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Engineering --- engineering --- machines and tools --- investigation of work process --- metrological support --- energy efficient technologies --- issues of material --- Engineering. --- Technology (General) --- Ukraine. --- Construction --- Industrial arts --- Technology --- materials science --- computational mechanics --- technical regulations

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This mixed methods study investigates why fewer than one in five impact evaluations integrates a value-for-money analysis of the development intervention being evaluated. This study distills four main insights from combined analysis of 33 semi-structured and unstructured interviews, surveys of 497 policy makers and 16 journal editors, and portfolio analyses of World Bank and worldwide impact evaluations. The study finds that low levels of training in cost data collection and analysis methods, together with a lack of standardization of the value-for-money assumptions (e.g., time horizons, discount rates, and economic or financial cost accounting) limit value-for-money integration into impact evaluations. Further eroding researchers' incentives, demand for cost evidence from the journals that publish impact evaluations is mixed. Ill-defined standards of rigor undermine editors' capacity to evaluate the quality of value-for-money analysis when it is integrated with impact evaluation evidence. Institutional funders of impact evaluations do not consistently demand that cost analysis be integrated into their funded evaluations. This study finds no evidence in support of the myth that policymakers do not demand cost evidence. Rather, it finds that researchers have few ways of knowing what kind of analysis policymakers need and when they need it. Improving the stock of impact evaluators who are cross trained in value-for-money methods, establishing standards in what constitutes rigor in costing, resolving methodological issues, and improving linkages between policymakers and researchers would lead to greater integration of value-for-money methods in impact evaluations.

Cost Accounting --- Cost-Benefit Analysis --- Cost-Effectiveness --- Development Economics and Aid Effectiveness --- Discount Rate --- Impact Evaluation --- Macroeconomics and Economic Growth --- Science and Technology Development --- Standards and Technical Regulations --- Statistical and Mathematical Sciences --- Time Horizon --- Value For Money

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This paper investigates how land size measurements vary across three common land measurement methods (farmer estimated, Global Positioning System (GPS), and compass and rope), and the effect of land size measurement error on the inverse farm size relationship and input demand functions. The analysis utilizes plot-level ata from the second wave of the Nigeria General Household Survey Panel, as well as a supplementary land validation survey covering a subsample of General Household Survey Panel plots. Using this data, both GPS and self-reported farmer estimates can be compared with the gold standard compass and rope measurements on the same plots. The findings indicate that GPS measurements are more reliable than farmer estimates, where self-reported measurement bias leads to over-reporting land sizes of small plots and under-reporting of large plots. The error observed across land measurement methods is nonlinear and results in biased estimates of the inverse land size relationship. Input emand functions that rely on self-reported land measures significantly underestimate the effect of land on input utilization, including fertilizer and household labor.

Agriculture. --- E-Business. --- Education. --- Land Measurement. --- Private Sector Development. --- Rural Development Knowledge and Information Systems. --- Rural Development. --- Science and Technology Development. --- Science Education. --- Scientific Research and Science Parks. --- Standards and Technical Regulations. --- Survey Methods.