NBER WORKING PAPER SERIESHUMAN CAPITAL AND CLIMATE CHANGE
Noam Angrist
Kevin Winseck
Harry A. Patrinos
Joshua S. Graff Zivin
Working Paper 31000
http://www.nber.org/papers/w31000

NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
March 2023

The authors thank Diego Ambasz and Javier Sanchez-Reaza for in-depth comments andexpertise. Additional useful comments were provided by Rafael E. De Hoyos Navarro. The
authors thank Domagoj Racic for his assistance in coding green party data in Europe andAnshuman Gupta for his careful coding of compulsory schooling law data. The views expressedherein are those of the authors and do not necessarily reflect the views of the National Bureau of
Economic Research.
At least one co-author has disclosed additional relationships of potential relevance for thisresearch. Further information is available online at http://www.nber.org/papers/w31000
NBER working papers are circulated for discussion and comment purposes. They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies
official NBER publications.
© 2023 by Noam Angrist, Kevin Winseck, Harry A. Patrinos, and Joshua S. Graff Zivin. All
rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without
explicit permission provided that full credit, including © notice, is given to the source.

Human Capital and Climate Change
Noam Angrist, Kevin Winseck, Harry A. Patrinos, and Joshua S. Graff Zivin
NBER Working Paper No. 31000
March 2023
JEL No. D72,H41,I20,I28,P16,Q01,Q5
ABSTRACT
Addressing climate change requires individual behavior change and voter support for pro-climate
policies, yet surprisingly little is known about how to achieve these outcomes. In this paper, weestimate causal effects of additional education on pro-climate outcomes using new compulsory
schooling law data across 16 European countries. We analyze effects on pro-climate beliefs,
behaviors, policy preferences, and novel data on voting for green parties – a particularly
consequential outcome to combat climate change. Results show a year of education increases proclimate beliefs, behaviors, most policy preferences, and green voting, with voting gains
equivalent to a substantial 35% increase.
Noam Angrist
University of Oxford
120 Walton St
Oxford OX2 6GG
United Kingdom
nangrist@gmail.com

Harry A. Patrinos
World Bank
1818 H Street NW
MSN G8-800
Washington, DC 20043
hpatrinos@worldbank.org

Kevin Winseck
University of California San Diegokwinseck@ucsd.edu

Joshua S. Graff Zivin
University of California, San Diego
9500 Gilman Drive, MC 0519
La Jolla, CA 92093-0519
and NBER
jgraffzivin@ucsd.edu

I

Introduction

The costs and consequences of climate change are enormous and multifaceted (Carleton and Hsiang,
2016; Graff Zivin and Neidell, 2013; Intergovernmental Panel on Climate Change, 2022; Isen, RossinSlater and Walker, 2017; Park, Behrer and Goodman, 2021), with monetized impacts estimatedto be as large as 20% of annual global GDP within a generation (Nordhaus, 2007). On current
trajectories, the world is on track to experience 2.7°C warming above pre-industrial levels within
the next century, far above the global goal of 1.5°C (Climate Action Tracker, 2022). Individual
behavior change and government policy are needed to dramatically alter the trajectory of emissions.
Despite the urgency and scale of the challenge, current efforts are underwhelming, in part because
sizable populations around the globe remain skeptical about climate change and policies to tackle
it (Bechtel, Scheve and van Lieshout, 2020; Dechezleprêtre et al., 2022; Sunstein et al., 2017).
Surprisingly little is known about how to overcome such resistance.
One promising approach is the accumulation of human capital through increased educational
attainment.1 More educated individuals may be better equipped to understand the complexities
of climate science and have more awareness of the risks of climate change. Descriptive correlations
suggests this might be true: a global survey found people with more education were more likely to
see climate change as a major threat (Pew Research Center, 2019). More education might also yieldtransferable skills across occupations, encouraging voting for policies which promote less-polluting
industries, such as renewable energy subsidies. Yet determining the causal effect of human capital
accumulation on pro-climate beliefs and behaviors is challenging. People who choose to pursue moreeducation are, by revealed preference, forward looking and thus more concerned with the futureconsequences of climate change. It might not be education that is causing pro-climate beliefs andactions, but rather time preferences. Reverse causality is another challenge: individuals who believe
in climate change might choose to pursue more education to better adapt to a changing world.
In this paper, we overcome causal inference challenges by assembling a new database on compulsory schooling laws (CSLs) to estimate the causal effect of human capital accumulation on aseries of climate outcomes in Europe. The use of CSLs as a plausibly exogenous shift in educational
attainment has a rich tradition in labor and health economics (Angrist and Krueger, 1991; Black,
Devereux and Salvanes, 2008; Brunello, Fort and Weber, 2009; Gathmann, Jürges and Reinhold,2015; Goldin and Katz, 1997; Lleras-Muney, 2005; Oreopoulos, 2006), but is much more limited on
climate.2 Moreover, due to data limitations, studies have been largely limited to single countries.
We build on this nascent climate literature leveraging 39 CSL reforms in 16 countries, identified
via a new reforms database and data-driven definition of CSLs that lead to meaningful educational
improvements. In addition, studies to date analyze limited outcomes. We study new climate outcomes which extend well beyond standard measures of beliefs and behaviors, also examining thehighly consequential domains of policy preferences and voting.
1
Human capital captures an individual’s knowledge and skills (Becker, 1962) and is typically measured by education
metrics including years of schooling (Barro, 2001) and learning (Angrist et al., 2021).
2
A small set of studies explore environmental outcomes (Meyer, 2015; Powdthavee, 2021).

