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Talking About the Weather:
Understanding Household Demand for Green
Investment
March 15, 2019
Abstract
In the summer of 2014, numerous weather events caused catastrophes throughout
Scandinavia. In the wake of these events, some investors came to hold beliefs about
expected future climate change that lie well outside the generally accepted scientific
consensus, dramatically overweighting the probability of unlikely adverse climate
scenarios. These investors traded into mutual funds that were labeled ‘green’ after
2014, but not before. This result is robust to the level of financial and environmental
knowledge, as well as pecuniary and non-pecuniary motives for investing. In sum,
weather calamities drive behaviorally biased investors into investment vehicles that
tout environmental sustainability.
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1 Introduction
Talking about the weather is often the perfect icebreaker, especially in Sweden, where
the summer of 2014 saw the highest temperatures on record throughout many parts of
Scandinavia. By the end of 2014, the Swedish Meteorological and Hydrological Insti-
tute (SMHI) had recorded new average annual high temperature records at 47 out of 100
weather stations throughout the country. These unusually high temperatures were asso-
ciated with an excessive number of weather-related anomalies, such as droughts, floods
and lightning strikes. Indeed, one of the worst wildfires in Sweden since the 1950s oc-
curred that summer, destroying an area the size of the District of Columbia.
In this paper, we show that these extreme weather events shaped opinions about the
environment, causing some individuals to hold extreme opinions about the likelihood of
future weather events. We then show that these opinions increase the likelihood of mak-
ing investments in mutual funds that claim to invest in an environmentally sustainable
way.
To do this, we administered a survey to almost 4,000 randomly selected Swedish
households in January and February 2018. The survey contains questions about both
environmental knowledge and beliefs about future climate-related calamaties such as
global temperature increases, food shortages and rising sea levels. We then match sur-
vey responses to detailed government registry data on household socio-economic status
and retirement savings choices. This allows us to connect beliefs about calamities along
with broader measures of financial and environmental knowledge and sophistication to
actual investment decisions.
Some respondents think that future climate-related catastrophes are very likely, far
more likely than would be implied by the accepted scientific consensus for worst case.
Their tendency to overweight low probability extreme events suggests that they are well
described by elements of prospect theory (Tversky and Kahneman (1992)). Indeed, we
show that increased exposure to localized weather calamities increases the probability of
holding such beliefs, which in turn demonstrates that these beliefs arise from a recency or
familiarity bias. After the summer of 2014—but not before—investors holding these be-
liefs trade their retirement accounts into positions that are heavily concentrated in mutual
fund holdings that are labeled as environmentally sustainable. The fact that their move-
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ment into “green” mutual funds occurs only after the calamities of 2014 but not before
suggests that extreme weather events caused certain individuals to overweight the prob-
ability of accelerated global warming, and that these fears then drove them into green
investments.
A natural alternative hypothesis is that investors simply see the risk return tradeoff
of green investments differently after witnessing weather calamities firsthand. To cap-
ture this, we measured alternative motives for holding green funds, such as whether the
respondent thinks green investments outperform, or whether they are driven by moral
considerations. Increased exposure to localized weather calamities does not make these
beliefs more likely. Nevertheless, our results hold even after controlling for these factors.
Moreover, we also measure the financial and environmental literacy of these respondents;
these factors have little impact on investment choices.
Our results add to a growing literature on socially responsible investing in general.
Our paper is especially connected to recent paper by Hartzmark and Sussman (2018),
which studies how the introduction of Morningstar ESG ratings affects mutual fund
flows. Their work focuses on aggregate fund flows in and out of high and low scoring
funds and suggests that investor irrationality is an important component of this effect.
Our work offers an account of the underlying behavioral mechanisms behind this result,
and is the first paper to consider how those interact with green investment decisions.
The remainder of the paper is structured as follows. First we describe the context of
the Swedish heat wave of 2014. This is provided in Section 2. Then we provide the demo-
graphics of our study population and present basic summary statistics for the questions
in our survey. This is presented in Section 3. Section 4 provides our main results. Section
5 concludes.
2 The year 2014: A Catastrophic Summer in Sweden
The weather in 2014 was exceptionally warm throughout the entirety of Sweden. The
Swedish Meteorological and Hydrological Institute (SMHI) recorded new average annual
temperature records at 47 out of 100 weather stations throughout the country.1 The sum-
1SMHI publication, The Year 2017 - Air Temperature, downloaded from www.smhi.se.
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mer of 2014 saw an extreme heatwave with average temperatures in July being 4 (in the
south) and 6 (in the north) degrees centigrade higher than normal. Figure 1 displays heat
maps obtained from SMHI for the month July, and compares the average temperature
in 2014 to those in 2012 to 2017. There were between 6 to 26 “heat wave days” (defined
as days with temperatures above 25 Centigrades) in the ten most northern weather sta-
tions above the Arctic Circle during 2014. By comparison, the norm would be zero or
only a handful of days in neighboring years. Hot air and humidity also released a record
number of lightning strikes, adding to the dramatic weather conditions.
Figure 1 here
High temperatures in July caused drought in many areas and are thought to be responsi-
ble for the worst Swedish wildfire since the 1950s. The fire eventually destroyed approx-
imately 150 square kilometers (or more than 37,000 acres, similar in size to the District of
Columbia) of forest and took eight months to extinguish. But more extreme weather was
to follow. In southern Sweden, there was a record of 100 millimeters of rainfall in Malmo
on a single day (August 31st), and later in the fall heavy rains in the west side of Sweden
caused severe floods in many cities, shutting down roads and trains and prompting citi-
zens to evacuate their homes. Figure 2 illustrates the severity of this shock by making a
similar yearly comparison for rainfall in the month of August between 2012 to 2017.
Figure 2 here
These events created a tremendous upsurge in public discourse surrounding climate
change. Figure 3 plots the number of average high temperature records for the 100
weather stations throughout the country along with then number of articles in the four
largest Swedish newspapers (Aftonbladet, Dagens Nyheter, Expressen and Svenska Dag-
bladet) that contain the phrase “climate change” in them.2 The spike in news relevance
beginning mid-way through 2014 and persisting through 2017 clearly illustrates that peo-
ple were talking about the weather in Sweden.
Figure 3 here
2Relatedly, Choi, Gao, and Jiang (2018) show that Google searches on climate change spike in the wakeof local high temperatures.
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Concomitantly, there was rapid growth in the number of financial products describ-
ing themselves as adhering to environmental, social or governance standards (ESG), es-
pecially throughout Europe. In Sweden, the Swedish Pension Authority started to label
ESG mutual funds in 2004, when 7% of around 700 mutual funds in the offering were
marked as such. It took almost ten years for their proportion to double to 13% in 2013,
but it quickly doubled again to 31% in 2016. As Figure 3 illustrates, By the end of 2017,
36% of all funds in the pension system have a pronounced ESG strategy of some sort,
even though there is no set industry standard for these guidelines.
