Journal of Economic Psychology 79 (2020) 102198
Contents lists available at ScienceDirect
Journal of Economic Psychology
journal homepage: www.elsevier.com/locate/joep
Worries of the poor: The impact of financial burden on the risk
attitudes of micro-entrepreneurs☆
T
Patricio S. Daltona, , Nguyen Nhungb, Julius Rüschenpöhlerc
⁎
a
b
c
Tilburg University, Department of Economics and CentER, Warandelaan 2, 5037 AB Tilburg, the Netherlands
Vrije Universiteit Amsterdam, Department of Finance, De Boelelaan 1105, 1081 HV Amsterdam, the Netherlands
Center for Effective Global Action, UC Berkeley, United States
ARTICLE INFO
ABSTRACT
JEL:
I32
C93
G02
We randomly expose the owners of small retail businesses in Vietnam to scenarios that trigger
financial worries and study the effect of this intervention on risk attitudes using an incentivecompatible elicitation method. We find that entrepreneurs exposed to financial worries behave
less risk-averse than those assigned to a placebo treatment. This effect is stronger for owners of
shops which are smaller and those less exposed to large income shocks in their everyday business.
We further show that the effect of financial worries on risk attitudes is not explained by changes
in the cognitive functioning of the treated. The findings are consistent with previous results from
laboratory experiments with students in developed countries. As such, the paper provides evidence for the external validity of these findings in the context of micro-entrepreneurship in a
developing country and points to financial worries as one understudied psychological channel for
the effect of material deprivation on decision-making.
Keywords:
Poverty
Risk preferences
Financial worries
Decision-making
Lab-in-the-field experiment
PsycINFO:
2340
2360
3120
1. Introduction
In recent years, scholars in economics have put forward the view that the disadvantageous conditions poor people live under can
trigger psychological processes which lead to counterproductive economic behavior and “behavioral” poverty traps (Haushofer &
Fehr, 2014). Indeed, material deprivation has been shown to affect various aspects of the decision-making process, including people’s
risk attitudes which can change in reaction to major negative income shocks (e.g., Bernile, Bhagwat, & Rau, 2017; Callen, Isaqzadeh,
Long, & Sprenger, 2014; Chuang & Schechter, 2015; Cohn, Engelmann, Fehr, & Maréchal, 2015; Vieider, Truong, Martinsson, & Nam,
2019) as well as minor individual life events (e.g., Bucciol & Zarri, 2015; Menkhoff, Schmidt, & Brozynski, 2006; Weber & Zuchel,
2005; Thaler & Johnson, 1990). However, the precise psychological channels through which such effects may operate are less clear.
Work on the cognitive and emotional processes underlying changes in risk attitudes has emphasized the role of cognitive load
(e.g., Deck & Jahedi, 2015; Gerhardt, Biele, Heekeren, & Uhlig, 2016), self-control (e.g., Fudenberg & Levine, 2011; Gerhardt,
Schildberg-Hörisch, & Willrodt, 2017), stress, emotions, and arousal (e.g., Jahedi, Deck, & Ariely, 2017; Lerner, Li, Valdesolo, &
This paper supersedes our earlier paper “Poverty, Income Volatility, and Cognitive Function: Evidence from Small Retailers in Vietnam”.
Corresponding author.
E-mail addresses: p.s.dalton@uvt.nl (P.S. Dalton), ruschenpohler@berkeley.edu (J. Rüschenpöhler).
☆
⁎
https://doi.org/10.1016/j.joep.2019.102198
Received 1 December 2018; Received in revised form 31 July 2019; Accepted 1 August 2019
Available online 06 August 2019
0167-4870/ © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/BY-NC-ND/4.0/).
Journal of Economic Psychology 79 (2020) 102198
P.S. Dalton, et al.
Kassam, 2015; Porcelli & Delgado, 2017). In this paper, we draw on the evidence from lab experiments on the effects of acute stress
on decision-making and study financial worries as one possible psychological channel through which negative income shocks can
influence risk attitudes.
Following a large body of research on priming manipulations in psychology and economics (see, Cohn & Maréchal, 2016, for a
review), we exposed owners of small retail businesses in a market in Haiphong, Vietnam, to hypothetical scenarios of unanticipated,
exogenous income shocks. Using a between-subject design, we randomly assigned one group of entrepreneurs to “hard scenarios”, in
which they are asked to imagine a series of large but ecologically valid negative shocks, such as the forfeiture of an essential asset.
Another group of entrepreneurs is randomly assigned to “easy scenarios” based on the same scripts but with income shocks negligible
in size. Following the scenarios, we elicit risk attitudes using the incentive-compatible elicitation method introduced by Gneezy and
Potters (1997). To isolate the specific effect on risk-taking from more general effects on cognitive performance, we further administer
Raven’s Matrices (Raven, Raven, & Court, 1998).
We obtain three main results. First, the treatment shocks succeeded in creating exogenous differences in financial worries and
levels of perceived stress. Second, we find that entrepreneurs assigned to the “hard scenarios” show lower levels of risk aversion than
those assigned to the “easy scenarios”. This effect is stronger for smaller shops and for entrepreneurs who are inexperienced with
large income shocks. Third, we provide evidence that general cognitive performance, as measured by Raven’s Matrices, was not
affected by the treatment. We conclude that the effect of financial worries we find is specific to risk attitudes, and cannot be explained
by broader theories of mental scarcity.
