# Why and how to measure household debt in informal economies?

Isabelle Guérin, Arnaud Natal, Govindan Venkatasubramanian

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Isabelle Guérin, Arnaud Natal, Govindan Venkatasubramanian. Why and how to measure household debt in informal economies?. ODRIIS Policy Brief No. 1, 2024, pp.1-5. hal-04622836

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# Why and how to measure household debt in informal economies?

Isabelle Guérin, Arnaud Natal, and G. Venkatasubramanian

<figure><img src="image_2.png" /></figure>

The surge in global household debt, particularly in developing countries, is underestimated due to widespread informal debt. Economists and statisticians must urgently address this issue. The *Observatory of Rural Dynamics and Inequality in South India* offers innovative methods for accurate household debt measurement.

real estate debt is a major contributor to household debt in the North, in the South, consumer debt plays a more significant role. Additionally, the true extent of indebtedness is often underestimated due to informal, unregulated, and unrecorded debt, which can be substantial. Therefore, it is crucial to develop specific methods for measuring indebtedness. This note elaborates on the methodological innovations implemented by the *Observatory of Rural Dynamics and Inequalities in South India* over the past two decades.

## Introduction

Although levels of household debt vary across different contexts, they constantly exhibit an upward trend. In OECD countries, the average level of household debt nearly doubled from 1995 to 2021, rising from 68% to 127% of disposable income.<fnref n="1" /> This upward trend is even more pronounced in the Global South. According to data from the Bank for International Settlements, household debt in emerging countries has increased from 28% to 50% of GDP between 2010 and 2022.<fnref n="2" /> While

## How to measure debt?

The omission of small informal loans partly explains the underestimation of family indebtedness [1]. Any attempt to measure debt is confronted with various biases. Certain debts are socially stigmatized, and individuals are reluctant to openly discuss them (social desirability bias). Research conducted in diverse settings such as the United States and South Africa has revealed that individuals tend to underesti-

<footnote n="1">¹https://doi.org/10.1787/de435f6e-en (accessed on 2 January 2024).</footnote>

<footnote n="2">²https://data.bis.org/topics/CREDIT_GAPS/data/BIS_PDQ_J3_1.0 (accessed on 2 January 2024).</footnote>
mate their debt by approximately fifty per-
cent [2]. Individuals may also forget cer-
tain debts, especially when they have mul-
tiple debts (memory bias). Moreover, what
may be classified as a debt by statisticians
or field economists, a sum that is to be
repaid, might be perceived differently by
villagers, for instance, as “financial aid” or
“money borrowed from elsewhere”, even
if it still involves an obligation to repay
(categorization bias). Furthermore, both
consciously or unconsciously, both men
and women may tend to undervalue the
debts of women, restricting them to min-
imal amounts that are solely allocated for
day-to-day expenses (gender bias). Addi-
tionally, conventional counting practices
present a challenge for statisticians. Tamil
villagers commonly reason in terms of
approximate, contextual, qualitative, and
comparative values rather than continu-
ous variables [3]. For instance, in response
to the question “how much money did
you borrow from this lender?”, some par-
ticipants may respond with phrases like
“Much too much” or conversely “he gave
me very little”.

Ethnography played a crucial role in
identifying these biases and developing
questionnaires that aimed to mitigate
them (complete elimination of biases is
unattainable). This involved using vernac-
ular terminology and avoiding derogatory
language, providing a comprehensive list
of expenses and inquiring about their
sources of funding instead of directly ask-
ing respondents about their debt levels.
Furthermore, interviews were conducted
separately with men and women, adopt-
ing a conversational approach to establish

rapport and instill confidence. To accom-
plish this, researchers immersed them-
selves in participants' social environments
and utilized shared cultural references
pertaining to local social, political, and re-
ligious aspects. Notably, careful attention
was paid to individuals' qualitative evalu-
ations of debt prior to soliciting quanti-
tative data (as individuals often possess
knowledge of their debt amounts; how-
ever, the subjective quality of the debt
may outweigh its actual quantity) [4].

To collect data, the *Observatory* con-
ducted a baseline survey of over 400
households in 2010 (known as the RUME
survey), and two follow-up household sur-
veys in 2016-17 and 2020-21 of the
same population (known as the NEEMSIS
waves).<fnref n="3" /> These surveys were conducted in
10 villages in Tamil Nadu, in the districts
of Kallakurichi and Cuddalore. This unique
longitudinal database provides valuable
insights into the changing dynamics of
the region over a span of three points in
time. It is worth noting, however, that our
sample is not intended to be representa-
tive of the entire population, but rather
serves to illustrate the diverse economic
and rural dynamics within the region.

Why worry about measuring
debt?

The objective of RUME (2010) and subse-
quent NEEMSIS (2016-17, 2020-21) data
is not to supplant national statistical sur-
veys, but rather to uncover the aspects
that are overlooked by delving into and
putting into context socio-economic pro-
cesses at the local and regional levels [5].

