Labor Market Returns to Two- and Four- Year Colleges. Paper by Kane and Rouse Replicated by Andreas Kraft
|
|
|
- Scot Smith
- 5 years ago
- Views:
Transcription
1 Labor Market Returns to Two- and Four- Year Colleges Paper by Kane and Rouse Replicated by Andreas Kraft
2 Theory Estimating the return to two-year colleges Economic Return to credit hours or sheepskin effects Human Capital Signaling Challenges Selection Bias Wrong self reported level of education Sample size
3 The National Longitudinal Survey of the High School Class of ,652 seniors from the high-school class of 1972 contains transcript information on all post-secondary schools reported by the students through 1979 Survey participants were last contacted in 1986 (~60%) Excluded: Self employed in 87 Participants that did not take survey every year Wages below $1.67 and above $60
4 Control Variables Race years of actual experience and experience squared dummies for region and size of the city dummies for part-time employment and region of the respondent's high school education dummies for self-reported education begun after 1979 parents' income percentage rank in high school NLS-72 test score
5 Male, no ability/ family background Source SS df MS Number of obs = 3,249 F(22, 3226) = Model Prob > F = Residual , R-squared = Adj R-squared = Total , Root MSE = lnhrwage Coef. Std. Err. t P> t [95% Conf. Interval] totcred totcred ( 1) totcred2 - totcred4 = 0 F( 1, 3502) = 7.43 Prob > F =
6 Male, including family/ ability background Source SS df MS Number of obs = 3,249 F(32, 3216) = Model Prob > F = Residual , R-squared = Adj R-squared = Total , Root MSE = lnhrwage Coef. Std. Err. t P> t [95% Conf. Interval] totcred totcred ( 1) totcred2 - totcred4 = 0 F( 1, 3216) = 0.02 Prob > F =
7 Male, including background and degree Source SS df MS Number of obs = 3,249 F(35, 3213) = Model Prob > F = Residual , R-squared = Adj R-squared = Total , Root MSE = lnhrwage Coef. Std. Err. t P> t [95% Conf. Interval] totcrd2b totcrd4b evcmpaa evcmpba evcmpgr
8 Hypothesis Testing ( 1) totcrd2b - totcrd4b = 0 ( 1) 2*totcrd2b - evcmpaa = 0 F( 1, 3213) = 3.54 Prob > F = F( 1, 3213) = 0.69 Prob > F = ( 1) evcmpaa = 0 ( 2) evcmpba = 0 ( 3) evcmpgr = 0 ( 1) 4*totcrd4b - evcmpba = 0 F( 1, 3213) = Prob > F = F( 3, 3213) = Prob > F =
9 Female, no ability/family background Source SS df MS Number of obs = 3,514 F(22, 3491) = Model Prob > F = Residual , R-squared = Adj R-squared = Total , Root MSE = lnhrwage Coef. Std. Err. t P> t [95% Conf. Interval] totcred totcred ( 1) totcred2 - totcred4 = 0 F( 1, 3491) = 1.22 Prob > F =
10 Female, including ability/ family background Source SS df MS Number of obs = 3,514 F(32, 3481) = Model Prob > F = Residual , R-squared = Adj R-squared = Total , Root MSE = lnhrwage Coef. Std. Err. t P> t [95% Conf. Interval] totcred totcred ( 1) totcred2 - totcred4 = 0 F( 1, 3481) = 0.00 Prob > F =
11 Female, including background and degree Source SS df MS Number of obs = 3,514 F(35, 3478) = Model Prob > F = Residual , R-squared = Adj R-squared = Total , Root MSE = lnhrwage Coef. Std. Err. t P> t [95% Conf. Interval] totcrd2b totcrd4b evcmpaa evcmpba evcmpgr
12 Hypothesis Testing ( 1) totcrd2b - totcrd4b = 0 ( 1) 2*totcrd2b - evcmpaa = 0 F( 1, 3213) = 3.54 Prob > F = F( 1, 3213) = 0.69 Prob > F = ( 1) evcmpaa = 0 ( 2) evcmpba = 0 ( 3) evcmpgr = 0 F( 3, 3213) = Prob > F = ( 1) 4*totcrd4b - evcmpba = 0 F( 1, 3213) = Prob > F =
13 National Longitudinal Survey of the Youth , year-olds in : 82% were sampled More recent cohort Better labor-force information Schooling info not as good
14 Control Variables controls for region and urban area in 1990 a dummy variable indicating employed part-time a dummy variable indicating missing type of college age in 1979 race parents' education Armed Forces Qualification Test score Actual experience
