GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No.105

The Degree of Judicial Enforcement and Credit Markets:
Evidence from Japanese Household Panel DataCharles Yuji Horioka and Shizuka Sekita

December 2009GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

The Degree of Judicial Enforcement and Credit Markets:
Evidence from Japanese Household Panel DataCharles Yuji Horioka†
Institute of Social and Economic Research, Osaka University
Shizuka Sekita‡Japan Society for the Promotion of Science (JSPS), Research Fellow
December 2009†

Corresponding author: Institute of Social and Economic Research, Osaka University, 6-1,
Mihogaoka, Ibaraki, Osaka 567-0047, Japan; Telephone: (81) 6-6879-8586/8574; Facsimile: (81)6-6878-2766; E-mail: horioka@iser.osaka-u.ac.jp‡
Institute of Social and Economic Research, Osaka University, 6-1, Mihogaoka, Ibaraki, Osaka
567-0047, Japan; Telephone: (81) 6-6879-8550; E-mail: sekita@iser.osaka-u.ac.jp

The Degree of Judicial Enforcement and Credit Markets:
Evidence from Japanese Household Panel Data
Abstract
In this paper, we conduct an empirical analysis of the impact of better judicial
enforcement on the probability of being credit rationed, loan size, and the probability ofbankruptcy using household-level data from the Japanese Panel Survey of Consumers,
conducted by the Institute for Research on Household Economics, in conjunction with
judicial data by court district on trial length and the ratio of the number of pending civil
trials to the number of incoming civil trials. Contrary to the predictions of the existing
theory, we find that better judicial enforcement increases the probability of being credit
rationed and decreases loan size. Furthermore, we find that better judicial enforcement
increases the probability of bankruptcy, a result that is consistent with lax screening
effects.
Keywords: Judicial enforcement; Credit allocation; Credit rationing; Bankruptcy;
Screening; Household Behavior; Borrowing
JEL classification numbers: D12; G21; G33; K12; K41; K42

Since the mid-1990s, the impact of legal systems on the economy has been thefocus of many theoretical and empirical investigations. As one example of this, manystudies, which originate with La Porta, Lopez-De-Silanes, Shleifer, and Vishny (1997),
have analyzed the relationship between legal systems and financial markets. In thesestudies, the following two channels through which the legal system affects financial
markets were identified: (1) the degree of judicial enforcement and (2) the content of thelaw.
In Japan, since laws apply to the nation as a whole, the content of the law isuniform in all judicial districts. However, the degree of judicial enforcement may varyfrom district to district. Thus, this paper focuses on differences in the degree of
judicial enforcement from district to district. The fact that the content of the law isuniform throughout the country in Japan makes it possible to distinguish the impact ofthe degree of enforcement from that of the content of the law, whereas this is notpossible in other countries, where different states have different laws.
The length of trials in Japan has become shorter and shorter over time. For
example, the average length of civil trial proceedings in district courts was 17.3 months
in 1973, 12.9 months in 1990, and 7.8 months in 2006. In 2003, the “Act on the
Expedition of Trials”1 was promulgated with the objective of concluding trials as
quickly as possible and protecting defendants’ rights through fair, appropriate, andadequate proceedings. Moreover, Japanese courts have conducted research on how to
improve the efficiency of trial proceedings. Several means have been used to achieve
this objective--for example, organizing issues more logically and intensively
investigating the most appropriate evidence.
Given these changes, the question that arises is how the duration of trials affects
economic behavior? Theory predicts that better judicial enforcement (i.e., faster court
proceedings) will decrease the probability of being credit rationed, increase loan size,
and increase the probability of bankruptcy. In this paper, we conduct an empiricalanalysis of the impact of better judicial enforcement (i.e., faster court proceedings) on
the probability of being credit rationed, loan size, and the probability of bankruptcy
using household-level data from the Japanese Panel Survey of Consumers, conductedby the Institute for Research on Household Economics, in conjunction with judicial databy court district on trial length and the ratio of the number of pending civil trials to thenumber of incoming civil trials.
There are at least three contributions of this paper. First, while many studies
conducted in Japan have analyzed the determinants of the probability of being credit
rationed and loan size, thus far no study has focused on the impact of the degree of
judicial enforcement on credit allocation. This is an important issue, especially inJapan, where the duration of trials has become shorter and is expected to become evenshorter in the future. Since the micro data on Japanese households from the “JapanesePanel Survey of Consumers”2 (hereafter the JPSC) contain detailed information on the
respondent’s residence, we could combine these data with data on judicial districts andanalyze the impact of the degree of judicial enforcement on credit allocation.
Second, in this paper, we controlled for more explanatory variables that capture
the local economic situation and local credit market activity than previous studies (for1
2

In Japanese, Saiban no Jinsokuka ni kansuru Houritsu.
In Japanese, Shouhi Seikatsu ni kansuru Paneru Chousa.
2example, gross domestic product, the bad loan ratio, market concentration, and thedepth of the credit market at the prefectural level). Since the pace at which district
courts function is affected by these local factors, it is crucial to control for these factors
in order to capture the pure impact of the degree of judicial enforcement.
Finally, our data set allowed us to investigate the impact of the degree of judicial
enforcement on the flow (rather than the stock) of debt. The current degree of judicial
enforcement can be expected to affect the amount of loans most recently granted bybanks, but all previous studies, with the exception of Fabbri (2002), employ the stock ofdebt to examine the impact of the degree of judicial enforcement on loan size. Most ofthese studies find the impact of the degree of judicial enforcement to be insignificant,but one possible reason for this is that the stock of debt reflects not only the current
choices of lenders and borrowers but also their past choices.
This paper is organized as follows: Section I surveys the results of previous
theoretical and empirical studies. In section II, the data used in our estimation are
described. In section III, the estimation method and estimation results are presented.
Finally, section IV concludes.
To summarize the main findings of this paper, we find that better judicial
enforcement increases the probability of being credit rationed and decreases loan size,
contrary to the prediction of the existing theory. We provide one possible
interpretation of these results at the end of Section III.B. Moreover, we find that betterjudicial enforcement increases the probability of bankruptcy, a result that is consistent
with lax screening effects.
I.
Previous Studies
In this section, we survey the theoretical and empirical literature on the impact ofthe degree of judicial enforcement on credit constraints, loan size, and bankruptcy.
First, we survey previous analyses of the impact of the degree of judicial
enforcement on credit constraints and loan size. Fabbri and Padula (2004) and Jappelli,
Pagano, and Bianco (2005) formalized the economic mechanism through which court
performance affects credit allocation. For example, Fabbri and Padula (2004) assumed
that a loan contract is securitized with collateral and that, if the borrower fails to repay,
the title to the collateral is transferred to the bank. The key assumption is that the
judicial system determines when the collateral is transferred to the bank in the case ofbankruptcy. If the enforcement procedure is slow, the probability that borrowers are
credit constrained might increase because borrowers’ incentive to repay loans is reduced.
In addition, slower court proceedings might reduce the equilibrium amount of debt
because banks would be expected to compensate for the lower liquidation value of thepledged collateral by raising interest rates.
Jappelli, Pagano, and Bianco (2005) employed Italian provincial data for the
1984-95 period as well as data on an indicator of judicial efficiency from the Italian
National Institute of Statistics (ISTAT) and found that the stock of pending trials per
thousand inhabitants (an indicator of poor judicial enforcement) was significantly
associated with (1) more overdraft loans (an indicator of credit constraints) and (2) alower lending-to-GDP ratio. All of these findings are consistent with their theoreticalpredictions. Moreover, Fabbri (2002) employed firm-level data from Spain for theyear 1998 as well as data on two indicators of the degree of judicial enforcement fromthe Spanish National Institute of Statistics (INE) and found that both indicators of poor
3