Our main results are in line with work from psychology on the effects of stress on risk-taking behavior. This literature shows that
acute stress, as experienced after a negative shock, generally facilitates sensation-seeking behavior (e.g., Piazza et al., 1993), attention to positive rather than negative pay-offs (e.g., Lighthall, Gorlick, Schoeke, Frank, & Mather, 2013; Petzold, Plessow, Goschke,
& Kirschbaum, 2010), habit-like memory (e.g., Schwabe, Joëls, Roozendaal, Wolf, & Oitzl, 2012; Vogel & Schwabe, 2016), automatic
or “System 1” decision-making (e.g., Margittai et al., 2016) as well as changes in risk-taking. For instance, Pabst, Schoof,
Pawlikowski, Brand, and Wolf (2013) and Starcke, Wolf, Markowitsch, and Brand (2008) report that participants exposed to a
socially threatening situation as part of the classical Trier Social Stress Test show increased risk taking. Koppel et al. (2017) and
Kirchler et al. (2017) present similar results with stress being imposed through application of pain and time pressure, respectively. In
particular, Kirchler et al. (2017) find that time pressure increases risk taking for losses. Related to this, Porcelli and Delgado (2009)
find that physical stress induction through the classic cold pressor task strengthens the reflection effect by which risk-taking increases
for decisions in the loss domain and decreases for gains. Finally, an adjacent literature finds that physiological stress inductions via
oral cortisol administration can also facilitate risk-seeking behavior (e.g., Putman, Antypa, Crysovergi, & Van Der Does, 2010; van
den Bos, Harteveld, & Stoop, 2009).
This study contributes to the literature in demonstrating the external validity of these lab findings in the context of the financial
worries of small-scale entrepreneurs in a developing country. In that, it adds to a rapidly growing literature of lab-in-the-field
experiments in developing countries looking at the channels through which poverty can affect preferences (see, e.g., Haushofer &
Fehr, 2014). More generally, the results speak to an emerging body of work, both lab-based and from the field, on the instability of
risk attitudes over time and in response to shocks (e.g., Kusev, van Schaik, Ayton, Dent, & Chater, 2009; Kusev et al., 2017; Mata,
Frey, Richter, Schupp, & Hertwig, 2018; Schildberg-Hörisch, 2018).
The remainder of the paper is organized as follows. In the next section, we describe the sample of Vietnamese micro-entrepreneurs
who took part in the experiment and provide a detailed account of the experiment and materials used. In Section 3, we describe the
measurement of key variables. Section 4 presents the results and Section 5 concludes.
2. Study outline
The study was carried out in May 2015 at Tam Bac Market in Hai Phong, Vietnam. After acquiring permission to run the
experiment, we conducted an extensive piloting exercise in a nearby market to adapt both experimental materials and some of our
outcomes measures to the local context, and subsequently ran the experiment. This section provides a detailed account of these study
activities.
2.1. Sample and randomization
The Tam Bac Market is one of the largest markets in urban Hai Phong, comprising several hundreds of registered small shops
concentrated within a sheltered area of approximately 2000 square meters.1 These shops typically occupy rectangular areas with an
average size of 4.5 square meters (median 3 square meters). Businesses are almost exclusively managed by one owner each, with few
exceptions of an entrepreneur owning more than one shop and employing a manager. The market offers a considerable variety of
products and each shop clearly specializes in some key products. As part of the market, shops are largely homogeneous with respect
to characteristics such as ownership, size, supervision, security, rules, levies, and marketing strategies. At the same time, the businesses display heterogeneity with respect to sales, profits, and income volatility due to differences in shop type, business location
within the market and thus relative exposure to loyal and casual customers, or unobservable characteristics such as ability.
1
Please refer to the Supplementary Materials to this paper for a map that locates Hai Phong within North-eastern Vietnam, a plan of the interior of
Tam Bac Market, and visual impressions of the market.
2
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P.S. Dalton, et al.
Using administrative data, we initially identified 769 shops as part of the market. We randomly assigned 300 shops in equal shares
to treatment (“hard scenarios”) and placebo groups (“easy scenarios”). Randomization was stratified by a dummy of above-median
business size, and dummies for the shop’s main product type on offer. We divided the market into ten areas and ran the experiment in
one area per day to minimize spill-overs. Shops were assigned randomly to enumerators stratified by experimental group, so as to
avoid experimenter demand effects.
Only 132 out of 300 shops assigned to experimental groups were still operative at the time of the study. 168 businesses had ceased
operations due to the closure of one area within the market. In interviews with local entrepreneurs, we verified that this area had
been closed by order of higher authorities and not due to the shop owners’ individual decisions. Shops generally did not relocate into
different areas of the market. All entrepreneurs whose shops were operative took part in the experiment and no interviews were
aborted.
In addition, we excluded from the main analysis eleven businesses: six which were above 16 m2 in size and thus largely surpassed
the median of 3 square meters (mean 3.89 square meters) and five which reported unreasonably high levels of income volatility
upwards of three times larger than median or mean volatility (median = 0.8, mean = 0.93).2 Hence, the final sample is comprised of
121 businesses, of which 65 were randomly assigned to the treatment (“hard scenarios”) and 56 to the placebo group (“easy scenarios”).
2.2. Hypothetical scenarios
The intervention consisted of inducing shop owners to think about financial scenarios that entailed negative shocks to income as
might be encountered in real life.3 In this, we made use of the priming methodology common in psychological research and lately
used in economics (see, Cohn & Maréchal, 2016, for a review). The experimental materials used to prime participants in both
treatment and placebo groups are based on an identical set of four hypothetical financial scenarios. Scenarios in both experimental
groups were designed to present participants with realistic situations of damage done to an asset they owned and used.
