Albert Rees Lecture, April Thomas Lemieux University of British Columbia

Transcription

1 Albert Rees Lecture, April Thomas Lemieux University of British Columbia

2 Thou shalt not include occupation dummies in a wage regression. But occupations are important and it works. Important t part of several explanations for changes in inequality Moving forward: some new evidence (2005 NGS) on what is behind occupational wage differentials.

3 It s totally endogenous, or to be more precise: Occupations may be a label l for wages Selection on unobservables / Roy model Can be hard and sometimes impossible to think of assigning occupations to people as a treatment, i.e. not clear that occupations are manipulable Both systematic and unsystematic measurement error may be quite substantial But before getting there, here s a quick reminder on how occupations are coded up in the CPS/census

4 Occupations coded up one the basis of the two following questions: What kind of work do you do, that is, what is your occupation? (For example: plumber, typist, farmer) What are your usual activities or duties at this job? (For example: typing, keeping account books, filing, selling cars, operating printing press, laying brick.) This information is then used by professional coders to assign a three digit census occupation code One important issue is that other information provided in the CPS (education, hopefully not wages...) may be used in the coding process

5 Under a traditional pay system (e.g. unionized workplace), pay in a given firm may only depend on job category (occupation) and seniority. Employers may change the job category of the worker to increase his/her wage, even if work remains the same Unclear to what extent this shows up in the CPS since job classifications are not directly asked in the two questions. BOTTOM LINE: If occupations are just a label for wages, they will explain a lot in terms of R square but little in terms of economics... Fortunately, the way occupations are coded somehow limits the potential for this problem

6 Workers self select in different occupation on the basis of their skills (both measured and unmeasured). May find large difference in average wages across occupations even if: There is no difference for the marginal worker (implication of simple Roy model) The ATE of being assigned to an occupation instead of another is equal to zero (but could be anything else) There is indeed lots of evidence of selection based both on measured and unmeasured skills across occupations. For example, GKLP (2005) find the following standard d deviations i of occupation effects at the 1-digit level using the NLSY. Raw: 0.181, OLS: 0.082, FE: But unlike the labelling model, here occupations do matter for the wage structure because returns to skill are not equalized across occupations

7 Figure 1: Roy model Wage "High wage" W2 "Low wage" W1 Low High S1 S2 Skill

8 Can we interpret occupation effects in a wage regression as treatment effects (LATE in a Roy model)? Unlike the case of skills, it not always clear what the underlying experiment would be in the case of occupations For example, it makes little sense to think of the treatment t t effect of being a surgeon relative to being a laborer for a high school dropout Part of the problem here is a support problem Need to both assign skills and occupations together Another pragmatic problem is that it is hard to think of good instruments for occupations. One suggestion is to use local demand shocks under the assumption that occupational wage differentials are set at the national level.

9 Mellow and Sider (1983) and Mathiowetz (1992) show that there is a lot of miscoding of occupations (based on responses by employers and employees). Independent coding of company record and interview yields large disagreement rates (24.3 % at the 1-digit level, 48.2 percent at the 3-digit level) Very problematic for panel data studies Not so surprising i given the way questions are asked and the coding is done. Errors are not only idiosyncratic. Abraham and Spletzer (2010) show large differences in the fraction of managers in data reported by employees (CPS) and employers (OES)

10 Occupations are not the only problematic (potentially endogenous) regressors included in wage regressions. But about any problem one can think of for, say, education, appears to be worse for occupations. Being careful before including occupation dummies in wage regressions is still a good advice to give graduate students...

11 There has been quite a bit of recent research where occupations play a salient role, especially in the wage structure/inequality literature: Nuanced view of technological og ca change: Autor, Levy and Murnane (2003), Goos and Manning (2007), Autor, Katz and Kearney (2006, 2008), Acemoglu and Autor (2011) Occupational specific capital and occupational mobility (Kambourov and Manovskii, 2009) And yes, Firpo, Fortin and Lemieux (2010)... These explanations appear to be empirically important, but it is not clear what to make of them because of the problems mentioned earlier.

12 ALM argue that one has to go beyond skill, and look at tasks and occupations to understand how computerization is reshaping the labor market Routine biased as opposed to skill biased technological change The fact that many of the routine occupations are in the middle of the skill distribution suggests this can potentially account for the polarization of wages observed during the 1990s. Goos and Manning (2007) look at a related issue for Britain, and find that composition effects linked to changes in the distribution of occupation explains a substantial share of the growth in inequality. More generally, the task-based approach can help understand the source of labor market substitutability between different groups: Skill groups (Autor and Acemoglu, 2011) Immigrants vs. Natives (Peri and Sparber, 2010)

13 Neal (1995) and Parent (1999) find that an important part of the return to firm-specific seniority is in fact a return to industry-specific specific seniority Suggest that industry specific human capital is important Kambourov and Manovski (IER 2009) argue that occupation-specific human capital is more important that industry-specific human capital They also argue (KM ReStud 2009) that occupational mobility has increased since the 1970s. Combining these two facts together could account for some of the growth in wage inequality. In a related contribution, Hoffmann (2010) shows that occupations accounts for a substantial share of lifecycle earnings inequality in an enriched dynamic Roy model with occupation-specific match components.

