Silviase/jgov_v1.4_full_ann huggingface dataset:
### 4. Data and Methodology

### 4.1 Data

Our main data source is the Japanese General Social Surveys (JGSS) $ ^{10} $ and is collected for nine years: 2000 to 2003, 2005, 2006, 2008, 2010, and 2012. This data comprise citizens aged between 20 and 89, who are chosen using stratification sampling of the population by regions (six regions) and by population size of cities/districts (four categories). The primary sampling units are based on the basic unit districts used for the Population Census. Each survey includes 2000 to 5000 observations and provides information about the following variables: age, sex, education, labor force state, occupation, employment status, firm size of the employer, work hours, and information about members in the household.

### 4.2 Methodology

We use a microsimulation model to produce individual income and loan repayment profiles, and then calculate fiscal costs of ICLs. $ ^{11} $ The structure of our simulation model is based on Higgins and Sinning (2013) and Higgins (2011), with some modifications. The simulations are conducted using three steps.

In the first step, we estimate distributions for the simulation. As the available data are categorical answers of income classes, they need to be converted into numeric answers using kernel density estimation (Appendix A1.1). We then pick up samples of university graduates aged 59 or under, and estimate two equations: probabilities of labor force states, $ P(L|M, C) $ with L=1 (Employed) and L=0 (Not employed); and probabilities of employment statuses, $ P(E|M, C, L=1) $ with E=1 (Full-time regular employment) and E=0 (Non-regular employment). The variables, M and C, show marital status and dependent child situations, respectively. The details are given in Appendix A1.2.

In the second step, we simulate individual annual revenue profiles, using the obtained distribution. Here, we will make specific assumptions of M and C. That is, we will deal with two cases:

• Case 1: all the males and females get married at age 29, and have a child at age 30; and

• Case 2: all the males and females remain single until age 59 with no child.

For each of the cases, the individuals' labor force state, L, is given by a draw, which follows the estimated P(L|M, C). If they are determined to stay outside the labor force, their revenues are set to zero. Otherwise, we go on to a next draw to decide employment status,
which follows $ P(E|M, C, L=1) $ . Finally, individual annual revenue is determined by a draw from an actual distribution, conditional on L=1 and E, M, and C, that is $ P(Y|E, M, C, L=1) $ . We then change time varying characteristics, such as age, marital status, presence of child, etc., and repeat the above procedures for every year (Dynamic Ageing). Therefore, income profiles of all individuals in the hypothetical cohort are obtained.

In the third step, we calculate fiscal costs of student loans for two repayment types, the ICL and fixed repayment student loan systems, in each case, for a comparison purpose. The formula of fiscal costs is given below,

$$ fiscal costs=\frac{PDV(Debt)-PDV(Repayments)}{PDV(Debt)}=\frac{2.36-PDV(Repayments)}{2.36}. $$

Here, debt outstanding is set to 2.36 million yen at age 22, which is the average debt outstanding of JASSO free-of-interest (or Type 1) loans at the end of FY2015. The discount rate is initially set to 0.1%, taking into account recent developments of long-term interest rates but is changed later as a parameter to examine variability of the fiscal costs. The above second and third steps are repeated for each member of the generated cohort.

A caveat is in order. We regard the information about age profiles gained from the cross section data as valid approximation to age profiles over time. Computational restraints due to the number of samples disallowed from using year dummies in the logit models given in Appendix 1. As a result, our simulations might be affected by cyclical and structural factors. Overall, there were slacks in labor markets in the sample periods, despite a large fluctuation before and after the Global Financial Crisis. The average of unemployment and jobs-to-applicants ratio were 4.73% and 0.73, respectively (Figure 2). The prolonged stagnation has made age-wage profiles flatter (Hamaaki, Hori, Maeda, and Murata, 2011).
Figure 2 Overall Labor Market Situations in Sample Years

Sources: Ministry of Internal Affairs and Communications; Ministry of Health, Labor and Welfare.

