[1] Address for correspondence: Martin Ravallion, Development Research Group, World Bank, 1818 H Street NW, Washington DC. These are the views of the authors, and should not be attributed to the World Bank or any affiliated organization. Not to be cited without permission. Financial assistance was provided by a joint British-Dutch-Swedish trust fund for studying the Social and Environmental Consequences of Growth-Oriented Policies. Helpful comments were received from Thomas Selden and seminar participants at the World Bank.
[2] However, there is conflicting evidence on this point (Levy and Chowdhury, 1994).
[3] A non-binding emissions curtailment policy applicable only to the developed nations was negotiated in 1992 (UN, 1992).
[4] Though this effect does not appear to have been discussed in the literature on environmental economics, it is widely appreciated in the literature on consistent aggregation of consumer demand models where inequality is often introduced as an additional variable in aggregate models.
[5] Here we use a well-known result from the literature on measuring inequality; see Atkinson (1970).
[6] Consumption accounts for trade, stored fuel, and changes in stocks; see Boden, Marland, and Andres (1995).
[7] We include carbon emissions from gas flaring, using estimates given in the UNSTAT database. Gas flaring emissions are generated as an unused by-product of oil drilling and typically constitute only about one percent of total emissions from fossil fuel use. Past work has often omitted this component. However, Heil (1997) found that models of emissions could be sensitive to the inclusion of gas flaring.
[8] This is one half of the area between the Lorenz curve and the diagonal, where the Lorenz curve gives the cumulative share of total income (on the vertical axis) held by the poorest x% of the population. If everyone has the same income then the Gini index is zero; if the richest person has all the income then its value is unity. Gini indices for income or consumption by country vary from 0.20 to 0.60. For a compilation of recent estimates by country see World Bank (1997).
[9] The countries in the data set are Australia, Bangladesh, Belgium, Brazil, Bulgaria, Canada, Chile, China, Colombia, Costa Rica, Cote d’Ivoire, Czechoslovakia, Finland, France, Honduras, Hong Kong, Hungary, India, Indonesia, Italy, Japan, Korea, Malaysia, Netherlands, New Zealand, Norway, Pakistan, Philippines, Poland, Portugal, Singapore, Soviet Union, Spain, Sri Lanka, Sweden, Taiwan, Thailand, Trinidad, Tunisia, United Kingdom, USA, and West
Germany.
[10] This is the same data we use later, after introducing within-country inequality. The picture looks very similar if we add the extra countries for which we do not have the inequality data.
[11] There is negative time trend independently of income, and we included this in the OLS regression. To correct for the spread of the observations over time we have used the estimated regression coefficient on time to line up all the observations to 1992. Thus, for the purpose of the graph in Figure 1, we add a(1992-t) from the measured emission rate for each year t, where a is the OLS estimate of the time trend.
[12] We also tried deleting the two largest countries, China and India, but population remained significant, and other results were very similar.
[13] Note that one must subtract the sum of log emissions from the OLS log likelihood in order to calculate the modified log likelihood required by this test. See Davidson and MacKinnon (1993, p.491) for details.