The effect of student loans on college enrollment: Evidence from municipality panel data in Japan

Journal Pre-proof The Effect of Student Loans on College Enrollment: Evidence from Municipality Panel Data in Japan Shinpei Sano

PII:

S0922-1425(19)30009-X

DOI:

https://doi.org/10.1016/j.japwor.2019.100979

Reference:

JAPWOR 100979

To appear in:

Japan & The World Economy

Received Date:

31 January 2019

Revised Date:

6 September 2019

Accepted Date:

11 September 2019

Please cite this article as: Sano S, The Effect of Student Loans on College Enrollment: Evidence from Municipality Panel Data in Japan, Japan and amp; The World Economy (2019), doi: https://doi.org/10.1016/j.japwor.2019.100979

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The Effect of Student Loans on College Enrollment: Evidence from Municipality Panel Data in Japan☆

Shinpei SANO※

Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba-shi, Chiba, 263-8522, Japan. E-mail: [email protected]

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※

☆

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The author acknowledges the financial support of KAKENHI(24730244, 16H06323) and Chiba University. Special thanks go to Takaaki Kawamoto who contributed to this study in an early stage. The author thanks two anonymous referees, Hideo Akabayashi, Ayako Kondo, Yusuke Jinnai, Ryuichi Tanaka, Takashi Unayama, Kazufumi Yugami, and seminar participants at the Western Economic Association International meeting, the Japanese Economic Association meeting, the Tokyo Labor Economics Workshop and Romacs, Osaka City University for helpful conversations and comments.

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Highlights

The effect of the criteria expansion for student loan eligibility on the college enrollment is

A difference-in-differences estimation by using municipal panel data from 1998 to 2003 is used.

The expansion of eligibility for student loans improves the male college enrollment rate by

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examined.

around 0.5 to 0.7% points.

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The female enrollment is less sensitive to the expansion of student loan eligibility.

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The impact of the student loan eligibility expansion is larger for low income areas.

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Abstract This study examines whether the criteria expansion for student loan eligibility promotes the college enrollment of high school graduates in Japan. In 1999, the Japan Student Services Organization revised the eligibility criteria of the student loan system based on household earnings. Before the revision, the maximum allowable earnings for student loan applications differed across regions; some region’s had lower criteria than

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others. After the revision, the criteria for regions with lower maximum allowable earnings were adjusted upwards to match regions with higher ones. We conducted a difference-in-differences estimation by using

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municipal panel data from 1998 to 2003. We found that the expansion of eligibility for student loans improved the male college enrollment rate by around 0.5 to 0.7% points, while female enrollment was less sensitive to

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the expansion of student loan eligibility. The impact of the student loan eligibility expansion is larger for low

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income areas.

Keywords: College enrollment; Student loans; difference-in-differences

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JEL classification: I22, I23, J24

1. Introduction

Human capital accumulation (e.g., via higher education provision) is one of the most important approaches to improving productivity and bridging income disparities. In Japan, the demand for higher education has increased in the past several decades, with increased employability along with the consequent decreased 2

unemployment rates for those with higher education. In 2010, approximately 45% of adults attained tertiary education, and the percentage of students continuing their tertiary education was an estimated 54.3%. The return to higher education is estimated to be around 5-10% in Japan (Sano & Yasui 2009, Nakamuro, Inui, & Yamagata 2017, Kikuchi 2017). Investment in higher education is heavily dependent on private sources in Japan. For example, of the

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total amount spent on tertiary-level education, 50.7% came from household expenditures (OECD, 2012). Additionally, the OECD highlights that although tertiary tuition fees are high, and financial aid is limited,

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Japan remains one of the countries with the lowest levels of public expenditure on tertiary education against their GDP: 0.5% compared to the OECD average of 1.1%. Policy makers and education researchers advocate

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for an increase of student aid options for students in higher education (Kobayashi, 2009).

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One of the major student aid sources in Japan is the student loans system offered by the Japan

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Student Services Organization (hereafter JASSO). The proportion of university students who are loan recipients was 38.2% in 2012. The loan amount offered by JASSO would sufficiently cover almost all the

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tuition fees at national/public institutions and 80-99% of the fees at private universities. JASSO’s loan

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facilities aim to provide financial assistance to academically excellent students who are unable to pursue their studies due to financial reasons. Understanding the mechanisms by which financial aid for household affects educational investment

in higher education would have important policy implications. For example, in the case of imperfect financial market, if low-income households with financial constrains invest less in their children’s education than 3

wealthier households, offering student loan by government can be justified on equity grounds. Evaluating the impact of student loan on investment in higher education offers the information about the design of student loan system such as the loan amount, the eligibility criteria for student loans. While the study of the impact of student loans on tertiary enrollment remains important for researchers and policymakers, there are two challenges to identify the impact in Japan. Firstly, the challenge in

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attempting to identify the causal impact of student loan on college enrollment is the endogeneity of receiving a loan. Since student loans are received by students with better academic backgrounds, their student loan

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variable could reflect unobserved student characteristics that affect the decision to go to college. In a vast literature, researchers have tried to eliminate the unobservable factors using experimental design. For example,

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Dynarski (2000, 2003), Cornwell, Mustard, & Sridhar (2006), Singell, Waddell and Curs (2006), Winters

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(2012), and related literatures utilize exogenous variation in both birth cohort and birth place to evaluate State

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Merit Aid Program in US1. It is not easy to find this situation, because Japanese student loan system is setting unitary.

