CO 2 Emissions and Income Inequality By Nicole Gruenewald, Stephan Klasen, Inmaculada Martínez- Zarzoso and Chris Muris Georg-August University of Göttingen AP1 Macro1 paper

Outline Motivation Underlying theories Related literature Data and empirical model Main results Conclusions

Motivation Income inequality a major (and rising) policy concern in developing countries: – Increases poverty, lowers poverty impact of growth, and lowers growth (?) Reducing carbon emissions a related concern; Question: How are the two related? Trade- offs or win-win? And how does this differ in richer vs. Poorer countries? Paper: Empirical assessment of relationship;

Underlying theories (1) Political economy (Boyce, 1994, 1998) Greater power inequality  more pollution (2) Aggregation bias (Heerink, Mulato and Bulte, 2001) If EKC relationship by income within a country Lower inequality  higher emissions (but Ravallion et al. 2001) (3) Inequality and Exclusion from the carbon economy Higher inequality, higher exclusion, lower emissions. (4) Emulation theory (Vleben, 1919) Higher inequality  higher emissions

Underlying theories(cont) Political economy (Boyce, 1994, 1998) – Via cost-benefit analysis and impacts on the rate of environmental time preference (short-run benefits, long-run costs) Normative Cost-Benefit Rule versus Positive Power- Weighted Social Decision Rule max  i b i where  i = power of i th individual  More unequal distributions of wealth and power tend to yield worse environmental outcomes

Underlying theories (cont) Political economy (Boyce, 1994, 1998) I.Incidence: Social decisions on environmental protection systematically favor those with more wealth and power over those with less II. Magnitude: More unequal distributions of wealth and power tend to yield worse environmental outcomes

Political economy (Boyce, 1994, 1998) I. Power-weighted social decision rule losers’ marginal cost winners’ marginal benefit Solid lines = marginal cost to losers and marginal benefit to winners (=  i b i for all b i 0, respectively). Dashed lines = power-weighted marginal cost to losers and marginal benefit to winners (=  i b i for all b i 0, respectively). E* = “optimal” level prescribed by cost-benefit analysis; E' = level under the power-weighted social decision rule when winners are more powerful than losers. But: Do powerful systematically favor more pollution/emissions? 0 $ Level of pollution E*E'E'

losers’ marginal cost winners’ marginal benefit Key: E* = “optimal” level prescribed by cost-benefit analysis; E' = level under the power-weighted social decision rule when winners are more powerful than losers; E" = level under the power-weighted social decision rule when losers are more powerful than winners 0 $ Level of environmentally degrading economic activity E* Political economy (Boyce, 1994, 1998) II. Magnitude: Two Types of Inefficiency E"E"E'E' Type-I inefficiency Type-II inefficiency

Aggregation bias: EKC: Higher inequality can reduce emissions But depends on type of redistribution and MPE! Ravallion et al (2001): Marg. Prop. to Emit and Inequality Y1Y1 Y2Y2 YmYm YmYm Y2Y2 Y1Y1 V f (Y) Underlying theories (cont) Y3

Underlying theories (cont) Inequality and the Carbon Economy – In poor countries, the poor lack access to modern energy and lead (largely) carbon-neutral lives (but issue of LUC!); – Higher inequality can increase the share of population outside of carbon economy; – Would lower emissions/capita – Effect particularly relevant in poorer countries! Emulation theory (Vleben, 1919) – Higher inequality  increase conspicuous (energy-intensive) consumption of all social groups, increasing emissions/capita No clear theoretical predictions!

Related literature Authors Dependent Variable EKCOther variables Income Inequality Sample and model Torras and Boyce (1998) Sulfur dioxide, Smoke, Heavy particles etc Yes GDP cubed Literacy Political rights Gini (+), low-, (-),high- income countries OLS Ravallion et al. (2000)CO2 per capitaYes Population and Gini interactions with all variables Time trend Gini (+), Interaction with GDP (+) 42 countries, Average Gini 1980s, OLS and FE Borghesi (2000)CO2 per capitaYes GDP cubed, Population density, Industry share, Interaction OLS: Gini (-), Interaction (+), FE: Gini not statist. Signinficant 37 countries, OLS, RE and FE Heerink et al. (2001)CO2 per capitaYesNoGini (-)64 countries, 1985 OLS

Novelties in this study Adjusted (more comparable) Gini index from Grün &Klasen (2008) Extended period and much larger country coverage; Modelling nonlinearlities between emissions and income inequality and interaction terms; Thorough robustness checks;

Data description Unbalanced panel dataset: 138 countries from 1960 to 2008: – CO2 from Oak Ridge Center – GNP from PWT – GINI from Grun and Klasen – Other variables from WDI

Empirical model The EKC model augmented with inequality is given by, L denotes natural logs Epc denotes CO 2 emissions per capita GDPpc is GDP per capita INEQ is the adjusted Gini index GDPINEQ is an interaction term between l GPDpc and lINEQ A composite error term and time dummies are added Below: Further Control Variables

Descriptives

Baseline Results Clear U-relationship for Gini plus positive interaction term!

Per capita emissions and income inequality Turning point in sample, but further right in richer countries!

Endogeneity Problems Reverse causality unlikely to be the case; Time-invariant unobserved heterogeneity captured by fixed effects; Problem of time-varying unobserved heterogeneity remains: – Tried various GMM (not stable); – Covariates (mostly do not change results) – Time varying external instruments hard to generate;

Main Conclusions Inverted U-shaped relationship between income and CO2 emissions (confirmed) U-shaped curve between Gini and CO2 emissions (new) Significant interaction term Robust result For rich countries: lower inequality associated with lower emissions (win-win); emulation effects? Aggregation bias and EKC? For poor countries: lower inequality increases emissions (carbon economy effects? Some descriptive support: rich: positive correlation inequality car/television ownership, poor: negative correlation.

Further Steps Further robustness checks: – Using broader emission data (including LUC) – Using UNIDO inequality data; – Using consumption-based emissions? Transmission channels: – Include variables that proxy channels in model; Work further on endogeneity issues.

Thanks for your attention

New developments Instrumental variable approach (FE, RE, BE estimates), Instruments: – Corruption – Political orientation – Education level – Labour regulations Quantile regressions Program evaluation approach

Instrumental Variables

Quantile regressions

Other ideas for instruments Brand new database which collects information on Minimum Wages, Unemployment Benefits and Employment Protection Legislation around the world. This database, which covers a long time span from 1980 to 2005, contains the following information for 91 countries: Minimum Wages, Unemployment Benefits, Employment Protection Legislation "Labour Market Regulation in Low-, Middle- and High- Income Countries: A New Panel Database" (2011), IMF working paper.Labour Market Regulation in Low-, Middle- and High- Income Countries: A New Panel Database Used in: Labor Market Regulations and Income Inequality: Evidence For A Panel of Countries By César Calderón, Central Bank of Chile; Alberto Chong, Inter-american Development Bank; Rodrigo Valdés, Central Bank Of Chile

Robustness A: annual data C : no Gini D: WDI Gini E: poverty gap F :Urban % H: richest half