Household Debt and Delinquency
over the Life Cycle
Sommarat Chantarat, Atchana Lamsam,
Krislert Samphantharak, and Bhumjai Tangsawasdirat∗

This paper uses loan-level data from Thailand’s National Credit Bureau to study
household debt over the life cycle of borrowers. We decompose two aggregate
and commonly used measures of debt—debt per capita and delinquency
rate—into components that unveil the extensive and intensive margins of
household indebtedness. We find a striking inverted-U life-cycle pattern of
indebtedness as predicted by economic theories. 然而, peaks are reached
at different ages for different loan products and different lenders. 我们也
find that debt has expanded over time for all age groups. Younger cohorts
seem to originate debt earlier in their lives than older generations. 同时,
older borrowers remain indebted well past their retirement age. 最后, 我们
find a downward pattern of delinquency over the life cycle. Our findings have
important policy implications on financial access and distress of households as
well as on economic development and financial stability of the economy.

关键词: 违法行为, demography, financial development, household debt,
life cycle
JEL codes: D14, D30, G20, J26, O16

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[右]esearch on household debt has lagged behind its sister literatures
on the asset side of the household balance sheet.
—Jonathan Zinman (2015)

∗Sommarat Chantarat: Puey Ungphakorn Institute for Economic Research (PIER), Bank of Thailand.
电子邮件: SommaraC@bot.or.th; Atchana Lamsam: PIER, Bank of Thailand. 电子邮件: AtchanLa@bot.or.th;
Krislert Samphantharak (corresponding author): PIER and School of Global Policy and Strategy, 大学
California San Diego. 电子邮件: krislert@ucsd.edu; Bhumjai Tangsawasdirat: Bhumjai Tangsawasdirat: Monetary
Policy Department, Bank of Thailand. 电子邮件: BhumjaiT@bot.or.th. This paper was part of our earlier working
文件 (Chantarat et al. 2017, 2018). We gratefully acknowledge Surapol Opasatien, Phadet Charoensivakorn,
Aurapat Rangsiwongs, Pakinee Pipatpoka, and Wassana Tongtiang at the National Credit Bureau (NCB) for their
kind provision of data and support. We thank Piti Disyatat and colleagues at PIER for their suggestions and
encouragement, especially Chonnakan Rittinon for providing great research assistance. We would like to thank the
managing editor and the anonymous referee for helpful comments and suggestions. The views expressed in this study
are our own and do not represent those of PIER, Bank of Thailand, or NCB. ADB recognizes “Korea” as the Republic
of Korea. The usual ADB disclaimer applies.

Asian Development Review, 卷. 37, 不. 1, PP. 61–92
https://doi.org/10.1162/adev_a_00141

© 2020 Asian Development Bank and
Asian Development Bank Institute.
在知识共享下发布
归因 3.0 国际的 (抄送 3.0) 执照.

62 Asian Development Review

我. 介绍

Household debt is a crucial component of the financial system. It helps
finance household consumption and investment as well as the operation of informal
business enterprises. Some debt instruments such as credit cards also serve as a
means of payment in the economy. 然而, high and rapidly rising household debt
can lead to debt burden and financial vulnerability of households, which in turn raise
serious concerns over the stability of the financial system. 此外, a high level
of debt may inhibit a household’s spending and consumption, a symptom known as
debt overhang, which in turn affects long-term growth of the aggregate economy.
This concern is of particular relevance today as we observe rising household debt
in many countries across the world—including a number of countries in Asia, A
region long known for its frugality.1 However, understanding household debt and its
implications on the economy is complicated. 一方面, households are diverse,
so we need to understand the heterogeneous nature of debt across borrowers. 这
goal of this paper is to contribute to this understanding by dissecting one aspect
of household debt, 即, indebtedness and delinquency over the life cycle. 在
特别的, this paper aims to answer two questions. 第一的, does household debt
follow a life-cycle profile predicted by economic theories and, 如果是这样, when does
deleveraging start? 第二, does the life-cycle pattern change over time across
队列?

Aggregate data show that household debt has increased in recent decades
in many countries. 例如, credit to households as a share of total credit
increased from 27% 在 1980 到 58% 百分比在 2000 in the Republic of Korea
(Beck et al. 2008). 最近, total bank credit to the household sector in
the Asia and Pacific region more than doubled relative to gross domestic product
(GDP) 之间 1995 和 2015 (Schularick and Shim 2017). The expansion is
observed in high-income economies such as Australia, 新西兰, the Republic
of Korea, and Singapore, as well as in middle-income countries such as Malaysia
and Thailand.2 In 2017, Thailand’s household debt, 在 69% of GDP, 是
highest among developing Asian economies; the only Asian country with higher
household debt was the Republic of Korea (94%). Other Asian economies with
similar household debt were Hong Kong, 中国 (69%) and Malaysia (68%), 尽管
Singapore and Indonesia experienced lower household debt than Thailand (57% 和
17%, 分别).

1看, 例如, The Economist (2017).
2The Appendix presents household debt-to-GDP ratios of selected economies in the Asia and Pacific region
based on data from the Bank for International Settlements (BIS). The only Asia and Pacific economy in the BIS
data that did not experience an increase in household debt to GDP is India (10.6% 在 2007 和 9.4% 在 2014). 还,
although household debt in advanced economies has declined or remained stable after the global financial crisis, 这
level of debt is still much larger than decades ago.

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Household Debt and Delinquency over the Life Cycle 63

Growing household debt is not necessarily a bad thing. If borrowers are
capable of servicing the debt, then it does not pose serious concerns on the
vulnerability of the borrowers and the stability of the financial system. 然而,
if debt accumulation comes from new loans with lower quality that result in
then measures to curb its adverse impacts must be
subsequent delinquency,
实施的. In order to understand the underlying risk of household debt, 我们
need to go beyond aggregate statistics and examine the distributional aspect, 在
特别的, who the debt holders are and where the delinquencies are. Several studies
have identified various characteristics of borrowers that are related to debt holding
and delinquency such as income, occupation, and location. This paper focuses on
another characteristic, 即, a borrower’s age. 更确切地说, we examine the
distribution of indebtedness and delinquency over the life cycle of debt holders. 这
rationales behind our study are twofold. 第一的, although there are extensive studies
of household portfolios, most have focused on savings and investment, 那是, 这
asset side of the household’s balance sheet; fewer studies have looked at debt, 那是,
the liability side. 第二, many of the economies that experience rising household
debt are also confronting a looming challenge from an aging society. Understanding
how debt and delinquency evolve over the life cycle of borrowers therefore provides
insights for relevant policy implications.
Despite its importance,

the understanding of household debt across a
borrower’s lifetime at a granular level has been limited, partly due to the lack of
数据. Existing literature that uses microdata usually relies on household surveys,
which have the advantage of covering all types of household debt in the formal,
semiformal, and informal financial sectors. 然而,
the data are prone to
inaccuracy, as households may have incentives to underclaim or overclaim their debt
situation. Household surveys are also often small and far from being representative
of the entire household sector of the country.

To overcome some of these problems, this paper uses granular administrative
debt data at the account level from Thailand’s National Credit Bureau (NCB) 从
2009 到 2016. Thailand serves as an ideal country for this study for several reasons.
第一的, based on data from the Bank for International Settlements (BIS), Thailand
is one of the countries with rapidly rising household debt in recent decades, 从
40.2% of GDP in 1994 到 69.5% 在 2014. 此外, the country is currently
experiencing a disruptive penetration of financial technology, or “fintech,” that
has the potential to significantly accelerate the process of credit expansion, 两个都
to existing debtors and to new borrowers. 同时, a growing middle-class
population has been accompanied by rising consumerism among the population.
最后, the country is experiencing a speedy change in demographic structure,
becoming an aging society. Specifically, the percentage of Thailand’s population age
65 and above will increase from 13% 在 2020 到 26% 在 2040, which would make
the country one of the world’s most rapidly aging economies (UN DESA 2015).
Understanding the age profiles of debt and delinquency is therefore important for
Thailand and other countries that are facing similar challenges.

