Are Economically âKinder, Gentler Societiesâ also Greener

Policy Analysis pubs.acs.org/est

Are Economically “Kinder, Gentler Societies” also Greener? Thomas Bernauer*,† and Tobias Böhmelt†,‡ †

ETH Zurich, Center for Comparative and International Studies & Institute for Environmental Decisions, Haldeneggsteig 4, 8092 Zurich, Switzerland ‡ University of Essex, Department of Government, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom S Supporting Information *

ABSTRACT: Several studies examining implications of the modern welfare state arrive at rather positive conclusions: generally, they find that economically “kinder, gentler societies”, that is, countries providing stronger state-sponsored social-safety nets for their people, perform better on various accounts, such as social and political stability, or economic performance. Recent research suggests that benign implications also exist for the environment in the sense that investing more in social policies may contribute to stronger environmental protection and higher environmental quality. We present theoretical arguments in favor, but also against this hypothesis, and evaluate it empirically with cross-sectional data for 68 countries. In contrast to previous studies, the results offer only weak and inconsistent support for the claim that social policies and environmental performance are systematically related. This means that governments of economically kinder, gentler societies would be ill advised to hope for positive “spillover effects” of social policies into the environmental realm. The findings also suggest, however, that more disaggregated analyses are necessary, since beneficial effects may exist in some environmental domains, but not in others.

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INTRODUCTION Why do some societies perform better than others in combining economic growth with environmental quality and protection? Generally, the existing research shows that, even when controlling for economic determinants and other relevant influences, democratic forms of government are likely to be more successful in this respect.1−10 On the public demand side, democracies are associated with a greater freedom to engage in scientific research on environmental problems and potential solutions. They are also characterized by a greater freedom of individuals, social groups, and political organizations to mobilize and voice demands for stronger environmental policies. On the political supply side, democratic leaders have to satisfy what the broader public wants if they seek to survive politically.11,12 Both factors push into the same direction, leading to the outcome that we observe stronger environmental policies in and also a better environmental performance of democracies, relative to societies with less democratic forms of government. Existing explanatory models that account for differences in environmental performance across countriesincluding democracies and nondemocratic statessuggest, however, that there is still a considerable amount of unexplained variance across cases.7,8,13 Recent studies drawing on the broader welfare-state literature in political science and economics14−20 have thus focused on additional, potentially relevant sociopolitical determinants of environmental performance, in particular, social policy.21 The basic argument here claims that economically “kinder, gentler societies”,22 that is, those countries with policies that seek to improve economic and © XXXX American Chemical Society

social development by pursuing a less individualistic approach to economic development and by providing a stronger statesponsored social-safety net for their people, are also likely to pay more attention to environmental problems. In turn, they adopt more ambitious environmental or “greener” policies that result in a more effective environmental protection.21,23 In the following, we try to shed more theoretical and empirical light on this hypothesis. We start by outlining arguments for why a stronger social policy could have a positive “spillover effect” on environmental protection, but also discuss claims that argue against this. We then run a series of empirical tests using aggregated and disaggregated cross-sectional data on social policy and environmental performance for 68 countries in order to examine the empirical evidence for and against the hypothesis. The results offer only weak and inconsistent support for the argument that countries with superior social policies perform better with regard to the environment, particularly when looking at different measures of environmental performance. This means that governments of economically kinder, gentler societies22 would be ill advised to hope for positive spillover effects of social policies into the environmental realm. Still, our findings do suggest that more disaggregated analyses are needed, since studying the effects on highly aggregated environmental performance measures tends to obfuscate, rather than clarify the welfare state-environment relationship. In fact, beneficial effects of social policy could well Received: March 22, 2013 Accepted: September 30, 2013

A

dx.doi.org/10.1021/es403362m | Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Environmental Science & Technology

Policy Analysis

exist in some environmental areas, but not in others. We finish the article with a concluding section that summarizes the findings, points to policy implications, and sketches options for further research.

