Repository of measurement and survey design resources
This is a list of resources on measurement and survey design relating to various topics. Topics are organized alphabetically for ease of navigation using the sidebar. We rely on crowd-sourced material to maintain this page and update it regularly. Feedback on the page, or suggestions of resources to add or remove, can be submitted using this form.
Welcome to the J-PAL Repository of Measurement and Survey Design Resources. The purpose of this repository is to provide an introduction to the measurement and survey design resources available in a particular subject or for a specific type of question. It is a companion piece to our Introduction to measurement and indicators and Survey design pages, compiling resources that discuss and provide guidance on specific issues related to the concepts introduced in those resources. In each section, you will find a list of resources, moving from more general to more specific, that are meant to introduce readers to the measurement tools, difficulties, and solutions in that particular topic.
These lists do not aspire to comprehensiveness but rather attempt to tackle the main points in measurement within a topic. They cover a variety of media, from blogs and academic papers to books and journal issues. While heavy synthesis is outside the scope of this resource, short descriptions of the papers, as well as introductions to each section written by J-PAL staff with expertise in that area, are provided for the reader’s ease of use.
The General section includes resources that provide an overview of measurement concepts and guidance on survey design. It also includes a few repositories of sample questionnaires and resources on remote surveying in light of the shifts during the Covid-19 pandemic. Some of the following sections are divided by research area, for example, financial inclusion, corruption, and environment & energy. Others, like the forthcoming section on measuring consumption, assets, income, prices, and poverty, cut across several research topics.
The current repository includes only a portion of the total number of sections, and we will continue to release more sections in phases over the coming months, so please check back for more.
As with our other research resources, this is meant to be a living resource. If you have feedback on any of the resources in this repository, or if you have recommendations for resources and/or further subjects to add, please fill out this form.
General measurement resources
- J-PAL research resource: Introduction to measurement and indicators, by Sarah Kopper and Katie Parry -- An overview of measurement concepts, measurement error, and common sources and types of data.
- J-PAL training presentation: Measurement: Outcomes, impacts, and indicators -- A lecture on measurement delivered at J-PAL’s 2019 Executive Education Training.
- Book: The analysis of household surveys, by Angus Deaton; The World Bank Group (1997/2018) -- A comprehensive guide to the qualitative and econometric methods for collecting and analysing survey data to understand the effect of household decisions.
- Blog post: Towards a survey methodology methodology, by Andrew Dillon (2016) -- An overview article on measurement issues.
- Broad overview: What can we learn from experimenting with survey methods?, by Joachim De Weerdt, John Gibson, and Kathleen Beegle (2020) -- An examination of the effect of different survey designs on the measurement of different indicators associated with consumption, household size, etc. It also highlights the learnings from recent literature on survey design. [Gated published version]
- Journal issue: Symposium on measurement and survey design, edited by David McKenzie and Mark Rosenzweig; Journal of Development Economics, Volume 98, Issue 1 (2012) -- A collection of curated articles on different measurement techniques and questions. [Gated]
- See also the Preface for symposium on measurement and survey design, by McKenzie and Rosenzweig (2012) [Gated]
- Survey guide: Measurement error in survey data, by John Bound, Charles Brown, and Nancy Mathiowetz; The Handbook of Econometrics (2001) -- Discusses the impact of measurement error and provides methods to minimise error. It also reviews a few validation studies and discusses their design. [Gated published version]
- Book: Sample surveys: Design, methods and applications, by C. R. Rao; The Handbook of Statistics, Volume 29, Part A (2009) -- A guide to different types of sampling methods and processing survey data with examples from experiences in the field. [Gated]
Practical guides to designing and implementing surveys
- J-PAL research resource: Survey design by Sarah Kopper and Katie Parry -- An introduction to the principles of survey design.
- Book: Designing household survey questionnaires for developing countries, edited by Margaret Grosh and Paul Glewwe; The World Bank Group, Vol. 1 (2000-05) -- Modules with detailed instructions on designing surveys for different sectors.