2

Europe is an ideal setting for this study. Countries in Europe enacted dozens of education
reforms in the twentieth century, expanding the number of years of education legally mandated
through compulsory schooling laws. At the same time, Europe has large, harmonized multi-country
surveys, enabling credible within- and cross- country analyses, with recent climate modules added
to the European Social Survey (ESS), which we analyze in this study. Moreover, Europe has arobust green party movement, which has an explicit environmental agenda.3 We codify a novel
dataset of green party voting outcomes, enabling identification of pro-climate voting behavior.
Our analysis focuses on outcome indices as well as on specific indicators within each index,
including comparisons between correlations and causal estimates. We find significant impacts onnearly all pro-climate measures. Our headline results show that an additional year of education
leads to an increase of 4.0 percentage points (PP) in pro-climate beliefs, 5.8 PP in behaviors, 1.0 PP
in policy preferences, and 3.6 PP in green voting. Relative to status quo rates, these impacts are
non-trivial, translating into 6.3% increase for beliefs, 8.5% for behaviors, 1.7% for policy preferences,
and a striking 35.0% increase for green party voting.
These results are notable since education has been conspicuously absent from most major climate change discussions.4 Our findings suggest expanding general education should be added to themenu of approaches considered in tackling one of the greatest modern threats to human well-being.
Indeed, human capital accumulation may be vital in shaping beliefs about the costs and benefitsof policies to reduce emissions (Dechezleprêtre et al., 2022) and extend directly to consequential
outcomes such as policy preferences and voting.
The rest of the paper is organized as follows. The next section describes our data. Section III
details our empirical strategy and Section IV presents our results. Some brief concluding remarks
are offered in Section V.

II

Data

Data on pro-climate outcomes – including beliefs, behaviors, policy preferences, and voting outcomes – come from the European Social Survey (ESS).5 The ESS is conducted biannually acrossdozens of European countries using stratified random sampling with a total sample size ranging
from 20,000 to 40,000 individuals per round. The ESS is a large microdata set capturing information on a host of social issues and is harmonized over time and across countries. In 2016, the
ESS introduced novel questions on climate outcomes, such as “how often do you do things to reduce energy use?” and “how likely are you to buy energy efficient appliances?” Moreover, the ESS
collected data on policy preferences such as “to what extent are you in favour or against using
public money to subsidise renewable energy such as wind and solar power?” Finally, we include
3

Green political parties’ environmental focus includes climate change, pollution, and industrial agriculture.
A recent analysis showed that only 24% of countries mention youth education in the context of the Paris Agreement (Kwauk, 2021) – a historic international treaty on climate change.
5
European Social Survey European Research Infrastructure (ESS ERIC). (2020).
ESS8 - integrated
file, edition 2.2 [Data set].
Sikt - Norwegian Agency for Shared Services in Education and Research.
https://doi.org/10.21338/ESS8E02 2
43

rich data on voting for green parties since 2002. Europe has a thriving green party movement in
32 countries. We codify a novel dataset of “green voting” across Europe based on party platforms.
Many political parties around the world have broad mandates, and are thus too general to explore
specific climate voting patterns. In contrast, green parties have an explicit environmental agenda,
enabling identification of pro-climate voting.
Table 1 shows the climate outcomes we consider in our analysis and Table A2 in the Online
Appendix includes the parties we classify as “green” in each country. Each climate outcome istransformed into a binary ‘pro-climate’ indicator if the response is equal to or above the median.
For example, a response is ‘pro-climate’ if the respondent answered “strongly in favor” or “somewhat
in favor” when asked about policies to subsidize renewable energy, since the median response is“somewhat in favor”. Alternatively, we also consider a continuous outcome, where 1 is the mostpro-climate response and 0 is the least.
In addition to analyzing individual outcomes, we aggregate climate outcomes into three indices:beliefs, behaviors, and policy preferences. Table 1 lists each question and denotes the index towhich it belongs; indices are simple within-individual averages. Our main results also include anindicator for whether respondents voted for a member of a green party in the last election for
countries where such a party exists.
Table 1: Climate Outcomes – Beliefs, Behaviors, Policy Preferences, and Voting
Question
Do you think the world’s climate is changing
Climate change good or bad impact across world
How worried about climate change
How much electricity should be generated from coal/hydro/solar
How worried too dependent on fossil fuels
How much thought about climate change before today
How likely to buy most energy efficient home appliance
How often do things to reduce energy use
Favor increase taxes on fossil fuels to reduce climate change
Favor subsidize renewable energy to reduce climate change
Favor ban of inefficient household appliances to reduce CC
Voted for green party in last national election

Beliefs✓✓✓✓✓

Behaviors

PolicyVoting

✓
✓
✓
✓
✓
✓
✓

Notes. Each outcome is grouped by index category. Each index is computed as an average for each individual acrossthe indicated questions. The final outcome, green voting, is a stand-alone binary outcome not aggregated withothers into an index. For beliefs about the source of electricity, we create a sub-index: the ESS has questions about
individuals’ opinions on electricity generation from coal, gas, hydroelectric, nuclear, solar, wind, and biofuel. Given
these outcomes are highly inter-related, we average pro-hydroelectric, pro-solar, and anti-coal beliefs. We exclude
indicators which might be collinear with renewables captured by solar and hydro-electric, such as wind, as well as
indicators with more ambiguous climate interpretations, such as nuclear.

We restrict our analysis to respondents at least 25 years old at the time they were surveyed tocapture effects for those who have completed their schooling. In particular, we analyze outcomesfor cohorts who received schooling and were affected by education reforms in the 1960s through the41980s and were adults being surveyed in the ESS from 2002 to 2018. In addition to climate andvoting outcomes, the ESS data contains birth year and years of education for every individual, which
are critical to mapping climate outcomes to cohorts of students affected by compulsory schoolinglaws, and who in turn experienced exogenous shocks to their educational attainment.
To examine the causal effect of education on climate outcomes, we leverage a new World Bank
dataset on compulsory schooling laws (CSLs) in Europe. Europe has had dozens of education
reforms throughout the twentieth century expanding the number of years of education legally
mandated through compulsory schooling laws. Figure A1 in the Appendix includes a map of thenumber of compulsory schooling law reforms which can be mapped to the ESS data over this time
period. For each CSL, we have information on the year it was passed, the year it came into effect,
and the new minimum schooling requirement under the law. For most CSLs, we also have theschool starting age, and assume this to be 6 years – the most common school starting age – for
CSLs for which it is missing; this lets us calculate the birth year of the first affected cohort. We
identify the CSL which applies to each respondent by finding the CSL that is applicable to theirbirth year cohort.
Together, these two unique datasets yield exogenous shocks to education which can be mapped
directly onto climate outcomes including beliefs, behaviors, policy preferences, and voting.