That being said, are stronger social policies and more ambitious environmental courses of action actually related systematically, and do stronger social policies cause better environmental performance? Are economically kinder, gentler societies22 really greener? To start with, the empirical answer is not as obvious as it might seem. For instance, Germany and Sweden appear to support the argument, since both countries have strong welfare states and are widely regarded as good environmental performers according to the Environmental Performance Index (EPI) and other measures of environmental quality. What about Greece or Italy, though? Both states have rather strong welfare states, not only in comparison with other industrialized countries, but also compared to most developing nations. Still, they are mediocre to poor environmental performers. Finally, what about the United States that has a rather weak welfare state and is, at best, a mixed environmental performer? At the theoretical level, a first potential and important caveat is that even if we observe a strong correlation of social and environmental policy, the relationship may not be causal, but may simply reflect a spurious correlation. For instance, richer or more democratic countries are likely to be more willing and better able to establish strong welfare states, and the same may hold for environmental protection.26,27 As a result, we are likely to observe both stronger social and environmental policies. However, both outcomes are rooted in higher income and/or democratic characteristics in the first place, meaning that stronger environmental protection policy is not caused by stronger social policy per se. In fact, and up till now, separate literatures on the welfare state and on environmental policy suggest that democratic forms of government and/or income levels are major drivers in these two policy domains.1−10,12−14,18,34−39 A second theoretical challenge pertains to the fact that economists generally remain divided over whether and under what conditions social policies have positive or negative effects on long-term economic growth, and about how economic growth affects the environment in the long run.40 This disagreement has important implications for the argument on the social policy-environment relationship. It could imply, for instance, that stronger social policies may even have a negative effect on economic growth, thus delaying “ecological modernization” and leaving countries with traditional polluting industries and little public demand for greener policies.41,42 Conversely, it is possible that stronger social policies support economic growth, but that higher growth may cause a larger ecological footprint, which negatively affects environmental quality.34,35,43 In both cases, stronger social policies are likely to have negative effects on environmental performance. Third, the impact of government size as an intervening variable between social policy and environmental protection could also play a role. Bernauer and Koubi,29 for instance, examine the influence of bigger governments on states’ air pollution emissions. The theoretical arguments behind this relationship are somewhat opposing, though. On one hand, “citizen-over-state theories” suggest that governments primarily function as public-good providers. Hence, the provision of public goods, including social welfare programs and social policies, increases with government size. On the other hand, Bernauer and Koubi29 highlight that the “state-over-citizen theories” argue that the relationship between government size and public-good provision may not necessarily be positive due to bureaucratic inefficiency and special interest-group influen-

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DOES SOCIAL POLICY MATTER FOR ENVIRONMENTAL PERFORMANCE? The main theoretical presumption underlying the claim of a positive effect of social policy on environmental performance is that the population of states with better social policies has a stronger socio-tropic orientation in general. This means that the average citizen, the median voter, and ultimately a larger part of the population of such countries is supposed to care more about the well-being of the community as a whole, relative to the wellbeing of herself (ego-tropic orientation), and relative to the average citizen in a country with a weaker welfare state. In the end, it is expected to a greater degree that the state pursues policies to this end.21,23−26 This socio-tropic orientation concerning economic and social matters may then extend to issues that are indirectly associated with democratic forms of government and economic improvement, such as sustainable development, or, more narrowly defined, environmental protection and quality.27,28 Environmental protection is a collective good whose benefits can be enjoyed by very large parts of the population, or even every inhabitant of a country; thus, it typically requires rather strong socio-tropic preferences of the electorate.1,2,9,12,29 Hence, states with a population that exhibits a stronger socio-tropic orientation with respect to economic and social welfare should also be more likely to pursue stronger environmental policies.23−26 In other words, the argument here expects an ideological spillover effect from prior social and economic policies to environmental policy, assuming that the same or similar factors motivating countries to adopt stronger social policies also drive them toward a more effective environmental protection. 21 And, in fact, given that most advanced industrialized countries have enacted their social policies well before embarking on more ambitious environmental policies (primarily as of the 1970s), a causal effect of social policy on environmental performance (rather than the reverse effect) is in principle possible. A similar and related argument for a positive relationship between a state’s social policy efforts and its degree of environmental protection pertains to the provision of public goods more directly.30−33 The basic claim in this literature states that a higher level of human capital is associated with higher pollution awareness among a country’s population. In turn, this leads to a decrease in pollution-intensive activities. The crucial point for this mechanism in the first place is that a society’s human capital can be increased due to a government’s higher spending on goods that benefit the population as a whole. As Lopez and his colleagues emphasize,30−33 these public goods explicitly pertain to education, health, and other social transfer expenditures. Moreover, “the reallocation of government spending may favor human capital-intensive activities to the detriment of physical capital-intensive industries, which tend to be among the most polluting ones.”33 Ultimately, due to mechanisms at both the macro and the micro level, the outcome of these two arguments is likely to be that a government’s higher efforts in social policy increase environmental protection. This mirrors the impact of the ideological spillover effect described above. B