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Secondly, there is insufficient research to examine the effects of student aid in Japan due to the lack

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of a proper data set for analysis of the determinants of college enrollment. Previous research in Japan used aggregated data (Zeni, 1998) or micro data with restriction. Nakamura (1993) used the Employment Status Survey and found a positive relationship between parental background and college enrollment only for co-resident high school student in metropolitan areas. Kobayashi (2009) also found a positive relationship

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Alternative identification strategy is comparing with the eligibility threshold (Guryan, 2001, Van Der Klaauw, 2002, Goodman 2008, Bruce and Carruthers 2014).

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between parental background and college enrollment for freshman using the Student Life Survey. Employment Status Survey, large sample cross-section data with rich information about household, can link the information between household situation and college enrollment only for co-resident college students after enrollment decision due to survey design. Student Life Survey has rich information about college students, but does not include information at the timing of enrollment decision. Neither previous study analyzed the effect

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of receipts of student loans prior to entrance into college on decision to enroll the college due to data limitation2.

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In this paper, we focus on the JASSO reform in 1999 to identify the effect of student loans on

college enrollment using municipality panel data to solve these problems. One of the most fundamental

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revisions of this reform was the change in the eligibility criteria for student loans based on household earnings

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depending on the recipient’s municipality of residence. Before the revision, the maximum allowable earnings

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for student loan applications differed across regions; the criteria for some regions were lower than others. After the revision, the criteria in the regions with lower maximum allowable earnings were adjusted upwards

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to match regions with higher ones. In other words, this meant that after the revision, more high school students

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living in regions with the lower criteria were eligible for JASSO college loans. This situation provided us with relevant and appropriate context for a natural experiment to identify the effect of eligibility expansion for student loans while controlling for unobserved factors that may affect the decision to enroll in tertiary education. Additionally, we use the municipality panel data based on the Basic School Survey. This survey,

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Itho and Suzuki (2003), Oguro and Watanabe (2008), and Shimoyama and Murata (2011) examined the effect of receiving student loans on the spending of college students using treatment model.

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school-related statistics, includes number of college and university students enrolled from surveyed school, but does not include the household-related information. Aggregating school information by school-located municipalities and merging to other municipality data sets allow us to analyze the relationship between household situation and enrollment decision3. This study makes at least two contributions to the extant literature. Firstly, we offer the evidence

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about the causal impact of student loan on college entrance. Our paper is in the line with previous studies that have applied exogenous variations, such as natural experiments, to identify the impact of student aid on

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educational outcomes using variation in both birth cohort and birth place such as State Merit Aid Program in US (Dynarski, 2000, 2003; Cornwell, Mustard, & Sridhar (2006), Singell, Waddell and Curs (2006), Winters

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(2012), Sjoquist, and Winters 2015). In this study, we extend the literature on this topic by evaluating the

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expansion effects of student loan eligibility by utilizing exogenous and regional variation in Japan with unite

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system. Secondly, evaluating the impact of student loan on college enrollment in Japan is important for educational policy. Despite heavier burden of tertiary education on private sector, Japan is one of the highest

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shares of tertiary-educated adult of all OECD countries. This study offers the evidence about the design of

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student loan system.

The remainder of this paper is organized as follows. Section 2 describes the educational system in

Japan in general with a specific focus on the 1999 reform. Section 3 describes the data, and Section 4 describes the empirical strategy employed in this study. Section 5 presents the empirical results. Finally,

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Kawamoto and Sano (2013) employs similar approach using JGSS. JGSS is individual data including household characteristics and respondent’s home prefecture lived in fifteen years old. However, the variation of the policy change comes from across municipalities, not prefectures, described in section 2.

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Section 6 concludes and discusses the study.

2. Institutional Background4 The Japanese education system consists of six years of compulsory education (elementary and lower secondary education such as junior high school), three years of upper secondary education (typically high

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school),5 and higher education (ranging from two years for college and four years for university). As of 2000, there were 86 national universities, 95 public universities, and 597 private universities as well as 395 two-year

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colleges in Japan. University enrolments have been rising at an average rate of 1.4% per year over the last three decades. There were about 2.8 million students in university and 0.15 million in college in 2000.

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The costs and benefits of attending college or university are as follows. Students prepare for the

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entrance examination to entry into a college or university, and students must pass the entrance exam between

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January and March before the semester commences in April. Tuition fees are the other direct cost of attending college or university. In 2000, the annual tuition fee was 520,800 yen for national universities and 817,952

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yen, on average, for private universities. According to Japan’s Ministry of Education, Culture, Sports, Science

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and Technology (MEXT), the ratio of tuition fees to family disposable income ranged from 1.5% to 2.7% in 2000. The benefit from graduating from college or university is high. The return to higher education is estimated to be around 5 to 10 % in Japan (Sano & Yasui, 2010, Nakamuro, Inui, & Yamagata, 2017, Kikuchi, 2017).

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Armstrong et al. (2018) explains the details of the Japanese education system and recent student loan reform. Almost all junior high school students go to high school (the enrollment rate was 98.1% in 2008).

