.502 (.500) other income (4.278) age 28.3 (7.258) high school?.644 (.479) black.458 (.499) number of children (1.104) # of ind. 831 # of obs. months 20,395

23 Empirical Main Linear Prob. s with Instruments Specification OLS IV-I IV-II Endogenous Variables Proportion not working (.006) (.09) -.55 (.105) Prior Work Experience.01 (.0004).027 (.003).018 (.004) Initial Wage.004 (.0003).0008 (.003).011 (.004) Cognitive.02 (.015).612 (.117).371 (.143) Manual.02 (.016).56 (.106).426 (.168) Non-routine.407 (.246) Cognitive Manual.007 (.011).566 (.104) (.223) Nonroutine Cognitive.283 (.093) Nonroutine Manual.261 (.149) Over-identification Test for IV-II: P-value=.25

24 Empirical Main Estimated Probability (Average Treatment Effect) analysis time IV_II CI_10_down ols CI_10_up raw

25 Effects of Specific Occupations? Empirical Main Note that occupations come with specific sets of skills. It make little practical sense to discuss the marginal effect of a specific task. We hence compare the estimated probabilities of given occupations Remember that eight occupations, including Cashiers, Receptionists, Waitresses, Cooks, Nursing Aids, Maids, Janitors and Assemblers comprised more than 50% of sample mothers.

26 Est. Prob. of Most Frequently Obs. Jobs Empirical Main analysis time Cashiers Nursing_Aids Cooks Janitors Receptionists Waitresses Maids Assemblers Receptionists, Assemblers and Nursing Aids are all statistically better than the worst, Maids. Middle packs, though, are not statistically different from each other.

27 Most Frequently Obs. Jobs Empirical Main Why are assemblers and receptionists much better? First and foremost, different job characteristics (assemblers are more likely to be unionized and paid higher, for example); As a side note, assemblers are a declining fashion (estimated growth in the next decade is -5%). On the other hand, receptionists have a notable feature Proportion Cognitive Manual Dispersion R NR R NR Cashiers 13 (%) Receptionists Waitresses Cooks Nursing Aids Maids Janitors Assemblers First, what is dispersion?

28 Dispersion Index Empirical D = K (N2 K i=1 N2 i ) N 2 (K 1) = K K 1 (1 N 2 i N 2 ), Main Used to describe the distribution of categorical variables Between [0,1] 0 means complete specialization 1 means equal share in all dimensions Choose K = 4: (rc, nrc, rm, nrm).

29 Specialization or Diversification? Empirical Main An interesting question is: Does it make sense for the low-skilled workers to specialize in one particular task, or diversify? Two related dimensions: Given same level of total skills, how much does specialization or diversification benefit? Given same specialization, how much do different levels of skill contribute?

30 Empirical Estimation Empirical Main We estimate the following regression model with the same IV strategy: y = β 0 +β 1 skill+β 2 dispersion+β 3 skill dispersion+β 4 X+ɛ

31 Specialization or Diversification? Empirical Main Skill Percentiles Dispersion p10 p25 p50 p75 p90 Spread p10 (sd).211 *** (.032).205 *** (.025).200 *** (.032).198 *** (.038).195 *** (.046) p *** (.031).174 *** (.016).180 *** (.016).184 *** (.021).188 *** (.029) p *** (.035).144 *** (.014).160 *** (.004).170 *** (.011).181 *** (.021) p (.044).102 *** (.024).133 *** (.022).152 *** (.027).171 *** (.035) p (.051).076 ** (.034).116 *** (.035).140 *** (.041).166 *** (.050)

32 Difference in Prob. Given Skills or Dispersions Empirical Main Same Skill Dispersions: [p90]-p[10] Same Dispersion Skills: [p90]-[p10] p90 (sd) (.087).147 * (.077) p (.076) (.062) p (.066).017 (.051) p ** (.053).068 (.058) p *** (.048).138 * (.078)

33 Summary Empirical Main 1. Fixing total task level: For jobs with higher levels of tasks performed, diversification or specialization does not seem particularly important; Diversification is particularly bad for workers who performed the lowest levels of total tasks. 2. Fixing degree of diversification: Intuitively, jobs with high levels of total tasks performed should have higher probability of succeeding. However, we see that jobs with moderate levels of diversification (dispersion between p25-p75), total task levels appear to be less important. On the other hand, task levels are highly crucial for jobs with high degrees of specialization (low DI) or diversification (high DI), at least in terms of magnitude.

34 - Estimated Probabilities for Most Frequent Jobs Empirical Main IV-II Ini. Work <= 12 Full-time Use 4-Consecutive Months Cashiers.173 (.025).094 (.012).090 (.009).080 (.011) Receptionists.314 (.042).117 (.017).196 (.034).296 (.025) Nursing Aids.131 (.019).094 (.015).102 (.017).066 (.014) Waitresses.191 (.029).071 (.012).110 (.018).138 (.016) Cooks.187 (.036).082 (.022).104 (.029).062 (.012) Maids.067 (.022).035 (.012).000 (.016) (.015) Janitors.144 (.038).082 (.021).035 (.027).015 (.015) Assemblers.232 (.019).085 (.016).170 (.008).130 (.011)

35 Concluding remarks Empirical Main Non-pecuniary characteristics affects a jobs potential to be a good stepping stone job! Total level is important, however: Specialization in a particular task can save some grounds. Diversification is particularly bad for workers who performed the lowest levels of total tasks. Suggestions on selection into treatment? Correction function approach developed by Wooldridge (2007) - ongoing. Hausman-Taylor (1981) estimator for panel data - ongoing