1 Updated Unified Category System for Census Occupations Peter B. Meyer US Bureau of Labor Statistics (but none of this represents official measurement or policy) SSHA 2006, Minneapolis; Nov 4, 2006 Outline 1. Tentative standard categories 2. Users and bug fixes 3. How Census assigns occupation codes 4. Imputation practice

2 Census Occupational Classifications U.S. Bureau of Census determines a list of 3 digit occupation codes each ten years Then puts one for employed respondents to the decennial Census and some other surveys Vast data is available in these categories:  CPS, ATUS, SIPP, NLS, ACS, decennial Census But not always consistently over long time spans Research efforts may require some standard

3 Tradeoffs in Classification Systems Precise job distinctions vs. Consistency, duration, and sample size  High tech occupations vs. other technical occupations blacksmith, database admin (shorter, more precise series) electrical engineer (longer evolving series )  “Superstars” jobs like athletes and musicians (need precision)  Licensed jobs (need long comparable occupations) Conformity with other data Avoid “sparseness” – many missing year-occ cells Meaning of occupation: function, tasks, skills, background, social class There is no perfect classification but there are tools & criteria for better ones.

4 Baselines to improve on IPUMS defined occ1950 for US workers recorded in ANY Census Working paper (Meyer and Osborne, 2005) defined classification of digit occupations 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 a not- elsewhere-classified category was given more scope)  3 categories added for 1960 data which doesn’t fit in

5 Some distinctions are lost in standardization 1970 code 1970 occupation title code 1980/90 component category titles Civilian Labor Force % of 1970 category 284 Sales workers, except clerks, retail trade 263 Sales workers, motor vehicles and boats 185, % 266 Sales workers, furniture and home furnishings 98, % 267 Sales workers; radio, television, hi fi, and appliances 76, % 268 Sales workers, hardware and building supplies 81, % 269 Sales workers, parts 39, % 274 Sales workers, other commodities 16, % 277 Street and door to door sales workers 2, % Census reports and IPUMS data show how many respondents would be coded in each of two classification systems.

6 User input and new data since 2005 Sent these programs to 19 people who expressed interest  Open-source code idea (helps find errors; also is public property) Corrections from users did come in  Philip Cohen, UNC Sociology, identified some problems/mistakes.  Sarah Porter, research assistant at U of Iowa working with Jennifer Glass, wrote a program to do some similar mappings. Comparing to that program I found mistakes in mine.  Dual-coded 1990/2000 data sets highlighted some surprises Experimented with imputations (example to follow) Visited the Census office where they assign these codes.

7 Census Bureau's National Processing Center in Jeffersonville, IN  Louisville, KY, is just south of it I interviewed four specialists who assign occupation & industry codes.

8 Information used when coding “what kind of work" “most important activities or duties" employer name “what kind of industry” city and state ("PSU") of respondent's home industry type (manufacturing, service, other) years of education, age, sex not income, although it was available before Jan '94 software. Tens of thousands of job titles are mapped to a code in a reference book they have, if industry also matches. Some cases may be "autocoded" by software and coder checks After coding, public use samples have 3-digit occupation code and 3-digit industry code Quality of assignments from public use samples are limited

9 Imputation: Statisticians and Actuaries Counts of Actuaries and Statisticians in Census Sample Actuaries Statisticians These were separate categories in and after 1970 But in 1960 they were all in “statisticians and actuaries” When standardizing (2005) they were put in “statisticians” Will try to infer which of the 1960 people were actuaries.

10 Statisticians and Actuaries Pooled all statisticians and actuaries Good predictors of whether respondent is an actuary:  Recorded in a later year  Employed in insurance, accounting/auditing, or professional services industries  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 Ran many logistic regressions predicting the actuaries

11 Statisticians and Actuaries For 1970 data that logistic regression predicts occupation right 88% of the time Revised counts of actuaries and statisticians after imputation Actuaries Statisticians

12  More accurate standardized “statistician” category  Longer actuary time series  Reduces sparseness – empty cells  Builds a technique for this data mining  Benefits scale up through IPUMS Statisticians and Actuaries Why work this arcane problem?

13 Imputing judges In 1960 Census, lawyers and judges were one category Later, they’re separate, and separate in “standard” system Without more info, we categorize all in 1960 as “lawyers”. We wish to impute which ones are judges Useful fact: private sector ones were all called lawyers Predictors for the public sector ones, of who’s a judge:  Older  Employed in state government  High salary income  Low business income  Educated less than 16 years  Employed at time of survey

14 Logit regression predicting judges in Census CoefficientStd errorp-value Year Age Age-squared Federal government employee State government employee Ln(salary) Ln(salary) squared Ln(salary) cubed Ln(business income) Fraction of earned income that is business income Education less than 16 years Years of formal education Is employed at time of survey Constant Dependent variable: maximum likelihood probability this individual is a judge.

15 Thus we assign judge occupation code gen logitindex = * year * age * age * age * indfed * indstate * lnwage * lnwage * lnwage * lnwage * lnwage * lnwage * lnbus * busfrac * (educyrs<16) * educyrs * employed /* 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 */ Threshhold probability is chosen to match the number of judges expected to be there, based on annual trend. Can get 83% accurate predictions from such a rule on 1970 data. This mis-assigns a few who should have stayed lawyers.

16 Newly Imputed Judges Lawyers Judges Respondents in Census samples after imputation

17 What's next? Use dual-coded CPS datasets with 1990 and 2000 codes to make a few more imputations Keep listening, seek more help, make it better. Publish variable at IPUMS.org Keep going? 1970 & 1980 dual coded data sets exist.

18 Industry and occupation coding Industry codes and occupations codes are assigned by the same group of people, at the same time for each respondent. Industry is almost always decided first. The people who do that are “coders” Procedures are carefully documented I wasn’t a “sworn” Census agent and couldn’t see it done, live

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

20 What new information would help referralists? Information about a job title Information about employer's city and state  not respondent’s But asking more questions would extend the CPS interview

21 Problems faced by referralists Too little information from respondent “Computer work" for “kind of work” Exaggeration (example: dot com businesses) Ambiguities:  "water company" for industry or employer  "surveyor" occupation  "boot" vs "boat" in handwriting Having to hurry Referralists confer with each other routinely, but sometimes make different choices from one another YES. Does technological change go along with occupational ambiguity? YES. Problems with computer work, biotech. Still no nanotech in classification.

22 The information coders have

23 Who's Doing the Coding There were about 12 coders and 14 referralists in October 2006 Referralistscoders 9+ Referralists have been coders before and usually have 9+ years of experience referralists I interviewed three referralists, and a supervisor The ones I met handled referrals from several surveys:  CPS, ATUS, SIPP, NLS, ACS  others on contract  All these use decennial Census occupation codes They DON’T handle the decennial Censuses

24 Information available to referralist Can match Employer name to a known employer from their Employer Name List (ENL), same as SSEL or Business Registry. Can look on the web for that employer Can study “little red book” - SOC manual or (less often) giant Dict Occ Titles 1991 or, I’m told, look up employer in Dun and Broadstreet data They try to make a coherent choice for industry and occupation together.

25 “Coders”“Referralists” “Coders” and “Referralists” Coders Coders follow carefully documented procedures from the Census headquarters in Suitland, MD Coders Coders with two years of experience are expected to assign 94 codes an hour, with 95% accuracy (which is checked) “Referralist" (aka statistical assistant) If there is not enough information to assign industry and occupation codes by procedure, the case is forwarded electronically ("referred") to a “Referralist" (aka statistical assistant)