15 Male1990_w~e Male1990_a~l Female1990~e Female1990~l b/se b/se b/se b/se evvocsch * (0.03) (0.04) (0.03) (0.04) only2yr 0.076* ** (0.03) (0.04) (0.03) (0.04) only4yr 0.083* 0.160*** (0.04) (0.05) (0.03) (0.05) both 0.088* 0.134* * (0.04) (0.05) (0.04) (0.06) aa 0.207*** 0.236*** 0.188*** 0.309*** (0.04) (0.05) (0.04) (0.05) ba 0.339*** 0.422*** 0.331*** 0.513*** (0.03) (0.04) (0.03) (0.05) graduate 0.442*** 0.669*** 0.426*** 0.573*** (0.05) (0.07) (0.06) (0.08) otherdeg * 0.315*** 0.348***
16 NLSY in 2012 Final year the cohort was surveyed Used sample went from 4,548 to 3,032 Higher standard errors Potential bias Gender Difference seems to be larger Some quite different Results from 1990
17 Variable Obs Mean Std. Dev. Min Max only2yr 5, only4yr 5, both 5, evvocsch 5, aa 5, ba 5, graduate 5, otherdeg 5, Variable Obs Mean Std. Dev. Min Max only2yr 3, only4yr 3, both 3, evvocsch 3, aa 3, ba 3, graduate 3, otherdeg 3,
18 Male, hourly rate Source SS df MS Number of obs = 1,416 F(29, 1386) = Model Prob > F = Residual , R-squared = Adj R-squared = Total , Root MSE =.5097 lnwage2012 Coef. Std. Err. t P> t [95% Conf. Interval] evvocsch only2yr only4yr both aa ba graduate otherdeg
19 Female, hourly rate Source SS df MS Number of obs = 1,616 F(29, 1586) = Model Prob > F = Residual , R-squared = Adj R-squared = Total , Root MSE = lnwage2012 Coef. Std. Err. t P> t [95% Conf. Interval] evvocsch only2yr only4yr both aa ba graduate otherdeg
20 Dummy for female, hourly rate Source SS df MS Number of obs = 3,032 F(30, 3001) = Model Prob > F = Residual , R-squared = Adj R-squared = Total , Root MSE = lnwage2012 Coef. Std. Err. t P> t [95% Conf. Interval] evvocsch only2yr only4yr both aa ba graduate otherdeg female
21 Male, annual earnings Source SS df MS Number of obs = 1,302 F(29, 1272) = Model Prob > F = Residual , R-squared = Adj R-squared = Total , Root MSE =.7566 lnearn12 Coef. Std. Err. t P> t [95% Conf. Interval] evvocsch only2yr only4yr both aa ba graduate otherdeg
22 Female, annual earnings Source SS df MS Number of obs = 1,477 F(29, 1447) = Model Prob > F = Residual , R-squared = Adj R-squared = Total , Root MSE = lnearn12 Coef. Std. Err. t P> t [95% Conf. Interval] evvocsch only2yr only4yr both aa ba graduate otherdeg
23 Female dummy, annual earnings Source SS df MS Number of obs = 2,779 F(30, 2748) = Model Prob > F = Residual , R-squared = Adj R-squared = Total , Root MSE = lnearn12 Coef. Std. Err. t P> t [95% Conf. Interval] evvocsch only2yr only4yr both aa ba graduate otherdeg female
24 Male1990 Male2012 Female1990 Female2012 b/se b/se b/se b/se evvocsch * (0.03) (0.05) (0.03) (0.04) only2yr 0.076* 0.151** * (0.03) (0.05) (0.03) (0.04) only4yr 0.083* 0.176** (0.04) (0.05) (0.03) (0.05) both 0.088* 0.147* (0.04) (0.06) (0.04) (0.05) aa 0.207*** 0.250*** 0.188*** 0.178*** (0.04) (0.06) (0.04) (0.05) ba 0.339*** 0.458*** 0.331*** 0.319*** (0.03) (0.05) (0.03) (0.04) graduate 0.442*** 0.800*** 0.426*** 0.565*** (0.05) (0.09) (0.06) (0.09) otherdeg * 0.315*** 0.294*** (0.07) (0.10) (0.06) (0.09)
25 Conclusion Similar returns to 2-year and 4-year credit hours There is a sheepskin effect Returns to college attendance have increased Returns to college completion have increased Problems: Sample size makes IV approach hard Potential biases
Final Exam - section 1. Thursday, December hours, 30 minutes
Econometrics, ECON312 San Francisco State University Michael Bar Fall 2013 Final Exam - section 1 Thursday, December 19 1 hours, 30 minutes Name: Instructions 1. This is closed book, closed notes exam.
İnsan TUNALI 8 November 2018 Econ 511: Econometrics I. ASSIGNMENT 7 STATA Supplement
İnsan TUNALI 8 November 2018 Econ 511: Econometrics I ASSIGNMENT 7 STATA Supplement. use "F:\COURSES\GRADS\ECON511\SHARE\wages1.dta", clear. generate =ln(wage). scatter sch Q. Do you see a relationship
Econ 371 Problem Set #4 Answer Sheet. 6.2 This question asks you to use the results from column (1) in the table on page 213.