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64 Asian Development Review

Our data cover the majority of formal loans to individuals in the country.
更确切地说, the data consist of over 60 million accounts from almost 20 百万
borrowers, representing 87% of the total household debt in the system in 2016.
The wide coverage and the granularity of the data allow us to decompose debt per
capita and delinquency rate, which are aggregate and commonly used measures of
debt, into components that unveil the extensive and intensive margins of household
indebtedness. This decomposition allows us to analyze debt holding, debt portfolio,
and delinquency for each age and cohort.3

We find a striking inverted-U life-cycle pattern of indebtedness as predicted
by economic theories. 然而, the peaks are reached at different ages for different
loan products and different lenders. We also find that debt has expanded over time
for all age groups. 尤其, the younger cohorts seem to originate debt earlier in
their lives than the older generations. 同时, older borrowers remain indebted
well past their retirement age. 最后, we find a downward pattern of delinquency
over the life cycle. Our findings have important policy implications on financial
access and distress of households as well as on economic development and financial
stability of the economy. These are especially relevant for economies with aging
人口.

The rest of the paper proceeds as follows. Section II discusses related
literature on household debt and delinquency over the life cycle. It also presents
evidence on how rising household debt over time is related to the life-cycle pattern.
Section III gives a background on credit markets in Thailand. Section IV provides
narratives on the data as well as descriptive statistics. Section V presents empirical
findings on debt holding, debt portfolio, and delinquency over the life cycle and also
presents a cohort analysis of debt and delinquency. The paper concludes in section
VI with policy implications for households and the aggregate economy.

二. Related Literature

This study is related to two strands of literature. The first is related to
consumption, 储蓄, and hence indebtedness of households over the life cycle.
The second is on rising household debt over time.

A.

Debt over the Life Cycle

The benchmark conceptual framework commonly used in the study of
debt over the life cycle is based on the classic Modigliani life-cycle hypothesis

3Recent availability of credit bureau data in some countries allows researchers to use the data to study various
aspects of debt and default of households. 看, 例如, Mian and Sufi (2015) for a study on the United States
(我们) during 2000–2010. To our best knowledge, our paper is one of the first that uses loan-level credit bureau data
to analyze debt and default in an emerging economy.

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Household Debt and Delinquency over the Life Cycle 65

(Modigliani 1966). Given that a person generally prefers smooth rather than
fluctuating consumption over time while his income usually has a hump-shaped
profile over the life cycle, he is likely to accumulate debt during the early years
of his life when his income is insufficient for his consumption. As he approaches
middle age and earns higher income, the need to borrow declines. He then begins to
pay down his debt, which is eventually paid off toward the end of his life. 其他
字, there is a leveraging–deleveraging dynamic over an individual’s life cycle,
implying an inverted-U pattern between age and indebtedness. In an economy where
individuals face debt constraints, especially those at a young age (due to a lack of
collateral and credit history) and those at an old age (due to costly liquidation of
debt outstanding at the time of death), the inverted-U pattern will be more humped
and less flat.

Empirical evidence supports the life-cycle hypothesis, which shows that debt
level and its composition change substantially over the life cycle. One of the earlier
studies is by Cox and Jappelli (1993), which uses the 1983 Survey of Consumer
Finance (SCF) 在美国 (我们) and finds that the desire for debt increases
until the age of the household head reaches mid-30s and then declines. Crook (2001)
uses the 1995 SCF and finds a decrease in demand for debt for household heads
older than 55 years old. Yilmazer and DeVaney (2005) analyze the 2001 SCF and
show that the likelihood of debt holding, and the amount of debt compared to total
assets decrease with the age of household heads.

最近, Crawford and Faruqui (2012) analyze household-level data
from the Canadian Financial Monitor and find an inverted-U pattern between the
age of the household head and the mean level of household debt in each year of the
data during 1999–2010. 为了 2010, indebtedness peaks at age 31–35 before gradually
declining. Using data from the Equifax/New York Fed Consumer Credit Panel,
Fulford and Schuh (2015) document similar strong life-cycle patterns of various
types of debts. 第一的, credit card debt begins to increase earlier in the life cycle until
年龄 50 and starts falling after age 60. 第二, few young people have mortgage,
but mortgage headcount increases with age until they turn 40 and then begins to
decline after age 60. 第三, individuals start having auto loans at a younger age, 和
经过 30 years old, almost 40% of individuals have auto loans. After that, the auto loan
headcount gradually declines and then sharply drops after age 60. 最后, student
loans present a distinct downward trend with age as individuals take this type of loan
early in life and repay over time as they age. Another debt product used heavily by
younger people is overdraft, as reported in a study by the US Consumer Financial
Protection Bureau. The propensity to overdraw on a bank account declines with
account holder age: 10.7% of the 18–25 age group have more than 10 overdrafts
每年, 相比 2.8% for those age 62 and over.

最后, unlike the literature on the life cycle of indebtedness, there are very
few studies on delinquency over the life cycle. An exception is Xiao and Yao
(2014) who analyze multiple datasets that are nationally representative of American

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66 Asian Development Review

families and document delinquency pattern by age and other demographic
特征. They find that younger households are more financially distressed
than their older counterparts and the presence of children increases the likelihood of
违法行为. Their finding suggests that younger households may experience more
financial difficulties that result in a higher likelihood of delinquency.

乙.

Rising Household Debt

The second strand of related research is on rising household debt. 这
literature has identified at least two major trends that contribute to rising household
debt
in recent decades: an increase in financial access and an increase in
consumerism of households.4 The first trend, including an increase in access to
credit by households, has been observed throughout the world—in developed,
新兴的, and underdeveloped economies. Deregulation of financial systems in
several countries brings about new financial institutions and competition, allowing
more people to have access to credit and existing borrowers to expand their loans.
Innovations in financial products provide new ways of consumer debt financing.
Various development initiatives implemented by government, especially those
through specialized financial institutions (SFIs), help underserved households to
gain access to loans. The advent of microfinance institutions results in financial
inclusion of households that would otherwise be left out. Enhanced financial
literacy facilitates households’ participation in credit markets. The creation of
credit bureaus reduces asymmetric information problems, which are one of the
most important frictions in financial markets. 最近, advances in digital
technology have further accelerated households’ access to financial products,
created new financial products that better serve households, and further reduced
transaction costs. Altogether,
these developments have resulted in the rising
household debt we have observed worldwide. Literature on the expansion of
financial access and its impacts on the economy is extensive. Dynan (2009) 和
Karlan and Morduch (2009) provide a survey of studies on this issue.

The second trend, an increase in consumerism, has been widely documented
by researchers focusing on consumer behaviors, including marketing and other
social sciences outside economics.5 Within economics, the idea about social status
and conspicuous consumption dates back at least to the century-old “keeping up
with the Joneses” argument by Veblen (1899). Alternative mechanisms analyzed
in recent studies include expenditure cascades (Frank, 莱文, and Dijk 2014)

4This argument does not consider short- and medium-term fluctuations. 例如, in the medium term,
Justiniano, Primiceri, and Tambalotti (2013) argue that the rapid rise and fall of US household debt between 2000
和 2007 cannot be explained by financial liberalization and subsequent tightening. They further argue that the credit
cycle was more likely accounted for by factors that impacted house prices through a collateral channel.

5See examples in an edited volume by Goodwin, Ackerman, and Kiron (1997).

