dispersed than in the modern industries. A more egalitarian traditional sector is depicted in the form of a taller and thinner distribution for agricultural population in Figure 1.

Income inequality can be estimated from the global income distribution data depicted in Figure 1. The Gini index for the world is equal to 67 %, which denotes a high level of inequality. In fact, the global Gini is about 28 points worse than that of the U.S. and even higher than the level observed in extremely unequal countries such as Mexico. As Bourguignon et al, (2004) noted: “if the world were a country, it would be among the most unequal countries of the world.” How much of this inequality can be explained by the disparity on average incomes between the agricultural group and the rest? Inequality decomposition analysis shows that a quarter of global income disparities can be explained by the difference in average incomes between the two groups of households, the remaining three quarters are due to within group income variation.

Based on the pre-established poverty line of 1 dollar (PPP) per day, the GIDD global income data also provide information about the differences in poverty incidence among the two population subgroups. Despite the fact that incomes are better distributed among the agricultural population (the Gini coefficient is 18 points lower in agriculture), lower average incomes in this sector result in higher poverty incidence: 31.7 percent of agricultural households are poor versus 8.1 percent among the non-agricultural households.

**Table 2: Characteristics of the Poor (for Developing Countries Only)**

|**Sector of employment**|**Primary School**<br>**Completed (%)**|**Age**|**Household**<br>**Size**|**Female**<br>**Headed (%)**|
|---|---|---|---|---|
|**Agricultural**|32.29|44.7|7.0|8.7|
|<br>**Non-Agricultural**|45.43|44.5|7.0|14|

Notes: (1) Primary school completed and age, refers to the household head. (2) Using data from the GIDD.

In terms of personal characteristics of the poor in and out of the agricultural sector, Table 2 shows that no noticeable differences are observed on the average age of the head and household size. However, poor people in agriculture tend to have lower education levels: just below a third of them has completed primary education. In agriculture, poor households headed by a woman are a small minority, close to 8 percent, significantly below the 14 percent observed in the non-agriculture segment (see Table 2).

Up to this point the welfare information on agricultural and non-agricultural populations has been derived by agglomerating all households within these two groups irrespectively of their nationality. In fact, the kernel _densities_ in Figure 1 exploit full income heterogeneity across households including variations between and within countries. Countries display large differences in terms of their population size, their level of development and the importance of the agricultural sector in their economies. These three country-specific characteristics are important determinants of the change of global poverty and global inequality. Clearly, as shown by **Figure 2** , global poverty would be strongly reduced in cases where China and India move towards higher income levels. Given their initial large share of global population and their position in the global income distribution, the economic expansion of these two giants is a key factor shaping the 5