III

Empirical Strategy

III.A

Compulsory Schooling Laws as an Instrument

Compulsory schooling laws are commonly used in the economics literature as an instrument for
educational attainment. We briefly review the necessary conditions for their use in our context.
First, compulsory schooling must affect educational attainment. While this may seem obvious, weshow in Section III.B that this relationship holds for many reforms, but does not necessarily holdfor all. Thus, following (Oreopoulos, 2006), we carefully identify reforms which bind – that is,reforms which affect a large enough share of students to have a detectable increase in educational
attainment. We restrict our sample to reforms with positive and significant first stages. Second,
compulsory schooling must affect climate outcomes through the educational attainment channel,
and not be confounded by other factors. Given the passing of compulsory schooling laws is anational, exogenous shock, resulting gains in education are largely orthogonal to other factors thatwould otherwise make the individual schooling decision endogenous. For example, a potential
confounding variable in the education-climate relationship is individuals’ valuation of the future(e.g. their discount rates or degree of present bias), which can simultaneously motivate them topursue education as an investment in their future, as well as be concerned about the future costs ofclimate change. Compulsory schooling laws that have a strong first stage overcome this confounderby mandating individuals to obtain greater educational attainment, regardless of these factors.
The plausibility of the assumption that CSLs affect climate outcomes only through the education
channel is further bolstered by the fact that most of the possible effects of CSLs on other mediating
5

factors, such as income, likely increase as a direct result of the education channel. This means ourestimate is the bundled effect of education, including changes in income and other mediators, thatcome with an exogenous increase in schooling. In line with both of these points, Table A4 in theOnline Appendix shows a strong first stage on education, while no statistically significant effect onother variables which should not be affected by CSL changes and would not operate through theeducation channel, such as gender or country of birth.
Our estimation strategy instruments for years of education using a series of indicators forwhether each compulsory schooling law binds for a given cohort of individuals. We construct these
indicators cumulatively, that is, the estimated effect of the current law is the marginal effect of
the law relative to the prior law. We run a two-stage least squares regression where the second
stage regresses our climate outcomes on predicted education based on the applicable compulsory
schooling laws, controlling for time trends and country fixed effects.6 For a given individual i we
estimate:

Eicy = αc + βr CSLicyr + Ty × δc + εicy

(1)b icyr + Ty × δc + εicy
Yicy = αc + βr E

(2)where CSLicyr is a binary indicator of whether an individual i in country c is a member of acohort y affected by the reform r, and is therefore in the treatment group.7 We estimate effects
across multiple countries and reforms, with CSLicyr representing a vector of binary indicators
across all included reforms r. In Equation (1) we estimate the first stage of the effect of CSLs
on educational attainment Eicy . Since educational attainment has trended upward over time, we
condition on a linear country-specific time trend Ty .8 We include country fixed effects δc given weanalyze results in a unified cross-country framework.9 We interact time trends and country fixed
effects to produce country-specific time trends. Standard errors are clustered at the country-law(e.g., the CSL) level, which is the level of treatment assignment. We estimate Equation (2), the6

We further Winsorize educational attainment at the 1 percent level, to minimize outlier bias and address spurious
coding in the ESS data of extreme values. With Winsorization, we have a minimum of two years of schooling anda maximum of 22 years. Without Winsorization, 414 respondents or 0.11% of our sample report at least 30 yearsof education, which clearly does not map to our standard notion of years of full-time education, even for individuals
with a PhD, motivating Winsorization. Otherwise, the maximum reported education is 60 years of schooling which
exerts undue leverage on the rest of the data. Nevertheless, our results also hold when using raw years of educationor topcoding at 20 years of education instead.
7
This is defined based on each respondents’ birth year and starting school age to derive when the reform would
first take effect for a given individual.
8Higher order time trends consume too much of the variation caused by the instrument, leaving insufficient identifying variation. We believe that the model with a quadratic time trend is misspecified, as evidenced by the fact thatsuch a model yields negative and highly statistically significant effects of education on household earnings, contradicting the economic consensus that education causally increases earnings (Angrist and Krueger, 1991; Ashenfelter
and Rouse, 1998; Card, 2001). In contrast, our model with linear country-specific time trends produces estimates
more consistent with the literature.
9
Omitted indicators are the earliest laws in each country, such that the earliest laws take the value of the country
fixed effect, and each subsequent law has a positive β estimate as long as the reform i increased education relative tothe country’s time trend.

6causal effect of additional education on a given climate outcome Yicy , with two-stage least squares,
where the first stage is estimated from Equation (1) with educational attainment instrumented by
CSL reforms.
This specification mirrors those most common in the economics literature (Acemoglu and Angrist, 2000; Lleras-Muney, 2005; Oreopoulos, 2006). It is important to note that these strategies
all identify local treatment effects of education that are applicable to individuals on the margin ofdropping out in the absence of the CSL. This is the policy-relevant estimate if the policy in question
is to increase minimum schooling requirements.

