metadata: authors: 0: author: Angrist,Noam
1: author: Winseck,Kevin
2: author: Patrinos,Harry Anthony
3: author: Zivin,Joshua S. Graff
count: Europe
docna: 0: docna: Human Capital and Climate Change
docty: Policy Research Working Paper
abstracts: cdata!: 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. This paper estimates causal effects
            of additional education on pro-climate outcomes using new
            compulsory schooling law data across 16 European countries.
            It analyzes effects on pro-climate beliefs, behaviors,
            policy preferences, and novel data on voting for green
            parties&#x2014;a particularly consequential outcome to
            combat climate change. Results show a year of education
            increases pro-climate beliefs, behaviors, most policy
            preferences, and green voting, with voting gains equivalent
            to a substantial 35% increase.
display_title: Human Capital and Climate Change
listing_relative_url: /research/2023/02/34008850/human-capital-climate-change
topicv3: Climate Change m1326829 567,Economics of Education m1327511 1540,Economic Analysis of Education m1327512 2124
publishtoextweb_dt: 2023-02-24T00:00:00Z
dois: 10.1596/1813-9450-10316
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1205875
1: author_key: NA
2: author_key: 95903
3: author_key: 1080236
model_extractions: Policy Research Working Paper 10316 Human Capital and Climate Change _Noam Angrist Kevin Winseck Harry Anthony Patrinos Joshua Graff Zivin_ Europe and Central Asia Region Office of the Chief Economist February 2023
Policy Research Working Paper 10316

# **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. This paper estimates causal effects of additional education on pro-climate outcomes using new compulsory schooling law data across 16 European countries. It analyzes 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 pro-climate beliefs, behaviors, most policy preferences, and green voting, with voting gains equivalent to a substantial 35% increase.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at hpatrinos@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Human Capital and Climate Change

Noam Angrist, Kevin Winseck, Harry Anthony Patrinos, and Joshua Graff Zivin<sup>∗</sup>

**JEL Codes:** D72, H41, I20, I28, P16, Q01, Q5

**Keywords:** Human Capital, Education, Climate Change, Compulsory Schooling Laws, Voting

> ∗Angrist: Oxford University, Youth Impact, World Bank (email: noam.angrist@bsg.ox.ac.uk); Winseck: University of California San Diego (email: kwinseck@ucsd.edu); Patrinos: World Bank (email: hpatrinos@worldbank.org); Graff Zivin: University of California San Diego, NBER (email: jgraffzivin@ucsd.edu).

> The authors thank Diego Ambasz and Javier Sanchez-Reaza for in-depth comments and expertise. Additional useful comments were provided by Rafael E. De Hoyos Navarro. The authors thank Domagoj Raˇci ́c for his assistance in coding green party data in Europe and Anshuman Gupta for his careful coding of compulsory schooling law data.
# **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 estimated to 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; Dechezleprˆetre 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.<sup>1</sup> 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 yield transferable 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 more education are, by revealed preference, forward looking and thus more concerned with the future consequences of climate change. It might not be education that is causing pro-climate beliefs and actions, 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 a series 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, J ̈urges and Reinhold, 2015; Goldin and Katz, 1997; Lleras-Muney, 2005; Oreopoulos, 2006), but is much more limited on climate.<sup>2</sup> 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 the highly consequential domains of policy preferences and voting.

> 1Human 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).

> 2A 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 a robust green party movement, which has an explicit environmental agenda.<sup>3</sup> 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 on nearly 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.<sup>4</sup> Our findings suggest expanding general education should be added to the menu 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 benefits of policies to reduce emissions (Dechezleprˆetre 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).<sup>5</sup> The ESS is conducted biannually across dozens 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

> 3Green political parties’ environmental focus includes climate change, pollution, and industrial agriculture.

> 4A 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.

> 5European 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~~

3
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 is transformed 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 most pro-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 to which it belongs; indices are simple within-individual averages. Our main results also include an indicator 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|Beliefs|Behaviors|Policy|Voting|
|---|---|---|---|---|
|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 efcient 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 inefcient household appliances to reduce CC|||✓||
|Voted for green party in last national election||||✓|

_Notes_ . Each outcome is grouped by index category. Each index is computed as an average for each individual across the indicated questions. The final outcome, green voting, is a stand-alone binary outcome not aggregated with others 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 to capture effects for those who have completed their schooling. In particular, we analyze outcomes for cohorts who received schooling and were affected by education reforms in the 1960s through the 4
1980s and were adults being surveyed in the ESS from 2002 to 2018. In addition to climate and voting 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 schooling laws, 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 the number 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 the school 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 their birth 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, we show in Section III.B that this relationship holds for many reforms, but does not necessarily hold for 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 a national, exogenous shock, resulting gains in education are largely orthogonal to other factors that would 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 to pursue education as an investment in their future, as well as be concerned about the future costs of climate change. Compulsory schooling laws that have a strong first stage overcome this confounder by 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 our estimate is the bundled effect of education, including changes in income and other mediators, that come with an exogenous increase in schooling. In line with both of these points, Table A4 in the Online Appendix shows a strong first stage on education, while no statistically significant effect on other variables which should not be affected by CSL changes and would not operate through the education channel, such as gender or country of birth.

