Is there a Green Dividend of National Redistribution?

Gürer, Eren; Weichenrieder, Alfons J. Article — Published Version Is there a Green Dividend of National Redistribution? The Journal of Economic Inequality Provided in Cooperation with: Springer Nature Suggested Citation: Gürer, Eren; Weichenrieder, Alfons J. (2023) : Is there a Green Dividend of National Redistribution?, The Journal of Economic Inequality, ISSN 1573-8701, Springer US, New York, NY, Vol. 22, Iss. 1, pp. 33-47, https://doi.org/10.1007/s10888-023-09579-5 This Version is available at: https://hdl.handle.net/10419/312386 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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ErenGürer1· AlfonsJ.Weichenrieder2 Received: 8 December 2022 / Accepted: 19 April 2023 © The Author(s) 2023 Abstract CO2 emissions are disproportionately caused by more affluent consumers. In the political debate, this fact has triggered the demand for income redistribution and wealth taxes not only to reduce inequality but also to reduce CO2 emissions. This paper calculates the possible size of a green dividend, i.e., a reduction in total national CO2 emissions, of redistribution in 26 countries and concludes that, for most EU countries, it is negative if the redistribution is efficient, in the sense that it keeps average incomes constant. If the redistribution introduces inefficiencies that lead to total income losses, the negative green dividend, otherwise associated with additional redistribution, may be avoided. Keywords Environment· Redistribution· CO2 emissions· Inequality· Green dividend JEL Classification Q56· D12· D30 1 Introduction There is growing awareness that CO2 emissions are disproportionately caused by the most affluent consumers. Chancel and Piketty (2015) estimate that 10% of the world’s population are responsible for approximately 45% of global CO2 emissions. Oxfam (2020) reckons that the richest five percent of the world’s population account for 37% of global CO2 emissions. When it comes to the responsibility for CO2 emissions, considerable attention recently has been given to the rich and super-rich. Barros and Wilk (2021) take stock of the emissions by 20 billionaires and suggest shaming as a measure to reduce their enormous CO2 footprints, which are driven by yachts, private jets and heating of multiple estates. For the EU, Ivanova and Wood (2020) calculate that the average carbon footprint of the top 1% of * Alfons J. Weichenrieder a.weic[email protected]t.de Eren Gürer [email protected] 1 Faculty ofEconomics andAdministrative Sciences, Middle East Technical University, 06800Ankara, Turkey 2 Faculty ofEconomics andBusiness Administration, Goethe University Frankfurt, Vienna University ofEconomics andBusiness & CESifo, 60323Frankfurt, Germany E.Gürer, A.J.Weichenrieder 1 3 households amounts to 55 tons of CO2-equivalent emissions per person, while in most EU countries the median polluter has a footprint of less than 10 tons. In the political debate, these asymmetric emissions trigger the demand for income redistribution and wealth taxes to reduce emissions by decreasing inequality. The claim has been made that less unequal countries may produce less CO2 emissions (see Kenner 2015; Dorling 2010). In this study, we define a green dividend as a reduction in total national CO2 emissions per capita, coming as a byproduct of more redistribution. In the academic literature, the question whether redistribution from rich to poor within a country could reduce CO2 emissions, and in that way could provide a green dividend, has received only limited attention. Brännlund and Ghalwash (2008) provide information based on Swedish household data on non-durable goods consumption. Using estimates of a non-linear demand system and pollution intensities of different consumption goods, they show that the income-pollution relationship is positive, but concave, which means that a more equal income distribution would increase, rather than decrease, the amount of three pollutants (CO2, NOx, SO2). This suggests that, contrary to what underlies the popular demand, no green dividend of redistribution would result. There is also scattered evidence from other countries. A working paper by Castellucci etal. (2010) applies the Brännlund and Ghalwash (2008) method to Italian data and also suggests a negative green dividend of redistribution. Sørheim (2021) finds that the carbon intensity of consumption of Norwegian households increases with income. A similar finding for urban China derives from Golley and Meng (2012). Duarte etal. (2012) and Lévay etal. (2021) find the opposite respectively for Spain and Belgium, where the pollution intensity of consumption seems to be lower for high incomes. Levinson and O’Brien (2019) identify concave Engel curves in the U.S. Unlike the present paper, they look at five different more local pollutants, omitting CO2. Apart from the studies based on micro data of consumption expenditure, several studies attempt to infer the relationship between income distribution and emissions via times series or panel data with macroeconomic variables. Often, these studies regress the log of national CO2 emissions on a