Updated Unified Category System for 1960-2000 Census Occupations

1. Updated Unified Category System for 1960-2000 Census Occupations Peter B. Meyer, OPT Brown Bag seminar, Oct 25, 2006 • Outline • Tentative standard categories • Users and bug fixes • How Census assigns occupation codes • Imputation practice

2. 1960 system from 1968-1970 1970 system from 1971-1982 1980 system from 1983-1991 1990 system from 1992-2002 2000 system from 2003-present Census Occupational Classifications • Census Bureau staff assign 3-digit occupations codes to respondents of decennial Census • The list of codes changes every Census • Current Population Survey (CPS) uses these codes: • Vast data is available in these categories • But time series don’t cover the whole period

3. Tradeoffs in Classification Systems • Duration vs. accuracy, precision • blacksmith, database admin (short precise series) • electrical engineer (long evolving series) • Number of occupations vs. sample size of each • Narrow distinctions may be of interest • Dental technicians • High tech occupations vs. other technical occupations • Licensed jobs • Conformity with other data

4. Desirable Attributes of a Classification • For each occupation, well-behaved time-series of: • mean wage • wage variance • fraction of the population • New criterion:SPARSENESS • One prefers a classification not be sparse, meaning it does not have many empty occ-year cells

5. Classification Current Phase Earlier working paper (Meyer and Osborne, 2005) defines a unified classification for Census & CPS 3-digit occupation codes from 1960 to present It was adapted from the 500+ categories in 1990 Census: • 379 categories have same name or almost same as 1990 • 125 were eliminated to help harmonize with other years • Example to follow • 19 categories have expanded (changed name or n.e.c. category) • 3 categories added for 1960 data which doesn’t fit in

6. Hard case; combined category here

7. Input from users and new data • Corrections from users

8. The information coders have

9. The information coders have

10. Statisticians and Actuaries Separate categories in and after 1970 In 1960 they were all in “statisticians and actuaries” When standardizing we put all these in “statisticians” Now we try to infer which people in this population were actuaries

11. Statisticians and Actuaries • Pooled all 1970-1990 statisticians and actuaries • Ran many logistic regressions predicting the actuaries • Good predictors of whether respondent is an actuary • Recorded in a later year • Employed in insurance, accounting/auditing, or professional services • Employed in private sector • High salary income • High business income, or to earn mostly business income • Is employed • Lives in Connecticut, Minnesota, Nebraska, or Wisconsin

12. Statisticians and Actuaries • For 1970-1990 a logistic regression can predict occupation right 88% of the time • Impute a prediction on 1960 data

13. Statisticians and Actuaries Why work this arcane problem? • More accurate statistician category, by later definition • Longer time series for actuaries • Reduces sparseness • Builds a technique

14. Lawyers and Judges • Combine all 1970-1990 lawyers and judges • Exclude all private sector employees because they are all lawyers (By definition? ) • In the remainder, predictors of judge, not lawyer: (judge is 1, lawyer is 0 in the next slide) • Older • Employed in state government • High salary income; low or no business income • Educated less than 16 years • Employed at time of survey • Can get 83% accurate predictions from such a regression

15. Logit Regression on 1970-1990 Census Sample

16. Can Use Those Coefficients in Stata gen logitindex = -.0046652 * year + .1549193 * age -.0006942 * age * age-1.4405086* indfed +.4986729 * indstate -1.795481 * lnwage +.0517015 * lnwage * lnwage+.0030016 * lnwage * lnwage * lnwage-.040749 * lnbus -.7140285 * busfrac+2.234934 * (educyrs<16)-.0442429 * educyrs +.2239105 * employed +13.0172 /* constant */ ; … gen logitval=exp(logitindex)/(1.0+exp(logitindex)) replace logitval=.0001 if !govtemployee /* this is a perfect predictor */ replace logitval=.0001 if !indfed & !indstate & !indlocal /* this too */ gen assigned = logitval>.46 /* Now ‘assigned’ has a 1 for imputed judges */

17. Newly Imputed Judges Respondents in Census samples after imputation

18. Preliminary Findings • There are opportunities to impute occupations occasionally with reasonable accuracy • The resulting records have “better-classified” occupations • slightly more accurate (in four categories) • Slightly less sparse (293 empty cells not 295) • Effects in a substantive regression not focused on these categories is tiny (What does it mean?)

