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Assets generate and help diversify income, provide collateral to access credit, alleviate liquidity constraints in the face of shocks, and are key inputs into empowerment. Despite the importance of individual-level data on asset ownership and control, and that most assets are owned by individuals, solely or jointly, it is typical for the micro data on asset ownership to be collected at the household level, often from only one respondent per household. Even when the data are collected at the individual level, with identification of reported or documented owners of a given asset within the household, the information is still often solicited from a single respondent. Further, the identification of owners is seldom paired with the identification of individuals who hold various rights to assets, limiting understanding of the interrelationships among ownership and rights, and whether these relationships vary across individuals. Through a review of the existing approaches to data collection and the relevant literature on survey methodology, this paper presents an overview of the current best practices for collecting individual-level data on the ownership and control of assets in household and farm surveys. The paper provides recommendations in three areas: (1) respondent selection; (2) definition and measurement of assess to and ownership and control of assets; and (3) measurement of the quantity, value, and quality of assets. Open methodological questions that can be answered through analysis of existing data or the collection and analysis of new data are identified for future research.

Asset Ownership --- Household Surveys --- Survey Methodology --- Wealth

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Research has provided robust evidence for the use of GPS technology to be the scalable gold standard in land area measurement in household surveys. Nonetheless, facing budget constraints, survey agencies often seek to measure with GPS only plots within a given radius of dwelling locations. Subsequently, it is common for significant shares of plots not to be measured, and research has highlighted the selection biases resulting from using incomplete data. This study relies on nationally-representative, multi-topic household survey data from Malawi and Ethiopia that exhibit near-negligible missingness in GPS-based plot areas, and validates the accuracy of a multiple imputation model for predicting missing GPS-based plot areas in household surveys. The analysis (i) randomly creates missingness among plots beyond two operationally relevant distance measures from the dwelling locations; (ii) conducts multiple imputation under each distance scenario for each artificially created data set; and (iii) compares the distributions of the imputed plot-level outcomes, namely, area and agricultural productivity, with the known distributions. In Malawi, multiple imputation can produce imputed yields that are statistically undistinguishable from the true distributions with up to 82 percent missingness in plot areas that are further than 1 kilometer from the dwelling location. The comparable figure in Ethiopia is 56 percent. These rates correspond to overall rates of missingness of 23 percent in Malawi and 13 percent in Ethiopia. The study highlights the promise of multiple imputation for reliably predicting missing GPS-based plot areas, and provides recommendations for optimizing fieldwork activities to capture the minimum required data.

Global Positioning System --- GPS --- Land Area Measurement --- Missing Data --- Multiple Imputation --- Survey Methodology

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'There have been two coordinated Point-in-Time (PiT) counts of homelessness in communities across Canada. The first took place in 2016 and the second in 2018. Communities are invited to participate in the third PiT count coordinated by ESDC in March and April 2020. This guide explains the Canadian PiT Count approach, including information on the methodology, the survey, and guidelines for communities to participate in the count'--Page 5.

2020 --- United States --- United States. --- Census, 2020 --- Population --- Data analysis --- Homeless shelter --- Homelessness --- Housing first --- Old age security --- Survey methodology --- Volunteering --- First nations --- Mental disorder --- Sampling (statistics)

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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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This book presents an up-to-date perspective on randomized response techniques (RRT). It discusses the most appropriate and efficient procedures of RRT for analysing data from queries dealing with sensitive and confidential issues, including the treatment of infinite and finite population setups. The book aims to spark a renewed interest among sampling experts who may have overlooked RRT. By addressing the missing topics and incorporating a wide range of contributors' works, it seeks to foster an appreciative academic environment and inspire a reformed and amended view of RRT. As the book unfolds, readers will gain valuable insights into the evolving landscape of RRT and its applications, positioning them at the forefront of this engaging field of study. On RRT, the literature has grown immensely since its inception in 1965 by S.L. Warner. Despite several books published on the subject, there are still two crucial topics missing from the existing RRT literature. This book aims to address these gaps and provide valuable insights to curious readers in the field. The book is mandatory reading for statisticians and biostatisticians, market researchers, operations researchers, pollsters, sociologists, political scientists, economists and advanced undergraduate and graduate students in these areas.

Sampling (Statistics). --- Computer science --- Mathematical statistics. --- Data protection. --- Survey Methodology. --- Probability and Statistics in Computer Science. --- Data and Information Security. --- Mathematics. --- Sampling (Statistics)