Debt and Health: The Impact of Over-indebtedness on

Mental Well-being in Sweden

By MARIA RÖNNGREN

Abstract

Household borrowing is a key element for consumption-smoothing over the life cycle.

However, over-indebtedness may induce negative health impacts through uncertainty, worries,

and shame for example. This paper examines how over-indebtedness affects the mental well-

being in Sweden between 2010-2018. The data is collected from several Swedish authorities at

the municipal and county level. In the attempt to estimate the causal relationship between

debt and health, a Bartik-like instrumental variable approach is used as an empirical strategy.

The main finding from the results is that an increase in the degree of over-indebtedness

improves mental health conditions but worsen excessive alcohol consumption. Nonetheless,

most of the estimates are imprecise and should not be interpreted as causal.

Keywords: debt, mental health, health behavior, instrumental variable, Bartik instrument Master’s Thesis in Economics Uppsala University Spring 2020 Supervisor: Hans Grönqvist

2

Table of Contents

1. Introduction ........................................................................................................................................ 3

2. Data & Descriptive statistics ............................................................................................................. 5

2.1 Measuring over-indebtedness ................................................................................................. 6

2.2 Health outcomes ...................................................................................................................... 7

2.3 Descriptive statistics .............................................................................................................. 8

3. Empirical method ............................................................................................................................. 11

3.1 General specification .............................................................................................................. 11

3.2 Instrumental variable approach: Bartik-like ........................................................................ 12

3.3 Limitations and potential threats ......................................................................................... 15

4. Results & Analyses ............................................................................................................................ 16

4.1 Main results ........................................................................................................................... 16

4.2 Heterogeneous effects ........................................................................................................... 20

4.3 Sensitivity analysis ................................................................................................................ 24

5. Conclusion & Discussion ................................................................................................................. 25

6. References .......................................................................................................................................... 27

Appendix ..................................................................................................................................................... 30

A.1 OLS and IV estimates of the average amount of debt ........................................................... 30

3

1. Introduction

Household indebtedness has been rising for a long time period and is now on its highest levels

ever in many countries. Since the mid 1990’s debts have increased more than disposable

income causing the debt ratio to climb above 100 in some countries, such as Sweden (Chmelar,

2013). While household borrowing is a key element in the economy allowing for consumption-

smoothing over the life cycle, over-indebtedness implies higher sensitivity to changes in

interest rates, which in turn jeopardize future disposable income and negatively affect people’s

well-being (Keese & Schmitz, 2014). Being over-indebted in a situation where individuals are

unable to manage financial commitments may lead to worries and uncertainty, shame, low

relative social status, social isolation, and other negative factors (Ahlström & Edström, 2015;

Ridley et al. 2020). High repayments of debt can hence provoke stress and negatively affect the

mental health as well as promote unhealthy behaviors, such as alcohol consumption (Clayton,

Liñares-Zegarra & Wilson, 2015), and physical health (Keese & Schmitz, 2014; Grafova, 2007).

On the other hand, being indebted can also be a consequence of having bad health. Both sets

of mechanisms can appear. Overall, the relationship between debt and health is raised as an

important social issue which could have policy implications both regarding health services and

financial services (Lindén, 2016; Meltzer et al., 2013).

Besides the potential simultaneous causality with debt both as a cause and a consequence

of health statuses, some additional endogeneity challenges need to be addressed to identify the

causal relationship between debt and health. One of them is the issue of omitted variable bias.

In general, it is difficult to control for everything that is correlated with both debt and health

to isolate the relationship of interest. Measurement also poses a challenge since it is not

obvious how to measure over-indebtedness or what markers of health outcomes to focus on,

that may potentially capture different aspects of individuals’ true health.

In this paper, I examine the effect of over-indebtedness on mental health in Sweden between

2010-2018. To do this I use data structured as a panel over Swedish municipalities and counties.

The data include measures of debt based on people in the register of the Swedish Enforcement

Authority (hereafter the SEA), which should provide a good proxy for over-indebtedness. As

there is a long process for ending up with debts in that register, indicating severe financial

problems (Swedish Data Protection Authority, 2020), this measure thus presumably provides

an accurate and valid measure of over-indebtedness in its most serious form. The health

outcomes are categorized into four types of measures; self-reported, medical prescriptions,

diagnoses, and insurance benefits of sickness payments, allowing me to document the effect

4

throughout the entire disease chain from having own symptoms to being forced into sick

leave. To estimate the causal impact of debt on health I use an instrumental variable approach.

The instruments are Bartik-like using the initial distribution of debt combined with the

exogenous Euro market interest rate to construct a “synthetic” path to the original debt trends

over time.

The main results show that higher average amounts of debt improve mental health conditions

but worsen excessive alcohol consumption. Some of the estimates are imprecise but even

economically significant effects cannot be interpreted as causal considering the sensitivity

analysis. However, the analysis of heterogeneous effects establishes interesting associations

between debt and higher ages together with high-income regions.

My results contribute to several different literatures. First, there is a vast literature on the

relationship between socio-economic status and health where especially the income-health

gradient in income is well investigated. For example, both Smith (1999) and Cutler, Lleras-

Money & Vogl (2011) discuss the varying income effects by age and that early childhood is the

most critical period for the impact on the health stock. Overall, higher income appears to

improve health outcomes. Up until recently the health effects of different low socio-economic

factors were unexplored (Richardson, Elliot & Roberts, 2013). The relationship between poverty

and mental health issues is investigated where causal evidence from poverty causing

depression and anxiety is found from randomized-controlled trials (e.g. Ridley et al. 2020).

Among such effects of low income and closely related employment aspects, which economists

mainly have been interested in, the literature of indebtedness and health is a quite new

research area with an increasing interest (Brown, Taylor & Wheatley Price, 2005). The existing

literature on this topic is spread out over several disciplines of research and the results

consistently find that high debts are associated with worse health conditions. There are studies

in medical and health related areas that distinguish between different maturities of debt

(Clayton, Liñares-Zegarra & Wilson, 2015), different types of debt (Meltzer et al., 2013; Jenkins

et al., 2008) and whether the debt is secured or unsecured (Richardson, Elliot & Roberts, 2013;

Hojman, Miranda & Ruiz-Tagle, 2016) for example.

Similarly, within the economics literature, Brown, Taylor & Wheatley Price (2005) find

that unsecured debt, such as arrears from credit cards, has a greater impact on psychological

well-being than mortgages, where no effect was found for households in the UK. Using

subjective measures of both debt and mental health according to the GHQ12 score (General

Health Questionnaire), they estimate the effect with a commonly used ordered probit model.

5

Interestingly, the results are confirmed additionally through the opposed investigation of how

increased savings lead to better health. Although the use of subjective measures of debt from

household surveys is common in many studies, they have also limitations. Both Gathergood

(2012) and Bridges & Disney (2010) discuss the threats to make inference based on self-

reported financial variables in the UK since the well-being could be influenced by perception

and lead to unreliable debt measures. Also, Dackehag et al. (2019) argue for a distinct

difference in using objective or subjective measures of health. In the Swedish context, they

only found effects of debt on self-reported measures while objective measures from

administrative registers rather show links from bad health to later payment difficulties. This

direction of the relationship is also discussed in Zimmerman & Katon (2005) after no effects of

debt on depression were found. Hence, the causal direction of the correlation has been

problematic to claim in different designs. In the attempt to estimate the causal effect of over-

indebtedness on health, a handful of studies use an instrumental variable approach (e.g.

Gathergood, 2012; Zimmerman & Katon, 2005). Other studies use less complicated strategies

such as excluding individuals (Keese & Schmitz, 2014) or using lagged debt variables (e.g.

Dackehag et al., 2019).

This thesis presents novel evidence from the Swedish context, which is of particular interest

given its low levels of income inequality (OECD, 2020). Further, I consider a more coherent

analysis using comprehensive data that covers a variety of health outcomes and a reliable

proxy of over-indebtedness. Lastly, to account for endogeneity concerns I exploit the method

of Bartik-like instruments. This combination contributes with a new broad overview of the

question of interest, which is possible when using data on the aggregate level in Sweden while

most of the previous studies discussed above use individual survey data in comparison.

The rest of the thesis is structured as follows: in section 2 the data is presented and described,

in the third section I explain the empirical methodology in the context of the thesis, in section

4 the results are presented together with analyses, and finally, section 5 concludes and

contains a more broad discussion about the results.

2. Data & Descriptive statistics

In this section, I describe the panel data that is used to answer the research question. The first

two parts consist of detailed descriptions of the different variables. The third part summarizes

some descriptive statistics.

6

2.1 Measuring over-indebtedness

The main explanatory variables of over-indebtedness are collected from the Swedish

Enforcement Authority (2020b) at the municipality level. The process from having an invoice

to pay to have debt in their register consists of several steps. To begin with, it is common to get

a reminder from the creditor before the errand goes to a debt collection demand from a

collection agency. The next step is for the creditor to contact the SEA to apply for a payment

injunction. In turn, the debtor is contacted by the authority to get fully informed about the

injunction and time to act. If there are still no payments transmitted the authority issues a

decision, a verdict. At this point, the debtor gets a record of non-payments (Swedish Data

Protection Authority, 2020). To proceed with the issue, the creditor can request execution of a

verdict; a request of help getting paid, and that the debt collection is now in the hands of the

agency. After receiving the “Debt to Pay”-letter without taken any actions, the SEA investigates

the assets of the debtor and finally moves forward with seizure orders or any other type of debt

collection. Between all these steps the debtor has the chance to act, either by paying or

objection. Hence, having debt at the SEA indicates really severe financial problems and it thus

seems plausible that the individual can be considered to be over-indebted (Swedish

Enforcement Authority, 2020a).

The different measures of debt that are used in my analysis are the share of debtors and the

average amount of debt per person in each municipality, the share of first debtors, and the

share of people being debtors for at least 20 years, so called infinite debtors. Besides the key

debt variable ‘average debt amount’ measuring the degree of over-indebtedness, the data

allows me to study whether there is a significant difference in being a debtor for the first time

and being a debtor for a long time. All debt variables are reported by gender and the most

crucial variables ‘average debt amount’ and ‘share of debtors’ are divided by age as well. This

makes it possible to split the sample into subsamples and make further analyses about

potential heterogeneous effects. In addition to the ideal experiment of exploiting random

allocation of debts, a desirable scenario would be to have further information about the

sources of debt, what the underlying nature and occasion is of the debt, for deeper analyses

and to be able to rule out some of the potential simultaneous causality (Meltzer et al., 2013).