Exogenous variation between treatment and placebo was introduced by varying the magnitude of damage done in otherwise
identical sets of scenarios. For example, an scenario designed to be perceived as burdensome and stress-inducing would illustrate the
forfeiture of an asset (e.g., a fridge breaking down). The corresponding placebo scenario, on the other hand, would depict minor
damages to the same asset. For the sake of brevity, throughout the paper, we label the set of treatment scenarios as “hard scenarios”
and the set of placebo scenarios as “easy scenarios”.4
In order to adapt the magnitudes of the two sets of otherwise identical financial scenarios to the local context, we conducted an
extensive pilot study with entrepreneurs from a neighboring market whose characteristics were similar to the study site. The aim of
these interviews was twofold: (i) to determine assets commonly owned and used by the type of entrepreneurs in our sample and (ii) to
understand which magnitudes would be burdensome yet realistic (“hard scenarios”) and which negligible yet noticeable (“easy
scenarios”).5 As an example, during the pilots we learned that almost all entrepreneurs owned and used a motorcycle for business
purposes. Through additional questions, we were able to form an estimate of how much it would cost to buy a new bike as well as
how much would have to be spent on a financially feasible and ultimately negligible repair service in case the vehicle breaks down.
We used this information to construct “Scenario 3”, which presents the case of a motorcycle breaking down with the costs to fix it
being VND 10 million (approx. US$ 920) in the “hard scenario” and VND 1 million (approx. US$ 92) in the “easy scenario”.
To verify that the magnitudes of the “hard scenarios” were indeed meaningful in the local context, we use additional, detailed
data collected on the entrepreneurs’ average monthly incomes.6 In the “hard scenarios”, entrepreneurs are, on average, exposed to
shocks commensurate to 43.5% of median monthly sales (28.69% of mean sales). Regarding negative income shocks reported to be
commonly experienced by the entrepreneurs in their day-to-day business, treatment shocks are of a magnitude of 93.13% of the
median difference between a month with bad sales and a month with sales typical for the firm.
2.3. Experimental procedure
For both experimental groups, the intervention was conducted according to the following procedure.7 After affirming their informed consent, participants were asked a series of demographic questions, personal and business-related, and were instructed to
solve a practice trial of Raven’s Matrices.8 Upon successful completion of the trial, the experiment would start.
The experiment itself is organized in four cycles, each of which was conducted according to the following procedure. Initially,
individuals were presented with the first hypothetical financial scenario (“hard” or “easy” depending on the experimental group).
2
In Section 4, we show that our results are robust to the inclusion of these businesses.
For the outline of this study, we drew inspiration from the experimental set-up and procedure of the New Jersey mall study laid out in Mani,
Mullainathan, Shafir, and Zhao (2013).
4
Please refer to the Supplementary Materials for an overview of the full set of scenarios with both treatment and placebo magnitudes.
5
Please refer to the Supplementary Materials for a more detailed account of the efforts we undertook to adapt the experimental material as much
as possible to the realities on the ground. With this information, we designed the set of four financial scenarios as well as the two versions of each
corresponding to the “hard” and the “easy scenarios”.
6
Further details about these data and their measurement can be found in Section 3.
7
Please refer to the Supplementary Materials for the sequence of events during the experiment.
8
Raven’s Standard Progressive Matrices and the specific items used are described in detail in Section 3.1.
3
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They were then given two minutes to process the information and were instructed to recall related experiences in their own lives and
imagine how the scenario would alter their own financial plans. Immersion was facilitated by follow-up questions that were designed
to prompt thoughts about possible consequences and strategies to solve the issue given the participants’ idiosyncratic financial
situation. Examples of questions include “do you have the money needed or could you get it on short notice?”, “would this create
problems for your long-term financial plans?”, or “would you need to sacrifice anything which would bring long-term consequences?”. Once two minutes had passed, the enumerator would immediately hand out the first set of five Raven’s Matrices and ask
the participant to complete it. Upon completion of the matrices, the enumerator would ask the participant for their answers to the
follow-up questions on the scenarios. This further assured (i) that participants would engage with the scenarios and possible solutions
to the issues portrayed throughout the performance task and (ii) that financial worries could spill over into the cognitive performance
task.
The procedure of this cycle was repeated four times, once for each scenario. Only when all four cycles were completed, we
measured the cumulative effect of the scenarios on individual risk attitudes using the investment game introduced by Gneezy and
Potters (1997). We subsequently measured overall stress levels using the Perceived Stress Scale (Cohen, Kamarck, & Mermelstein,
1983; Cohen & Williamson, 1988).9 A typical intervention lasted 40 min in total, in the end of which participants received a lump
sum payment of VND 50.000 (approx. US$ 2.3) as their participation fee. To this were added between 0 and VND 90,000 (US$ 4.14)
as a consequence of the investment game. In accordance with common practice in psychology, the Raven’s Standard Progressive
Matrices were neither incentive-compatible nor timed. Overall, the average shop owner earned about VND 100,000 (approx. US$
4.6).
3. Data
This section describes the methods to measure each of the outcomes and variables used to examine heterogeneous treatment
effects. It concludes with descriptive statistics from the data and randomization tests to establish balance between experimental
groups.
3.1. Measurement of main variables
Risk attitudes
As our main outcome of interest, we focus on risk attitudes which we measure through the incentive-compatible elicitation
method introduced by Gneezy and Potters (1997). Since its inception, it has been used in various contexts (for a review see Charness,
Gneezy, & Imas, 2013). In the course of this game, the individual is endowed with an amount X (VND 30,000 or US$2.75) and may
decide to invest any amount x = {0 x X } in a risky bet which is known to triple the amount invested with probability p = 1/2 . By
coin toss, it thus yields the dividend 3x + (X x ) or X x . Since the expected value of investing in the risky asset exceeds the
expected value of the safe asset, a risk-neutral (and, in turn, risk-seeking) person should invest the entire endowment, while a riskaverse person may invest less. The fraction of money invested x / X is the variable used to measure risk attitudes. Through investing
their money in the risky asset, participants could gain up to VND 90,000 (US$ 4.14).