3 Data and Empirical Setting
Our data are collected and matched in four steps. First, we worked in conjunction with
Statistics Sweden to administer a survey in January and February 2018. The survey in-
vitation was sent out by mail to 20,000 respondents, but respondents completed the sur-
vey online. Second, these survey results were matched to each respondent’s registry data
from various sources, including the Swedish Tax Authority, which is maintained by Statis-
tics Sweden. This step allows us to combine their environmental views with a large set
of demographic and wealth characteristics, including in which of the 290 municipalities
where the respondent lives. In the third step, we add the complete transaction histo-
ries—this includes the timing and size of any trades as well as the holdings at a point
in time—from the Swedish Pension Authority (SPA), the details of which are described
below. From the SPA, we also obtain data on fund characteristics, which allows us to
determine whether a fund is labelled as an ESG investment choice. The final step merges
on data from the Swedish Meteorological and Hydrological Institute (SMHI). From the
SMHI, we obtain weather warnings issued between 2010 and 2017. Warnings are issued
at a regional level; there are 21 distinct administritive regions. The warnings are graded
from 1 (some risks and disturbances to transport and other parts of society); 2 (danger,
damage and larger disturbances); and 3 (serious danger, serious damage and major dis-
turbances). We match regional warning data to the municipalities for which we have
survey data, which allows us to provide direct evidence of how exposure to the weather
calamities of 2014 affected public opinion.
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In the remainder of this section, we explain the Swedish pension system in more detail
and the supply of ESG funds. We then show the data on individual allocations in our
sample and explain our survey measures and results sorted on investor characteristics.
3.1 The Swedish Pension System
In June of 1994, the Swedish Parliament passed legislation that transformed the public
pension system from one based on defined benefit to one based partly on defined contri-
bution. A full account of this transition is beyond the scope of this paper; our purpose
here is to highlight the key features as they pertain to our analysis.3 Prior to 1994, the
system was a flat-rate universal benefit system augmented by an earnings-related sup-
plement. The period between 1994 and 1999 allowed for the accumulation of two types
of accounts. One is a defined contribution plan funded on a pay-as-you-go basis based
on a contribution rate of 16% of labor income, analogous to Social Security in the United
States. An additional 2.5% of labor income was credited to an individual account man-
aged by the pension authority. This operates in a manner similar to a 401(k) plan in the
United States, but as part of the state pension, rather than an occupational pension as is
common with 401(k) plans. Starting from 2000, individuals were allowed to control how
this account was invested by allocating this portion of their account across as many as
five different funds.4 In 2000, there were 456 funds available, a number that has grown
to 892 at the end of 2017. Swedish Pension Authority (SPA) started to label ESG funds in
2004, when 7% of around 700 funds in the offering were marked ESG funds. By the end
of 2017, 36% of all funds in the pension system have a pronounced ESG strategy of some
sort, even though there is not a set industry standard for these guidelines.
3The 1990s were a period of tremendous economic change and upheaval in Sweden more generally. Theexact details of the transition are complicated and are discussed at length in Palme, Sunden, and Soderlind(2007) and Palmer (1995).
4Cronqvist and Thaler (2004) and a follow-up paper Cronqvist, Thaler, and Yu (2018) discuss the roleof nudging people to make a choice or falling into the default fund. Anderson and Robinson (2018) usea similar approach to the one considered here in documenting how beliefs about fees and performanceaffected pension choices.
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3.2 Green Investment Options in the Swedish Pension System
The SPA maintains complete records of all trades and portfolio balances in the pension
system going back to 2000. We obtain these records for those respondents who completed
our survey. In addition, we manually collect fund characteristics from the catalogue that
is printed and mailed to first time savers as well as listed on the SPA website. The ESG
label was introduced in 2004, and lets fund companies label themselves as investing with
restrictions determined by ethical or environmental considerations (so-called negative se-
lection funds as in Hong and Kacperczyk (2009)). This information is required to be clearly
provided in all information and marketing about the funds, but there is no standard or
minimum requirements given by the SPA to which funds must adhere in order to earn
this label.
Figure 4 here
Figure 4 shows how total pension savings in our survey have evolved over time across
traditional and ESG investments. Prior to 2014, the amount of money trading into ESG
funds was tiny relative to the amount trading into non-ESG funds. For example, in 2012
58 million SEK was traded into non-ESG funds while only 3 million SEK were traded
into ESG funds. After the summer of 2014 the amount flowing into ESG funds grows
dramatically. In 2015, 31 million SEK were traded into ESG funds while 44 million SEK
were traded into non-ESG funds. By 2016 the trading into ESG funds matches non-ESG
funds, and by 2017 the total outstanding balances held in ESG funds outweigh the amount
held in non-ESG funds.
Panel A of Table I shows spectacular growth in the offering of ESG funds from 2010
to 2017, and Figure 3 traces out the full history from 1999 (which are the funds given
in the catalogue in 2000). In 2010, there was 839 funds offered in the system of which
89 were ESG funds - a fraction of almost 11%. Panel B of Table I shows a snapshot of
the porfolio holdings as of December 2017. We have 3,667 respondents after matching
with the pension data, of which 1,193 never made an active choice and so were still in
the default fund as of 2017.5 It is difficult to tell if respondents chose the default fund
because of its ESG profile, so we confine the rest of the analysis to those actively chosing5The default fund technically consists of two funds: an equity fund and a bond fund to which savers are
allocated depending on age.
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funds within the pension system. The third to the fifth row of Panel B sorts investors
in accordance with their ESG holdings defined for different weights. There were 1,827
investors with a positive share of ESG labelled funds in their pension portfolio, and 647
with no weight. At the threshold of at least 50% ESG funds, there were 1,434 investors
owning ESG and 1,040 not. Finally, 840 respondents held all ESG labelled funds at the
end of 2017, which corresponds to 34% of all those selecting their own funds—a fraction
that is close to the overall offering of funds.
Table I here
3.3 Surveying Swedish Environmental Beliefs
To measure environmental literacy and see how it squares with perceived literacy and
a general understanding of financial matters, we invited 20,000 Swedish households by
regular mail to participate in an online financial and environmental literacy survey.6 A
total of 4,257 respondents completed the survey where 3,993 remain after having deleted
incomplete responses, or almost 20% of the invited sample; this shrinks to 3,667 after hav-
ing matched them to the pension data. The survey is matched to registry data obtained
from Statistics Sweden, from where we obtain standard information on gender, age and
income, but also detailed information about level and subject of study and the area in
which people live (divided into 291 districts/municipalities). The location of individuals
also allows us to match on Green Party election outcomes, which is a control variable used
alongside population density in the regression analysis that follows. Table II provides a
demographic breakdown of the respondents.
Table II here
Relative to the overall Swedish population, we have an over-representation of older,
wealthier, better-educated respondents in our sample. Almost half of the individuals in
our sample went to college and 57% of our respondents are 45 or older, while only 41%
of the Swedish working age population is in this age range. Also, women are slightly
over-represented in our study. Statistics Sweden offers sampling weights that allow us to
6The results of the financial and environmental test and its questions are discussed in Anderson andRobinson (2019).