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Household Debt and Delinquency over the Life Cycle 67

and status goods (Bursztyn et al. 2017). 然而, most studies that link the role
of relative standing in society to household behaviors focus on the effects on
consumption and assets, while the studies on the effects on liabilities and financing
attract less attention.6

The pervasiveness of the consumer society and the stagnant income of the
middle and lower social classes imply a higher demand for consumer goods and
consumer durables by households that do not have sufficient income to finance such
purchases, resulting in the origination and accumulation of credit card, 个人的,
auto, and mortgage debts. A few studies that examine the relationship between
social status and debt include Georgarakos, Haliassos, and Pasini (2014) WHO
analyze data from the Dutch National Bank Household Survey. They find that,
everything else equal, a higher average income in the social circle, as perceived
by a household, increases a household’s tendency to borrow and the likelihood
of future financial distress, as reflected in the debt service ratio and the loan-to-
value ratio of households. In another study, Bricker, Ramcharan, and Krimmel
(2014) link household financial decision data from SCF with neighborhood data
from the American Community Survey and find that a household’s position in the
income distribution relative to its close neighbors is positively associated with its
expenditure on high-status cars, its level of indebtedness, and the riskiness of its
portfolio. 最后, a study of Singaporean households by Lee, 森, and Qian (2017)
provides more evidence. All else equal, households residing in condominiums
(those who are more likely to care more about social status) spend substantially
more on conspicuous goods, have more credit card debt, and have more delinquent
debt on their credit cards than their counterparts living in subsidized public housing.
They find no difference in spending on inconspicuous consumption of these two
groups.7

C.

Implications of Rising Household Debt on the Life Cycle

Although the two major trends discussed above are likely to be responsible
for rising household debt across all ages, in theory they may have heterogeneous
impacts across age groups, resulting in a changing debt profile over the life cycle.
Compared to middle-age individuals, younger consumers are likely to face more
financial constraints (Attanasio and Weber 2010). 例如, they have shorter or

6One of the reasons is that many households are willing to display their consumption and assets while keeping

their indebtedness private.

7The relationship between consumerism and indebtedness is also related to inequality since growing income
inequality has a potential to further exacerbate the mismatch between a household’s conspicuous wants and its own
funds. 然而, the relationship between inequality and indebtedness is challenged by Coibion et al. (2016). 这
authors use household-level debt data during 2000–2012 from the Equifax/New York Fed Consumer Credit Panel
and show that low-income households in high-inequality areas accumulated less debt relative to their income than
low-income households in lower-inequality regions. They also show that the price of credit is higher and access to
credit is harder for low-income households in high-inequality regions.

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68 Asian Development Review

nonexistent credit history and fewer collateralizable assets, making it difficult for
them to qualify for loans. Financial development that helps reduce this information
barrier will result in a disproportionate increase in credit to the younger generation
as compared to their older counterparts when they were at the same age. 同样地,
the elderly also experience credit constraints because they have a shorter remaining
life expectancy than working-age individuals, hence a shorter time to pay back
loans. Financial innovations such as reverse mortgages allow them to gain access to
loans that people their age generations ago would otherwise not have had access to.
相似地, increasing longevity and a later retirement age contribute to individuals
remaining indebted much later in their lives.

同样地, an upward trend in consumerism can also have impacts on debt
profile over the life cycle of borrowers. These effects could be magnified by other
factors such as psychological or cognitive biases. 例如, a recent survey
shows that younger consumers are more likely to make compulsive purchases (他
Issa 2017).

Empirical studies have confirmed these predictions. 例如, 克劳福德
levels of Canadian households are
and Faruqui (2012) find that mean debt
systematically greater for those with household heads born in a later year or
a younger cohort. Studies also show that there is an expansion in indebtedness
distribution at both ends of the age spectrum over time, 那是, the youth and
the elderly. For young adults, Houle (2014) uses data from four waves of the US
National Longitudinal Surveys of Youth and analyzes indebtedness of three cohorts
of young adults in the 1970s, 1980s, and 2000s. He finds that the most recent cohort
began accumulating debt earlier than previous cohorts. 此外, there is a shift in
the debt portfolio toward noncollateralized loans, including student loans over time.
最后, young adults from a lower social class background have disproportionately
taken on more unsecured debt over time, compared to their more advantaged
同行. 相似地, Hodson and Dwyer (2014) study the 1997 wave of the
National Longitudinal Surveys of Youth and the SCF to investigate indebtedness of
millennials (defined as those born around 1982 or after). They find that, compared
to an earlier generation (Gen X), millennials took on greater amounts of debt at an
earlier age. Their debt increased sharply once they reached 18 years old. By their
mid-20s, 多于 20% had student loan debt and more than 30% had auto and
credit card debts. Millennials also had historically high rates of homeownership
in their early 20s compared to previous generations. 最后, 李, 森, and Qian
(2017) find that the relationship between conspicuous consumption and credit card
debt in Singapore is concentrated among younger, male, single individuals.

For the elderly, Lusardi and Mitchell (2013) use data from the Health and
Retirement Study and the National Financial Capability Study and examine three
different cohorts of Americans age 56–61 in different time periods: 1992, 2002,
和 2008. They find that more recent cohorts have taken on more debt and face
more financial insecurity, mostly because they purchased more expensive homes

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Household Debt and Delinquency over the Life Cycle 69

with smaller down payments.8 Similarly, Kuhn, Schularick, and Steins (2017)
study the oldest data from the SCF and find that those born between 1945 和
1964 experience rising debt-to-income ratios as they age. Kim (2015) analyzes the
distributions of household debt in the Republic of Korea and the US and finds that
the proportion of household debt held by older households has increased.

This paper contributes to the literature on household debt over the life cycle
以各种方式. 第一的, most existing studies rely on data at the household level
and analyze the distribution of debt based on the age of household heads. 给定
that each household consists of members with different ages, a household is not
an appropriate unit of observation for the study of debt over the life cycle. 在
对比, this study uses granular data at the account level that allow us to better
study indebtedness of each individual, including the portfolio of his or her debt.
尤其, granular data allow us to decompose the aggregate, commonly used
measures of debt per capita and delinquency rate into components that unveil the
extensive and intensive margins of household indebtedness. This decomposition in
turn allows us to analyze debt holding, debt portfolio, and delinquency for each age
and cohort. 第二, while other studies only analyze debt holdings, this paper also
examines the number of loan accounts and financial institutions, hence providing
insights on both extensive and intensive margins of indebtedness. This paper also
studies loan performance over the life cycle, exploring the relationship between age
and delinquency and hence providing new insights on the quality of loans across age
团体. 最后, unlike existing studies that use data from developed countries, ours
is one of the first that examines this issue in the context of an emerging economy.

三、. Background on Credit Markets in Thailand

The financial system in Thailand represents what we typically observe
in developing economies, where both formal and informal sectors coexist. 这
is also the case for credit markets where loans are made. At one end, formal
credit providers include (domestic and foreign) commercial banks, special financial
机构 (Government Savings Bank, Government Housing Bank, Export-Import
Bank, Small and Medium Enterprise Development Bank, Islamic Bank, 和
Bank for Agriculture and Agricultural Cooperatives), and other nonbank financial
机构 (such as credit card, personal loan, insurance, and hire purchase or
leasing companies). At the other end, informal credit providers include friends and
relatives as well as moneylenders in rural villages. Between these two ends lie
semiformal credit providers such as cooperatives, production groups, and village
funds.

8Related to growing indebtedness of the elderly, Higgs et al. (2009) use nine rounds of data from the United
Kingdom Family Expenditure Survey collected between 1968 和 2005 and study consumption expenditure by retired
households. Their findings show a growing extent of ownership of key goods in retired households.