enforcement (viz., the length of trials and the number of proceedings that last more than
one year divided by the total number of concluded proceedings) have a negative impact
on the logarithm of total credit granted during 1998 and on the stock of financial debt.
Furthermore, by using firm-level data from Italy for the year 1991 together with the
ISTAT data, she found that an indicator of better judicial enforcement (viz., the ratio ofcompleted judicial proceedings to the total number of pending proceedings) has apositive impact on the stock of total debt and that an indicator of poor judicial
enforcement (viz., the length of first trials) has a negative impact thereof.3
On the other hand, many papers obtain results that do not necessarily support thetraditional view of judicial efficiency. For example, Fabbri and Padula (2004) useddata from the 1989, 1995, and 1998 waves of the “Survey of Household Income and
Wealth (SHIW)” together with the ISTAT data and found that the ratio of the backlog ofpending trials to the number of incoming trials (an indicator of poor judicial
enforcement) has a significantly positive impact on the probability of being credit
constrained, which is consistent with their theory, but that it does not have a significant
impact on the amount of debt. Magri (2007) used the 1989, 1995, and 1998 waves of
SHIW, the same data set used by Fabbri and Padula (2004), but used a different measureof judicial efficiency: the average time for recovery, which was obtained from aquestionnaire sent by the Bank of Italy to Italian banks. She found that recovery time
does not have a significant impact on the probability of being rationed or loan size.
Alessandrini, Presbitero, and Zazzaro (2008) used the last three waves of Italianfirm-level data for the 1995-2003 period together with the ISTAT data and found thatthe efficiency of courts in recovering bad loans increases the probability of being
rationed.
Next, we survey previous analyses of the relationship between the degree of
judicial enforcement and bankruptcy. Many economists and legal experts argue thatthe primary economic function of credit markets is to provide cheap credit. In order toaccomplish this goal, they advocate protecting creditor rights strongly. However,credit markets also fulfill other functions, such as the screening of projects. Zazzaro(2005) models the bank’s choice of the quality of screening technology anddemonstrates that, since improvements in the degree of judicial enforcement might
reduce the bank’s incentive to adequately screen borrowers, access to credit might beharder (easier) for good-type (bad-type) borrowers. Consequently, better judicial
enforcement would worsen credit allocation and increase the bankruptcy rate (seeManove, Padilla, and Pagano (2001) for similar results). Jappelli, Pagano, and Bianco(2005) found that the stock of pending trials per thousand inhabitants (an indicator ofpoor judicial enforcement) is significantly associated with a lower ratio of
nonperforming loans to total loans, which is consistent with the theoretical result of
Zazzaro (2005). Grant and Padula (2006) used Italian household data from
Findomestic Banca for the 1995-99 period together with the ISTAT data and found thatthe length of trials does not have a significant impact on the probability of repayment.
This result is not surprising because the data they used specializes in unsecured credit,
and the main channel through which the degree of judicial enforcement affects
repayment behavior is collateral.
3

The length of second and third (appeal) trials does not have a significant impact on the stock of
total debt.
4

The present paper first tests whether better judicial enforcement decreases theprobability of being rationed and increases loan size (Sections III.A and B).
Surprisingly, the estimation results of this paper are opposite in sign to the theoreticalpredictions of the traditional view, and we provide one possible interpretation at the endof Section III.B. We then examine the impact of the degree of judicial enforcement on
the probability of bankruptcy in Section III.C. Our findings are consistent with the lax
screening effect of Zazzaro (2005), whereby better judicial enforcement increases theprobability of bankruptcy by worsening the quality of credit allocation.
II.

Data

In this section, we discuss the data sources used in our analysis and present
descriptive statistics of our variables pertaining to the degree of judicial enforcement.
A. Household Data
The Japanese Panel Survey of Consumers (JPSC) is a panel survey of young Japanesewomen that has been conducted annually since 1993 by the Institute for Research on
Household Economics.4 This paper employs data from the 2003-07 waves of thissurvey because these waves asked respondents whether or not they (or their spouses)
were credit constrained during the past year.5 While the respondents are all women,
the survey questions pertain to the respondents as well as their family members,
including spouses, children, and parents. The number of observations in 2003, 2004,
2005, 2006, and 2007 was 2136, 1977, 1863, 1770, and 1694, respectively; thus, ourstudy used an unbalanced panel. After excluding observations that had missing values
for the variables included in our analysis, the number of observations that remained was
between 1200 and 1500 in each year. In sections III.A and III.B, we use only thoseobservations in which the household applied for a loan during the past year in order to
identify households that were rationed by banks. Households that applied for a loan
during the past year comprise just over 10% of the total (=710/6862). In particular,such households numbered 166, 157, 125, 150, and 112 in 2003, 2004, 2005, 2006, and2007, respectively.
There are three advantages to using data from the JPSC. The biggest advantage
of using the JPSC data is that this data set includes detailed information regarding the
respondent’s place of residence. Thus, we were able to match observations from the
JPSC data with the judicial data of the relevant district court (see section II.B for
details). The second advantage of the JPSC is that it collects data on the size of loans
granted by financial institutions during the survey year. In many previous studies
regarding the degree of judicial enforcement, data on the flow of debt are not available
4

In Japanese, Kakei Keizai Kenkyuusho.
While questions pertaining to credit constraints were included in the 1993 wave as well as in all
waves after 1998, until 2002, the survey only asked whether respondents (or their spouses) had everbeen credit constrained, and thus it is impossible to distinguish exactly when they were credit
constrained. For this reason, in this study, we do not use the 1993-2002 waves. In addition,
unfortunately, in the 2003 wave, respondents aged between 24 and 29 were asked about whether
they had ever been credit constrained. Therefore, we had no choice but to assume that respondents
aged between 24 and 29 in 2003 who had ever been credit constrained were credit constrained
during the past year.
55and hence data on the stock of debt are used. However, the stock of debt reflects thepast as well as current choices of lenders and borrowers, whereas the current degree of
judicial enforcement would be expected to affect the credit amount most recently
granted by banks. Thus, in our study, we use data on the flow of debt as our measureof loan size. The third advantage of the JPSC is that, although it does not collect data on
whether or not the respondent applied for a loan, it is possible to identify loan applicantsby using questions on the flow of debt in conjunction with those on credit constraints(see section III.A for details).
B. Data on Judicial Districts
There are several types of courts in Japan: the Supreme Court, high courts, district
courts, summary courts, and family courts. When a borrower fails to repay his or her
loan and the lender wishes to seize the borrower’s property and sell it through a court
order, the lender must appeal to a district court. In principle, when a plaintiff (lender)
wishes to appeal to a court, the competent court is that of the district where thedefendant (borrower) lives or where the collateral is located. We used data on all 50
district courts, taken from the Public Relations Division of the Supreme Court and theAnnual Report of Judicial Statistics, published by the General Secretariat of theSupreme Court of Japan. All prefectures other than Hokkaido have one district,
whereas Hokkaido has four. This means that it is necessary to obtain information
regarding the city in which Hokkaido respondents reside. Fortunately, our data set
collects information regarding the city, town, or village in which the respondent lives.
Thus, we were able to match observations from the JPSC data with judicial data on therelevant district court.6
In our study, we employed two indicators of the degree of judicial enforcement.
The first indicator is the length of trials in each district court during the 2003-07
period.7 Data on the length of trials include all first civil trials in district courts.
They represent the average amount of time between the date of the initial recording of atrial and that of the court verdict in each year.8 In the regression analysis, we use threedummy variables (1st Enforcement Quartile1, 2nd Enforcement Quartile1, and 3rd
6

In fact, the court performance of high courts and summary courts may also affect credit allocation.
This is because high courts have jurisdiction over appeals lodged against judgments of district courts.
Another reason is that in order to seize the borrower’s property through a court order, lenders need toobtain official documents that show the existence of the right to claim loan repayment fromsummary courts. Thus, in section III, we also use judicial data on high courts and summary courts
to conduct robustness checks.
7
We would like to thank the Public Relations Division of the Supreme Court for providing us withdata on the length of trials in each judicial district. Since the data are for the 1989-2006 period, weconstructed the length of trials for the year 2007 by using linear, log, exponential, quadratic, andpower approximations and by choosing the approximation with the highest R-squared for each
judicial district. The equations calculated by Excel are as follows: Y = a + b * X, Y = a + b *log(X), Y = a * exp(b * X), Y = a + b * X + c * X2, and Y = a * Xb, respectively. Y is the length oftrials, and X is the year.
8
In order to avoid measurement error, previous studies used indicators of the degree of judicial
enforcement that excluded cases with no relation to loan contracts. Unfortunately, we were unable
to obtain data on the length of trials broken down by the type of case. Thus, the average length oftrials for all civil cases is used in this study. However, for the second indicator of judicial
enforcement, we excluded all work- and family-related cases.
6