Policy Analysis

Table 1. Variables and Descriptive Statistics variable

description

source

mean

SD

49

75.96

15.92

HREP

based on the EPI’s environmental health (ENVHEALTH) category, composed of three categories: environmental burden of disease, water, and air (without pesticide regulation indicator)

2010 EPI Data

EREP

based on six of the original categories forming the EPI’s ecosystem vitality (ECOSYSTEM) category: air pollution, water, biodiversity and habitat, forestry, fisheries, and agriculture (climate change dropped from categories)

2010 EPI Data49

68.71

8.52

GREP

based on the EPI’s climate change (CLIMATE) category: GHG emissions per capita, CO2 emissions per electricity generation, and industrial GHG emission intensity

2010 EPI Data49

50.18

15.59

SPI

social policy index ranking

Krishnakumar and Tellez Minnig50

34.5

19.77

POPDENS

population density: population per km2

229.68

931.21

GDPCAP

gross domestic product per capita

18 223.78

14 844.56

DEMO

democracy index

CPI

corruption perception index

EFW

economic freedom in a country

EFI

ethnic fractionalization index for ethnic diversity in a country

Extrapolated from 2010 EPI Data49 Extrapolated from 2010 EPI Data49 Economist Intelligence Unit’s Index of Democracy51 Transparency International52 Gwartney, Hall, and Lawson53 Alesina et al.54

GLOB

KOF Index of Globalization

Dreher55

ces. Eventually, this argument suggests that countries with bigger governments are unlikely to be more environmentalfriendly or greener, although they are more likely to have better social policies. In fact, Bernauer and Koubi29 find a negative relationship between government size and environmental quality as measured by lower SO2 emissions. Again, this questions the postulated environmental-improving impact of social policies. Fourth, an empirical shortcoming of the existing literature concerns the level of aggregation of the outcome variable, that is, environmental performance. It is important to consider complex relationships between income levels, various types of pollution, and social policy in this context. On one hand, with regard to the relationship between income and pollution, the literature on the Environmental Kuznets Curve (EKC) shows that the link between income levels and environmental performance or, conversely, pollution can have various functional forms. These forms can range from linear specifications with a positive or negative slope to U-shaped or inverted U-Shaped and S-shaped curves.34,35,44 That is, pollution increases or decreases with a state’s growing income level, or the relationship may also be nonlinear. The main reason for this empirical ambiguity is that emission pollution reduction, or, more generally, environmental degradation is subject to a different set of political and economic factors, contingent on whether effects of pollution are felt primarily locally, beyond national borders, or even globally, and what the economic implications of reducing (or increasing) pollution are. A closely related reason is that some forms of environmental risk can be shifted to other countries or even into the global commons, whereas such risk-shifting is more difficult for other environmental risks.43−48

6.86

1.8

5.04

2.33

7.1

0.72

0.39

0.25

70.26

14.62

scaling theoretical range is [0; 100], with higher values indicating better environmental performance theoretical range is [0; 100], with higher values indicating better environmental performance theoretical range is [0; 100], with higher values indicating better environmental performance we rescaled the original variable so that higher values indicate stronger social policies higher values indicate higher density higher values indicate higher GDP per capita higher values indicate more democracy

higher values indicate more corruption higher values indicate more economic freedom higher values indicate more ethnic diversity higher values indicate that a country is more open (globalized)

On the other hand, when considering social policy in an EKC model of pollution (or environmental performance) and assuming that social policy becomes stronger with growing income levels of a society, the partial effect of social policy on environmental performanceif there is anyis unlikely to be uniform across different types of pollution. We may expect, however, that countries prioritize efforts to clean up the local environment as they grow wealthier, and only become willing to invest more resources in tackling larger scale environmental problems, such as climate change, later on. This is likely to imply that, if social policy has any positive causal effect on environmental performance, it should be most visible for local pollution problems, and least visible for environmental problems of a larger scale and whose negative impacts (e.g., on public health) are not directly observable. In brief, plausible theoretical arguments, founded on existing theories and empirical evidence on the economic-environmental nexus, can be made in favor as well as against a positive effect of social policy on environmental performance. Moreover, we should expect the effect of social policy on environmental performance to vary across types of environmental degradation. In other words, at the theoretical level, the answer to whether economically kinder, gentler societies22 are also greener is ambiguous and empirical research has to play the arbiter.