- Book: Designing household survey samples: Practical guidelines, by the United Nations’ Department of Economic and Social Affairs Statistics Division (2008) -- A practical guide on designing household surveys, implementing them, and analyzing the data while accounting for the sampling frame and non-sampling error.
- Broad overview: Articles on DIME’s Wiki, including:
- Survey guide: The World Bank’s Living Standards Measurement Study (LSMS) site has many resources and papers on measurement and survey design, many of which are listed below.
- Broad overview: Guidelines for best practice in cross-cultural surveys, Survey Research Center, Institute for Social Research, University of Michigan (2016), including:
- Broad overview: EGAP's 10 things to know about survey design, by Gabriella Sacramone-Lutz -- Contains broad guidelines on some aspects of survey design.
- Blog post: Development Impact blog posts on measurement and survey design.
- Broad overview: Observational field research, by Laura Brown -- A brief overview of what observational research is and the types of data collection associated with it.
This section contains sources for finding sample questionnaires across sectors; topic-specific sample questionnaires can also be found under the relevant topic header. Note that project data is typically stored with the questionnaire and can be found by following the links below.
- Datahub for Field Experiments in Economics and Public Policy, by J-PAL and IPA; Harvard Dataverse -- Includes survey questionnaires, research results, replication code, and documentation from studies conducted by J-PAL and IPA affiliated researchers.
- The World Bank’s Microdata Catalog -- Central cache of questionnaires and data used and produced by the World Bank.
- Model surveys from the Demographic and Health Surveys Program -- DHS questionnaire modules on household composition, biomarkers, and behavior related to the health of women, men and children.
- The International Food Policy Research Institute's Database of Household and Community Surveys -- A library of datasets resulting from household and community surveys conducted by IFPRI, many of which are open access and contain the survey questionnaires.
- International Household Survey Network -- A catalogue of surveys, research data and documentation from the IHSN.
- J-PAL research resource: Resources for conducting remote surveys, by Michael Gibson and Sarah Kopper (2020).
- Using mobile phones to collect panel data in developing countries, by Brian Dillon (2011) discusses the experience of collecting high frequency household data using mobile phones in rural Western Tanzania in 2009-2010. [Gated]
- One example of a study using SMS polls to study health behavior is Tracking health seeking behavior during an Ebola outbreak via mobile phones and SMS, by Shuo Feng, Karen A. Grépin, and Rumi Chunara (2018).
- J-PAL research resource: Best practices for conducting phone surveys, by Sarah Kopper and Anja Sautmann (2020)
- Webinar: Adaptations for phone surveys: A webinar with Tavneet Suri -- Webinar on guidance on pivoting to phone surveys in light of the Covid-19 pandemic.
- Survey guide: Mobile phone panel surveys in developing countries: A practical guide for microdata collection, by Andrew Dabalen, Alvin Etang, Johannes Hoogeveen, Elvis Mushi, Youdi Schipper, and Johannes von Engelhardt (2016) -- A practical guide to designing and conducting phone surveys.
- Survey guide: Remote surveys on the DIME Wiki -- Brief overview of different kinds of remote surveys.
Research on response rates and mode effects
- Briefs: Phone survey methods on Innovation for Poverty Action's (IPA) RECOVR hub, including evidence briefs on:
- Blog post: Reducing attrition in phone surveys, by Berk Özler and P. Facundo Cuevas (2019) -- Discusses small changes that can dramatically improve attrition rates for phone surveys.
- Paper: Call me maybe: Experimental evidence on frequency and medium effects in microenterprise surveys, by Robert Garlick, Kate Orkin, and Simon Quinn (2020) -- Testing the reliability of phone surveys in microenterprise surveys. [Gated published version]
- Paper: Please call again: Correcting nonresponse bias in treatment effect models, by Luc Behaghel, Bruno Crépon, Marc Gurgand, and Thomas Le Barbanchon (2015) -- Propose and test a method to reduce non-response bias in surveys that involve repeated sequential attempts to obtain a response.
- J-PAL webinar: Webinar on how to use SurveyCTO plug-ins for phone surveys, by SurveyCTO, IPA, and J-PAL South Asia.
