Student Loan Debt: Causes and Consequences DISSERTATION

Transcription

1 Student Loan Debt: Causes and Consequences DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Xue Wu Graduate Program in Economics The Ohio State University 2015 Dissertation Committee: Professor Lucia Dunn, Advisor Professor Stephen Cosslett Professor Trevon Logan

2 Copyrighted by Xue Wu 2015

3 ABSTRACT This dissertation employs data from the National Longitudinal Survey of Youth (NLSY97) and data from the Consumer Finance Monthly (CFM) to empirically investigate the causes and consequences of rising student loan debt in recent years and evaluate the new initiatives in the White House s latest budget proposal which aim at easing Americans student loan debt burden. Chapter 3 examines the relationship between student loan debt and postgraduation living arrangements for college graduates in the United States, taking labor market factors into account. Using data from NLSY97, I explore both the parent-youth coresidence outcomes at a point in time and the dynamics of college graduates' movements back home. I estimate a random effects panel probit model and a discrete time proportional hazard model. The estimation results show that an increase in the amount of student loan debt owed at the time of graduation significantly increases the hazard of moving back home after graduation. Chapter 4 uses NLSY97 data to investigate the factors that affect the amount of educational loans students borrow to attend college, with a particular interest in whether parental divorce would affect a college student s educational debt burden. The estimation results indicate that youths from divorced families are more likely to have a higher debt burden upon leaving school than youths from intact families. Among the youths from ii

4 divorced families, those whose biological parents lived in states that permit courts to extend child support beyond the age of 18 for college expenses (post-majority states) do not seem to take fewer loans than those whose biological parents lived in non-postmajority states. Chapter 5 evaluates the new initiatives in the White House s latest budget proposal which aim at easing Americans student loan debt burden. The new initiatives would enable a greater number of Americans with federal student loans to enroll in the income-based repayment program known as Pay as You Earn program (PAYE). I examine the costs of this plan in depth. I use data from CFM, a survey which contains extensive information on household debt and credit, interest rates of repayment, expectations and attitudes. I find that more than 10 percent of borrowers in my sample will receive loan forgiveness after they make regular payments for 20 years under the PAYE program. Projecting to the total U.S. population, this program would result in a great amount of student loan debt forgiveness. iii

5 DEDICATION To my parents iv

6 ACKNOWLEDGMENTS First, I would like to express my deepest gratitude to my advisor Professor Lucia Dunn for her supervision and guidance on my academic research. This dissertation would not have been accomplished without her support and encouragement. I would like to thank Professor Stephen Cosslett for his advice in the process of my research. I greatly appreciate his willingness to provide his expertise and support whenever necessary. I would like to thank Professor Trevon Logan for his insights and enthusiasm on my research. I also wish to thank the entire faculty and staff of the Economics Department who have also been helpful in my completion of this program. I would like to thank John- David Slaughter, who provided technical assistance for my research. Last but not least, I would like to thank my parents for their understanding and encouragement. They were my ultimate source of strength and made it possible for me to achieve this work. v

7 VITA B.S., Economics, Beijing International Studies University M.A., Economics, Bowling Green State University M.S., Applied Statistics, Bowling Green State University M.A., Economics, The Ohio State University FIELDS OF STUDY Major Field: Economics vi

8 TABLE OF CONTENTS ABSTRACT... ii DEDICATION... iv ACKNOWLEDGMENTS... v VITA... vi LIST OF TABLES... x LIST OF FIGURES... xii CHAPTER 1: INTRODUCTION... 1 CHAPTER 2: DATA... 6 CHAPTER 3: THE IMPACT OF STUDENT LOAN DEBT ON COLLEGE GRADUATES CO-RESIDENCY WITH PARENTS Introduction Previous Literature Theoretical Model Data Description Descriptive Statistics vii

9 3.6 A Random Effects Panel Probit Model for Co-residence A Duration Model for Moving Back Home Conclusion CHAPTER 4: EFFECTS OF PARENTAL DIVORCE ON COLLEGE STUDENTS EDUCATIONAL DEBT BURDEN Introduction Previous Literature Data Description Descriptive Statistics Tobit Model Estimation Conclusion CHAPTER 5: AN EMPIRICAL INVESTIGATION OF STUDENT LOAN DEBT REPAYMENT Introduction The PAYE Program Data and Methodology Empirical Results Conclusion viii

10 REFERENCES APPENDIX A: NLSY97 CO-RESIDENCE QUESTIONS ix

11 LIST OF TABLES Table 1. Summary Statistics of the Whole Sample and the Youths Who Owed a Positive Amount of Student Loans at the Time of Graduation Table 2. Summary Statistics for the Amount of Student Loans Owed at Graduation for Youths Who Have Positive Amount of Loans Table 3. Proportions of Youths Who have ever Moved Back Home after Graduation Table 4. Random Effects Panel Probit Model for Co-residence (Controlling for Annual Income) Table 5. Random Effects Panel Probit Model for Co-residence (Controlling for Annual Full-Employment Status) Table 6. Random Effects Panel Probit Model for Co-residence (Controlling for both Annual Income and Annual Full-Employment Status) Table 7. Discrete Time Proportional Hazard Model which Allows for Unobserved Heterogeneity in the Form of Random Effects (Labor Market Variable: Annual Full Employment Status) Table 8. Discrete Time Proportional Hazard Model which Allows for Unobserved Heterogeneity in the Form of Random Effects (Labor Market Variable: Annual Income) x