Econ 371 Problem Set #4 Answer Sheet 6.2 This question asks you to use the results from column (1) in the table on page 213. a. The first part of this question asks whether workers with college degrees
u panel_lecture . sum
u panel_lecture sum Variable Obs Mean Std Dev Min Max datastre 639 9039644 6369418 900228 926665 year 639 1980 2584012 1976 1984 total_sa 639 9377839 3212313 682 441e+07 tot_fixe 639 5214385 1988422 642
Labor Force Participation and the Wage Gap Detailed Notes and Code Econometrics 113 Spring 2014
Labor Force Participation and the Wage Gap Detailed Notes and Code Econometrics 113 Spring 2014 In class, Lecture 11, we used a new dataset to examine labor force participation and wages across groups.
ECON Introductory Econometrics. Seminar 4. Stock and Watson Chapter 8
ECON4150 - Introductory Econometrics Seminar 4 Stock and Watson Chapter 8 empirical exercise E8.2: Data 2 In this exercise we use the data set CPS12.dta Each month the Bureau of Labor Statistics in the
Your Name (Please print) Did you agree to take the optional portion of the final exam Yes No. Directions
Your Name (Please print) Did you agree to take the optional portion of the final exam Yes No (Your online answer will be used to verify your response.) Directions There are two parts to the final exam.
Example 2.3: CEO Salary and Return on Equity. Salary for ROE = 0. Salary for ROE = 30. Example 2.4: Wage and Education
1 Stata Textbook Examples Introductory Econometrics: A Modern Approach by Jeffrey M. Wooldridge (1st & 2d eds.) Chapter 2 - The Simple Regression Model Example 2.3: CEO Salary and Return on Equity summ
The Multivariate Regression Model
The Multivariate Regression Model Example Determinants of College GPA Sample of 4 Freshman Collect data on College GPA (4.0 scale) Look at importance of ACT Consider the following model CGPA ACT i 0 i
Econometrics is. The estimation of relationships suggested by economic theory
Econometrics is Econometrics is The estimation of relationships suggested by economic theory Econometrics is The estimation of relationships suggested by economic theory The application of mathematical
Categorical Outcomes. Statistical Modelling in Stata: Categorical Outcomes. R by C Table: Example. Nominal Outcomes. Mark Lunt.
Categorical Outcomes Statistical Modelling in Stata: Categorical Outcomes Mark Lunt Arthritis Research UK Epidemiology Unit University of Manchester Nominal Ordinal 28/11/2017 R by C Table: Example Categorical,
Advanced Econometrics
Advanced Econometrics Instructor: Takashi Yamano 11/14/2003 Due: 11/21/2003 Homework 5 (30 points) Sample Answers 1. (16 points) Read Example 13.4 and an AER paper by Meyer, Viscusi, and Durbin (1995).
Assignment #5 Solutions: Chapter 14 Q1.
Assignment #5 Solutions: Chapter 14 Q1. a. R 2 is.037 and the adjusted R 2 is.033. The adjusted R 2 value becomes particularly important when there are many independent variables in a multiple regression
Heteroskedasticity. . reg wage black exper educ married tenure
Heteroskedasticity. reg Source SS df MS Number of obs = 2,380 -------------+---------------------------------- F(2, 2377) = 72.38 Model 14.4018246 2 7.20091231 Prob > F = 0.0000 Residual 236.470024 2,377.099482551
You created this PDF from an application that is not licensed to print to novapdf printer (http://www.novapdf.com)
Monday October 3 10:11:57 2011 Page 1 (R) / / / / / / / / / / / / Statistics/Data Analysis Education Box and save these files in a local folder. name:
Dummy variables 9/22/2015. Are wages different across union/nonunion jobs. Treatment Control Y X X i identifies treatment
Dummy variables Treatment 22 1 1 Control 3 2 Y Y1 0 1 2 Y X X i identifies treatment 1 1 1 1 1 1 0 0 0 X i =1 if in treatment group X i =0 if in control H o : u n =u u Are wages different across union/nonunion
*1A. Basic Descriptive Statistics sum housereg drive elecbill affidavit witness adddoc income male age literacy educ occup cityyears if control==1
*1A Basic Descriptive Statistics sum housereg drive elecbill affidavit witness adddoc income male age literacy educ occup cityyears if control==1 Variable Obs Mean Std Dev Min Max --- housereg 21 2380952
1) The Effect of Recent Tax Changes on Taxable Income
1) The Effect of Recent Tax Changes on Taxable Income In the most recent issue of the Journal of Policy Analysis and Management, Bradley Heim published a paper called The Effect of Recent Tax Changes on
Effect of Education on Wage Earning
