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The internationally recognised methodology for collecting and using R&D statistics, the OECD's Frascati Manual is an essential tool for statisticians  and science and innovation policy makers worldwide. It includes definitions of basic concepts, data collection guidelines, and classifications for compiling R&D statistics.  This updated edition contains improved guidelines reflecting recent changes in the way R&D takes place and is funded and the wider use of R&D statistics and definitions. It provides new chapters dedicated to the practical aspects of collecting R&D data in different sectors, as well as new guidance on capturing different aspects of public support for R&D such as tax incentives.

Research --- Scientific surveys --- Statistical methods --- Surveys, Scientific --- Science --- Science research --- Scientific research --- Science and state --- Information services --- Learning and scholarship --- Methodology --- Research teams

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There is an inherent tension between traditional norms and survey protocols for quantitative data collected in the developing world. Unexpected interactions between the interviewer and respondent can lead to interviewer effects in the data, particularly in the case of subjective or sensitive questions. This paper makes use of a unique data set available from Timor-Leste containing subjective and objective questions to study these effects. In addition to their age and gender, data were collected from the interviewers regarding their opinions on the subjective questions prior to fieldwork. Fixed effects and mixed effects logit models are used to examine the main effects and interactions between interviewer and respondent characteristics. More objective measures serve as a pseudo control group. The paper finds interviewer effects in the both subjective and objective data, but the magnitude is considerably stronger for subjective questions. The paper also finds that female respondents are more susceptible to influence based on the interviewer's beliefs. Despite methodological shortcomings, the study highlights the need to consider more fully the impact of traditional cultural norms when conducting quantitative surveys in the developing world on topics that are outside the standard objective questions.

Gender & Law --- Housing & Human Habitats --- Interviewer Effects --- Justice Surveys --- Labor Policies --- Science Education --- Scientific Research & Science Parks --- Subjective Data --- Survey Methodology --- Timor-Leste

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When measuring inequality of opportunity, researchers usually opt to eliminate within-type variation. Provided that in practice it is impossible to observe all circumstances, this implies that the researcher estimates a lower bound of the true level of inequality of opportunity. By using data drawn from 27 Demographic Household Surveys (circa 2008), it is found that lower bound estimates can have substantial measurement error, and that measurement error can vary considerably across countries. As a consequence, lower bound estimates of inequality of opportunity can demand too little redistribution to equalize inequalities due to circumstances and can make the "traditional" cross-country comparisons misleading.

Education --- Equity and development --- Gender --- Gender & law --- Inequality --- Inequality of opportunity --- Lower bounds --- Measurement error --- Poverty reduction --- Science and technology development --- Science education --- Scientific research & science parks

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When measuring inequality of opportunity, researchers usually opt to eliminate within-type variation. Provided that in practice it is impossible to observe all circumstances, this implies that the researcher estimates a lower bound of the true level of inequality of opportunity. By using data drawn from 27 Demographic Household Surveys (circa 2008), it is found that lower bound estimates can have substantial measurement error, and that measurement error can vary considerably across countries. As a consequence, lower bound estimates of inequality of opportunity can demand too little redistribution to equalize inequalities due to circumstances and can make the "traditional" cross-country comparisons misleading.

Education --- Equity and development --- Gender --- Gender & law --- Inequality --- Inequality of opportunity --- Lower bounds --- Measurement error --- Poverty reduction --- Science and technology development --- Science education --- Scientific research & science parks

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This paper is a practical guide for researchers and practitioners who want to understand spillover effects in program evaluation. The paper defines spillover effects and discusses why it is important to measure them. It explains how to design a field experiment to measure the average effects of the treatment on eligible and ineligible subjects for the program in the presence of spillover effects. In addition, the paper discusses the use of nonexperimental methods for estimating spillover effects when the experimental design is not a viable option. Evaluations that account for spillover effects should be designed such that they explain the cause of these effects and whom they affect. Such an evaluation design is necessary to avoid inappropriate policy recommendations and neglecting important mechanisms through which the program operates.

Data Collection --- Disease Control & Prevention --- Field Experiments --- Impact Evaluation --- Indirect Treatment Effect --- Labor Policies --- Population Policies --- Program Mechanisms --- Science Education --- Scientific Research & Science Parks --- Spillover Effects