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Student loans are one of the major sources of income for students and household. One of the most important student loan providers is JASSO. The proportion of university students that were loan recipients was 38.2% in 2012. Of these loans, according to the Survey of Student Aid, JASSO student loans accounted for about 70% of overall student financial aid given in 2003. JASSO loans cover almost all the tuition fees at public universities and 80–90% at private ones. Although students can finance their college costs (e.g., tuition

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fees, living costs, etc.) through allowances from their families and earnings from part-time jobs, student loans remain crucial for a number of potential entrants. JASSO student loans consist of Category I (interest-free)

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and Category II (interest bearing) and can cover almost all annual tuition fees. JASSO selects loan

applications based on the student’s character profile, health, academic achievements, and his/her family's

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financial situation (based on taxable earnings varied by family size and so on). Each year, there are two

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application periods: before entering university (around October,6 approximately 20% of loans) and after

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entering university (around July). For our research, we will focus on the pre-entry applications as they reflected enrollment motivation.

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Since its establishment in 1943, JASSO has been reformed several times until 2003. The

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fundamental reforms were implemented in 1984 and 1999. This paper focuses on the reform implemented by JASSO in 1999. The background of 1999 reform was response to increasing demand for higher education and diversifications of students in the 1990s. MEXT tried to shift towards providing loans with emphasis on the degree of financial difficulty so that loans could be provided to students who wish to lend; shift from

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Application and acceptance timings vary by high school. However, students must decide to apply for a student loan at the start of 12th grade. Further, students receive their acceptance letter before taking the entrance examination.

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merit-based to need-based. To achieve this goal, MEXT planned to expand the quantity of recipients of Category II student loans introduced in 1984. The expansion of Category II student loans was in line with the national policy of utilizing Fiscal Investment and Loan Program (FILP) addressing the falling birthrate and the aging population, because while Category I student loan was contributed from the general account, Category II was contributed from FILP (JASSO 2006, Shirakawa and Maehata 2012). This reform expanded the

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Category II student loans while relaxing the selection standards regarding the applicants’ academic achievements and financial situation. At that time, household earnings criteria (the maximum allowable

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taxable earning for the provision of student loans) that depended on the recipient’s municipality of residence for not only Category II but also Category I were changed to meet the most generous conditions.

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This paper focus on the change to the maximum allowable taxable earning for the provision of

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student loans occurred by revisions in the JASSO reforms in 1999. Allowed taxable earnings varied with the

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number of dependent families, occupation (self-employment or not), and place of residence. JASSO has set a maximum allowable taxable earnings level for applications across individual regions. A region is defined as a

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set of municipalities corresponding to the Public Assistance System in Japan. According to the Handbook of

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Public Assistance, the municipalities were divided into six groups based on the standard of living and population size. The upper and second upper classes are called first class areas 1 and 2, the third and fourth classes are called second class areas 1 and 2, and the fifth and bottom classes are called third class areas 1 and 2. Table 1 tabulates example municipalities into their respective class. Region A is defined by JASSO as first class areas 1 and 2, while region B was defined as the second and third areas. 9

Table 1. Class-area Classifications in 2000 Example

Class Area

Municipalities

1st class

Regio

Special Wards of

Yokoham

area1

nA

Tokyo

a

1st class

Regio

area2

nA

Sapporo

2nd class

Regio

area1

nB

2nd class

Regio

area2

nB

3rd class

Regio

area1

nB

3rd class

Regio

area2

nB

Osaka

Sendai

Chiba

Hiroshima

Mito

Shizuoka

Nara

Hitachi

Mishima

Sasebo

Hirisaki

Imabari

Yuki

Sasayama

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Kyoto

including some of the towns and villages including many the towns and

a

villages

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Uwajim

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Source: The Ministry of Health, Labour and Welfare (eds.), The Handbook of Public Assistance (Seikatsu

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Hogo Techo).

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Prior to the reform, the maximum allowable taxable earnings in region A was higher than region B. For example, prior to the reform in 1996, the maximum allowable taxable earnings for those living in region A was around 3.2 million, while for those living in region B it was around 3.0 million yen. After the reform, the maximum allowable taxable earnings in region B was adjusted upwards to match that of region A. Figure 1 illustrates the time series and regional patterns of the maximum allowable taxable earnings. 10

3.25

3.2 3.15 3.1 3.05 3

2.95 1996[1997]

1997[1998]

1998[1999]

1999[2000]

2000[2001]

fisical year [observed year] region B

2001[2002]

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region A

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Max allowable income (million yen)

3.3

Figure 1. Change in maximum allowable taxable earnings by region for the pre- and post-reform period

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Notes: The calculations were based on applications for student loans, published by JASSO. Each level of the maximum allowable taxable income (million yen: 1 US dollar is approximately 100 yen) is based on with the observed year in brackets.

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three-member households and Category I student loans. The figures on the horizontal axis are the fiscal year,

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The figures on the horizontal axis are the fiscal year, with the observed year in brackets.7 The figures on the vertical axis are the maximum allowable annual taxable earnings, in million yen. The line with

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the diamond symbol is a time series pattern of the maximum allowable income for region A and the line with

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the square symbol is the same for region B. As illustrated in the graph, during the pre-reform period, the maximum allowable taxable earnings differed by region. However, in the post-reform period, the maximum allowable income was standardized for both regions following region A’s allocation. Note that taxable

Note that the fiscal year is different from the observed year. Twelfth-grade students in 1999, who enroll in university in 2000, were not affected by the reform, but twelfth-grade students in 2000, who enrolled in universities in 2001, were affected. We describe the detail in section 4. 7

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earnings are different from actual disposable income for a household. The taxable earnings for an application are calculated from income, including wages, pension, and so on, minus the tax deduction, which varies depending on income class and number of dependents. The maximum allowable taxable earnings differ depending on whether the applicant is self-employed or not. It can be difficult for households applying for student loans to estimate their accurate taxable income. If taxable earnings on the application does not exceed

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the maximum allowable taxable earnings, there would be a high probability of receiving a student loan. The change in maximum allowable taxable earnings for student loan applications was exogenous for households.