14 Add occupations, as summarized by the task contents from the O*NET to a standard d decomposition of the changes in inequality. Tasks used are aimed at capturing technological change (routine, computer use, etc.) and offshorability (work onsite, face-to-face, etc.). The change to the return to these tasks are an important part of the story both for: The broad-based based increase inequality during the 1980s. The polarization of wages during the 1990s.

15 A. Detailed Wage Structure Effects 1976/78 to 1988/90 0 e Change Log Wage Union Education Experience Technology Offshorability Quantile 15

16 B. Detailed Wage Structure Effects 1988/90 to 2000/02 e Change 0 Log Wage Union Education Experience Technology Offshorability Quantile 16

17 We don t really know what to make of occupations, but they do a great job when you stick them into your analysis... Is there any way we can make studies that use occupations more convincing? Find some good IVs? Unlikely to happen. The remainder of the lecture will focus on two promising i avenues that have been explored recently Occupational licensing The task approach I will then show some empirical i evidence based on the 2005 Canadian National Graduates Survey (NGS)

18 Like unions, this potentially creates a gap between marginal products and wages (Friedman and Kuznets, 1945) Recent work on this by Kleiner (2006), Kleiner and Krueger (2009) Why is this interesting? The phenomena has been growing substantially over time When licensing is important, we get a close connection between skills (field of study) and occupation. Can almost think of occupations as a measure of field-specific skills/education

19 From Krueger and Kleiner, 2009

20 A more parsimonious way of modelling the effect of occupations on wages is to replace occupations dummies by measures of the task content of occupations. W ij = Σγ k T jk +Xβ i β + ε ij Where T jk is the measure of the intensity of task k in occupation j. Similar to Lancasterian/hedonic approach in demand theory/io Two occupations with the same task content will be assigned the same wage Provides a natural measure of distance between occupations

21 Poletaev and Robinson (2008) implement this approach using task measures from the DOL and a principal components analysis. Tasks are interpreted as skill requirements Using the DWS, they show that wage loss on displacement depends on distance between old and new job Gathmann and Schoenberg (2010) follow a related approach using German data Use more traditional measures of tasks (manual, cognitive, etc.) instead of a principal components approach Workers develop task-specific skills over time, depending on the task mix of occupations This approach yields several implications confirmed in the data: More likely to move to closer occupations after spending more time in a given occupation Wage change depends on distance

22 Deals with the labelling problem: two occupations with similar task requirements no longer labelled as different occupations with different wages Tasks more interpretable than dummies Measurement error problem may be less severe When tasks are viewed as skill requirements, we get a closer mapping between skills and occupations (as summarized by tasks) than in the traditional approach. For example, if different occupations require different levels of cognitive skills, the treatment of increasing cognitive skills (through education) becomes the same as moving up the individual in the distribution of occupations A few downsides... Still have some of the problems mentioned earlier (endogenous choice of occupation/tasks) Under the skill interpretation, tasks only capture general skills

23 2007 follow up of a sample of people who graduated from post-secondary education programs in Canada in 2005 Over 50 percent of young people (55.4 percent of years old in the 2006 census) now getting such degrees. Information on the following variables: Field of study (FOS) Could you get a license? Did you get a license? How related is your job to your education?

24 Upon completion of your (certificate / diploma / degree) program, were there steps that you could take to obtain a licence to practise, register with your regulatory college/professional association, or obtain a professional designation? Have you obtained this licence, registration or professional designation? i By obtained I mean that you have met all of the requirements, which include, but may not be limited to, successful completion of all national and/or provincial examinations.

25 Basic goal: can we explain occupation wage effects by economic factors we better understand? Transition between field of study and occupations How well can occupational choice be explained by field of study (skills)? Is the mapping closer in the case of occupations with licensing? Effect of occupations on wages. How much of it can be explained by: Field of study (specific skills) Tasks (general skills) Licensing Is one s job related to his/her education

26 Table A1: Descriptive statistics ti ti from the 2005 NGS Highest degree Job related to education College or CEGEP Closely related University up to Somewhat related bachelor's degree Not related Post graduate Could get a license: Age in 2005 Under Did get a license: Currently a student: Female: Note: observations. All statistics weighted using sample weights.

