Essays in Behavioral and Labor Economics = Ensayos en Economia del Comportamiento y Laboral [electronic resource]
저자
발행사항
Ann Arbor : ProQuest Dissertations & Theses, 2023
학위수여대학
Cornell University Economics
수여연도
2023
작성언어
영어
주제어
학위
Ph.D.
페이지수
1 online resource(294 p.)
지도교수/심사위원
Advisor: O'Donoghue, Ted.
Just like factories require physical capital-machinery, buildings, tools-to produce output, workers rely on their human capital-abilities, skills, knowledge-to accomplish job tasks. Human capital is a fundamental determinant of worker productivity and, consequently, of wages. But human capital and wages are not determined in a vacuum. Market participants like universities, firms, and peer networks shape the production of human capital and the determination of wages.This dissertation examines behavioral or "nonstandard" forces that determine human capital and affect the price of labor. I show that cognitive endurance is a highly-rewarded skill in the labor market (Chapter 1), that limited firm sophistication affects the distribution of wages (Chapter 2), and that the wage return to attending elite universities is closely linked to the value of their alumni networks (Chapter 3).In Chapter 1, "Cognitive Endurance, Talent Selection, and the Labor Market Returns to Human Capital," I study the importance of cognitive endurance-the ability to sustain performance on a cognitively-demanding task over time-for success in college and the labor market. I use college-admission-exam records from 15 million Brazilian high school students and develop a method to decompose test scores into fatigue-adjusted ability and cognitive endurance. I find that cognitive endurance has a significant wage return. Controlling for fatigue-adjusted ability and other student characteristics, a onestandard-deviation higher endurance predicts a 5.4% wage increase. I also document positive associations between endurance and college attendance, college quality, college graduation, firm quality, and other outcomes. Finally, I show how systematic differences in endurance across students interact with the exam design to determine the sorting of students to colleges.In Chapter 2, "Coarse Wage-Setting and Behavioral Firms," I study why firms often pay new hires round-numbered salaries-a puzzling fact from the perspective of canonical wage-determination models. I posit that, in the presence of uncertainty about the fully-optimal salary, firms might rely on a rule of thumb or heuristic as an approximation-a form of pricing I refer to as "coarse wage-setting." I test this hypothesis using contracted salaries of 280 million hires in Brazil. First, I show that firms that tend to hire workers at round-numbered salaries are less sophisticated and have worse market outcomes. Motivated by this reduced-form evidence, I develop a wage-posting model in which optimization frictions lead to the adoption of coarse wages and provide evidence supporting three predictions of the model using two research designs. Finally, I show that coarse wage-setting generates within-firm wage compression, increases nominal wage stickiness, and interacts with policies that affect the wage distribution, such as changes in the minimum wage.In Chapter 3, "The Direct and Spillover Effects of Large-scale Affirmative Action at an Elite Brazilian University" (with Cecilia Machado and Evan Riehl), we study a selective university in Brazil that adopted large-scale race- and income-based affirmative action. We link admission records to national employer-employee data to show that a key benefit of attending the university is access to high-paying firms affiliated with its alumni. We find that affirmative action increased disadvantaged students' access to these firms and raised their early-career earnings. But both of these benefits faded as their careers progressed. In addition, the increase in student body diversity lowered the job prospects and earnings of the university's most highly ranked students. Our findings show that affirmative action may be less effective at reducing income disparities when the benefits of admission depend on a university's alumni networks.
l igual que las fabricas requieren de capital fisico-maquinas, edificios y herramientas-para producir bienes y servicios, los trabajadores dependen de su capital humano-habilidades, destrezas y conocimientos-para llevar a cabo tareas laborales. El capital humano es un determinante fundamental de la productividad de los trabajadores y, por lo tanto, de los salarios. Sin embargo, el capital humano y los salarios no se determinan en el vacio. Los agentes del mercado, como universidades, empresas y conexiones sociales, influyen en la produccion de capital humano y la determinacion de los salarios.Esta disertacion examina factores "no estandar" que determinan el capital humano y afectan el precio del trabajo. Demuestro que la resistencia cognitiva es una habilidad altamente recompensada en el mercado laboral (Capitulo 1), que la limitada sofisticacion de las empresas afecta la distribucion de los salarios (Capitulo 2) y que el retorno salarial de asistir a universidades de elite esta estrechamente vinculado