12 Table 9. Summary Statistics for the Whole Sample: Sample Size and Percentages Table 10. Detailed Summary Statistics for the Whole Sample Table 11. Detailed Summary Statistics for Youths in Divorced Families Table 12. Detailed Summary Statistics for Youths in Intact Families Table 13. Detailed Summary Statistics for Youths in Divorced Families: by Post-majority States and Non-post-majority States Table 14. Tobit Model Estimation for the Whole Sample Table 15. Tobit Model Estimation for Youths Received College Degrees Table 16. Age Distribution of the Whole Sample Table 17. Loan Forgiveness Assuming Income Growth Rate is 1.5 Percent Table 18. Loan Forgiveness Assuming Income Growth Rate is 1.2 Percent Table 19. Total Loan Forgiveness when Projected to 5 Million New Applicants Table 20. Total Loan Forgiveness when Projected to the Nation xi

13 LIST OF FIGURES Figure 1. Average Student Debt Outstanding among Households with Student Debt (in 2010 dollars) Figure 2. Trends of Tuition and Required Fees and Grants for Degree Granting 4-Year Public Schools: xii

14 CHAPTER 1: INTRODUCTION In recent years, the rising costs of higher education have made financing for higher education more important. One of the major sources for financing higher education is student loans. Generally, there are two types of student loans: federal student loans, which are funded by the federal government, and private student loans, which are made by a lender such as a bank, credit union, state agency, or a school. Federal student loan borrowers usually have to start repaying their federal student loans 3 to 9 months after they graduate, leave school, or change enrollment status to less than half-time. Many private student loans require payments while the student is still in school. There have been growing concerns that students are being overwhelmed with debt to finance their higher education. During the recent hard economic times, fewer college graduates are securing jobs upon graduation which means paying back what they have borrowed is getting increasingly difficult. In 2011, 54 percent of recent college graduates were jobless or underemployed (Associated Press, Half of Recent College Grads Underemployed or Jobless, Analysis Says, 2012). According to the Education Department, 10 percent of federal student loan borrowers who entered repayment in fiscal year 2011 defaulted on their loans by the end of fiscal year This is up from 9.1 percent the year before and is the sixth consecutive annual increase since 2005 (The Wall Street Journal. U.S. News: Debt Takes a Bite out of Pay --- Government Ramps up 1

15 Wage Garnishment of Defaulted, June ). The mountain of student-loan debt is affecting every aspect of college graduates lives. According to a Gallup-Purdue University study, college graduates without any student loan debt were seven times more likely to be happy and thriving in most areas of their lives compared to those with more than $40,000 debt (The Wall Street Journal. U.S. News: Colleges Don't Buy Happiness -- - Elite Schools Have No Monopoly on Thriving Graduates, Poll of 30,000 Finds, May ). The study also finds that the mountain of student loan debt also prevents new college graduates from being able to start a new business. About 26 percent of graduates with no undergraduate debt started their own businesses, compared with just 20 percent of those carrying student loan debt from $20,000 to $40,000 (The Wall Street Journal. U.S. News: Colleges Don't Buy Happiness --- Elite Schools Have No Monopoly on Thriving Graduates, Poll of 30,000 Finds, May ). Many other studies have found that student loan debt also delays marriage and other adult decisions following college graduation. Chapter 3 explores the impact of student loan debt on one particular aspect of college graduates lives --- college graduates living arrangements. In this chapter, I examine the effect of student loan debt on the phenomenon of U.S. college graduates moving back home to live with their parents, after taking labor market factors into account. Previous research that examined this phenomenon looked at young adults in general and focused on the roles of labor market factors in their residence decisions. This study is the first to specifically examine the effect of student loan debt on the moving 2

16 back home behavior of college graduates, after taking labor market factors into account. To document and understand the moving-back-home behavior of college graduates, I construct an annual panel of parent-youth co-residence situations based on retrospective co-residence questions and the household roster from the National Longitudinal Survey of Youth (NLSY97). I estimate a random effects panel probit model to examine the impact of student loan debt on parent-youth co-residence outcomes at a point in time. I find that college graduates who have higher amounts of student loan debt are more likely to live at home with parents after graduation. I also estimate a discrete time proportional hazard model that allows for unobserved heterogeneity in the form of random effects to explore the dynamics of college graduates movements back home. The estimation results show that an increase in the amount of student loan debt owed at the time of graduation significantly increases the hazard of moving back home after graduation. Chapter 4 explores the causes of the rising student loan debt situation in recent years. In recent years, college tuition and fees have been rising at a faster rate than financial aid, forcing college students and their parents to shoulder a growing portion of college costs. The widening gap between financial aid and college costs is making it increasingly difficult for low-income parents to pay for college, and single parents are disproportionately affected since they experience greater economic hardship than married parents. Chapter 4 investigates the factors that affect the number of educational loans students borrow to attend college, with a particular interest in the parental divorce situation. This chapter contributes to the literature on the relationship between parental 3

