*Topicality/Definitions Democracy Promotion Includes Military Intervention
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- DEMOCRACY REDUCES EMISSIONS—YOUR STUDIES IGNORE THAT THE EFFECT IS LARGER FOR HIGH EMITTING COUNTRIES
- DEMOCRACIES REDUCE EMISSIONS—PAPERS THAT DON’T USE QUANTILE REGRESSION WILL DRASTICALLY UNDERESTIMATE THE EFFECT OF DEMOCRACY ON EMISSIONS REDUCTION
Democracy Good – CO2 EmissionsDEMOCRACY DECREASES EMISSIONS IN THE HIGHEST EMITTING COUNTRIES—PREVIOUS STUDIES THAT DON’T PARSE THE DISTRIBUTION ARE WRONG You et al 15 [WAN-HAI YOU a , HUI-MING ZHU a , KEMING YU b and CHENG PENG, Hunan University and Brunel University, Democracy, Financial Openness, and Global Carbon Dioxide Emissions: Heterogeneity Across Existing Emission Levels, World Development Vol. 66, pp. 189–207, 2015] The objective of this study is to explore the impact of democracy on pollution using the panel quantile regression model, which takes into consideration unobserved individual heterogeneity and distributional heterogeneity. While the democracy–pollution nexus has drawn economists’ interest in recent years, the main contribution of this study is to examine the sensitivity of the democracy–pollution nexus to the conditional distribution of pollution. We have argued that emissions research should start being more concerned with the effects of explanatory variables on different parts of the emission distribution. While average effects certainly are an important feature to examine, it is also interesting to understand what happens at the extremes of a distribution. We believe that quantile regression model can help us obtain a more complete picture of the factors affecting emissions. Using this methodology, we are able to assess the determinants of emissions throughout the conditional distribution, with particular focus on the most and least emissions countries–those that arguably are of the most interest. In particular, we examine whether greater democracy and more financial openness consistently reduce emissions among the most and the least emissions countries. In general, we find that population size has a positive and significant effect on pollution. Population size increases emissions, with the strongest effects at the low of conditional distribution. The effect of the percentage of industrial activity is significant in the uppermost quantile, suggesting that within the most emissions countries, increasing the percentage of industrial activity does not reduce emissions. The quadratic specifications suggest an EKC inverted-U relationship between emission and income. However, the estimated turning point is far above all countries’ income levels. Therefore, the global emissions–income relationship is essentially monotonic. Our results are similar to Holtz-Eakin and Selden (1995). We do not find any significant effect of trade openness on pollution. However, the impact of democracy on pollution is not uniform across conditional distribution of pollution according to the magnitude and sign of coefficients and this conclusion is further confirmed by the inter-quantile test, which is designed to examine whether the observed differences along the estimated coefficients are statistically significant across quantiles. Democracy is positively associated with emissions for the least emissions individuals (or countries), while the relationship is negative for the most emissions countries. This gives insights that the democracy–pollution nexus may have been not fully studied in previous studies that focused on mean effects. Another key implication of our findings is that financial openness has no significant effect on pollution at almost all quantiles. Our main findings are generally robust when the alternative estimation methods and alternative model specifi- cations are employed. In terms of policy implications, our findings suggest that highest emissions countries could benefit the most from increasing democracy levels. Among the least emission nations, higher democracy level and greater financial openness do not appear to reduce emissions. The key implication of our findings is that blanket emissions control policies are unlikely to succeed equally across countries with different emissions levels. For instance, greater democracy is likely more effective in the conditionally most emissions nations. To be effective, emissions control initiatives should be tailored differently across the most emissions and least emission nations, especially with respect to the role of democracy. DEMOCRACY REDUCES EMISSIONS—YOUR STUDIES IGNORE THAT THE EFFECT IS LARGER FOR HIGH EMITTING COUNTRIES You et al 15 [WAN-HAI YOU a , HUI-MING ZHU a , KEMING YU b and CHENG PENG, Hunan University and Brunel University, Democracy, Financial Openness, and Global Carbon Dioxide Emissions: Heterogeneity Across Existing Emission Levels, World Development Vol. 