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In their simplest form, they lead to the well\u2010known difference\u2010in\u2010differences estimator. CRC models yield estimates of ATE for \u201cmovers\u201d (i.e., cross\u2010sectional units whose treatment status changed over time) while ATE for \u201cstayers\u201d (i.e., cross\u2010sectional units who retained the same treatment status over time) are not identified. We study additional restrictions on selection into treatment that lead to the identification of ATE for stayers by an extrapolation from quantities identified by the CRC model. We discuss estimation and testing of the extrapolation's validity, then use our results to estimate the returns to agricultural technology adoption among maize farmers in Kenya.<\/jats:p>","DOI":"10.1002\/jae.2789","type":"journal-article","created":{"date-parts":[[2020,6,13]],"date-time":"2020-06-13T14:45:39Z","timestamp":1592059539000},"page":"917-939","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Average treatment effects for stayers with correlated random coefficient models of panel data"],"prefix":"10.1002","volume":"35","author":[{"given":"Valentin","family":"Verdier","sequence":"first","affiliation":[{"name":"Department of Economics University of North Carolina  Chapel Hill North Carolina"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,7,13]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/0034-6527.00321"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1573-4412(07)06072-2"},{"key":"e_1_2_8_4_1","doi-asserted-by":"crossref","unstructured":"Abraham S. &Sun L.(2018).Estimating dynamic treatment effects in event studies with heterogeneous treatment effects\u00a0(SSRN Scholarly Paper ID 3158747). 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