17 divorce and children s outcomes by investigating the effect of parental divorce on college students educational indebtedness. Using data from NLSY97, I find that, controlling for other factors, the divorce of parents results in a student taking on significantly more educational debt to attend college. In addition, I find that post-majority child support laws, which permit courts to extend child support beyond the age of 18 or to explicitly order child support for college expenses, may not bring about the desired effect. Among children from divorced families, those whose biological parents lived in post-majority states are not borrowing less through student loans than those whose biological parents lived in non-post-majority states. Increasing levels of student loan debt driven by rising college prices, combined with a weak economy suggest the need for more programs to be designed to alleviate student loan borrowers financial distress, for example, a repayment program in which loan payments are capped based on the borrower s income. Chapter 5 evaluates the new initiatives in the White House s latest budget proposal which aim at easing Americans student loan debt burden. The new initiatives would enable a greater share of Americans with federal student loans to enroll in the income-based repayment program known as the Pay as You Earn program (PAYE). This program caps borrowers monthly student loan payments at 10 percent of their disposable income regardless of how much they owe, and forgives loan balances after 20 years of regular payments (10 years for those who work in public or nonprofit jobs). I examine the costs of this plan in depth. I use data from the Consumer Finance Monthly (CFM), a survey that contains extensive 4

18 information on household debt and credit, interest rates of repayment, expectations and attitudes. I find that more than 10 percent of borrowers in my sample will receive loan forgiveness after they make regular payments for 20 years under the PAYE program. Among those who receive forgiveness, the average loan balances forgiven is $2,100. The worst scenario occurs when income growth rate is low and all the borrowers are young people who do not plan to pay off their loans early. On average, each borrower receives more than $3,000 loan forgiveness. Projecting to the total U.S. population, this program would result in a great amount of student loan debt forgiveness. 5

19 CHAPTER 2: DATA The data for Chapter 3 and Chapter 4 are taken from the National Longitudinal Survey of Youth (NLSY97). NLSY97 is a longitudinal survey sponsored and directed by the U.S. Bureau of Labor Statistics and conducted by the National Opinion Research Center at the University of Chicago, with assistance from the Center for Human Resource Research at The Ohio State University. The survey includes 8,984 individuals from the cohort born between 1980 and 1984, which is the same cohort that has been labeled as the boomerang generation in the popular press. The original cohort is comprised of two subsamples: the baseline sample which contains 6,748 youths, and a supplemental oversample of 2,236 Black and Hispanic youths who were also born during the same years. Most of the youths have been interviewed approximately annually since 1997 up to the most recent survey year The survey contains extensive information on labor market experience, schooling, educational outcomes, geographic residence, and detailed information on the youth s family and relationship background. In particular, schooling, employment, and household formation data are collected in the form of event histories. The survey also ascertains information on schools attended, field of study, financial aid obtained, and educational loans taken as long as a youth is enrolled in school in a survey year. The highest grade attained is collected at every round of interview. The month and year in which the degree is obtained is also reported. 6

20 In the first year of the survey (1997), a Parent Questionnaire was administered to the youths parents. It collected extensive background information from one of the youth s biological parents. If no biological parent lived in the youth s primary household, another adult household member was selected for the Parent Questionnaire according to predetermined criteria. In the Parent Questionnaire, the resident adult was asked to provide information about his or her background, significant events in life including marital history and employment history, general state of health, income and assets. NLSY97 has many strengths over the other survey data on young adults. First, the observations from NLSY97 are more recent than those from studies from the National Center for Education Statistics (NCES), which are often used in the related literature on college students. Second, the survey contains extensive information on all schools attended, financial aid obtained in each year, and educational loans taken in each year. Third, young adults are linked to their parents through the Parent Questionnaire. Therefore, the researcher is able to ascertain information such as the parents level of education and income and assets. Also, information on parental relationship can be obtained through the youths answers to retrospective questions about their parents. Fourth, the restricted NLSY97 Geocode data contains detailed information of U.S. state, county, and metropolitan statistical area of residence (MSA) for each respondent in each round of the survey. Having access to the restricted NLSY97 Geocode data allows me to match MSA of residence information from the Geocode data with other data sets to generate new measures. In Chapter 3, I match MSA of residence information from the 7

