Measures of cognitive, noncognitive, and technical skills are increasingly used in development economics to analyze the determinants of skill formation, the role of skills in economic decisions, or simply because they are potential confounders. Yet in most cases, these measures have only been validated in high-income countries. This paper tests the reliability and validity of some of the most commonly used skills measures in a rural developing context. A survey with a series of skills measurements was administered to more than 900 farmers in western Kenya, and the same questions were asked again after three weeks to test the reliability of the measures. To test predictive power, the study also collected information on agricultural practices and production during the four following seasons. The results show the cognitive skills measures are reliable and internally consistent, while technical skills are difficult to capture and very noisy. The evidence further suggests that measurement error in noncognitive skills is non-classical, as correlations between questions are driven in part by the answering patterns of the respondents and the phrasing of the questions. Addressing both random and systematic measurement error using common psychometric practices and repeated measures leads to improvements and clearer predictions, but does not address all concerns. The paper provides a cautionary tale for naive interpretations of skill measures. It also points to the importance of addressing measurement challenges to establish the relationship of different skills with economic outcomes. Based on these findings, the paper derives guidelines for skill measurement and interpretation in similar contexts.

Measures of cognitive, noncognitive, and technical skills are increasingly used in development economics to analyze the determinants of skill formation, the role of skills in economic decisions, or simply because they are potential confounders. Yet in most cases, these measures have only been validated in high-income countries. This paper tests the reliability and validity of some of the most commonly used skills measures in a rural developing context. A survey with a series of skills measurements was administered to more than 900 farmers in western Kenya, and the same questions were asked again after three weeks to test the reliability of the measures. To test predictive power, the study also collected information on agricultural practices and production during the four following seasons. The results show the cognitive skills measures are reliable and internally consistent, while technical skills are difficult to capture and very noisy. The evidence further suggests that measurement error in noncognitive skills is non-classical, as correlations between questions are driven in part by the answering patterns of the respondents and the phrasing of the questions. Addressing both random and systematic measurement error using common psychometric practices and repeated measures leads to improvements and clearer predictions, but does not address all concerns. The paper provides a cautionary tale for naïve interpretations of skill measures. It also points to the importance of addressing measurement challenges to establish the relationship of different skills with economic outcomes. Based on these findings, the paper derives guidelines for skill measurement and interpretation in similar contexts.

"How can educators determine the most effective approaches for measuring students' social-emotional and self-regulation skills? And how can they use the data to improve their own practice? This book brings together leading experts from multiple disciplines to discuss the current state of measurement and assessment of a broad range of noncognitive skills and present an array of innovative tools. Chapters describe measures targeting the individual student, classroom, whole school, and community; highlight implications for instructional decision making; examine key issues in methodology, practice, and policy; and share examples of systematic school- and district-wide implementation"--

Measuring non-cognitive skills using self-report instruments in developing countries has two main challenges: such instruments are expensive and have significant biases. Survey Effort, defined as the response rate in surveys, is a promising alternative to be used as a proxy for non-cognitive skills. This paper analyze the correlation between survey effort with socioemotional skills for low-income Brazilian students. We show that survey effort predict student's conscientiousness and agreeableness skills. We also find the presence of heterogeneous correlation in girls and students with different socioeconomic status.

This book constructs a measure of education inequality using time-series cross-national data and utilizes real-world examples based on author interviews. It provides insights into how classic trade theory might be applied more broadly to generate expectations not only about income distribution, but also about investment in human capital. The project explores the ways in which global trends toward urbanization and democratization might be expected to impact education inequality. The author addresses contemporary issues in politics, such as growing income inequality, the backlash against globalization and free trade, and concerns that democratic institutions are elite-dominated and unresponsive to the needs of common citizens.

This is an open access title available under the terms of a CC BY-NC-SA 3.0 IGO licence. It is free to read at Oxford Scholarship Online and offered as a free PDF download from OUP and selected open access locations. Detailed analyses of poverty and wellbeing in developing countries, based on household surveys, have been ongoing for more than three decades. The large majority of developing countries now regularly conduct a variety of household surveys, and the information base in developing countries with respect to poverty and wellbeing has improved dramatically. Nevertheless, appropriate measurement of poverty remains complex and controversial. This is particularly true in developing countries where (i) the stakes with respect to poverty reduction are high; (ii) the determinants of living standards are often volatile; and (iii) related information bases, while much improved, are often characterized by significant non-sample error. It also remains, to a surprisingly high degree, an activity undertaken by technical assistance personnel and consultants based in developed countries. This book seeks to enhance the transparency, replicability, and comparability of existing practice. In so doing, it also aims to significantly lower the barriers to entry to the conduct of rigorous poverty measurement and increase the participation of analysts from developing countries in their own poverty assessments. The book focuses on two domains: the measurement of absolute consumption poverty and a first order dominance approach to multidimensional welfare analysis. In each domain, it provides a series of flexible computer codes designed to facilitate analysis by allowing the analyst to start from a flexible and known base. The book volume covers the theoretical grounding for the code streams provided, a chapter on 'estimation in practice', a series of 11 case studies where the code streams are operationalized, as well as a synthesis, an extension to inequality, and a look forward.