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
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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
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.
(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.
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.
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-
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)
(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)
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)
(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)
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)
(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.
(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.