21 NLSY97 Geocode data with Freddie Mac House Price Index (FMHPISM) data to generate a measure of typical housing price in the MSA of residence for each youth in each round of the survey. Details on the data set and the ways in which some of the variables are constructed will be discussed in Chapter 3 and Chapter 4. The data for Chapter 5 are taken from the Consumer Finance Monthly (CFM), a large monthly random household telephone survey conducted by the Center for Human Resource Research at The Ohio State University. The CFM is an on-going national survey, which started in February 2005 and is adding at least 300 new cases each month. CFM has many strengths over the other survey data on consumer finance. First, this survey collects household data on consumer finances and includes many unique variables that are not available in other consumer finance surveys. The survey contains extensive information on all types of household debt, the interest rates of repayment, psychological debt stress, default, bankruptcy, expectations and attitudes. Compared with the Survey of Consumer Finances (SCF), which is the most widely used public dataset covering households financial issues, the CFM provides more detailed information on consumer behavior such as information on whether the borrowers plan to pay off each type of debt in advance. Second, the SCF takes place every three years and the data are two years old when published; the CFM is a monthly survey and provides the most up-to-date data on consumer finance to reflect the newest changes of American consumers spending habits and financial management. Third, the CFM questions have been constructed to be as individualized as possible. When a respondents says don t know or refuses to answer a 8

22 survey question, a series of unfolding bracket questions are used to get as much information as possible. In the unfolding bracket questions, a threshold is presented. The respondent is asked whether or not the amount is less than or more than the threshold. Based on the answers to the unfolding bracket questions, an estimated value can be calculated to reflect the true value of the data. Details on the data set and the ways in which some of the variables are constructed will be discussed in Chapter 5. 9

23 CHAPTER 3: THE IMPACT OF STUDENT LOAN DEBT ON COLLEGE GRADUATES CO-RESIDENCY WITH PARENTS 3.1 Introduction In recent years, the returns to a college degree have risen substantially. However, the cost of higher education has risen even more quickly. Between 1992 and 2012, the wage premium for college rose by 30 percent (Current Population Survey), while real tuition and fees at public and private nonprofit four-year colleges rose by 92 percent and 57 percent, respectively (Trends in College Pricing 2012, Table A2). These rising costs have made financing for higher education more important. One of the major sources for financing higher education is student loans. Generally, there are two types of student loans: federal student loans, which are funded by the federal government, and private student loans, which are made by a lender such as a bank, credit union, state agency, or a school. Although student loans have been a readily accessible financial source for college education, there has been growing concerns that students are being overwhelmed with debt to finance their higher education. According to the Survey of Consumer Finances (SCF), from 1992 to 2010, average student debt outstanding among households with student debt (in 2010 dollars) rose from $10,763 to $25,905 (see Figure 1). According to a report from Federal Reserve Bank of New York, overall student debt rose 12 percent to 10

24 $1.08 trillion in Student debt has become the second-largest form of household debt, after mortgages. $30,000 $25,000 $20,000 $15,000 $10,000 Average Student Loans Amount Outstanding among Households with Student Debt $10,763 $11,373 $17,419 $17,050 $19,439 $22,669 $25,905 $5,000 $ Source: Author s Calculation from the Survey of Consumer Finances (SCF), Figure 1. Average Student Debt Outstanding among Households with Student Debt (in 2010 dollars) Students are having a hard time repaying their student loans. Students usually have to start repaying their federal student loans 3 to 9 months after they graduate, leave school, or change enrollment status to less than half-time. Many private student loans require payments while the student is still in school. College debt continues to increase 11

25 among college graduates and fewer graduates are securing jobs upon graduation, which means paying back what they borrowed is getting increasingly difficult. The mountain of student-loan debt is preventing new college graduates from being able to take chances such as starting a new business. Many studies have found that student loan debt also delays marriage and other adult decisions following graduation from college. In recent years, more young adults have been observed to live in the home of their parents. According to the U.S. Census Bureau, the proportion of young men age 25 to 34 living in the home of their parents rose from 14 percent in 2005 to 19 percent in 2011 and rose from 8 percent to 10 percent over the same period for women. A similar pattern has been found for 18 to 24 year olds over the same period (The United States Census Bureau, 2011). This new trend has captured the attention of the popular press, which has popularized the term boomerang generation, referring to young adults who move back in with their parents after having initially moved away from home. Since student loan debt among young adults has expanded substantially in the past decade, it is meaningful to examine whether massive student loan debt by college graduates has played a role in their boomerang behavior. This chapter examines the effect of student loan debt on the moving back home behavior of college graduates, taking labor market factors into account. I have explored both the parent-youth co-residence outcomes at a point in time and the dynamics of college graduates' movements back home. I estimate a random effects panel probit model and a discrete time proportional hazard model. The estimation results show that college 12