III.B

First Stages: the Effect of CSLs on Education

Compulsory Schooling Laws (CSLs) legally mandate an increase in educational attainment, often byraising the minimum school leaving age. For example, in 1963, Italy increased minimum schoolingfrom 5 years of education to 8 (equivalent to increasing the minimum school leaving age from 11 to
14 years old). We carefully identify reforms for which there is a strong first stage – that is, where
an increase in required years of schooling by CSLs substantially increases average educational
attainment, net of the time trend, rather than assume all CSLs increase education, or that allindividuals are affected by CSLs. While legally enforceable, changes to CSLs will only have astrong first stage if they are enforced, rolled out rapidly, and bind for those who would otherwise
not proceed to attain more schooling without the law (e.g., some individuals may attain 8 years ofeducation in Italy even before it was legally required).
We estimate Equation 1 on all rounds of the ESS with standard errors clustered by country×law.10
We define and analyze binding first stages as those that are positive and statistically significant
with a t-statistic greater than 1.96, indicating a robust relationship. Figure 1 shows the 16 countries with relevant reforms (and up to 39 country-reforms, with multiple binding reforms in somecountries). Table A3 in the Online Appendix shows all first stages with positive effects, including
those that are not statistically significant. Countries in the main analysis include Albania, Austria,
Belgium, Bulgaria, Czechia, Germany, Denmark, Hungary, Iceland, Italy, Lithuania, Luxembourg,
Latvia, Russia, Slovakia, and Ukraine. We exclude countries, such as the United Kingdom, wherereforms occurred at the sub-national level and do not map cleanly to the ESS data.
10

By using all rounds of the ESS to determine strong first stages, we have more power to estimate the true effectof compulsory schooling laws beyond the country’s time trend.

7Figure 1: Compulsory schooling law changes with positive and significant effects on education bycountry. This figure shows the number of compulsory schooling law changes with strong first stage estimates bycountry. The map show the number of reforms that are positive and statistically significant. Countries in themain analysis include Albania, Austria, Belgium, Bulgaria, Czechia, Germany, Denmark, Hungary, Iceland, Italy,
Lithuania, Luxembourg, Latvia, Russia, Slovakia, and Ukraine.

IV

Results

Results on our three main pro-climate indices - beliefs, behaviors, and policy preferences - as wellas green voting are shown in Table 2. An additional year of education leads to an increase of 4.0
percentage points (PP) in pro-climate beliefs, 5.8 PP in behaviors, 1.0 PP in policy preferences,and 3.6 PP in green voting. These impacts translate into a 6.3% increase for beliefs, 8.5% forbehaviors, 1.7% for policy preferences, and a whopping 35.0% for green party voting. Panel B
of Table 2 shows the results with continuous outcomes to ensure results are not driven by binary
threshold values defined as being “pro-climate”. Results remain consistent. Point estimates are
positive and p-values also follow a similar pattern. For example, an additional year of education haslarge and statistically significant effects on pro-climate beliefs and behaviors, with p-values below <0.001 in both panels. Of note, while effect directions and statistical significance can be compared,
the magnitudes in Panels A and B are not directly comparable.11 In Online Appendix Figure A3
we include a series of robustness tests, such as various time trends and inclusion of all CSLs withpositive first stages, not just those that are statistically significant, among others. Results show
slightly dampened effects, but consistently large and positive effects.
In Figure 2, we compare the causal effects derived from IV estimates on the three pro-climate
indices and green voting to their corresponding OLS correlation estimates, expressed in terms ofstandard deviations for comparability between outcomes. In Figure 2 and Table 3 we analyze
11

In Panel A, a one unit change in the outcome is the difference between being below and above median, whereas
in Panel B, a move from 0 to 1 means changing from the most anti-climate response to the most pro-climate.

8

outcomes using binary indicators for ease of interpretation. Results are similarly robust whether
using binary or continuous outcomes. The gains shown in Table 2 translate to 0.152 standard deviation increase for pro-climate beliefs, a 0.184 increase for behaviors, a 0.033 increase for policies,
and a 0.130 for green party voting. Moreover, IV causal estimates are substantially larger than
OLS estimates for beliefs, behaviors, and voting. One important potential explanation for theselarger causal estimates is downward bias in the OLS estimates due to income effects. More educated individuals are often richer, and richer individuals are often more conservative – a standard
assumption in political economy models (Meltzer and Richard, 1981) – and thus might be less proclimate. Indeed in Table A1 in the Online Appendix we see correlations exactly along these lines.
The substantial increase in causal IV estimates relative to OLS estimates – more than a tripling inmagnitude – highlights the importance of credible causal identification of the effects of education
on pro-climate outcomes.
Table 2: The effect of education on pro-climate outcomes.

(1)
Pro-climate
beliefs(2)
Pro-climate
behaviors(3)
Pro-climate
policypreferences

(4)
Green
votingPanel A: indicators for above-median climate stanceYears of education
0.040
0.058
0.010
(0.007)(0.009)(0.009)[0.000][0.000][0.225]
Mean
0.640
0.682
0.627
Percent change
6.3 %
8.5 %
1.7 %

0.036
(0.013)[0.005]0.103
35.0 %

Panel B: continuous pro-climate variables
Years of education
0.018
0.035
(0.004)(0.007)[0.000][0.000]
Mean
0.635
0.633
Percent change
2.9 %
5.6 %
Observations
17353
17349
Clusters
36
36

0.036
(0.013)[0.005]0.103
35.0 %
52493
39

0.005
(0.006)[0.408]
0.601
0.8 %
16950
36

Notes: This table shows the causal effect of a year of education on each pro-climate outcome index, as in Equation(2). The outcome in Panel A denotes effects on being pro-climate defined in binary terms (relative to the median).
Panel B shows averages of the continuous outcomes, where 1 is the most pro-climate response to each question and
0 is the least. Standard errors clustered by country×CSL in parentheses. Standard errors are in parentheses andp-values are in brackets.