The data covers the period 2010-2018 and is restricted by the lack of data for earlier years.

Observations named “missing” or “other” are excluded from the data. These represent

individuals not registered in any of Sweden’s 290 municipalities, such as foreign residents or

estates, and constitutes about 25% of the total number of over-indebted each year.

7

Additionally, in contrast to the previous studies that use subjective measures of debt from

surveys, I exploit the advantage of using an objective proxy for over-indebtedness. Since this

measure of debt is not influenced by perceptions or certain conditions of the individuals, such

as health statuses, it can be considered as reliable (Gathergood, 2012).

In the analysis, I include several control variables to increase the precision of the estimates;

age, gender, population size, the share of individuals with two or more years of upper

education, unemployment rate, divorce rate, the share of foreign born residents, and average

disposable income. All of them are conducted by Statistics Sweden (2020).

2.2 Health outcomes

The mental health outcomes can be categorized into four subgroups of measures; self-

reported, prescriptions of drugs, diagnoses, and sickness payments from the insurance system.

The self-reported measures are collected from the Public Health Agency (2020) and their

survey “The National Public Health Survey” (Nationella folkhälsoenkäten). The variables show

the share of people that experienced ‘stress’, ‘reduced mental well-being’, ‘anxiety’, ‘sleeping

problems’, and ‘alcohol risk consumption’. They are also in the form of moving averages in 4-

year intervals, which yield five observed time periods between 2010-2018.1 Prescriptions and

diagnoses are both collected from the National Board of Health and Welfare (2020).

Prescriptions of medical drugs measure the proportion of consuming people and are

‘antidepressants’ (ATC code N06A), ‘anxiolytics’ (ATC code N05B), ‘hypnotics’ (sleeping pills,

ATC code N05C), ‘alcohol abuse’ (ATC code N07BB) and ‘antibiotics’ (ATC code J01). The

diagnoses are measured in a similar way where ‘alcohol related diagnoses’, ‘mood disorders’

(ICD10: F30-39), ‘stress related diseases’ (ICD10: F40-48), and further also ‘tumors’ (ICD10:

C00-D48) are included. These will capture the effect on hospitalizations due to depression and

general mental illness. Hence, tumors together with antibiotics will act like a placebo in the

sensitivity analysis where we do not expect to find an effect from debt. It is, however, possible

that these health outcomes capture other underlying health aspects, so called “co-morbidities”.

Both the self-reported, drugs and diagnoses are measured on the county level divided by

gender while drugs and diagnoses are divided by age as well.

Finally, as Dackehag et al. (2019) argue, to control for more general health issues, received

benefits from the Social Insurance (2019) are included. These variables are measured by gender

in the form of the share of sickness cases, both specifically for people with severe stress (ICD10:

1 In the regressions for these health outcomes, I use moving average of debt and controls as well.

8

F43), more broadly psychological illness (ICD10 F00-99), and sickness in general. A sickness

case is defined as a period of continuously received compensation for work absence longer

than 14 days, including both sickness, rehabilitation, and work injuries. Insurance payment for

F43 and in general are measured on the municipal level, while psychological F00-F99 is on the

county level. The former has some missing values for some of the municipalities and years. The

reason for being missing cannot be noticed, hence they are considered to be missing at

random and not causing bias in the estimates.

In summary, the health outcomes are both subjective and objective measures on different

levels of illness to capture the width of health, and to be able to distinguish between the two

types of measures as Dackehag et al. (2019) show importance of.

2.3 Descriptive statistics

To get a general overview of the data, some descriptive statistics are presented in this section.

The trends of the two main explanatory variables of debt are shown in figure 2.1 below. The

total number of people in over-indebtedness and their total amount of debt in MSEK are

displayed with different y-axes.

In the first 4 years of the studied

period, up until 2013, these two

variables are following the same

patterns. After that point in time,

interestingly the number of

indebted people starts to decrease

while the debt amount continues to

increase. According to the Swedish

Enforcement Authority (2020c), the

decrease in debtors can partly be

explained by the increase in

decisions of debt reliefs and a

period with a booming economy and low interest rates. In such periods it is easier for people to

get out of indebtedness and stabilize tough situations by starting a new job for instance. At the

same time, the ones who already are having severe financial difficulties have growing debts,

and as a result, the average amount per debtor has increased since 2013. Additionally, the

FIGURE 2.1 Number of debtors and their total amount of debt per year in Sweden.

9

number of people in over-indebtedness is quite volatile and is expected to increase again once

the economy turns into a recession with high unemployment for example. With that in mind, I

choose to mainly focus on the average debt amount per person as the endogenous variable.

To get a deeper insight into the regional differences that are used in the analysis, with account

for the population as well, the distribution of debt changes over the studied period 2010-2018

within all municipalities is presented in a histogram in figure 2.2 below.

In figure 2.2 the distribution of changes in debt amount is shown. It is centered around slightly

above 0 in log changes where most of the municipalities have increased the average amount of

debt per resident. The peak is shown for near 100 municipalities to have an increase of 10-20 %

between 2010-2018. Only about 25% of the municipalities perform a decline in average debt

amounts. On both sides of the distribution, there are a few outliers. Lomma in Skåne län is the

municipality with the largest decrease in average debt amount with -1.69 log changes,

corresponding to around 80% decrease during the studied time period. On the other hand,

Tjörn in Västra Götalands län has the most increase by 177 %, almost twice the amount of debt

FIGURE 2.2 Distribution of change in debt amount within municipalities between 2010-2018 in Sweden. Bin width = 0.1 log.

10

per person. However, the variation between years within the time interval 2010-2018 is not

captured in the figure but used in the analysis.

TABLE 2.1 Summary statistics.

Full sample Debt < Median Debt Median

Variable Mean SD Mean SD Mean SD

Stress (%) 12.48 3.48 15.25 2.42 9.70 1.70

Reduced mental well-being (%) 12.42 3.21 14.88 2.35 9.96 1.71

Anxiety (%) 31.63 6.63 37.67 3.14 25.60 2.25

Sleeping problems (%) 33.16 5.94 38.58 2.43 27.73 2.39

Alcohol consumption (%) 15.31 4.17 11.71 1.94 18.91 2.23

Antidepressants (%) 9.24 0.92 9.20 0.82 9.29 1.01

Anxiolytics (%) 5.39 0.72 5.38 0.83 5.41 0.59

Hypnotics (%) 8.19 0.66 8.35 0.51 8.01 0.75

Alcohol abuse (%) 0.24 0.04 0.25 0.05 0.23 0.04

Antibiotics (%) 18.82 2.32 18.96 2.44 18.68 2.18

Mood disorders (%) 0.88 0.19 0.85 0.18 0.92 0.20

Stress related (%) 0.89 0.24 0.86 0.21 0.94 0.26

Alcohol related (%) 0.22 0.08 0.19 0.05 0.25 0.10

Tumors (%) 3.43 0.51 3.36 0.46 3.51 0.56

Sickness: severe stress (%) 1.03 0.54 1.03 0.56 1.03 0.51

Sickness: psychological illness (%) 2.55 1.48 3.57 1.35 1.43 0.45

Sickness: general (%) 19.67 4.96 19.29 5.02 20.05 4.88

Debtors (%) 3.70 1.12 3.11 0.77 4.30 1.10

Avg debt amount/person (KSEK) 6.58 3.14 4.67 0.82 8.48 3.43

First debtors (%) 0.72 0.16 0.66 0.13 0.78 0.17

Infinite debtors (%) 1.15 0.42 0.92 0.27 1.37 0.43

Population (log) 9.84 0.96 9.94 0.94 9.74 0.97

Education (%) 20.48 6.47 21.71 6.91 19.27 5.75

Marital status (%) 0.42 0.11 0.40 0.10 0.45 0.12

Employment status (%) 3.90 0.72 3.87 0.68 3.94 0.76

Ethnicity (%) 12.98 6.05 11.48 4.54 14.46 6.93

Disposable income/person (MSEK) 0.18 0.03 0.18 0.03 0.18 0.03

Table 2.1 Notes. Percentages of the population. Mean is based on the region for each variable; municipality or county.

FIGURE 2.3 Number of prescriptions of mental illness drugs in Sweden.

FIGURE 2.4 Number of mental illness diagnoses in Sweden.

11

The use of medication for mental health issues has had an increasing trend through all years

since 2010. The diagnoses within the same area follow a similar pattern. This is shown above in

figure 2.3 and 2.4. Clearly, the total mental well-being is worsened throughout the investigated

period according to the objective health registers in Sweden. Overall, the underlying

relationship of interest between debt and mental illness is positive. In table 2.1 of the summary

statistics, the mean and standard deviation of all variables used are presented. In column 1 the

mean is based on all years between 2010 and 2018 and on the specific regional level each

variable is measured. In line with expectations, it can be seen that the share of people

experiencing mental issues decreases from self-reported to medical prescriptions, and again to

diagnoses as the level of health gets more severe. This cannot entirely be seen for the fourth

category of sickness payments since these variables measure a broader sense of psychological

and general health. In columns 2 and 3 the sample is divided by the median in average debt

amount per person. Among the self-reported measures, it is noticed that worse health

conditions are perceived among the counties with low debt amounts, except for excessive

alcohol consumption. The opposite is seen for antidepressants and anxiety among the drugs

and for all diagnoses, where the counties with higher debts are having worse mental health.

The pattern among sickness cases is more ambiguous with no change between debt regions in

severe stress, a larger share of psychological cases in low debt regions, and a higher share of

general cases in high debt regions. As different health statuses differ between low and high

debt regions it is shown that health is driven by other factors as well, for which regression

analysis is needed.

3. Empirical method

In the following section, the empirical strategy is explained in more detail. Further, a

discussion about the identifying assumptions, potential threats and limitations are also

included.