lag structure of the Gini index and other variables. For the U.S., Baek and Gweisah (2013) find a positive short and long-term association of the Gini index with the level of CO2 emissions. Demir etal. (2019) find a negative long run association for Turkey, while Uzar and Eyuboglu (2019) empirically claim a positive association.1 Cheng etal. (2021) report a positive relationship between income inequality and direct CO2 emissions in China; Ghazouani and Beldi (2022) look at seven Asian economies and also argue that a higher level of inequality increases emissions. For OECD countries, Hailemariam etal. (2020) focus on the top income inequality in a macro-econometric approach and claim that the top 10% share is positively associated with CO2 emissions. Hübler (2017) derives mixed results dependent on the specific panel data model. Knight etal. (2017) suggest an empirical association of wealth inequality and national CO2 emissions in high-income countries. A problem of macro studies that exploit the time dimension is that interactions between income and technical progress, as well as potentially important omitted variables (such as regulation, international commitments, political upheavals), are not included in the analysis. A causal interpretation of these regressions is therefore difficult. Redistribution via 1 Bae (2018) looks at the interaction between mitigation instruments and the income distribution. Bruckner etal. (2022) evaluate the effect of possible further initiatives of international poverty reduction on CO2 emissions. The results suggest a modest positive impact of poverty reduction on global CO2 emissions. The connection between inequality and local air quality is studied in Kasuga and Takaya (2017). 34 Is there aGreen Dividend ofNational Redistribution? 1 3 tax-transfer schemes may have a different impact on emissions than empirically observed distributional variation that, among other factors, may derive from changes in trade patterns, innovation, or unionization. The present study avoids these issues by following a microeconomic approach. It resembles Brännlund and Ghalwash (2008) in using household budget surveys to study the link between income distribution and CO2 emissions. Our study differs from existing studies in several ways. First, unlike Brännlund and Ghalwash (2008) and other papers that consider specific countries, we investigate 26 European countries simultaneously thanks to harmonized datasets. Second, we have access to kgCO2-equivalent emissions of 200 products (direct plus indirect emission products), a considerably higher number than analyzed in previous studies. Brännlund and Ghalwash (2008) use eight products, Castellucci etal. (2010) exploit three direct emission products. Sørheim (2021), Golley and Meng (2012), Duarte etal. (2012) respectively study emission intensities of 61 products, 42 sectors, and 27 economic activities. Third, emission intensities utilized in this study are derived from a multi-regional input–output model. Hence, we do not have to impose the assumption that imported products have the same environmental impact as domestically produced products, a limitation acknowledged by Brännlund and Ghalwash (2008), Golley and Meng (2012) and Duarte etal. (2012).2 This limitation may be important if production locations of goods consumed by rich vs. poor systematically differ. For 26 European countries, we simulate a redistributive tax-transfer policy which comes on top of existing policies. It adds a flat tax of ten percent to finance a lump sum payment for every household. We derive that, for 22 countries, such an income redistribution would result in a negative green dividend, i.e., CO2 emissions would increase. Only four countries would have a reduced level of CO2 emissions. Even for those, the green dividend is limited. In the case of the UK, if additional redistribution leads to a 3.5 percentage point decrease in the Gini coefficient, this reduces CO2-equivalent emissions per capita by 40kg, which amounts to a mere 0.47% of per capita emissions in the year 2010. We also explore scenarios when the tax-transfer system is costly and therefore shrinks average income. A vast literature on the deadweight loss of taxation (excess burden) suggests that a transfer system may resemble a leaky bucket: increased taxes could lead to distorted incentives and behavioral effects on labor markets may shrink output. On top of losses due to behavioral changes, taxes imply losses because of compliance and administrative costs. We address these losses in a stylized way by asking how growth-adverse a tax system must be to overturn the results derived under the assumption of a neutral taxtransfer system. We find that if slightly more than 15% of the tax revenues are assumed to be wasted, then this would be enough to generate a green dividend in all countries. This means that, in most countries, only a costly, wasteful redistribution system may carry a green dividend, i.e., lower CO2 emissions; however a costless system does not. The reminder of the paper is organized as follows. The next section introduces our methodology and Sect.3 presents our data. Section4 contains the main analysis, Sect.5 concludes. Technical details are described in the Appendix. 