9Figure 2: Effects of Education on Pro-Climate Outcomes - Standardized causal estimates vs. correlations. This figure plots estimates from our main IV specification which
captures causal estimates compared to the OLS estimate which shows correlational estimates, bothwith linear country-specific time trends and country fixed effects. The OLS regression is restricted
to the same sample as the IV. The indices are standardized and expressed in terms of standard
deviations. 90% confidence intervals shown from standard errors clustered at the country×law.
While Table 2 shows our primary results, the panels of Table 3 break down each of the indicesinto their components, showing positive and significant estimates on nearly every sub-outcome. In
terms of specific indicators, on beliefs, we find one year of education has a 4.5 percentage point
increase in thinking the world’s climate is changing, with similar effects on worrying about climatechange and worrying about dependency on fossil fuels. We also find effects on beliefs in favor of
pro-clean energy captured in an index composed of being pro-solar, pro-wind, and anti-coal. In
terms of behaviors, we find 4.1 and 6.0 percentage point increases in reducing energy use and buying
energy efficient appliances, respectively, with a 7.1 PP increase in having thought about climatechange before today. For policy preferences, we find a 2.2 PP increase in favoring bans on thesale of inefficient appliances and a 2.4 PP increase on favoring subsidies for renewable energy. In
contrast, we find no effect on preferences to increase taxes on fossil fuels, a result that attenuates
our policy index despite two of the three components being strongly positive. This result suggeststhat individuals may be less supportive of pro-climate policies when the costs of those actions are
salient and run counter to self-interest (Dechezleprêtre et al., 2022), such as through immediatetax increases. The impacts on green voting are larger than those for policy preferences, suggestingthat rather than promote individual policies, a broad commitment to a green agenda might attract
the most voter support from more educated citizens.

10Table 3: Effect of education on each element of pro-climate outcome indices.

Panel A: pro-climate
beliefs

Years of education(1)
Think theworld’s climate
is changing

Climate Outcomes(2)(3)
Worried about
CC has badCC(not good)
impact across
world

(4)
Procleanenergybeliefs(5)
Too dependent
on fossil
fuels

0.025
(0.009)[0.007]
16842
36
0.717
3.4 %

0.048
(0.015)[0.001]
16517
36
0.654
7.4 %

0.045
(0.016)[0.005]
16926
36
0.542
8.2 %

0.024
(0.014)[0.091]
15986
35
0.566
4.2 %

0.052
(0.010)[0.000]
16473
36
0.760
6.8 %

Panel B: pro-climate
behaviors

Thought about
CC before
today

Likely to buy
most efficient
appliance

How often do
things to reduceenergy use

Years of education

0.071(0.010)[0.000]
17240
36
0.688
10.3 %

0.060(0.014)[0.000]
16888
36
0.664
9.0 %

0.041
(0.016)[0.012]
17059
36
0.706
5.8 %

Panel C: pro-climate
policy preferences &votingFavor increase
taxes on fossil
fuels to reduce
CC

Favor
subsidise
renewableenergyFavour ban saleof inefficienthouseholdappliances

Green
VotingYears of education-0.008(0.017)[0.631]
16417
36
0.544
-1.5 %

0.024(0.011)[0.028]
16734
36
0.748
3.3 %

0.022
(0.010)[0.037]
16611
36
0.589
3.7 %

0.036
(0.013)[0.005]
52493
39
0.103
35.0 %

Observations
Clusters
Mean
Percent change

Observations
Clusters
Mean
Percent change

Observations
Clusters
Mean
Percent change

Notes: This table shows point estimates for each of the elements of the indices. Panel A shows the elements of thebeliefs index, Panel B the behaviors index, and finally Panel C shows both the policy preferences index and green
voting. Outcomes are binary, so multiplying the point estimate by 100 yields the percentage point increase in thelikelihood of having a pro-climate stance on the given outcome from an additional year of education. Standard
errors clustered at the country×law level in parentheses and p-values in brackets. Estimates include country fixed
effects and country-specific linear time trends. “CC” means “climate change”.

11V

Conclusion

Climate change poses existential risks to the planet and generates trillions of dollars in annual costs
to society. While changing pro-climate beliefs, behaviors, policy preferences, and voting is difficult,a promising approach is through more education. This paper provides strong causal evidence that
education can impact a range of pro-climate outcomes. We find that an additional year of education
is linked with increases in pro-climate beliefs, behaviors, most policy preferences, and green voting,with voting gains equivalent to a large 35% increase – effects which are particularly consequential
to promote pro-climate policies.
While education is often a footnote in climate change agendas, this paper reveals the promise of
education as an additional tool to combat climate change. Europe in particular is a context whereclimate change is receiving substantial attention, including efforts such as the European GreenNew Deal, yet education remains an underutilized lever. Moreover, while educational attainment
has expanded dramatically in recent decades, the median school reform law in 2020 in Europe
guaranteed only 10 years of schooling, a full two years below a complete primary and secondary
education of 12 years. These gaps are even more dramatic in the developing world; in sub-SaharanAfrica educational reform laws only guarantee 8 years of schooling on average. Expanding access to
education has traditionally been believed to play a transformative role in the economic and social
well-being of societies – it now also appears to play a vital role in the battle against climate change.