3.1 General specification

The aim of the empirical strategy is to estimate the causal effect of over-indebtedness on

mental health outcomes. The general model of interest has the following form:

𝑀𝑒𝑛𝑡𝑎𝑙 ℎ𝑒𝑎𝑙𝑡ℎ𝑖𝑡 = 𝛼 + 𝛽𝐷𝑒𝑏𝑡𝑖𝑡 + 𝛾𝑋𝑖𝑡 + 𝑓𝑖 + 𝜆𝑡 + 𝛿𝑗𝑡𝑅𝑒𝑔 × 𝑌𝑒𝑎𝑟 + 휀𝑖𝑡 (1)

12

𝑀𝑒𝑛𝑡𝑎𝑙 ℎ𝑒𝑎𝑙𝑡ℎ is the outcome variable for municipality or county 𝑖 and year 𝑡, and 𝐷𝑒𝑏𝑡 is the

main explanatory variable leading to 𝛽 is the coefficient of interest. 𝑋 is a set of covariates,

which are specified in section 2.1. 𝑓 denotes municipality/county fixed effects which capture all

region specific characteristics that are constant over time and have an impact on health, such

as local persistent levels of risk aversion. 𝜆 denotes time fixed effects and capture effects with

an impact on health that changes over time but not across regions, such as technological

changes. The interaction term between counties and time is the region-by-time fixed effect

and controls additionally for specific changes in counties in different time periods in case some

time effects are affecting the counties differently. This effect is only included in those

specifications estimated at the lower municipal level (where 𝑖 = municipality and 𝑗 = county),

which is for two of the 15 different outcomes; sickness cases for severe stress and general cases.

When exploiting this panel structure with included fixed effects it uses the within region and

time variation to rule out the potential omitted variable bias due to unobserved heterogeneity.

This relies on the identifying assumption of strict exogeneity of debt, meaning no correlation

between the error term 휀 and debt remains. The remaining concerns regarding omitted

variable bias would only be from those varying both over time and between municipalities or

counties. Besides the observed characteristics included as controls, there are likely several

unobserved time varying variables determining both indebtedness and mental health.

Dackehag et al. (2015) discuss for example that individual specific factors, such as financial

knowledge and expectations or confidence and attitudes to debt, can explain why people run

into debts. Genetics is further argued by Zimmerman & Katon (2005) to have an important role

in the risk for mental disorders. This may also be related to family background, social norms,

and peers in the surroundings (Dackehag et al., 2015) and hence influence both the financial

situation and health status.

For these reasons, equation (1) is likely not sufficient to estimate a causal relationship

between debt and health. To be able to interpret the results causally, the problems with

simultaneous causality and unobserved confounders need to be addressed in some way.

3.2 Instrumental variable approach: Bartik-like

One potential way to deal with the above endogeneity problems is to instrument the measure

of debt by using an instrumental variable (IV) design. Here, over-indebtedness is instrumented

by using shift-share instruments, also called Bartik-instruments. This type of instrument can

be considered to create a “synthetic” distribution of average debt amounts and the number of

debtors. Following Boustan et al. (2013), the initial distribution of amounts of debt and

13

indebted people in 2010 is used to predict the distribution in subsequent years when

combining the initial distribution with national exogenous shocks in interest rates. An increase

in the interest rate should intuitively lead indebted people to fall into over-indebtedness and

the share of debtors in the register of the SEA increases. For those already indebted should also

their debt amount increase in case of a rise in the interest rate correspondingly. By holding the

local area of debt distribution fixed at 2010 in each municipality or county as the share-

variable, and then let the prediction vary based on national patterns; the shift-variable, the

instrument is constructed by interacting the two variables. First, the two different share-

variables (𝜃𝑖1, 𝜃𝑖

2) that are used are calculated as the following:

𝜃𝑖1 =

𝐷𝑒𝑏𝑡 𝑎𝑚𝑜𝑢𝑛𝑡𝑖,2010𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑖,2010

𝐷𝑒𝑏𝑡 𝑎𝑚𝑜𝑢𝑛𝑡𝑆𝑊𝐸,2010𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑆𝑊𝐸,2010

𝜃𝑖2 =

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑒𝑏𝑡𝑜𝑟𝑠𝑖,2010𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑖,2010

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑒𝑏𝑡𝑜𝑟𝑠𝑆𝑊𝐸,2010𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑆𝑊𝐸,2010

(2)

𝑖 = municipality or county

In the first share-variable, the initial distribution of debt amount per resident is used where

each share corresponds to one municipality or county. Similarly, for the second share-variable,

the distribution of the share of debtors is used. Below are the designs of the final instruments

(𝑍𝑖𝑡1 , 𝑍𝑖𝑡

2 ) expressed:

𝑍𝑖𝑡1 = 𝜃𝑖

1 × 𝐸𝑈 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑅𝑎𝑡𝑒𝑆𝑊𝐸,𝑡 𝑍𝑖𝑡2 = 𝜃𝑖

2 × 𝐸𝑈 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑅𝑎𝑡𝑒𝑆𝑊𝐸,𝑡 (3)

The first instrument in equation (3) uses the initial distribution of debt amounts together with

the shift of the Euro market interest rate. The rate is measured as a yearly average of the 6

months maturity rate (Riksbanken, 2020). This is assumed to predict ‘average debt amount’,

thus be the main instrument used throughout the analysis. In the same way, the second

instrument uses the distribution of debtors in 2010 to interact with the Euro interest rate. This

is a better predictor when the number of debtors is estimated and will only be used in the part

of the analysis where the effects of first and infinite debtors are distinguished.

There are two assumptions to fulfill for instrument validity; relevance and exogeneity. The

exogeneity assumption consists of two parts where the instrument is assumed to be both

randomly assigned and excludable. Only the first assumption about relevance is testable

through the first stage regression in the IV approach.

14

𝐷𝑒𝑏𝑡𝑖𝑡 = 𝜋0 + 𝜋1𝑍𝑖𝑡𝑘 + 𝜌𝑋𝑖𝑡 + 𝜆𝑡 + 𝑢𝑖𝑡 (4)

To fulfill relevance, the instrument needs to have a significant impact on the endogenous

variable, hence 𝐶𝑜𝑣(𝐷𝑒𝑏𝑡𝑖𝑡 , 𝑍𝑖𝑡𝑘 ) ≠ 0 in equation (4). For this reason, the instrument based on

amounts are likely to be a better fit for ‘average debt amount’, while the one based on numbers

of debtors may rather predict ‘share of

first and infinite debtors’. 𝑋 is the same

set of covariates used in the general

specification in the previous subsection.

Year dummies are also included. The

results from the first stages for the main

endogenous debt variable are presented

in table 3.1 to the left. The instrument is

standardized to have a mean equal to 0

and a standard deviation of 1 and show

positive significant coefficients in the specifications separated for both counties and

municipalities. An increase by 1 standard deviation “Bartik-shock” results on average in

approximately 1600 SEK increase in debt amount per person at the municipal level and 1500

SEK at the county level. Also, the F-statistics are high enough to exceed the rule of thumb F >

10, indicating that the instrument is not weak at any regional level. Hence, the relevance

assumption is fulfilled.

The other part of instrument validity, the exogeneity condition, is specified as

𝐶𝑜𝑣(𝑍𝑖𝑡𝑘 , 𝑢𝑖𝑡|𝑋𝑖𝑡 , 𝜆𝑡) = 0. Conditional on the covariates and time effects, the instruments should

be uncorrelated with the error term. Hence, when there are no omitted variables related to

both the instrument and health, the instrument is as good as randomly assigned. Additionally,

the instrument should have no direct effect on health to fulfill the excludability; the only

channel to health should be through debt. By construction, the shifts in the shift-share

methodology can be considered as shocks, from where exogeneity is assumed. The Euro

market interest rate is induced by international macroeconomic factors, nothing the Swedish

households are able to manipulate. Hence, the EU interest rate is considered to be as good as

randomly assigned. It drives the Swedish market interest rate because of macroeconomic

connections such as trade for instance, and is in turn affecting household debt but is not

directly related to health. In addition, the interest rate is assumed to only affect health through

TABLE 3.1 First stage estimates. Average debt amount

- municipal 𝑍1 - county 𝑍1

1.612*** (0.184)

1.509*** (0.363)

F-statistic 76.83 17.32

No. observations 36540 2646

Table 3.1 Notes. Significance level: *** = 1%, ** = 5%, * = 10%. Robust standard errors in parentheses. Control variables: age, gender, population, education, employment status, marital status, ethnicity, and disposable income. Year dummies are included. All regressions are average-weighted by population.

15

the impact on debt. No other factor than debt is supposed to be affected by the Euro interest

rate and in turn, has an impact on mental well-being. Using these instruments and assuming

the validity assumptions are true, then the IV-approach accounts for all omitted factors at the

regional level stated in the general specification. Thus, there is no need to control for regional

fixed effects. I do however include time fixed effects to increase precision.

Assuming instrument validity, the general causal estimate of interest consists of two parts; the

reduced form estimate, where I regress the health outcomes on the instruments, and the first

stage estimate presented above. These are expressed in equation (5) below.

𝛽𝐼𝑉 =𝑑𝐻𝑒𝑎𝑙𝑡ℎ/𝑑𝑍𝑘

𝑑𝐷𝑒𝑏𝑡/𝑑𝑍𝑘 (5)

3.3 Limitations and potential threats

As mentioned in the introduction of the thesis, some potential issues have been considered

and addressed in terms of identification and causality for this research question. Though, there

might still be limitations in the methodology and potential threats to identify the causal effect

of interest. Posing the highest threat to the instrumental variables approach, is the use of

invalid instruments. Such instruments can generate more bias in the IV-estimates than in OLS

where the endogeneity problem remains. Even though the instrument is relevant enough (see

table 3.1) the exogeneity condition might be violated. If so, the endogeneity problems of both

potential omitted time- and region-varying confounding variables and simultaneous causality

would not be solved, and therefore, the IV estimates will be biased.

Generally, it is difficult to find valid instruments in practice. Since the exogeneity condition is

not statistically testable, it only relies on theoretical intuition and knowledge within the

context. Even though the interaction between the initial distribution of debt amounts together

with the Euro interest rate should be randomly assigned to Swedish households, the most

problematic part is to ensure that the Bartik instrument exclusively affects health via the

measure of debt. Yet, all possible channels between the interest rate and health are hard to

consider. Another limitation associated with the use of instrument, given that it is assumed to

be valid, is when the estimates only reflect the average treatment effect of those who increase

their debt or run into debt because of the instrument, not the overall average effect between

different amounts or between indebted and unindebted. Such “LATE”-interpretation (local

average treatment effect) of the IV estimates requires an additional assumption; monotonicity.