Enforcement Quartile1), which represent quartiles of the distribution of the length oftrials, with the highest quartile (8.8 months or more) being the excluded category.
More specifically, 1st Enforcement Quartile1 is a dummy variable that equals one if thelength of trials is less than 7.4 months and zero otherwise, 2nd Enforcement Quartile1 is
a dummy variable for trials between 7.4 and 8.1 months, and 3rd Enforcement Quartile1
is a dummy variable for trials between 8.1 and 8.8 months. Thus, these dummy
variables indicate better judicial enforcement as compared to the excluded category.
The second indicator of the degree of judicial enforcement that we use is the ratioof the number of pending civil trials to the number of incoming civil trials in each
district during the 2003-07 period. The data include all civil trials except for workand family-related cases. The ratio of the number of pending civil trials to the numberof incoming civil trials reflects the duration of future trials, while the length of trials(the first indicator) reflects the duration of current and past trials. While many
previous studies have used the number of pending trials as an indicator of poor judicial
enforcement, they have used different normalization measures such as population, thenumber of judges, and the number of court personnel. In our analysis, we normalized
the number of pending trials by the number of incoming trials, as done by Fabbri andPadula (2004), but the estimation results do not change even if we use different
normalization measures such as population and the number of judges. As in the caseof the first indicator of the degree of judicial enforcement, we used three dummy
variables for the second indicator-- namely, 1st Enforcement Quartile2, 2nd Enforcement
Quartile2, and 3rd Enforcement Quartile2, with the highest quartile (45.5 or higher)
being the excluded category. 1st Enforcement Quartile2 is a dummy variable that
equals one if the pending rate is less than 39.9; 2nd Enforcement Quartile2 is a dummy
variable for pending rates between 39.9 and 43.0, and 3rd Enforcement Quartile2 is adummy variable for pending rates between 43.0 and 45.5. Thus, these dummy
variables indicate better judicial enforcement than the excluded category.
In the first section, we stated that the duration of court proceedings in Japan has
declined over time. The question that arises is how the length of trials in Japan
compares to that in other countries. According to “The Second Report on the
Acceleration of Trials,”9 the length of first civil trials in 2004 was 8.3 months in Japan,
22.4 months in England, 9.6 months in France, and 8.5 months in the U.S. The only
country with shorter trials than Japan (7.2 months) was Germany. In addition,
Djankov, La Porta, Lopez-de-Silanes, Shleifer (2003) compared the duration of theprocess for collecting on a check returned for non-repayment in 109 countries, andaccording to their study, Japan ranked seventh from the bottom, with the process
lasting 60 months.10 Thus, it can be stated that the duration of trials in Japan is shorteven by international standards.
(Insert Figures 1 and 2 here)

9

In Japanese, Saiban no Jinsokuka ni kakaru Kenshou ni kansuru Houkokusho (Dai Ni-kai).
http://www.courts.go.jp/about/siryo/jinsoku/hokoku/02/index.html
10
Djankov, La Porta, Lopez-de-Silanes, Shleifer (2003) also calculate the average duration of theprocedure for evicting a residential tenant for nonpayment of rent and find that the average durationof such a procedure in Japan is 363 months and that Japan ranks 87 among the 109 countries in theirsample.
7

We now present data on the two indicators of the degree of judicial enforcement
across different districts in Japan. Figures 1 and 2 show data on the length of trails andthe ratio of the number of pending trials to the number of incoming trials, respectively,in each district court. Figure 1 shows data on the length of trials in all 50 district
courts in Japan, with the upper half of this figure showing data for 2000 and the lower
half showing data for 2006, and as is evident from this figure, the length of trials wasmuch shorter in 2006 than it was in 2000 in all districts, meaning that the degree of
judicial enforcement improved throughout the country. The median length of trials
was 9.0 months in 2000 but only 7.7 months in 2006. As can be seen from the graybars, which indicate districts in which trials are longer than the median, poor judicial
enforcement persists in some areas. For instance, if we were to divide Japan into eightregions (namely, Hokkaido, Tohoku, Kanto, Chubu, Kinki, Chugoku, Shikoku, andKyushu), we would conclude that the degree of judicial enforcement is the worst in
Chubu, Kanto, and Shikoku. By contrast, trials in Hokkaido were particularly short inboth 2000 and 2006.
Figure 2 shows data on the ratio of the number of pending trials to the number ofincoming trials for all 50 districts, and as can be seen from this figure, this variable
shows similar patterns to those for the length of trials shown in Figure 1. While theratio of pending trials to incoming trials declined in 2006, it is still high in Chubu andKanto. In addition, the ratio of pending trials to incoming trials in Hokkaido is smaller
than the median, indicating that the degree of judicial enforcement is higher in
Hokkaido than it is in other areas.
III.

Results

In this section, we present the results of our empirical analysis of the impact ofbetter judicial enforcement on the probability of being rationed (Section III.A), on loan
size (Section III.B), and on the probability of bankruptcy (section III.B).
A. The Probability of Being Credit Constrained and the Degree of Judicial
Enforcement
According to the theoretical model of Fabbri and Padula (2004), households are lesslikely to be credit constrained when loan contracts are enforced more strongly becausehouseholds’ incentive to repay increases. In this section, we test whether betterjudicial enforcement decreases the probability of being rationed using both a pooling
logit model and a random effects logit model.
Rit* = Xita +Eitb + νi+εit,
Rit = 1 if Rit* > 0
Rit = 0 if Rit* ≤ 0(1)Rit* is an unobserved variable that is related to an observed variable on credit
constraints Rit, and X are economic and demographic household characteristics that
affect loan supply and demand, and E are three dummy variables (1st Enforcement
Quartile, 2nd Enforcement Quartile, and 3rd Enforcement Quartile) that indicate betterjudicial enforcement compared to the excluded category (see section II B for details).
Thus, the expected signs of the marginal effects of E are negative.
8In order to identify credit-constrained households, we first need to identify loan
applicants because households that did not apply for a loan cannot, by definition, be
rationed by banks. While the JPSC does not ask any questions about whether or nothouseholds applied for a loan, it does collect data on the amount of loans granted byfinancial institutions during the past year. We defined households that were granted
credit during the past year as well as those that were not granted credit and whose loan
applications were rejected as loan applicants.
To determine whether households are credit constrained, we employed the
responses to the following questions in the survey: “Was your (or your spouse’s) loan
application turned down during the past year?” and “Was you (or your spouse’s) loan
amount reduced when you applied for a loan during the past year?”11 Respondents
who replied “yes” to one or both of the above questions were regarded as being credit
constrained.
With regard to the household characteristics that influence credit rationing, we
employed (1) the age and the square of the age of respondents (AGE, AGED); (2) the
logarithm of the monthly disposable income of respondents (LINC),12 (3) a dummy
variable that equals one if respondents are self-employed (SELF-EMPLOYED); (4) adummy variable that equals one if respondents are unemployed (UNEMPLOYED); (5)the ratio of the monthly amount of loan repayments to monthly income (REPAY); (6) adummy variable that equals one if respondents work in the same company in which
they worked a year earlier (TENURE); (7) a dummy variable that equals one if the
respondent graduated from college (COLLEGE); (8) the logarithm of the sum of thevalue of financial assets (bank and postal deposits and investment securities) and thevalue of real assets (land and housing) (LWEALTH); (9) the logarithm of loans
outstanding (LLOAN); (10) a dummy variable that equals one if the respondent owns
her own home (HOME); (11) a dummy variable that equals one if the respondent ismarried (MARRIED); (12) the number of children (CHILD); (13) a dummy variable that
equals one if respondents live in metropolitan areas (METRO); (14) a dummy variable
for medium city (MEDIUM CITY); (15) seven area dummies (AREA1–7); and (16)dummies for each year (YEAR2004–07).13 In addition, in order to take account of theregional economic situation and credit market activity, we included the per capita grossprefectural product in each prefecture (PREFECTURAL GDP), the ratio of
11