12References
Acemoglu, Daron, and Joshua Angrist. 2000. “How Large Are Human-Capital Externalities? Evidence from Compulsory Schooling Laws.” NBER Macroeconomics Annual, 15: 9–59.
Publisher: The University of Chicago Press.
Angrist, Joshua D., and Alan B. Krueger. 1991. “Does Compulsory School Attendance Affect
Schooling and Earnings?*.” The Quarterly Journal of Economics, 106(4): 979–1014.
Angrist, Noam, Simeon Djankov, Pinelopi K. Goldberg, and Harry A. Patrinos. 2021.
“Measuring human capital using global learning data.” Nature, 592(7854): 403–408. Number:
7854 Publisher: Nature Publishing Group.
Ashenfelter, Orley, and Cecilia Rouse. 1998. “Income, Schooling, and Ability: Evidence froma New Sample of Identical Twins*.” The Quarterly Journal of Economics, 113(1): 253–284.
Barro, Robert J. 2001. “Human Capital and Growth.” American Economic Review, 91(2): 12–17.
Bechtel, Michael M., Kenneth F. Scheve, and Elisabeth van Lieshout. 2020. “Constantcarbon pricing increases support for climate action compared to ramping up costs over time.”
Nature Climate Change, 10(11): 1004–1009. Number: 11 Publisher: Nature Publishing Group.
Becker, Gary S. 1962. “Investment in Human Capital: A Theoretical Analysis.” Journal of
Political Economy, 70(5): 9–49. Publisher: University of Chicago Press.
Black, Sandra E., Paul J. Devereux, and Kjell G. Salvanes. 2008. “Staying in the Classroom
and Out of the Maternity Ward? The Effect of Compulsory Schooling Laws on Teenage Births.”
The Economic Journal, 118(530): 1025–1054.
Brunello, Giorgio, Margherita Fort, and Guglielmo Weber. 2009. “Changes in Compulsory Schooling, Education and the Distribution of Wages in Europe.” The Economic Journal,
119(536): 516–539.
Card,

David.

Persistent

2001.

“Estimating

EconometrictheProblems.”

Returnto

Econometrica,

Schooling:

Progress

69(5):

1127–1160.

on

Some
eprint:https://onlinelibrary.wiley.com/doi/pdf/10.1111/1468-0262.00237.
Carleton, Tamma A., and Solomon M. Hsiang. 2016. “Social and economic impacts ofclimate.” Science, 353(6304): aad9837. Publisher: American Association for the Advancement of
Science.
Climate Action Tracker. 2022. “Warming Projections Global Update November 2022.” Publisher: Climate Action Tracker.

13Dechezleprêtre, Antoine, Adrien Fabre, Tobias Kruse, Bluebery Planterose, Ana
Sanchez Chico, and Stefanie Stantcheva. 2022. “Fighting Climate Change: International
Attitudes Toward Climate Policies.” NBER Working Paper Series.
Gathmann, Christina, Hendrik Jürges, and Steffen Reinhold. 2015. “Compulsory schooling reforms, education and mortality in twentieth century Europe.” Social Science & Medicine,
127: 74–82.
Goldin, Claudia, and Lawrence Katz. 1997. “Why the United States Led in Education: Lessons
from Secondary School Expansion, 1910 to 1940.” National Bureau of Economic Research, Inc
NBER Working Paper 6144.
Graff Zivin, Joshua, and Matthew Neidell. 2013. “Environment, Health, and Human Capital.” Journal of Economic Literature, 51(3): 689–730.
Intergovernmental Panel on Climate Change. 2022. “Climate Change 2022: Impacts, Adaptation and Vulnerability.” IPCC Report 2022.
Isen, Adam, Maya Rossin-Slater, and Reed Walker. 2017. “Relationship between season ofbirth, temperature exposure, and later life wellbeing.” Proceedings of the National Academy of
Sciences, 114(51): 13447–13452. Publisher: Proceedings of the National Academy of Sciences.
Kwauk, Christina. 2021. “Who’s making the grade on climate change education ambition?”
Lleras-Muney, Adriana. 2005. “The Relationship Between Education and Adult Mortality in
the United States.” The Review of Economic Studies, 72(1): 189–221.
Meltzer, Allan H., and Scott F. Richard. 1981. “A Rational Theory of the Size of Government.” Journal of Political Economy, 89(5): 914–927. Publisher: The University of Chicago
Press.
Meyer, Andrew. 2015. “Does education increase pro-environmental behavior? Evidence from
Europe.” Ecological Economics, 116: 108–121.
Nordhaus, William D. 2007. “A Review of the Stern Review on the Economics of Climate
Change.” Journal of Economic Literature.
Oreopoulos, Philip. 2006. “Estimating Average and Local Average Treatment Effects of Education when Compulsory Schooling Laws Really Matter.” American Economic Review, 96(1): 152–
175.
Park, R. Jisung, A. Patrick Behrer, and Joshua Goodman. 2021. “Learning is inhibited byheat exposure, both internationally and within the United States.” Nature Human Behaviour,5(1): 19–27. Number: 1 Publisher: Nature Publishing Group.
Pew Research Center. 2019. “A look at how people around the world view climate change.”
14

Powdthavee, Nattavudh. 2021. “Education and pro-environmental attitudes and behaviours:
A nonparametric regression discontinuity analysis of a major schooling reform in England andWales.” Ecological Economics, 181: 106931.
Sunstein, Cass R., Sebastian Bobadilla-Suarez, Stephanie C. Lazzaro, and Tali Sharot.
2017. “How People Update Beliefs about Climate Change: Good News and Bad News.” Cornell
Law Review, 102(6): 1431–1444.

15

A
A.A

Appendix
Correlations between Education, Income, and Conservatism

Table A1: Correlations between education, income, and conservatism.

IncomeSchooling
0.383

Raw
Conservatism
0.068

Residualized
Schooling Conservatism
0.295
0.061

Notes: This table shows correlation coefficients between income and both schooling and conservatism.
Conservatism reflects where respondents self-report falling on a 0-1 scale where 1 is most right-leaning and 0 is mostleft-leaning on the political spectrum. Years of schooling is the Winsorized years of education attained, as in themain text. Lastly, income is the self-reported household income decile, normalized to fall on the 0-1 range. Raw
correlations are simply the correlation coefficients in our main analysis sample. Residualized coefficients are theresult of first residualizing income, schooling, and conservatism on country fixed effects and country-specific linear
time trends as in the main analysis.