26 graduates with a higher amount of student loan debt are more likely to be living with their parents at some time after graduation. The increase in the amount of student loan debt owed at the time of graduation significantly increases the hazard of moving back home afterward by 3 percent. In other words, taking 1 percent more in student loan debt is associated with a 50.7 percent of chance of moving back home sooner than those who do not take on the 1 percent extra student loan debt. The remainder of this chapter proceeds as follows. Section 3.2 reviews previous related literature. Section 3.3 presents a theoretical model. Section 3.4 describes the data sets used in the econometric analysis and Section 3.5 describes characteristics of the data. Section 3.6 reports the estimates of a random effects panel probit model. Section 3.7 reports the estimates of a duration model. Section 3.8 provides conclusions. 3.2 Previous Literature My research is related to two lines of work. First, many studies have focused on the post-college impact of student loan debt. Chambers (1992) examines the career choices of law school students and finds that students with higher debts are more likely to take jobs in large private law firms and less likely to take jobs in government or legal services. Field (2006) examines the influence of educational debt aversion on the career choice of law school students. She finds that students who received tuition waivers are more likely to take jobs as public interest lawyers than students who received an equivalent amount of loan repayment assistance. She interprets these results as debt 13

27 aversion, since the debt at the earlier stage is perceived to be more costly, although the amount of debt is essentially the same. Minicozzi (2005) uses data from the National Post-Secondary Student Aid Survey to estimate the effect of educational debt on the wage growth of college graduate students. She finds that college graduates with higher education debt tend to choose a high-paying first job, but their subsequent wage growth rate is low. Rothstein and Rouse (2007) find that debt causes graduates to choose substantially higher-salary jobs and reduces the probability that students choose low-paid "public interest" jobs. Shand (2008) uses the Survey of Consumer Finance (SCF) data to examine the impact of educational debt and consumer debt on the propensity of homeownership. She finds that educational debt is associated with reduced homeownership rates. Another line of work has examined the determinants of young adults behavior with regards to moving back home with parents. Several studies focus on older cohorts and use either retrospective questions about the timing of the first movement back home or annual data on living arrangements. DaVanzo and Goldscheider (1990) use data from the National Longitudinal Study of the High School Class of 1972 to analyze the determinants of returning home of young adults. They estimate cross-sectional logistic regressions for living at home as a function of characteristics in the previous survey for youths who lived away from home at the time of the last survey. They find that both income and unemployment have a significant impact on the behavior of living at home. Ermisch (1999) uses data from the British Household Panel Survey to estimate probit 14

28 models of annual return to the parental home. He finds that a spell of unemployment during the year increases the annual probability of return to parental home. Goldscheider and Goldscheider (1999) estimate proportional hazard models for returning home using data on the age when youths first left and first returned home. However, they have not studied the effects of labor market variables on these movements due to the lack of labor market data. A more recent work by Kaplan (2012) focuses on a new cohort of youths and uses data from National Longitudinal Survey of Youth (NLYS97). He constructs a new monthly panel data set based on retrospective monthly parent-youth co-residence questions in the NLSY97 and examines the relationship between the dynamics of parentyouth living arrangements and labor market outcomes for youths in the United States who do not go to college. He estimates duration models that allow for unobserved heterogeneity and finds that labor market factors play an important role in determining the dynamics of parent-youth living arrangements. Due to the availability of a key question from NLSY97 that is needed in the study, Kaplan (2012) only focuses on youths who do not go to college. These studies all look at young adults in general and focus on the roles of labor market factors in their residence decisions. None of these studies have focused on college graduates. This chapter attempts to document and understand college graduates moving back home behavior and to examine the effect of student loan debt on this phenomenon, taking labor market factors into account. 15

29 3.3 Theoretical Model My theoretical model is based on the economic analysis of parental-youth coresidence decisions described by Ermisch and Di Salvo (1997). In the model, parents are altruistic toward their children (the youths). The parents utility is a function of their consumption of housing and other goods as well as the utility of the youths. Housing is a local public good for a household. It is assumed that housing services per person are not affected by household size. Parents are altruistic and can provide the youth with monetary support in the form of explicit financial transfers and non-monetary support in the form of shared residence. The model has a two-stage structure. At the first stage, parents choose their own housing, consumption of other goods and financial transfer to maximize their utility, subject to their income, the price of housing and their knowledge of the youth s preferences. At the second stage, the youth takes parental financial transfer, his or her income, and the price of housing as given, and chooses his or her own housing, whether to co-reside with parents and consumption of other goods to maximize his or her utility. Ermisch and Di Salvo (1997) showed that whether the financial transfers are made depends on parental income relative to the youth s income. When the ratio is large enough, financial transfers are made both during co-residence and when the youth lives alone; when the ratio is in an intermediate range, no financial transfers are made during co-residence, but they are made when the youth lives alone; when the ratio is low enough, no financial transfers are made in either case. Based on the data from NLSY97, only a small fraction of youths who graduated from college receive financial transfers 16