2 Castellucci etal. (2010) study direct emission products and, hence, imports are not relevant. An exception is Sørheim (2021), which also derives emission intensities from a multi-regional model. 35 E.Gürer, A.J.Weichenrieder 1 3 2 Methodology Simulating how households’ CO2 emissions would change with income may be done in a variety of ways. One approach is to estimate individual demand curves for various goods categories using a structural model. With these demand curves, it is possible to derive a counterfactual demand after some redistribution is applied. In a second step, CO2 intensities of final output goods are used to calculate CO2 emission changes from the income effects on demand. This approach is used by Brännlund and Ghalwash (2008). Several assumptions must be imposed for its implementation. For example, some functional form of demand must be specified. In addition, the estimation assumes differently affluent households face the same prices, which has been empirically contested by the literature on shopping behavior (e.g., Aguiar and Hurst, 2007). A simpler approach, utilized in the present paper, is to regress the actual, observed CO2 emissions of households over some explanatory variables that include a proxy of their permanent incomes, household type and size. After altering households’ net incomes with, e.g., a redistributive policy, counterfactual CO2 emissions can be predicted using the previous regression’s coefficients. While this method requires no assumption on the homogeneity of prices and no specific functional form of demand, it also comes with caveats. An implicit assumption in using this approach is that consumers’ goods demand, and hence CO2 emissions, are changed in the same way irrespective of the source of income changes (e.g., market influences on income vs. tax changes). The same assumption, however, is made for approaches that exploit demand estimates through structural modeling. Let cpre i denote kgCO2-equivalent consumption of household i residing in any country (for brevity country indices are suppressed) directly observed in the data at 2010. Note that cpre represents the household’s kgCO2-equivalent consumption before the redistributive policy experiment. Our regression specification that predicts CO2 consumption reads: where pk,i denotes a set of household-type dummies with k∈{1, 2, …,7} indicating whether household i belongs to household-type group k . Household-type classification is as follows: 1one adult, 2two adults, 3more than two adults, 4one adult with dependent children, 5two adults with dependent children, 6more than two adults with dependent children, 7others. Note that there are only 22 observations of group 7 in Spain. Parameter epre i represents the total annual expenditure (permanent income) of household i before redistribution (observed in the data) and si indicates household size. Variables 𝛽1 k ,.., 𝛽5 k are the regression coefficients associated with the terms that include different orders of total expenditure. It should be noted that household-type information is missing in Sweden. Thus, explanatory variables in the regressions of Sweden are epre i ,.., ( epre i ) 5 and si . We run the regression specification given in (1) separately for each country and collect the predicted values 𝛼 ,  λk ,  𝛽1 k ,..,  𝛽 5 k,  𝛿 and 𝜀 i with k∈{1, 2, …,7} . In the next step, we perform the redistributive policy experiment by taxing the 10% of epre i and redistributing the resulting tax revenue equally to every household country-by-country. Let epost i denote the total expenditure (permanent income) of household i located in any country after redistribution. Resulting kgCO2-equivalent consumption of any household can be predicted by: (1) c pre i=𝛼+ 7 ∑ k=1 𝜆kpk,i+ 7 ∑ k=1 𝛽1 kpk,i∗epre i+ 7 ∑ k=1 𝛽2 kpk,i∗(epre i)2+ 7 ∑ k=1 𝛽3 kpk,i∗(epre i) 3 + 7 ∑ k=1 𝛽4 kpk,i∗(epre i)4+ 7 ∑ k=1 𝛽5 kpk,i∗(epre i)5+𝛿si+𝜀i 36 Is there aGreen Dividend ofNational Redistribution? 1 3 Hereafter, one can calculate kgCO2-equivalent consumption per-capita, cpost , in every country. Country-by-country differences, cpre −cpost , yield the bars presented below in Fig.5. 3 Data Our data comes from two sources.3 Information on households’ good demands is taken from the 2010 European Union Household Budget Surveys (EU HBSs) provided by Eurostat. For most of our 26 European countries, we facilitate 63 different product baskets (51 for Germany and 59 for Sweden). In a next step, the goods demands need to be transformed into the direct and indirect CO2 emissions that go with these product baskets.4 For this, we use the 2010 wave of EXIOBASE v3.8.2 database, which relies on country-specific input–output matrices and technical coefficients to yield CO2 emissions of 200 different product categories in each country. More specifically, EXIOBASE gives kgCO2-equivalent emissions per euro spent on these 200 product categories.5 Because of national differences in transport costs, input–output matrices, and production processes these data differ between countries. As the product categories between the EU HBSs and EXIOBASE3 differ, a rule on how to