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RESEARCH DESIGN The most comprehensive empirical analysis on the relationship between social policy and environmental performance is provided by a recent article from Kerret and Shvartzvald,21 who find strong empirical evidence in support of the “economically kinder, gentler society claim,” that is, higher C



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Table 2. Baseline Models of Environmental Performancea

social policy efforts also improve a country’s environmental performance. The authors conclude that “the results demonstrate the important role of social policy in explaining differences in the environmental performance of countries”.21 In the following, we build on and seek to extend this study in the following ways. First, by relying on these authors’ empirical data and model specifications, we construct a series of baseline models of social policy and environmental performance. Afterward, our contribution focuses on the improvement of the methodology and the examination of the results’ robustness. Moreover, and closely following our theoretical discussion, we disaggregate the outcome variables, because the analyses in the prior step suggest that the effects of social policy on the aggregated outcome measures are not robust, but likely to vary over the disaggregated subindicators. Finally, we also introduce and analyze additional data for environmental performance in a last step. Eventually, it seems that there is little empirical support for the hypothesis that social policies positively affect environmental performance. The main conclusion we draw is that aggregating several environmental performance indicators into composite indices and seeking to explain variation therein does not provide much analytical clarity. Hence, while the argument as such linking social policy and environmental performance seems intriguing, studying the implications of social policy and other political factors for environmental performance on a more disaggregated basis seems to be more useful.

Model 1 (HREP) SPI

Model 2 (EREP)

0.25 (1.72)b

0.26 (1.19)

0.05 (0.51) 0.18 (1.02) 0.09 (0.67) 0.15 (0.80) 0.05 (0.41) −0.26 (−2.65)d 68 16.32d 0.62

−0.15 (−1.00) 0.28 (1.05) 0.23 (1.16) −0.56 (−2.01)c 0.34 (1.87)b 0.20 (1.36) 68 2.44c 0.13

GLOB SPIbGLOB POPDENS GDPCAP DEMO CPI EFW EFI N F Adj. R2

Model 3 (GREP) −2.57 (−3.67)d −1.34 (−4.97)d 3.41 (4.05)d 0.25 (1.94)b −0.57 (−2.51)c 0.10 (0.63) 0.20 (0.81) 0.02 (0.14) −0.19 (−1.46) 68 5.73d 0.39

a

Models are based on OLS regression. Constant included in models but omitted from presentation. Table entries are standardized beta coefficients with t-statistics in parentheses. All three dependent variables are scaled so that higher values indicate better performance. b p < 0.10. cp < 0.05. dp < 0.01.

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BASELINE MODEL ESTIMATIONS We start by building our baseline models that follow the core estimations in Kerret and Shvartzvald.21 By relying on the information provided in this study as closely as possible, we use the same raw data for compiling our data set. That is, we constructed three composite indices for environmental performance (HREP, EREP, and GREP) as well as all of the explanatory variables used by Kerret and Shvartzvald.21 Table 1 identifies these variables and shows some descriptive statistics. Table 2 demonstrates that we are basically able to replicate the principal results of Kerret and Shvartzvald’s21 core models that we employ as our baseline estimations. Model 1 reveals that the SPI variable, which captures how strong the social policy of a country is as higher values pertain to economically “kinder, gentler societies,” has a positive and significant effect on HREP at conventionally acceptable levels. The effect of SPI is also positive in the model that focuses on EREP (Model 2), but not statistically significant. These two findings somewhat support the argument that a stronger socio-tropic orientation of a society, which is presumably associated with higher social policy efforts, has a positive effect on countries’ ability to solve environmental problems that have a direct and visible impact on humans (measured by HREP), whereas the effect on ecosystem-related performance (measured by EREP) is weaker. With a final baseline model, we examine the argument that stronger social policy efforts induce a better performance with respect to global environmental problems (measured by GREP), contingent on how open or “globalized” a country is (Model 3). This argument rests on the mechanism that citizens and, arguably, also their governments care more about environmental conditions on a larger scale if they are more exposed to the world beyond national borders. To facilitate interpretation of this contingent effect, we follow Braumoeller56 and Brambor et al.57 by plotting the marginal effects of SPI50 along the values of the globalization variable.55 Figure 1 graphs

the results. Based on these three baseline models, we now proceed with additional analyses that offer harder tests for the robustness of these estimations.