19. Census Bureau's National Processing Center in Jeffersonville, IN

20. Who's Doing the Coding • There are “Coders” and “Referralists” • Coders follow carefully documented procedures, most likely from the Census National Headquarters in Suitland, MD • In many cases there is not enough information to assign industry and occupation codes • Such unresolved cases are forwarded electronically ("referred") to a “Referralist" • Coders with two years of experience are expected to produce 94 code assignments an hour, with 95% accuracy (codes are checked)

21. Who's Doing the Coding • There were about 12 coders and 14 referralists in October 2006 • Referralists have been coders before and usually have 9+ years of experience • I interviewed three referralists, and a supervisor, but no coders during my October 2006 visit • The ones I met handled referrals from several surveys: • CPS, ATUS, SIPP, NLS, ACS, and others on contract • All of the above surveys use decennial Census occupation codes • Industry and occupational codes for Decennial Censuses are assigned by other employees, not the ones who permanently work in Jeffersonville now (???)

22. "kind of work" "principal duties" employer name city and state ("PSU") of respondent's home (not workplace) industry, already coded industry type (manufacturing, service, other) years of education, age, sex not income, although it was available before Jan '94 software. Information Available to a Coder The industry is normally coded before the occupation. Referralist can match Employer name to a known employer from the Employer Name List (ENL), possibly the same as SSEL Some cases are "autocoded" before coder sees

23. Problems and Problematic Cases • “Computer work" for occupation (???) • Too little information from respondent • Exaggeration (example: dot com businesses) • Ambiguities: • "water company" for industry or employer • "surveyor" occupation • "boot" vs "boat" in handwriting • hurrying • Referralists confer with each other routinely, but sometimes make different choices from one another • Does technological change go along with occupational ambiguity? YES. Problems with computer work, biotech. Still no nanotech in classification.

24. What Would Improve Their Coding Accuracy or Speed? • Information about a job title • Information about employer's city and state • [show CPS 1993 questions] (???) • But! Asking more questions would extend the interview • Retrieved from "http://econterms.net/pbmeyer/research/occs/wiki/index.php?title=Brown_bag_Oct_25"

25. Questions for Occupational Time Series Hypotheses for time series of consistently-defined occupations: • Have high tech jobs had rising earnings inequality? [yes] • Superstars effect? [yes] • Is nurturing work valued less (England et al)? • Have mathematical occupations grown in size or pay? • Measuring payoffs to skills • Have better job-search technologies reduced inequality within job categories? (as predicted by Stigler (1960) Researchers sometimes use only industry, not occupation, or limit time span of study to keep consistent occupation

26. "What's Next?" • Make next working paper and program code available • Publish at IPUMS • Accumulate more classification systems, techniques, criteria, and expert opinions • New wiki of all classifications

27. Thank You.

28. Tasks Inputs Meaning of Occupation • Worker’s tasks • Worker’s function (identified e.g. by inputs and outputs) • example: blacksmiths vs forging machine operators • example: teachers of different subjects and ages of students • Sometimes other distinctions • Hierarchically (apprentices, foremen, supervisors) • Certification • Skills • Industry (activity of the employing organization) • To some extent these are separate labor markets, with separated job search, wage setting, unemployment experiences. Outputs

29. Occupation Attributes I • Strength (1-5 from DOT) • Reasoning (1-6 from DOT) • Mathematical reasoning (1-6 from DOT) • Language use (1-6 from DOT) • Duration of specific training (from DOT) • Nurturing (0/1) (England et al, 1994) • many others, potentially

30. Occupation Attributes II • % urban (e.g. doctor in rural area) • often involves traveling (or required mobility earlier) • rate of growth • % of immigrants • authority (0/1) (England et al, 1994) • high tech • regulated • unionized • use of machines • involves advocacy; or repair; or negotiation