Effect of Education on Wage Earning Group Members: Quentin Talley, Thomas Wang, Geoff Zaski Abstract The scope of this project includes individuals aged 18-65 who finished their education and do not have
The data definition file provided by the authors is reproduced below: Obs: 1500 home sales in Stockton, CA from Oct 1, 1996 to Nov 30, 1998
Economics 312 Sample Project Report Jeffrey Parker Introduction This project is based on Exercise 2.12 on page 81 of the Hill, Griffiths, and Lim text. It examines how the sale price of houses in Stockton,
An Examination of the Impact of the Texas Methodist Foundation Clergy Development Program. on the United Methodist Church in Texas
An Examination of the Impact of the Texas Methodist Foundation Clergy Development Program on the United Methodist Church in Texas The Texas Methodist Foundation completed its first, two-year Clergy Development
Professor Brad Jones University of Arizona POL 681, SPRING 2004 INTERACTIONS and STATA: Companion To Lecture Notes on Statistical Interactions
Professor Brad Jones University of Arizona POL 681, SPRING 2004 INTERACTIONS and STATA: Companion To Lecture Notes on Statistical Interactions Preliminaries 1. Basic Regression. reg y x1 Source SS df MS
Time series data: Part 2
Plot of Epsilon over Time -- Case 1 1 Time series data: Part Epsilon - 1 - - - -1 1 51 7 11 1 151 17 Time period Plot of Epsilon over Time -- Case Plot of Epsilon over Time -- Case 3 1 3 1 Epsilon - Epsilon
Example 7.1: Hourly Wage Equation Average wage for women
1 Stata Textbook Examples Introductory Econometrics: A Modern Approach by Jeffrey M. Wooldridge (1st & 2nd eds.) Chapter 7 - Multiple Regression Analysis with Qualitative Information: Binary (or Dummy)
The SAS System 11:03 Monday, November 11,
The SAS System 11:3 Monday, November 11, 213 1 The CONTENTS Procedure Data Set Name BIO.AUTO_PREMIUMS Observations 5 Member Type DATA Variables 3 Engine V9 Indexes Created Monday, November 11, 213 11:4:19
Cameron ECON 132 (Health Economics): FIRST MIDTERM EXAM (A) Fall 17
Cameron ECON 132 (Health Economics): FIRST MIDTERM EXAM (A) Fall 17 Answer all questions in the space provided on the exam. Total of 36 points (and worth 22.5% of final grade). Read each question carefully,
The relationship between GDP, labor force and health expenditure in European countries
Econometrics-Term paper The relationship between GDP, labor force and health expenditure in European countries Student: Nguyen Thu Ha Contents 1. Background:... 2 2. Discussion:... 2 3. Regression equation
tm / / / / / / / / / / / / Statistics/Data Analysis User: Klick Project: Limited Dependent Variables{space -6}
PS 4 Monday August 16 01:00:42 2010 Page 1 tm / / / / / / / / / / / / Statistics/Data Analysis User: Klick Project: Limited Dependent Variables{space -6} log: C:\web\PS4log.smcl log type: smcl opened on:
Problem Set 9 Heteroskedasticty Answers
Problem Set 9 Heteroskedasticty Answers /* INVESTIGATION OF HETEROSKEDASTICITY */ First graph data. u hetdat2. gra manuf gdp, s([country].) xlab ylab 300000 manufacturing output (US$ miilio 200000 100000
F^3: F tests, Functional Forms and Favorite Coefficient Models
F^3: F tests, Functional Forms and Favorite Coefficient Models Favorite coefficient model: otherteams use "nflpricedata Bdta", clear *Favorite coefficient model: otherteams reg rprice pop pop2 rpci wprcnt1
Handout seminar 6, ECON4150
Handout seminar 6, ECON4150 Herman Kruse March 17, 2013 Introduction - list of commands This week, we need a couple of new commands in order to solve all the problems. hist var1 if var2, options - creates
Problem Set 6 ANSWERS
Economics 20 Part I. Problem Set 6 ANSWERS Prof. Patricia M. Anderson The first 5 questions are based on the following information: Suppose a researcher is interested in the effect of class attendance
Technical Documentation for Household Demographics Projection
Technical Documentation for Household Demographics Projection REMI Household Forecast is a tool to complement the PI+ demographic model by providing comprehensive forecasts of a variety of household characteristics.
[BINARY DEPENDENT VARIABLE ESTIMATION WITH STATA]
![[BINARY DEPENDENT VARIABLE ESTIMATION WITH STATA]](/thumbs/85/91912134.jpg "[BINARY DEPENDENT VARIABLE ESTIMATION WITH STATA]")
Tutorial #3 This example uses data in the file 16.09.2011.dta under Tutorial folder. It contains 753 observations from a sample PSID data on the labor force status of married women in the U.S in 1975.