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adjust our regressions for these sampling differences, so that our results can be taken as
though they are drawn from a stratified random sample of the population.
Overall, we find that about a third in our sample is in the default fund, which is sim-
ilar to the results in Anderson and Robinson (2018). Default fund investors tend to be
lower income, young females. The default fund has since inception a clear ESG profile,
having an governance policy that not only screen companies, but also one that involves
expressing opinions about sustainable behavior through meetings with management and
shareholder voting. In this respect, one can argue that the default fund is a low cost al-
ternative to obtain an ESG portfolio for most investors, but it is of course also likely that
many investors fall into default because of their lack of interest in investments in general.
For this reason, we focus on the holdings and trades of active investors, which are those
that at some point in time chose a different portfolio. At the end of 2017 there were 3,667
individuals in our sample, of which 2,474 had chosen their portfolio.
The three last columns of Table II shows the fraction in our sample that had some
(a non-zero weight), most (over 50%) or all (100%) of their portfolio invested in an ESG
labelled fund. A majority of 74% had a non-zero portfolio weight in an ESG fund, but
that might not be so surprising given that the menu offering contains over 36% ESG funds
together with the fact that most people hold more than one fund. Increasing the threshold
to be above 50% decreases the share considerably, and about one-third of the sample hold
an ESG-only portfolio. Women and those having studied environmental science are more
likely to invest in these funds. For income, we find that wealthier individuals are more
likely to invest in ESG funds. We find that all in the youngest age group hold ESG funds,
but 98 per cent of these individuals are in the default fund. This suggests caution in
interpreting this number. In the next section we show that environmental concerns are
greater for younger and lower income respondents, but the results in Table II actually
shows that the fraction of all ESG portfolios increases somewhat with age.
3.4 Motivations for Green Investment
Our survey includes four basic types of questions: financial literacy questions, environ-
mental literacy questions, questions on household behavior, and questions that gauge
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one’s own perceptions of financial literacy and environmental matters.7
The main focus of this study is to measure possible motivations for holding green in-
vestments, where we specifically focus on climate change calamities. Even if temperature
change has been in focus of the policy discussion, its effects on food shortage and sea level
rise has been powerful illustrations of the consequences of extended periods of drought
and melting ice in the arctic region. We ask:
• “In the next 20 years...”
– “The average temperature on earth rises by more than one Centigrade”
– “Food shortage will increase”
– “The sea level will increase by over one meter”
The responses on a five point Likert scale ranging from “Very Unlikely” to “Very
Likely” are displayed in Figure 5 and shows that 80% of the respondents in our sample
finds a steep temperature change likely or very likely, 65% believes that food shortage will
increase and 47% that the world sea level will increase by more than one meter. The en-
vironmental calamity outcomes are obviously difficult to predict, but over a twenty year
horizon, they tend to be pessimistic outcomes compared to current scientific consensus.
Figure 5 here
The fact that almost 40% of respondents find that a one degree temperature change
is very likely within such a short time frame is surprising given current scientific pre-
dictions. According to the United Nations and the Intergovernmental Panel of Climate
Changes (IPCC), it is unlikely that the average temperature would rise by one degree
centigrade in twenty years, since the current temperature increase is measured to be
around a rate of 0.17 Centigrades per decade, and historically have been about one Centi-
grade since the early 1920’s. A one-half centigrade increase in global average temperature
per decade would be considered a worst-case scenario by these estimates.
Likewise, a sea-level increase of one meter in a twenty year period far exceeds con-
sensus estimates for sea level increases. For example, the IPCC report in their worst cace
7The financial literacy test is a modified version explained in Anderson and Robinson (2018) which isbased on Lusardi and Mitchell (2007) and Lusardi and Mitchell (2011).
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scenarios from 2014 that there is a 95% probability that the sea level rise will be less than
one meter by 2100.
Broadly speaking, global hunger and undernourishment have been decreasing over
the last twenty years. According to FAO, IFAD and WFP (2015), food shortage in many
regions of the world can be closely tied to conflicts and natural disasters, but generally
diminishes with economic growth. Thus, a belief in increased food shortage is also likely
to indicate a pessimistic outlook tied to environmental concerns.
We create a measure of a respondent’s focus on climate calamities by summing the
number of “Very Likely” responses for each of the three questions. These responses are
correlated. Of those finding a one degree temperature rise very likely, 46% and 29% also
foresee food shortages and a sharply rising sea level. Our measure therefore covers a
somewhat larger fraction, 47% of the sample compared to the 39% of those only fearing a
rise in temperature. A more comprehensive analysis of the individual responses to these
questions can be found in Anderson and Robinson (2019), in which the survey method
and results are described in detail.
Apart from fears, there is a range of other motives to invest into ESG. Those previously
considered can be categorized into pecuniary (“Doing well”) and non-pecuniary (“Doing
good”). Pecuniary motives are those that include aspects of behavior that eventually will
lead to better economic outcomes for everyone. The non-pecuniary motives are different
from the pecuninary in that they not necessarily lead to better economic outcomes, but
is a goal worthwhile sacrificing for a better or cleaner world. This set of motivations are
often also referred to as “warm-glow” motivations in that they also may give important
reputational externalities.
We attempt to measure the alternative mechanisms leading to ESG investments by
attitudes towards two statements in our survey, each measured on a five point Likert scale
ranging from “Strongly Agree” to “Strongly Disagree”. We code a dummy equal one for
“Strongly Agree” ( fraction reported within parenthesis) to the following statements:
• Pecuniary (14%)
– “Environmentally sustainable investments generate higher returns in the long
run”
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• Non-Pecuniary (25%)
– “A clean planet is more important for me than economic welfare”
As within different forms of weather calamities, there is considerable overlap between
the two measures of pecuniary motivations. Almost two-thirds of those thinking that
green investments generate higher returns also thinks that a clean planet is more impor-
tant than economic welfare. Our measure of calamities also overlaps the two other moti-
vations, where about one-third prefers a clean planet over economic welfare and one-fifth
believes in higher returns from green investments.
Table III reports a set of Probit regressions to show how the motivations are related
to demographics and geography. In Columns (1) through (4) use the Calamity dummy
as the dependent variable. Column (1) shows that fears of environmental fears are more
common among the young, among lower-income individuals, and among females. En-
vironmental fears are less common among those who have studied economics and busi-
ness. Also, individuals with higher environmental and financial literacy scores think that
future calamities are very likely.
Table III here
In Column (2) we add the beliefs about the test scores which we solicit immediately
after taking the test. When the beliefs are added, the test scores themselves become in-
significant. This is a result that echoes that of Anderson, Baker, and Robinson (2017), who
find that someone’s beliefs about their knowledge of the domain in question (overestima-
tion) is more important for predicting their behavior and beliefs than what they actually
know. Column (2) shows that self-perceptions of environmental and financial literacy are
related to belief in future calamities.
