The importance of non-cognitive skills emerging from such studies has led some to argue that the role of cognitive skills in shaping socioeconomic outcomes (Jensen 1998) may have been overstated and that such over-emphasis on cognitive skill to the exclusion of other factors can bias evaluation of interventions and public or private investments to promote human capital (Heckman 2000).<sup>2</sup> Yet, for many sectors, evidence on the impact of non-cognitive skills on economic outcomes in low-income contexts is only beginning to emerge. For example, studies argue that non-cognitive skills predict the probability of business innovation for micro-entrepreneurs (De Mel _et al._ 2009) and default for micro-loan applicants (Klinger _et al._ 2013).

Exploring the impacts of personality traits on adoption of agricultural technology in Sub-Saharan Africa is germane for two reasons. First, agriculture provides the main income source for the vast majority of SubSaharan Africans and is therefore fundamentally important to poverty reduction, but the continent is far from the agricultural technology frontier and most increases in agricultural production have so far been due to expansion along the extensive rather than intensive margin. Second, many studies find traditional measures of human capital to have little impact on production efficiency or adoption of beneficial innovations (Huffman 2001), suggesting that other factors may be key determinants of these outcomes. Moreover, some factors identified in recent literature as strong predictors of adoption decisions–e.g. the size of an individual’s network or his/her risk preferences–may arguably be affected by personality traits.

# **3. Data and descriptive statistics**

This section uses survey data to characterize the study setting, which is dynamic and well integrated with peri-urban labor markets, and to highlight differences among sample farmers. We explain how key dimensions of personality traits are measured and how they differ among individuals in our sample before discussing in detail the channels through which they might affect technology adoption and technical efficiency.

# **3.1 Study setting, data sources, and key household characteristics**

We use data from the Kpong and Weta irrigation schemes, located in Ghana’s Eastern and Volta Regions. Cultivators of rice plots were selected randomly from a listing of all cultivators in the schemes during the 2013 major season. With the sample stratified by gender and scheme,<sup>3</sup> data were collected on agricultural production, household income, livestock and other assets, credit, decision making, and expenditures in the 2012 major season (April-September). After dropping non-useable data,<sup>4</sup> we have 1,194 cultivators with 1,778 rice parcels.

> 2 An analogy may be the shift from a focus on school enrollment to a focus on improving school quality (e.g. Hanushek and Woessmann 2008). 3 All female farmers were included to allow sufficient statistical power for analysis by gender sub-group.

> 4 Of the 1,600 farmers in the sample, 159 were dropped because they did not cultivate irrigated rice parcels, 80 because accurate harvest information was not available, and another 184 due to missing control variables.

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