30 from their parents. Since I am interested in the college graduates co-residence phenomenon, I will focus on the last case in which no financial transfers are made no matter whether the youth co-resides with the parents or not. Consider both the youths and the parents preferences represented by a constant elasticity of substitution utility function, U i = [αc ρ i + βh ρ i ] 1 ρ ρ < 1, i = y, p. The elasticity of substitution between housing and other consumption is given by σ = 1 1 ρ. The utility maximization problems of the youths and the parents are as follows. Youths: s. t. max U y = [αc ρ y + βh ρ y ] 1 ρ c y + (1 r)ph y + l w y Parents: max U p = [αc ρ p + βh ρ p ] 1 ρ + ρ η[αcy + βh ρ y ] 1 ρ s. t. c p + ph p w p where h y and h p are consumption of housing for the youth and the parents, respectively. c y and c p are consumption of other goods for the youth and the parents. r indicates whether the youth and the parents co-reside (r = 1) or live apart (r = 0). The value of financial transfers depends on whether the youth and the parents are co-residing. p is the price of housing in terms of consumption of other goods. l is the amount of student loan debt owed by the youth. For simplicity, I assume the youth and the parents are faced with the same housing price. w y and w p are income of the youth and the parents. η is the degree of parents altruism. 17

31 Since we are considering the case where parents do not make financial transfers to the youth, the youth s and the altruistic parents decisions are compatible. It is sufficient to examine how the student loan debt payment affects the youth s utility ratio. The youth s indirect utility function while living away from parents is V y 0 = (w y l)(α σ + β σ p ρ 1 ρ ρ 1 ) ρ The youth s indirect utility function while co-residing with parents is V 1 y = (α(w y l) ρ + βh ρ 1 p ) ρ The effects of the youth s income and the parents income on the youths utility ratio V 0 y ( ) V 1 y ( ) can be derived as: ln ( V y 0 V1 ) y ln (w y l) = 1 α ln ( V y 0 V y 1 ) ρ (w y l) (α(w y l) ρ + βh ρ p ) > 0 ρ h p ln (w p ) = β (α(w y l) ρ + βh ρ p ) < 0 These results indicate that youths with low income and high student loan debt payment are more likely to co-reside with parents. Youths with richer parents are more likely to co-reside with parents. derived as: The effects of housing price on the youths utility ratio V 0 y ( ) V 1 y ( ) can be 18

32 where ε p = ln ( V y 0 V1 ) y ln (p) = ph y (w y l) ε p [1 + α β ((w y l) h p ) ρ ρ 1 (1 ph p w p ) 1 is the parents price elasticity of housing demand. The sign of ln ( V y 0 V y 1) ln (p) depends on the responsiveness of the parents housing demand to price. My empirical analysis focuses on the effects of student loan debt on parent-youth co-residence. I assume parents, who are altruistic, are always able to provide the youth with non-monetary support in the form of shared residence. Therefore, it is plausible to assume that an increase over time in the price of housing would have little effect on parents housing demand. It can be shown that when the parents price elasticity of housing demand is relatively small (ε p < 1), the effects of housing price on the youths utility ratio is negative ( ln ( V y 0 V y 1) ln (p) < 0). A higher housing price increases the probability of parent-youth co-residence. ρ ] 3.4 Data Description In this section, I describe the data set and the ways in which some of the variables were constructed. The sample is drawn from the NLSY97, a longitudinal survey of 8,984 individuals from the cohort born between 1980 and This is the same cohort that has been labeled the boomerang generation in the popular press. The original cohort is 19

33 comprised of two subsamples, the baseline sample which contains 6,748 youths and a supplemental oversample of 2,236 Black and Hispanic youths who were also born during the same years. Most of the youths were interviewed approximately annually since The survey contains extensive information on labor market behavior, educational outcomes and detailed information on the youth s family and relationship background. The survey also contains information on employment status, income, and debt. In selecting my sample, I focus on 1,903 youths who have received a bachelor s degree or a more advanced degree (a master s degree, a Ph.D. degree or a professional degree) by the most recent survey year (survey year 2011), who have never served in the military and who have at least one parental figure listed on the household roster in the first round of survey. Since there are only 12 youths who graduated before year 2003, I restrict the time frame of my analysis to be from 2003 to The panel nature of the NLSY97 allows me to follow a youth from 1 to 9 years after the youth received a college degree. Since students usually have to start repaying their federal student loans 3 to 9 months after they graduate, I assume all students in my sample start paying back their loans 6 months after leaving school. Generally most students will be included in the sample 6 months after graduation. Since some youths continue to pursue a more advanced degree (a master s degree, a Ph.D. degree or a professional degree) after they graduate from college and the main interest of this paper is to examine the living arrangement of youths when they are out of school, I include youths in the sample based 20