Our estimation strategy instruments for years of education using a series of indicators for whether 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.<sup>6</sup> For a given individual _i_ we estimate: where _CSLicyr_ is a binary indicator of whether an individual _i_ in country _c_ is a member of a cohort _y_ affected by the reform _r_ , and is therefore in the treatment group.<sup>7</sup> 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_ .<sup>8</sup> We include country fixed effects _δc_ given we analyze results in a unified cross-country framework.<sup>9</sup> 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), the

> 6We 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 and a maximum of 22 years. Without Winsorization, 414 respondents or 0.11% of our sample report at least 30 years of 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 education or topcoding at 20 years of education instead.

> 7This 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 that such 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.

> 9Omitted 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 to the 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 of dropping 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 by raising the minimum school leaving age. For example, in 1963, Italy increased minimum schooling from 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 all individuals are affected by CSLs. While legally enforceable, changes to CSLs will only have a strong 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 of education 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.<sup>10</sup> 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 some countries). 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, where reforms occurred at the sub-national level and do not map cleanly to the ESS data.

> 10By using all rounds of the ESS to determine strong first stages, we have more power to estimate the true effect of compulsory schooling laws beyond the country’s time trend.

7
<!-- Start of picture text --> Positive+SignificantNumber of 4 FASs .<br>First Stages 2<br>Europe<br>1<br>Ge<br>3<br>ms<br><!-- End of picture text -->
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 these larger 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 in magnitude – highlights the importance of credible causal identification of the effects of education on pro-climate outcomes.

Table 2: **The effect of education on** **<u>pro-climate</u> outcomes** .

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Pro-climate<br>beliefs|Pro-climate<br>behaviors|Pro-climate<br>policy<br>preferences|Green<br>voting|
|_Panel A: indicators _|_for above-me_|_dian climate st_|_ance_||
|Years of education|0.040|0.058|0.010|0.036|
||(0.007)|(0.009)|(0.009)|(0.013)|
||[0.000]|[0.000]|[0.225]|[0.005]|
|Mean|0.640|0.682|0.627|0.103|
|Percent change|6.3 %|8.5 %|1.7 %|35.0 %|
|_Panel B: continuous _|_pro-climate _|_variables_|||
|Years of education|0.018|0.035|0.005|0.036|
||(0.004)|(0.007)|(0.006)|(0.013)|
||[0.000]|[0.000]|[0.408]|[0.005]|
|Mean|0.635|0.633|0.601|0.103|
|Percent change|2.9 %|5.6 %|0.8 %|35.0 %|
|Observations|17353|17349|16950|52493|
|Clusters|36|36|36|39|

_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 and p-values are in brackets.

9
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Table 3: **Effect of education on each element of pro-climate outcome indices** .

|||Clima|te Outcomes|||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|_Panel A: pro-climate_|Think the|CC has bad|Worried about|Pro-|Too de-|
|_beliefs_|world’s climate|(not good)|CC|clean|pendent|
||is changing|impact across||energy|on fossil|
|||world||beliefs|fuels|
|Years of education|0.045|0.024|0.052|0.025|0.048|
||(0.016)|(0.014)|(0.010)|(0.009)|(0.015)|
||[0.005]|[0.091]|[0.000]|[0.007]|[0.001]|
|Observations|16926|15986|16473|16842|16517|
|Clusters|36|35|36|36|36|
|Mean|0.542|0.566|0.760|0.717|0.654|
|Percent change|8.2 %|4.2 %|6.8 %|3.4 %|7.4 %|
|_Panel B: pro-climate_|Thought about|Likely to buy|How often do|||
|_behaviors_|CC before|most efcient|things to reduce|||
||today|appliance|energy use|||
|Years of education|0.071|0.060|0.041|||
||(0.010)|(0.014)|(0.016)|||
||[0.000]|[0.000]|[0.012]|||
|Observations|17240|16888|17059|||
|Clusters|36|36|36|||
|Mean|0.688|0.664|0.706|||
|Percent change|10.3 %|9.0 %|5.8 %|||
|_Panel C: pro-climate_|Favor increase|Favor|Favour ban sale|Green||
|_policy preferences &_|taxes on fossil|subsidise|of inefcient|Voting||
|_voting_|fuels to reduce|renewable|household|||
||CC|energy|appliances|||
|Years of education|-0.008|0.024|0.022|0.036||
||(0.017)|(0.011)|(0.010)|(0.013)||
||[0.631]|[0.028]|[0.037]|[0.005]||
|Observations|16417|16734|16611|52493||
|Clusters|36|36|36|39||
|Mean|0.544|0.748|0.589|0.103||
|Percent change|-1.5 %|3.3 %|3.7 %|35.0 %||

_Notes:_ This table shows point estimates for each of the elements of the indices. Panel A shows the elements of the beliefs 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 the likelihood 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”.

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# **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 where climate change is receiving substantial attention, including efforts such as the European Green New 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-Saharan Africa 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.

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