66, pp. 189–207, 2015] The determinants of CO2 emissions have attracted many researchers over the past few decades. Most of studies, however, ignore the possibility that effect of independent variables on CO2 emissions could vary throughout the CO2 emission distribution. We address this issue by applying quantile regression methods. We examine whether greater democracy and more financial openness consistently reduce emissions among the most and least emission nations. Our results show that the effect of democracy on CO2 emissions is heterogeneous across quantiles. Among the most emissions nations, greater democracy appears to reduce emissions, but more financial openness does not appear to reduce it. DEMOCRACIES REDUCE EMISSIONS—PAPERS THAT DON’T USE QUANTILE REGRESSION WILL DRASTICALLY UNDERESTIMATE THE EFFECT OF DEMOCRACY ON EMISSIONS REDUCTION You et al 15 [WAN-HAI YOU a , HUI-MING ZHU a , KEMING YU b and CHENG PENG, Hunan University and Brunel University, Democracy, Financial Openness, and Global Carbon Dioxide Emissions: Heterogeneity Across Existing Emission Levels, World Development Vol. 66, pp. 189–207, 2015] More recently, some attention has been paid to the relationship between the institutional quality (e.g., democracy) and pollution. Romuald (2011) argues that many environmental problems can be explained by institutional failure and bad government methods. Goel et al. (2013) argue that many policies have been implemented to influence (directly or indirectly) economic agents to internalize environmental externalities. A key factor behind the success of these policies is the institutional quality of a country. In this context of the literature, some researchers have been paid to the democracy– pollution nexus and some researchers have assessed the effect of political freedom on pollution. The results of such studies are, however, contradictory. In this paper we mainly focus on the democracy–environmental pollution nexus. Some theorists believe that democracy can improve the environmental quality of a country, while others argue that may not improve the environmental quality or may even worsen it. Empirically, the results are mixed. The studies by Torras and Boyce (1998), Barrett and Graddy (2000), Li and Reuveny (2006), and Farzin and Bond (2006) argue thatdemocratization makes citizens better informed and better enabled to protest. Torras and Boyce (1998) find that democracy has in general a positive and significant effect on environmental quality, especially in low-income countries. Harbaugh, Levinson, and Wilson (2002) find there exists a consistent negative relationship between sulfur dioxide and the democracy level of a country. Farzin and Bond (2006) find evidence that the country’s level of democracy and its associated freedoms isrelated positively to environmental quality. Bernauer and Koubi (2009) find that democracies and especially presidential systems have a positive effect on air quality. However, several scholars find that democracy may not improve the environmental quality or may even worsen it (Midlarsky, 1998; Roberts & Parks, 2007; Scruggs, 1998). For example, Roberts and Parks (2007) conclude that democracy has almost no impact on carbon emissions. Scruggs (1998) also find an insignificant relationship between democracy level and three environmental indicators (dissolved oxygen demand, fecal coliform, particulate emissions), once income inequality is included. Nevertheless, Midlarsky(1998) finds that a higher democracy level is associated with a worse environmental performance. Though many literatures are concerned with the relationship between democracy and environmental quality, it is safe to say that extant empirical evidence on democracy–pollution nexus is mixed. We argue that the main shortcoming of these studies is that the result may be biased due to neglect the distributional heterogeneity. In addition, financial openness may also play a significant role in reducing environmental pollution (Tamazian & Bhaskara Rao, 2010; Jalil & Feridun, 2011; Tamazian, Chousa, & Vadlamannati, 2009). Tamazian et al. (2009) argue that an improvement in financial infrastructure (based on the openness of capital account) may contribute to the efficient technological use and, therefore affect the environmental degradation as well. However, research on financial openness and its effects on pollution is more recent, and in relative infancy. So far we have found no studies to establish the relationships between democracy, financial openness, and environmental pollution accounting for distributional heterogeneity in panel quantile regression framework. 