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70 Asian Development Review

With developments in the financial sector in recent decades, the formal
financial sector is becoming more important in Thailand, while the informal sector
has been in decline. Specifically, 在 2016, one in three of the Thai population has
debt from the formal financial sector. 同时, the percentage of households
with debt in the informal sector quickly declined from 20% 在 2007 to under 10%
在 2013.9

然而, household debt is concentrated among a small group of borrowers.
Specifically, the top 10% of borrowers account for over 62% of total formal debt.
It is mostly concentrated among the following loan subcategories: personal and
business loans, loans from commercial banks, loans held by borrowers outside the
working-age group, loans in urban areas and in Bangkok and the vicinity. The top
10% of borrowers tend to be homeowners and live in urban areas and in Bangkok
and its vicinity. 正如预期的那样, the bottom group has fewer accounts and fewer distinct
products than the middle group, which in turn has fewer accounts and products than
the top group. Personal loans are highly prevalent, while housing loans appear very
limited. Specifically, 17% of the Thai population have personal loans, 其次是
auto and credit card loans at 9% each. 同时, 仅有的 4% of the population have
housing loans.

In terms of credit providers, among the formal lenders, loans from special
financial institutions are less prevalent relative to those of commercial banks and
other institutions with a larger outreach, but their average loan size is larger
when compared to other types of lenders. Specifically, the number of borrowers
from special financial institutions is only one-third of the number of borrowers
from commercial banks, but the median debt per borrower is almost double that
of commercial banks. This reflects the role of special financial institutions as
state-owned enterprises, established with each specific law, so each one has its own
mandate and offers certain types of loans, whereas commercial banks offer a wider
range of loan products.

IV. 数据

This paper uses account-level consumer loan data submitted to Thailand’s
NCB by its members from 2009 到 2016.10 The data cover almost all loans from the
formal financial sector to ordinary persons in Thailand.11 The NCB data are suitable

9For details on household debt in Thailand, see Chantarat et al. (2017) and Suwanik, Chantarat, 和

Samphantharak (2018).

10The data are as of the end of each year, except for the data for 2016 which are at the end of July. 那里
是 90 NCB members in total: (我) 19 banks—all 15 Thai commercial banks and four foreign bank branches; (二) six
specialized financial institutions (SFIs); 和 (三、) 65 nonbank financial institutions such as credit card, personal loan,
insurance, and hire purchase or leasing companies.

11然而, the data do not include loans from the Student Loan Fund, cooperatives, village funds, and loans
from the informal financial sector, such as money lenders or community-based institutions. The data also do not
include loans to juristic persons, which are maintained in a separate database at the bureau.

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Household Debt and Delinquency over the Life Cycle 71

桌子 1. Overview of Credit Bureau Data

Coverage

2009

2010

2011

2012

2013

2014

2015

七月 2016

Number of financial institution

68

69

75

78

78

80

86

90

members

Number of accounts (百万)
Number of borrowers (百万)
Total loan outstanding (兆

32.63
11.65
4.39

33.93
12.29
4.92

37.86
13.36
6.05

41.99
14.73
7.14

46.63
15.98
8.11

47.63
16.07
8.44

48.47
15.94
8.69

60.51
19.25
9.80

baht)
少于 30 days past due
31–60 days past due
61–90 days past due
91–120 days past due
121–300 days past due
Greater than 300 days past

到期的

3.85
0.08
0.04
0.02
0.11
0.30

4.39
0.08
0.03
0.02
0.09
0.31

5.32
0.10
0.05
0.02
0.10
0.45

6.40
0.12
0.05
0.02
0.12
0.44

7.24
0.16
0.08
0.04
0.14
0.44

7.63
0.20
0.08
0.04
0.12
0.37

7.86
0.26
0.07
0.04
0.13
0.32

Total delinquent loansa (兆

0.42

0.42

0.57

0.57

0.62

0.53

0.50

baht)

Number of credit review for

8.70

9.90

11.44

13.90

13.76

12.75

9.48

8.78
0.28
0.11
0.04
0.18
0.42

0.64

7.12

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5.27

6.95

9.00

7.63

17.66

30.04

24.61

23.76

new loans (百万)

Number of credit review for
existing loans (百万)

aDelinquent loans are loans that are more than 90 days past due.
来源: Authors’ calculations from National Credit Bureau data.

for our study for various reasons. 第一的, unlike data collected from surveys that tend
to miss certain groups, such as the very high-income households, the NCB data
cover a wide range of the population, consisting of the majority of formal loans to
individuals in the Thai economy. 第二, the data contain account-level information
that makes the analysis of household debt at a granular level possible and helps
unveil the heterogeneity across households. 第三, the coverage and granularity of
the data together allow us to study loan portfolios of individuals when they borrow
from multiple financial institutions. This analysis would not be possible with data
from each lender separately.

桌子 1 shows the coverage of the data. As of July 2016, there were 60.51
million active loan accounts from 19.25 million borrowers that contributed to a total
loan outstanding of 9.8 trillion baht (乙) 或者 87% of the total household debt in the
系统. The data also contain information on days past due for each loan account.
This information allows us to study delinquent loans, which we define in this paper
as loans that are more than 90 days past due. With this definition, the total value of
delinquent loans was B0.64 trillion or 6.5% of the total loan outstanding.

the account

The NCB data contain information that reflects four granular dimensions
在
等级: (我) information on the borrower (age and postcode of
mailing address); (二) information on the loan product, which we group into six
类别: 住房, automobile, motorcycle, credit card, business loans,12 和

12Business loans include commercial loans and loans for agriculture.

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72 Asian Development Review

personal and other loans;13 (三、) information on the lender, which we group into
three broad categories: commercial banks,14 SFIs,15 和别的 (nonbank) financial
机构;16 和 (四号) quantitative information on credit line, loan outstanding, 和
days past due (and hence the utilization ratio and delinquency status) that we can
use as outcome variables in our analysis.17 In particular, we define delinquent loans
as those that are more than 90 days past due. 换句话说, delinquency is an
indicator of financial unhealthiness.18

桌子 2 provides descriptive statistics about the borrowers. It shows that the
median age of borrowers is 43 years old. The majority of borrowers live in Bangkok
and its vicinity (29%). 实际上, half of borrowers live in urban areas.19 A median
borrower holds two loan accounts, has one loan product, and borrows from one
lender. The distributions of credit line and loan outstanding per borrower are very
skewed, with the means as high as B0.77 and B0.51 million and the medians only
at B0.28 and B0.15 million.20 Similarly, the mean delinquent amount per borrower
is B0.20 million, while the median is only B0.06 million. Approximately 7% 的
the borrowers are new clients. The utilization rate, defined as the ratio of current
balance to credit line, averages at 0.65 per borrower.

桌子 3 provides summary statistics of loans. The top panel presents the
allocation of accounts, outstanding loans, and delinquency across products and
lenders. Personal and other loans occupy the highest delinquency share among all
loan products, while commercial banks account for the highest delinquency share
among lenders. Personal and other loans raise concerns for the financial system as
they occupy the largest share of accounts (35.5%), the second-largest share of debt
(27.8%), and the largest share of delinquency (31.8%). Auto and housing loans also
raise concerns for the financial system as they contribute to the second and the

13Personal loans include secured loans (例如, car for cash, home for cash) and unsecured loans or clean loans
(例如, cash card, multipurpose loans). The majority of other loan products include overdraft and other hire purchase
or leasing.

14A commercial bank is defined on a solo or stand-alone basis. 那是, its subsidiaries such as credit card and
hire purchase or leasing companies that are also members of NCB are categorized as separate entities under other
financial institutions.

15Specialized financial institutions (SFIs) include Government Savings Bank, Government Housing Bank,
Export-Import Bank, Small and Medium Enterprise Development Bank, Islamic Bank, and Bank for Agriculture and
Agricultural Cooperatives.

16Other financial institutions are nonbank financial institutions such as credit card companies, hire purchase
or leasing companies, insurance companies, and cooperatives. We thus refer to other financial institutions as nonbank
financial institutions.

17A credit line from each borrower is computed as the aggregate of credit lines from all loan products held
by the borrower. 然而, in the case of borrowers with multiple credit cards from the same financial institution, 全部
credit cards are collectively under the same credit line.