Figure 1. Contingent effect of social policy (SPI) on climate policy performance (GREP). Estimates are based on Model 3 in Table 2. Vertical bars indicate 90% confidence intervals for point estimates. Horizontal line indicates a marginal effect of zero. Rug plot along xaxis illustrates distribution of observations of GLOB.

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EXTENSION OF BASELINE MODELS: RESULTS AND DISCUSSION We extend our baseline models in a 3-fold way. First, we analyze potential outlier cases in the sample, which might overor underestimate the impact of SPI on environmental performance. Second, we study the composition of the D



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Figure 2. Scatter plots of main explanatory and dependent variables.

the dependent variable one employs, Singapore stands out as a highly influential observation in all of the three models. According to Cook’s D,58 we can clearly identify this city-state as a potential case that is likely to drive the results disproportionately. In fact, when dropping those outlier cases from the analysis, the results change. Table 4 summarizes the main findings after we omit the identified outliers from our sample; and we use again a graphical interpretation (Figure 3) for the interaction effect (SPI*GLOB).

dependent variables more thoroughly and, following our theoretical argument above, employ more disaggregated measures. Finally, we alter the model specification of GREP (Model 3, Table 2). As shown below, once we analyze these issues carefully and move beyond the baseline specifications, the empirical evidence for the main hypothesis, that is, that stronger social policies have a positive effect on environmental performance, becomes inconclusive and arguably weaker than previous studies might suggest. Outlier Cases. Figure 2 shows scatter plots for the social policy variable (SPI) and the three environmental performance indices (HREP, EREP, and GREP), respectively. This simple visual illustration already highlights two crucial points. First, it is rather difficult to detect a systematic correlation between SPI and any of the three dependent variables. Second, the sample might include several influential outliers, that is, observations that deviate from the overall pattern and, thus, are likely to disproportionately drive the results reported in Table 2. This seems to be particularly the case for HREP and GREP. On the one hand, the scatter plot of the former indicates that there are several countries, which are located between 50 and 65 on the x-axis and 90 and 100 on the y-axis. On the other hand, the plot for GREP emphasizes that numerous countries are located at the lower and upper bounds of SPI, which might affect the findings summarized in Table 2. Using Cook’s distance measure (D),58 we can identify the outlier cases more formally. After calculating D, we employ the common cutoff value of D > 4/n and review the results in Table 3. While there are only a few countries in the sample that exceed the cutoff value, that is, six states at most depending on Table 3. Outlier Cases

a

Table 4. Regression Results, Excluding Outliersa Model 4 (HREP) SPI

HREP

EREP

GREP

Tunisia (0.086) Belgium (0.155) Luxembourg (0.246) Singapore (34.836)

Norway (0.064) Ivory Coast (0.068) Cyprus (0.073) Singapore (29.671)

0.20 (1.63)

0.45 (2.00)b

−0.11 (−1.65) 0.16 (0.93) 0.39 (3.11)d −0.03 (−0.20) −0.01 (−0.05) −0.31 (−3.50)d 62 24.52d 0.73

−0.22 (−1.75)b −0.02 (−0.08) 0.22 (1.04) −0.48 (−1.66) 0.34 (1.94)b 0.16 (0.93) 64 2.48c 0.14

GLOB SPIbGLOB POPDENS GDPCAP DEMO CPI EFW EFI N F Adj. R2

a

Jordan (0.105) Iran (0.116) India (0.121) Venezuela (0.241) Luxembourg (0.290) Singapore (42.014)

Model 5 (EREP)

Model 6 (GREP) −3.36 (−5.24)d −1.61 (−6.23)d 4.46 (5.72)d −0.18 (−1.90b) −0.65 (−3.34)d 0.22 (1.43) −0.01 (−0.05) 0.13 (0.97) −0.35 (−2.92)d 64 9.18d 0.54

a

Models are based on OLS regression. Constant included in models but omitted from presentation. Table entries are standardized beta coefficients with t-statistics in parentheses. bp < 0.10. cp < 0.05. dp < 0.01.