### 4.3 Detailed Specifications of Student Loan Schemes

It is necessary to specify details of loan schemes to calculate fiscal costs. We will make the ICL scheme as close to the newly launched one in April 2017, as possible. As the new scheme is launched as a Type I loan, the interest rate charged is set to 0%. The repayments are set to 9% of taxable income (i.e., tax base of local inhabitant taxation) that is calculated as annual revenues minus income deductions. We will take into account only three income deductions: employment deduction, social contributions deduction, and basic deduction. We will ignore the other deductions, such as spouse tax deduction. If the borrowers' annual revenues are below 3 million yen, they are exempted from repayments. This exemption is available for at most 10 years. After exhausting the exemption period, borrowers are required to repay a minimum of 24,000 yen per year, even if they earn nothing. Note that the minimum payments are clearly against the spirit of the ICLs, but are incorporated in the current ICL scheme. We will assume that, even if borrowers become dependents, they make the same repayments as they do when they are singles. We do not consider how much their supporters earn and whether they may change their type of repayment from ICL to fixed repayment, though these are handled in the actual system.

We will also calculate fiscal costs of the Type I fixed repayment loan scheme for comparison. Therefore, the interest rate of loans is again set to 0%. The repayment is set to 160,000 yen for 15 years, which is determined by the JASSO. However, this exemption scheme is available for current Type I loans. That is, if borrowers' annual revenues are below 3 million yen, they are exempted from repayments for at most 10 years.
### 5. Simulation Results

### 5.1 Earnings

Let us look at the simulated earnings, which are of critical importance, as emphasized by Dearden (2017). As for Case 1 shown in Figure 3, male earnings tend to increase with larger disparities, as males become older. As a result, the repayment exemption is only available for those in their 30s to 40s, who are below the 10th percentile in the earning distribution, and is more widely applicable to those in their 50s.

The graph for females tells a very different story. Only those who belong to the top tail can enjoy increases in earnings, while earnings of the rest stay almost flat after the age of 30. In fact, those appearing at the 75th percentile of the earning distribution are below the threshold of 3 million yen. Therefore, most females would be eligible for repayment exemptions throughout their working lives, if the exemptions were applied without the limit of 10 years.

Gender difference is less visible in Case 2 as shown in Figure 4. This is because females are more likely to continue working due to the assumption that they stay single. This is also because single males tend to include low-income earners, and male earnings are likely to be lower than in Case 1. The threshold of 3 million yen is located at around the 25th percentile and the median of the male and female earnings distributions, respectively.

Earnings developments greatly reduce the pace of debt outstanding over time. Figure 5 shows the debt outstanding of both sexes in Cases 1 and 2, together with the fixed repayment type loan counterparts. In Case 1 (shown on the upper left of Figure 5), on an average, males complete repaying their debts in their late 30s, while females, again on an average, do not complete repaying, and still hold some debt at age 59. This is because females leave their jobs at 30 after their marriage. Their revenues are lower than 3 million yen and remain almost unchanged during the simulation period, thereby leaving them in debt at age 59.
Figure 3 Simulated Earnings in Case 1

(2) Females
Figure 4 Simulated Earnings in Case 2

(1) Males

(2) Females
Figure 5 Debt Developments over Repayment Periods

Simulated value, averaged over all persons at each age

Note that fixed repayments may greatly change situations (shown on the upper right of Figure 5). After enjoying 10-year exemptions, females have to repay 160,000 yen, which enables them to complete repaying the debts at age 47. $ ^{12} $ Since fixed repayments are lower than those in the ICL scheme, males delay getting out of debt by five years.

As for Case 2, both males and females continue to work because they remain single during the simulation period. In terms of ICLs, males complete repaying their debts at about age 45, later than the age in Case 1, thereby reflecting lower wages of single workers. Almost all females complete repayments at age 59. In the fixed repayment scheme, both males and females complete repayment at age 46, much earlier than repayment for ICLs for females.