assign the 63 HBS groups into the 200 EXIOBASE3 groups is required. To achieve this, we rely on a concordance table provided by Ivanova and Wood (2020). A couple of examples may be helpful to illustrate how CO2 coefficients of EXIOBASE are linked to the EU HBSs. In the EU HBSs, the air travel expenditures of households are recorded under a category named ‘Passenger transport by air’. At the same time, we observe the CO2-equivalent emissions induced by one Euro expenditure on ‘Kerosene’ (jet fuel) and on ‘Air transport services’ in EXIOBASE. According to the concordance tables of Ivanova and Wood (2020), 25% of the money spent on air transport services is attributable to expenditure on kerosene, whereas the remaining 75% is attributable to air transport services. Accordingly, CO2 emissions caused by each household’s expenditure on air travel can be determined. Similarly, the concordance tables of Ivanova and Wood (2020) suggest that the HBS expenditure category ‘Electricity expenditure’ can be broken into several sub-categories such as ‘Electricity by coal (6%)’, ‘Electricity by gas (14%), ‘Electricity by nuclear (40%)’, ‘Transmission and Distribution Services (37%)’. The CO2-equivalent (2) c post i=𝛼 + 7 ∑ k=1  λkpk,i+ 7 ∑ k=1  𝛽1 kpk,i∗epost i+ 7 ∑ k=1  𝛽2 kpk,i∗(epost i)2+ 7 ∑ k=1  𝛽3 kpk,i∗(epost i) 3 + 7 ∑ k=1  𝛽4 kpk,i∗(epost i)4+ 7 ∑ k=1  𝛽5 kpk,i∗(epost i)5+ 𝛿si+𝜀 i 3 The datasets and data preparation procedures are described in more detail in Appendix. 4 A direct CO2 emission occurs in the use phase of a product (e.g., when a household buys gasoline and burns it while driving). An indirect emission occurs when a consumer purchase leads to emissions in the production chain. 5 EXIOBASE reports CO2 content of goods in per euro expenditure in basic prices (excluding trade & transport margins and taxes). EU HBSs provide expenditures in purchaser prices. We follow Ivanova and Wood (2020) and convert HBS expenditures in purchaser prices into basic prices by removing taxes and allocating trade & transport margins into associated products. 37 E.Gürer, A.J.Weichenrieder 1 3 emissions associated with the sub-categories are observable in EXIOBASE and, thus, one can calculate the total amount of emissions caused by each household’s electricity demand. In the HBSs, reported expenditures by households may be too large or too small to match country-wide levels despite the representative nature of the EU HBSs. We follow Ivanova and Wood (2020) in scaling HBS expenditures of each household proportionately to match country level expenditures reported in EXIOBASE (national accounts). On average, this leads to a scaling factor of 1.37. Scaling of expenditures for specific goods comes at a cost as it may alter total household level expenditures in asymmetric and slightly arbitrary ways: a household’s expenditure for motor vehicles may be scaled up if it bought a car, whereas for another household that did not buy a car, expenditure cannot be scaled up. We identify four product categories that reduce the correlation between total raw expenditures and total scaled expenditures in a visible way and exempt them from scaling if under reported: – Motor vehicles, trailers and semi-trailers (25 out of 26 countries under report) – Kerosene (5 out of 26 countries under report) – Financial intermediation services, except insurance and pension funding services (26 out of 26 countries under report) – Other business services (26 out of 26 countries under report) The first two categories frequently have zero expenditures for households, as car purchases and holiday flights clearly may not occur each year.6 Many households with zero reported expenditure for the last two categories may simply not be aware of having those expenditures. Outliers exist in every country, both in terms of CO2-equivalent consumption and total household expenditure. We drop households that constitute the top 0.1% of household CO2 consumption and the top 0.1% of household total expenditure. The total number of dropped observations amounts to 425 out of 274.396 (the remaining sample size after performing other cleaning procedures is further described in Appendix). While it is widely accepted that, in general, the climate effect of CO2 emissions does not depend on where and in what way CO2 is emitted, there is one exception: aviation. The radiative forcing that measures the climate relevance of all side effects to CO2 emissions is deemed higher for aviation, because high layer contrails and other effects tend to aggravate aviation induced global warming. This was emphasized by the International Panel on Climate Change, IPCC (1999). One way of expressing the climate effect of aviation is to divide the sum of climate effects (radiative forcing) by the radiative forcing that resulted from aviation’s CO2 emissions alone, leading to a frequently used upscaling factor of about three (Wit etal. 2005, p. 34). While more recent literature seems to suggest a somewhat smaller average factor, a large variation of estimates between 1.9 and 5 still prevails (cf. Jungbluth and Meili 2019, p. 405). We decided to introduce a factor three for CO2 emissions from kerosene. Considering the role of inequality for CO2 emissions, it is important to recognize the role of kerosene in this research, as air travel services are disproportionally consumed by the rich. This weighting has not been done in previous work on the nexus between inequality and CO2 emissions. However, we argue that ignoring the fact that a ton of CO2 emitted via air travel has a larger climate impact than a ton emitted by public buses 6 Scaling of kerosene consumption from air travel may also be problematic if some of national consumption should be attributed to foreign flight guests. 