16

A.B

Green Party CodingCountry
Austria
Belgium
Switzerland
CyprusCzechia
Germany
Denmark
Estonia
Spain
FinlandFranceHungary
Ireland
IcelandLithuania
LatviaNetherlands
NorwayPortugal
Sweden

Abbr.
AT
BE
CH
CY
CZ
DE
DK
EE
ES
FI
FR
HU
IE
IS
LT
LV
NL
NO
PT
SE

Table A2: Green party coding.
Green Parties
GrüneGroen!, Ecolo
Green Party
The Cyprus Green PartyČeská pirátská strana
Alliance 90/The Greens
SF Socialistisk Folkeparti, Alternativet
Erakond Eestimaa Rohelised
En Comú Podem, Iniciativa per Catalunya-Verds
Green League
EELV (Europe Ecologie Les Verts)
LMP (Lehet Más A Politika)
Green Party
Vinstri hreyfinguna - grænt framboõ
Lithuanian Green Party (LZP)
Zaļo un Zemnieku savienı̄baGreen Left
Miljøpartiet De Grønne
PAN - Pessoas-Animais-Natureza
Miljöpartiet de gröna

Notes: An individual is coded as voting green if they reported voting for one of the listed parties in the last election.
Missing responses and those from countries with no green parties in the relevant election are coded as missing.
Those who voted for a different party in countries with green parties at the time are coded as not voting green.

17

A.C

First Stage Estimates

In this paper, we leverage a new dataset on compulsory schooling laws in Europe from the World
Bank, which is one of the largest datatabases on CSLs to date. Figure A1 shows the number ofcompulsory schooling law reforms by country.

Figure A1: Number of compulsory schooling Law (CSL) reforms by country. The map shows all
CSL changes that can be mapped to the ESS data. Note that a British reform commonly used in literature
is excluded from our analysis, because this reform is region-specific and the ESS data does not have enoughgeographic granularity to accurately assign regional laws to respondent’s individual level climate outcomes.
Figure A2 shows all first stages with positive effects, in addition to the first stages with both
positive and highly statistically significant effects included in the main analysis in the paper. Exact first stage estimates are included in the table below. We provide these estimates to give acomprehensive picture of where CSLs bind and have large effects versus where effects are smaller.

Figure A2: Positive CSL changes by country. This figure shows the number of compulsory
schooling law changes with positive (but not necessarily significant) first stage estimates by country.

18

Table A3: CSL changes with any education effect
Reform & Year

Estimate

Positive

Positive+Significant

AL8

2.079

✓

✓

1963

( 0.271) [ 0.000]

AT9

0.595

✓

✓

1966

( 0.201) [ 0.004]

BE8

1.217

✓

✓

1919

( 0.122) [ 0.000]

BG8

0.718

✓

✓

1960

( 0.349) [ 0.042]

CH9

0.069

1970

( 0.209) [ 0.741]

CY6

0.194

1962

( 0.480) [ 0.687]

CZ9

0.363

1948

( 0.013) [ 0.000]

DE13

0.502

1992

( 0.044) [ 0.000]

DE4

0.315

1920

( 0.015) [ 0.000]

DE8

0.981

1946

( 0.026) [ 0.000]

DK7

0.893

1958

( 0.247) [ 0.000]

DK9

0.109

1972

( 0.310) [ 0.726]

EE6

0.850

1920

( 0.776) [ 0.276]

EE8

0.729

1958

( 0.666) [ 0.276]

ES8

0.331

1970

( 0.355) [ 0.353]

FI6

1.136

1921

( 1.399) [ 0.419]

FR10

0.116

1967

( 0.061) [ 0.059]

HU10

0.568

1961

( 0.173) [ 0.001]

✓
✓
✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓
✓
✓
✓
✓
✓
✓

19

✓

HU8

1.077

1945

( 0.095) [ 0.000]

IE9

0.182

1972

( 0.203) [ 0.371]

IS7

1.361

1936

( 0.555) [ 0.016]

IT8

1.040

1963

( 0.516) [ 0.046]

LT11

0.023

1980

( 0.045) [ 0.607]

LT5

1.894

1937

( 0.051) [ 0.000]

LT7

0.766

1953

( 0.030) [ 0.000]

LT8

1.477

1958

( 0.082) [ 0.000]

LT9

0.135

1980

( 0.124) [ 0.276]

LU10

0.717

1977

( 0.098) [ 0.000]

LU11

0.785

1993

( 0.019) [ 0.000]

LV5

1.176

1937

( 0.055) [ 0.000]

LV7

0.188

1953

( 0.032) [ 0.000]

LV8

0.736

1958

( 0.088) [ 0.000]

NL10

0.142

1973

( 0.162) [ 0.382]

NL7

0.169

1928

( 0.292) [ 0.565]

NL8

0.570

1950

( 0.289) [ 0.051]

NL9

0.220

1969

( 0.173) [ 0.207]

NO7

0.761

1936

( 0.772) [ 0.327]

✓

✓

✓
✓

✓

✓

✓

✓
✓

✓

✓

✓

✓

✓

✓
✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓
✓
✓
✓
✓

20

NO9

0.105

1969

( 0.809) [ 0.897]

PL8

0.309

1966

( 0.463) [ 0.506]

PT6

1.170

1964

( 0.804) [ 0.148]

PT9

0.756

1986

( 0.750) [ 0.316]

RU5

1.482

1937

( 0.039) [ 0.000]

RU7

0.855

1953

( 0.024) [ 0.000]

RU8

0.651

1958

( 0.086) [ 0.000]

RU9

0.069

2004

( 0.096) [ 0.472]

SE9

0.357

1963

( 0.864) [ 0.680]

SK8

1.338

1948

( 0.153) [ 0.000]

SK9

0.709

1948

( 0.145) [ 0.000]

UA12

0.714

2002

( 0.024) [ 0.000]

UA5

1.575

1937

( 0.077) [ 0.000]