We next test how weather events may shape climate fears. We use the geographical
variation where respondents live in the cross-section and local weather warnings data to
which they were more likely to be exposed in 2014. It is of course likely that individuals
also react to weather shocks outside of their geographical proximity, either through media
exposure or because personal connections to other parts of the country make weather
events there salient. This would, all else equal, make our results weaker. We separately
include milder Class 1 warnings in Column (3) and more severe Class 2 warnings in
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Column (4).8 We find that people living in areas of a greater number of Class 2 warnings
are much more likely to hold fears of climate calamities, whereas this is not the case for
the milder Class 1 warnings.
In order to rule out that the weather warnings do not coincide with other motives, we
separately run regressions with Pecuniary and Non-Pecuniary motives for green invest-
ments as the dependent variable in Column (5) and (6). Although these motivations share
many of the characteristics, as for instance being more popular among lower-income,
young females, but having been exposed to weather warnings does not explain holding
these beliefs.
The fact that exposure to a greater number of weather anomalies predicts holding
the belief that future weather calamities are extremely likely, but has no effect on the
other motivations, is important for two reasons. First, it provides support for the key
assumption behind our identification strategy–that the extreme weather of 2014 was a
shock that changed opinions. Second, it provides evidence that these changes in opinion
work through a recency or familiarity bias.
4 Actual Fund Choices
To investigate the propensity to choose ESG funds, we use Probit regressions where the
dependent variable takes the value of one if the holdings or trades correspond to one of
the three ESG specifications—Some, Most, or All—defined in Panel B and C of Table I.
We first present the results for the year 2017 and then contrast panel estimates from 2012
to 2014 with those from 2015 to 2017.
4.1 Fund Holdings and Trades in 2017
In Table IV we present results from Probit regressions on holdings of ESG for all individ-
uals that made a choice (who were not in the default fund at the end of 2017). In Column
(1) through (5), the dependent variable labelled “All” takes the value of one if the fund
portfolio contained only ESG funds. The regression shows that owning ESG funds are
more likely among people living in areas of higher Green party votes, but insignificant
8There are very few Class 3 warnings in the data and none in 2014.
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for all other independent variables we consider. The specification in Columns (1) through
and (2) is made with our without controlling for the type of fund. Fund controls are the
portfolio weights to four fund type categories: Equity, Mixed, Bond and Target funds.
The climate Calamities dummy is introduced in Columns (3) and is strongly significant,
and remains so when introducing separate dummies for pecuniary (higher green returns)
or non-pecuniary (cleaner planet) motives in Column (4) and (5). The point estimates re-
veal that those strongly agreeing to quick global warming, increased food shortage and
sharp sea level rise are around 5% more likely to hold an all ESG portfolio at the end of
2017.
Table IV here
In Column (6) of Table IV, we re-define the dependent variable with the condition to
have at least 50% ESG labelled funds in the portfolio at the end of 2017. The results are
broadly the same as having a 100% weight, if not somewhat stronger. Finally, when we
use the lowest threshold on defining ESG which is a non-zero weight marked “Some” in
Column (7), the correlation with climate Calamaties are considerably weaker and cut in
half. The overall results therefore point to that weather Calamities is associated with a
conscious choice of a portfolio tilt towards ESG funds.
One possible explanation for the preceding results is that the tilt towards green funds
arises because funds reclassify themselves, not because investors necessarily make active
choices. Given the fact that the number of ESG-labelled funds has grown rapidly since
2014, this possibility bears careful consideration.
To control for this possible explanation, in Table V we use trades (or rebalances) of
portfolios into ESG labelled funds as the dependent variable. (Specifically, the trades
summarized in Panel C of Table I.)9 In Column (1) and (2) we first model the decision to
trade by creating a dummy equal one if the individual traded during the year, and zero
otherwise. The results show that the average trader is an older male with higher financial
literacy. When introducing the pecuniary motives and calamities in Column (2), we find
that they are less likely to hold non-pecuniary beliefs.
Table V here9When creating dummy variables for the corresponding trade categories, we first average the ESG hold-
ings across trades in cases there were more than one trade.
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In Column (3) of Table V, we only consider the 382 trades made during 2017 and again
find that the climate calamities measure is highly significant. The point estimate implies
that respondents having climate fears are 15.6% more likely to trade into an all ESG fund.
In Column (4), we present the Probit regression for the 50%, and find a weaker effect. In
Column (5), using the non-zero threshold as the dependent variable, we can not establish
a relation between climate calamaties and porfolio trades at all.
To summarize, in this subsection we establish a link between fears of climate calami-
ties and the propensity to both hold and trade ESG funds in the year 2017. In line with the
hypothesis that people indeed act on their beliefs, we find that this effect is much stronger
for portfolios that are concentrated to ESG holdings. We find weak or no evidence for
alternative mechanisms driving this behavior, such as for pecuniary or non-pecuniary
reasons.
4.2 Fund Holdings and Trades Before and After the 2014 Heat Wave
In the next step, we investigate to which extent the survey responses can explain recent
years portfolios and trades compared to those preceding the climate shock in 2014. We
divide the time-series of portfolio holdings into two periods: those occuring between
2015 and the end of sample in 2017, and those in the preceding three years 2012 to 2014.
We control for year fixed effects and cluster standard errors on the individual level in all
specifications.
Table VI display the results on holdings. Columns (1) to (2) shows separate regres-
sions for the time periods before and after the weather event using the widest definition
of ESG fund holdings, “Some” where all portfolios with a non-zero weight is included
in the specification. We find no evidence of a relation between weather calamities and
ESG investors. In Column (3) and (4) we repeat this analysis and change the dependent
variable to be “Most” ESG holdings (above 50%), and in Column (5) to (6) “All” (100%).
The results show that portfolio holdings are not affected by fears of weather calamities in
the years before 2014, but is strongly related to those after the weather event.
Table VI here
In Table VII, we repeat the analysis for trades and introduce the difference in fees in the
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portfolio that was traded to compared to the previously held portfolio as a control vari-
able. This is to rule out the possibility that our results would be driven by a motivation
to minimize fees. The results are similar, but stronger compared to those in Table VI.
We find no relation between climate fears and trades for low intensity ESG trades, but
a very strong effect for high intenstiy rebalances. The marginal probability estimate for
our Calamity dummy reveals that there is a 10% increased chance that those with climate
fears traded into an ESG portfolio in period after 2014, but close to zero before.
Table VII here
5 Discussion and Conclusion
This paper connects extreme weather events to the financial decisions made by the people
who are exposed to them. It not only provides evidence that extreme weather calamities
affect both the supply and the demand for environmentally responsible financial invest-
ments, but it explores a variety of economic mechanisms by which this might occur.
The increased salience of environmental issues induced by being exposed to extreme
weather events could operate through several channels. One channel is that individu-
als might believe, rightly or wrongly, that in the future the returns to environmentally
sustainable investments will be higher than previously expected. Altering the perceived
risk/return tradeoff of so-called green investments might induce them to increase their
holdings for purely pecuniary reasons, regardless of any inherent concern for the envi-
ronment or their fellow man. Our results provide little support for this explanation.