Many previous studies of borrowing constraints such as Cox and Jappelli (1993) and Duca andRosenthal (1993) include households that were discouraged from applying for a loan. While thesurvey we used also collects the information needed to identify discouraged borrowers, we did notinclude them in rationed households because discouraged borrowers fall in the subsample of those
who did not apply for a loan. However, we also tried classifying discouraged households asrationed households as a robustness check at the end of this section.
12
The reason why we did not use annual disposable income is that the survey asks respondents
about annual income earned in the previous calendar year and taxes paid in the previous calendar
year. Thus, if we had used annual disposable income, we would not have been able to use data for2007. In order to avoid a further reduction in the number of observations, we employed monthly
disposable income, which is available for the current calendar year.
13
Our data set includes data on married as well as single respondents. With respect to AGE,
AGED, LINC, SELF-EMPLOYED, UNEMPLOYED, REPAY, TENURE, and COLLEGE, we use dataon the respondent’s husband if the respondent is married and data on the respondent herself only if
she is single because husbands are more likely to be the household head or the primary income
earner.
9

nonperforming loans to total lending in regional banks by prefecture (BAD LOANS), theHerfindahl-Hirschman Index (the sum of the squared individual shares of lending inregional banks by prefectures) (HERFINDAHL), and the ratio of total lending in eachprefecture to prefectural GDP (DEPTH).14
In the estimation, we focus only on households that applied for a loan during thepast year. This is because we cannot determine whether or not households wererationed by banks if they did not apply for a loan. However, there is a possibility thatusing this subsample will result in sample selection bias because there might be acorrelation between the unobservable factors that decide the probability of applying fora loan and those that decide the probability of being credit constrained. Although weused a maximum-likelihood probit model with sample selection, 15 the correlation
between error terms of the two equations was not statistically different from zero forany specifications. 16 Thus, we used only the sample of loan applicants in ourestimation.
(Insert Table 1 here)
Table 1 presents characteristics of loan applicants broken down by whether or notthey are credit-rationed.17 Rationed households comprise 21% of the total (149/710).
A comparison of rationed households with non-rationed households shows that rationed
households have lower income, wealth, homeownership, debt, tenure, educational
attainment, and per capita gross prefectural product than non-rationed households. In
addition, rationed households are more likely to be self-employed or unemployed andless likely to live in metropolitan areas. Moreover, rationed households have morechildren and higher loan repayments than non-rationed households. With respect tothe judicial variables of interest, the means of 2nd Enforcement Quartile1 and 1st
Enforcement Quartile2 are slightly higher in the case of rationed households than they
are in the case of non-rationed households, which implies that better judicial
enforcement increases the probability of being rationed. Looking at the means of 2nd
14

Magri (2007) controlled for the portion of the loan that is recovered in the event of a borrower’s
bankruptcy, but unfortunately, we were unable to control for this variable because of the lack of data.
15
We used the heckprob command in STATA 9.
16
Magri (2007) also found that the hypothesis that the errors in the two equations are uncorrelated
cannot be rejected.
17
The total number of observations, including loan applicants and non-applicants was 6862, and thenumber of households whose loan applications were rejected and/or whose loan size was reduced
was 149. Thus, on average, only 2% (149/6862) of households were credit constrained during the2003 to 2007 period. However, if we include discouraged borrowers in rationed households inaddition to rejected and reduced households, the share of rationed households increases to 4%(292/6862). Kohara and Horioka (2006), who used different waves of the same survey that weused, found that 7.61%, 9.29%, and 15.40% of households were rejected, reduced, and/or
discouraged in 1993, 1998, and 2003, respectively. In our study, the proportion of rationed
households is far less, but this difference is due mainly to the difference in the definition of credit
constraints. While Kohara and Horioka (2006) include households that were credit constrained in
the past in credit-constrained households, we include only households that were credit-constrained
during the past year in credit-constrained households. If we use the same definition as that of
Kohara and Horioka (2006), the proportion of credit-constrained households increases to 10%(128/1226) in 2007, which is relatively similar to the proportion of rationed households calculated
by Kohara and Horioka (2006).
10and 3rd Enforcement Quartile2, however, they are higher in the case of non-rationed
households than they are in the case of rationed households. Thus, the impact of betterjudicial enforcement is not clear from the descriptive statistics.
(Insert Table 2 here)
We turn now to the estimation results. The results for the pooling logit model
are shown in columns (1) and (2) of Table 2, whereas the results for the random effects
logit model are shown in columns (3) and (4) of the same table.18 Let us first consider
the impact of household characteristics on the probability of being rationed. From theviewpoint of supply-side adverse selection, the debt ceiling should be lower for younger
borrowers and therefore an increase in AGE should relax credit constraints. In mostcases, AGE has a significantly negative impact on the probability of being rationed, as
expected. For example, the marginal effects of AGE and AGED in (3) indicate that theprobability of being rationed decreases until borrowers are about 41 years old (0.0826 /(2 * 0.0010)) and then approaches zero. With respect to the impact of income, LINC
does not have a significant impact in any case. The insignificance of the marginal
effect of LINC is contrary to expectation because an increase in income would be
expected to relax credit constraints and is also contrary to the empirical evidence for the
U.S. but is consistent with the findings of Kohara and Horioka (2006), who found that,in Japan, income is not an important factor in determining the probability of being
rationed. With respect to the impact of LWEALTH, we found that it has a significantly
negative impact in most cases, which is as expected because if households’ assets arehigher, we would expect the demand for credit to decrease and the debt ceiling toincrease. In addition, the marginal effects of HOME are insignificant in all cases,
which is contrary to our expectation because we expected homeownership to be a proxyfor collateral and for previous good credit records. LLOAN was found to have asignificantly negative impact on the probability of being rationed. We expected thatthe higher are loans outstanding, the greater would be the probability of being rationed,
but the negative impact we find might imply that large amounts of outstanding loans
function as an indicator of good credit records in the past. While SELF-EMPLOYED
was found to have a significantly positive impact on the probability of being rationed inmodel (2), as expected, the marginal effects of UNEMPLOYED were found to be
insignificant in all cases. REPAY had a significantly positive impact on the probabilityof being rationed in all cases, which is as expected because the higher is the value of
REPAY, the lower will be the ability to repay. The results for TENURE imply that if
tenure is longer, the probability of being rationed decreases, as expected. Moreover,while Kohara and Horioka (2006) found that college graduates are significantly lesslikely to be borrowing constrained, in our results, the marginal effect of COLLEGE wasinsignificant, as found by Jappelli (1990). MARRIED has a significant positive impact
on the probability of being rationed. This result is contrary to our expectation because,in general, we would expect married couples to be less likely to be rationed because
their desired borrowing is lower than that of single people due to economies of scale18

We performed a likelihood-ratio test and found that the null hypothesis—that the proportion of
the total variance contributed by the panel-level variance component equals zero—was rejected.
This indicates that the panel-level variance component is important and thus that a random effects
logit model is preferable to a pooling logit model.
11and because their debt ceiling is higher than that of single people due to factors such as
lower mobility. With respect to CHILD, we found that it has a significantly positive
impact, which is as expected because if households have more children, we would
expect their desired borrowing to increase and their credit constraints to become tighter.
With respect to the remaining explanatory variables, it appears that city size (METRO
and MEDIUM CITY) does not have a significant impact on the probability of being
rationed. Additionally, prefectural variables such as BAD LOANS and HERFINDAHL
do not have a significant impact on the probability of being rationed, while
PREFECTURAL GDP (DEPTH) has a significant negative (positive) impact on theprobability of being rationed in some cases.
With respect to the judicial variables of interest, none of the three dummy
variables for Enforcement Quartile2 had a significant impact on the probability of being
rationed. On the other hand, 2nd Enforcement Quartile1 had a significantly positive
impact on the probability of being rationed in model (1) of Table 2 even though all of
the marginal effects of Enforcement Quartile1 are insignificant in model (3) of the sametable. The results for the pooling logit model suggest that better judicial enforcement
increases the probability of being rationed, contrary to theoretical prediction.
Considering the magnitude of the marginal effects of 2nd Enforcement Quartile1 inmodel (1) of Table 2, the probability of being rationed is 11 percentage points higher injudicial districts with second-best judicial enforcement than in judicial districts with theworst judicial enforcement. As stated before, the percentage of rationed households
was 21%, which means that moving from the worst judicial enforcement district to thesecond-best judicial enforcement district increases the probability of being rationed by
52% ((11 / 21) * 100).
As robustness checks, we first tried using dummy variables pertaining to thelength of trials and to pending rates in high courts rather than in district courts. Highcourts are located in eight major cities in Japan, and some high courts have branch
courts (there are six branch courts in all of Japan). Thus, the judicial data on highcourts consist of only 14 categories in each year, which means that variation in the
judicial data on high courts across individuals is considerably smaller than that in the
judicial data on district courts. As expected, dummy variables pertaining to the lengthof trials and pending rates in high courts either did not have a significant impact on theprobability of being rationed or were dropped during the estimation due to colinearity.
Second, we tried using dummy variables pertaining to pending rates in summary
courts.19 The judicial data on summary courts consist of 50 categories in each year,
which is the same number as in the case of district courts. The dummy variables
pertaining to pending rates in summary courts did not have a significant impact on
credit allocation. This result is consistent with the results presented in Table 2, which
are based on dummy variables pertaining to pending rates in district courts, andsuggests that the future duration of trials might be irrelevant in determining theprobability of being rationed.20
Lastly, we tried including discouraged borrowers, who were previously excluded
because they did not apply for a loan, in rationed households. We found that this19