EC327: Limited Dependent Variables and Sample Selection Binomial probit: probit
EC327: Limited Dependent Variables and Sample Selection Binomial probit: probit. summarize work age married children education Variable Obs Mean Std. Dev. Min Max work 2000.6715.4697852 0 1 age 2000 36.208
Two-stage least squares examples. Angrist: Vietnam Draft Lottery Men, Cohorts. Vietnam era service
Two-stage least squares examples Angrist: Vietnam Draft Lottery 1 2 Vietnam era service 1980 Men, 1940-1952 Cohorts Defined as 1964-1975 Estimated 8.7 million served during era 3.4 million were in SE Asia
Testing the Solow Growth Theory
Testing the Solow Growth Theory Dilip Mookherjee Ec320 Lecture 5, Boston University Sept 16, 2014 DM (BU) 320 Lect 5 Sept 16, 2014 1 / 1 EMPIRICAL PREDICTIONS OF SOLOW MODEL WITH TECHNICAL PROGRESS 1.
Quantitative Techniques Term 2
Quantitative Techniques Term 2 Laboratory 7 2 March 2006 Overview The objective of this lab is to: Estimate a cost function for a panel of firms; Calculate returns to scale; Introduce the command cluster
Impact of Household Income on Poverty Levels
Impact of Household Income on Poverty Levels ECON 3161 Econometrics, Fall 2015 Prof. Shatakshee Dhongde Group 8 Annie Strothmann Anne Marsh Samuel Brown Abstract: The relationship between poverty and household
An analysis of the relationship between economic development and demographic characteristics in the United States
University of Central Florida HIM 1990-2015 Open Access An analysis of the relationship between economic development and demographic characteristics in the United States 2011 Chad M. Heyne University of
Solutions for Session 5: Linear Models
Solutions for Session 5: Linear Models 30/10/2018. do solution.do. global basedir http://personalpages.manchester.ac.uk/staff/mark.lunt. global datadir $basedir/stats/5_linearmodels1/data. use $datadir/anscombe.
Model fit assessment via marginal model plots
The Stata Journal (2010) 10, Number 2, pp. 215 225 Model fit assessment via marginal model plots Charles Lindsey Texas A & M University Department of Statistics College Station, TX lindseyc@stat.tamu.edu
Ownership structure and corporate performance: evidence from China
Name: Kaiyun Zhang Student number: 10044965/6262856 Track: Economics and Finance Supervisor: Liting Zhou Ownership structure and corporate performance: evidence from China Abstract This paper examines
The impact of cigarette excise taxes on beer consumption
The impact of cigarette excise taxes on beer consumption Jeremy Cluchey Frank DiSilvestro PPS 313 18 April 2008 ABSTRACT This study attempts to determine what if any impact a state s decision to increase
. ********** OUTPUT FILE: CARD & KRUEGER (1994)***********.. * STATA 10.0 CODE. * copyright C 2008 by Tito Boeri & Jan van Ours. * "THE ECONOMICS OF
********** OUTPUT FILE: CARD & KRUEGER (1994)*********** * STATA 100 CODE * copyright C 2008 by Tito Boeri & Jan van Ours * "THE ECONOMICS OF IMPERFECT LABOR MARKETS" * by Tito Boeri & Jan van Ours (2008)
Modeling wages of females in the UK
International Journal of Business and Social Science Vol. 2 No. 11 [Special Issue - June 2011] Modeling wages of females in the UK Saadia Irfan NUST Business School National University of Sciences and
sociology SO5032 Quantitative Research Methods Brendan Halpin, Sociology, University of Limerick Spring 2018 SO5032 Quantitative Research Methods
1 SO5032 Quantitative Research Methods Brendan Halpin, Sociology, University of Limerick Spring 2018 Lecture 10: Multinomial regression baseline category extension of binary What if we have multiple possible
Estimating Consumer Price Inflation by Household
Estimating Consumer Price Inflation by Household Jess Diamond Hitotsubashi University Kota Watanabe Meiji University Tsutomu Watanabe University of Tokyo Aim Of The Study Seek to shed light on 2 issues:
GGraph. Males Only. Premium. Experience. GGraph. Gender. 1 0: R 2 Linear = : R 2 Linear = Page 1
GGraph 9 Gender : R Linear =.43 : R Linear =.769 8 7 6 5 4 3 5 5 Males Only GGraph Page R Linear =.43 R Loess 9 8 7 6 5 4 5 5 Explore Case Processing Summary Cases Valid Missing Total N Percent N Percent
Visualisierung von Nicht-Linearität bzw. Heteroskedastizität
Visualisierung von Nicht-Linearität bzw. Heteroskedastizität. use..\wooldridge\stata\wage2, clear. scatter wage IQ Kommentar: Folie 38. graph copy a3, replace. summ IQ Variable Obs Mean Std. Dev. Min Max
Example 8.1: Log Wage Equation with Heteroscedasticity-Robust Standard Errors
1 Stata Textbook Examples Introductory Econometrics: A Modern Approach by Jeffrey M. Wooldridge (1st & 2nd eds.) Chapter 8 - Heteroskedasticity Example 8.1: Log Wage Equation with Heteroscedasticity-Robust
Sean Howard Econometrics Final Project Paper. An Analysis of the Determinants and Factors of Physical Education Attendance in the Fourth Quarter
Sean Howard Econometrics Final Project Paper An Analysis of the Determinants and Factors of Physical Education Attendance in the Fourth Quarter Introduction This project attempted to gain a more complete
Dummy Variables. 1. Example: Factors Affecting Monthly Earnings
Dummy Variables A dummy variable or binary variable is a variable that takes on a value of 0 or 1 as an indicator that the observation has some kind of characteristic. Common examples: Sex (female): FEMALE=1
Allison notes there are two conditions for using fixed effects methods.
