Recent occupation concepts applied to historical U.S. Census data

1. Recent occupation concepts applied to historical U.S. Census data Peter B. Meyer US Bureau of Labor Statistics (but none of this represents official measurement or policy; Views and findings are those of the author not the agency) ESSHC 2008, Lisbon, March 1, 2008 • Outline • Brief history of U.S. Census occupations • Recent practices • Can standardize to recent definitions? • Groups at the margins

2. Relevant history of U.S. Census occupation concepts • Constitution requires counts every ten years for defining political districts and taxes by state. • It excludes Indians (Native Americans). • First collected in 1790. • Idea of collecting occupations recurs. • 1850: “Profession, occupation, or trade” asked. • 1870: Slave category disappears. • 1902: Bureau of Census established. • 1920: Most Indians now included in principle. • 1950 and since: Relatively stable practices.

3. Can standardize occupation categories over time that look like recent ones? • IPUMS (Matt Sobek) defined occ1950 for 1850-present • Meyer and Osborne (2005) defined 1990-based classification from 1960 to present. Plan to improve, correct, and extend that. Let us look at recent Census practices occupation variable. • Census “coders” in a single location assign 3-digit industry and occupation codes • They follow carefully documented practices. • I interviewed four experienced ones. • They work just north of Louisville, in Jefferson, Indiana

4. Information used when coding • “what kind of work" • “most important activities or duties" • employer name • “what kind of industry” • home city and state • years of education • age • sex • before 1994, had income too This information is available when choosing “industry” and “occupation”  • Tens of thousands of job titles are mapped to a code in a reference book they have, if industry matches what is expected. • Some cases may be "autocoded" by software; coder checks. • Coder with two years experience should assign 94 codes per hour with 95% “accuracy”, which is checked. • Cases not meeting the rules go to “referralist” (specialist) • They have 9+ years of experience.

5. Problems faced by referralists • Having to hurry • Ambiguity; too little information from respondent • “computer work" for “kind of work” • "water company" for industry or employer • "surveyor" occupation • "boot" vs "boat" in handwriting • exaggeration (example: dot com businesses) • Referralists confer with each other routinely, but sometimes make different choices from one another

6. Tangential motivation:Example of standardizing • In 1960 Census, “lawyers and judges” was one category • Later, “lawyers” and “judges” were separate • We can impute which 1960 ones are judges for standardizing comparisons to later data. • In 1970-1990 these variables predict who’s called a judge: • Employed in public sector, especially in state government • Older • Employed in state government • High salary income • Low business income • Educated less than 16 years • Employed at time of survey

7. Newly imputed lawyers and judges Using that information in a logistic regression on people in 1960: “lawyers and judges,” can split them with estimated 80% accuracy: Seems to be 80% accurate. In this case, can estimate a time series of earnings separately for judges and lawyers more accurately. Let us go back to look at the earlier period to see how far it is from this data-processing world.

10. Tentative conclusions • Realistic to apply current occupation concept back to 1910-1940, sometimes earlier • Before 1930 it makes sense to adjust for adult women in home-based economy and Indians • In 1850 maybe only 35% of population would have a “Census occupation”; now 65% or more. • More research is feasible to get better at this.

11. The information coders have