For summary courts, no data are available on the length of trials.
We also tried using the length of trials and that of the ratio of the number of pending trials to thenumber of incoming trials instead of dummy variables thereon and found that the marginal effects ofthe two are insignificant with respect to the probability of being rationed.
20

12

caused the impact of judicial enforcement on the probability of being rationed to
become more significant. More specifically, in the pooling logit model, all the dummy
variables for Enforcement Quartile1 had a significantly positive impact on theprobability of being rationed. Moreover, in the random effects logit model, 2nd and 3rd
Enforcement Quartile1 had a significantly positive impact on the probability of being
rationed. These results provide further support for the view that better judicial
enforcement increases the probability of being rationed, as shown in model (1) of Table
2, and are again contrary to theoretical prediction.
B. Loan Size and the Degree of Judicial Enforcement
In theory, if the degree of judicial enforcement is weak, banks would be expected to try
to compensate for the lower liquidation value of the pledged collateral by raisinginterest rates, and this will reduce loan size in equilibrium. In this section, we test
whether better judicial enforcement increases loan size. According to the JPSC data,
12% of loan applicants were not granted any credit during the past year. In order totake account of the widespread presence of zeros regarding loan size, we conductedboth pooling Tobit21 and random effects Tobit estimations.22
Lit = Xita +Eitb +νi+εit(2)

The dependent variable we use in this analysis is the logarithm of loan size
granted by banks during the past year, while the explanatory variables are the same as
those described in Section III.A. Thus, the expected signs of the marginal effects ofbetter judicial enforcement E are positive.
(Insert Table 3 here)
The estimation results are shown in Table 3. Looking first at the impact ofhousehold characteristics, LWEALTH has a significantly positive impact in every case,which is as expected because if households have more wealth, they can pledge morecollateral to lenders. On the other hand, HOME has a significantly negative impact onloan size, which might be because owning a home lowers the demand for new (housing)loans. LLOAN has a significantly positive impact on loan size, which might be theresult of a simultaneity problem arising from the fact that LLOAN includes not only
loans granted more than one year earlier but also loans granted during the past year.
The inference is supported by the fact that if we exclude loans granted during the past
year from LLOAN, the marginal effects of LLOAN become insignificant in most cases.
Furthermore, we found that TENURE has a significantly positive impact on loan size,
which is consistent with our expectations. MARRIED has a significantly negative
impact on loan size, and this might be because married couples have a lower demand forloans as a result of economies of scale in the consumption of durables. In addition,
CHILD has a significantly negative impact on loan size, which might be because the21

We utilized an interval regression with left-censoring of observations and robust standard errors
adjusted for clusters in households to estimate our Tobit model.
22
The null hypothesis (that the panel-level variance component equals zero) was rejected. This
result indicates that the panel-level variance component is important and thus that a random effects
Tobit model is preferable to a pooling Tobit model.
13

debt ceiling allowed by banks decreases when households have more children.
With respect to the judicial variables of interest, the marginal effects of all
dummies pertaining to Enforcement Quartile2 were insignificant. On the other hand,
the marginal effects of all dummy variables pertaining to Enforcement Quartile 1 weresignificantly negative. These results are again contrary to our expectation that betterjudicial enforcement increases the credit granted by lenders. Moreover, the magnitudeof the impact of a change in the degree of judicial enforcement is considerable. The
estimation results derived from the random effects Tobit model suggest that movingfrom a judicial district with the worst judicial enforcement to judicial districts with thebest, second best, and third best judicial enforcement reduces loan size by 78%((exp(–1.5215) – 1) * 100), 76% ((exp(–1.4181) – 1) * 100), and 56% ((exp(–0.8137) –1) * 100), respectively.
In order to check the robustness of our results, we first tried using the length oftrials and the ratio of the number of pending trials to the number of incoming trials
instead of using dummy variables thereon. We found that the marginal effect of thelength of trials was significantly positive with respect to loan size both in the pooling
Tobit and random effects Tobit models, which is consistent with the results shown in
Table 3. Second, we tried using dummy variables pertaining to the pending rates insummary courts23 and found that the dummy variables pertaining to pending rates insummary courts did not have significant impact on loan size, which is also consistent
with the results shown in Table 3.
Based on the estimation results presented in sections III.A and III.B, we canconclude that better judicial enforcement increases the probability of being rationed anddecreases loan size. These results are opposite in sign to the theoretical results ofFabbri and Padula (2004) and Jappelli, Pagano, and Bianco (2005). We provide one
possible theoretical explanation of our findings below:24
Credit markets operate under substantial asymmetries of information. For
example, borrowers may be either creditworthy and honest, or dishonest. The former
type of borrower repays its debts as long as it has the ability to do so, while the latter
type of borrower never repays its debts. Although the lender understands thatborrowers are heterogeneous, she is unable to distinguish one from the other. Assume
that there are two ways to screen borrowers. One is a more costly but more effective
way, which produces a more informative signal about the type of borrower. The otheris a less costly but less effective way. In particular, think of delayed informationrevelation as a less effective way of screening. There may not be sufficient
information about some borrowers when they apply for a loan, but with some
low-intensive monitoring, more information might become available later. Examplesof what this type of screening might involve are: (a) cutting the amount of the initialloan, and conditional on adverse information not being revealed later or on good
information being revealed later, increasing the loan amount after some time and (b)rejecting an initial application, presenting the borrower with a harsher budget constraint
for a while so that the borrower disciplines herself, and then extending the loan. There is
one crucial assumption that is needed here—namely that if the more costly but more
effective screening method is used at the optimal intensity, then the benefits from23

For summary courts, no data are available on the length of trials.
This interpretation was suggested by an anonymous referee of this journal.
appreciate his/her enormous help and contribution.
24