Alternatively, scenes of environmental devastation might cause a moral awakening,
whereby investing sustainably becomes a moral imperative. Raising social consciousness
could cause individuals to increase their allocations to green investments regardless of
whether the expected financial returns are higher or lower than previously thought. Our
results also provide little support for this explanation.
Instead, our results suggest that some individuals, after being exposed to extreme
weather events, overweight the probability of extremely unlikely future weather events
will occur. It is these individuals, and not others, who are more likely to tilt their re-
tirement portfolios towards green investments. Thus, elements of prospect theory seem
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important for understanding the demand for green investments. This suggests that be-
havioral channels are likely an important mechanism for understanding how exposure to
extreme events affects portfolio choice.
These findings are important for several reasons. First, as climate change induces
increasing weather volatility, the exposure to extreme weather events is likely to increase
in the future. Thus the scope for overweighting to play into decision-making is likely
to be important going forward. More generally, households and institutional investors
alike are increasingly being asked to make investment choices based not only on standard
pecuniary risk and return tradeoffs but also on the environmental or social performance
of companies and mutual funds. The difficulty in measuring these alternative dimensions
leaves them even more susceptible to the types of behavioral biases that have been shown
to affect financial decision-making in other contexts.
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References
Anderson, Anders, Forest Baker, and David T. Robinson, 2017, Precautionary savings,retirement planning and misperceptions of financial literacy, Journal of Financial Eco-nomics 126, 383–398.
Anderson, Anders, and David T. Robinson, 2018, Who feels the nudge? Knowledge, self-awareness and retirement savings decisions, Swedish House of Finance working paperNo. 17-15.
, 2019, Knowledge, fear and beliefs: Understanding household demand for greeninvestments, Swedish House of Finance working paper.
Andersson, Mats, Patrick Bolton, and Frederic Samama, 2016, Hedging climate risk, Fi-nancial Analyst Journal 72, 13–32.
Choi, Darwin, Zheyu Gao, and Wenxi Jiang, 2018, Attention to global warming, Workingpaper, SUHK Business School, The Chinese University of Hong Kong.
Cronqvist, Henrik, and Richard H. Thaler, 2004, Design choices in privatized social-security systems: Learning from the Swedish experience, American Economic Review,Papers and Proceedings 94, 424–428.
, and Frank Yu, 2018, When nudges are forever: Inertia in the swedish premiumpension plan, Working paper, University of Chicago.
FAO, IFAD, and WFP, 2015, The state of food insecurity in the world 2015. Meeting the2015 international hunger targets: taking stock of uneven progress., Rome, FAO.
Hartzmark, Samuel M., and Abigail B. Sussman, 2018, Do investors value sustainability?a natural experiment examining ranking and fund flows, Working Paper, University ofChicago.
IPCC, 2014, Climate change 2014: Synthesis report. Contribution of working groups I,II and III to the Fifth Assessment Report of the Intergovernmental Panel on ClimateChange [Core writing team, R.K. Pachauri and L.A. Meyer (eds.)]., IPCC, Geneva,Switzerland, 151 pp.
Lusardi, Annamaria, and Olivia S. Mitchell, 2007, Baby boomer retirement security: Theroles of planning, financial literacy, and housing wealth, Journal of Monetary Economics54, 205–224.
, 2011, Financial literacy and planning: Implications for retirement well-being,in Annamaria Lusardi, and Olivia S. Mitchell, ed.: Financial Literacy: Implications forRetirement Security and the Financial Marketplace . pp. 17–39 (Oxford University Press)Michigan Retirement Research Center, WP 2015-108.
Palme, Marten, Annika Sunden, and Paul Soderlind, 2007, How do individual accountswork in the swedish pension system?, Journal of the European Economic Association 5,636–646.
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Tversky, Amos, and Daniel Kahneman, 1992, Advances in prospect theory: Cumulativerepresentation of uncertainty, Journal of Risk and Uncertainty 5, 297–323.
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Table I: Fund Menu, Portfolio Holidings and Rebalances of ESG Funds
This table presents the number of individuals in sample making changes (portfolio rebalances) within the Swedish PensionSystem in 2017. The first row displays the number of individuals in each bucket of number of changes ranging from 0 to 5 and over5 trades, and shows that 427 individuals of 3,667 made at least one trade. The second row shows the same distribution conditionalon that the portfolio change contained at least partially (non-zero weight) an ESG labelled fund. The third row dispalys the samedistribution with the condition that the portfolio change was made to an all ESG labelled portfolio. Data of portfolio changes andfund holdings are obtained from the SPA.
Panel A: Number of funds / YearType of funds 2010 2011 2012 2013 2014 2015 2016 2017All available SPA funds 839 873 854 885 886 881 873 892of which ESG 89 99 99 118 146 187 270 325Fraction ESG 10.6% 11.3% 11.6% 13.3% 16.5% 21.2% 30.9% 36.4%
Panel B: Number of funds in portfolio 2017Holdings (0) (1) (2) (3) (4) (5) ESG>0 TotalDefault fund 1,193 1,193Active 865 453 316 307 533 1,827 2,474Some ESG funds 647 389 343 288 285 522 1,827 2,474Most ESG funds 1,040 389 292 208 193 352 1,434 2,474All ESG funds 1,634 389 240 102 57 52 840 2,474
Panel C: Number of portfolio changes 2017Changes (0) (1) (2) (3) (4) (5) (>5) TotalFull sample 3,285 264 50 21 11 5 31 3,667Changed to some ESG funds 65 231 35 14 7 5 25 382Changed to mostly ESG funds 168 166 27 7 3 3 8 382Changed to all ESG funds 260 109 8 1 2 1 1 382
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Table II: Sample Characteristics
This table reports sample proportions and summary statistics across some key characteristics of the responses of the surveyquestions about environmental beliefs as well as ESG pension fund holdings. Column (1) shows the sample proportions and Column(2) the corresponding population average for Sweden. Columns (3) through (5) present the proportions of respondents stronglyagreeing to the following three groups of statements: Calamities takes the value of one if agreeing to: “How likely do you thefollowing globals scenarios are in the next twenty year”, followed by the following three statements “The average temperature onearth rises by more than one degree Celsius”, “Shortage of food will increase” and “The seawater level will increase by over onemeter” and zero otherwise. Pecuniary takes the value of one if agreeing to the following statement: “In the long run, environmentallysustainable investments generate higher returns” and zero otherwise. Non-Pecuniary takes the value of one if strongly agreeing to:“A clean planet is more important for me than economic welfare”. Column (6) shows the proportion of individuals in the pensiondefault fund. Column (7) shows the fraction of respondents not in the default fund, holding a non-zero weight in an ESG labelledfund as of the end of 2017. Columns (8) and (9) dispaly the same fraction, but defining the holdings to be over 50% (Most) or 100%(All) of their portfolio in an ESG fund. There are 3,667 individuals in the full sample (first six columns), of which 2,474 actively chosetheir portfolio (the last three columns).