General cognitive performance
In order to control for potential effects on cognitive functioning through cognitive load, we additionally administer the classic
Standard Progressive Matrices developed by Raven (Raven et al., 1998) which contain exercises of matching pictorial patterns.10
Conceptually, cognitive functioning is thus defined as the underlying latent trait measured by the aggregate score on Raven’s Progressive Matrices. Raven’s Matrices are widely used in psychology and economics. They are a non-verbal test to measure abstract
reasoning skills unconfounded by numeracy and literacy. Due to logistical and time constraints, we only administered 20 of the full
set of 60 matrices. Prior to the experiment, we conducted a pilot study in which we selected the 20 matrices that would maximize
power in discriminating cognitive performances in the local context. In a neighboring market with similar characteristics to that of
the study site, we asked entrepreneurs to solve the complete range of 60 Raven’s Matrices. We subsequently selected the 20 matrices
that discriminated best between individual participants’ performances.11 As is common in the literature, we imposed no time limit for
the completion of the task. For each participant, we randomized the order of the items but kept it constant across participants. That
way difficulty does not increase monotonically with time but conditions are the same for everyone. As the final score, we use the
aggregate of correct answers to calculate a percentage score.
Financial worries
In addition to the main outcomes, we ask a set of questions to verify the success of the treatment shock to generate differences in
financial worries. Conceptually, financial worries are, in line with the cognitive load literature, a secular increase in the use of the
amount of finite working memory capacity. We use the follow-up questions asked after each scenario to measure the extent to which
individuals showed preoccupation about their financial prospects. Questions are adapted to each scenario; examples are: “Do you
9
See Section 3 for further details on the measurement of our main variables.
We also administered a version of the numeric Stroop Test (see, e.g., Wolach, McHale, & Tarlea, 2004) to complement the Raven’s Matrices and
control for another facet of cognitive functioning. However, due to a programming error, we were unable to compute the final score commonly
referred to as the “Stroop Effect”.
11
Please refer to Supplementary Materials for the steps we took in order to select the most suitable set of matrices.
10
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P.S. Dalton, et al.
have the money needed or could you get it on short notice?”, “[w]ould this create problems for your long-term financial plans?”, and
“[w]ould you need to sacrifice anything which would bring long-term consequences?”. From the aggregate of these items, we
construct the percentage score which we use in the final analysis.
Stress
To further verify the capacity of the treatment shock to generate differences in stress levels, we use the 10-item Perceived Stress
Scale (Cohen et al., 1983) to measure perceived stress. Questions are designed to capture how uncontrollable and unpredictable
respondents perceive their situation. Questions include how nervous or how stressed an individual felt in the past month, and
whether difficulties were perceived to be mounting in a way that they could not be overcome. Individuals answered on a 5-point scale
ranging from 0 (“never”) to 4 (“very often”). To set the time frame of the question, each item was prefixed by “[i]n the past month,
[…]”. While this does not match the exact time frame of the experiment, we expected this measure to respond to the emotional
priming caused by the intervention. We used a percentage score constructed from the aggregate of the responses in the final analysis.
Additional firm characteristics
In order to investigate the heterogeneity of treatment effects on different subgroups, we measure business size and the month-tomonth volatility of income the entrepreneur commonly experiences. In order to obtain the shop’s size, we asked each respondent for a
subjective estimation of the size of their premises. Enumerators were instructed to estimate independently and ask back to confirm if
their estimation did not match the estimation of the entrepreneur.12 We measured income volatility by eliciting a set of estimates for
monthly sales. Specifically, we ask: “Every business experiences good and bad times with sales being high in one month and low in
another. Regarding your own business, when you think of a very good [very bad] month you had, how high [low] were your total
sales?”. Additionally, we asked respondents to estimate their shop’s total sales in a typical month. We construct our measure of
monthly income volatility by normalizing the difference between total sales of a good month Revgood and total sales of a bad month
Revbad by the total sales of a typical month Revtypical . Hence, volatility is defined as V =
(Revgood
Revbad )
Revtypical
.
3.2. Descriptive statistics
Table 1 presents descriptive statistics on key characteristics on the firm- and individual level. Columns 1 to 3 summarize key
characteristics of the entrepreneurs who participated in our study and provides some descriptive statistics on their businesses. The
average shop owner in our sample is 47 years old and has been running his/her business for about 18 years. Most (93%) are female
and have no employees (77%). Only 25% of the shop owners have a bank account. About 91% own the premises of their shop and
about 86% own a house. Monthly sales (US$; PPP adjusted, as are all following values) are at US$ 6,347 on average and the mean of
monthly profits at US$ 586. This yields a plausible profit margin of roughly 9% on average. The median month-by-month volatility of
income, that is the difference between a good and a bad month’s sales normalized by a typical month’s sales, is reported to be as high
as one typical month’s worth of sales (median =.98). Our data shows a diverse range with respect to monthly profits, with a median
of roughly US$ 367, a lower quartile of US$ 275 and an upper quartile of US$ 551. The average entrepreneur in our sample has
liabilities of about US$ 2,900. 90% of the entrepreneurs report to be able to borrow the amount of a typical month’s worth of profits
within one week’s notice and thus do not seem to be liquidity constrained with respect to financing working capital.