Practical survey guides
- J-PAL South Asia’s Transitioning to CATI checklist, by Saurabh Bhajibhakare, Ambika Chopra, Putul Gupta, and Mustufa Patel (2020) -- Guide to implementing and monitoring CATI surveys.
- J-PAL South Asia’s Quality assurance for CATI, by Saurabh Bhajibhakare (2020) -- Best practices on conducting and supervising phone surveys with guidance on back checks and high frequency checks.
- Guide to work-from-home data collection, by DIME Analytics -- Guide to conducting remote surveys during the Covid-19 pandemic.
- 60_Decibel’s Remote survey toolkit (2020) -- Guidance on conducting phone surveys, different available methods with some example question sets.
- SurveyCTO’s Computer-assisted telephone interviewing (CATI) starter kit, by Marta Costa -- Guide to CATI using SurveyCTO.
- J-PAL South Asia’s Exotel-SurveyCTO plugin -- Useful for making anonymous calls from within a SurveyCTO form.
- Survey guide: J-PAL South Asia’s Budgeting for phone surveys during the Covid-19 pandemic, by Putul Gupta (2020) -- Guide to budgeting for trainings, personnel and equipment for phone surveys during the Covid-19 pandemic.
- Survey guide: Remote surveying in a pandemic: Handbook, by Steve Glazerman, Michael Rosenbaum, Rosemarie Sandino, and Lindsey Shaughnessy (2020) -- Comprehensive guide to all aspects of remote surveying including IRB and data protection, respondent tracking and logistics.
- Blog post: Impact evaluations in the time of Covid-19, part 1, by Markus Goldstein and Florence Kondylis (2020) -- Early pandemic guidance on how the pandemic is likely to affect research.
- Blog post: Practical tips for implementing remote surveys in the time of the Great Lockdown, by Maria Jones, Roshni Khincha, Florence Kondylis, and Lysca Uwamariya (2020) -- Tips on logistics, training, data collection and security while conducting remote surveys.
- Blog post: Mobile phone surveys for understanding Covid-19 impacts: Part I sampling and mode, by Kristen Himelein, Stephanie Eckman, Charles Lau, and David McKenzie (2020) -- Discussion on planning and budgeting for sampling frames and different methods for phone surveys with examples.
- Blog post: Mobile phone surveys for understanding Covid-19 Impacts: Part II response, quality, and questions, by Kristen Himelein, Stephanie Eckman, Charles Lau, and David McKenzie (2020) -- Discussion of reasonable response rates, examples of methods to improve response rates and data monitoring methods.
- Webinar: Adapting to Covid-19: Overview of data collection and phone surveying with SurveyCTO, video by SurveyCTO (2020).
Corruption in governance and service provision
Corruption, or when bureaucrats and elected officials misuse their positions or break rules for private gain, is difficult to measure due to its illicit and often secretive nature. Directly observing corrupt activities like bribery by government officials, neglecting official duties, or tax avoidance can be challenging as officials may change or conceal their behavior in response to being monitored. Further, traditional survey approaches are unlikely to elicit truthful responses as officials may be unwilling to confess to corruption due to social desirability bias. Alternative methods of measuring corruption that rely on asking about citizens’ experience with corrupt officials (e.g. “have you paid a bribe for a service before?”) or their perceptions of corruption may be biased, outdated, or incomparable across contexts (Olken 2009). Perception-based indices and rankings may also provide limited insight into the type, causes, or consequences of corruption in a given context (Banerjee, Mullainathan, and Hanna 2012).
Given these challenges, open questions around measuring corruption include: How can researchers measure corruption without distorting public officials’ behavior or eliciting a biased response? What is the best way of measuring social norms around corruption? What types of corruption, if any, can citizen reports shed valuable insights on? Can e-governance reforms that improve the collection of administrative data also improve our ability to measure corruption? More reliable measures of corruption help us better answer policy-relevant questions like the effects of corruption on the efficiency of public service delivery, and the effectiveness of anticorruption policies and programs.
