{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T05:12:12Z","timestamp":1693545132674},"reference-count":65,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T00:00:00Z","timestamp":1568937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,1,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>Missing values are a major problem in all econometric applications based on survey data. A standard approach assumes data are missing at random and uses imputation methods or even listwise deletion. This approach is justified if item nonresponse does not depend on the potentially missing variables\u2019 realization. However, assuming missingness at random may introduce bias if nonresponse is, in fact, selective. Relevant applications range from financial or strategic firm-level data to individual-level data on income or privacy-sensitive behaviors. In this paper, we propose a novel approach to deal with selective item nonresponse in the model\u2019s dependent variable. Our approach is based on instrumental variables that affect selection only through a partially observed outcome variable. In addition, we allow for endogenous regressors. We establish identification of the structural parameter and propose a simple two-step estimation procedure for it. Our estimator is consistent and robust against biases that would prevail when assuming missingness at random. We implement the estimation procedure using firm-level survey data and a binary instrumental variable to estimate the effect of outsourcing on productivity.<\/jats:p>","DOI":"10.1093\/ectj\/utz016","type":"journal-article","created":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T15:11:31Z","timestamp":1568992291000},"page":"88-114","source":"Crossref","is-referenced-by-count":1,"title":["Information technology outsourcing and firm productivity: eliminating bias from selective missingness in the dependent variable"],"prefix":"10.1093","volume":"23","author":[{"given":"Christoph","family":"Breunig","sequence":"first","affiliation":[{"name":"Department of Economics, Emory University, 1602 Fishburne Drive, Atlanta, GA 30322"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Kummer","sequence":"additional","affiliation":[{"name":"University of East Anglia, GaTech & ZEW, REG 2.25E, Norwich Research Park, Norwich NR4 TJ7, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joerg","family":"Ohnemus","sequence":"additional","affiliation":[{"name":"Centre for European Economic Research (ZEW), P.O. 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