16

The instrument is then assumed to affect the individuals in the same direction, meaning that

all who are affected by an increase in the interest rate will increase their debt amounts. It is

difficult to identify these people who get affected by the instrument, thus for whom the effect

represents. Even though it would be possible to observe the subgroup of treated individuals,

another related weakness is the difficulty to distinguish between always-takers and compliers;

the ones who always increase their debt amounts independently of the instrument and the

ones who increase the amounts due to the interest rate, respectively. This is also associated

with the fact that the estimates of 𝛽𝐼𝑉 may differ depending on the choice of instruments due

to different specific groups affected. If another instrument would have been chosen it is

possible that another subgroup of individuals would be affected, and the effects of debt on

health showing different signs or magnitudes. Hence, IV estimates have strong internal validity

for the specific groups but may have little external validity for the whole population.

4. Results & Analyses

Section 4 presents the results from the models specified in the previous part and my analysis of

the effect of over-indebtedness on health. In the following subsections, I show results also for

heterogeneous effects and robustness tests.

4.1 Main results

In the first step of the analysis an OLS specification (1), without area fixed effects and no

account has taken to the bias problems, is estimated for each health outcome variable shown

in table 4.1. This is a summarized table from the complete tables A1-A4 in the appendix. Robust

standard errors are used in all analyses. In table A5 in the appendix a similar summary is

presented but with clustered standard errors to control for potential correlation between

observations within regions. However, these results do not differ remarkably and thus only the

robust standard errors are used hereafter. The effects of the average amount of debt per person

on the self-reported measures in panel A are mostly negative. For reduced mental well-being

and risk consumption of alcohol, the effects are statistically significant at the 5% level and 10%

level, respectively. Only on sleeping problems debt show a positive impact where an increase

in average debt amount of 1000 SEK leads to a 0.35%-points increase on average in the share of

people using hypnotics, holding all controls constant. Further, in the second step of the

analysis, the IV regressions are estimated. These are presented in model (2). Then, all

coefficients turn insignificant besides the effect on stress. At the 5% significance level, higher

17

TABLE 4.1 Summarized OLS and IV estimates of the relationship between average debt amount and psychological outcomes.

Panel A. Self-

reported

Stress Reduced mental well-being Anxiety Sleeping problems Alcohol consumption

(1) OLS (2) IV (1) OLS (2) IV (1) OLS (2) IV (1) OLS (2) IV (1) OLS (2) IV

Average debt

amount (KSEK)

-0.078

(0.096)

1.158**

(0.510)

-0.213**

(0.085)

0.140

(0.313)

-0.144

(0.131)

0.550

(0.474)

0.354***

(0.116)

0.257

(0.420)

-0.247*

(0.143)

0.209

(0.547)

R-sq.

No. observations

0.884

210

0.779

210

0.878

210

0.867

210

0.946

210

0.936

210

0.938

210

0.937

210

0.863

210

0.851

210

Panel B. Drugs Antidepressants Anxiolytics Hypnotics Alcohol abuse

(1) OLS (2) IV (1) OLS (2) IV (1) OLS (2) IV (1) OLS (2) IV

Average debt

amount (KSEK)

-0.297***

(0.020)

-0.436***

(0.153)

-0.173***

(0.013)

-0.291**

(0.120)

-0.294***

(0.023)

-0.637***

(0.207)

0.014***

(0.001)

0.021***

(0.007)

R-sq.

No. observations

0.930

2646

0.927

2646

0.912

2646

0.908

2646

0.938

2646

0.925

2646

0.866

2646

0.860

2646

Panel C.

Diagnoses

Mood disorders Stress related Alcohol related

(1) OLS (2) IV (1) OLS (2) IV (1) OLS (2) IV

Average debt

amount (KSEK)

-0.024***

(0.004)

0.008

(0.029)

-0.029***

(0.005)

-0.058

(0.039)

0.023***

(0.003)

0.070***

(0.015)

R-sq.

No. observations

0.667

2646

0.649

2646

0.634

2646

0.625

2646

0.791

2646

0.595

2646

Panel D. Sickness Sickness: severe stress Sickness: psychological Sickness: general

(1) OLS (2) IV (1) OLS (2) IV (1) OLS (2) IV

Average debt

amount (KSEK)

0.021***

(0.005)

0.135*

(0.071)

0.011

(0.030)

-0.422

(0.401)

0.078***

(0.021)

0.099

(0.112)

R-sq.

No. observations

0.742

1764

0.620

1764

0.861

378

0.773

378

0.786

5220

0.786

5220

Controls

Regional dummies

Year dummies

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Table 4.1 Notes. Significance level: *** = 1%, ** = 5%, * = 10%. Robust standard errors in parentheses. 𝑍1 is used as instrument. Control variables included: age, gender, population, education, employment status, marital status, ethnicity, and disposable income. All regressions are average-weighted by population.

18

amounts of debt by 1000 SEK per person increase the share of people with high stress levels by

over 1 %-point on average. In a calculation example, assume 150 000 people are living in a

region. Between 2010-2018 the mean of the moving average shows that around 12.5 % were

feeling stressed (see table 2.1 over summary statistics). If a 1000 SEK increase in average debts

among the residents leads to about 1.2%-points increase in the share of stressed people, that

means an additional 1800 people on average in that region feel problems with stress. However,

the other estimates are still positive, which may indicate an overall positive relationship

between amounts of debt and worse perceived mental health.

Panel B shows the impact of average debt amounts on the use of drugs for mental health

issues. The effects on antidepressants, anxiolytics, and hypnotics are significantly negative at

the 10% level in the first OLS estimation. When regressing with the Bartik-like instrument the

coefficients remain negative and the effect gets larger in magnitude. Though, the effect on

medical prescriptions for alcohol abuse is significant and positive but small in both

specifications. The IV estimate shows that if debt amounts per person increase by 1000 SEK the

use of pharmaceuticals for alcoholics increases by 0.02 %-points on average, given everything

else constant. In relation to the mean of alcohol abuse between 2010-2018 per region this is an

increase of 9% and thus economically significant. The health outcomes of diagnoses are

presented in panel C. Consistently with the drugs, all are negatively related to amounts of debt

while alcohol related diagnoses are shown to have a positive relationship with higher debts.

Comparing the OLS estimates and the IV estimates, only the coefficient on alcohol related

diagnoses remains significant. Similarly as before, the magnitude is larger in the IV

estimations. This is also the case in the final panel D over insurance payments from sickness

cases. Both the specific cases due to severe stress and the general sickness show positive effects

in the OLS regressions, while there is only on severe stress that the average amount of debt has

a precise impact in IV estimation.

To summarize, the estimates in the first specification in table 4.1 establish there is a

relationship between the amount of debt and mental health. In the attempt to causally

interpret the results by using instruments, an effect of debt is only found for a few of the

outcomes. Most of them also show the reverse sign compared to the hypothesis. However,

comparing the two specifications the IV estimates are often larger in magnitude than the OLS.

One potential explanation could be measurement error in the endogenous variable. In this

context, utilizing rich register data from the SEA, that does not seem probable. Another more

likely reason could be that the instrument estimates the “LATE”; local average treatment effect,

discussed previously in section 3.3, i.e. the average effect of debt for those who run into debt

19

because of the instrument. The effect is estimated for the people who increase their debt

amount in the register of the SEA because of their reaction to the national trends in the

interest rates. If this group of individuals, the compliers, are more responsive in terms of

health changes in over-indebtedness compared to the general population, then this may

explain why the IV estimates are larger than the OLS. In table 4.2, the reduced form

regressions of mental health on the different Bartik-instruments are given. If the final IV

estimates do not hold, it is yet interesting to study the reduced forms as they do not rely on

the exclusion restriction.

Among the self-reported outcomes, the instrument has a positive effect on stress, while the

other perceived health statuses are not affected significantly from the exogenous variation in

the instrument. The direct effect on many of the drug outcomes in panel B is negative. An

increase by one standard deviation of “Bartik-shock” in the instrument 𝑍1 lead to a decrease in

the share of people using antidepressants by 0.66%-points for example. The corresponding

value on the share of anxiety drug consumption is a decrease by 0.44%-points. These declining

results are consequences of the indirect effect the instrument has on the average amount of

debt, which in turn affects the use of mental related drugs. Since the reduced form estimates

are one part of the causal estimation, these negative effects may explain the negative effects

Table 4.2 Notes. Significance level: *** = 1%, ** = 5%, * = 10%. Robust standard errors in parentheses. Control variables included: age, gender, population, education, employment status, marital status, ethnicity, and disposable income. Year dummies are included. All regressions are average-weighted by population.

TABLE 4.2 Reduced form estimates.

Panel A. Self-

reported

Stress Reduced mental

well-being

Anxiety Sleeping

problems

Alcohol

consumption

Instrument: 𝑍1

0.648***

(0.151)

0.078

(0.169)

0.308

(0.232)

0.144

(0.260)

0.117

(0.284)

No. observations 210 210 210 210 210

Panel B. Drugs Antidepressants Anxiety Hypnotics Alcohol abuse

Instrument: 𝑍1

-0.657***

(0.238)

-0.438**

(0.178)

-0.961***

(0.297)

0.031***

(0.012)

No. observations 2646 2646 2646 2646

Panel C.

Diagnoses

Mood disorders Stress related Alcohol related

Instrument: 𝑍1

0.012

(0.043)

-0.087

(0.061)

0.106***

(0.024)

No. observations 2646 2646 2646

Panel D.

Sickness

Sickness: severe

stress

Sickness:

psychological

Sickness:

general

Instrument: 𝑍1

0.109*

(0.056)

-0.285*

(0.164)

0.173

(0.203)

No. observations 1764 378 5220

20

found in the IV estimation in table 4.1. One potential explanation of why negative effects are

found as a consequence of that the interest rate has positive effects on debt (see the first stage

estimates in table 3.1), is the possibility that the excludability assumption is violated. The

interest rate may influence mental health through other channels than debt exclusively. Other

national macroeconomic factors, such as unemployment rates, could potentially be affected by

interest rates and in turn have an impact on health.