While measuring corruption is difficult, researchers have made remarkable progress in doing so in the past few years, including through the use of a variety of innovative approaches that directly measure corruption and begin tackling some of these questions. This includes:
- Surveyors accompanying truck drivers on delivery routes, dressed as their assistants, to record bribes paid to police at checkpoints (Olken and Barron 2009)
- Combining GPS-tracked company vehicle data with administrative data to measure corrupt behavior among bureaucrats of a large public service provider (Schonholzer et al., ongoing)
- Comparing villagers’ perceptions of corruption to an objective measure (e.g. the difference between government-reported expenditure for a road building project and the estimated cost of actually building the road according to independent engineers; Olken 2009).
The papers that follow include many more examples of methods that can be used to measure corruption in governance and service provision, including through audits, public expenditure tracking surveys, market inference, and more. For a discussion on the different measurement approaches and their applicability, see the MITx Micromasters Course on Political Economy and Economic Development.
- The World Bank's Worldwide Governance Indicators (1996-2019) -- See the comprehensive documentation for a discussion of their methodology.
- The World Bank’s Enterprise Surveys -- Surveys firms to collect information on informal payments for utilities, licenses, contracts etc.
See Bai et.al (2019) for an example using data from the WB Enterprise surveys to compare corruption across countries.
- International Crime Victims Surveys (ICVS) -- Includes a question on bribe payments by individuals in 49 countries.
- V-Dem: Global Standards, Local Knowledge -- Includes variables to measure corruption.
- Paper: Eight questions about corruption, by Jakob Svensson (2005) -- Provides a definition of corruption and then discusses the level of corruption in different countries, the different ways to reduce corruption, and the impact of corruption on growth.
- Paper: Corruption in developing countries, by Benjamin Olken and Rohini Pande (2012) -- A review of the different measurement techniques and the existing evidence.
- Paper: Section 4 (measurement), of Corruption, by Abhijit Banerjee, Sendhil Mullainathan, and Rema Hanna (2012) -- A review of different measurement methods and their application in the literature.
- Book: New advances in experimental research on corruption, edited by Danila Serra, Leonard Wantchekon, R. Mark Isaac, and Douglas A. Norton; Emerald Group Publishing Limited, Vol. 15 (2012) -- Reviews the research on corruption measurement and reduction generated from laboratory and field experiments. [Gated]
- Paper: Survey techniques to measure and explain corruption, by Ritva Reinikka and Jakob Svensson (2003) -- Reviews the use of Public Expenditure Tracking Surveys (PETS), provider surveys, and enterprise surveys for measuring corruption in education, health, and private businesses.
- Book: Are you being served? New tools for measuring services delivery, edited by Samia Amin, Jishnu Das, and Markus Goldstein; the World Bank, Vol. 1 (2008) -- Examples of using different methods and tools for measuring public service delivery.
- Book: Advances in experimental political science, edited by James N. Druckman and Donald P. Green; Cambridge University Press, Vol. 1 (2021) - A comprehensive guide to the next experimental methods, data collection, analysis, and challenges. [Gated]
- Book: Corruption: What everyone needs to know, by Ray Fisman and Miriam A. Golden; Oxford University Press (2017) - An overview of corruption and its causes and consequences with examples from around the world. [Gated]
International indicators of corruption and governance
- Paper: A measurement assessment approach: Assessing the varieties of democracy corruption measures, by Kelly M. McMann, Daniel Pemstein, Brigitte Seim, Jan Teorell, and Staffan I. Lindberg (2017) -- Develops an approach to assess the reliability and validity of corruption measures, and tests the assessment method on the V-Dem measure.