Panel Data 3: Conditional Logit/ Fixed Effects Logit Models Richard Williams, University of Notre Dame, http://www3.nd.edu/~rwilliam/ Last revised April 2, 2017 These notes borrow very heavily, sometimes
An Analysis Summary of Factors Affecting China Assembled Funds Trust Products Expected Return Rate
Open Journal of Business and Management, 2016, 4, 273-281 Published Online April 2016 in SciRes. http://www.scirp.org/journal/ojbm http://dx.doi.org/10.4236/ojbm.2016.42029 An Analysis Summary of Factors
Determinants of FII Inflows:India
MPRA Munich Personal RePEc Archive Determinants of FII Inflows:India Ravi Saraogi February 2008 Online at https://mpra.ub.uni-muenchen.de/22850/ MPRA Paper No. 22850, posted 22. May 2010 23:04 UTC Determinants
Don t worry one bit about multicollinearity, because at the end of the day, you're going to be working with a favorite coefficient model.
In theory, you might think that dummy variables would facilitate a simple and compelling test for bias or discrimination. For example, suppose you wanted to test for gender bias in pay. It's really very
Advanced Industrial Organization I Identi cation of Demand Functions
Advanced Industrial Organization I Identi cation of Demand Functions Måns Söderbom, University of Gothenburg January 25, 2011 1 1 Introduction This is primarily an empirical lecture in which I will discuss
Relation between Income Inequality and Economic Growth
Relation between Income Inequality and Economic Growth Ibrahim Alsaffar, Robert Eisenhardt, Hanjin Kim Georgia Institute of Technology ECON 3161: Econometric Analysis Dr. Shatakshee Dhongde Fall 2018 Abstract
Chapter 11 Part 6. Correlation Continued. LOWESS Regression
Chapter 11 Part 6 Correlation Continued LOWESS Regression February 17, 2009 Goal: To review the properties of the correlation coefficient. To introduce you to the various tools that can be used to decide
A New Look at Technical Progress and Early Retirement
A New Look at Technical Progress and Early Retirement Lorenzo Burlon* Bank of Italy Montserrat Vilalta-Bufí University of Barcelona IZA/RIETI Workshop Changing Demographics and the Labor Market May 25,
Religion and Volunteerism
Religion and Volunteerism Abstract This paper uses a standard Tobit to explore the effects of religion on volunteerism. It analyzes cross-sectional data from a representative sample of about 3,000 American
Impact of Stock Market, Trade and Bank on Economic Growth for Latin American Countries: An Econometrics Approach
Science Journal of Applied Mathematics and Statistics 2018; 6(1): 1-6 http://www.sciencepublishinggroup.com/j/sjams doi: 10.11648/j.sjams.20180601.11 ISSN: 2376-9491 (Print); ISSN: 2376-9513 (Online) Impact
Module 9: Single-level and Multilevel Models for Ordinal Responses. Stata Practical 1
Module 9: Single-level and Multilevel Models for Ordinal Responses Pre-requisites Modules 5, 6 and 7 Stata Practical 1 George Leckie, Tim Morris & Fiona Steele Centre for Multilevel Modelling If you find
Final Exam, section 1. Thursday, May hour, 30 minutes
San Francisco State University Michael Bar ECON 312 Spring 2018 Final Exam, section 1 Thursday, May 17 1 hour, 30 minutes Name: Instructions 1. This is closed book, closed notes exam. 2. You can use one
ECON Introductory Econometrics Seminar 2, 2015
ECON4150 - Introductory Econometrics Seminar 2, 2015 Stock and Watson EE4.1, EE5.2 Stock and Watson EE4.1, EE5.2 ECON4150 - Introductory Econometrics Seminar 2, 2015 1 / 14 Seminar 2 Author: Andrea University
Sociology Exam 3 Answer Key - DRAFT May 8, 2007
Sociology 63993 Exam 3 Answer Key - DRAFT May 8, 2007 I. True-False. (20 points) Indicate whether the following statements are true or false. If false, briefly explain why. 1. The odds of an event occurring
Does Globalization Improve Quality of Life?
