1. 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

2. Outline Underlying theories Related literature Data and empirical models Main results and further developments Conclusions

3. Theoretical arguments Political economy (Boyce, 1994, 1998) – Greater power inequality  more pollution – More unequal distributions of wealth and power tend to yield worse environmental outcomes (+) Aggregation bias (Heerink, Mulato and Bulte, 2001) Lower inequality  higher emissions Concave ekc at househoold level Redistribution  increase pollution (-) Emulation theory (Vleben, 1919) Higher inequality  higher emissions (+)

4. Related literature Authors Dependent Variable EKCOther variablesIncome InequalitySample and model Torras and Boyce (1998) Sulfur dioxide, Smoke, Heavy particles etc Yes GDP cubed Literacy Political rights Gini (+), low-, (-),high- income 19-42 countries 1977-1991 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, 1975-1992 OLS and FE Borghesi (2000)CO2 per captaYes GDP cubed, Population density, Industry share, Interaction OLS: Gini (-), Interaction (+), FE: Gini not statist. Signinficant 37 countries, 1988-1995 OLS, RE and FE Heerink et al. (2001)CO2 per capitaYesNoGini (-)64 countries, 1985 OLS

5. Data description Adjusted Gini index from Grün &Klasen Extended period Modelling non-linearlities between emissions and income inequality

6. Data description We use an unbalanced panel data set covering 138 countries from 1960 to 2008 CO2 Oak Ridge Center GDP from PWT GINI from Grün & Klasen Other variables from WDI

7. 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

8. Per capita emissions and income inequality

9. Baseline Results

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

11. New developments Instrumental variable approach (FE, RE, BE estimates) – Corruption – Political orientation – Education level – Labour regulations Simultaneous equation model (3SLS) Quantile regressions

12. Instrumental Variables

13. Simultaneous equation model

15. Quantile regressions

17. Main Conclusions Inverted U-shaped relationship between income and CO2 emissions U-shaped curve between Gini and CO2 emissions Inequality decreases emissions only for low levels of inequality Problem: finding adequate instruments

18. 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 Minimum wages, calculated on a monthly basis Median wages, calculated on a monthly basis Average wages, calculated on a monthly basis Unemployment Benefits First and second-year gross replacement rates Overall average GRR for two years UB coverage Employment Protection Legislation Maximum advance notice Advance notice period after 9 months of service, in months Advance notice period after 4 years of service, in months Advance notice period after 20 years of service, in months Maximum Severance payment Severance payment after 9 months of service, in months: a lump-sum payment to the dismissed employee at the time of cessation of employment Severance payment after 4 years of service, in months

19. References "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 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