<footnote n="3">³https://neemsis.hypotheses.org/ (accessed on 2 January 2024).</footnote>
<figure><img src="image_3.png" /><figcaption>**Figure 1:** Incidence of indebtedness and outstanding debt</figcaption></figure>

Source: RUME (2010), NSSO 70th round (2013), NEEMSIS (2016-17, 2020-21), NSSO 77th round (2019); authors' calculations.

Consequently, these data expose a distinct void in official statistical surveys.

This is particularly evident when examining household debt. When examining the prevalence of indebtedness (i.e., the percentage of households with at least one active loan), RUME and NEEMSIS data indicate that approximately 99% of households have debts, while national data such as the All India Debt and Investment Survey (AIDIS) from the NSSO (2013, 2019) reveal that only 40% of households were indebted in 2013 and 37% in 2019 (see Figure 1). Additionally, the average outstanding debt (the amount yet to be repaid) estimated using NEEMSIS data in 2020-21 surpasses that estimated using AIDIS data in 2019 by more than threefold (in current rupees, INR 180,400 and INR 53,000 respectively).

AIDIS data continue to be valuable for capturing temporal trends, such as the gradual decline and subsequent resurgence of informal household debt over

the past half-century. Nonetheless, these data alone fail to provide a comprehensive understanding of the magnitude of household debt. In the past, underestimating household debt has already led to serious crises. In contemporary India, it is clear that policymakers focus excessively on so-called"financial exclusion" (i.e., households lacking access to formal financial services) and underestimate the scale of rural household debt.

## Household financial vulnerability: an escalating concern

Specific indicators are needed to capture the consequences of debt in terms of financial vulnerability. Debt ratios are the most widely used indicators, and the most telling is probably the debt service ratio (DSR). It represents the proportion of gross annual income devoted to repaying the annual debt (capital and interest). From Figure 2, it is evident that the fi-
<figure><img src="image_4.png" /><figcaption>Figure 2: Debt service ratio</figcaption></figure>

Source: RUME (2010), NEEMSIS (2016-17, 2020-21); authors' calculations.

financial condition of households is deteriorating. In 2010, approximately 44% of their annual income was allocated towards debt repayment, whereas this proportion has escalated to 68% in 2020-21. This is of particular concern for Dalits (formerly known as untouchables) who belong to the so-called lowest castes. Unlike the “middle” and “upper” castes, Dalits have witnessed a consistent increase in their average debt servicing. The gravity of the situation is magnified for households falling under the lowest tercile of income earners, that is, the poorest 33% of households. In 2010, these households apportioned approximately 60% of their annual income for debt repayment, whereas this proportion has surged to over 140% in 2020-21. In simpler terms, for every 100 rupees earned, a staggering 140 rupees is dedicated solely to debt repayment.

## Conclusion

While many efforts have been made to better measure income, consumption and wealth in the economies of the Global South, very little attention has been paid to the reliable measurement of indebtedness. The scale of household indebtedness and its constant rise do, however, raise real public policy questions, since debt and over-indebtedness have significant consequences in terms of impoverishment, mental health, forced migration, and similar concerns. It is imperative for economists and statisticians to promptly address this matter, and the endeavors of the Observatory represent an initial stride in this direction.

## References

[1] Pallavi Chavan. “Debt of Rural Households in India: A Note on the All-India Debt and Investment Survey”. In: *Re-
*view of Agrarian Studies* 2.1 (2012), pp. 151–161.

[2] Dean Karlan and Jonathan Zinman. "Lying About Borrowing". In: *Journal of the European Economic Association* 6.23 (2008), pp. 510–521. DOI: 10.1162/jeea.2008.6.2-3.510.

[3] Arjun Appadurai. "Small-Scale Techniques and Large-Scale Objectives". In: *Conversations between Economists and Anthropologists: Methodological Issues in Measuring Economic Change in Rural India*. Ed. by Pranab Bardhan. New-York: Oxford University Press, 1989, pp. 250–282.

[4] Isabelle Guérin, Govindan Venkata-subramanian, and Santosh Kumar. *The Indebted Woman: Kinship, Sexuality, and Capitalism*. Culture and Economic Life. Redwood City, CA, USA: Stanford University Press, 2023.

[5] Himanshu, Praveen Jha, and Gerry Rodgers, eds. *The Changing Village in India: Insights from Longitudinal Research*. Delhi, India: Oxford University Press, 2016. DOI: 10.1093/acprof:oso/9780199461868.001.0001.

## Observatory of Rural Dynamics and Inequalities in South India (ODRIIS)

The Observatory analyzes over the past 15 years how structural changes in India are reshaping the organization of work, migration, social hierarchies, and household livelihoods. The Observatory also observes, measures, and understands inequalities to contribute to the theoretical renewal of the concepts of agriculture and ecology; labour and knowledge; money, debt, and finance. To achieve these objectives, the Observatory collects longitudinal data in rural and peri-urban areas of South India using a multidisciplinary approach; shares quantitative data and survey tools; contributes to academic debates and policymaking through publications; and trains social sciences students in research.

The aim of this Policy Brief series is to disseminate the Observatory's empirical, methodological and theoretical work to provide the general public and decision-makers with relevant information based on original, evidence-based data.