However, besides these negative effects, there are some positive coefficients found for two

of the sickness payment outcomes as well as for two of the diagnoses. Noticeable is also the

positive relationship between the instrument and alcohol variables for all degrees of

categories. Just as the IV results in table 4.1, the reduced form effects show precise estimates

specifically for the alcohol abuse drugs and alcohol related diagnoses.

Overall, most of the significant main results are negative. Hence, an increase in the average

amount of debt per person lowers the level of bad mental well-being which is a result against

the hypothesis. But the results also show how increased debt amounts lead to excessive alcohol

consumption. It is rather the health behavior that is affected and changes in the lifestyle act

like a consequence of increased amount of debt.

4.2 Heterogeneous effects

The advantage of having data divided by gender and age allows me to study potential

heterogeneous effects of debt. Also, in addition to the main variable of debt that is used

throughout the thesis, I analyze data on the share of first debtors and infinite debtors to

investigate whether the period of indebtedness has a different impact on mental health

outcomes. Below, in table 4.3, these results are presented. First, I run the first stage regressions

of these two endogenous debt variables on the instrument Z2 for both regional levels to test for

relevance. Here, it is plausible to assume Z2 to predict the share of debtors rather than Z1

using debt amounts. In the bottom right corner of the table, the estimates of the instrument

show strong enough and positively significant impact on both the share of first and infinite

debtors. A 1 standard deviation increase of “Bartik-shock” in the instrument results in

approximately 0.1 percentage points increase in the share of first debtors and 0.15%-points

increase in the share of infinite debtors, both at the county level.

The estimates in panel A on self-reported outcomes are very imprecise for the effect of first

debtors. However, the effect of infinite debtors is economically significant and positive on

21

stress and anxiety. A 1%-point increase in the share of debtors among those who have been

indebted for at least 20 years leads to an increase of near 12%-points in the share of people

feeling stressed, which corresponds to an increase of 100% on average. Panel B shows the

effects on medical drugs. For both first debtors and infinite debtors, there is a positive impact

on the use of anxiolytics, significantly different from zero at the 10% level. Although, being a

first debtor has a larger impact than being a debtor for a long time. A 1%-point increase in the

share of first debtors leads to 4.3%-points increase on average in the share of people

consuming anxiolytic drugs. Further, there is also a positive impact of infinite debtors on

alcohol abuse medicine. In panel C the estimates on stress related diagnoses are also shown to

be positive for both types of debtors. Similarly, the magnitude of the effect is larger for the first

TABLE 4.3 IV estimates of being first and infinite debtor on psychological outcomes.

Panel A. Self-

reported

Stress Reduced mental

well-being

Anxiety Sleeping

problems

Alcohol

consumption

First debtor (Z2) 1845.0

(34085)

172.9

(3362)

993.4

(18433)

363.2

(6814)

171.9

(3564)

Infinite debtor (Z2) 11.67***

(2.862)

1.093

(2.606)

6.281*

(3.683)

2.297

(3.990)

1.087

(4.587)

No. observations 210 210 210 210 210

Panel B. Drugs Anti-depressants Anxiolytics Hypnotics Alcohol abuse

First debtor (Z2) 1.415

(2.285)

4.313*

(2.540)

-0.637

(1.811)

0.196

(0.135)

Infinite debtor (Z2) 0.924

(1.431)

2.817*

(1.511)

-0.416

(1.191)

0.128*

(0.075)

No. observations 378 378 378 378

Panel C.

Diagnoses

Mood disorders Stress related Alcohol related 1st stage:

county Z2

First debtor (Z2) -0.680

(0.534)

1.505*

(0.855)

0.032

(0.215)

0.097***

(0.023)

[16.99]

Infinite debtor (Z2) -0.444

(0.357)

0.983*

(0.541)

0.021

(0.138)

0.148***

(0.040)

[13.48]

No. observations 378 378 378 378

Panel D. Sickness Sickness: severe

stress

Sickness:

psychological

Sickness: general 1st stage: municipal

Z2

First debtor (Z2) 1.740

(1.396)

-1.922

(2.377)

-4.975*

(2.947)

0.086***

(0.009)

[98.37]

Infinite debtor (Z2) 1.892

(1.669)

-1.255

(1.685)

-2.129*

(1.266)

0.120***

(0.016)

[153.10]

No. observations 1764 378 5220 5220

Table 4.3 Notes. Significance level: *** = 1%, ** = 5%, * = 10%. Robust standard errors in parentheses. F-statistic in brackets. Control variables included: gender, population, education, marital status, employment status, ethnicity, and disposable income. Year dummies are included. All regressions are average-weighted by population.

22

debtors. For the sickness outcomes in panel D, the effects of being first and infinite debtors are

similar in magnitude and positive on payments received due to severe stress and negative on

psychological sickness cases. Only the effects on general sickness cases are statistically

significant, where being a first debtor has more than twice as large negative impact than for

the infinite debtors. A 1%-point increase in first debtors lead to almost 5%-points decrease in

general sickness cases on average.

Henceforth, the main results presented in the previous subsection are deeper analyzed by

splitting the sample by gender, age, and income regions. In table 4.4 the effects of the average

amount of debt on a smaller set of outcomes, drugs and diagnoses, are presented, separated for

women and men using the instrument. The main results in table 4.1 present negative effects on

antidepressants, anxiolytics, and hypnotics, while positive on alcohol abuse. The effects on

antidepressants and anxiolytics are here seen to be driven by men. On the other hand, the

TABLE 4.4 IV estimates of debt amount on drugs and diagnoses separated for women and men.

TABLE 4.5 IV estimates of debt amount on drugs and diagnoses separated for young and old.

Women Men

Panel A. Drugs Anti-

depressants

Anxiolytics Hypnotics Alcohol

abuse

Anti-

depressants

Anxiolytics Hypnotics Alcohol

abuse

Average debt

amount (Z1)

-0.894

(0.559)

-0.562

(0.492)

-1.588**

(0.633)

0.049***

(0.015)

-0.168**

(0.068)

-0.132**

(0.063)

-0.237*

(0.129)

0.013

(0.008)

Panel B. Diagnoses

Mood disorders

Stress related

Alcohol related

Mood disorders

Stress related

Alcohol related

Average debt

amount (Z1)

-0.025

(0.120)

-0.316*

(0.174)

0.097**

(0.050)

0.012

(0.017)

-0.022

(0.019)

0.073***

(0.015)

No. observations 1323 1323 1323 1323 1323 1323 1323 1323

Table 4.4 Notes. Significance level: *** = 1%, ** = 5%, * = 10%. Robust standard errors in parentheses. Control variables included: age, population, education, marital status, employment status, ethnicity, and disposable income. Year dummies are included. All regressions are average-weighted by population.

Age: 26-34 Age: 55-64

Panel A. Drugs Anti-

depressants

Anxiolytics Hypnotics Alcohol

abuse

Anti-

depressants

Anxiolytics Hypnotics Alcohol

abuse

Average debt

amount (Z1)

0.299

(0.772)

-0.365

(0.625)

-0.604

(0.815)

-0.057

(0.089)

-0.132

(0.177)

0.078

(0.099)

-0.224*

(0.131)

0.014

(0.013)

Panel B.

Diagnoses

Mood

disorders

Stress

related

Alcohol

related

Mood

disorders

Stress

related

Alcohol

related

Average debt

amount (Z1)

0.192

(0.396)

-0.039

(0.372)

-0.129

(0.195)

-0.040

(0.030)

-0.110**

(0.055)

0.036*

(0.020)

No. observations 378 378 378 378 378 378 378 378

Table 4.5 Notes. Significance level: *** = 1%, ** = 5%, * = 10%. Robust standard errors in parentheses. Control variables included: gender, population, education, marital status, employment status, ethnicity, and disposable income. Year dummies are included. All regressions are average-weighted by population.

23

effects on hypnotics and alcohol abuse are mostly from women. In panel B of diagnoses, the

positive effect on alcohol related ones is seen for both men and women, reflecting the same

result found in 4.1. Among women, there is also a negative effect of average amount on stress

related diagnoses.

Table 4.5 introduces the results divided by age for the same outcomes. Two of seven age

groups are compared, one to represent young people between 26-34 years and one to represent

an older part of the population between 55-64 years. The effect of the average amount of debt

is only significant for older people. Hypnotics and stress are negatively affected by an increase

in amounts of debt, while alcohol diagnoses increase with higher debt amounts. An increase in

debt amount with 1000 SEK per person will decrease the share of people with stress diagnoses

by 0.1 percentage points among the elderly. This is economically significant when put in

relation to the average in each county where the share of stress diagnoses is 0.89% during the

studied period, hence around 11% decrease.

As a final sample split, I divide the counties into low income counties and high income

counties. Compared to the median in both 2010 and 2018 the same counties were below, for

which I divided the sample such that 10 counties are below the median and 11 counties are at or

above the median. In table 4.6 it is clear that almost all of the coefficients are significant for

the high income regions. Similar to the main results in table 4.1 most of the effects are negative

but both alcohol outcomes, for both drugs and diagnoses, are positively affected by an increase

in the average amount of debt. However, among the low income regions, the effect on medical

prescriptions for alcohol abuse is twice as large as for the richer areas. Also, the effect on

TABLE 4.6 IV estimates of debt amount on drugs and diagnoses separated for low and high income regions.

Income < Median Income Median

Panel A. Drugs Anti-

depressants

Anxiolytics Hypnotics Alcohol

abuse

Anti-

depressants

Anxiolytics Hypnotics Alcohol

abuse

Average debt

amount (Z1)

0.612

(0.534)

1.115*

(0.612)

-0.282

(0.497)

0.028*

(0.017)

-0.750***

(0.210)

-0.446***

(0.142)

-0.694***

(0.230)

0.014*

(0.008)

Panel B.

Diagnoses

Mood

disorders

Stress

related

Alcohol

related

Mood

disorders

Stress

related

Alcohol

related

Average debt

amount (Z1)

-0.103

(0.077)

0.018

(0.073)

-0.057*

(0.032)

-0.011

(0.033)

-0.091*

(0.047)

0.086***

(0.019)

No. observations 1260 1260 1260 1260 1386 1386 1386 1386

Table 4.6 Notes. Significance level: *** = 1%, ** = 5%, * = 10%. Robust standard errors in parentheses. Control variables included: gender, population, education, marital status, employment status, ethnicity, and disposable income. Year dummies are included. All regressions are average-weighted by population.