al valor de sus redes de exalumnos (Capitulo 3).En el Capitulo 1, "Resistencia cognitiva, seleccion de talento y rendimientos del capital humano en el mercado laboral", estudio la importancia de la resistencia cognitiva-la capacidad de mantener el rendimiento en una tarea cognitivamente exigente a lo largo del tiempo-para el exito en la universidad y el mercado laboral. Utilizo registros de examenes de ingreso a la universidad de 15 millones de estudiantes de secundaria brasilenos y desarrollo un metodo para descomponer las puntuaciones de las pruebas en habilidad ajustada por fatiga y resistencia cognitiva. Encuentro que la resistencia cognitiva tiene una prima salarial significativa. Controlando por la habilidad ajustada por fatiga y otras caracteristicas de los estudiantes, una resistencia superior en una desviacion estandar predice un aumento salarial del 5,4%. Tambien documento asociaciones positivas entre la resistencia cognitiva y la asistencia a la universidad, la calidad de la universidad, la graduacion universitaria, la calidad de la empresa y otras variables. Finalmente, muestro como las diferencias sistematicas en la resistencia entre los estudiantes interactuan con el diseno del examen para determinar la clasificacion de los estudiantes en las universidades.En el Capitulo 2, "Determinacion salarial tosca y conducta no estandar de las empresas", estudio por que las empresas a menudo pagan a los nuevos empleados salarios en numeros redondos, un hecho desconcertante desde la perspectiva de los modelos canonicos de determinacion de salarios. Postulo que, en presencia de incertidumbre sobre el salario optimo, las empresas usan heuristicas como una aproximacion, una forma de fijacion de salario a la que me refiero como "determinacion salarial tosca". Pongo a prueba esta hipotesis utilizando los salarios de 280 millones de trabajadores en Brasil. Primero, muestro que las empresas que tienden a contratar trabajadores con salarios redondos son menos sofisticadas y tienen peores resultados en el mercado. Motivado por estos hechos estilizados, desarrollo un modelo de fijacion de salarios en el que fricciones de optimizacion llevan a la adopcion de salarios toscos y proporciono evidencia que respalda tres predicciones del modelo utilizando dos estrategias empiricas. Finalmente, muestro que el establecimiento de salarios toscos genera compresion salarial dentro de la empresa, aumenta la rigidez salarial nominal e interactua con politicas que afectan la distribucion de salarios, como cambios en el salario minimo.En el Capitulo 3, "Los efectos directos y de derrame de la accion afirmativa a gran escala en una universidad brasilena de elite" (con Cecilia Machado y Evan Riehl), estudiamos una universidad selectiva en Brasil que adopto una politica de accion afirmativa a gran escala basada en raza e ingresos. Vinculamos los registros de admision con datos nacionales de empleadores y empleados para demostrar que un beneficio clave de asistir a la universidad es el acceso a empresas que pagan alto salarios y que estan afiliadas a su red de exalumnos. Descubrimos que la accion afirmativa aumento el acceso de los estudiantes desfavorecidos a estas empresas e incremento sus ingresos iniciales en la carrera. Sin embargo, ambos beneficios disminuyeron a medida que sus carreras avanzaron. Por otra parte, el aumento en la diversidad del cuerpo estudiantil redujo las perspectivas laborales y los ingresos de los estudiantes mas destacados de la universidad. Nuestros hallazgos muestran que la accion afirmativa puede ser menos efectiva para reducir las disparidades de ingresos cuando los beneficios de la admision dependen de las redes de exalumnos de la universidad.
서지정보 내보내기(Export)
닫기소장기관 정보
닫기권호소장정보
닫기오류접수
닫기오류 접수 확인
닫기음성서비스 신청
닫기음성서비스 신청 확인
닫기이용약관
닫기학술연구정보서비스 이용약관 (2017년 1월 1일 ~ 현재 적용)
학술연구정보서비스(이하 RISS)는 정보주체의 자유와 권리 보호를 위해 「개인정보 보호법」 및 관계 법령이 정한 바를 준수하여, 적법하게 개인정보를 처리하고 안전하게 관리하고 있습니다. 이에 「개인정보 보호법」 제30조에 따라 정보주체에게 개인정보 처리에 관한 절차 및 기준을 안내하고, 이와 관련한 고충을 신속하고 원활하게 처리할 수 있도록 하기 위하여 다음과 같이 개인정보 처리방침을 수립·공개합니다.
주요 개인정보 처리 표시(라벨링)
목 차
3년
또는 회원탈퇴시까지5년
(「전자상거래 등에서의 소비자보호에 관한3년
(「전자상거래 등에서의 소비자보호에 관한2년
이상(개인정보보호위원회 : 개인정보의 안전성 확보조치 기준)개인정보파일의 명칭 | 운영근거 / 처리목적 | 개인정보파일에 기록되는 개인정보의 항목 | 보유기간 | |
---|---|---|---|---|
학술연구정보서비스 이용자 가입정보 파일 | 한국교육학술정보원법 | 필수 | ID, 비밀번호, 성명, 생년월일, 신분(직업구분), 이메일, 소속분야, 웹진메일 수신동의 여부 | 3년 또는 탈퇴시 |
선택 | 소속기관명, 소속도서관명, 학과/부서명, 학번/직원번호, 휴대전화, 주소 |
구분 | 담당자 | 연락처 |
---|---|---|
KERIS 개인정보 보호책임자 | 정보보호본부 김태우 | - 이메일 : lsy@keris.or.kr - 전화번호 : 053-714-0439 - 팩스번호 : 053-714-0195 |
KERIS 개인정보 보호담당자 | 개인정보보호부 이상엽 | |
RISS 개인정보 보호책임자 | 대학학술본부 장금연 | - 이메일 : giltizen@keris.or.kr - 전화번호 : 053-714-0149 - 팩스번호 : 053-714-0194 |
RISS 개인정보 보호담당자 | 학술진흥부 길원진 |
자동로그아웃 안내
닫기인증오류 안내
닫기귀하께서는 휴면계정 전환 후 1년동안 회원정보 수집 및 이용에 대한
재동의를 하지 않으신 관계로 개인정보가 삭제되었습니다.
(참조 : RISS 이용약관 및 개인정보처리방침)
신규회원으로 가입하여 이용 부탁 드리며, 추가 문의는 고객센터로 연락 바랍니다.
- 기존 아이디 재사용 불가
휴면계정 안내
RISS는 [표준개인정보 보호지침]에 따라 2년을 주기로 개인정보 수집·이용에 관하여 (재)동의를 받고 있으며, (재)동의를 하지 않을 경우, 휴면계정으로 전환됩니다.
(※ 휴면계정은 원문이용 및 복사/대출 서비스를 이용할 수 없습니다.)
휴면계정으로 전환된 후 1년간 회원정보 수집·이용에 대한 재동의를 하지 않을 경우, RISS에서 자동탈퇴 및 개인정보가 삭제처리 됩니다.
고객센터 1599-3122
ARS번호+1번(회원가입 및 정보수정)