18Our definition of delinquent loans is therefore more stringent than just loans with late payment, but less
stringent than nonperforming loans. Specifically, the classification of nonperforming loans by the Bank of Thailand
involves both loans that are more than 90 days past due and other qualitative assessments.

19Urban areas are defined by postcode, with more than 50% of its area classified as a municipality.
20The value of the Thai baht fluctuated between B29 and B36 per US dollar during 2009–2016. At the end

of July 2016, the exchange rate was about B36 per US dollar.

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Household Debt and Delinquency over the Life Cycle 73

桌子 2. Descriptive Statistics of Borrowers, 七月 2016

Mean Median

SD Min Max

氮

Borrower characteristics
年龄 (年)
Region

Bangkok and vicinity (=1)
中央 (=1)
北 (=1)
Northeast (=1)
南 (=1)
Urban (=1)A

Portfolio characteristics
Number of accounts
Number of distinct loan products
Number of financial institutions used
Total credit line (million baht)
Total loan outstanding (million baht)
Total delinquent loan (million baht)
Credit history and utilization
Number of years in NCB data
New borrower (=1)
Have delinquent loan this year (=1)
Have delinquent loan in the past year (=1)
Utilization rateb (%)

44.16

43.00

12.36

20

80

18,813,985

0.29
0.18
0.15
0.25
0.11
0.51

3.14
1.60
2.08
0.77
0.51
0.20

5.02
0.07
0.17
0.16
0.65

已经
已经
已经
已经
已经
已经

2.00
1.00
1.00
0.28
0.15
0.06

5.00
已经
已经
已经
0.71

已经
已经
已经
已经
已经
已经

3.11
0.81
1.66
3.84
3.07
1.10

2.73
已经
已经
已经
0.36

0
0
0
0
0
0

1
1
1
0
0
0

1
0
0
0
0

1
1
1
1
1
1

167
6
8
4,220
8,990
973

8
1
1
1
1

16,039,856
16,039,856
16,039,856
16,039,856
16,039,856
16,039,856

19,245,461
19,245,461
19,245,461
19,245,461
19,245,461
3,189,527

19,245,461
19,245,461
19,245,461
15,252,626
19,245,461

N = number of observations, na = not available, NCB = National Credit Bureau, SD = standard deviation.
笔记: Some data for age and location are missing, hence the smaller sample size with these identifiers.
aPostcode is defined as an urban area if more than 50% of the area belongs to municipalities.
bUtilization rate is defined as a ratio of current balance to credit line.
来源: Authors’ calculations from National Credit Bureau data.

third-largest shares of delinquency even though housing loans account for only
5.4% of the total number of accounts, due to the large size of each loan.21 In
对比, credit card debt occupies the second-largest share of accounts (30.7%),
but they contribute much less to the total debt outstanding and delinquency due to
their small sizes. By lender, commercial banks account for the largest share of debt
outstanding and delinquency, while nonbank financial institutions account for the
largest share of accounts. The bottom panel presents the allocation across borrower
age groups and locations. The shares of borrowers, loans, and delinquencies are
highest among borrowers in working-age groups (highest for those age 46–60 years,
followed by 36–45 years and 25–35 years) and borrowers in Bangkok and the
vicinity.

21用于比较, although housing debt accounts for the largest share of total loan outstanding at 33.2%,
the number is still lower than that of every G7 economy where more than 50% of outstanding household debt in
2012 was in mortgages. Other debt products of the “Big Four” banks in the US were student loans, vehicle loans, 和
credit cards. See Zinman (2015).

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74 Asian Development Review

桌子 3. Dissecting Aggregate Debt and Delinquency by Loan Product, Lender, 和
Borrower, 七月 2016

Loan Product and Lender

Loan product
Housing
汽车
Motorcycle
Credit card
Personal loan and others
Loan for business

Lender

Commercial banks
SFIs
Other financial institutionsa

Borrowers

年龄

Younger than 25
From 25–35
From 36–45
From 46–60
Older than 60

Region

Bangkok and vicinity
中央
北
Northeast
南
Urban

Share of
帐户 (%)

Share of Loan
Outstanding (%)

Share of Delinquent
Loan (%)

5.4
11.2
2.2
30.7
35.5
15.0

35.2
26.6
38.2

33.2
20.8
0.5
3.8
27.8
13.9

51.7
33.9
14.4

19.8
23.4
2.7
8.1
31.8
14.0

45.6
29.4
25.0

Share of

Share of Loan

Borrowers (%) Outstanding (%)

Share of Delinquent
Loan (%)

3.2
27.6
27.9
32.2
9.1

29.4
18.3
15.4
25.4
11.4
51.0

1.1
22.6
33.0
36.1
7.2

36.4
17.2
13.7
21.0
11.7
58.8

1.3
24.8
34.2
34.0
5.7

35.4
18.1
13.7
18.7
14.1
58.1

SFI = specialized financial institution.
aOther financial institutions include credit card companies, hire purchase companies, insurance companies,
and cooperatives.
来源: Authors’ calculations from National Credit Bureau data.

V. Empirics

This section presents empirical findings on debt holding, debt portfolio, 和
delinquency over the life cycle. For each issue, we first present a simple plot of
descriptive statistics by age. We then perform a regression analysis that allows us to
control for macroeconomic and individual fixed effects. 最后, we end the section
with a cohort analysis of debt and delinquency.

A.

Debt Holding over the Life Cycle

The granularity of the NCB data allows us to quantify the debt situation at the
borrower level. We consider two measures. 第一的, debt headcount is defined as the
number of individuals with debt divided by the total population. It is a measure that

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Household Debt and Delinquency over the Life Cycle 75

provides information on the prevalence of debt across individuals in the economy,
那是, what fraction of the population is indebted.22 Second, debt per borrower is
defined as the average loan outstanding of each indebted individual. This measure
indicates debt intensity that each indebted individual experiences. These two
microlevel measures thus decompose the commonly used and aggregate measure
of debt per capita into two parts: (我) debt prevalence at the extensive margin and
(二) debt intensity at the intensive margin.

Total outstanding debt
Population

= Number of borrowers
Population

· Total outstanding debt
Number of borrowers

Debt per capita = Debt headcount · Debt per borrower

We first explore the age profile of debt prevalence and debt intensity, 作为
shown in Figure 1. The top-left chart shows an inverted-U relationship between
age and debt headcount: the headcount increases with age, peaks at 50% around an
early age of 30 years old, remains at 40%–50% until reaching retirement age, 和
then declines sharply afterwards. This is consistent with young and old individuals
facing more credit constraints. The top-right chart gives us additional information
about debt over the life cycle from the perspective of debt intensity, 那是, debt per
borrower. 全面的, we observe the expected debt accumulation when individuals are
young and enter the labor force and then debt decumulation as borrowers retire.
然而, borrowers’ debt intensity remains high, well past the retirement age,
raising a concern over the indebtedness of an aging population.23

The center-left panel of Figure 1 further presents the age profiles of debt
prevalence by loan product. Personal, auto, and credit card debt headcounts peak at
30%, 20%, 和 20%, 分别, for borrowers in the young working-age group
(around 28–35 years old). Individuals thus begin having credit card, auto, 和
especially personal loans at an early age. These patterns are consistent with large
access to loans from nonbank institutions, which include several hire purchase or
leasing and credit card companies. The pattern peaks at 40% among the young
working-age group, as shown in the bottom-left panel of Figure 1. 相似地, 这
center-right panel of Figure 1 presents the age profiles of debt intensity by loan
product and further shows a high intensity of housing and auto debts among the
young working-age group. The median housing debt per borrower is high for very
young borrowers and gradually declines for the older borrowers. This pattern likely
reflects the repayment of mortgage for each individual as well as a general increase
in housing price over time (IE。, younger homebuyers purchased their home at more
最近的, higher prices than the older generations who purchased their home a long

22In the household finance literature, this measure is also known as the penetration rate or the participation

速度.