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With regard to this, we compared the changing educational outcomes by region. Since a region is a set of municipalities, we implemented a difference-in-differences (DD) analysis using municipality panel data.

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It is worth mentioning that no other education policies were implemented in 1999. The changes in

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educational policy from the 1990s to 2000s that could affect one’s decision to go to college other than the

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1999 JASSO loan reform were the alleviation of College Setting Standard and the revision of the curriculum standard. The alleviation of College Setting Standard refers to the rules related to the establishment and

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expansion of colleges capacities. It was difficult for university establishments to expand college capacities,

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because the rules strictly regulated the quantity. After the alleviation of College Setting Standard in 1991, university establishments easily established and expanded their capacities. In fact, the college capacity per 10,000 high school students was 867.6 in 2000, compared with 493.3 in 1990. Although this alleviation occurred at the national level and not at a regional level, it could affect the enrollment decisions of high school students sensitive to the capacity changes in local areas. To deal with this problem, we employed the capacity 12

of colleges per high school students in the prefecture per year as a control variable. Another factors affecting enrollment decisions could be the curriculum standard revision. In Japan, materials taught and the teaching hours at elementary, secondary, and high schools are regulated by the curriculum standards issued by MEXT. The curriculum standards have been revised every decade since 1947. The revision after the 1989 revision was made in 1999, yet this revision did not affect the enrollment decision

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of high school students in 1999. The announcement was made in 1999; however, the implementation of the curriculum standards revision for high school students was implemented in 2003. Therefore, the

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announcement of the curriculum standard revision could not affect the enrollment decision.

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3. Empirical Framework

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The schooling model suggests that a person will decide to go to college if the present value of his/her possible

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lifetime earnings with tertiary qualification exceeds the present value of earnings with a high school diploma. This model also suggests that a decline in college enrollment fees would correlatively increase the enrollment

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rate. In this context, the expansion of eligibility for student loans can be interpreted as a reduction in college

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enrollment costs.

Our identification strategy relies on the evaluation of state merit aid using variations in both birth

cohort and birth state (Cornwell, Mustard, & Sridhar (2006), Singell, Waddell and Curs (2006), Winters (2012)). To evaluate the expansion effect of applicant eligibility for student loans on college enrollment across Japanese municipalities, we compare the treatment group (region B) with the control group (region A) to 13

obtain a DD estimate. The basic approach is to compare changes in educational outcomes in municipalities where applicant eligibility for student loans was expanded due to the reform (i.e., region B) with municipalities where no change occurred (i.e., region A). The basic estimation for Equation (1) is as follows: 𝑌𝑖𝑡 = 𝛽 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 ∗ 𝑃𝑜𝑠𝑡𝑖𝑡 + 𝜏𝑡 + 𝑐𝑖 + 𝑋𝑖𝑡 𝛾 + 𝑅𝑖 ∗ 𝜏𝑡 + 𝜖𝑖𝑡 , (1)

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where Y indicates the college enrollment rate, i indicates the municipality, and t indicates the year. Our specification allows the multiple year t. Treatment is an indicator variable equal to one if the municipality was

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subject to reform, and zero otherwise. The treatment dummy is defined as cities in region B, which correspond to the second level and lower regions of the Public Assistance System. Post dummy is an indicator variable

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equal to one if the years were post-reform, and zero otherwise. 𝜏𝑡 includes year fixed effects and 𝑐𝑖 includes

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the municipality fixed effects. The year fixed effects capture the time-variant but common factors for the

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municipality. The municipality fixed effect captures the time-invariant and unobservable factors that affect enrollment. X includes the covariates that affect enrollment (per-capita household income and student/teacher

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ratio in high school). The capacity of colleges in the region where high school students live is regarded as

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another factor that affects college enrollment rates (Kobayashi 2009, Kikuchi 2017). DD estimator might be sensitive to the change of capacity in local areas, because many local cities and towns are involved in the treatment group. To deal with this problem, we added the capacity of colleges per high school students in the prefecture as a control variable. Additionally, ϵ is the error term. The source of the variation of the treatment dummy comes from across municipalities, because treatment status is determined by municipalities in region 14

B. Our specification allows the municipality fixed effects to capture the time-invariant, unobservable factors that affect enrollment but do not to capture the time-variant ones. The municipality specific trend is not included in our specification because the treatment dummy has the same variation as the municipality specific trend. Instead, we include prefecture specific trends, 𝑅𝑖 ∗ 𝜏𝑡 , to capture the time-variant, unobserved effects. All estimations in this study use weights based on the number of high school students in the previous year to

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adjust the imbalance of population size between treatment and control groups. Our parameter of interest is β, which represents the coefficient of the interaction term between the

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post-reform dummy and treatment dummy. Unfortunately, we cannot estimate the effects of the recipient of a student loan on college enrollment, because JASSO’s loan approval rate is unavailable at the municipality

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level. Instead, we interpret β as the expansion effect of applicant eligibility on college enrollment

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(Intention-to-treat effect).

