{"id":19,"date":"2014-05-02T16:56:27","date_gmt":"2014-05-02T14:56:27","guid":{"rendered":"http:\/\/people.unil.ch\/danieloesch\/?page_id=19"},"modified":"2026-03-03T09:02:59","modified_gmt":"2026-03-03T08:02:59","slug":"scripts","status":"publish","type":"page","link":"https:\/\/people.unil.ch\/danieloesch\/scripts\/","title":{"rendered":"Scripts for Social Class & OEP"},"content":{"rendered":"

For social class,<\/strong> you find below codes in Stata, R, SPSS and SAS for constructing my class scheme (discussed here in 2006<\/a> and 2023<\/a>). To write your own code, refer to the Excel sheets below, which show how occupations are assigned to classes.<\/p>\n

For Stata<\/strong>, Ben Jann (University of Bern) has written the Stata module crosswalk<\/a> which creates my class scheme based on ISCO (“ssc install crosswalk” & “help crosswalk”). For R<\/strong>, the same is possible using the DIGCLASS package<\/a>, developed by the EU-JRC DIGCLASS team.<\/p>\n

For occupations’ earning potential OEP <\/strong>(discussed here<\/a>), you can download the Excel files that assign OEP values to occupations here<\/a> for ISCO-88 and ISCO-08 (1- to 4-digit). Besides the common scale, these files also show the specific OEP values for Germany, Sweden, Switzerland, the UK and the US.<\/p>\n

For Stata<\/strong>, the Stata module crosswalk<\/a> creates OEP values based on ISCO. For R<\/strong>, the DIGCLASS package<\/a> does the same, constructing OEP values for ISCO occupations.<\/p>\n

Occupations (ISCO) into classes<\/h6>\n

Excel<\/a> for occupations measured with ISCO-08 (4-digits)<\/p>\n

Excel<\/a> for occupations measured with ISCO-08 (2-digits)<\/p>\n

Excel <\/a>for occupations measured with ISCO-88 (4-digits)<\/p>\n

Excel <\/a>for occupations mesured with ISCO-88 (2-digits)<\/p>\n

European Social Survey (ESS)
<\/span><\/h6>\n

For a summary of my class schema and its coding in the ESS, see pdf \"image_pdf\"<\/a><\/p>\n

ESS 2002-2006 (rounds 1 to 3) in Stata<\/a><\/p>\n

ESS 2008-2010 (rounds 4 and 5) in Stata<\/a><\/p>\n

ESS 2012-2020 (rounds 6 to 10) in Stata<\/a><\/p>\n

ESS 2002-2010 (rounds 1 to 5) in\u00a0R<\/a><\/strong> or in SPSS<\/a><\/p>\n

ESS 2012-2023 (rounds 6 to 11) in R<\/a><\/strong> or in SPSS<\/a><\/p>\n

International Social Survey Programme (ISSP) + MOSAiCH<\/h6>\n

ISSP 1987-2013 (+ cumulated dataset on Social Inequalities) in Stata<\/a> with a table<\/a> for variable names<\/p>\n

ISSP 2014-2017 in Stata<\/a><\/p>\n

ISSP 2010, 2011 in SPSS<\/a><\/p>\n

MOSAiCH-ISSP 2017 in Stata<\/a> (for Switzerland)<\/p>\n

MOSAiCH-ISSP 2019 in Stata<\/a> (for Switzerland)<\/p>\n

MOSAiCH-ISSP 2022 in\u00a0R<\/strong><\/a> (for Switzerland)<\/p>\n

German Socio-Economic Panel (SOEP)<\/h6>\n

SOEP (CNEF equivalent file, all availables waves) in Stata<\/a><\/p>\n

Swiss Household Panel (SHP)<\/h6>\n

SHP 1999 up to latest wave in Stata<\/a><\/p>\n

SHP 1999 up to latest wave in SPSS<\/a><\/p>\n

SHP 1999 up to latest wave in SAS<\/a><\/p>\n

Swiss Election Studies (SELECTS)<\/h6>\n

SELECTS 2019 in Stata<\/a>, R<\/a> or SPSS<\/a><\/p>\n

SELECTS 2015 in Stata<\/a>, R<\/a> or SPSS<\/a><\/p>\n

SELECTS 2011 in SPSS<\/a><\/p>\n

SELECTS 2007 in SPSS<\/a><\/p>\n

SELECTS 2003 in SPSS<\/a><\/p>\n

SELECTS 1999 in SPSS<\/a><\/p>\n

SELECTS 1995 in SPSS<\/a><\/p>\n\n\n

<\/p>\n\n\n\n

Luxembourg Income Study (LIS)<\/strong><\/p>\n\n\n\n

Using LIS data and variables for occupation, education and employment status, this R-code<\/a> <\/strong>creates a 4-class variable for many countries and years, discussed in Moawad & Oesch (2025)<\/a>:<\/p>\n\n\n\n