<?xml version='1.0' encoding='UTF-8'?><metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns="http://dublincore.org/documents/dcmi-terms/"><dcterms:title>Bureaucrat Assignments as Instruments of Political Control: Evidence from Land Administration Officials in India</dcterms:title><dcterms:identifier>https://doi.org/10.7910/DVN/BXZL6P</dcterms:identifier><dcterms:creator>agnihotri, anustubh</dcterms:creator><dcterms:creator>Dasgupta, Aditya</dcterms:creator><dcterms:creator>Devesh Kapur</dcterms:creator><dcterms:publisher>Harvard Dataverse</dcterms:publisher><dcterms:issued>2026-03-09</dcterms:issued><dcterms:modified>2026-03-09T20:06:46Z</dcterms:modified><dcterms:description>This paper investigates how politicians use personnel policy to control, and sometimes corrupt, bureaucratic behavior, focusing on the assignment of bureaucrats to geographical posts. With a matching model, we show that when posts vary in desirability, politicians can use assignments as carrots/sticks to control bureaucrats even when they cannot be fired. The argument is tested with a nationwide survey of Indian officials who regulate/administer land, a lucrative asset and source
of political rents in fast-growing countries. With a novel design, we show that officials prefer posts with urban amenities, good staffing, and hometown proximity. These preferences are intense in salary-equivalent terms. Ruling parties pressure officials to bend regulations by strategically assigning them to their preferred posts but threatening transfer should they fail to comply, especially in locations with high corruption potential (volume of rural-to-urban land transactions). The findings highlight bureaucrat assignments as a potent instrument of political control/corruption of bureaucracy.</dcterms:description><dcterms:subject>Social Sciences</dcterms:subject><dcterms:subject>Bureaucracy, Transfers, Political Economy, State Capacity</dcterms:subject><dcterms:date>2026-03-09</dcterms:date><dcterms:contributor>agnihotri, anustubh</dcterms:contributor><dcterms:dateSubmitted>2025-06-14</dcterms:dateSubmitted><dcterms:source>Agnihotri, Anustubh, Aditya Dasgupta, and Devesh Kapur. 2022. “All India Tehsildar Survey.” https://doi.org/10.7910/DVN/BXZL6P Harvard Dataverse</dcterms:source><dcterms:rights>This dataset is made available with limited information on how it can be used. You may wish to communicate with the Contact(s) specified before use.</dcterms:rights></metadata>