4. Data

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The fundamental data source of our study is the Basic Municipality Data taken from the Statistical

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Information Institute for Consulting Analysis.8 This data set includes some demographic information for the municipalities that was derived from various surveys conducted by the Japanese government. For example, the college enrollment rate and number of high school students are taken from the Basic School Survey, conducted by MEXT.

8

Author purchased CD-ROM in 2010.

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The college enrollment rate, our variable of interest, comes from the Basic School Survey. We explain the details of this survey related to our empirical strategy. The Basic School Survey is conducted every May for all schools in Japan to obtain basic information about each school, such as the number of students. The Basic School Survey is comprised of two survey questionnaires, a School questionnaire and Graduation situation questionnaire. The School questionnaire includes information such as the number of students

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enrolled as of the first of May in the survey year, while the Graduation situation questionnaire includes information such as the number of graduates in March of the last school calendar year. For example, the Basic

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School Survey in survey year 2000 had the number of high school students in twelfth grade in 2000, who were born in 1982, and the number of graduates in 2000, who were born in 1981. The JASSO reform was

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implemented in 1999; the post-reform cohort was born after 1982, while the pre-reform cohort was born

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before 1981. Therefore, the post-reform period corresponds to the 2001 survey year for the Basic School

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Survey.

The college enrollment rate is collected per high school municipality in the Basic School Survey.

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Additionally, the eligibility criteria for student loans is based on household earnings depending on the

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recipient’s municipality of residence before college enrollment. Thus, treatment status is determined by the recipient’s municipality of residence before college enrollment. We use the college enrollment rate, defined as number of college and university students enrolled

divided by 18-year-old bracket population, aggregated by school location municipality. We also use the college enrollment rate by gender. Unfortunately, we cannot separate the data between colleges and 16

universities. In addition, we cannot identify the specific colleges or universities high school graduates attended. We use one-year lagged student-teacher ratio in high school and one year lagged per capita taxable income for covariates to control the enrollment to college for households. The student-teacher ratio in high school is defined as the ratio of the number of high school students and number of high school teachers, which

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comes from the Basic School Survey. Unfortunately, these variables do not divide by gender or grade. Therefore, the student-teacher ratio in high school is a proxy variable for overall school quality of the schools

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located in the municipality. Per capita taxable income, from the National Tax Survey, indicates the household economic situation of households located in the municipality. Both covariates are taken as one year lagged to

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fit the timing of the observed year of college enrollment rate. We employed the capacity of colleges per

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10,000 high school students in the prefecture as a control variable. The capacity of colleges in prefectures

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comes from the Digest of University published by the Association of Education. Our sample period is restricted to 1998 to 2003, although the Basic Municipality Data contains

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information from 1980, based on the following reasons. First, we apply the DD method to focus on the effect

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of reform on college enrollment driven only by the JASSO reform. The DD method is used for detecting short-rum effects and relies on the parallel trend assumption. We decided to use data from after 1998 after checking the parallel trend assumption, as described below. Second, the number of municipality mergers increased rapidly after 2004. The number of municipalities was 3212 in 2003, 3132 in 2004, 2521 in 2005, and 1821 in 2006. Municipality codes in our 17

data set are recorded at the survey time, figures in the data set for the post-merger period are merged by the new municipality code. We cannot retroactively apply the pre-merger codes to the figures. For this reason, we exclude samples after 2004 in the analysis. In addition, we exclude samples with missing values. The sample size of our municipality panel data from 1998 to 2003, after the above sample restrictions, is approximately 11,000 with 1900 municipalities. Table 2 presents the descriptive statistics

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divided into four groups: the treatment and control groups both before and after reform. Our estimation is based on a comparison across the groups with a parallel trend prior to the reform

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and not the groups with similar characteristics. Therefore, the magnitude of variables, such as enrollment rate, potentially differs across the control and treatment groups. In fact, according to Table 2, all variables for the

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treatment group are smaller than those for the control group. Although there are magnitude differences, we

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focused on the trend change from the reform. Because an advanced country such as Japan has experienced

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the tendency matters.

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higher tertiary education enrollment rate, the impact on the magnitude is not important; rather, the impact on

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Table 2. Descriptive statistics of the municipality data Treatment Pre-Reform 1998-2000

Post-Reform 2001-2003

Std. Dev.

Mean

Std. Dev.

Enrollment Rate

29.92

18.93

30.46

18.14

Enrollment Rate (Male)

28.64

19.96

30.21

19.06

Enrollment Rate (Female)

31.53

19.75

31.21

19.16

Per capita Household income

31.11

4.05

31.05

3.78

12.93

3.52

12.57

3.41

776.10

518.48

877.83

560.22

2nd class area1

0.07

0.25

2nd class area2

0.04

0.20

3rd class area1

0.33

0.47

3rd class area2

0.56

0.50

teacher The capacity of colleges per 10000 high school students in prefecture

N

0.07

0.25

0.04

0.20

0.33

0.47

0.56

0.50

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High school student / High school

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Mean

5290

5167

Control

Post-Reform 2001-2003

Std. Dev.

Mean

Std. Dev.