Sample Pop. Green Beliefs Def. ESG holdingsProp. Prop. Calamities Pecuniary Non-Pec. Fund Some Most All
Overall 100.00 100.00 0.47 0.13 0.26 0.33 0.74 0.58 0.34Pop. Wtd. . . 0.49 0.15 0.25 0.41 0.72 0.57 0.34
GenderMen 48.70 51.10 0.45 0.12 0.23 0.30 0.75 0.56 0.32Women 51.30 48.90 0.49 0.15 0.29 0.35 0.73 0.60 0.36
Age18-24 4.23 15.50 0.61 0.11 0.23 0.98 1.00 1.00 1.0025-34 15.19 22.90 0.60 0.16 0.26 0.74 0.57 0.48 0.3035-44 19.55 20.80 0.51 0.16 0.30 0.39 0.74 0.56 0.3345-54 27.02 22.00 0.42 0.12 0.24 0.17 0.77 0.59 0.3255-65 34.01 18.90 0.42 0.12 0.25 0.15 0.73 0.59 0.36
Income0-111 8.45 25.00 0.56 0.16 0.33 0.73 0.71 0.54 0.25111-287 34.14 24.90 0.48 0.15 0.27 0.40 0.70 0.57 0.38287-399 31.20 25.20 0.45 0.13 0.24 0.25 0.74 0.57 0.34399+ 25.63 25.00 0.45 0.10 0.24 0.17 0.78 0.61 0.32
EducationSome school 5.40 17.40 0.49 0.16 0.19 0.32 0.69 0.57 0.34High school 39.19 44.00 0.45 0.12 0.21 0.29 0.70 0.55 0.34College 55.03 38.60 0.48 0.14 0.30 0.35 0.77 0.60 0.34Studied Env/Bio 1.88 . 0.59 0.19 0.35 0.42 0.75 0.60 0.38Studied Econ/Bus 10.31 . 0.41 0.14 0.22 0.25 0.71 0.54 0.32
LocationUrban 34.14 . 0.51 0.14 0.29 0.39 0.78 0.60 0.35Rural 65.86 . 0.45 0.13 0.24 0.29 0.72 0.57 0.34
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Table III: Motives to be Green: Calamaties, Pecuniary and Non-Pecuniary
This table reports the results of Probit regressions where the dependent variable takes the value of one if the response is“Strongly agree” to one or several statements. Calamities takes the value of one if agreeing to: “How likely do you the followingglobals scenarios are in the next twenty year”, followed by the following three statements “The average temperature on earth risesby more than one degree Celsius”, “Shortage of food will increase” and “The seawater level will increase by over one meter” andzero otherwise. Pecuniary takes the value of one if agreeing to the following statement: “In the long run, environmentally sustainableinvestments generate higher returns” and zero otherwise. Non-Pecuniary takes the value of one if strongly agreeing to: “A cleanplanet is more important for me than economic welfare”. Dependent variables Warnings Class 1 and 2 counts the number of weatherwarnings in each category in 2014, Perceived and Actual financial and environmental literacy denote self-assessed and actual scoreon a five question test. Log income refers to disposal income, Age is divided by ten and Female denotes a dummy equal to one forwomen, zero otherwise. Urban and Green denote population density and the share of green party voters in the municipality of therespondent. University, ECON and ECO student are education indicator variables for subjects having a university degree or havingstudied Economics/Business or Biology/Geography/Environmental science at any level since high school. Sampling weights areused.
(1) (2) (3) (4) (5) (6)VARIABLES Calamities Calamities Calamities Calamities Pecuniary Non-Pec.
Warnings Class 2 0.015*** 0.003 0.008(0.006) (0.004) (0.005)
Warnings Class 1 -0.000(0.001)
Perceived Env. Lit. 0.043*** 0.043*** 0.044*** 0.029*** 0.029***(0.011) (0.011) (0.011) (0.008) (0.010)
Perceived Fin. Lit. 0.001 0.001 0.001 0.006 -0.007(0.011) (0.011) (0.011) (0.008) (0.009)
Env. Lit. 0.022** 0.009 0.009 0.008 0.013* 0.033***(0.010) (0.010) (0.010) (0.010) (0.007) (0.009)
Fin. Lit. 0.024*** 0.015 0.015 0.015 -0.012 0.002(0.009) (0.010) (0.010) (0.010) (0.008) (0.009)
Log Income -0.037*** -0.037*** -0.037*** -0.036*** -0.010* -0.035***(0.009) (0.009) (0.009) (0.009) (0.006) (0.008)
Age -0.048*** -0.055*** -0.055*** -0.056*** -0.011* 0.006(0.008) (0.008) (0.008) (0.008) (0.006) (0.007)
Female 0.053*** 0.067*** 0.067*** 0.068*** 0.035** 0.069***(0.020) (0.020) (0.020) (0.020) (0.014) (0.017)
Urban 0.008 0.008 0.008 0.003 0.005 0.006(0.008) (0.008) (0.008) (0.008) (0.006) (0.007)
Green 0.001 0.001 0.001 0.003 0.002 0.008***(0.004) (0.004) (0.004) (0.004) (0.003) (0.003)
University -0.023 -0.027 -0.027 -0.032 0.005 0.040(0.069) (0.070) (0.070) (0.069) (0.046) (0.063)
ECO student 0.069 0.050 0.050 0.044 0.046 0.045(0.074) (0.073) (0.073) (0.073) (0.057) (0.063)
ECON student -0.071** -0.063** -0.063** -0.062* 0.021 -0.015(0.031) (0.031) (0.031) (0.031) (0.024) (0.027)
Observations 3,667 3,667 3,667 3,667 3,667 3,667Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
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Table IV: Holding Green Mutual Funds in 2017
This table reports the results of Probit regressions where the dependent variable takes the value of one if the respondent hasmore than 50% of their government premium pension savings invested in ESG labelled funds; zero otherwise. The independentvariables follow those from Table III. The sample represents the 2,277 respondents who were not in the default fund at the endof 2017. Independent variables follow those of Table III. Data of portfolio changes and fund holdings are obtained from the SPA.Sampling weights are used.