27 Table 1a: Distribution of occupation by field of study, 2005 NGS Occupation: Soc. Sc. & Proportion Duncan Manag. Business Science Health educ. Arts Sales Trades Manuf. in field: index: Field of study: Business Physical & life sc Math, computer & info Engineering & related Health & fitness Education Social sc. & law Humanities Arts & communications Other Proportion in occupation:

28 Table 1b: Distribution of occupation by gender and degree, 2005 NGS A: Gender Soc. Sc. Proportion Duncan Manag. Business Science Health & educ. Arts Sales Trades Manuf. in field: index: Men Women All B: Degree Some college Bacc. Degree Post graduate All

29 Table 2: Pseudo R square in multinomial logit for occupational choice Sample: All Could get Did get Job related License & Covariates: 1: Gender license license to education related 2: Degree : FOS : All No. Obs Note: McFadden's pseudo R square based on the formula R square =1 lnl(m _ full)/lnl(m _ intercept)

30 Table A2: Tasks by occupation Manual Analytical Interactive Management Business Science Health Soc. Sc. & educ Arts Sales Trades Manufacturing Note: Constructed using the measures provided in Gathmann and Schoenberg (2010)

31 Table 3: Standard deviations of occupation wage effects in the 2005 NGS Adjusted Standard Variance for: deviation 1: nothing : age, gender, & education 3: 2 & FOS : 3, license & related 5: 3 & tasks : 4 & tasks

32 Table A3: Wage regressions, 2005 NGS [1] [2] [3] [4] [5] [6] Female (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) College (0.018) (0.019) (0.019) (0.019) (0.018) (0.018) Bachelor ) (0.016) (0.017) (0.017) (0.017) (0.017 (0.017) Age < (0.019) (0.019) (0.019) (0.019) (0.018) (0.018) Age (0.021) (0.020) (0.020) (0.020) (0.020) (0.020) Age (0.023) (0.022) (0.022) (0.021) (0.021) (0.021) Other regressors none Occ. FOS occ & FOS Occ, FOS, Tasks, FOS, license & license & related related Adjusted R square

33 Table A3: Wage regressions, 2005 NGS (continued) [1] [2] [3] [4] [5] [6] Occupation: Business (0.022) (0.022) (0.021) Science (0.024) (0.026) (0.025) Health (0.024) (0.030) (0.030) Soc. Sc. & educ (0.021) 021) (0.022) 022) (0.022) 022) Arts (0.030) (0.030) (0.030) Sales (0.024) (0.024) (0.024) Trades (0.036) (0.037) (0.036) Manufacturing (0.039) (0.038) (0.037) Other regressors FOS FOS FOS Tasks, FOS, (demogr. always in) license & license & related related Adjusted R square

34 Table A3: Wage regressions, 2005 NGS (continued) [1] [2] [3] [4] [5] [6] Field of Study: Education (0.028) (0.029) (0.029) (0.028) Arts & comm (0.032) 032) (0.033) 033) (0.032) 032) (0.031) 031) Humanities (0.030) (0.030) (0.029) (0.029) Soc. sc. & law (0.026) (0.026) (0.025) (0.025) Business (0.025) (0.025) (0.024) (0.024) Phys. & life sc (0.034) (0.034) (0.033) (0.033) Mth Math, comp. & info (0.033) (0.034) (0.033) (0.032) Engin. & related (0.027) (0.028) (0.027) (0.026) Health & fitness (0.026) (0.031) (0.030) (0.028) Other regressors Occ. Occ. Occ. Tasks, (demogr. always in) license & license & related related Adjusted R square

35 Table A3: Wage regressions, 2005 NGS (continued) [1] [2] [3] [4] [5] [6] Could get license (0.013) (0.013) Did get lic (0.016) (0.016) Job very related (0.015) (0.014) Job somewhat rel (0.017) (0.016) Tasks: Manual (0.221) Analytic (0.281) Interactive (0.156) Other regressors Occ. FOS Occ. Occ. FOS (demogr. always in) FOS FOS Adjusted R square

36 Field of study (specific skills) explain a large fraction of the choice of occupation Especially in cases where workers can get a license Most of the occupation wage differentials can be explained using the following factors: Standard demographics Field of study (specific human capital) Tasks (general capital / skill requirements) Getting a license / relatedness of job and education

37 Preliminary analysis from the 2005 NGS suggests that most of the occupation wage differentials can be explained by factors we better understand: Field of study Task content of work Especially the case in occupations where workers can get a license Probably better to include these variables instead of occupations. But controlling for occupations in a wage regression may not be such a bad thing to do when these other variables are not available. But many problems remain, (e.g. endogenous choice of field of study), as in the case of other standard covariates