12.41

49.12

11.85

Enrollment Rate

48.83

Enrollment Rate (Male)

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Mean

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Pre-Reform 1998-2000

43.60

13.19

46.32

11.91

52.66

13.64

51.33

13.22

41.04

6.12

40.49

6.68

17.07

1.98

16.57

2.06

######

1627.09

2554.34

1688.00

1st class area1

0.61

0.49

0.61

0.49

1st class area2

0.39

0.49

0.39

0.49

Enrollment Rate (Female) Per capita Household income teacher

na

High school student / High school

The capacity of colleges per 10000 high

Jo

ur

school students in prefecture

N

385

4. Empirical Results 4.1. Basic Results

19

375

Table 3 presents the results of the DD estimation based on equation (1) to control the unobserved heterogeneity by using municipality fixed effects from the 1998 to 2003 sample. The treatment dummy is defined as cities in region B, in which eligibility for student loans was expanded due to the JASSO reform. Post dummy is an indicator variable equal to one if the years were post-reform, and zero otherwise. The DD estimator indicates the coefficient of the interaction term between the treatment and post dummy. We present

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results with and without covariates, such as student teacher ratio, per capita taxable income, the capacity of

Table 3 DD using 1998-2003 data

Dependent Variable

(2)

(3)

College Emrollment Rate 1998-2003

Year Effects

(6)

(7)

(8)

(9)

College Emrollment Rate(Female)

1998-2003

1998-2003

0.393

0.378

0.536**

-0.139

-0.0140

0.678**

0.671**

0.587**

0.281

(0.320)

(0.309)

(0.261)

(0.495)

(0.493)

(0.342)

(0.269)

(0.258)

(0.278)

na

Treatment*Post

(5)

College Emrollment Rate(Male)

lP

Sample Period

(4)

re

(1)

-p

colleges per 10,000 high school students in a prefecture and prefecture specific trend.

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

N

Y

Y

N

Y

Y

N

Y

Y

N

N

Y

N

N

Y

N

N

Y

Observations

11,217

11,217

11,217

11,084

11,084

11,084

11,014

11,014

11,014

# of municipalities

1,920

1,920

1,920

1,911

1,911

1,911

1,909

1,909

1,909

Effect Covariates

ur

Municipality Fixed

Jo

Prefecture Trend

Notes: The dependent variable is college enrollment rate. All estimations in this study use weights based on the number of high school students in the previous year. The figures in parentheses are clustered robust standard errors at the municipality level. *, **, and *** represent significance at 10%, 5%, and 1%, respectively. Covariates includes per capita household income, student-teacher ratio in high school and the 20

capacity of colleges per 10,000 high school students in a prefecture.

Columns (1) - (3) in Table 3 give the results for college enrollment rate with and without covariates and prefecture specific trend. Our estimates for the college enrollment rate show the coefficient of interaction term between the treatment and post-reform dummy is positive but statistically insignificant in the regression

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with and without covariates (columns (1) and (2)). Column (3), our preferred specification, shows that the coefficient estimate becomes larger and statistically significant at the 5% level.

There may be a heterogonous effect of the reform on the enrollment decision between males and

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females, because they faced different costs in the schooling decision. Columns (4) - (9) in Table 3 tabulate the

re

DD estimation results as differentiated by gender group. Columns (4) - (6) provide the results for male college

lP

enrollment rate, which controls for year and municipality fixed effects, with and without covariates and prefecture specific trend. The coefficient of interaction term between the treatment and post-reform dummy is

na

negative and statistically insignificant when we do not control for prefecture specific trend (columns (4) and (5)). However, the coefficient estimate for the male college enrollment rate becomes positive and statistically

ur

significant at the 5% level when we control for covariates and prefecture specific trend (column (6)).

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Columns (7) - (9) provide the results for the female college enrollment rate. Our estimates for the college enrollment rate show that the coefficient of interaction term between the treatment and post-reform dummy is positive and statistically significant in the regression without the covariates and prefecture time trend (Columns 7 and 8). However, the coefficient estimate for the college enrollment rate of female becomes statistically insignificant when we control for the prefecture specific trend (Column 9). 21

For the analysis of male students, the estimation results indicate a similarity to the results of the overall sample. However, for female students, the results demonstrate a lesser degree of difference in terms of the overall reform impact. This result indicates that males are more sensitive to the availability of financial aid for enrollment than females. These results indicate that the expansion of eligibility for student loans improved college enrollment

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for male applicants. The specification allowing the year, municipality fixed effect, covariates, and prefecture specific trend is preferred due to controlling factors that affect the enrollment decision. The magnitude of the

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expansion of eligibility for student loans is around a 0.5 to 0.7% increase in the college enrollment rate. Given that around 28 percent of our male sample lives in their resident prefecture, an increase of 0.5 to 0.7

re

percentage points is not incredibly large, but it is certainly non-trivial.

lP

Interestingly, females are less sensitive to the expansion of eligibility for student loans. One

na

interpretation is that female students would avoid the student loan burden. Because the labor market is strict for females, their life-time income would not be enough to repay their loans (Armstrong et al., 2018). Another

ur

interpretation is that male students potentially crowded out female students. Males are more sensitive to the

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expansion of eligibility for student loans because larger number of them attended college. After controlling the capacity of colleges and universities during the reform, males could have crowded out female students.

4.2. Robustness Check The parallel trend assumption is the most important assumption for the difference-in-differences model. It is 22

not easy to test the parallel trend assumption directly, because the counterfactual trend for the treatment group could be unobservable. Alternatively, to test the parallel trend assumption, we performed a DD estimation using a “placebo” reform year.