24

anxiolytics is significantly positive and on the alcohol related diagnoses in panel B is negative,

as opposed to those with higher income.

To summarize, the negative debt effects on hypnotics are mainly driven by older women in

high income regional areas. The positive impact of amount of debt on alcohol related

diagnoses is also mostly associated with older people in richer counties but both women and

men are affected. The alcohol diagnoses in low income regions are instead decreasing with

higher amounts of debt. The outcome of stress related diagnoses appear to be negatively

affected for older women in wealthier local areas, as the impact on hypnotics but is smaller in

magnitude.

4.3 Sensitivity analysis

As additional health outcomes in the data, I included antibiotics among the drug prescriptions

and tumors among the diagnoses. This allows me to study the effects of debt on two health

outcomes expected to be unaffected by debt and hence control the robustness of the main

results. In table 4.7, without taking the necessary instruments for causal interpretation into

account, the OLS estimates indicate there is a close to zero but positive relationship between

the average amount of debt and both antibiotics and tumors. The estimates are not statistically

significant. Turning to the IV results, the

coefficients turn to be significant with debt

amounts having effects on both antibiotics

and tumors, positive and negative

respectively. These significant results are

not in line with expectations. This is an

indication that the estimates found among

the main results and heterogeneous effects

may not be interpreted as causal. However,

these effects may also be a consequence of

doctors finding “co-morbidities” at the same

time when individuals search for help for

other illness issues. Thus, mental health

issues may correlate with prescriptions of

antibiotics and tumor diagnoses.

TABLE 4.7 OLS and IV estimates of debt on

antibiotics and tumors.

Antibiotics (1) OLS (2) IV

Average debt

amount (KSEK)

0.004

(0.028)

0.657**

(0.296)

No. observations 2646 2646

Tumors (1) OLS (2) IV

Average debt

amount (KSEK)

0.030

(0.022)

-0.371**

(0.164)

No. observations 2646 2646

Controls

Regional dummies

Year dummies

Yes

No

Yes

Yes

No

Yes

Table 4.7 Notes. Significance level: *** = 1%, ** = 5%, * = 10%. Robust standard errors in parentheses. 𝑍1 is used as instrument. Control variables: gender, population, education, marital status, employment status, ethnicity, and disposable income. All regressions are average-weighted by population.

25

The main results are sensitive to changes when including different sets of controls. Looking at

table A1 in the appendix, the majority of the IV estimates on the self-reported outcomes start

with negative significant results when the instrument alone estimates the effect of debt

amounts. The inclusion of additional controls changes the sign of the effect as well as the

significance removes. Similar changes can be seen in table A2 over diagnoses, although the IV

estimates of average debt amount instead appear significant in the last specifications (5)

including all controls. Contrary, the coefficient on antidepressants does not vary between the

specifications which indicate that the instrument seems to fulfill exogeneity and explain the

variation in that specific medication. Among the diagnoses in table A3, only the effects on

alcohol related diagnoses remain stable throughout the different IV specifications. Mood

disorders and stress related diseases are close to zero and imprecise. The last complete table

A4 shows how all estimates on sickness cases are larger in specification (5) with all controls

than in specification (2) with the instrument alone. Conditional on all the controls, the impact

of an increase in debt amount per person is even larger on the sickness cases for work absence.

5. Conclusion & Discussion

The main finding from this thesis is that a higher degree in over-indebtedness improves

mental health. On several of the outcomes, there were no effects found. An outstanding result

is however that higher amounts of debt seem to change the lifestyle related health behavior

and particularly increase excessive alcohol consumption. While alcohol behavior should

intuitively correlate with other types of mental issues, the effect of debt appears in opposite

directions for these two. One potential explanation for this result could be that increasing the

level of drinking can be seen as a concrete action to other underlying problems, which then

will be the main factor seen in the results. However, as in previous literature, it is in general

difficult to isolate the causal relationship between debt and health and to claim the direction

in particular. As discussed earlier in the methodology, an important threat to identification is

the use of non-legitimate instruments. As seen in the main results and based on the sensitivity

analysis the exclusion restriction is likely violated and the instrument is invalid. Thus, the

findings may not be interpreted as causal.

Even though the effects might not be causal, the findings of that higher amounts of debt

appear to affect mental health positively poses some interesting thoughts. Firstly, Smith (1999)

argues based on the fundamental theory that health is a stock, a function of initial health

endowment together with the total history of past and current health decisions and occasions.

26

The most crucial time for the impact on the stock is found in early childhood and the most

developing years as young. Hence, later life experiences, such as running into debt, should

have a smaller impact on the health stock and may explain the uncertain health effects.

Secondly, another important issue from childhood regarding causality is a potential third

confounder such as genetics or parental background (Cutler, Lleras-Muney & Vogl, 2011).

Under what conditions individuals grow up may be essential to how they handle money and

view their well-being. Not only the influence of parents should be considered, but also friends,

teachers, and other peers in the social circle.

Third, I think a common feature as a debtor is that they are likely deniers. The

combination of ignorance and lack of knowledge leads to uncertainty regarding the debts. A

large part of the debt amounts may be related to the increase in unsecured debts, such as from

gambling for example. These people are living in the moment and not thinking about the

consequences. At the same time, they are feeling ashamed and there is always a will of “keep

up with the Joneses”. Towards friends and family, they hide and deny the situation they have

put themselves into. These behavioral characteristics of pushing the problems ahead over and

over again may lead to them never realizing their real health condition and are less inclined to

seek help. Hence, these people will not be recorded in the objective health measures of drugs,

diagnoses, and sickness cases payments. Among the scarce literature of objective measures of

health, Dackehag et al. (2019) do not find any effects running from debt to

psychopharmaceutical substances. Most of the evident empirical research is based on

subjective measures, both on debt and health. However, even though there are some positive

effects found on perceived stress in this thesis, the data over the self-reported outcomes as

moving averages in counties are less precise with less variation and then hard to estimate

precisely. Further, such behavioral aspects would be interesting to address and study for future

research within the context of indebtedness and health.

Conclusively, despite the improved effects on mental health from higher debt amounts against

the hypothesis and the limitations in the methodology, a key point from this thesis is the

increase in alcohol consumption at a risk level. These results should not suggest implications

of encouraging to run into debts to get healthier, but rather improve debt knowledge in early

education for example, considering the health behavior effects. However, the difficulty to

isolate the causal effect running from over-indebtedness to health opens for further analyses.

27

6. References

Ahlström, R. & Edström, S. 2014. “Överskuldsättning och ohälsa”. Rapport 2014:16

Konsumentverket.

Boustan, L., Ferreira, F., Winkler, H. & Zolt, E.M. 2013. “The Effect of Rising Income Inequality

on Taxation and Public Expenditures: Evidence from U.S. Municipalities and School Districts,

1970-2000”. Review of Economics and Statistics, 95(4), pp. 1291-1302.

Bridges, S. & Disney, R. 2010. “Debt and depression”. Journal of Health Economics, 29(3), pp.

388-403.

Brown, S., Taylor, K. & Wheatley Price, S. 2005. “Debt and distress: Evaluating the

psychological cost of credit”. Journal of Economic Psychology, 26(5), pp. 642-663.

Chmelar, A. 2013. Household Debt and the European Crisis (No 13). Brussels: European Credit

Research Institute (ECRI). Available: http://www.ecri.eu/publications/research-

reports/household-debt-and-european-crisis [Downloaded: 2020-03-11]

Clayton, M., Liñares-Zegarra, J. & Wilson, J. 2015. “Does debt affect health? Cross country

evidence on the debt-health nexus.” Social Science & Medicine, 130, pp. 51-58.

Cutler, D., Lleras-Muney, A. & Vogl, T. 2011. ”Socioeconomic Status and Health: Dimensions

and Mechanisms”. In: Glied, S. & Smith, P. (ed.) The Oxford Handbook of Health Economics.

Oxford: Oxford University Press, pp. 123-163.

Dackehag, M., Ellegård, L-M., Gerdtham, U. & Nilsson, T. 2015. ”Ekonomisk knapphet,

skuldsättning och hälsa”. The Swedish Enforcement Authority.

Dackehag, M., Ellegård, L-M., Gerdtham, U. & Nilsson, T. 2019. ”Debt and mental health: new

insights about the relationship and the importance of the measure of mental health”. European

Journal of Public Health, 29(3), pp. 488-493.

Gathergood, J. 2012. “Debt and Depression: Causal Links and Social Norm Effects”. The

Economic Journal, 122(563), pp. 1094-1114.

Grafova, I. 2007. “Your Money or Your Life: Managing Health, Managing Money”. Journal of

Family and Economic Issues, 28(2), pp. 285-303.

Hojman, D., Miranda, A. & Ruiz-Tagle, J. 2016. ”Debt trajectories and mental health”. Social

Science & Medicine, 167, pp. 54-62.

Jenkins, R., Bhugra, D., Bebbington, P., Brugha, T., Farrell, M., Coid, J., Fryers, T., Weich, S.,

Singleton, N. & Meltzer, H. 2008. “Debt, income and mental disorder in the general

population”. Psychological Medicine, 38(10), pp. 1485-1493.

28

Keese, M. & Schmitz, H. 2014. “Broke, Ill and Obese: Is There an Effect of Household Debt on

Health?”. Review of Income and Wealth, 60(3), pp. 525-541.

Lindén, E. 2016. ”Skapar skulder ohälsa eller ohälsa skulder?”. Vetenskap & Hälsa.

Meltzer, H., Bebbington, P., Farrell, M. & Jenkins, R. 2013. “The relationship between personal

debt and specific common mental disorders”. The European Journal of Public Health, 23(1), pp.

108-113.

National Board of Health and Welfare. 2020. Statistikdatabas. Stockholm: Socialstyrelsen.

Available: https://www.socialstyrelsen.se/statistik-och-data/statistik/statistikdatabasen/

[Downloaded: 2020-03-11]

OECD. 2020. Income inequality (indicator). Paris: The Organization for Economic Co-

Operation and Development. Available: https://data.oecd.org/inequality/income-

inequality.htm [Downloaded: 2020-03-12]

Public Health Agency. 2020. Faktablad om psykisk ohälsa. Stockholm: Folkhälsomyndigheten.

