Abstract
In this paper we develop a methodology for identifying a population group surveyed latently in the (target) survey relevant for further processing, for example poverty calculations, but surveyed explicitly in another (source) survey, not suitable for such processing. Identification is achieved by transferring the binary information from the source survey to the target survey by means of a logistic regression determining group affiliation in the source survey by use of variables available also in the target survey. In the proposed methodology we improve on common matching procedures by optimizing the cut-value of the probability which assigns group affiliation in the target survey. This contrasts with the commonly used “Hosmer-Lemeshov” cut-values for binary categorization, which equates between the sensitivity and specificity curves. Instead we improve group identification by minimizing the sum of total errors as a percent of total true outcomes.
The Jewish ultra-orthodox population in Israel serves as a case study. This idiosyncratic community, committed to the observance of the Bible is only latently observed in the surveys typically used for poverty calculation. It is explicitly captured in the social survey, which is not suitable for poverty measurement.
This procedure is useful for ex-post enhancement of survey data in general.
References
Berman, Eli and Ruth Klinov (1997). Human Capital Investment and Nonparticipation: Evidence from a Sample with Infinite Horizons (Or: Mr. Jewish Father Stops Going to Work), Jerusalem, The Maurice Falk Institute for Economic Research in Israel, Discussion Paper (97.05), 1-36.Search in Google Scholar
Berman, Eli (2000). Sect, Subsidy, and Sacrifice: An Economist’s View of Ultra-Orthodox Jews, The Quarterly Journal of Economics, 904-952.10.1162/003355300554944Search in Google Scholar
Bigman, D., and P.V. Srinivasan (2002). Geographical Targeting of Poverty Alleviation Programs: Methodology and Applications in Rural India. Journal of Policy Modeling, 24, 237-255.10.1016/S0161-8938(02)00108-4Search in Google Scholar
Dahan, M. (1998). The Ultra-Orthodox Jews and Municipal Authority, Part 1 – Income Distribution in Jerusalem, in Hebrew, The Jerusalem Institute for Israel Studies, Research Series, 79, 1-50.Search in Google Scholar
Degani, Avi and Rina Degani (2000). The Demand for Housing in the Haredi Sector, Institute for Spatial Analysis Ltd., September, 1-170.Search in Google Scholar
Flug, Karnit and Nitsa (Kaliner) Kasir (2003). Poverty and Employment and the Gulf between Them, Israel Economic Review, Vol. 1, p. 55-80.Search in Google Scholar
Frenkel, Alona, Pavel Soyfer and Yoram Mayshar (2003). Potential Income as a Measure of Poverty in Israel, Working Paper, Maurice Falk Institute, (in Hebrew), 1-30.Search in Google Scholar
Friedman, Menachem (1991). The Haredi (Ultra-Orthodox) Society – Sources, Trends and Processes, in Hebrew, Summary in English, The Jerusalem Institute for Israel Studies, Jerusalem.Search in Google Scholar
Glewwe, Paul and Jacques Van der Gaag (1990). Idenifying the Poor in Developing Countries: Do Different Definitions Matter? World Development, 18 (6), 803-815.10.1016/0305-750X(90)90003-GSearch in Google Scholar
Gottlieb, Daniel and Nitsa Kasir (2004). Poverty in Israel and a Strategy for its Reduction, in Hebrew, The Bank of Israel, www.bankisrael.gov.il, 1-46.Search in Google Scholar
Gottlieb, Daniel and Roy Manor (2005). On the Choice of a Poverty Measure: The Case of Israel, 1997 to 2002, in Hebrew, Abstract in English, forthcoming, The Bank of Israel, 1-54.Search in Google Scholar
Gottlieb, Daniel (2007). Poverty and Labor Market Behavior in the Ultra-Orthodox Population in Israel, (in Hebrew), Economics and Society Program, The Van Leer Institute, Jerusalem, 1-55.Search in Google Scholar
Gurovich, Norma and Eilat Cohen-Kastro (2004). Ultra-Orthodox Jews – Geographic Distribution and Demographic, Social and Economic Characteristics, 1996-2001, in Hebrew, Summary in English, Working Paper Series, No. 5, Central Bureau of Statistics – Demography Sector.Search in Google Scholar
Hadjicostas, Petros and George C. Hadjinicola (2001). The Asymptotic Distribution of the Proportion of Correct Classifications for a Holdout Sample in Logistic Regression, Journal of Statistical Planning and Inference, Vol. 92 (1-2), January, 193-211.Search in Google Scholar
Hentschel, J., J.O. Lanjouw, P. Lanjouw and J. Poggi (2000). Combining Survey Data to Trace the Spatial Dimensions of Poverty: A Case Study of Ecuador, World Bank Economic Review, 14, (1), 147-65.10.1093/wber/14.1.147Search in Google Scholar
Hosmer, David W. and Stanley Lemeshow (2000). Applied Logistic Regression. 2nd Edition, New York: John Wiley & Sons Inc.10.1002/0471722146Search in Google Scholar
Schares, G., et al. (2003). Regional Distribution of Bovine Neospora Caninum Infection in the German State of Rhineland-Palatinate Modeled by Logistic Regression, International Journal of Parasitology, Vol 33 (14), 1631-1640.10.1016/S0020-7519(03)00266-2Search in Google Scholar
Schutter, E.M.J. et al. (1998). Estimation of Probability of Malignancy Using a Logistic Model Combining Physical Examination, Ultrasound, Serum CA 125, and Serum CA 72-4 in Postmenopausal Women with a Pelvic Mass: An International Multicenter Study”, Gynecologic Oncology, Vol. 69, (1), 56-63.10.1006/gyno.1998.4942Search in Google Scholar
Stegeman, J.A. et al. (2006). Establishing the Change in Antibiotic Resistance of Enterococcus Faecium Strains Isolated from Duch Broilers by Logistic Regression and Survival Analysis. Preventive Veterinary Medicine, 74 (1): 56–66.Search in Google Scholar
Tal Commission (2000). Report on the arrangement concerning the recruitment of Yeshiva students to the IDF (in Hebrew), July,Search in Google Scholar
© 2009 Daniel Gottlieb et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.