(1) (2) (3) (4) (5) (6) (7)VARIABLES All All All All All Most Some
Calamities 0.046** 0.043* 0.045* 0.042* 0.025(0.023) (0.023) (0.023) (0.024) (0.022)
Pecuniary 0.030 0.038 0.022 0.011(0.036) (0.038) (0.039) (0.035)
Non-Pecuniary -0.019 0.018 0.053**(0.028) (0.030) (0.026)
Perceived Env. Lit. -0.001 -0.001 -0.003 -0.004 -0.003 -0.003 0.002(0.013) (0.013) (0.013) (0.013) (0.013) (0.014) (0.013)
Perceived Fin. Lit. -0.016 -0.017 -0.017 -0.018 -0.018 -0.008 -0.014(0.013) (0.013) (0.013) (0.013) (0.013) (0.014) (0.013)
Env. Lit. -0.005 -0.003 -0.003 -0.003 -0.003 0.001 -0.005(0.012) (0.012) (0.012) (0.012) (0.012) (0.013) (0.012)
Fin. Lit. -0.004 -0.003 -0.004 -0.004 -0.004 -0.012 -0.001(0.012) (0.012) (0.012) (0.012) (0.012) (0.013) (0.012)
Log Income 0.013 0.012 0.013 0.013 0.013 0.017 0.017(0.014) (0.014) (0.014) (0.014) (0.014) (0.015) (0.013)
Age 0.014 0.025** 0.027** 0.027** 0.028** 0.036*** 0.041***(0.012) (0.012) (0.012) (0.012) (0.012) (0.013) (0.012)
Female 0.012 0.001 -0.001 -0.002 -0.001 0.007 -0.038(0.024) (0.024) (0.024) (0.024) (0.024) (0.026) (0.023)
Urban -0.004 -0.001 -0.001 -0.001 -0.001 0.015 0.024***(0.009) (0.009) (0.009) (0.009) (0.009) (0.010) (0.009)
Green 0.009** 0.009** 0.009** 0.009** 0.009** 0.004 0.002(0.004) (0.004) (0.004) (0.004) (0.004) (0.005) (0.004)
University 0.016 0.010 0.009 0.007 0.008 0.086 0.002(0.087) (0.085) (0.085) (0.085) (0.086) (0.087) (0.088)
ECO student 0.093 0.083 0.083 0.084 0.086 0.012 0.078(0.094) (0.093) (0.093) (0.093) (0.094) (0.098) (0.065)
ECON student -0.032 -0.033 -0.031 -0.033 -0.033 -0.040 -0.026(0.034) (0.034) (0.034) (0.034) (0.034) (0.038) (0.035)
Observations 2,474 2,474 2,474 2,474 2,474 2,474 2,474Fund controls No Yes Yes Yes Yes Yes Yes
Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
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Table V: Choosing Green Mutual Funds in 2017
This table reports the results of Probit regressions where the dependent variable takes the value of one if the respondentshas rebalanced their portfolio in 2017. In Column (1) and (2) the dependent variable takes the value of one for the 382 respondentsthat made at least one rebalancing of their portfolio, zero otherwise. Columns (3) to (5) displays displays the same regression focusingon the 382 that made at least one trade. In Column (3) the dependent variable takes the value of one if the respondent rebalancedtheir porfolio to an all ESG portfolio. In Column (4), the indicator variable takes the value of one if the rebalancing into a portfolioof at least 50% ESG labelled funds, and Column (5) if greater than zero; the indicator variable is zero otherwise. and (5) repeats theanalyis of Column (4) but uses more than 50% of their government premium pension savings invested in ESG labelled funds; zerootherwise. The regressions include several sets of controls. Individual Characeteristics an Knowledge controls follow those fromTable III and IV but educational dummies are dropped due to collinearity in Columns (3) through (5). We include year fixed effectsand portfolio weights in four fund categories: Equities, Mixed, Bond and Target funds. Data of portfolio changes and fund holdingsare obtained from the Swedish Pension Authority. Sampling weights are used.
(1) (2) (3) (4) (5)VARIABLES All Trades All Trades All ESG Mostly ESG Some ESG
Calamities 0.005 0.132** 0.113* -0.021(0.010) (0.058) (0.060) (0.044)
Pecuniary -0.000 0.026 0.111 0.028(0.015) (0.091) (0.089) (0.053)
Non-Pecuniary -0.023** -0.097 -0.097 -0.045(0.012) (0.069) (0.082) (0.061)
Perceived Env. Lit. -0.000 0.000 0.023 0.006 -0.013(0.006) (0.006) (0.036) (0.035) (0.027)
Perceived Fin. Lit. 0.005 0.005 -0.041 0.004 0.013(0.006) (0.005) (0.037) (0.037) (0.026)
Env. Lit. -0.006 -0.005 0.002 0.003 -0.007(0.005) (0.005) (0.031) (0.033) (0.023)
Fin. Lit. 0.014*** 0.014*** -0.022 0.017 0.019(0.005) (0.005) (0.032) (0.032) (0.021)
Log Income 0.013** 0.012** -0.093* -0.136** -0.093**(0.005) (0.005) (0.053) (0.059) (0.042)
Age 0.024*** 0.024*** 0.008 -0.007 0.045**(0.004) (0.004) (0.028) (0.029) (0.022)
Female -0.028*** -0.026** 0.027 -0.026 -0.082*(0.010) (0.010) (0.062) (0.065) (0.046)
Urban -0.001 -0.001 -0.034 0.021 0.022(0.004) (0.004) (0.024) (0.023) (0.015)
Green -0.001 -0.001 0.016 0.001 0.005(0.002) (0.002) (0.011) (0.011) (0.009)
University -0.044* -0.044*(0.024) (0.024)
ECO student -0.012 -0.011(0.043) (0.043)
ECON student -0.010 -0.009(0.015) (0.015)
Observations 3,667 3,667 382 382 382Fund controls Yes Yes Yes Yes Yes
Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
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25
ii
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ii
ii
Tabl
eV
II:T
rade
sB
efor
ean
dA
fter
the
2014
Hea
tWav
e
This
tabl
ere
port
sth
ere
sult
sof
Prob
itre
gres
sion
sw
here
the
depe
nden
tva
riab
leta
kes
the
valu
eof
one
ifth
ere
spon
dent
sha
sre
bala
nced
thei
rpo
rtfo
lioin
the
year
sbe
fore
oraf
ter
the
heat
wav
ein
2014
.The
sam
ple
“Bef
ore”
cont
ains
allt
rade
sdu
ring
the
year
s20
12to
2014
,and
the
sam
ple
”Aft
er”
for
2015
to20
17.T
hede
pend
entv
aria
ble
inC
olum
n(1
)and
(2)t
akes
the
valu
eof
one
for
the
resp
onde
nts
that
mad
eat
leas
ton
etr
ade
into
apo
rtfo
liow
ith
apo
siti
veES
Gw
eigh
t,ze
root
herw
ise.
InC
olum
n(3
)an
d(4
),th
ein
dica
tor
vari
able
take
sth
eva
lue
ofon
eif
the
reba
lanc
ing
into
apo
rtfo
lioof
atle
ast5
0%ES
Gla
belle
dfu
nds,
and
Col
umns
(5)a
nd(6
)ift
hetr
ade
was
into
anal
l(10
0%)E
SGpo
rtfo
lio;t
hein
dica
tor
vari
able
isze
root
herw
ise.
Con
trol
vari
able
sfo
llow
thos
ein
Tabl
eII
I.Ye
arfix
edef
fect
san
dfu
nd-t
ype
cont
rols
are
incl
uded
inal
lspe
cific
atio
ns.D
ata
ofpo
rtfo
lioch
ange
san
dcl
assi
ficat
ions
are
obta
ined
from
the
SPA
.Sam
plin
gw
eigh
tsar
eus
ed.