14

We greatlyinformation generated by the less costly screening method do not outweigh the costs. If
this assumption is satisfied, the intensity of the costly screening method will be high andthe costless screening method will become redundant when the degree of judicial
enforcement is weak. However, when the degree of judicial enforcement is strong, theintensity of the costly screening method will be reduced, and the benefits from the lesscostly screening method will outweigh the costs. As argued above, by its very nature,
this screening method might involve cutting the initial loan size and denying approval toelicit more information.
C. The Probability of Bankruptcy and the Degree of Judicial Enforcement
If the degree of judicial enforcement is strong, banks may not screen borrowers
adequately and erroneously evaluate bad (good) borrowers as good (bad) ones, resulting
in an increase in the probability of bankruptcy. In this section, we test whether or not
better judicial enforcement increases the probability of bankruptcy. Although we wereinclined to take advantage of the panel nature of the data, panel logit estimation did notwork well because only a small number of households went bankrupt. Therefore, wechose to conduct a pooling probit estimation.
Bi* = Xia +Eib + Aic + νi,
Bi = 1 if Bi* > 0
Bi = 0 if Bi* ≤ 0(3)Bit* is an unobserved variable that is related to observed data on bankruptcy B.
To construct the dependent variable, we used the following two questions: “Did you
experience personal bankruptcy or the bankruptcy of your own company during the past
year?” and “Did you have difficulty making loan repayments during the past year?”
The former question is the most appropriate one for testing our hypothesis, and thus thelatter question appears to be redundant. However, since households that went bankrupt
comprise only 0.2% of the total (14/6222), commonly used statistical procedures suchas probit might not be estimated. Therefore, we also took account of information
regarding difficulties with loan repayments because households experiencing suchdifficulties presumably have a higher probability of future bankruptcy. Morespecifically, we defined a dummy variable that equals one if households answered “yes”
to at least one of these two questions and zero otherwise and used it as our dependent
variable.
For explanatory variables that affect bankruptcy, we used the same explanatory
variables X as in sections III.A and III.B, with the exception of UNEMPLOYED.
Moreover, we used three dummy variables A that indicate adverse events.25 The threedummy variables we used are DIVORCE (whether or not the respondent was divorced
during the past year), LOST JOB (whether or not the respondent or her family members
lost their jobs during the past year),26 and SICK (whether or not the respondent or her
family members were sick during the past year). E represents three dummy variables(1st Enforcement Quartile, 2nd Enforcement Quartile, and 3rd Enforcement Quartile), as
25

See, for example, Fay, Hurst, and White (2002) for an empirical study that investigates the impactof adverse events on personal bankruptcy.
26
We dropped UNEMPLOYED because we wanted to include LOST JOB and because
UNEMPLOYED and LOST JOB are highly correlated.
15

in sections III.A and III.B.
positive.

Thus, the expected signs of the marginal effects of E are(Insert Table 4 here)

The estimation results are shown in Table 4. Looking first at the estimation
results pertaining to household characteristics, although the level of income does nothave a significant impact on the probability of bankruptcy, all adverse events(DIVORCE, LOST JOB, and SICK) that reduce the ability to repay loans have asignificantly positive impact on the probability of bankruptcy. Moreover, themagnitudes of these effects are also pronounced: DIVORCE, LOST JOB, and SICK
increase the probability of bankruptcy by 1.97, 1.01, and 0.11 percentage points,
respectively. Since the share of bankrupt households is 0.56%, divorce, losing a job,
and becoming sick increase the probability of bankruptcy by 352%, 180%, and 20%,
respectively. Moreover, although we found that LWEALTH, SELF-EMPLOYED,REPAY, TENURE, MARRIED, BAD LOANS, and HERFINDAHL have a significant
impact on the probability of bankruptcy although the magnitudes of their marginal
effects are not very large.
Let us look next at the role played by the degree of judicial enforcement. As
expected, the 1st and 2nd Enforcement Quartile1 and 1st Enforcement Quartile2 all havea positive impact on the probability of bankruptcy. These results imply that betterjudicial enforcement increases the probability of bankruptcy, as shown in Zazzaro(2005). To appreciate the magnitude of this distortion, we computed how the
probability of bankruptcy changes if the average household moves from a judicial
district with the worst judicial enforcement to a district with better judicial enforcement.
Using the results for Enforcement Quartile1 as an example, moving from a judicial
district with the worst judicial enforcement to a district with the best and second-best
judicial enforcement increases the probability of bankruptcy by 0.16 and 0.21
percentage points, respectively. Since the proportion of bankrupt households is 0.56%,
the probability of bankruptcy is 29% and 38% higher in judicial districts with the bestand second-best judicial enforcement than it is in judicial districts with the worst
judicial enforcement.
IV.
Conclusion
In this paper, we conducted an empirical analysis of the impact of better judicial
enforcement (i.e., faster court proceedings) on the probability of being credit rationed,
loan size, and the probability of bankruptcy using household-level data from theJapanese Panel Survey of Consumers, conducted by the Institute for Research on
Household Economics, in conjunction with judicial data by court district on trial lengthand the ratio of the number of pending civil trials to the number of incoming civil trials.
The existing theory predicts that, if the degree of judicial enforcement is strong,
the household’s incentive to repay its loans will increase and banks will be less likely toration credit. In addition, since better judicial enforcement makes the liquidation valueof the pledged collateral higher, banks should reduce interest rates and loan size should
increase in equilibrium when the degree of judicial enforcement is strong. However,contrary to this traditional view, we found that better judicial enforcement increases theprobability of being credit rationed and decreases loan size. The following is a16possible interpretation of this result. Since the intensity of costly screening is reduced
if the degree of judicial enforcement is strong, a lender might not have sufficient
information about a borrower and thus might reject the initial loan application and cut
the initial loan size in order to elicit more information if the degree of judicial
enforcement is strong.
Furthermore, we explored the impact of the degree of judicial enforcement on theprobability of bankruptcy and found that better judicial enforcement increases theprobability of bankruptcy. This finding is consistent with the lax screening effect,
whereby better judicial enforcement increases the probability of bankruptcy because
improvements in the degree of judicial enforcement reduce the bank’s incentive toscreen borrowers adequately and worsen the quality of credit allocation.
In sum, our estimation results found that better judicial enforcement facilitates therecovery of loans but that it may be socially harmful because it reduces the amount ofcredit granted to households and increases their bankruptcy rate. Thus, better judicial
enforcement may not necessarily be a good thing.

17

Acknowledgments
We would like to thank David Blau, Lucia Dunn, Shinya Kajitani, Mari Kan, Wataru
Kureishi, Howard P. Marvel, Colin R. McKenzie, Masao Ogaki, Tae Okada, Hiroko
Okudaira, Kei Sakata, Keiko Tamada, Midori Wakabayashi, Bruce Weinberg, Keiko
Yoshida, and all members of Groupe d’Analyse et de Theorie Economique (GATE),
Lyon, France, for their useful advice and suggestions. Moreover, we greatly appreciatedetailed comments provided by Alberto Zazzaro, which enabled us to significantly
improve our paper. In addition, we would like to thank the Institute for Research on
Household Economics for providing micro data from the Japanese Panel Survey ofConsumers for the 2003-07 period. This paper was prepared while the second author,
Shizuka Sekita, was visiting GATE. Any remaining errors are our own.

18

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19

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20

Asahikawa
KushiroSapporoHakodate
Aomori
Iwate
Miyagi
Akita
Yamagata
Fukushima
Ibaragi
Tochigi
Gunma
Saitama
Chiba
Tokyo
Kanagawa
NiigataToyamaIshikawa
Fukui
Yamanashi
NaganoGifu
Shizuoka
Aichi
Mie
Shiga
Kyoto
Osaka
Hyogo
Nara
Wakayama
Tottori
Shimane
Okayama
Hiroshima
Yamaguchi
Tokushima
KagawaEhime
Kochi
Fukuoka
Saga
Nagasaki
Kumamoto
Oita
MiyazakiKagoshima
Okinawa

0
14

12

10
Asahikawa
KushiroSapporoHakodate
Aomori
Iwate
Miyagi
Akita
Yamagata
Fukushima
Ibaragi
Tochigi
Gunma
Saitama
Chiba
Tokyo
Kanagawa
NiigataToyamaIshikawa
Fukui
Yamanashi
NaganoGifu
Shizuoka
Aichi
Mie
Shiga
Kyoto
Osaka
Hyogo
Nara
Wakayama
Tottori
Shimane
Okayama
Hiroshima
Yamaguchi
Tokushima
KagawaEhime
Kochi
Fukuoka
Saga
Nagasaki
Kumamoto
Oita
MiyazakiKagoshima
Okinawa

Figure 1: The Length of Civil Trials (in Months) in District Courts (Upper Graph: 2000; Lower Graph: 2006)14
Hokkaido
Tohoku
Kanto
Chubu
Kinki

21

Chugoku
Shikoku
Kyushu

12

10

8

6

4

2

8

6

4

2

0

Source: The Public Relations Division of the Supreme Court. Note: The length of trials measures the time elapsed between the date of the initialrecording of a trial and that of the court verdict. Trials include all first civil trials in district courts.