Available: https://www.folkhalsomyndigheten.se/psykisk-ohalsa/ [Downloaded: 2020-03-11]

Richardson, T., Elliot, P. & Roberts, R. 2013. “The relationship between personal unsecured

debt and mental and physical health: A systematic review and meta-analysis”. Clinical

Psychology Review, 33(8), pp. 1148-1162.

Ridley, M., Rao, G., Schilbach, F. & Patel, V. 2020. “Poverty, Depression, and Anxiety: Causal

Evidence and Mechanisms”. Working Paper no 27157, Cambridge: National Bureau of Economic

Research.

Riksbanken (2020). Statistik – Sök räntor & valutakurser. Stockholm: Sveriges riksbank.

Available: https://www.riksbank.se/sv/statistik/sok-rantor--valutakurser/ [Downloaded: 2020-

05-02]

Smith, J. 1999. “Healthy Bodies and Thick Wallets: The Dual Relation Between Health and

Economic Status”. Journal of Economic Perspectives, 13(2), pp. 145-166.

Social Insurance. 2020. Statistik om sjukpenning och rehabiliteringspenning. Stockholm:

Försäkringskassan. Available: https://forsakringskassan.se/statistik/sjuk/sjuk-och-

rehabiliteringspenning [Downloaded: 2020-03-11]

Statistics Sweden (2020). Statistikdatabasen. Stockholm: Statistiska Centralbyrån. Available:

http://www.statistikdatabasen.scb.se/pxweb/sv/ssd/ [Downloaded: 2020-05-02]

29

Swedish Data Protection Authority. 2020. Betalningsanmärkningar. Available:

https://www.datainspektionen.se/vagledningar/inkassokrav-eller-

betalningsanmarkning/betalningsanmarkningar/ [Downloaded: 2020-03-11]

Swedish Enforcement Authority. 2020a. Om någon är skyldig dig pengar – kan vi hjälpa till,

Stockholm: Kronofogden. Available:

https://www.kronofogden.se/Ansokaombetalningsforelaggande.html [Downloaded: 2020-04-

06]

Swedish Enforcement Authority. 2020b. Statistik. Stockholm: Kronofogden. Available:

https://www.kronofogden.se/Statistik.html [Downloaded: 2020-03-11]

Swedish Enforcement Authority. 2020c. Historiskt få skuldsatta hos Kronofogden. Stockholm:

Kronofogden. Available: https://www.kronofogden.se/81460.html [Downloaded: 2020-05-02]

Zimmarman, F. & Katon, W. 2005. “Socioeconomic status, depression disparities, and financial

strain: What lies behind the income-depression relationship?”. Health Economics, 14(12), pp.

1197-1215.

30

Appendix

A.1 OLS and IV estimates of the average amount of debt TABLE A1. OLS and IV estimates of the relationship between amount of debt and self-reported outcomes. 𝑍1 is used as instrument. Self-reported Stress Reduced mental well-being Anxiety

(1) OLS (2) IV (3) IV (4) IV (5) IV (1) OLS (2) IV (3) IV (4) IV (5) IV (1) OLS (2) IV (3) IV (4) IV (5) IV Average debt amount (KSEK) Gender Education Employment status Marital status Ethnicity Disposable income Population (log)

-0.078 (0.096)

5.016*** (0.842) 0.102** (0.040) 0.521** (0.220) 0.352

(3.483) 0.193*** (0.057) -5.487

(11.820) 0.349** (0.153)

-0.596*** (0.124)

1.311*** (0.398)

14.098*** (2.534)

1.062* (0.609)

12.270*** (4.077)

0.152*** (0.056)

0.826*** (0.305) -1.779

(6.382)

1.158** (0.510)

11.899*** (3.192) 0.099* (0.059) 0.412

(0.386) -12.845* (6.652)

0.269*** (0.080) -22.850

(23.186) 0.092

(0.209)

-0.213** (0.085)

4.006*** (0.853) 0.069* (0.040) 0.356

(0.242) 10.473***

(2.872) -0.059 (0.051) 4.339

(11.580) 0.819*** (0.148)

-0.603*** (0.127)

0.722** (0.309)

9.805*** (1.941)

0.083 (0.389) 5.256** (2.551)

0.147*** (0.032) 0.470** (0.210) 8.828** (4.354)

0.140 (0.313)

5.968*** (1.974) 0.068

(0.042) 0.325

(0.249) 6.711

(4.697) -0.037

(0.057) -0.611

(13.521) 0.746*** (0.171)

-0.144 (0.131)

11.857*** (1.173) 0.061

(0.056) 0.703** (0.294) 6.006

(4.998) 0.059

(0.078) 4.911

(13.986) 0.759*** (0.210)

-1.522*** (0.228)

0.994** (0.388)

18.610*** (2.535)

0.387 (0.529)

14.427*** (3.534)

0.165*** (0.049)

0.865*** (0.268) 7.034

(6.220)

0.550 (0.474)

15.720*** (3.088) 0.059

(0.061) 0.642* (0.335) -1.401 (7.495) 0.101

(0.087) -4.834

(19.409) 0.616** (0.238)

R-sq. No. obs.

0.884 210

0.464 210

0.714 210

0.780 210

0.779 210

0.878 210

0.495 210

0.749 210

0.857 210

0.867 210

0.946 210

0.676 210

0.903 210

0.936 210

0.936 210

Sleeping problems Alcohol consumption

(1) OLS (2) IV (3) IV (4) IV (5) IV (1) OLS (2) IV (3) IV (4) IV (5) IV Average debt amount (KSEK) Gender Education Employment status Marital status Ethnicity Disposable income Population (log)

0.354*** (0.116)

14.306*** (0.877) -0.081

(0.052) 0.845*** (0.272) -3.078

(3.690) -0.085

(0.069) 15.202

(13.672) 0.621*** (0.184)

-1.387*** (0.141)

0.128 (0.272)

11.205*** (1.702)

0.093 (0.537)

11.367*** (3.450) 0.014

(0.039) 0.733*** (0.271) 0.124

(6.172)

0.257 (0.420)

13.769*** (2.403) -0.081 (0.050)

0.854*** (0.269) -2.048 (5.454) -0.091 (0.069) 16.557

(14.229) 0.641*** (0.194)

-0.247* (0.143)

-6.138*** (1.385) 0.052

(0.062) 0.202

(0.293) 16.955***

(4.571) -0.192*** (0.068)

57.604*** (16.059)

0.192 (0.183)

1.047*** (0.104)

0.688 (0.458) -2.658

(2.795)

-0.169 (0.504)

-9.254*** (3.275)

0.149*** (0.050) -0.549** (0.262)

14.531** (6.375)

0.209 (0.547) -3.597

(3.250) 0.050

(0.058) 0.162

(0.331) 12.085 (8.085) -0.164** (0.077)

51.197** (22.102)

0.097 (0.226)

R-sq. No. obs.

0.937 210

0.752 210

0.928 210

0.932 210

0.937 210

0.863 210

0.735 210

0.759 210

0.849 210

0.851 210

Table A1. Notes. Significance level: *** = 1%, ** = 5%, * = 10%. Robust standard errors in parentheses. Year dummies are included. All regressions are average-weighted by population.

31

TABLE A2. OLS and IV estimates of the relationship between amount of debt and drugs. 𝑍1 is used as instrument. Drugs Antidepressants Anxiolytics

(1) OLS (2) IV (3) IV (4) IV (5) IV (1) OLS (2) IV (3) IV (4) IV (5) IV Average debt amount (KSEK) Age Gender Education Employment status Marital status Ethnicity Disposable income Population (log)

-0.297*** (0.020)

2.874*** (0.056)

-2.863*** (0.133)

0.052*** (0.008)

0.036*** (0.006)

2.743*** (0.217) 0.015

(0.011) -9.251*** (1.593) -0.094

(0.065)

-0.364** (0.177)

-0.175* (0.106)

2.560*** (0.179)

-4.251*** (0.682)

-0.424*** (0.127)

2.812*** (0.228)

-2.703*** (0.686) 0.026* (0.013) 0.046** (0.018)

3.016*** (1.018)

-0.436*** (0.153)

2.855*** (0.060)

-2.608*** (0.281) 0.025

(0.032) 0.048*** (0.016)

3.173*** (0.514) 0.002

(0.019) -0.642 (9.662) 0.006

(0.130)

-0.173*** (0.013)

2.327*** (0.050)

-1.377*** (0.087)

-0.034*** (0.006)

-0.015*** (0.004)

0.855*** (0.144)

0.137*** (0.009)

-10.358*** (1.111)

0.250*** (0.042)

0.098 (0.160)

0.190 (0.142)

1.294*** (0.233)

-4.151*** (0.915)

0.303* (0.182)

1.023*** (0.316)

-4.299*** (1.008)

0.056*** (0.017)

-0.077*** (0.024)

-4.237*** (1.485)

-0.291** (0.120)

2.311*** (0.051)

-1.163*** (0.233) -0.058** (0.025) -0.005 (0.011)

1.218*** (0.396)

0.126*** (0.014) -3.095 (7.473)

0.334*** (0.095)

R-sq. No. observations

0.930 2646

. 3213

0.878 2646

0.927 2646

0.927 2646

0.912 2646

. 3213

0.704 2646

0.726 2646

0.908 2646

Hypnotics Alcohol abuse

(1) OLS (2) IV (3) IV (4) IV (5) IV (1) OLS (2) IV (3) IV (4) IV (5) IV Average debt amount (KSEK) Age Gender Education Employment status Marital status Ethnicity Disposable income Population (log)

-0.294*** (0.023)

4.053*** (0.070)

-1.628*** (0.138)

-0.087*** (0.009)

-0.084*** (0.007)

-0.961*** (0.230)

0.210*** (0.014)

-9.283*** (1.868)

0.466*** (0.062)

-0.154 (0.223)

0.062 (0.234)

2.899*** (0.383)

-4.331*** (1.526)

0.360 (0.265)

2.378*** (0.459)

-5.287*** (1.478) 0.061** (0.026)

-0.165*** (0.035)

-7.652*** (2.196)

-0.637*** (0.207)

4.007*** (0.078) -0.999** (0.400)

-0.156*** (0.041)

-0.055*** (0.020) 0.101

(0.700) 0.178*** (0.025) 11.944

(12.854) 0.712*** (0.157)

0.014*** (0.001)

-0.012*** (0.003) -0.007 (0.005)

-0.002*** (0.000)

0.004*** (0.000) -0.001 (0.012)

-0.006*** (0.001)

1.526*** (0.137) -0.000 (0.002)

0.004 (0.006)

0.008 (0.007)

0.046*** (0.012)

0.136*** (0.046)

0.001 (0.008)

0.061*** (0.015)

0.139*** (0.046)

-0.003*** (0.001)

0.006*** (0.001)

0.238*** (0.068)

0.021*** (0.007)

-0.011*** (0.004) -0.020 (0.013) -0.001 (0.001)

0.003*** (0.001) -0.022 (0.023)

-0.005*** (0.001) 1.101** (0.471) -0.005 (0.006)

R-sq. No. observations

0.938 2646

. 3213

0.784 2646

0.836 2646

0.925 2646

0.866 2646

0.210 3213

0.649 2646

0.732 2646

0.860 2646

Table A2 Notes. Significance level: *** = 1%, ** = 5%, * = 10%. Robust standard errors in parentheses. Year dummies are included. All regressions are average-weighted by population.

