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Occupation-Based Socioeconomic Index with Percentile Ranks University of Pennsylvania ScholarlyCommons Population Center Working Papers (PSC/PARC) Population Studies Center 12-1-2020 Occupation-Based Socioeconomic Index with Percentile Ranks Xi Song University of Pennsylvania, [email protected] Yu Xie Princeton University, [email protected] Follow this and additional works at: https://repository.upenn.edu/psc_publications Part of the Demography, Population, and Ecology Commons, Family, Life Course, and Society Commons, Inequality and Stratification Commons, and the Work, Economy and Organizations Commons Recommended Citation Song, Xi, and Yu Xie. 2020. "Occupation-Based Socioeconomic Index with Percentile Ranks." University of Pennsylvania Population Center Working Paper (PSC/PARC), 2020-59. https://repository.upenn.edu/ psc_publications/59. This paper is posted at ScholarlyCommons. https://repository.upenn.edu/psc_publications/59 For more information, please contact [email protected]. Occupation-Based Socioeconomic Index with Percentile Ranks Abstract In this paper, we propose a method for constructing an occupation-based socioeconomic index that can easily incorporate occupational structure changes. The resulting index is the education percentile rank of an occupation for a given cohort, based on contemporaneous information pertaining to education composition and the number of workers at the occupation level. An occupation may experience an increase or decrease in its ranking when either education or size of relevant occupations change. The method is flexible in dealing with changes in occupation and education measurements over time. Applying the method to U.S. history from the mid-nineteenth century to the present day, we derive the index using the IPUMS U.S. Census microdata from 1850 to 2000 and the American Community Surveys (ACS) from 2001 to 2018. Compared to previous occupational measures, this new measure takes into account occupational status evolvement caused by long-term secular changes in occupational distributions and education composition. The resulting percentile rank measure can be easily merged with social surveys and administrative data that include occupational measures based on the U.S. Census occupation codes and crosswalks. Keywords occupation, socioeconomic status, percentile rank, continuous measure Disciplines Demography, Population, and Ecology | Family, Life Course, and Society | Inequality and Stratification | Social and Behavioral Sciences | Sociology | Work, Economy and Organizations This working paper is available at ScholarlyCommons: https://repository.upenn.edu/psc_publications/59 Occupation-Based Socioeconomic Index with Percentile Ranks Xi Song Department of Sociology University of Pennsylvania Yu Xie Department of Sociology Princeton University Center for Social Research Peking University November 2020 Keywords: occupation, socioeconomic status, percentile rank, continuous measure Word count (including text and footnotes): 11,218 words, 3 tables, 4 figures * We thank Hao Dong, Joseph Ferrie, Catherine Massey, Jonathan Rothbaum, and Karen Rolf for helpful discussions; and our research interns, Pengtao Deng, Kai Feng, Xudong Guo, Yuqing Hu, Yuyang Hu, Shiyuan Li, Lemeng Liang, Di Tong, Chuqiao Yuan, Xiaohang Zhao, Xiuqi Yang, Xia Zheng, and Ruixuan Zhong. Direct all correspondence to Xi Song, 3718 Locust Walk, Philadelphia, PA 19104 ([email protected]) or Yu Xie, 186 Wallace Hall, Princeton University, Princeton, NJ 08544 ([email protected]). Occupational codes and percentile rank files presented in this paper can be downloaded from the project website (https://osf.io/x7rnw/). ABSTRACT In this paper, we propose a method for constructing an occupation-based socioeconomic index that can easily incorporate occupational structure changes. The resulting index is the education- percentile rank of an occupation for a given cohort, based on contemporaneous information pertaining to education composition and the number of workers at the occupation level. An occupation may experience an increase or decrease in its ranking when either education or size of relevant occupations change. The method is flexible in dealing with changes in occupation and education measurements over time. Applying the method to U.S. history from the mid- nineteenth century to the present day, we derive the index using the IPUMS U.S. Census microdata from 1850 to 2000 and the American Community Surveys (ACS) from 2001 to 2018. Compared to previous occupational measures, this new measure takes into account occupational status evolvement caused by long-term secular changes in occupational distributions and education composition. The resulting percentile rank measure can be easily merged with social surveys and administrative data that