Research Computing >> Resources & reference >> Economic and social classifications
ECONOMIC AND SOCIAL CLASSIFICATIONS
U.S. 1987 SIC classification (used by CRSP and Compustat)
- Official North American Industrial Classification System (NAICS) site, at the Bureau of the Census. Includes 1997 NAICS and 1987 SIC Correspondence Tables
- United Nations Classifications Registry: Includes the International Standard Classification (ISIC), and the Standard International Trade Classification (SITC).
- Eurostat's Classification Server, includes downloadable concordance tables between different international classifications (NACE, ISIC, CLIO, etc)
- Economic classifications, from the Institut de Recherches Économiques et Sociales (Catholic University of Louvain, Belgium). Includes links to concordance tables between international and national classifications (some information is presented in French, Italian, or German)
- Industry Concordances, by Jon Haveman
References on using industrial classifications in finance and accounting
- Bhojraj Sanjeev, Charles M. C. Lee and Derek Oler (2003). "What's My Line? A Comparison of Industry Classification Schemes for Capital Market Research." Journal of Accounting Research; 41(5), pages 745-774. (Full text available through Ingenta)
Abstract: This study compares four broadly available industry classification schemes in a variety of applications common to capital market research. Standard Industrial Classification (SIC) codes have been available since 1939 but are being replaced by North American Industry Classification System (NAICS) codes. The Global Industry Classifications Standard (GICS)SM system, jointly developed by Standard & Poor's and Morgan Stanley Capital International (MSCI), is popular among financial practitioners, whereas the Fama and French  algorithm is used primarily by academics. Our results show that GICS classifications are significantly better at explaining stock return comovements, as well as cross-sectional variations in valuation multiples, forecasted and realized growth rates, research and development expenditures, and various key financial ratios. The GICS advantage is consistent from year to year and is most pronounced among large firms. The other three methods differ little from each other in most applications.
- Guenther, David A. and Andrew J. Rosman (1994). "Differences between COMPUSTAT and CRSP SIC Codes and Related Effects on Research". Journal of Accounting and Economics, 18(1), pages 115-28.
Abstract: Differences between SIC codes assigned to companies by COMPUSTAT and CRSP are examined. Large differences are observed at two-, three-, and four-digit levels. Correlations of intra-industry monthly stock returns are larger and variances of intra-industry financial ratios are smaller for industries based on COMPUSTAT codes. Replication of a portion of Freeman and Tse (1992) produces significant results using COMPUSTAT codes, consistent with the original research, but insignificant results for CRSP codes.
- Kahle, Kathleen M and Ralph A. Walkling (1996). "The impact of industry classifications on financial research". Journal of Financial and Quantitative Analysis, 31(3), pages 309-35. (Full text available through ABI/Inform)
Abstract: Using approximately 10,000 firms jointly covered by Compustat and CRSP from 1974 to 1993, a study finds substantial differences in the SIC codes designated by the 2 databases. More than 36% of the classifications disagree at the 2-digit level and nearly 80% disagree at the 4-digit level. The study examines the impact of these differences upon financial research in several ways: 1. It is shown that classification of utilities, financial firms, and conglomerate acquisitions are affected by the choice of CRSP versus Compustat SIC codes. 2. It is shown that industry classification matters in financial research by illustrating that size- and industry-matched comparisons are more powerful than pure size matches. 3. The specification and power of Compustat versus CRSP classifications are tested by simulating a typical financial experiment in which sample firms are matched to control firms by industry. The results include: 1. Compustat matched samples are more powerful than CRSP matched samples in detecting abnormal performance. 2. Nonparametric tests outperform parametric tests.