23The general retirement age for civil servants in Thailand is 60 years old.

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76 Asian Development Review

数字 1. Life Cycle of Debt Prevalence and Intensity

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SFI = specialized financial institution.
来源: 作者的计算.

过去). The data show that individuals in Thailand start to have a high intensity of
housing debt at a much younger age than those in the US where debt per borrower
peaks later in the middle of working age (Fulford and Schuh 2015). Auto loans
exhibit a different pattern, with a relatively stable median debt per borrower across

Household Debt and Delinquency over the Life Cycle 77

all working ages, although the median is higher for the young and declining for the
老的. Credit card and business loans, 然而, show the expected inverted-U patterns
in which we find an increase in debt per borrower among young borrowers and a
decrease for the elderly. The figure also shows that the prevalence of personal, credit
卡片, and business loans among retirees remains quite high. The amount of personal
loans per borrower also peaks after retirement age. The high debt prevalence and
intensity of personal loans after retirement could also reflect the limited loan choices
of the retirees. One possible explanation for the high headcount of credit card debt
among the retirees could be the use of credit cards as a payment mechanism.

The age profiles of debt echo the limited housing debt—only one in 10 的
the working-age population in their 40s have housing loans. This is considered
very low relative to the US, where around 40% of borrowers in their 40s have a
mortgage (Fulford and Schuh 2015).24 Apart from housing loans, access to credit
card also appears limited. Credit card debt prevalence peaks at 20%, which again
is a lot lower than in the US where 63% of the population have at least one credit
卡片 (Consumer Financial Protection Bureau 2015).25 数字 1 further shows that
access to business loans appears quite late and peaks among those in retirement age.
Possible explanations include the difficulty of the young in setting up or expanding
a business due to their lack of credit history and collateral as well as limited
经验. 或者, another possible explanation is that formal sector jobs are
easier to get for younger people and hence they have limited demand for business
loans.

The age profile of debt intensity is vastly diverse across lenders, reflecting
different roles of commercial banks, SFIs, and nonbank financial institutions in the
经济. Individuals have access to loans from nonbank financial institutions at
a younger age as these institutions focus on providing auto and credit card loans.
The debt headcount from SFIs is higher for working-age individuals who are also
close to retirement. The median debt per borrower of loans from commercial banks
is much higher for very young borrowers, peaks at 21 years old, and declines
afterwards. The median peaks later around the age of 35 for loans from SFIs.

下一个, we examine how debt profile over a person’s life cycle has changed over
time from 2009 到 2016. There are more borrowers (extensive margin) and debt per
borrower is larger (intensive margin) at every age over time, especially among the
younger generation. 数字 2 presents the age profiles of debt prevalence and debt

24There are several factors that contribute to this finding. 第一的, the finding is partly driven by the fact that a
large portion of the Thai population live in rural areas with very limited access to mortgage loans. The debt headcount
in urban areas is about 20% (Chantarat et al. 2018). 第二, the land and housing markets in Thailand are less active
than those in the US because Thailand has lower job mobility across cities. 此外, extended families remain
widespread in Thailand where children live with their parents and inherit their land and house.

25相似地, this finding is partly driven by the debt headcount in rural areas, which was very low (少于
10%). Considering only urban areas, the debt headcount is higher than 30% (Chantarat et al. 2019). 此外, 这
Thai economy remains largely cash based due to its large informal sector; the use of credit cards is not widespread.

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78 Asian Development Review

数字 2. Life Cycle of Debt Prevalence and Intensity over Time

来源: 作者的计算.

intensity by year. Generally speaking, these age profiles are similar to what we see
图中 1 为了 2016. 然而, 数字 2 reveals additional insights. 第一的, it shows
that household debt in Thailand has expanded, as evident from the upward shift of
the curve over time. 第二, credit expansion is not uniform across age groups,
with the younger borrowers experiencing the most expansion. 更确切地说, debt
headcount and debt per borrower increase most for individuals around 30 years old.
This descriptive finding suggests that the younger generations have accumulated
debt earlier in their lives than the older ones. We return to a regression analysis with
age and cohort fixed effects later in this section.

Looking across different types of loans, we observe heterogeneity in debt
accumulation by loan category, with remarkable increases in debt headcounts
of personal, auto, and credit card loans among the younger generation.26 The
expansion of debt headcount for auto loans takes place during 2011–2013.27 Debt
headcounts for housing and business loans increase over the period of our study,
but the magnitudes are relatively small when compared to other loan products. 这
debt headcounts of loans from commercial banks and other (nonbank) financial
institutions increase most for borrowers around 30 years old, while the headcount
from SFIs expands most for those around 40 years old. For debt intensity, we find
that debt per borrower for housing loans rises tremendously for young borrowers,
while that for credit card loans increases most for individuals age 40–60.

To isolate the age effects from macroeconomic and individual fixed factors,
we perform a regression analysis of debt per borrower, controlling for year fixed

26See Chantarat et al. (2018) for detail.
27This observation is likely due to the first-car buyer tax rebate scheme. See Muthitacharoen, Samphantharak,

and Chantarat (2019) for the impact of the scheme on household debt in Thailand.

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Household Debt and Delinquency over the Life Cycle 79

数字 3. Age Regression Coefficients of Debt per Borrower (controlling for cohort effect)

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SFI = specialized financial institution.
笔记: Y-axis scaling varies across loan products and lenders to show the variation in life-cycle patterns for each loan.
来源: 作者的计算.

effects, interactions between year and location (postcode) fixed effects, and location
or individual fixed effects. Specifically, we run the following two regressions:

做,A, j,t = α + βa + γ j + δt + κ j,t + εi,A, j,t

和

做,A, j,t = α + βa + λi + δt + κ j,t + εi,A, j,t

(1)

(2)

where yi,A, j,t is debt per borrower, α is the constant term, βa is the age a fixed effect,
γ j is the location j fixed effect, δt is the year t year fixed effect, κ j,t is the location-
year fixed effect for location j and year t, λi is the individual borrower i fixed effect,
and εi,A, j,t is the residual.

数字 3 presents regression coefficients of age variables estimated by
方程 (2). Our results show that, after controlling for individual (hence cohort)
and time-location fixed effects, the regression coefficients increase with age until
大约 60 years and then decline. The interpretation is that, for those below
60 years old, older individuals have higher debt than younger ones, implying debt
accumulation over this age range. 同样地, for those above 60 years old, 这
finding implies debt decumulation. The overall pattern is similar to the narrative we
discussed earlier. 然而, the regression analysis shows that the life-cycle pattern

80 Asian Development Review

数字 4. Number of Accounts and Lenders

FI = financial institution.
来源: 作者的计算.

of overall debt per borrower peaks at 60 年. 换句话说, borrowers keep
accumulating debt until retirement age, and then begin to deleverage at retirement.28
总共, our analysis of the life cycle of debt outstanding shows that there is
an inverted-U pattern as predicted by the life-cycle hypothesis. The peak estimated
by regression analyses is at 60 年, which is also the common retirement age in
Thailand. 然而, the pattern is heterogeneous across loan products and lenders.
尤其, the peaks for auto and credit card debts are achieved earlier when
borrowers are still young; the patterns for lenders, with the earliest peak for nonbank
financial institutions, mirror those for these loan products.

乙.

The Life Cycle of a Loan Portfolio

The granularity of the data allows us to study the life cycle of each
individual’s loan portfolio. 数字 4 presents the proportion of borrowers in each
age group by the number of accounts and by the number of lenders. The figure
reveals that the life cycle of the number of loan accounts held by each individual
also follows a nonlinear pattern—the fraction of borrowers with multiple loan
accounts increases with age for young groups, then remains relatively stable during
the working age of 30–60, and finally decreases with age for retired borrowers. 这
age profile of the number of lenders delivers a similar pattern.

