development agencies including the World Bank and non-governmental organizations (NGOs) (Delgado and Siamwalla 1999).

On income diversification, the most relevant studies for Zimbabwe are those by Piesse et al. (1998) and by Kinsey et al. (1998). Piesse et al. (1998) find that in remote areas, non-farm income sources increase income inequality but in areas better connected to the large urban market of Harare, that it decreases income inequality. They suggest that in rural areas less well connected to urban centers, the agrarian power structures allow those with higher farm incomes to better exploit non-farm income sources. With better access to urban markets, they suggest that opportunities for non-farm employment are less dependent on these power structures and are therefore more equalizing of income. Kinsey et al. (1998) examine 400 resettled households in rural Zimbabwe over a 13 year period and find that income diversification is a coping strategy used during times of drought, but that the income sources that can be tapped are likely to be lowreturn activities such as day jobs or agricultural piecework.

The existing empirical studies on income diversification have several limitations. There is little focus on the role of income diversification under urban settings, despite the fact that urban households face some of the same risks as their rural counterparts such as varying returns to labor, market failures and the risks of structural adjustment. Most current empirical work on rural income diversification uses the non-farm income share in the total income as a measure of diversification. This approach has some shortcoming, including the fact that it does not account for heterogeneity in the non-farm income sources. Finally, there is little or no work comparing income diversification patterns before and after economic shocks. This study attempts to extend the empirics on income diversification to fill these gaps.

# **4.   Measuring Income Diversification**

Attempts to quantify income diversification, so far mostly available for rural areas, have focused mainly on estimation of non-farm income share in the total household income (e.g. Block and Webb 2001; Barrett and Webb 2001; Lanjouw et al. 2001). The assumption in these studies is that higher share of non-farm income amounts to higher income diversification and less vulnerability to weather related shocks, a major risk factor in rural areas where agriculture is the mainstay of livelihood. Some important difficulties are associated with the use of non-farm income share as a measure of diversification. For instance, non-farm income share as a proxy indicator for income diversification gives equal risk-mitigation weight to households deriving a given percent of non-farm income, for instance, from 1 versus 3 sources or 1 versus 3 income generating household members. We argue that different sources of income exhibit varying degree of liquidity and vulnerability to risk; the same amount non-farm income from a single source and multiple sources will have different implications for household risk management. Non-farm income share also has less relevance in urban areas where most income sources tend to be non-farm.

This paper proposes a richer measure of income diversity based on a more disaggregated classification of income sources beyond the simple farm and non-farm categorization. Our diversification index incorporates the number of different income sources (N) and income 4