include occupational measures based on the U.S. Census occupation codes and crosswalks. Occupation-Based Socioeconomic Index with Percentile Ranks 1. Introduction One of the key features of human society is the vast variability in social attributes. Not only is any social attribute highly heterogeneous at the individual level, but an individual’s social attributes are also multidimensional in nature, manifested in education, occupation, income, wealth, personal reputation, community status, and family background, among many other characteristics. Hence, developing quantitative measurements of an individual’s social position is very difficult, as it is impractical to incorporate all these detailed measures. Many early studies in search of socioeconomic indicators have suggested occupation as a simple—yet arguably the single most important—indicator of socioeconomic status (Blau and Duncan 1967; Caplow 1954; Featherman and Hauser 1978; Warner, Meeker, and Eells 1949), a measure that is highly associated with one’s ability, characteristics, and training, from which one can infer social prestige (Gross 1959; Kahl 1957; Reiss 1961). Compared to income and wealth, occupation is publicly known to others (Goldberger 1989; Hauser et al. 2000) and often the only item consistently collected in historical registration and records and widely available in social surveys. For more than a century, occupational measures have been widely used in both government statistics and social science research. Yet, making good use of occupation data in sociological studies is fraught with methodological challenges. One difficulty is the assurance of measurement comparability across studies, populations, and time. Broadly speaking, the development of occupational measures has evolved along two major lines: (1) one that groups occupations into categories (e.g., Glass 1954; Lipset and Bendix 1959; Sorokin [1927] 1959; Erikson, Goldthorpe, Portocarero 1979; Jonsson et al. 2009; Weeden and Grusky 2005; Hauser 1 1980; Edwards 1938; Wright 1997); and (2) another that represents occupations with a unidimensional, continuous scale based on occupational prestige or socioeconomic scores (e.g., Davis 1927; Svalastoga 1959; Duncan 1961; Hauser and Warren 1997; Treiman 1977; Ganzeboom, Luijkx, and Treiman 1989; Nakao and Treas 1994; Hodge, Siegel, and Rossi 1964; Hauser and Featherman 1977; Nam and Powers 1983). Both the class and gradational approaches are widely accepted, tested, and debated for their strengths and limitations (see a review in, e.g., Jonsson et al. 2009). Yet, most research thus far has focused almost exclusively on modern, industrialized societies. Very few researchers have developed occupational measures for past populations or transitional societies before or during industrialization. One exception is Treiman (1976), who matched an incomplete list of occupations observed in the U.K., U.S., Italy, and Nepal from the fifteenth to nineteenth centuries to 1968 Standard Occupational Scale Categories. More recently, van Leeuwen, Maas, and their collaborators devised the Historical International Standard Classification of Occupations (HISCO)1 and the Historical International Standard Class Scheme (HISCLASS) for occupations in preindustrial, agrarian societies in Western Europe from the eighteenth to the twentieth centuries (van Leeuwen and Maas 2011; van Leeuwen, Maas, and Miles 2002). Despite their theoretical and conceptual significance, these two measures are not widely used in empirical work for at least three main reasons. First, the HISCO taxonomy contains 1,675 occupational groups, many of which are specific to a particular population, period, or region. Empirical data, especially those from small 1 These structural changes lead to significant differences between occupational classifications in historical and contemporary societies. Van Leeuwen, Maas, and Miles (2002) created the Historical International Standard Classification of Occupations (HISCO), which provides a systematic basis of occupational titles and definitions for a variety of countries in the nineteenth and twentieth centuries. Specifically, they rely on the 1,506 occupational categories in the 1968 version of the International Standard Classification of Occupations scheme to derive the 1,000 most frequent occupational titles from eight countries from the seventeenth to the twentieth centuries. 2 samples, contain insufficient information to support statistical analysis at its full classification. To simplify the measure, van Leeuwen, Maas, and their collaborators further developed the HISCLASS scheme with 12 social classes ranging from different tiers of professionals and managers to lower and unskilled workers and farmers.2 Although HISCLASS is more manageable than HISCO, the division of
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