Asahikawa
KushiroSapporoHakodate
Aomori
Iwate
Miyagi
Akita
Yamagata
Fukushima
Ibaragi
Tochigi
Gunma
Saitama
Chiba
Tokyo
Kanagawa
NiigataToyamaIshikawa
Fukui
Yamanashi
NaganoGifu
Shizuoka
Aichi
Mie
Shiga
Kyoto
Osaka
Hyogo
Nara
Wakayama
Tottori
Shimane
Okayama
Hiroshima
Yamaguchi
Tokushima
KagawaEhime
Kochi
Fukuoka
Saga
Nagasaki
Kumamoto
Oita
MiyazakiKagoshima
Okinawa

0.8

0.8

0.7

0.6

Asahikawa
KushiroSapporoHakodate
Aomori
Iwate
Miyagi
Akita
Yamagata
Fukushima
Ibaragi
Tochigi
Gunma
Saitama
Chiba
Tokyo
Kanagawa
NiigataToyamaIshikawa
Fukui
Yamanashi
NaganoGifu
Shizuoka
Aichi
Mie
Shiga
Kyoto
Osaka
Hyogo
Nara
Wakayama
Tottori
Shimane
Okayama
Hiroshima
Yamaguchi
Tokushima
KagawaEhime
Kochi
Fukuoka
Saga
Nagasaki
Kumamoto
Oita
MiyazakiKagoshima
Okinawa

Figure 2: Number of Pending Trials / Number of Incoming Trials in District Courts (Upper Graph: 2000; Lower Graph: 2006)
0.7
Hokkaido
Tohoku
Kanto
Chubu
Kinki

22

Chugoku

Source: Annual Report of Judicial Statistics. 1. Civil Cases (2000, 2006). General Secretariat, Supreme Court.
Note: Trials include all civil trials in district courts with the exception of work- and family-related cases.
Shikoku
Kyushu

0.6

0.5

0.4

0.3

0.2

0.1

0

0.5

0.4

0.3

0.2

0.1

0

Table 1: Characteristics of Households that Applied for a Loan
Full
Non-Rationed
Rationed
Mean
S.D.
Mean
S.D.
Mean
S.D.
st
0.25
0.44
0.25
0.44
0.25
0.43
1 Enforcement Quartile1 (dummy variable)
0.19
0.39
0.18
0.39
0.23
0.42
2 nd Enforcement Quartile1 (dummy variable)
rd
0.30
0.46
0.30
0.46
0.30
0.46
3 Enforcement Quartile1 (dummy variable)
st
0.24
0.43
0.23
0.42
0.29
0.45
1 Enforcement Quartile2 (dummy variable)
nd
0.29
0.45
0.30
0.46
0.26
0.44
2 Enforcement Quartile2 (dummy variable)
rd
0.22
0.41
0.23
0.42
0.17
0.38
3 Enforcement Quartile2 (dummy variable)
AGE
37.33
8.00
37.53
7.71
36.58
9.00
INC (in tens of thousands of yen)
26.27
15.16
27.21
14.38
22.73
17.39
WEALTH (in tens of thousands of yen)1610.162063.73
1812.852172.03846.981345.35
HOME (dummy variable)
0.60
0.49
0.65
0.48
0.44
0.50
LOAN (in tens of thousands of yen)956.401185.51
1006.01
1195.18
769.621132.88
SELF-EMPLOYED (dummy variable)
0.13
0.33
0.11
0.32
0.19
0.39
UNEMPLOYED (dummy variable)
0.04
0.20
0.03
0.17
0.09
0.28
REPAY (in ratio form)
0.35
0.42
0.32
0.38
0.43
0.57
TENURE (dummy variable)
0.77
0.42
0.82
0.39
0.60
0.49
COLLEGE (dummy variable)
0.23
0.42
0.25
0.43
0.14
0.35
MARRIED (dummy variable)
0.77
0.42
0.77
0.42
0.75
0.43
CHILD
1.62
1.15
1.57
1.13
1.79
1.20
METRO (dummy variable)
0.23
0.42
0.24
0.43
0.17
0.38
MEDIUM CITY (dummy variable)
0.65
0.48
0.64
0.48
0.71
0.45
PREFECTURAL GDP (in billions of yen)
4.37
1.29
4.43
1.32
4.15
1.16
BAD LOANS (in percent)
5.14
1.92
5.10
1.90
5.29
1.98
HERFINDAHL (in percent)
54.58
23.82
54.02
23.92
56.70
23.36
DEPTH (in ratio form)
0.68
0.38
0.69
0.39
0.65
0.34
Number of Observations
710
561
149
Note: Data was taken from the Japanese Panel Survey of Consumers for the 2003-07 period. “Full” refers to all households that applied for a loan during
the past year. “Non-Rationed” refers to households that applied for a loan but were not rationed during the past year. “Rationed” refers to households
that not only applied for a loan but also had their loan applications rejected and/or their loan amount reduced during the past year.

23

Table 2: Judicial Enforcement and the Probability of Being Credit Rationed
Pooling Logit

Random Effects Logit

Marginal EffectMarginal Effect(Standard Error)
Explanatory Variable
1st

Enforcement Quartile1

2 nd Enforcement Quartile1(1)(Standard Error)(2)0.03730.0514(0.0623)(0.1415)0.1066 *3

Enforcement Quartile1(0.1383)

0.04850.1081(0.0471)(0.1127)

1 st Enforcement Quartile2
2 nd Enforcement Quartile2
3 rd Enforcement Quartile2
AGE
AGED
LINC
LWEALTHHOME
LLOAN
SELF-EMPLOYEDUNEMPLOYEDREPAYTENURECOLLEGEMARRIED

METRO-0.0178-0.0519(0.0560)(0.1436)-0.0308-0.0785(0.0404)(0.1066)-0.0375-0.0740(0.0426)(0.1003)-0.0430 **-0.0435 **-0.0826 *-0.0816 *(0.0175)(0.0177)(0.0451)(0.0448)0.0005 **0.0005 **0.0010 *0.0010 *(0.0002)(0.0002)(0.0006)(0.0006)

0.00270.00330.00510.0054(0.0061)(0.0064)(0.0174)(0.0177)-0.0089 **-0.0088 **-0.0156 *-0.0152(0.0037)(0.0038)(0.0094)(0.0095)

0.01800.0188-0.0135-0.0139(0.0507)(0.0511)(0.1333)(0.1337)-0.0337 ***-0.0331 ***-0.0742 ***-0.0726 ***(0.0050)(0.0049)(0.0218)(0.0224)0.15050.17510.07880.0966 *(0.0541)(0.0554)(0.1236)(0.1183)0.00370.0096

0.07300.0744(0.0832)(0.0865)(0.2364)(0.2360)

0.1173 ***0.1134 ***0.2284 **0.2151 **(0.0396)(0.0390)(0.1085)(0.1112)-0.1310 **-0.1387 **-0.2774 **-0.2888 **(0.0547)(0.0561)(0.1158)(0.1206)-0.0065-0.0107-0.0425-0.0516(0.0497)(0.0495)(0.1168)(0.1160)

0.0729 *(0.0397)

CHILD

(4)

0.1514(0.0647)rd(3)

0.0727 *(0.0402)0.0443 **0.0426 **0.1915 *(0.1063)0.0895 **0.1880 *(0.1088)0.0857 **(0.0183)(0.0184)(0.0435)(0.0436)-0.0289-0.0252-0.0496-0.0436(0.0520)(0.0551)(0.1482)(0.1537)

24

Table 2: Judicial Enforcement and the Probability of Being Credit Rationed(Cont’d)
MEDIUM CITY
PREFECTURAL GDP
BAD LOANSHERFINDAHL

0.01030.00820.02670.0159(0.0423)(0.0435)(0.1106)(0.1093)-0.0616 *-0.0599 *-0.1360-0.1209(0.0358)(0.0340)(0.0908)(0.0898)-0.0054-0.0071-0.0136-0.0162(0.0105)(0.0101)(0.0265)(0.0257)-0.0000

0.0000-0.00020.0001(0.0011)(0.0010)(0.0023)(0.0022)0.4116

0.4339(0.3539)(0.3457)

0.1954DEPTH

0.2147 *(0.1279)
Number of Observations
2

Wald Chi (33)2Prob > Chi
Log Likelihood

Pseudo R-squared2

LR test of rho = 0: chibar (01)Prob >= Chibar2(0.1244)

710

710

710

710

132.91122.27

85.55

87.66

0.00000.00000.00000.0000-281.06277-282.45244-272.80040-273.48984

0.22950.2257

-

-

16.52

17.93

-

-

0.000

0.000

Note: The dependent variable is a dummy variable that equals one if the household’s loan
application was rejected and/or its loan amount was reduced during the past year and zero otherwise.
In addition, we also included seven area dummies and four year dummies, but their marginal effects
and standard errors are not shown. Moreover, the marginal effects of the continuous variables were
evaluated at their means and those of dummy variables show the change in the dependent variable
when the value of the dummy variable changes from zero to one. Furthermore, the standard errors
in the pooling logit model were adjusted for clustering on households and those in the random
effects logit model were bootstrapped. Finally, *, **, and *** indicate significance at the 10%, 5%,
and 1% levels, respectively.