Columns 4 to 6 of Table 1 show tests of randomization that compare the characteristics of the entrepreneurs and their firms
assigned to “hard scenarios” with those assigned to “easy scenarios”. These tests show that the randomization procedure created
groups that are comparable in terms of basic characteristics. The only characteristic which shows a difference in means at the 10percent level is business age, with firms assigned to the “easy scenarios” being of slightly higher age than those assigned to the “hard
scenarios”. Other characteristics do not show significant differences across groups. This suggests that the randomization was successful in achieving balance across experimental groups.
4. Results
This section presents results from manipulation checks, main treatment effect regressions, as well as robustness checks, including
randomization inference.
4.1. Manipulation checks
As a first step, we confirm that the intervention succeeded in creating exogenous variation in financial worries and perceived
stress. Table 2 presents results from OLS regressions of financial worries and perceived stress on assignment to the “hard scenarios”.13
The results suggest that the experimental manipulation was effective. As Columns (1) to (3) show, individuals exposed to the “hard
scenarios” expressed significantly more worries (29.9% increase) about future finances and their ability to meet their hypothetical
obligations than participants exposed to the “easy scenarios”. Furthermore, as Columns (4) to (6) suggest, this difference in the
intensity of preoccupations is accompanied with a difference in stress levels. Treated participants report average stress levels which
are 6.7% (0.41 standard deviations) higher than those of the control. This effect is robust across specifications and sizeable given
12
Since shops occupy one or more standard, rectangular spaces of the market, we are confident that these estimations is largely accurate.
In these and all following regression models we add stratification controls and firm-level controls likely related to the outcome to the specification to gain precision in the estimates.
13
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fairly low levels of stress at baseline (mean = 0.295). We conclude that the exogenous variation the treatment created was meaningful,
both statistically and in terms of its effect size.
4.2. Treatment effects on risk attitudes
4.2.1. Average treatment effect
Table 3 presents OLS regression estimates of the main treatment effect on risk attitudes.14 We observe that in reaction to the
experimental manipulation of financial worries participants show a significant decrease in risk aversion. Individuals exposed to the
“hard scenarios” invest, on average, 12.2% (0.36 standard deviations) more in the risky bet than those assigned to placebo (see
Column 3).
4.2.2. Heterogeneous treatment effects
We further investigate whether treatment effects vary between subgroups of the sample that differ in the size of their business and
in the income volatility they typically face. We conjecture that treatment effects will be larger for smaller firms and for those who are
not used to face income volatility. To test these conjectures, we run OLS regressions in which we interact the treatment with size and
income volatility. Table 4 presents the results from these regressions. As Columns 1 and 2 indicate, entrepreneurs with shops smaller
than the median who are exposed to the “hard scenarios” invest, on average, 20.8% more in the risky bet. On the contrary, for owners
of larger businesses, changes in risk-taking are not significant, and in fact very close to zero. Columns 3 and 4 show that only
inexperienced shop owners, those typically exposed to volatility levels below the sample median, adjust their behavior to invest
20.6% more in the risky bet. On the contrary, treatment does not significantly change the risk attitudes of those already accustomed
to higher levels of volatility.
Additional analyses confirm that this effect is associated with an increased stress response. Specifically, entrepreneurs used to
lower levels of volatility report a sizeable increase of 11.6% (0.76 standard deviations) in stress levels, while those used to abovemedian levels of income volatility show no sign of increased stress.15 This finding is in line with Bernile et al. (2017) who posit that
the intensity of income shocks mediates the direction of their effect on risk-taking behavior.
4.3. Treatment effects on cognitive performance
We further estimate treatment effects on cognitive functioning to check whether the impact on risk taking is amenable to broader
explanations by theories of cognitive load. Table 5 presents OLS regression estimates of the effect of assignment to the “hard scenarios” on the entrepreneurs’ aggregate scores on the Raven’s Matrices. In contrast to Mani et al. (2013) and Mullainathan and Shafir
(2013), but in line with recent large-scale evidence by Fehr, Fink, and Jack (2019) and Carvalho, Meier, and Wang (2016), we find no
effect on cognitive functioning in any of the specifications. This is unlikely to be due to the predictability of the income shock, as
proposed by Lichand and Mani (2019), as the hypothetical scenarios used in this study have no immediate precedent in the real world
which participants could have used to form expectations about potential outcomes. Within our sample of low-income entrepreneurs
in a developing country, theories of mental scarcity do not seem to account for the effects we find on risk attitudes.
4.4. Robustness checks
We perform additional robustness checks to assure that the results we find are not driven by the exclusion of outliers or sensitive
to the estimation method.16 We estimate the same OLS regression models as outlined in Sections 4.1 and 4.2 with the full sample
including the eleven initially excluded outliers. All treatment effects are robust in size and sign, and remain statistically significant.
We additionally perform exercises of randomization inference as suggested by Gerber and Green (2012) and Athey and Imbens
(2017).17 These exercises instill confidence that, despite the moderate sample size, treatment effects are not homogeneously zero.
Magnitudes of treatment effects on perceived stress and risk attitudes remain almost unchanged.18
5. Conclusion
In this paper, we present results from a lab-in-the-field experiment in Vietnam designed to estimate the causal impact of financial
worries on risk-taking behavior among micro-entrepreneurs. We show that financial worries increase self-reported levels of stress and
reduces participants’ levels of risk aversion, especially of those with shops smaller in size and less experienced with income volatility.
This is in line with lab-based work on risk-taking under stress (e.g., Kirchler et al., 2017; Koppel et al., 2017; Pabst et al., 2013;
14
We use the term “treatment effect” to denote “intention-to-treat effects”. Since no interviews were aborted, the intention-to-treat effect can be
interpreted as the average treatment effect.
15
These additional analyses are available from the authors upon request.