- Paper: Are international databases on corruption reliable? A comparison of expert opinion surveys and household surveys in sub-Saharan Africa, by Mireille Razafindrakoto and François Roubaud (2010) -- A comparison of data from household surveys on perception of corruption and expert opinions to comment on the reliability of expert opinions and related biases. [Gated published version]
Specific approaches to measuring corruption
- Paper: Corruption perception vs corruption reality, by Benjamin Olken (2009) -- Examines the reliability of villagers’ perception of corruption in a project by comparing it with the “missing expenditure.” Missing expenditure is the difference between the actual cost of the project and the individual’s perception. [Gated published version]
- Paper: Parochial politics: Ethnic preferences and politician corruption, by Abhijit Banerjee and Rohini Pande (2009) -- Uses expert surveys to measure perceptions about how corrupt a candidate is. They report a high correlation between journalist’s perception about the candidate with actual data.
Through survey estimates of bribes
- Paper: Who must pay bribes and how much? Evidence from a cross-section of firms, by Jakob Svensson (2003) -- Examines information collected on bribe payments by surveying Ugandan firms. [Gated published version]
- Paper: Firm growth and corruption: Empirical evidence from Vietnam, by Jie Bai, Seema Jayachandran, Edmund J. Malesky and Benjamin Olken (2019) -- Examines data collected from 10,000 firms on their perception of corruption and bribe payments in Vietnam. [Gated published version]
- See also Reid and Weigel (2019), who use self-reported estimates to measure the impact of financial incentives on bribes.
Through direct observation
- Paper: Comparing corruption in a laboratory and in the field in Burkina Faso and in Canada, by Olivier Armantier and Amadou Boly (2013) -- Compares the results of direct observation of corruption in laboratories in Canada and Burkina Faso to the corruption in the field in Burkina Faso. [Gated]
- Paper: The simple economics of extortion: Evidence from trucking in Aceh, by Benjamin Olken and Patrick Barron (2009) -- Observed bribes paid by truck drivers at police checkpoints or weigh stations in Indonesia. They also asked truck drivers to self-report bribe payments to check the accuracy of reported values. [Gated published version]
- For papers on observing bribes see An empirical study of corruption in ports, by Sandra Sequeira and Simeon Djankov (2010), who use direct observation of bribes at ports in Mozambique and South Africa. In How to subvert democracy: Montesinos in Peru, John McMillan and Pablo Zoido (2004) use detailed records of the former Chief of Police, Montesinos; the paper compares the amount of bribes paid to different institutions.
By comparing estimated and actual expenditure
Public Expenditure Tracking Surveys (PETS):
Survey guide: Using public expenditure tracking surveys to monitor projects and small-scale programs /A guidebook, by Margaret Koziol and Courtney Tolmie (2010) -- A practical guide for implementing PETS.
Broad overview: Following the money: Do public expenditure tracking surveys matter?, by Geir Sundet (2008) -- Discusses the limitations of wide adoption of PETS and suggests solutions and alternative methods.
Broad overview: Implementing public expenditure tracking surveys for results: Lessons from a decade of global experience, by Asli Gurkan, Kai Kaiser, and Doris Voorbraak (2009) -- A review of previous implementations of PETS and a summary of their lessons and challenges.
Broad overview: Public expenditure tracking and service delivery surveys: A review of design and implementation issues, by Ritva Reinikka (2002) -- A presentation on the need, features, benefits and implementation of PETS.
An example of using PETS is given in Local capture: Evidence from a central government transfer program in Uganda, by Ritva Reinikka and Jakob Svensson (2004), where the authors compared allocated grants to schools with the actual grants received to measure “leakage” or amount of funds siphoned off by politicians. This is one of the first uses of PETS to track corruption. [Gated published version]
- Paper: Tax rates and tax evasion: Evidence from “missing imports” in China, by Raymond Fisman and Shang-Jin Wei (2004) -- Compares China’s reported imports and Hong Kong’s reported exports of the same products to measure the impact of the tariff schedule on reported trade. [Gated published version]
- Paper: Monitoring corruption: Evidence from a field experiment in Indonesia, by Benjamin Olken (2007) -- Compares the actual cost of the infrastructure project with engineers’ estimated costs. [Gated published version]
- For another paper on infrastructure projects, see Proposal for a new measure of corruption, illustrated with Italian data, by Miriam Golden and Lucio Picci (2005), where the authors compare the value of existing infrastructure with the amount allocated for the projects.