Similar to the analysis of debt over the life cycle, we perform a regression
analysis to control for macroeconomic and individual fixed effects. Specifically, 我们
estimate equation (2) by using the number of accounts and the number of lenders per
borrower as the dependent variable, 做,A, j,t. 数字 5 plots the regression coefficients

28For detailed regression results, see Tables A.2–A.4 in Chantarat et al. (2018).

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Household Debt and Delinquency over the Life Cycle 81

数字 5. Age Regression Coefficients of Number of Accounts and Lenders (controlling
for cohort effect)

来源: 作者的计算.

数字 6. Life Cycle of a Loan Portfolio

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SFI = specialized financial institution.
来源: 作者的计算.

by age. The overall pattern is similar to the narrative we discussed earlier, 与
peak at 57 years for both regressions.29

最后, 数字 6 illustrates the composition of loan portfolios. The left
panel presents the portfolio of loan products for each borrower by age. It shows
a striking life-cycle pattern. Auto loans account for a significant share of total loan
outstanding for young borrowers but are almost nonexistent for older borrowers.
Business loans show the opposite pattern. Personal loans, 然而, have constantly
contributed a large portion of the portfolio throughout the life cycle while housing
and credit card loans both accounted for relatively smaller portions. The right panel
shows the portfolio of lenders for each borrower by age. 再次, it shows a clear
life-cycle pattern. 其他 (nonbank) financial institutions dominate the portfolio of

29For detailed regression results, see Table A.5 in Chantarat et al. (2019).

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82 Asian Development Review

younger borrowers, while SFIs occupy the largest share of the portfolio of the older
borrowers. The portfolio of lenders mirrors the life-cycle pattern in the portfolio of
loan products.

总之, our study confirms an inverted-U pattern that reflects the
life-cycle profile of debt. Based on the regression analysis, the number of accounts
and the number of lenders peak at 57 年, roughly consistent with the peak of debt
per borrower found earlier, which is around the common retirement age in Thailand.
Our findings also reiterate the role of nonbank financial institutions for lending to
the young and SFIs for the old. 最后, we find that the younger generation seems to
originate debt earlier in the life cycle than their older counterparts. Older borrowers
also continue to be indebted well past their retirement age.

C.

Delinquency over the Life Cycle

Parallel to debt, we study the life cycle of delinquency at the borrower level.
Delinquency headcount is defined as the number of borrowers with delinquent loans
divided by the total number of borrowers. This measure represents the prevalence of
delinquency among borrowers. Delinquent debt per delinquent borrower is defined
as the value of delinquent loans that each delinquent borrower has. This measure
thus tells us about the intensity or severity of delinquency faced by delinquent
borrowers. These microlevel measures give us information beyond what we get from
the aggregate delinquency measure, the delinquency rate, defined as the percentage
of loan outstanding in the economy that is delinquent, which provides us with no
information on the distribution of delinquent loans and the burden on borrowers.
Parallel to the decomposition of debt outstanding presented earlier, our measures
of delinquency can be presented as components of the aggregate delinquency
措施.

Total outstanding debt
Number of borrowers
= Number of delinquent borrowers
Number of borrowers

· Total delinquent debt
Total outstanding debt

·

Total delinquent debt
Number of delinquent borrowers

Debt per borrower · Delinquency rate

= Delinquency headcount · Delinquent debt per delinquent borrower

数字 7 presents the age profiles of delinquency headcount and median
delinquent debt per delinquent borrower. The top-left chart shows a downward-
sloping profile, with the delinquency headcount highest at 21% for borrowers age 29
and declining as age increases. The top-right chart shows that, with the exception of
high delinquent debt per delinquent borrower for very young borrowers, the overall
delinquency intensity increases with age and remains relatively constant at around

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Household Debt and Delinquency over the Life Cycle 83

数字 7. Life Cycle of Delinquency Prevalence and Intensity

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SFI = specialized financial institution.
来源: 作者的计算.

B0.06 million for the working-age population and then declines for those older than
60 years old. This pattern mimics what we discussed earlier about the life cycle of
debt intensity.

The age profiles of delinquency are heterogeneous across loan products. 为了
个人的, auto, credit card, and business loans, delinquency headcounts are high

84 Asian Development Review

for the younger borrowers and later decline, with the highest headcounts at around
30 years old for personal, credit card, and business loans and at 23 years old
for auto loans. 关于 20% of the young working-age borrowers have delinquent
personal loans. 全面的, delinquency headcounts show a stable or downward trend
as borrowers age for most loan products. A possible explanation is that older
borrowers have a longer credit history and more collateral, allowing for better
loan screening and lower incentive to default. Housing debt appears to have a low
delinquency headcount and is uniform across ages, which is what we expect for
secured loans.

There are vastly mixed patterns of delinquency intensity in the age profiles of
different loan products. Housing and business loans exhibit decreasing delinquent
debt per delinquent borrower as age increases. This finding mirrors what we
discussed earlier about debt intensity in Figure 1. 相比之下, auto, credit card,
和, to some extent, personal loans show the opposite pattern, 那是, 他们是
increasing with age. Delinquent debt per delinquent borrower for personal loans
deserves special attention because the intensity increases with age and remains
high after retirement, while the delinquency headcounts remain persistently high
还有. 还, alarming is credit card debt—the delinquency intensity continues to
stay high past the retirement age (although the decline in delinquency headcount
makes the situation less worrisome than personal loans). These findings raise a
concern about the debt burden of retirees. In the case of auto loans, we also find
high delinquent debt per delinquent borrower for the elderly but a low delinquency
headcount, implying that the defaults were likely on expensive cars.

Across lenders, we find large delinquency headcounts of loans from other
(nonbank) financial institutions (which also peak among the young working-age
团体), while delinquency headcounts of loans from SFIs remain stable across
all ages and do not decline after retirement. More explicitly, 数字 7 shows that
the delinquency headcounts of debt from commercial banks and other (nonbank)
financial institutions peak among borrowers age 30 (在大约 12% 和 20%,
分别), while the delinquent headcount of SFIs seems to be similar for all
borrowers age 30 以上, around 10%–15%. Delinquent debt per delinquent
borrower for nonbank financial institutions is also high among older borrowers,
while the delinquency headcount declines with age. This finding is likely driven by
the high delinquency intensity of credit card debt, which exhibits a similar pattern.
数字 8 presents age profiles of delinquency headcounts over time and
shows that the declining delinquency headcount is evident in every year during
2009–2016. Further analysis reveals heterogeneity in the profiles across loan
products and lenders.30 The drop is significant for housing, credit card, and personal
loans. 然而, auto and business loans experience an increase in delinquency
prevalence during the same period, especially among younger borrowers. If we

30See Chantarat et al. (2018) for detail.

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Household Debt and Delinquency over the Life Cycle 85

数字 8. Life Cycle of Delinquency Prevalence over Time

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来源: 作者的计算.

compare across lenders, we find that delinquency headcounts decrease over time
for loans from nonbank financial institutions. Delinquency headcounts, 然而,
increase over time for loans from commercial banks and SFIs among the young
working-age population. They decline over time among the older population.

Similar to earlier analyses, we run probit regressions of equations (1) 和 (2)
where the dependent variable, 做,A, j,t, is an indicator variable that takes the value of
1 if an individual has at least one delinquent loan and 0 否则. 数字 9 plots the
probit regression coefficients by age, controlling for macroeconomic and individual
fixed effects. The overall pattern is similar to the narrative we discussed earlier.31

总之, our analysis reveals an overall downward-sloping pattern of
delinquency over the life cycle. This is consistent to the finding using US data by
Xiao and Yao (2014). 然而, the pattern is heterogeneous across loan products
and lenders. Business and credit card loans seem to experience an increasing
delinquency headcount over time.

D.