16
The results of these exercises are reported in Tables 1 and 2 of the Supplementary Materials.
17
We use Stata code provided by Heß (2017).
18
Additional analyses confirm that the results from the randomization inference exercises are robust to including outliers. These results are
available upon request.
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P.S. Dalton, et al.
Porcelli & Delgado, 2009; Putman et al., 2010; Starcke et al., 2008). In focusing on micro-entrepreneurs as an occupation that
characterizes the typical employment of the poor in the developing world (Gollin, 2008), we provide evidence for the external
validity of these lab-based findings in the developed world.
Notably, we observe a decrease in risk aversion among our sample of female entrepreneurs, while the lab-based literature tends to
report equivalent results predominantly for men (e.g., Lighthall, Mather, & Gorlick, 2009; Lighthall et al., 2012; Preston, Buchanan,
Stansfield, & Bechara, 2007; van den Bos et al., 2009). We contend that the difference in context as well as potential selection into
entrepreneurship may play a role in explaining this difference, but acknowledge that more research is needed to rigorously test these
conjectures. We also find that cognitive functioning as measured by performance in the Raven test remains unaltered by the exposure
to financial worries, which makes it unlikely that more general theories of mental scarcity along the lines of Mullainathan and Shafir
(2013) account for our findings.
Our results differ from a part of the literature studying changes in risk preferences after negative shocks, which finds choices to
become less risky. For instance, Callen et al. (2014) show that an aggregate score of exposure to acts of violence promotes a preference for certainty.19Cohn et al. (2015) show that when professional traders are primed with a scenario of a recession they take less
risks. These studies differ in at least three crucial ways from ours. First, unlike Callen et al. (2014), we do not measure risk attitudes in
reaction to repeated and potentially traumatizing events. The stress that violence imposes may have different behavioral and neurobiological consequences than the more controllable effect of commonly occurring income shocks of realistic magnitude (along the
lines of Bernile et al., 2017). Similarly, priming a financial recession as in Cohn et al. (2015) is arguably subject to the same caveat.
Second, both Callen et al. (2014) and Cohn et al. (2015) find an association between fearful reactions and diminished risk taking.
This is in line with the literature on the impact of emotions on choice (e.g., Lerner & Keltner, 2001; Lerner et al., 2015; Raghunathan
& Pham, 1999) which finds fearful and anxious individuals to reduce risk taking. Our study is not able to isolate all potential
emotional states triggered by financial worries, and their effects on risk-taking behavior. We do observe changes in perceived stress,
but financial worries could in principle have also triggered anxiety, fear, sadness or anger, which we do not directly observe. We
conjecture that due to the realistic magnitude of our treatment shocks in the context of the participants’ everyday business, it is
unlikely that participants were experiencing anxiety or fear, but we leave the test of such conjecture for future research. If our
treatment prompted emotional states of anger or sadness, our findings would be consistent with the literature which generally finds
angry and sad individuals to seek greater risks (see Lerner et al., 2015, for a review). Again, this is all at the level of conjecture, and
more research is needed to pin down the emotional channels through which financial worries may affect financial risk taking in the
context of small-scale entrepreneurship.
Finally, the population of interest in Cohn et al. (2015) consists of high-income financial professionals who trade assets in the
developed world. Ours is comprised of small low-income entrepreneurs in a developing country. There is ample evidence suggesting
that individuals in poverty are more prone to seek risks in a variety of domains, such as in decisions to borrow at high interest rates
(Bertrand, Morse, Bertrand, & Morse, 2011; Dobbie & Skiba, 2013), to gamble at unfavorable odds and play the lottery (Haisley,
Mostafa, & Loewenstein, 2008), or to make risky health choices (Cutler & Lleras-Muney, 2010; Lynch, Kaplan, & Salonen, 1997;
Marmot et al., 1991). This paper shows that financial worries associated with poverty may be one of the potential mechanisms
explaining lower risk aversion under poverty.
This study has some potential limitations. One limitation concerns the validity of the method we use to elicit risk attitudes.
Although we apply an incentive-compatible method that is widely used in the economics literature (see, e.g., Charness et al., 2013),
there is evidence showing that risk preferences change considerably when measured using different methods (e.g., Pedroni et al.,
2017). A second limitation pertains to the timing between the scenarios and the observed effects. Since risk attitudes are measured
right after the treatment, we cannot exclude the possibility that the treatment effects we observe are relatively fleeting. That is, our
hypothetical scenarios may not be suited to impose stress for longer than the 40 min of the intervention, as shocks of real-life
proportions would. This feeds into a broader discussion on the potentially differential effects of the acute stress created by income
shocks and the chronic stress associated with permanent material deprivation. A third limitation arises from the priming method we
use to create exogenous variation of financial worries. Though popular in psychology and economics, rendering the memory of a
shock salient may not have the same impact on risk attitudes as experiencing the same shock in real life.
In sum, our findings invite further research on the effect of financial worries on the various aspects of financial decision-making.
This literature merits more attention also from the standpoint of feasibility: we show that inducing financial worries experimentally is
affordable and feasibly done in the field with non-invasive methods and working with less educated populations. Additional work on
the topic would complement the study of decision-making under mental scarcity (Mani et al., 2013; Mullainathan & Shafir, 2013;
Shah, Mullainathan, & Shafir, 2012) and has the potential to provide valuable contributions to understanding the persistence of
poverty.
Acknowledgements
We are grateful to Do Thuy Dung, Do Phuong Dung, Pham Thuy Dung, Nguyen Huy Nam, and Ly Viet Hoa for excellent research
assistance in the field. This paper has benefited from comments of participants at the 7th Wageningen-Lucerne-Tilburg Development
19
In an important contribution, Vieider (2018) shows that the empirical findings of Callen et al. (2014) are potentially confounded by systematic
noise, and rejects explanations based on a preference for certainty in favor of explanations based on random choice. We acknowledge that caution is
needed when interpreting the results reported by Callen et al. (2014).