38 Is there aGreen Dividend ofNational Redistribution? 1 3 would systematically distort our calculations that are concerned about distributional issues. Because air travel is consumed disproportionately by the more affluent, the weighting of kerosene tends to produce a higher green dividend compared to using no weight. While EU HBSs contain self-reported household income, we found this variable to be fraught with considerable noise, including negative values. For example, CO2 consumption vs. reported net incomes curve exhibits a second peak at the bottom of the distribution. This may reflect a temporary income shock such as capital losses. We therefore decided to take scaled reported household expenditure as a measure of permanent income as in Brännlund and Ghalwash (2008). Figure1 reports our calculations of the CO2 footprint for four different household types in each country. The ranking of countries is based on mean (per-capita) kgCO2-equivalent consumption (green dots); in each country, the footprint of the 10th and 90th percentiles and the median are also reported.7 Figure2 illustrates the unweighted average of CO2-equivalent consumption shares for each consumption expenditure decile. Across countries, the top 30% percent, in terms of consumption expenditure, is responsible for more than 50% of CO2. 4 Redistribution and CO2 emissions It is recognized that convexity or concavity of the Engel curves for carbon intensive consumption is decisive for the role of redistribution on carbon emissions (Brännlund and Ghalwash 2008). In the case of a concave relationship, redistribution, which puts more households from the ends of the income distribution to the middle, should tend to increase total emissions. The opposite should be the case if the Engel curve is convex. The five panels in Fig.3 illustrate Engel curves for CO2 consumption for five selected countries. We find concave curves for the vast majority of countries, exemplified in Fig.3 by Germany, France and Belgium. Convex curves, on the other hand, are depicted in Fig.3 for Ireland and the UK; they are also found in Luxemburg and Cyprus. While the role of the Engel curve’s shape is recognized in the literature, it is unclear how large the changes of CO2 emissions from redistribution are. In this section, we therefore simulate a policy measure that reduces national inequality. This policy measure, which is added to the existing system in each country, collects from each household 10 percent of total expenditure (our proxy of permanent income) and uses the revenues to pay a limited, universal demogrant to each household. Neither the tax, nor the demogrant is conditioned on household size. This policy measure would not affect all countries in the same way. If a country exhibits a rather equal distribution to begin with, then our measure has a modest impact compared to a pre-existing situation of a very unequal distribution. Figure4 illustrates the impact of our policy measure on the Gini coefficient, measured in percentage points (ppt.), in all 26 countries. For most countries, a flat tax of 10% plus a lump sum grant would reduce the Gini coefficient between three and four ppt., where three ppt., for example, equals the difference in the Gini coefficient for disposable income between Portugal and Sweden in 2019.8 7 Unexpectedly, Cyprus ranks highest in mean CO2 footprint, which is not due to a single expenditure category, but based on several CO2-intensive consumption categories. 8 Based on data from the OECD inequality database (https:// data. oecd. org/ inequ ality/ incomeinequ ality. htm). 39 E.Gürer, A.J.Weichenrieder 1 3 Fig. 1 CO2 footprints across countries. Note: Belgium (BE), Bulgaria(BG), Czech Republic (CZ), Cyprus (CY), Denmark (DK), Germany (DE), Estonia (EE), Finland (FI), Greece (EL), Spain (ES), France (FR), Croatia (HR), Hungary (HU), Italy (IT), Ireland (IE), Lithuania (LT), Latvia (LV), Luxembourg (LU), Malta (MT), Poland (PL), Portugal (PT), Romania (RO), Slovenia (SI), Slovakia (SK), Sweden (SE), United Kingdom (UK) Fig. 2 Distribution of CO2 across consumption expenditure deciles. Note: The length of the bar for decile 1 (and analogous for the other bars) is derived by calculating the CO2 consumption share of those 10 percent of the population with the smallest total expenditure in each country and then taking the unweighted average across the 26 countries 40 Is there aGreen Dividend ofNational Redistribution? 1 3 Kasuga, H., Takaya, M.: Does inequality affect environmental quality? Evidence from major Japanese cities. J. Clean. 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