UA7

1.144

1953

( 0.043) [ 0.000]

UA8

0.474

1958

( 0.141) [ 0.001]

UA9

0.102

1996

( 0.156) [ 0.517]

Obserations

✓
✓
✓
✓
✓

✓

✓

✓

✓

✓

✓
✓
✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

3159271

Notes: This table shows first stage estimates for Equation (1) for each CSL that positively affects educational
attainment. The point estimate is the effect on educational attainment following each CSL’s implementation, controlling for country-specific linear time trends and country fixed effects. The numbers following each country code
indicate the years of schooling required by each law (AL8 requires 8 years of schooling in Albania). The listed year is

21

A.D

CSL Validity Test

After first CSL

Observations

Table A4: Validity test
(1)
(2)
(3)
Male
Born in Country Years of Educationb/se/pb/se/pb/se/p
0.009
0.008
1.016
(0.011)(0.005)(0.144)[0.430][0.107][0.000]159091159057
159169Standard errors in parentheses. P-values in brackets.
Notes: This table shows the coefficient on the indicator for being after the first CSL change in a country while
additionally controlling for country fixed effects and linear country-specific time trends. The outcomes are (1) an
indicator for the respondent being male, (2) an indicator for being born in the country they are surveyed in, and (3)Winsorized years of education. The small and nonsignificant estimates in Columns (1) and (2) along with the large
and highly significant estimate on years of education support the validity of the instrument, as CSL changes affectschooling without a discernible effect on predetermined outcomes like gender and birth country, suggesting that
there are not other important confounders at play. Note that while the ESS has plenty of other outcomes that could
be tested in this manner, gender and birth location are the primary ones that we do not expect to be influenced byeducation, as these are determined before the amount of schooling is realized.

A.E

Robustness and Alternate Specifications

In this section, we consider the robustness of our estimates to several modeling decisions. We
analyze results with all positive first stages (not just those that are statistically significant as in themain analysis), as well as using all reforms. In addition, we analyze results with alternative timetrends such as squared time trends. Finally, rather than using indicators for compulsory schoolinglaws as the instrument for educational attainment, we use the current level of the minimum schoolingrequirement rather than a binary indicator, controlling for the upward time trends and country fixed
effects.
Figure A3 shows a plot of estimates across these robustness tests, showing broadly similar
patterns and robustness. We see slightly dampened effects across various robustness tests, whichis to be expected, however positive and large effects of education on pro-climate outcomes persist.
the first birthyear affected by the law. CSL changes not included in this table have nonpositive first stage estimates.
Standard errors are in parentheses.
2
Notes: Regressions of indicators for male and being born in the same country as being surveyed in on theindicators for each CSL in the presence of country fixed effects and country-specific linear time trends.

22

Figure A3: Robustness checks. Figure shows IV estimates for the four outcome indices under alternative timetrend specifications and inclusion criteria for the first stage. The main specification is as in Section III (linear countryspecific time trend and country fixed effects for CSL changes with positive statistically significant effects). SquaredTime Trend is the same as Main but replaces the country-specific linear birth year term with a country-specificsquared birth year term (centered on 1950). Cohort Fixed Effect is the main specification replacing the countryspecific linear time trend with birth cohort indicators (allowing for a completely flexible European time trend, but
without variation by country beyond a country-specific intercept). All Laws is the main specification including allreforms as instruments. Alternate is the secondary IV specification where the instrument is the number of years ofschooling interacted with country. 90% confidence intervals shown from standard errors clustered at the country×law.

23

A.F

ESS Question Text and Pro Environmental Beliefs Definitions

We include exact question working and coding for our main pro-climate outcomes.
• Importance to care for nature and environment: (ESS 2016 and 2018) Now I will
briefly describe some people. Please listen to each description and tell me how much each
person is or is not like you. Use this card for your answer. She/he strongly believes thatpeople should care for nature. Looking after the environment is important to her/him.
• How likely to buy most energy efficient home appliance: If you were to buy a large
electrical appliance for your home, how likely is it that you would buy one of the most energy
efficient ones?
0 Not at all likely - 10 Extremely likely• How often do things to reduce energy use: There are some things that can be done to
reduce energy use, such as switching off appliances that are not being used, walking for shortjourneys, or only using the heating or air conditioning when really needed. In your daily life,
how often do you do things to reduce your energy use?
• How much electricity should be generated from coal: The highlighted box at the
top of this card shows a number of energy sources that can be used to generate electricity.
Please take a moment to look over them. How much of the electricity used in [country] shouldbe generated from each energy source? First, how much of the electricity used in [country]
should be generated from coal?
• How worried too dependent on fossil fuels: How worried are you about [country] being
too dependent on using energy generated by fossil fuels such as oil, gas and coal?
• Do you think the world’s climate is changing: You may have heard the idea that theworld’s climate is changing due to increases in temperature over the past 100 years. What isyour personal opinion on this? Do you think the world’s climate is changing?
• How much thought about climate change before today: How much have you thought
about climate change before today?
• How worried about climate change: How worried are you about climate change?
• Climate change good or bad impact across world: How good or bad do you think the
impact of climate change will be on people across the world? Please choose a number from 0
to 10, where 0 is extremely bad and 10 is extremely good.
0 Extremely bad - 10 Extremely good
• Favour increase taxes on fossil fuels to reduce climate change: To what extent are youin favour or against the following policies in [country] to reduce climate change? Increasing
taxes on fossil fuels, such as oil, gas and coal.
24• Favour subsidise renewable energy to reduce climate change: To what extent are
you in favour or against the following policies in [country] to reduce climate change? Using
public money to subsidise renewable energy such as wind and solar power.
• Favour ban of least energy efficient household appliances to reduce climate change:
To what extent are you in favour or against the following policies in [country] to reduce climatechange? A law banning the sale of the least energy efficient household appliances.

25