3 Thus, our contribution is complementary to this research. We add to the extant literature by: (a) examining the joint effects of democracy and financial openness on environmental degradation in one framework. The theoretical literature is aware of a possible joint effect of democracy and financial openness on environmental degradation. But none of the existing empirical studies has analyzed this relationship satisfactorily. The previous studies include either democracy or financial openness in the EKC framework. In this paper, we test the joint importance of democracy and financial openness on pollution. Brune and Guisinger (2003) show a positive impact of democracy on financial openness. Similarly, Kirch and Terra (2012) argue that financial decisions may be strongly influenced by the institutional quality of a country. Quinn (2000) acknowledges the possibility of reverse causality from financial liberalization to democratic reversals. Given the relationship between democracy and financial openness, if one or both constructs are misrepresented in the model, there is a substantial likelihood that the coefficient of one variable is contaminated by another variable. In the interest of addressing our research question, we run three specifications of the model. Specification I includes both democracy and financial openness; Specification II includes only the democracy factor; and Specification III includes only the financial openness factor; (b) by employing a quantile regression model with panel data, notably developed by Koenker (2004), we extend the earlier analysis by looking the impact of democracy not only on the mean but also on the shape of the conditional distribution of environmental pollution. In particular, we examine whether greater democracy and more financial openness consistently reduce emissions among the most and the least emissions countries. Are there different causes of emissions in high emissions nations compared to the least emissions countries? To the best of our knowledge, no studies have investigated the relationship between democracy, financial openness, and environmental quality in quantile regression framework. The motivation to use quantile regression on emissions equation is twofold. 4 First, quantile regression is able to describe the entire conditional distribution of the dependent variable (in our case emissions) and thus help us obtain a more complete picture of the factors affecting pollution emissions. 5 Specifically, quantile regression estimator gives one solution to each quantile. Therefore, we may assess how policy variables affect countries according to their position on the conditional emission distribution (Mello & Perrelli, 2003). Using this methodology, we are able to assess the determinants of emissions throughout the conditional distribution, with particular focus on the most and least emissions countries—those that are arguably of the most interest. From a policy perspective, it is more interesting to understand what happens at the extremes of a distribution. Chestnut, Schwartz, Savitz, and Burchfiel (1991) argue that both humans and ecosystems are more seriously affected at high concentrations of pollutions. Hence, it is important to learn about the behavior of emissions at high levels of pollution. However, policy analyses using OLS techniques are not particularly suitable to target environmental protection policies toward high emissions countries. In addition, it is often the case that the characteristics of the locations or countries that experience pollution levels below or above the mean are intrinsically different. In the type of consideration, the focus is no longer on the mean effect, but on the full distribution of pollution emissions. Second, the quantile regression estimator is robust to outlying observations on the dependent variable and quantile regression can be more efficient than OLS regression when the error term is non-normal. 6 This is of particular advantage in emissions equation setting where the emission distribution is typically characterized by thick tails, 7 as can be seen, for instance, in Flores, Flores-Lagunes, and Kapetanakis (2014). At present, only a few, albeit important papers, have applied panel quantile regression fixed effect model to investigate the relationship between income and pollution (Damette & Delacote, 2012; Flores et al., 2014; Yaduma, Kortelainen, & Wossink, 2013). 8 These three papers, however, have not included explanatory variables to explicitly account for the role of democracy and financial openness in the income–environmental nexus. To the best of our knowledge, no paper has yet thoroughly investigated the joint role of economic, financial openness and democracy variables on CO2 emissions in the panel quantile regression model framework. The remainder of the paper is organized as follows. Section 2 outlines the methodology used within this paper. Section 3 describes the data used in this paper. The empirical results of panel quantile regression models are presented in Section 4. Section 5 concludes the paper. 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