Table 4 Placebo Tests (2)

Dependent Variable

(3)

(4)

(5)

College Emrollment Rate All

Male

Female

All

Male

2001-2002

Treatment * "Post" -0.250

0.146

-0.0832

0.0466

-0.0897

(0.299)

(0.463)

(0.398)

(0.278)

(0.409)

(0.478)

Y

Y

Y

Y

Y

Y

Municipality Fixed Y

Covariates

Y

Prefecture Trend

Y

Observations

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

3,779

3,733

3,765

3,741

3,699

3,724

1,896

1,874

1,893

1,891

1,878

1,885

ur

# of municipalities

Y

na

Effect

re

Year Effects

-0.0348

lP

dummy

Female

-p

1999-2000

Sample Period

(6)

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(1)

Jo

Notes: The dependent variable is college enrollment rate. “Post” dummy is one for year 2000 for the columns (1)–(3) and “Post” dummy is one for year 2002 for the columns (4)–(6). All estimations in this study use weights based on the number of high school students in the previous year. The figures in parentheses are clustered robust standard errors at the municipality level. *, **, and *** represent significance at 10%, 5%, and 1%, respectively. Covariates includes per capita household income student-teacher ratio in high school and the capacity of colleges per 10,000 high school students in a prefecture. To examine the parallel trend assumption, we divided the data into two periods and only used the

23

samples from two years: pre-reform (1999-2000) and post-reform (2001–2002). Table 4 presents the difference-in-differences results for the effects of student loan reform on the college enrollment rate using pre-reform and post-reform periods. All regressions include dummies for year and municipality fixed effects, covariates, and prefecture specific trends. Columns (1)–(3) in Table 4 show the DD results from 1999 to 2000, when the reform had not yet

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been actually implemented. “Post” dummy is one for year 2000 for the columns (1)–(3). The coefficient of interaction term between the treatment and “post” dummy is statistically insignificant in all cases, which

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implies that the pre-trend is parallel in the treatment and control groups. Columns (4) –(6) in Table 4 show the DD results from 2001 to 2002, namely the two years after the reform. “Post” dummy is one for year 2002 for

re

the columns (4)–(6). The coefficients of the interaction term between the treatment dummy and “post” dummy,

lP

defined as one for year 2001, are not statistically significant in any cases.

na

One alternative to our baseline estimation is to conduct an event style analysis in which we allow the effects of the expansion of eligibility for student loans by reform to vary over time by including separate

ur

treatment dummies for each year before and after the reform in event time. This would also implement a

Jo

placebo test since there should be no effect in the years immediately before the reform. The equation for the event style analysis basic estimation is as follows:

𝑌𝑖𝑡 = 𝛿𝑡 ∑3−2 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 ∗ 𝑒𝑣𝑒𝑛𝑡_𝑦𝑒𝑎𝑟𝑖𝑡 + 𝜏𝑡 + 𝑐𝑖 + 𝑋𝑖𝑡 𝛾 + 𝑅𝑖 ∗ 𝜏𝑡 + 𝜖𝑖𝑡 , (2) where the event year indicates the survey year minus reform year. We allow the year, municipality fixed effect, covariates, and the prefecture specific trend. 24

Panel B. Male 1 -1

-.5

0

0

.5

1

2

Panel A. All

-2

-1

1 event year

2

3

-2

-1

1 event year

2

3

-.5

0

.5

ro of

1

1.5

Panel C. Female

-1

1 event year

2

3

-p

-2

Figure 2. Coefficients of the impact of the reform on the rate of college enrollment

re

Notes: The coefficients and their 95% confidential intervals are taken from Table A. Figures in the horizontal

lP

axis are the event year, defined as the survey year subtracted from the reform year.

na

Figure 2 shows a visual representation of the results of an event style analysis based on the estimation result reported in Appendix Table A. Each point indicates the time series pattern of the reform

ur

impact, and the solid lines indicate a 95% confidential interval. The horizontal axis is the event year, defined

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as the survey year subtracted from the reform year. Panel A indicates the result for the college enrollment rate. The coefficients of the interaction terms between the year and treatment dummy from two years and one year before the reform are not statistically significant, which implies that the parallel trend assumption is satisfied. The coefficient of the interaction term between the treatment dummy and one year after becomes positive and statistically significant at the 5% level, and it remains positive and statistically significant at the 10% level 25

after two years. Panel B in Figure 2 shows the result for the male college enrollment rate. The coefficients of the interaction terms between the year and treatment dummy from and one year before the reform are not statistically significant at the 5% level, but, in the case of two years before, the coefficient is statistically significant at the 5% level. The coefficient of the interaction term between the treatment dummy and from one

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year to two years after becomes positive and statistically significant at the 5% level. In the case of females, shown in panel C, the parallel trend assumption is satisfied. The coefficient of the interaction term between

-p

the treatment dummy and from one year to three years after are positive but statistically insignificant at the 5% level.

re

The overall result is driven from the male result. The magnitudes of coefficient are the largest at one

lP

year after, gradually decreasing. The impact of the expansion of student loan eligibility on college enrollment

na

lasted at least three years, and might return to the original trend.

4.3. Estimation Result by Class Area

ur

In this subsection, we investigate whether our estimation results are heterogeneous across income

Jo

groups using the constitution of treatment group. The treatment group is defined as region B, where applicant eligibility for student loans expanded due to the reform. Region B consists of second-class and third-class areas, corresponding to the Public Assistance System in Japan, described in Section 2. The class area is set based on a standard consumption level using the National Consumption Survey. Roughly speaking, residents in the second-class area are wealthier than residents in the third-class area. We then interact the post-reform 26

dummy with the dummy for each class area: second-class area1, second-class area2, third-class area1, and third-class area2. We run the DD estimation using the 1998 to 2003 sample, allowing the year, municipality fixed effect, covariates, and the prefecture specific trend.