University of Tennessee, Knoxville Trace: Tennessee Research and Creative Exchange University of Tennessee Honors Thesis Projects University of Tennessee Honors Program 5-2017 Does Globalization Improve
Limited Dependent Variables
Limited Dependent Variables Christopher F Baum Boston College and DIW Berlin Birmingham Business School, March 2013 Christopher F Baum (BC / DIW) Limited Dependent Variables BBS 2013 1 / 47 Limited dependent
institution Top 10 to 20 undergraduate
Appendix Table A1 Who Responded to the Survey Dynamics of the Gender Gap for Young Professionals in the Financial and Corporate Sectors By Marianne Bertrand, Claudia Goldin, Lawrence F. Katz On-Line Appendix
Chapter 6 Part 3 October 21, Bootstrapping
Chapter 6 Part 3 October 21, 2008 Bootstrapping From the internet: The bootstrap involves repeated re-estimation of a parameter using random samples with replacement from the original data. Because the
Impact of Minimum Wage and Government Ideology on Unemployment Rates: The Case of Post-Communist Romania
International Journal of Humanities and Social Science Vol. 2 No. 1; January 2012 Impact of Minimum Wage and Government Ideology on Unemployment Rates: The Case of Post-Communist Romania Abstract Bogdan
The impact of foreign direct investments on economic growth and employment
The impact of foreign direct investments on economic growth and employment in Albania Abstract PhD (C.) Lorena Çakërri University Ismail Qemali Vlorë, Albania Assoc. Prof. Filloreta Madani University Ismail
Youth unemployment, churn and stalled careers: The UK youth labour market in the 2008/9 recession
Youth unemployment, churn and stalled careers: The UK youth labour market in the 2008/9 recession Dr Neil Lee Department of Geography & Environment / Centre for the Analysis of Social Exclusion LSE n.d.lee@lse.ac.uk
Review questions for Multinomial Logit/Probit, Tobit, Heckit, Quantile Regressions
1. I estimated a multinomial logit model of employment behavior using data from the 2006 Current Population Survey. The three possible outcomes for a person are employed (outcome=1), unemployed (outcome=2)
ADRRI JOURNAL OF ARTS AND SOCIAL SCIENCES. ADRRI JOURNALS ( ISSN: ISSN-L: VOL. 14, No.6 (2), November, 2016
ADRRI JOURNAL OF ARTS AND SOCIAL SCIENCES ADRRI JOURNALS (www.adrri.org) VOL. 14, No.6 (2), November, 2016 The Impact of Changes in Monetary Policy Rates on the Lending and Financial Performance of Banks
Testing Capital Asset Pricing Model on KSE Stocks Salman Ahmed Shaikh
Abstract Capital Asset Pricing Model (CAPM) is one of the first asset pricing models to be applied in security valuation. It has had its share of criticism, both empirical and theoretical; however, with
Trade Imbalance and Entrepreneurial Activity: A Quantitative Panel Data Analysis
Scholedge International Journal of Business Policy & Governance ISSN 2394-3351, Vol.04, Issue 11 (2017) Pg 116-123. DOI: 10.19085/journal.sijbpg041101 Published by: Scholedge Publishing www.thescholedge.org
Willingness to Pay for Green Electricity in Tianjin, China: Based on Contingent Valuation Method
Willingness to Pay for Green Electricity in Tianjin, China: Based on Contingent Valuation Method Li-Qiu Liu College of Management and Economics, Tianjin University the 40 th June 20, 2017, IAEE, Singapore
Stat 328, Summer 2005
Stat 328, Summer 2005 Exam #2, 6/18/05 Name (print) UnivID I have neither given nor received any unauthorized aid in completing this exam. Signed Answer each question completely showing your work where
a. Explain why the coefficients change in the observed direction when switching from OLS to Tobit estimation.
1. Using data from IRS Form 5500 filings by U.S. pension plans, I estimated a model of contributions to pension plans as ln(1 + c i ) = α 0 + U i α 1 + PD i α 2 + e i Where the subscript i indicates the
A Study of the Impact of Social Welfare Policies on Household Saving. Rate in China. Borui Xiao. Advised by. Professor Lakshman Krishmurthi
A Study of the Impact of Social Welfare Policies on Household Saving Rate in China By Borui Xiao Advised by Professor Lakshman Krishmurthi Submitted to the Department of Mathematical Methods in Social
CHAPTER 2 ESTIMATION AND PROJECTION OF LIFETIME EARNINGS
CHAPTER 2 ESTIMATION AND PROJECTION OF LIFETIME EARNINGS ABSTRACT This chapter describes the estimation and prediction of age-earnings profiles for American men and women born between 1931 and 1960. The
The distribution of the major economies effort in the Durban platform scenarios
The distribution of the major economies effort in the Durban platform scenarios Low climate IMpact scenarios and the Implications of required Tight emission control Strategies Massimo Tavoni (FEEM), Elmar
Memorandum. Human Resources Division
Memorandum Human Resources Division TO: FROM: RE: Vacellia Clark, Chief Examiner Civil Service Commission Human Resources Staff Establish a Passing Score for Animal Control Officer DATE: October 30, 2013
Notes are not permitted in this examination. Do not turn over until you are told to do so by the Invigilator.
