_al._ 2014). Use of data on land transfers from registries as a frame is one option to cross-check and complement data from household samples that would also allow obtaining ‘objective’ land prices.<sup>11</sup> Descriptive statistics for households who did or did not purchase land over the last 5 years in table 5 for Uganda and Nigeria suggest that sales markets had a mildly redistributive effect and transferred land to those with lower endowments.<sup>12</sup> At the same time, those who purchased land expanded existing holdings, are better educated, have significantly higher income and assets, and apply slightly more inputs than those in autarky. Infrastructure access seems to matter less for land purchases than for rentals, something that could be due to a number of reasons (e.g. buyers having access to transport).

# **4. Econometric approach and results**

Subject to the limitations of data availability, econometric analysis suggests that, while land rental markets fail to fully equalize factor ratios, they contribute to structural transformation by helping to transfer land to productive but land-poor and relatively labor-rich households. Yet, the fact that female plot managers or owners, especially unmarried ones, almost universally attain much lower levels of productivity than the rest and could increase their income by leasing out land points towards significant barriers to land market participation at least for this group.

# **4.1 Gendered determinants of productivity**

To assess levels and determinants of productive performance, we estimate country-specific stochastic frontier production (Coelli 1998) at household level using a Cobb-Douglas functional form: l (1) where, _i_ indexes households, _Yii_ is the value of crop output, (YYii) = ββl (XXii) + γγMMii + vv _Xii_ is a vector of inputs including operated land area, ii −uuii where, _i_ indexes households, _Yii Xii_ number of days of family labor, expenses on hired labor and fertilizer, and value of agricultural assets, _Mi_ is a vector that includes the share of household land managed either jointly or by a female, _β_ and _γ_ are vectors of parameters to be estimated, _ui_ proxies for technical inefficiency and _νi_ is an error term _._<sup>13</sup> Each producer’s distance from the frontier can then be used as a measure of inefficiency in subsequent regressions of land market participation to assess whether it is the more productive who are able to access land through rental or sales markets as expected if other factor markets function well or whether imperfections in other markets (e.g. those for credit) imply that land is acquired for reasons not directly related to agricultural production.

> 11 In many countries, a desire to avoid taxes, together with an outdated valuation system, implies that prices for registered sales may be systematically underdeclared, something that is often associated with weak governance. Documenting such differences could be a valuable contribution to the policy dialogue.

> 12 Figures for land ownership are 2.72 ha vs. 4.43 ha for owned area, and 1.09 ha vs. 1.94 ha for owner area per adult in Niger and 0.70 ha vs. 1.38 ha for owned area, and 0.30 ha vs. 0.65 ha for owned area per adult in Uganda.

> 13 Formally _ui_ is a ne-sided non-negative error term assumed to be half-normally distributed with unknown mean _μu_ and variance _σu_ 2 to _νi_ is a two-sided white noise error term assumed to be normally distributed with mean 0 and variance _σν_<sup>2</sup> .

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