Table 5 Treatment as Rank DD using 1998-2003 (2)

(3)

College

College

College

Emrollment Rate

Emrollment Rate

Emrollment Rate

(Male)

(Female)

1998-2003

1998-2003

1998-2003

0.466

0.354

0.479

(0.298)

(0.395)

0.440

0.595

(0.396)

(0.566)

0.615**

0.615

0.261

(0.284)

(0.388)

(0.324)

0.553*

0.992**

0.116

(0.465)

(0.303)

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Observations

11,217

11,092

11,153

Number of prefcity

1,911

1,911

1,909

Post*2nd class area2

Post*3rd class area1

Post*3rd class area2

(0.326)

Y

Municipality Fixed

Covariates

ur

Effect

na

Year Effects

Jo

Prefecture Trend

-p

Post*2nd class area1

(0.340) 0.398

(0.458)

re

Sample Period

lP

Dependent Variable

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(1)

Notes: The dependent variable is the college enrollment rate. All estimations in this study use weights based on the number of high school students in the previous year. The figures in parentheses are clustered robust standard errors at the municipality level. *, **, and *** represent significance at 10%, 5%, and 1%, respectively. Covariates includes per capita household income, student-teacher ratio in high school and the capacity of colleges per 10,000 high school students in a prefecture. 27

Table 5 shows the estimation results for heterogeneous, cross-income groups using the 1998 to 2003 sample. Columns (1) shows the coefficient of interaction term between the post-reform dummy and each class dummy in the third class, are positive and statistically significant. Interestingly, the lower the income level, as indicated by class area dummy, the larger the magnitudes of the coefficient of interaction term. The impact of

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the expansion of student loan eligibility is larger for areas where lower income residents with credit constraints live.

-p

Columns (2) and (3) show the results by gender. While the male result is the same as the overall result, females did not respond to the expansion of student loan eligibility in each class area. This result

re

indicates that males are more sensitive to financial aid access for enrollment than females living in lower

na

lP

income area.

5. Concluding Remarks

ur

This study examines whether the criteria expansion for student loan eligibility promotes the college

Jo

enrollment of high school graduates in Japan. The Japan Student Services Organization revised the eligibility criteria of the student loans system in 1999 based on household earnings. Before the revision, the maximum allowable earnings to apply for student loans differed across regions; the criteria in some regions was lower than others. After the revision, the criteria in regions with lower maximum allowable earnings were adjusted upward to match regions with a higher maximum. 28

We conducted a difference-in-differences estimation by using municipal data from 1998 to 2003. We found that the expansion of eligibility for student loans improved the male college enrollment rate by around 0.5 to 0.7% points, while the female enrollment rate is less sensitive to the expansion of student loan eligibility. The impact of the expansion of student loan eligibility is larger for regions with lower income residents. Certainly, more analyses using a data set with an individual level, a larger sample size, and in

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different periods are necessary to generalize our findings for definitive policy suggestions. These analyses are

Jo

ur

na

lP

re

-p

left for future research.

29

Appendix A Table A Genialized DD using 1998-2003 (1)

(2)

(3)

College

College

College

Emrollment Rate

Emrollment Rate

Emrollment Rate

(Male)

(Female)

1998-2003

1998-2003

1998-2003

0.305

0.892**

0.249

(0.266)

(0.439)

(0.450)

-0.182

-0.289

(0.199)

(0.312)

0.587**

0.815**

(0.271)

(0.346)

(0.284)

0.529*

0.777**

0.393

(0.376)

(0.342)

0.684

0.0657

(0.332)

(0.427)

(0.372)

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

11,217

11,092

11,153

1,911

1,911

1,909

Dependent Variable

Sample Period

reform

reform

Treatment* 1 year after reform

(0.278) Treatment* 3 years after 0.468

Year Effects Municipality Fixed Effect Covariates

Jo

Observations

ur

Prefecture Trend

na

lP

reform

re

Treatment* 2 years after reform

Number of prefcity

0.0437

(0.262) 0.447

-p

Treatment* 1 year before

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Treatment* 2 years before

Notes: The dependent variable is college enrollment rate. The figures in parentheses are clustered robust standard errors at the municipality level. *, **, and *** represent significance at 10%, 5%, and 1%, respectively. Covariates includes per capita household income and student-teacher ratio in high school.

30

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Dynarski, M. S. (2003). Does aid matter? Measuring the effect of student aid on college attendance and completion. American Economic Review, 93(1), 279-288.

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Goodman, Joshua. (2008). Who merits financial aid? Massachusetts’ Adams Scholarship. Journal of Public

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jittaityousa no kohyo data wo motiite"[Scholarship and Cousumer Behavior of Students], Seikatsu keizaigaku kenkyu, No.33, pp.19-32

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Shirakawa, Y. and Maehata, Y. (2012). “Nihon” [Japan], Kobayashi, Masayuki edit “Kyoiku kikai heno

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cyo-sen” [The Challenge to Equal Opportunity of Education], Toshindo, pp. 47-104 Singell, Larry D., Glen R. Waddell, and Bradley R. Curs. (2006). HOPE for the Pell? Institutional effects in the intersection of merit-based and need-based aid. Southern Economic Journal 73, no. 1, pp.79-99.

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