Cohort Analysis of Debt and Delinquency

Given the age of each borrower in the data, we can analyze debt and
delinquency by birth cohort. The findings are displayed in Figure 10, where each
of the lines represents a unique birth cohort of borrowers over different ages

31For detailed regression results, see Tables A.6–A.8 in Chantarat et al. (2019).

86 Asian Development Review

数字 9. Age Regression Coefficients of Delinquency Probability (controlling for
cohort effect)

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SFI = specialized financial institution.
笔记: Y-axis scaling varies across loan products and lenders to show the variation in life-cycle patterns for each loan.
来源: 作者的计算.

during a period of 8 years from 2009 到 2016 in the data. The advantage of this
cohort analysis is that it allows us to examine prevalence and intensity of debt and
delinquency from two different approaches. 第一的, for a given age, we can compare
debt and delinquency across different cohorts or generations, while controlling for a
particular position in their life cycle. 第二, we can trace a given cohort over time
and examine debt and delinquency over the life cycle of the same cohort, 尽管
controlling for cohort specific effects.

数字 10 shows that debt accumulation implied by debt headcounts follows
an inverted-U pattern, while over time, individuals who were born later start having
debt earlier in their lives. 第一的, for a given age, the top chart in Figure 10 节目
that the debt headcount of the younger generation is uniformly above that of the
older generation. 例如, at the age of 30, the cohort born in 1985 has a
debt headcount of almost 50%, while the cohort born in 1981 has a less than 40%
headcount. This result suggests that the younger generations seem to have had more
access to credit than the older ones when they were at the same age. 或者,
for each level of debt headcount, we can see that the younger cohorts arrived at
that debt level faster than the older cohorts. 例如, the cohort born in 1975
reached the debt headcount of 40% when they were about 38 years old, 而

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Household Debt and Delinquency over the Life Cycle 87

数字 10. Life Cycle of Debt and Delinquency by Cohort

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cohort born in 1980 和 1990 achieved that same level of debt headcount when they
只是 31 和 25 years old, 分别.

第二, for a given cohort, the chart shows that the lines have positive slopes
at younger ages and become negative for older ages. This finding suggests an
inverted-U dynamic of leveraging and deleveraging over the life cycle. The chart

88 Asian Development Review

also shows that the peaks of debt headcounts appear at earlier ages and at higher
levels for younger cohorts, further confirming that the younger generations had
access to debt faster and earlier than the older generations. 最后, the chart reveals
that the cohort age with the highest debt headcount in the data is the cohort of
borrowers born in 1981 when they were 33 years old. 更确切地说, this cohort
had the highest debt headcount in the entire data at over 50% 在 2014.

We also find that debt accumulation and decumulation over the life cycle and
over time implied by debt intensity mimic the pattern found in debt headcounts, 但
the highest debt intensity is reached later than the highest headcount. The middle
chart of Figure 10 shows the median debt per borrower. 再次, we see the overall
inverted-U debt intensity dynamics over the life cycle. The younger cohorts seem
to have a higher median debt per borrower than the older cohorts when they were
at the same age, and the difference is highest at younger ages. 或者, 这
younger cohorts achieve a given median debt per borrower earlier in their lives than
the older cohorts.

For delinquency, we find that delinquency headcounts decline with age over
the life cycle. They also decline over time for all cohorts. These findings are shown
in the bottom chart of Figure 10. For each cohort, we observe a general downward-
sloping line, implying that delinquency headcounts decrease when borrowers
become older. For each age, the delinquency headcounts of the younger cohorts are
lower than the older ones, suggesting that delinquency headcounts have declined
over time for all age groups.

We also perform a regression analysis for debt and delinquency by age and
cohort. The results are consistent with our earlier narrative that younger cohorts
seem to be more indebted than earlier cohorts, controlling for age-specific effects.
相似地, delinquency seems to be lower over time, with younger cohorts being
less delinquent. This could reflect technology or knowledge that allow for better
loan screening (less severe adverse selection) and better loan collection (less severe
moral hazard).32

六、. Conclusion and Policy Implications

This paper uses loan-level data to study the life cycle of household debt in
Thailand. The wide coverage and the granularity of the data allow us to decompose
the aggregate and commonly used measures of debt per capita and delinquency
rate into components that unveil the extensive and intensive margins of debt and
违法行为. This decomposition allows us to analyze prevalence, intensity, 和
distribution of indebtedness over the life cycle. We find striking life-cycle patterns
of indebtedness, but they are heterogeneous across loan products and lenders. 我们

32For detailed results, see Table A.9 in Chantarat et al. (2018).

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Household Debt and Delinquency over the Life Cycle 89

also find an expansion of debt over time, while delinquency has been in decline for
most loan products. 最后, we find a downward pattern of delinquency over the
life cycle. Our findings yield important implications on both individual households
and the economy at large. We discuss them in this section.

A.

Implications for Households

Our findings show that Thai people have become indebted earlier in
their lives. Delinquency headcounts are also highest among younger working-
age borrowers. This raises a concern that these individuals may face difficulty
in getting loans in the future. We also find that debt remains high for many
borrowers after retirement. Policies that enhance access to necessary credit, 这样的
as for housing and for business investment, are thus critical, but such policies need
to target individuals with potential to repay. In this respect, the “data revolution”
and financial innovations have already opened up new opportunities in resolving
information asymmetry and other inefficiencies in the credit market—a necessary
step to unlock access to credit especially among the underbanked population.33
Policies that promote access to savings in preparation for retirement are also
批判的, as well as policies that enhance access to necessary credit for high-potential
retirees, such as reverse mortgage. These policies are crucial as the country is
demographically aging and longevity of individuals is increasing. Financial literacy
and planning programs that can effectively raise both financial awareness and
discipline among households are especially critical. These programs should target
the young population in school before they enter the labor and credit markets.
Our study helps identify the groups of borrowers with delinquency vulnerability.
尤其, we raise concerns over the young working-age population that have
high delinquency headcount and intensity.

乙.

Implications on the Economy

The high debt intensity among households, especially among the young
working-age group which have a large propensity to spend, could lead to
debt overhang and a prolonged sluggish consumption and investment spending,
particularly among these young borrowers. This in turn could weaken domestic
aggregate demand—one of the main growth engines of the economy. 一个高
debt burden implies increasing vulnerability for households and thus to the
overall financial system and economy if delinquency is positively correlated across
households. These macroeconomic implications thus amplify the importance of
policies that aim to balance financial access and financial stability.

33The data revolution broadly includes the emergence of big data and the advancement in computing

technology that allow complex analyses of borrowers and loans.

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90 Asian Development Review

综上所述, this study illustrates that debt is very heterogeneous in many
方面. In order to understand the situation of household debt and design
appropriate policies, aggregate data are not sufficient and granular data that cover
the majority of the financial system are needed. This paper exemplifies the potential
of credit bureau data in generating new knowledge about household debt. 钥匙
limitation of the data is the lack of informal and semiformal debts, 例如,
loans from cooperatives, educational loans from the Student Loan Fund, 也
borrower’s income and savings data. Augmenting the data to cover this necessary
information will open up new opportunities for researchers and policy makers in
using this dataset to answer relevant policy questions necessary for effective policy
design and targeting.34

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附录

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Household Debt to Gross Domestic Product of Selected
Asia and the Pacific Economies (%)

澳大利亚
新西兰
Republic of Korea
Thailand
Malaysia
日本
香港, 中国
新加坡
People’s Republic of China
印度尼西亚

1984

39.9

26.1

54.7

1994

51.5
41.9
47.3
40.2

71.6
38.6
28.9

2004

95.8
75.5
62.6
44.2
54.1A
68.0
57.7
46.3
10.9A
10.9

2014

118.3
88.8
84.3
69.0
68.9
66.0
65.6
60.6
36.1
17.1

aData for Malaysia and the People’s Republic of China are for 2006.
来源: Bank for International Settlements. https://www.bis.org/statistics/
totcredit.htm (访问过 16 十月 2019).

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