(1)
(2)
(3)
(4)
(5)
(6)
VAR
IABL
ESSo
me
Befo
reH
WSo
me
Aft
erH
WM
ostB
efor
eH
WM
ostA
fter
HW
All
Befo
reH
WA
llA
fter
HW
Cal
amit
ies
-0.0
49**
0.02
9-0
.012
0.10
6**
-0.0
14**
*0.
083*
*(0
.023
)(0
.038
)(0
.009
)(0
.044
)(0
.005
)(0
.037
)Pe
cuni
ary
-0.0
500.
014
-0.0
030.
108
0.00
30.
068
(0.0
31)
(0.0
59)
(0.0
15)
(0.0
75)
(0.0
09)
(0.0
62)
Non
-Pec
unia
ry0.
085*
*0.
026
0.00
40.
001
-0.0
02-0
.068
*(0
.036
)(0
.047
)(0
.012
)(0
.055
)(0
.005
)(0
.040
)Fe
edi
ff.
0.07
1-0
.071
-0.0
22-0
.210
*0.
001
-0.1
34(0
.054
)(0
.105
)(0
.024
)(0
.116
)(0
.009
)(0
.097
)
Obs
erva
tion
s1,
419
1,04
51,
419
1,04
51,
419
1,04
5C
hara
cter
isti
csYe
sYe
sYe
sYe
sYe
sYe
sK
now
ledg
eco
ntro
lsYe
sYe
sYe
sYe
sYe
sYe
sFu
ndco
ntro
lsYe
sYe
sYe
sYe
sYe
sYe
sYe
arFE
Yes
Yes
Yes
Yes
Yes
Yes
Stan
dard
erro
rsin
pare
nthe
ses
***
p<0.
01,*
*p<
0.05
,*p<
0.1
26
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Figure 1: July temperatures in Sweden 2012-2017
This figure display heat maps of differences from average temperatures over Sweden in July for the years 2012 to 2017. Yellow denotes
normal, blue to purple below, and orange to red above average temperatures.The maps are obtained from the Swedish Meteorological
and Hydrological Institute, downloaded from www.smhi.se.
2012 2013 2014
2015 2016 2017
27
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Figure 2: August rainfall in Sweden in 2012-2017
This figure display heat maps of rainfall in percentages of normal over Sweden in August for the years 2012 to 2017. Yellow denotes
average (100%), green to blue above, and orange to red below normal rainfall. The maps are obtained from the Swedish Meteorological
and Hydrological Institute downloaded from www.smhi.se.
2012 2013 2014
2015 2016 2017
28
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“green˙investment˙v5” — 2019/3/15 — 19:35 — page 29 — #29 ii
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Figu
re3:
The
Emer
genc
eof
ESG
Fund
sO
ver
Tim
e
This
figur
esh
ows
how
ESG
mut
ualf
unds
have
prol
ifer
ated
over
tim
ein
the
Swed
ish
Prem
ium
Pens
ion
Syst
emas
per
Dec
embe
rea
chye
aras
pres
ente
din
the
PPA
fond
broc
hure
.The
bars
show
tota
lnum
ber
offu
nds,
split
upin
perc
ento
fESG
and
non-
ESG
fund
sin
the
offe
ring
.The
ESG
labe
lwas
intr
oduc
edin
2004
.The
grey
line
depi
cts
the
num
ber
ofne
ws
item
sfr
omth
efo
urla
rges
t
new
spap
ers
inSw
eden
(Aft
onbl
adet
,Dag
ens
Nyh
eter
,Exp
ress
enan
dSv
ensk
aD
agbl
adet
)eve
ryye
arco
ntai
ning
the
wor
d“C
limat
ech
ange
”.Th
eda
tafo
rfu
nds
are
colle
cted
from
the
SPA
fund
cata
logu
esan
dw
ebpa
ge.T
heda
taon
new
sco
vera
geis
from
the
Nat
iona
lLib
rary
ofSw
eden
(ww
w.k
b.se
).
100%
100%
100%
100%
100%
93%
93%
94%
92%
91%
91%
89%
89%
88%
87%
84%
79%
69%
64%
7%7%
6%8%
9%9%
11%
11%
12%
13%
16%
21%
31%
36%
0102030405060708090100
0
100
200
300
400
500
600
700
800
900
1000
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
No. of temperature records
No. of funds
Year
Non-
ESG
labe
lled
fund
sES
G la
belle
d fu
nds
Artic
les o
n "C
limat
e Ch
ange
"Hi
gh te
mpe
ratu
re re
cord
s
29
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ii
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Figu
re4:
Tota
lPen
sion
Savi
ngs
and
ESG
Hol
diin
gs
This
figur
esh
ows
the
sam
ple
hold
ings
fund
sdi
vide
din
toES
Gan
dno
n-ES
Gan
dth
ede
faul
tfun
d(a
lso
anES
Gla
belle
dfu
nd).
The
hold
ings
are
split
upin
the
amou
nts
trad
edin
toea
chca
tego
ry
ever
yye
ar.
2029
3942
3143
5554
6898
137
157
197
252
4871
9598
8011
212
011
417
5
216
277
256
228
236
719
3253
33
6281
79
58
67
5944
38
44
69
1113
10
1426
27
34
39
6413
121
6
247
11
35
4
3
45
3
12
14
31
38
58
0
100
200
300
400
500
600
700
800
900
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
SEKm
Defa
ult
Hold
oth
er fu
nds
Trad
ed in
to o
ther
fund
s
Hold
ESG
Trad
ed in
to E
SG
30
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Figu
re5:
Surv
eyR
espo
nses
toC
limat
eC
alam
itie
s
This
figur
esh
ows
the
surv
eyre
spon
ses
toth
ree
ques
tion
sab
out
clim
ate
cala
mit
ies
ona
five
poin
tLi
kert
scal
era
ning
from
Very
Uni
kely
toVe
ryLi
kely
.Th
equ
esti
onis
“In
the
next
20ye
ars”
follo
wed
byth
ree
stat
emen
ts:
i)“T
heav
erag
ete
mpe
ratu
reon
eart
hin
crea
ses
bym
ore
than
one
Cen
tigr
ade”
;ii)
“Foo
dsh
rtag
ein
crea
ses”
;and
iii)“
The
wor
ldse
ale
velr
ises
than
mor
eth
anon
e
met
er”. 0%10
%
20%
30%
40%
50%
Don'
t kno
wVe
ry u
nlik
ely
Uni
kely
Nei
ther
Lik
ely
orU
nlik
ely
Like
lyVe
ry li
kely
Clim
ate
cala
miti
es: "
In th
e ne
xt 2
0 ye
ars.
..""T
he a
vera
ge te
mpe
ratu
re o
n ea
rth
incr
ease
s mor
e th
an o
ne C
entig
rade
"
"Foo
d sh
orta
ge in
crea
ses"
"The
wor
ld se
a le
vel r
ises m
ore
than
one
met
er"
31