UNIVERSITY OF EAST ANGLIA School of Economcs Man Seres PG Examnaton 2016-17 BANKING ECONOMETRICS ECO-7014A Tme allowed: 2 HOURS Answer ALL FOUR questons. Queston 1 carres a weght of 30%; queston 2 carres
Enrollment Type. Proportion of Non AAS Students by Enrollment Type. UW Colleges Campus Profile: UW Fox Valley
Ten Year Enrollment Trends by Enrollment Type Fall AAS High School Special Audit Other Total Enrollment 2008 1473 62 88 5 13 1641 2009 1520 111 106 1 10 1748 2010 1583 118 97 9 24 1831 2011 1615 105 79
STATA Program for OLS cps87_or.do
STATA Program for OLS cps87_or.do * the data for this project is a small subsample; * of full time (30 or more hours) male workers; * aged 21-64 from the out going rotation; * samples of the 1987 current
CHAPTER 4 DATA ANALYSIS Data Hypothesis
CHAPTER 4 DATA ANALYSIS 4.1. Data Hypothesis The hypothesis for each independent variable to express our expectations about the characteristic of each independent variable and the pay back performance
Role of Accounting Information in Assessing Stock Prices in Bangladesh
International Journal of Business and Social Research Volume 07, Issue 10, 2017. 18-25 Article Received: 15-09-2017 Accepted: 30-09-2017 Available Online: 05-10-2017 ISSN 2164-2540 (Print), ISSN 2164-2559
CHAPTER 4 ESTIMATES OF RETIREMENT, SOCIAL SECURITY BENEFIT TAKE-UP, AND EARNINGS AFTER AGE 50
CHAPTER 4 ESTIMATES OF RETIREMENT, SOCIAL SECURITY BENEFIT TAKE-UP, AND EARNINGS AFTER AGE 5 I. INTRODUCTION This chapter describes the models that MINT uses to simulate earnings from age 5 to death, retirement
Enrollment Type. UW Colleges Campus Profile: UW Marathon County. Proportion of Non AAS Students by Enrollment Type
Ten Year Enrollment Trends by Enrollment Type Fall AAS High School Special Audit Other Total Enrollment 2008 1250 12 62 11 28 1363 2009 1292 22 57 13 15 1399 2010 1315 25 41 14 14 1409 2011 1266 15 41
Enrollment Type. UW Colleges Campus Profile: UW Manitowoc. Proportion of Non AAS Students by Enrollment Type
Ten Year Enrollment Trends by Enrollment Type Fall AAS High School Special Audit Other Total Enrollment 2008 507 10 16 1 6 540 2009 489 14 31 5 9 548 2010 573 11 20 4 4 612 2011 624 10 20 4 6 664 2012
Notice that X2 and Y2 are skewed. Taking the SQRT of Y2 reduces the skewness greatly.
Notice that X2 and Y2 are skewed. Taking the SQRT of Y2 reduces the skewness greatly. The MEANS Procedure Variable Mean Std Dev Minimum Maximum Skewness ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
The relationship between income inequality and economic growth. in OECD countries, including South Korea
The relationship between income inequality and economic growth in OECD countries, including South Korea A Thesis Submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University
Risk Tolerance Profile of Cash-Value Life Insurance Owners
Risk Tolerance Profile of Cash-Value Life Insurance Owners Abed Rabbani, University of Missouri 1 Zheying Yao, University of Missouri 2 Abstract Life insurance, a risk management tool, generally provides
Market Approach A. Relationship to Appraisal Principles
Market Approach A. Relationship to Appraisal Principles 1. Supply and demand Prices are determined by negotiation between buyers and sellers o Buyers demand side o Sellers supply side At a specific time
Final Exam, section 2. Tuesday, December hour, 30 minutes
San Francisco State University Michael Bar ECON 312 Fall 2018 Final Exam, section 2 Tuesday, December 18 1 hour, 30 minutes Name: Instructions 1. This is closed book, closed notes exam. 2. You can use
Final Exam, section 1. Tuesday, December hour, 30 minutes
San Francisco State University Michael Bar ECON 312 Fall 2018 Final Exam, section 1 Tuesday, December 18 1 hour, 30 minutes Name: Instructions 1. This is closed book, closed notes exam. 2. You can use
STATA log file for Time-Varying Covariates (TVC) Duration Model Estimations.
STATA log file for Time-Varying Covariates (TVC) Duration Model Estimations. This STATA 8.0 log file reports estimations in which CDER Staff Aggregates and PDUFA variable are assigned to drug-months of