### 5.2 Fiscal Costs

Simulating earnings will allow us to calculate the fiscal costs defined in Section 3. The results are summarized in Figure 6 that shows the fiscal costs for a range of discount rates. We will concentrate on two cases with discount rates equal to 0.1% and 2%, respectively. In general, setting the discount rate is related to our projection of the future course of the Japanese economy. If we are pessimistic and think that getting out of the current extraordinary monetary easing may be unlikely in the foreseeable future, the discount rate should be almost zero. On the other hand, if we are optimistic about the future of the economy and expect the
monetary easing to be corrected soon, the rate should be set to a certain positive figure. Taking into account the above, we will pick up these discount rates and further investigate their fiscal consequences.

Figure 6 Fiscal Costs Comparison between the Current ICL and Fixed Repayments by Case

Fiscal cost = [2.36 - PDV(repayment flows from age 22 to 59 averaged over all persons)] / 2.36 * 100

Table 1 shows the results, together with their fix repayment counterparts. As expected, setting discount rates greatly affects the fiscal costs. Roughly speaking, apart from large differences observed in female ICL cases, the fiscal costs are almost in the same magnitude for both discount rates: they fall in the range of 1 to 2% for 0.1% discount rate, and almost around 20% for 2% discount rate. In other words, as long as borrowers continue to work, consequences due to the different natures of repayment schemes – income contingent or fixed – are rather minor. As high-income earners are allowed to complete repaying earlier, fiscal costs for males are lower in the ICL scheme than those in the fixed repayments.

It is notable that applying ICL to females in Case 1 turns out to be very costly: twice compared to that of male counterparts for the discount rate of 2%, and nine times for 0.1%, respectively. The higher costs of the ICLs are accounted for by females unwilling to provide market services. The costs are higher as the discount rate is set lower. Weighted averages of both cases could suggest an overall picture because both cases are likely to represent two extremes in terms of female labor supply behaviors.

However, the results depend on the assumption that borrowers' labor statuses changed every year, and therefore, their annual revenues are highly volatile. This comes from the assumption that an individual's labor status in period t is determined independently of that in period t-1. The resulting high labor mobility is likely to reduce the fiscal costs, given the
minimum earnings eligible for repayments. $ ^{13} $

Table 1 Simulation Results of Fiscal Costs with Mobility

Discount rate	Sex	Case 1	Case 2	Weighted Avg.
ICL	FR	ICL	FR	ICL	FR
0.1%	Total	5.1%	1.4%	1.3%	1.4%	4.3%	1.4%
Male	1.1%	1.3%	1.2%	1.4%	1.1%	1.3%
Female	10.1%	1.6%	1.5%	1.5%	8.5%	1.6%
2%	Total	27.3%	24.2%	22.7%	24.1%	26.4%	24.2%
Male	18.7%	21.8%	21.5%	23.6%	19.3%	22.1%
Female	38.0%	27.3%	24.3%	24.8%	35.4%	26.8%

Note: FR stands for “Fixed Repayment.” Full mobility means that labor statuses are determined by simulations every year.

Therefore, we will recalculate the fiscal costs with the assumption of no mobility. To be specific, we will calculate the fiscal costs if the borrowers stay at the same percentile of the earnings distributions during their lives. In other words, the borrowers are assumed to track the earning age profiles shown in Figures 2 and 3, if they happen to be located at one of the particular percentiles of 10, 25, 50, 75, and 90. This assumption is again obviously extreme, but could be helpful to assess how biased the above results are.

The changed assumption will produce Tables 2, which summarizes detailed information about males and females in Table A2 and A3, respectively (see Appendix 2). The impacts on the results, except for those of males in Case 1, are huge. The costs may increase by more than 10 percentage points for males in Case 2, and more than 20 percentage points for females in both cases. The increase in costs for males in Case 1 may be mild, about 4 to 5 percentage points.