The SPI effect becomes insignificant in Model 4 (HREP), but now achieves statistical significance at the 10% level in Model 5 (EREP). According to Figure 3, the overall pattern in Model 6 (GREP) remains unchanged, though. That being said, note that this result goes against commonly cited claims in the literature that social policies are likely to have a stronger and more immediate effect on HREP than on EREP.21 Composition of Environmental Performance Measures. Both the HREP and EREP indices (we deal with the GREP measure further below) lump together or, more

Cook’s D in parentheses. Cut-off value of D > 4/n equals 0.059. E



Policy Analysis

subindicators. Eventually, the outcome is a substantial amount of measurement error and imprecision in the aggregated data. With regard to trade, Dorussen,65 for example, argues that aggregated trade-flow measures are likely to be too abstract as “trade in some goods should have a bigger impact on the likelihood of conflict than trade in others.” Moreover, Goenner66 claims that “trade of strategic commodities may influence international relations differently than the same volume of toys traded between nations.” To assess the implications of this aggregation in our setup, we run the regression models used for HREP and EREP (Table 2) for the individual components of those composite indices. Table 5 (for HREP) and Table 6 (for EREP) summarize the Table 5. Disaggregated HREP Components Regression Resultsa Figure 3. Contingent effect of social policy (SPI) on climate policy performance (GREP). Estimates are based on Model 6 in Table 4. Vertical bars show 90% confidence intervals for point estimates. Horizontal line indicates marginal effect of zero. Rug plot along x-axis illustrates distribution of observations of GLOB.

SPI N F Adj. R2

technically, aggregate a very heterogeneous set of indicators that do not necessarily connect theoretically and empirically to the same underlying concepts (these subcategories are summarized in Table 1). For example, the fisheries and the air-pollution component of the EREP indicator areprimarily, theoretically but also empiricallyvery distinct factors that are likely to be affected by different influences as well. The point we are trying to make is that aggregating these subvariables into a larger index is likely to be misleading as this leads to a loss of information and does not necessarily stand on theoretical grounds, but only empirical ones in the form of correlations. While correlations between variables can point to a meaningful association, they could also suggest a link thatfrom a theoretical point of viewhardly makes sense at all. The most prominent example in this regard is perhaps the highly statistically significant correlation between stork populations and human birth rates,59 although Sparks and Tryjanowski raised this issue in terms of environmental research as well.60 Moreover, the hierarchy of countries of the 2010 EPI list leaves it sometimes unclear how variables are counted, measured, and attributed. Ultimately, this not only questions the meaningfulness of many of the indicators that are aggregated into the EPI, but alsoand most importantly the aggregation as such. From our view, it seems plausible that this has a huge impact on the alleged positive or negative relationship between kinder, gentler societies22 and environmental protection. In fact, the existent economics and political science literature raises similar concerns with regard to GDP, democracy, or trade measures. With regard to GDP, Ravallion,61 Gadrey and Jany-Catrice,62 and van den Bergh,63 for instance, argue that nationally aggregated GDP measures cannot capture intrastate regional variances, ignore many activities and resources that are not commercial, but affect a country’s economic wealth, or that GDP “represents an estimate of the costs instead of the benefits of all marketrelated economic activities in a country, and that it does not capture all social costs as it omits external costs.”63 In terms of democracy indices, Treier and Jackman64 contend that aggregated variables suffer from arbitrariness of the weighting scheme and are likely to discard much of the variation in the

Model 7 (EH)

Model 8 (AIR_H)

Model 9 (WATER_H)

Model 10 (AGCLTR)

0.21 (1.62) 68 24.33d 0.71

0.22 (1.25) 68 8.85d 0.45

0.32 (2.06)c 68 13.43d 0.56

0.00 (0.02) 68 1.62 0.06

a

Models based on OLS regression. Control covariates as specified in Model 1 (Table 2) and constant included in models but omitted from presentation. Table entries are standardized beta coefficients with tstatistics in parentheses. bp < 0.10. cp < 0.05. dp < 0.01.