25

Table 3: Judicial Enforcement and Loan Size

Explanatory Variable
1st

Enforcement Quartile1

2 nd Enforcement Quartile1
3 rd Enforcement Quartile11stPooling Tobit

Random Effects Tobit

Marginal EffectMarginal Effect(Standard Error)(Standard Error)-1.6918 ***-1.5215 ***(0.6617)(0.5158)-1.7145 ***-1.4181 ***(0.5832)(0.4798)-0.9435 **-0.8137 **(0.4146)(0.3559)

Enforcement Quartile2

2 nd Enforcement Quartile2
3

rd

Enforcement Quartile2

AGE
AGED
LINC
LWEALTHHOME
LLOAN
SELF-EMPLOYEDUNEMPLOYEDREPAYTENURECOLLEGEMARRIED
CHILD
METRO

0.22550.1571(0.7102)(0.6544)0.23400.2044(0.5237)(0.5327)0.63000.4304(0.4669)(0.4411)0.21690.20390.19050.1741(0.2128)(0.2122)(0.1795)(0.1782)-0.0024-0.0022-0.0021-0.0019(0.0026)(0.0026)(0.0022)(0.0022)0.03610.02660.03980.0323(0.0820)(0.0821)(0.0726)(0.0732)

0.1381 ***0.1354 ***0.1026 ***0.0995 ***(0.0394)(0.0397)(0.0364)(0.0361)-1.6883 ***-1.6963 ***-1.3240 ***-1.3273 ***(0.4209)(0.4296)(0.4367)(0.4290)0.9339 ***0.9297 ***0.8399 ***0.8379 ***(0.0693)(0.0699)(0.0451)(0.0463)

0.55940.46930.46630.4020(0.5836)(0.5861)(0.5616)(0.5468)-0.1760-0.2481-0.2498-0.2945(1.4252)(1.4455)(1.2855)(1.2659)-0.1075-0.1267

0.07900.0502(0.2663)(0.2718)(0.3213)(0.3362)0.77980.8574 *0.65760.7454 *(0.5270)(0.5342)(0.4487)(0.4509)

0.39820.43340.4371

0.4611(0.3555)(0.3514)(0.3532)(0.3656)-0.9992 *-1.0121 *-0.8612 *-0.8681 *(0.5393)(0.5523)(0.4689(0.4839)-0.4378 **-0.4257 **-0.4080 **-0.4074 **(0.2234)(0.2223)(0.1811)(0.1815)0.43040.35430.37870.3095(0.6164)(0.6354)(0.5592)(0.5592)

26

Table 3: Judicial Enforcement and Loan Size (Cont’d)
MEDIUM CITY
PREFECTURAL GDP
BAD LOANSHERFINDAHL
DEPTH

0.23690.24110.21530.2257(0.5154)(0.5252)(0.4249)(0.4200)-0.17400.0145-0.2106-0.0332(0.3917)(0.3574)(0.4045)(0.3341)0.02180.07410.0126

0.0586(0.1091)(0.1054)(0.1088)(0.1077)-0.00290.0007-0.0041-0.0004(0.0120)(0.0111)(0.0119)(0.0107)0.7346-0.1304

0.78550.0267(1.3432)(1.2800)(1.2463)
710

710

710

(1.3922)710

Wald Chi (33)337.08326.05343.99359.6820.00000.00000.00000.0000-1868.818-1872.38-1860.7007-1864.3334

-

-

16.23

16.09

-

-

0.000

0.000

Number of Observations
2

Prob > Chi
Log Likelihood2

LR test of sigma_u = 0: chibar (01)Prob >= Chibar

2

Note: The dependent variable is the logarithm of the loan amount granted during the past year. We
also controlled for seven area dummies and four year dummies, but their marginal effects andstandard errors are not shown. Moreover, marginal effects of continuous variables were evaluatedat means and those of dummy variables are discrete changes as dummy variables change from zero
to one. In addition, standard errors in the pooling Tobit model were adjusted for clustering on
households and those in the random effects Tobit model were bootstrapped. Finally, *, **, and ***indicate significance at the 10%, 5%, and 1% levels, respectively.

27

Table 4: Judicial Enforcement and the Probability of Bankruptcy
Pooling Probit
Explanatory Variable
1

st

2

nd

3

rd

1

st

2

nd

3

rd

Enforcement Quartile1

Marginal EffectMarginal Effect(Standard Error)(Standard Error)

0.0016 **(0.0015)

Enforcement Quartile1

0.0021 ***(0.0014)

Enforcement Quartile1-0.0002(0.0002)0.0012 *Enforcement Quartile2(0.0011)0.0007

Enforcement Quartile2(0.0007)-0.0002

Enforcement Quartile2(0.0003)
DIVORCE

0.0197 ***(0.0151)

LOST JOB

0.0101 ***(0.0069)

SICK
AGE
AGED
LINC
LWEALTHHOME
LLOAN
SELF-EMPLOYED

0.0011 ***(0.0156)0.0106 ***(0.0070)0.0015 ***(0.0008)(0.0010)-0.0000-0.0001(0.0001)(0.0001)

0.00000.0000(0.0000)(0.0000)-0.0000-0.0000(0.0000)(0.0000)-0.0001 ***-0.0001 ***(0.0000)(0.0000)-0.0003-0.0003(0.0004)(0.0005)

0.00000.0000(0.0000)(0.0000)

0.0015 ***(0.0013)

REPAY

0.0224 ***0.0001 *(0.0001)28

0.0019 ***(0.0016)0.0002 *(0.0001)

Table 4: Judicial Enforcement and the Probability of Bankruptcy (Cont’d)
TENURECOLLEGE-0.0033 ***-0.0043 ***(0.0019)(0.0024)-0.0001-0.0001(0.0002)(0.0003)0.0007 ***MARRIEDCHILD
METRO
MEDIUM CITY
PREFECTURAL GDP(0.0004)(0.0005)-0.0000

0.0000(0.0001)(0.0001)

0.00010.0001(0.0003)(0.0004)-0.0001-0.0001(0.0003)(0.0004)

0.0002-0.0000(0.0002)(0.0003)0.0001 *BAD LOANSHERFINDAHL
DEPTH
Number of Observations
2

Wald Chi (35)0.0009 ***0.0001(0.0001)(0.0001)-0.0000 ***-0.0000 ***(0.0000)(0.0000)-0.0011-0.0006(0.0009)(0.0011)

6222

6222

211.57200.7620.00000.0000Prob > Chi
Log Pseudolikelihood-142.60904-144.66652
Pseudo R-squared
0.34040.3309Note: The dependent variable is a dummy variable that equals one if the household experienced
personal bankruptcy and/or the bankruptcy of its own company during the past year and/or faceddifficulties with loan repayments during the past year and zero otherwise. We also controlled forseven area dummies and four year dummies, but their marginal effects and standard errors are notshown. In addition, the marginal effects of the continuous variables were evaluated at their means
and those of dummy variables show the change in the dependent variable when the value of thedummy variable changes from zero to one. Moreover, standard errors were adjusted for clusteringon households. Finally, *, **, and *** indicate significance at the 10%, 5%, and 1% levels,
respectively.

29