7
Journal of Economic Psychology 79 (2020) 102198
P.S. Dalton, et al.
Economics workshop, the ENTER Jamborée 2016 at UCL, the M-BEES 2016 Conference at Maastricht University, and the 2016
Research Day Workshop at Universidad del Rosario. We are especially grateful to Daan van Soest, Thorsten Beck, and Elena Cettolin
for useful detailed comments. This paper was written in the framework of the research project “Enabling Innovation and Productivity
Growth in Low Income Countries (EIP-LIC/PO5639)”, funded by the Department for International Development (DFID) of the United
Kingdom and implemented by Tilburg University and partners. Website: www.tilburguniversity.edu/dfid-innovation-and-growth.
Appendix A. Summary statistics
See Table 1.
Table 1
Descriptive statistics and verification of randomization.
Sample Characteristics
Number of
Observations
Full Sample
Mean
SD
Assigned
N = 121
Gender (1 = Male)
Age (Years)
Business Size (m2)
Business Age (Years)
Total Number of Employees
Has Business Registration Certificate (Yes/No)
Total Monthly Sales (US$ PPP)b
Total Monthly Profits (US$ PPP)b
Monthly Income Volatility
Outstanding Loans (US$ PPP)b
Has Access to Liquidity on Short Notice (Yes/No)
Owns Business Premises (Yes/No)
Owns House or Flat (Yes/No)
Has Bank Account (Yes/No)
Generates Any savings from Monthly Profits (Yes/No)
Total Savings (US$ PPP)b
Has Personal or Business Insurance (Yes/No)
121
121
121
121
121
121
121
111
121
118
117
120
120
120
120
91
121
0.07
47.39
3.89
18.18
0.31
0.99
4399.43
561.75
0.93
2635.58
0.9
0.91
0.83
0.24
0.29
3583.49
0.59
0.26
9.95
2.49
7.99
0.72
0.09
6853.76
694.14
0.47
7443.43
0.30
0.29
0.38
0.43
0.46
10397.57
0.49
Means by Treatment
Hard
Scenario
Assigned
n = 65
Easy
Scenario
Assigned
n = 56
0.11
46.78
3.75
17.02
0.31
1
5090.55
626.09
0.88
2513.96
0.89
0.94
0.86
0.23
0.23
3767.90
0.57
0.04
48.09
4.05
19.54
0.30
0.98
3597.23
477.31
0.97
2784.74
0.90
0.87
0.78
0.25
0.36
3358.61
0.61
Hard – Easy
Differences in Means
(t-Test or
2)
0.13a
0.47
0.51
0.08
0.98
0.28a
0.23
0.27
0.30
0.85
0.84a
0.21a
0.25a
0.76a
0.11a
0.85
0.67a
Notes:Outstanding Loans: Total amount in USD PPP the shop owner currently owes in loans. Liquidity on Short Notice: “Are you able to borrow
an amount equal to your profits in a normal month in one week from now?” (Yes/No) Ownership: “Do you own your business premises?” and “Do
you or your family own a house or a flat?” (Yes/No) Total Savings Total amount the shop owner currently holds in savings. Insurance:
Variable = 1 if “yes” to “Are you personally currently covered by any kind of insurance?” or “yes” to “Is your business currently covered by any kind
of insurance?”. The last column shows p-values of differences in means tests using t-tests or Pearson- 2 tests for dichotomous variables (marked
by a). For variables marked by b the PPP conversion factor used is = 10, 879.11 (Source http://data.un.org).
Appendix B. Manipulation checks
See Table 2.
Table 2
Impact of treatment on financial worries and perceived stress.
(1)
Financial
Worries
(Fraction “yes”)
(2)
(3)
Hard scenarios
0.273∗∗∗
(0.050)
0.283∗∗∗
(0.049)
Constant
0.304∗∗∗
(0.036)
No
No
Stratification Controls
Additional Firm-level Controls
(4)
Perceived
Stress
(Composite Score)
(5)
(6)
0.299∗∗∗
(0.051)
0.060∗
(0.029)
0.063∗
(0.028)
0.067∗
(0.028)
0.298∗∗
(0.101)
0.283∗
(0.120)
0.295∗∗∗
(0.022)
0.363∗∗∗
(0.045)
0.383∗∗∗
(0.059)
Yes
No
Yes
Yes
No
No
Yes
No
Yes
Yes
(continued on next page)
8
Journal of Economic Psychology 79 (2020) 102198
P.S. Dalton, et al.
Table 2 (continued)
R-rquared
Sample Size
Outcome Mean in “Easy Scenarios”
Outcome SD in “Easy Scenarios”
(1)
Financial
Worries
(Fraction “yes”)
(2)
(3)
0.198
121
0.304
0.268
0.245
121
0.304
0.268
0.277
121
0.304
0.268
(4)
Perceived
Stress
(Composite Score)
(5)
(6)
0.036
121
0.295
0.162
0.096
121
0.295
0.162
0.122
121
0.295
0.162
This table presents treatment effects of the estimated impact of the financial scenarios on financial worries and perceived stress. In the first three
columns, the outcome is the proportion of four questions on financial worries the respondent answered with “yes”. Questions include “would this
create problems for your long-term financial plans?”, or “would you need to sacrifice anything which would bring long-term consequences?”. In the
fourth to sixth columns, the outcome is a composite score of ten questions as part of the Perceived Stress Scale (Cohen et al., 1983; Cohen &
Williamson, 1988). Questions include, e.g., “How often have you felt nervous or stressed?”. Stratification controls are dummy variables for the shop
space being above or below the median and dummies for the main product of the shop. Additional firm level controls are the entrepreneur’s gender,
age of the business, and its sales in a typical month. Statistical significance is highlighted by: ∗ p<0.05, ∗∗ p<0.01, ∗∗∗ p<0.001. Robust standard
errors are reported in parentheses.