32

TABLE A3. OLS and IV estimates of the relationship between amount of debt and diagnoses. 𝑍1 is used as instrument. Diagnoses Mood disorders Stress related

(1) OLS (2) IV (3) IV (4) IV (5) IV (1) OLS (2) IV (3) IV (4) IV (5) IV Average debt amount (KSEK) Age Gender Education Employment status Marital status Ethnicity Disposable income Population (log)

-0.024*** (0.004)

-0.071*** (0.010)

-0.456*** (0.031)

0.008*** (0.002)

0.010*** (0.002)

-0.259*** (0.048)

0.022*** (0.002)

3.484*** (0.377)

-0.099*** (0.012)

0.043 (0.034)

0.040 (0.030) -0.017 (0.051)

-0.695*** (0.196)

-0.012 (0.024) -0.002

(0.042) -0.312** (0.132)

0.024*** (0.002)

0.016*** (0.003) -0.018

(0.189)

0.008 (0.029)

-0.067*** (0.011)

-0.516*** (0.063) 0.015** (0.006) 0.007** (0.003)

-0.359*** (0.100)

0.025*** (0.004) 1.475

(1.811) -0.122*** (0.025)

-0.029*** (0.005)

-0.142*** (0.013)

-0.507*** (0.045)

0.010*** (0.003)

0.017*** (0.002)

-0.249*** (0.062)

0.035*** (0.003)

2.964*** (0.518)

-0.108*** (0.016)

0.043 (0.039)

0.034 (0.038) -0.098

(0.065) -0.747*** (0.244)

-0.033 (0.031) -0.074

(0.053) -0.287* (0.167)

0.029*** (0.003)

0.027*** (0.005) 0.061

(0.243)

-0.058 (0.039)

-0.146*** (0.014)

-0.455*** (0.078) 0.004

(0.008) 0.020*** (0.004) -0.160

(0.129) 0.033*** (0.004) 4.743* (2.432) -0.087** (0.034)

R-sq. No. observations

0.667 2646

. 3213

0.271 2646

0.617 2646

0.649 2646

0.634 2646

. 3213

0.225 2646

0.584 2646

0.625 2646

Alcohol related

(1) OLS (2) IV (3) IV (4) IV (5) IV Average debt amount (KSEK) Age Gender Education Employment status Marital status Ethnicity Disposable income Population (log)

0.023*** (0.003)

-0.069*** (0.005)

-0.096*** (0.011)

-0.003*** (0.001) 0.000

(0.001) -0.366*** (0.024)

0.011*** (0.001)

2.462*** (0.189)

0.011*** (0.004)

0.090*** (0.019)

0.082*** (0.015)

-0.089*** (0.024)

-0.332*** (0.093)

0.097*** (0.018)

-0.137*** (0.031)

-0.337*** (0.098)

0.014*** (0.002) -0.006** (0.002)

-0.772*** (0.141)

0.070*** (0.015)

-0.063*** (0.006)

-0.183*** (0.030) 0.006** (0.003) -0.004** (0.002)

-0.512*** (0.049)

0.015*** (0.002) -0.467 (0.930) -0.023** (0.011)

R-sq. No. observations

0.791 2646

. 3213

. 2646

0.208 2646

0.595 2646

Table A3. Notes. Significance level: *** = 1%, ** = 5%, * = 10%. Robust standard errors in parentheses. Year dummies are included. All regressions are average-weighted by population.

33

TABLE A4. OLS and IV estimates of the relationship between amount of debt and sickness cases. 𝑍1 is used as instrument. Sickness Sickness: severe stress Sickness: psychological

(1) OLS (2) IV (3) IV (4) IV (5) IV (1) OLS (2) IV (3) IV (4) IV (5) IV Average debt amount (KSEK) Gender Education Employment status Marital status Ethnicity Disposable income Population (log)

0.021*** (0.005)

-1.069*** (0.046)

0.014*** (0.003)

0.601*** (0.211)

-0.024*** (0.003)

-3.871*** (0.470) -0.033

(0.022)

0.014 (0.013)

0.020 (0.028)

-1.315*** (0.186)

0.130* (0.075)

-2.063*** (0.496) -0.003 (0.002)

-2.253** (0.936)

0.135* (0.071)

-1.629*** (0.348)

0.027*** (0.009)

0.060 (0.378)

-0.037*** (0.009)

-6.138*** (1.823) -0.069* (0.039)

0.011 (0.030)

-1.514*** (0.251) 0.013

(0.012) -0.210*** (0.074) 1.545* (0.925)

-0.060*** (0.018)

-12.811*** (3.530)

0.330*** (0.093)

-0.033 (0.115)

-0.037 (0.092)

-1.987*** (0.586)

-0.016 (0.359) -2.114 (2.364) -0.010 (0.023) -0.101 (0.065) -0.017 (3.189)

-0.422 (0.401) 0.798

(2.115) 0.011

(0.018) -0.143

(0.123) 5.360

(3.355) -0.077*** (0.028) -5.709

(8.548) 0.427*** (0.135)

R-sq. No. observations

0.742 1764

0.295 3976

0.708 1764

0.576 1764

0.620 1764

0.861 378

0.371 378

0.835 378

0.838 378

0.773 378

Sickness: general

(1) OLS (2) IV (3) IV (4) IV (5) IV Average debt amount (KSEK) Gender Education Marital status Ethnicity Disposable income Population (log)

0.078*** (0.021)

-10.885*** (0.378) -0.061** (0.024)

4.518*** (0.905)

-0.136*** (0.017)

-30.39*** (3.333) 0.139

(0.180)

0.063 (0.098)

0.041 (0.096)

-11.676*** (0.658)

0.041 (0.115)

-12.517*** (0.700)

-0.149*** (0.012) -0.298 (1.187)

0.099 (0.112)

-10.984*** (0.609) -0.059** (0.028)

4.427*** (0.946)

-0.138*** (0.022)

-30.798*** (4.038) 0.139

(0.179)

R-sq. No. observations

0.786 5220

0.254 7830

0.746 5220

0.773 5220

0.786 5220

Table A4. Notes. Significance level: *** = 1%, ** = 5%, * = 10%. Robust standard errors in parentheses. Year dummies are included. All regressions are average-weighted by population.

34

TABLE A5. Summarized OLS and IV estimates of the relationship between average debt amount and psychological outcomes.

Panel A. Self-

reported

Stress Reduced mental well-being Anxiety Sleeping problems Alcohol consumption

(1) OLS (2) IV (1) OLS (2) IV (1) OLS (2) IV (1) OLS (2) IV (1) OLS (2) IV

Average debt

amount (KSEK)

-0.078

(0.100)

1.158***

(0.293)

-0.213

(0.135)

0.140

(0.351)

-0.144

(0.184)

0.550

(0.686)

0.354*

(0.189)

0.257

(0.576)

-0.247*

(0.334)

0.209

(0.353)

R-sq.

No. observations

0.884

210

0.779

210

0.878

210

0.867

210

0.946

210

0.936

210

0.938

210

0.937

210

0.863

210

0.851

210

Panel B. Drugs Antidepressants Anxiolytics Hypnotics Alcohol abuse

(1) OLS (2) IV (1) OLS (2) IV (1) OLS (2) IV (1) OLS (2) IV

Average debt

amount (KSEK)

-0.297***

(0.058)

-0.436**

(0.190)

-0.173***

(0.042)

-0.291**

(0.147)

-0.294***

(0.053)

-0.637***

(0.175)

0.014***

(0.003)

0.021*

(0.012)

R-sq.

No. observations

0.930

2646

0.927

2646

0.912

2646

0.908

2646

0.938

2646

0.925

2646

0.866

2646

0.860

2646

Panel C.

Diagnoses

Mood disorders Stress related Alcohol related

(1) OLS (2) IV (1) OLS (2) IV (1) OLS (2) IV

Average debt

amount (KSEK)

-0.024**

(0.008)

0.008

(0.032)

-0.029**

(0.013)

-0.058

(0.063)

0.023***

(0.007)

0.070***

(0.013)

R-sq.

No. observations

0.667

2646

0.649

2646

0.634

2646

0.625

2646

0.791

2646

0.595

2646

Panel D. Sickness Sickness: severe stress Sickness: psychological Sickness: general

(1) OLS (2) IV (1) OLS (2) IV (1) OLS (2) IV

Average debt

amount (KSEK)

0.021***

(0.007)

0.135*

(0.075)

0.011

(0.042)

-0.422**

(0.170)

0.078***

(0.030)

0.099

(0.106)

R-sq.

No. observations

0.742

1764

0.620

1764

0.861

378

0.773

378

0.786

5220

0.786

5220

Controls

Regional dummies

Year dummies

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Table A5. Notes. Significance level: *** = 1%, ** = 5%, * = 10%. Clustered standard errors in parentheses. 𝑍1 is used as instrument. Control variables included: age, gender, population, education, employment status, marital status, ethnicity, and disposable income. All regressions are average-weighted by population.