34 on the following criteria. For youths who pursued no further education after graduating from college, I start to follow them from 6 months after they received their bachelor s degree. For youths who pursued a more advanced degree within one year of college graduation, I start to follow them from 6 months after they received their advanced degrees. For youths who pursued a more advanced degree years after graduation, I start to follow them from 6 months after they received their bachelor s degrees but excluded them from the sample when they were working on their more advanced degrees and then continue to follow them from 6 months after they received their advanced degrees. Based on the above criteria, on average, youths are in the sample for 5.7 years. Co-residence Variables The dependent variable of my analysis is a dichotomous measure of whether the youth is living at home during the years after graduation. Information about parent-youth co-residence in the NLSY97 can be obtained in two ways. The ideal way is to construct a parent-youth co-residence panel based on a set of retrospective questions about monthly co-residence. During each interview from rounds 2 to 6 (survey years ), respondents were asked to list each period of one month or more in which they lived separately from each of their parents. Based on the responses to these co-residence questions across rounds, it is possible to reconstruct a monthly panel of parent-youth co-residence outcomes for each respondent. However, the last year this question was asked is Since the group of people I am interested in is 21

35 college graduates and due to the age of the NLSY97 cohort, most of the respondents graduate from college after Therefore, this question does not work in my case. Starting from round 7 (survey year 2003), the above retrospective co-residence question was replaced with two questions that ask about the month and year that a youth first lived away from his or her parents, and the month and year when the youth returned home for a period of at least three months (see Appendix A). By piecing together the responses to these co-residence questions across rounds, it is possible to reconstruct a panel of parent-youth co-residence outcomes for each respondent. However, using these questions as the basis for co-residence information has two shortcomings. First, these questions only provide information on the timing of the youth s first time moving back in with parents for at least three months. The subsequent movements out of or back in the home are unknown. Second, the youths are not asked to ignore periods when they temporarily moved back home during summer break. For youths who moved back home during summer break after they first moved out of home, the information on the timing of their moving back in with parents after college cannot be obtained from these questions. To make up for these shortcomings, I also use the responses to some questions from the household roster. Before round 7 (survey year 2003), the household roster records the relationship to the youth of all individuals living in his or her permanent household, which does not necessarily correspond to the current residence of the youth. However, starting from round 7, the household roster records the relationship to the youth of all individuals living 22

36 in his or her current household. Since most of the respondents in the NLSY97 cohort graduated from college after 2003, I can use the household roster of rounds 7 to 15 (survey years ) to obtain a youth s subsequent parent-youth co-residence outcomes after the first return. In this paper, I define a youth as living away from his or her parents in a given year only if he or she is observed to be not living with any of his living parent figures or parent in-law figures during a survey year. Conversely, a youth is defined to be living at home if he reports living with at least one parent figure or parent in-law figure during a survey year. By combining the responses to the retrospective co-residence questions and the responses to questions about the relationship to the youth of all current household members, I construct a yearly panel of post-college parent-youth co-residence outcomes for all respondents in my sample. Student Loan Debt Variable The focal independent variable in my analysis is student loan debt. My measure of student loan debt is the loans owed at the time when the respondent graduated from school. For each term enrolled, respondents are asked about the amount of government subsidized loans or other types of loans they have borrowed while attending the institution and about the amount of loans still owed. I adjust the dollar amount of the loans owed in each term for inflation and then sum up the amount of loans a respondent owed during each term while in college. 23

37 Labor Market Variables Labor market controls include a measure of the youth s annual income, a measure of the youth s annual full-employment status and a measure of the youth s parents income. The income measure of the youth is based on the created variable, annual wage and salary income, from the NLSY97. The measure for the youth s annual fullemployment status is constructed from the employment event history which includes a weekly employment record of the youth in a given week and created variables for total hours worked in the week. A youth is defined as fully-employed in a given year if he or she is recorded as working more than 30 hours per week and more than 40 weeks per year. The income measure of the youth s parents is based on the parents reported income from the parental questionnaire in the first round of the survey (survey year 1997). Housing Price Variable The parent-youth co-residence outcomes can be influenced by the housing price of the metropolitan statistical area (MSA) in which a young adult resides. Based on the analysis of the theoretical model, individuals who are faced with a higher rental or housing price are more likely to move back in with parents. It is important to control for the housing price each respondent is faced with when he or she makes the decision of whether to move back with parents. NLSY97 Geocode data contains detailed information of U.S. state, county, and MSA of residence for each respondent in each round of the survey. I match MSA of residence information from the Geocode data of the NLSY97 with Freddie Mac House Price Index (FMHPISM) data and generate a measure of the 24

38 typical housing price in the MSA of residence for each youth in each round of the survey. Due to the lack of annual MSA data for youths parents, I use the housing price in the MSA of residence for the youth as an approximate housing price that both the youth and the parents are faced with when the youth makes the decision of whether to move back with parents. Additional Control Variables Additional control variables in the analysis include gender, race, ethnicity, highest degree obtained, annual parental transfers, marital and cohabitation status. The youth s highest degree obtained is categorized into master s degree, Ph.D. degree and professional degree with the bachelor s degree as the baseline. There is little doubt that the outcomes of parent-youth co-residence are strongly influenced by factors that relate directly to household formation: marriage and cohabitation relationships. To the extent that the data allow, it is thus important to control for these factors when trying to ascertain the role of student loans in affecting co-residence outcomes. 3.5 Descriptive Statistics In this section, I report summary statistics for various observable characteristics of the youths in the sample. In all the analysis, I used a custom set of cross-sectional weighs to account for oversampling and attrition during the period 2003 to The summary statistics for variables that do not change over the sample period are shown in Table 1. It reports that of the 1,903 youths from the whole sample, 1,250 (66%) 25