Table 6. Disaggregated EREP Components Regression Resultsa

SPI N F Adj. R2

Model 11 (AIR_E)

Model 12 (WATER_E)

Model 13 (BIODIV)

Model 14 (FOREST)

Model 15 (FISH)

0.01 (0.06) 68 2.81c 0.16

0.13 (0.65) 68 4.24d 0.25

0.04 (0.16) 68 0.98 0.00

0.42 (2.04)c 68 4.36d 0.26

0.05 (0.24) 68 2.26c 0.12

a Models based on OLS regression. Control covariates as specified in Model 2 (Table 2) and constant included in models but omitted from presentation. Table entries are standardized beta coefficients with tstatistics in parentheses. bp < 0.10. cp < 0.05. dp < 0.01.

results. While the coefficients of the SPI variable are all positively signed, this effect is only significant for the water component in the HREP model (Model 9) and the forest category in the EREP model (Model 14). In other words, the findings for the SPI variable shown in Table 2 are driven by a very small subset of components that are aggregated into HREP and EREP − in fact, only two out of nine subvariables. Another indication that the different components of HREP and EREP, respectively, are likely to capture very different latent variables in the sense of theoretical constructs is given by the fact that the coefficients of the control variables (not reported in Tables 5 and 6) change signs across Models 7−10 and Model 11−15. We thus conclude that aggregation of the nine components into two composite indices obfuscates, rather than clarifies the impact of social policy on environmental performance. Global Environmental Policy. One main finding in the literature is that the effect of social policy on environmental performance, and particular on GREP, is contingent on a country’s openness toward the international system, that is, F



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Figure 4. Social policy effects on climate change policy and emissions. Values on y-axis indicate “average marginal effects of SPI.” Vertical bars show 90% confidence intervals for point estimates. Horizontal lines indicate marginal effect of zero. Note that all three dependent variables are scaled so that higher values (in a range of 0 to 1) indicate better performance. Rug plots along x-axes illustrate distribution of observations of GLOB

variables that capture the climate change problem more directly. The plots in Figure 4 do not support the argument that countries with stronger social policies are more climate-friendly, though. There is neither a direct positive effect of SPI on climate policy or emission-related climate performance, nor (in two of the three models) an indirect effect contingent on globalization. In the model of emission trends, where the contingent effect is significant, it acts in the opposite direction of what the hypothesis expects: the effect of SPI becomes adverse with increasing globalization.

globalization; (see, e.g., Table 2 and Figure 1 above; for a theoretical argument on this see, e.g., Ward43). The first potential problem with this result is that the composite GREP index consists of indicators measuring emissions, whereas HREP and EREP are comprised of a larger set of indicators including measures of policy stringency. Moreover, using the exact same explanatory model for local and global forms of environmental problems is theoretically hard to justify, since the global environmental problems involve global collective action of states and policymakers are likely to have very different incentive structures across the different environmental-problem types; for instance, local health-related environmental issues such as water pollution, compared to greenhouse gas emissions.13 In other words, the mechanisms behind different types of environmental pollution often differ and politicians might have strong incentives to deal with some issues, but not all. Cao and Prakash67 emphasize that issue visibility is likely to be a key factor here. To examine the results for climate change related environmental performance more directly, we use a different model specification and data from Bättig and Bernauer.8 This allows us to distinguish climate policy and emissions, and to use explanatory models that are more specific to the climate change problem. Figure 4 summarizes the findings in graphical form. In detail, the plots in this figure are based on OLS regression models (analyzing the same cross-sectional sample of 68 countries as above with robust standard errors), which rely on a disaggregated approach and clearly defined dependent variables. These three dependent variables, which are based on Bättig et al.68 and essentially mirror the GREP variable from a theoretical lens in a disaggregated version, are (a) policy output, that is, states’ ratification behavior, financial contributions, and countries’ reporting behavior under the UNFCCC (the exact operationalization is given in Bättig and Bernauer8); (b) the natural log of CO2 emission levels per capita as 1990− 2004 averages; and (c) CO2 emission trends, that is, the average annual growth rate of CO2 emissions per capita in 1990−2004. Against this background, we incorporate the SPI variable, the globalization item, and the interaction term of these two into Models 1, 3, and 4 of Bättig and Bernauer8 (those covariates that are additionally considered in these models are described in the Supporting Information) in order to explain their impact on states’ global environmental policies in a disaggregated fashion and with clearly defined dependent

