Inequality in households’ carbon footprint and its relation to income distribution . 55

To evaluate the inequality in the carbon footprint between poor and rich households, we rank the households based on their income distribution. By doing this, we can analyze the degree of concentration in household emissions ordered in terms of increasing value of

56 household income and not according to emissions¹⁷. The concentration index, commonly known as the pseudo-Gini index, of household carbon footprint captures the inequality in emissions between the rich and poor households. This shows to what degree the rich households emit more than poor households.

Table 3.2 shows the income inequality and the degree of concentration in household carbon footprint. Results show that the two indices are moving in different directions. Income inequality shows a slight decrease while the concentration index or the pseudo CO2 Gini shows a relatively huge increase. This means that in 2000, the households are more unequal in income than in emissions but that in 2006, the opposite is true where households are more unequal in emissions than in income. This increase is driven by the rising emission share of the richest households and the declining emission share of the poorest households. Although there is rising share in both income and emissions among rich households, it is only with emissions that we observed a rather large increase in the share (Table 3.2). In addition, this rising emission inequality could also be affected by the large number of households in the poor quintile who use less energy in their consumption.

A related concept to the concentration of household emissions is the Kakwani index. It captures how emission inequality between poor and rich households differs from the income inequality. This is represented by the difference in the pseudo-Gini index of emissions and the Gini index of household income. We document a declining Gini for income but a rising pseudo-Gini for emissions. The difference in the concentration indexes leads to a different sign of the Kakwani index. In 2000, results show a negative Kakwani index while in 2006, results show a positive Kakwani index. This means that in 2000, the carbon footprint is more equally distributed than the income but in 2006, the carbon footprint tends to be less equally distributed than the income. Evidently, there is a worsening carbon footprint inequality among households in the Philippines, however income inequality tends to improve marginally.

Table 3.2. Income inequality and emission inequality.

Year Income Gini Simple CO2 Gini Pseudo CO2 Gini Kakwani

2000 0.478 0.498 0.455 -0.023

2006 0.474 0.516 0.475 0.001

Note: The simple Gini is computed by ranking the households based on emissions and not on income but the pseudo Gini of CO2 is computed by ranking the households based on per capita income.

We disaggregate both the income and emission distribution into quintiles to observe in more detail the differences between income and emission inequality (Table 3.3). The lowest quintile represents the poorest 20% of households and the highest quintile represents the

17 If we rank households based on emission and compute the degree of inequality, we are getting the “simple”

Gini index of household emission and not the concentration. For comparison, we also compute the “simple” Gini index of emission and present the results in the next section.

57 richest 20%. Within the same quintile, we rank the households based on their income and derive the income inequality as well as the concentration index of household emissions. By grouping households into quintiles, we narrow down the huge differences in income and make the households more homogenous in their lifestyles. We observe that both the income or emission inequality in the 2^nd, middle and 4^th quintile is relatively low compared to the poorest and richest quintile. This shows that households in the middle-income quintile have more homogeneous lifestyles than the households in the extreme quintiles. In both years, the richest quintile posted a negative Kakwani index, implying that the distribution in income is more unequal than the distribution in emissions. In contrast, the poorest quintile posted a positive Kakwani index implying that the distribution in emissions is more unequal than the distribution in income.

Among poor households, the inequality in the carbon footprint is larger than the income inequality while among rich households, the inequality in the carbon footprint is smaller than the income inequality. This is represented by the crossing of the Lorenz curve of income and the concentration curve of household carbon footprint¹⁸ (Figure 3.1). This observation is plausible because among rich households, there is somewhat a threshold in the consumption level while income will be unbounded because rich households have more varied sources of income. Hence, emission inequality, which is derived from consumption, is lower than the income inequality among rich households. In contrast, poor households have very limited sources of income yet their consumption is high and more variable relative to their income.

This makes emission inequality higher than the income inequality among poor households.

Table 3.3. Concentration indexes and Kakwani index by income quintile.

Income Gini Pseudo CO₂ Gini Kakwani index

2000 2006 2000 2006 2000 2006

Poorest 0.1647 0.1467 0.1742 0.1669 0.0094 0.0202

2nd 0.0699 0.0701 0.0941 0.0867 0.0242 0.0166

Middle 0.0710 0.0720 0.0863 0.0885 0.0153 0.0165

4th 0.0917 0.0918 0.0815 0.0975 -0.0102 0.0057

Richest 0.2847 0.2853 0.2120 0.2300 -0.0727 -0.0553

Overall 0.478 0.474 0.455 0.475 -0.023 0.001

18 Both the Lorenz and concentration curve is conditional on income.

58 Figure 3.1. Lorenz curve of income and concentration curve of household carbon emissions.

3.4.3. Simple inequality in household carbon emissions.

Aside from computing emission inequality ranked by income, we also compute emission inequality based on the ranking of household carbon footprint. Padilla and Serrano (2006) refer to this as the “simple” inequality in carbon emissions. We used the coefficient of variation (CV), Gini index and Theil index to capture the degree of spread, concentration and entropy of households’ carbon footprint. We also compute the inequality in income for comparison. Our findings are in rhyme with our previous results revealing that households are more unequally distributed in the carbon footprint than in income (Table 3.4). We also document a worsening inequality in household carbon emissions and a slight improvement income inequality. This observation supports our earlier finding about the concentration of household emission.

Table 3.4 shows that in 2000 households were more unequal in income but were more equal in emissions as measured by the CV. The higher the CV the higher is the degree of disparity among households. By 2006, results reveal that households are more unequal in their carbon footprint than income. With regards to concentration and entropy, the Gini and Theil indexes are consistent in showing that the carbon footprint inequality is higher compared to income in both years. The decline in the Gini index for income is consistent with what was reported by Albert and Ramos (2010) that income inequality in the Philippines went slightly down from 2000 to 2006. This is consistent with Table 3.1 suggesting that there has been a decline in income gap.

59 Table 3.4. Household inequality index by emissions, income and expenditure.

Variables CV Gini Index Theil Index

2000 2006 2000 2006 2000 2006

Emission 1.284 1.253 0.498 0.516 0.468 0.493

Income 1.401 1.234 0.478 0.474 0.394 0.382

Note: When computing for emission inequality, households are ranked based on their emissions and when computing for income inequality, households are ranked based on income.