Overall Table 2 shows that reduced mobility could increase the fiscal costs by up to 15 to 20 percentage points, depending on the assumption of the discount rate. To obtain more specific estimates, it is necessary to get the information at time t-1, and model how labor statuses at time t are related to the information at time t-1. Since the JGSS collects current information only, it is impossible for us to further investigate this point. Thus, allowing for
time dependency will require use of new information sources. For example, use of the labor force survey could provide transition matrices of labor statuses, and there are also several empirical studies on transitions among labor states. How to incorporate such information into our microsimulation exercises is beyond this study, and should be our future task.

Table 2 Simulation Results of Fiscal Costs without Mobility

Discount rate	Sex	Case 1	Case 2	Weighted Avg.
ICL	FR	ICL	FR	ICL	FR
0.1%	Total	26.0%	1.4%	23.3%	1.3%	25.5%	1.4%
Male	4.7%	1.2%	17.6%	1.3%	7.3%	1.3%
Female	53.3%	1.6%	30.6%	1.4%	49.0%	1.5%
2%	Total	41.4%	23.6%	39.6%	23.0%	41.1%	23.5%
Male	23.1%	21.6%	35.0%	22.8%	25.5%	21.8%
Female	64.9%	26.2%	45.5%	23.3%	61.2%	25.6%

Note: FR stands for “Fixed Repayment.” No mobility means that individuals are assumed to stay at the same percentile of revenue distributions during their lives.

### 5.3 Discussions

The simulation results show that female labor supply behaviors exert a significant influence on the fiscal costs. In particular, if married females provide more labor services in the market at higher wages, it will reduce fiscal costs. In this context, the government's initiatives related to female labor, such as the Plan to Realize the Dynamic Engagement of All Citizens, are very important.

The merit of ICLs is its insurance function against unfortunate labor market outcomes. Therefore, its universal application to students is desirable. This could mean that females who may be unwilling to continue working after their marriages are also eligible for the ICL scheme, thereby increasing the fiscal costs. Such would-be housewives could account for one out of seven among the current third-year female students (Figure 7). Therefore, in addition to an equity issue emphasized by the Ministry of Finance, $ ^{14} $ the universality issue is likely to pose an efficiency problem. Armstrong and Chapman (2017) argue for universality, pointing that the equity concerns could be alleviated by allowing an option of front-loaded payments that wealthy Australian students (as per their research) prefer to utilize and may be useful in Japan as well.

Even if the simulation results are accepted, it is necessary to get information about possible
benefits of the ICL in the context of cost-benefit analysis. A benefit of the ICL could be measured as the effects that the ICLs enable discouraged students for economic reasons under the fixed repayment loans to study at tertiary institutions. $ ^{15} $ Estimating such a benefit is beyond this study.

Figure 7 Career Preference Survey Results of Third-year Female Students

Source: Univ. of Kyoto, Univ. of Tokyo and DENTSU Scholarship Foundation (2015) “Report on University Students’ Career Preference: Changes over the period of 2007, 2010 and 2013”

### 6. Conclusion

This study has estimated the fiscal costs incurred by ICLs launched in April 2017 using a microsimulation approach. Examining single and married couple cases, the study has detected three factors to affect how costly the loan scheme is: the discount rate, female working conditions, and income mobility.

The case studies show that the range of the possible fiscal costs is very wide, and that the costs are, on average, 20 percentage points higher than their fixed repayment counterparts. The largest costs may amount to about 40% of the mean loan outstanding at the time of graduation. This means that the amount would not be repaid in PDV terms. This amount could
be reduced, depending on the three aforementioned factors. Some examples are: the discount rate falls from 2%; more married females continue working at higher wages; and more income dynamics are introduced, such as changes in the percentages of income distributions during individuals' lives. All these changes will cut fiscal costs.

The lowest cost would lie in the range of 1 to 5% of the mean loan outstanding. The possible range is very large and, therefore, needs to be reduced, which is the most important task in this microsimulation exercise. A promising way would be to incorporate time dependency into the draw performed every year to determine an individual's labor status. This makes it necessary to model the individual probabilities of labor status as conditional on the debtors' past situations. This should require information other than that provided by the JGSS.

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