as the costs are likely to be enormous while most of the benefits will accrue to wealthy land owners who would be paid inflated prices by the government for the land they “offer”. The fact that _mailo_ parcels are estimated to fetch US$ 156 per acre in rental markets, compared to US$ 29 for customary and US$ 65 for freehold lands, a difference that is maintained if one looks at the median instead where rental prices are US$ 43, 30, and 15, respectively. This suggests that inflated expectations have also affected hypothetical land rental prices, thus negatively affecting potential renters’ ability to gain access to such lands through market mechanisms. Finding ways to increase security of _mailo_ tenants in a way that is more incentive compatible (and less costly) than the open-ended promise of financing under the land fund will be critical.

Comparing hypothetical land values to the total value of production net of cash input costs suggests that with the exception of _mailo_ areas, land sales prices are, at about 7 times profits, in line with international evidence. Figures on input use also suggest that, with only 1% of producers using fertilizer and 4% applying pesticides, use of chemical inputs is very low; in contrast to about 50% who purchase seeds and 32% who rely at least partly on hired labor. This is consistent with other studies of Ugandan agriculture (Pender 2004). There are significant differences across tenure regimes in the extent to which higher value root crops, fruits, or cooking banana ( _matooke_ ) is cultivated. While these are likely to be rooted in inherent variation of agro-ecological suitability, soil fertility, and market access, the high correlation of such regional features with tenure status implies that, to avoid misinterpretation of the tenure variable, we will need to adjust for such inherent productivity differences.

Table 2 presents key household characteristics for all households and by quartile of the asset distribution. The importance of land and of policies that affect it is illustrated by the fact that land constitutes more 60% of the total assets owned by sample households. With a Gini coefficient of 0.80 for the total value of non-land assets and the bottom quartile owning less than US$ 50 and the top quartile more than 100 times this amount, inequality in non-land assets is considerable. We find a similar result in terms of land values (the sum of the value of owned and occupied parcels) though not as the same level of magnitude as the value of non-land assets. The value of land held by the top quartile (US$ 5,939) is 10 times higher than that by the bottom quartile. There is also a high correlation between ownership of human and physical capital assets as well as income levels. Although our data do not allow comparison over time, they are not inconsistent with evidence suggesting a widening of the gap in assets as well as educational achievement between rural and urban areas, increasing inequality, and a rise of rural poverty following a good record of poverty reduction in the country (Kappel _et al._ 2005, Uganda Bureau of Statistics 2005).

The table illustrates that a key difference between rich and poor households is that the former have access to a more diversified range of income sources, especially wage income (33% for the top as compared to less than 15% for all the three bottom quartiles), and sale of livestock products (62% as compared to less 13