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IMPLICATIONS AND AVENUES FOR FURTHER RESEARCH In contrast to what the existing literature frequently argues theoretically and empirically finds, our results are somewhat sobering as we are unable to find robust empirical evidence for the claim that countries with better social policies are also likely to be greener. This means that governments of economically “kinder, gentler societies”22 would be ill advised to hope for positive spillover effects of social policies into the environmental realm. They may be more environmental-friendly with respect to a few specific aspects of environmental performance, such as water pollution or forest management. It does not appear that there is much of an empirical basis for many other aspects of environmental performance, though, including environmental policies in general, emission levels per capita, and emission trends. Reminiscent of a person who stands with her feet in two separate buckets of water, with temperatures of 0 and 90 °C, respectively, and appears to feel fine on average, aggregating several environmental performance dimensions into broad composite indices does not seem to offer a useful clarification of social policy implications for the environment. Instead, future research should distinguish positive and perhaps also negative effects of social policy on particular facets of environmental performance, and try to systematically develop explanations for variation in effects across diverse environmental domains. And, in fact, our findings do suggest that more disaggregated analyses are in need as beneficial effects could well exist in some environmental domains, but not in others. In light of this, the basic assumption so far has been that the SPI item50 is indeed a valid and reliable measure of countries’ social policy and development efforts. Note, however, that this G



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ACKNOWLEDGMENTS We thank the editor of Environmental Science & Technology, Julie Zimmerman, and three anonymous reviewers for helpful comments and suggestions.

paper’s main claim, that is, that aggregation loses information and, hence, trying to identify meaningful relationships on the basis of correlations between highly aggregated variables is misleading, is also likely to apply to SPI.50 Future research should, thus, try to pursue disaggregation at both ends by distinguishing more thoroughly the effects of particular facets of social policy on particular facets of environmental performance. Data limitations essentially prevent us from conducting such an analysis here, since reliable information on states’ social policies or spending is generally available for OECD countries only. Analyzing such a sample, either with cross-section or panel data, may certainly improve our knowledge, but might be limited in its generalizability. Similarly, a shortcoming of our article is arguably the crosssectional nature of the data, which prevents us from modeling temporal dynamics and is likely to induce the problem of omitted variable bias.69,70 Moreover, it is difficult to test causal effects based on cross-sectional data in nonexperimental settings. The reason for using these cross-sectional data is twofold, however. First, the most comprehensive study21 on the relationship between social policy efforts and environmental protection also employs those cross-sectional data from the sources we cited in Table 1 above. Departing from this too much would lead to the loss of comparability between this study’s results and our findings. Hence, the cure might be worse than the disease. Second, time-series cross-section data for the dependent variable of environmental protection do exist, for example, emission indicators as frequently used in the literature, although Ward43 raises the concern that despite their importance, “these indicators are hardly a broad enough basis on which to build an analysis,” while “coverage is patchy, time series are short, and annual data are largely unobtainable for poorer countries.” That being said, regardless of the fact that welfare data hardly vary over time, we lack reliable and valid time-series cross-section data for countries’ social policies that cover (a) different dimensions of social policy as well as socialpolicy spending at the same time and (b) a larger set of countries.21,20,71 Particularly the latter point seems crucial from our point of view as one of the strengths of this paper is that more countries are covered than simply the OECD states. Generally, scholars only examine OECD countries, though. Hence, more data-compilation efforts seem necessary to further shed light on the nexus between states’ social policy and their environmental performance as the impact of social-policy efforts on a state’s environmental performance remains unsettled.

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REFERENCES

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ASSOCIATED CONTENT

S Supporting Information *

Additional information as noted in the text. This material is available free of charge via the Internet at http://pubs.acs.org.

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Policy Analysis

AUTHOR INFORMATION

Corresponding Author

*(T.B.) Phone: +41 44 632 63 85; fax: +41 44 632 12 89; email: [email protected]. Notes

The authors declare no competing financial interest. H



Policy Analysis

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