<mark>powerful implication is that inter-household variation in the shadow price of inputs and outputs based on endowments, distance to market, etc. cannot explain the relationship, as much of the existing literature implies when suggesting that both equity and efficiency goals might be advanced by progressive land transfer programs that would redistribute land from larger land owners to those with smaller holdings. Consistent with the findings of Barrett, Bellemare, and Hou (2010), the striking within-household inverse relationship raises novel puzzles about farmers’ behaviors that have yet to draw much research attention.</mark>

# **_7. Farmers do not significantly vary input application rates according to perceived soil quality._**

Yields and crop response to input use depend on the soil organic matter or soil carbon status of the plot. We might therefore expect that farmers would allocate inputs according to their beliefs about plot and soil quality. Instead, using simple descriptive statistics and not controlling for other factors, we find that input use is virtually the same across plots characterized by households in Uganda, Malawi, and Tanzania as “good,” “average,” and “poor” and based on the self-reported erosion status of plots. Regression analysis of within-farm variation on more than 26,000 plots on 14,000 farms holding constant observable and unobservable farm-level factors reveals that plots deemed “average” or “poor” in quality are statistically significantly more likely to receive inorganic fertilizer applications than are plots categorized as “good”, however these variables explain only a tiny amount of within-household fertilizer allocation decisions and this relationship does not hold over self-reported erosion status. If “poor” and eroded plots, in particular, have suffered serious nutrient mining and to the extent that plot quality matters, then this surprising finding may signal a knowledge gap among farmers and raises important questions about the accuracy and drivers of farmer perceptions of soil quality. This may signal a need for renewed efforts at extension programming around soil fertility and, possibly, the need to invest in simple soil quality tests.

# **_8. Few households use credit to purchase modern inputs._**

In all LSMS-ISA countries except Ethiopia, less than 1 percent of cultivating households used credit— either formal or informal—to purchase improved seed varieties, inorganic fertilizer, or agro-chemicals, signaling a dramatic breakdown in financial services that could help households invest in modern inputs. The cross-country, nationally representative data reinforce widespread perceptions of the weakness of agricultural input credit markets in the region. M <mark>uch scope remains for deepening rural financial markets, despite recent advances in money transfer systems based on mobile phone platforms, the proliferation of microfinance institutions, etc.</mark>

# **_<mark>9. Gender differences in input use exist at the farm and plot level.</mark>_**

<mark>Male headed households statistically significantly apply, use, and own more modern agricultural inputs than do female headed ones, consistent with the conventional wisdom. Similarly, plots owned or managed by women, who control less than a quarter of all cultivated plots, are less likely to receive modern agricultural inputs and receive lesser amounts when applied. Gender differences in modern agricultural input use, both among and within households, merit more attention as they may lead to needless productivity losses and food insecurity.</mark> 52