39 owed a positive amount of educational loans at the time when they graduated. 59 percent of the youths in the whole sample are female. 16 percent are Black and 13 percent are Hispanic. 12 percent of the youths received a Master s degree and only a few youths received a Ph.D. degree or a professional degree. The group of youths who graduated with a positive amount of student loans shares almost the same characteristics as the whole sample. All Youths Youths with Positive Amount of Student Loan Debt Proportion S.D. Proportion S.D. Female Black Hispanic Master s degree Ph.D. degree Professional degree N 1,903 1,250 Table 1. Summary Statistics of the Whole Sample and the Youths Who Owed a Positive Amount of Student Loans at the Time of Graduation The summary statistics for the amount of student loans owed at the time of graduation for youths who have a positive amount of loan are shown in Table 2. We see that for those ever enrolled with educational loans, their mean educational debt owed at 26

40 the time when they graduated is $20,155. Half of the youths owed about $15,000 educational loans when graduated from college. The highest amount of loans owed reached almost $150,000. Mean 20, Standard Deviation 17, Minimum th Percentile 2, th Percentile 7, th Percentile 15, th Percentile 27, th Percentile 53, Maximum 149, N 1,250 Amount of Student Loan Debt Owed at Graduation in 2011 dollars Table 2. Summary Statistics for the Amount of Student Loans Owed at Graduation for Youths Who Have Positive Amount of Loans Table 3 reports the proportions of youths who have ever moved back home after graduation for different groups from the sample. For all groups of youths, more youths with a positive amount of educational debt have ever moved back home after graduation than youths without debt. Note that almost half of those with a positive amount of debt have moved back home after graduation. Among youths without educational debt, more 27

41 than half of Blacks and Hispanics have been recorded as ever returning home after graduation. This indicates that it is important to control for these demographic factors that can affect co-residence outcomes. Among youths who graduated with a positive amount of student loans, about 60 percent of Blacks and Hispanics have been recorded as ever returned home. Youths without Student Loans: N=653 All Youths 40% 48.6% Female 45.5% 46.9% Black 53.5% 57.8% Hispanic 56% 62.5% Youths with Positive Amount of Student Loans: N=1,250 Table 3. Proportions of Youths Who have ever Moved Back Home after Graduation In the remainder of this chapter, I explore the extent to which the amount of student loan debt at graduation time is associated with post-school parent-youth coresidence outcomes. 3.6 A Random Effects Panel Probit Model for Co-residence In this section, I describe the random effects panel probit model for moving back home and report the regression estimates. 28

42 I model the relationship between co-residence outcome and student loan debt with a random effects panel probit model. H it = X it β + γe i + α i + ε it i = 1,2,, N; t = 1,2,, T with H it = 1 {Hit >0} α i ~i. i. d. N(0, σ α 2 ); ε it ~i. i. d. N(0, σ ε 2 ) ε it and α i are assumed to be random α i and ε it are mutually independent as well as independent of X it and E i where H it is latent co-residence status, H it is the observed co-residence status which indicates whether the individual is living at home or not in a given year. E i includes the amount of student loan debt at the time of graduation, a measure of the parents income, an indicator for female, an indicator for Black, an indicator for Hispanic, an indicator of a master s degree, indicator of a Ph.D. degree, and an indicator of professional degree. X it includes a measure of annual housing price in the MSA where the youth lives, annual marital status, annual income, full-employment status, and annual parental transfer. I include year dummies to control for macroeconomic factors. Considering the facts that younger adults are more likely to be living with parents than older ones and that younger adults are more likely to have attended school in more recent years when student loan burdens have increased, I also control for youths age at graduation. In the theoretical model, a youth moves back home to live with parents when his or her utility of living 29

43 apart is less than the utility received when living in the parental home. Youths will differ in their tastes concerning living with parents. The term ε it from the regression model catches unobserved characteristics such as the youths tastes. Note that it is possible that a youth who has a higher tastes for living with parents decided in advance to move in with parents after graduation and therefore took more loans during college. However, I primarily focus on college graduates transition in parental co-residence after a period of independent living. I will interpret my results as reflective of the effect of student loan debt on parent-youth co-residence. It is outside the scope of this study to explain the mechanisms through which a youth becomes indebted. When we assume youths unobserved characteristics independent of variables in X it and E i, β and γ can be estimated consistently. The estimation results of three different specifications of the model are reported in Tables 4, 5 and 6 respectively. Table 4 reports the results from the model with annual income controlled. Table 5 reports the results from the model with annual fullemployment status controlled. Table 6 reports the results from the model with both of annual income and annual full-employment status controlled. 30