Appendix C. Treatment effects
C.1. Average treatment effects on risk attitudes
See Table 3.
Table 3
Impact of treatment on risk attitudes (investment game).
Fraction of Money Invested in Investment Game
Hard scenarios
Constant
Strata Controls
Additional Firm-level Controls
R-squared
Sample Size
Outcome Mean in Easy Scenarios
Outcome SD in Easy Scenarios
(1)
(2)
(3)
0.125∗
(0.056)
0.124∗
(0.056)
0.122∗
(0.057)
0.731∗∗∗
(0.046)
0.632∗∗∗
(0.096)
0.543∗∗∗
(0.120)
No
No
0.043
119
0.731
0.337
Yes
No
0.078
119
0.731
0.337
Yes
Yes
0.099
119
0.731
0.337
This table presents treatment effects on risk attitudes as measured in an incentive-compatible investment game (Gneezy & Potters, 1997). The
dependent variable is the proportion of money invested in a risky bet. Stratification controls are dummy variables for the shop space being above or
below the median and dummies for the main product of the shop. Additional firm level controls are the entrepreneur’s gender, age of the business,
and its sales in a typical month. Statistical significance is highlighted by: ∗ p<0.05, ∗∗ p<0.01, ∗∗∗ p<0.001. Robust standard errors are reported in
parentheses.
C.2. Heterogeneous treatment effects on risk attitudes
See Table 4.
Table 4
Heterogeneity in treatment impact on risk attitudes for shops with below- and above-median business size and income volatility.
Fraction of Money Invested in Investment Game
Hard scenarios
(1)
(2)
(3)
(4)
0.209∗
(0.083)
0.208∗
(0.085)
0.207∗∗
(0.070)
0.206∗∗
(0.071)
(continued on next page)
9
Journal of Economic Psychology 79 (2020) 102198
P.S. Dalton, et al.
Table 4 (continued)
Fraction of Money Invested in Investment Game
(1)
(2)
(3)
(4)
Hard scenarios ∗ AM size
−0.189
(0.106)
−0.196
(0.109)
AM size
0.208∗
(0.084)
0.202∗
(0.088)
Hard scenarios ∗ AM volatility
−0.172
(0.111)
−0.171
(0.113)
AM volatility
0.024
(0.092)
0.014
(0.095)
Constant
Strata Controls
Additional Firm-level Controls
R-squared
Sample Size
Outcome Mean for AM Group in Easy Scenarios
Outcome SD for AM Group in Easy Scenarios
F-test (p-Value): Hard Scenarios + Interaction
0.639∗∗
(0.069)
0.513∗∗
(0.124)
0.722∗∗
(0.062)
0.552∗∗
(0.128)
No
No
0.098
119
0.847
0.240
0.768
Yes
Yes
0.125
119
0.847
0.240
0.853
No
No
0.077
119
0.731
0.337
0.685
Yes
Yes
0.135
119
0.731
0.337
0.693
This table presents treatment effects on risk attitudes for shops above and below the median of (i) the size of the business premises (Columns 1 and
2) and (ii) the reported month-to-month volatility (Columns 3 and 4). Treatment effects are estimated by simple OLS regressions including a
treatment dummy, an interaction term between treatment and explanatory variable, and a dummy of the explanatory variable. Columns (1) and (3)
show specifications without controls, while Columns (2) and (4) control for stratification dummies (a dummy for above/below median shop size and
dummies for the main product of the shop) and additional controls (the entrepreneur’s gender, the age of the business, and business sales in a typical
month). F-tests are used to show treatment effects on shops above the median of the explanatory variable. Statistical significance is highlighted by: ∗
p<0.05, ∗∗ p<0.01, ∗∗∗ p<0.001. Robust standard errors are reported in parentheses.
C.3. Treatment effects on cognitive performance
See Table 5.
Table 5
Impact of Treatment on Cognitive Functioning (Raven’s Matrices).
Fraction of Raven’s Matrices Correctly Solved
(1)
(2)
(3)
Hard scenarios
0.051
(0.043)
0.044
(0.043)
0.020
(0.042)
Constant
0.419∗∗
(0.031)
0.542∗∗
(0.076)
0.600∗∗
(0.095)
No
No
0.011
121
0.419
0.235
Yes
No
0.053
121
0.419
0.235
Yes
Yes
0.111
121
0.419
0.235
Stratification Controls
Additional Firm-level Controls
R-squared
Sample Size
Outcome Mean in Easy Scenarios
Outcome SD in Easy Scenarios
Notes: This table presents treatment effects on cognitive functioning as measured by the Standard Progressive Matrices (Raven et al., 1998). The
dependent variable is the proportion of total items solved correctly. Stratification controls are dummy variables for the shop space being above or
below the median and dummies for the main product of the shop. Additional firm level controls are the entrepreneur’s gender, age of the business,
and its sales in a typical month. Statistical significance is highlighted by: ∗ p<0.05, ∗∗ p<0.01, ∗∗∗ p<0.001. Robust standard errors are reported in
parentheses.
10
Journal of Economic Psychology 79 (2020) 102198
P.S. Dalton, et al.
Appendix D. Supplementary material
Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.joep.2019.
102198.
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