- For another paper using administrative data, see Corruption and the costs of redistribution: Micro evidence from Indonesia, by Benjamin Olken (2006). [Gated published version]
- Paper: Just rewards? Local politics and public resource allocation in south India, by Timothy Besley, Rohini Pande, and Vijayendra Rao (2011) -- Measures the likelihood of being a beneficiary of a transfer program based on status and power. [Gated published version]
- Another example is given by Banerjee et al. (2020), who use digital and mobile monitoring to reduce leakages in public spending.
From market inference
Paper: Estimating the value of political connections, by Raymond Fisman (2001) -- Obtained measure of firm’s political connectedness from a local consulting firm and compared how the stock prices changed when the president fell ill to estimate the value of the connections. [Gated published version]
Paper: Public sector pay and corruption: Measuring bribery from micro data, by Yuriy Gorodnichenko and Klara Sabirianova Peter -- Compares the pay and consumption gap between private and public sector employees to measure the amount of bribes or informal payments to public sector employees in Ukraine. [Gated published version]
- Paper: Electoral accountability and corruption: Evidence from the audits of local governments, by Claudio Ferraz and Frederico Finan (2011) -- Uses audit reports in Brazil municipalities to create a measure of corruption. [Gated published version]
- Paper: Governance and the effectiveness of public health subsidies: Evidence from Ghana, Kenya and Uganda, by Rebecca Dizon-Ross, Pascaline Dupas, and Jonathan Robinson (2017) -- Measures corruption in health program administration with audits, back-checks and decoy visits.
- Paper: Missing in action: Teacher and health worker absence in developing countries, by Nazmul Chaudhury, Jeffrey Hammer, Michael Kremer, Karthik Muralidharan and F. Halsey Rogers (2006) -- A review of surveys recording teachers’ and health worker absence during unannounced visits in six developing countries.
- Another example is Duflo, Hanna and Ryan (2012), who use cameras to reduce instances of teacher absenteeism.
Through other methods
- Paper: Using field experiments in international relations: A randomized study of anonymous Iicorporation, by Michael Findley, Daniel Nielson, and J.C. Sharman (2013) -- Measures the willingness of firms to break the laws by proposing the formation of a shell company. [Gated Published Version]
- Paper: Misunderestimating corruption, by Aart Kraay and Peter Murrell (2016) -- A tool-specific example of using random response methods to measure the prevalence of reticent methods in an enterprise survey. [Gated published version]
Energy and environment
Research in environment, energy, and climate change encompasses a range of topics and with them challenges and opportunities in measurement unique to the sector. Some of the topics included in the sector include greenhouse gas emission reductions; measures to help people cope and live with the effects of climate change; pollution reduction and sustainable natural resource management; and access to affordable, reliable, and clean energy sources.
Generating evidence in environment, energy, and climate change is becoming increasingly urgent as emissions increase, global warming progresses, and communities start to feel the effects of a changing climate. Climate change is highly inequitable, with low-income communities being hardest hit by climate and weather shocks, while at the same time having the fewest resources to adapt. To better understand the impacts of programs, technologies, and policies, researchers are exploring new ways of combining different sources of data. Combining remote sensing data, satellite data, or administrative data collected by governments and utility companies with ground-truth and survey data has the potential to unlock insights about the efficacy of climate solutions. Technological innovations in, for example, sensor technology can produce more granular data on air quality and pollution (Khanna, 2000; O’Neill et al., 2003), allowing for new and innovative combinations with data on welfare losses and health. Policies and research on environment and climate change often face challenges in using available data to predict and measure the impacts of environmental shocks - a challenge that is being met with developments in predictive modeling to inform policy and humanitarian interventions.
Lastly, understanding human behavior and household-level measures to face environmental and energy challenges as well as mitigate and adapt to climate change opens up more questions. Researchers tackle these questions by studying incentive structures (Jayachandran et al., 2017; Hanna et al., 2016), effective regulation enforcement and monitoring (Duflo et al., 2013; Ghanem and Zhang, 2014), and energy consumption and conservation behavior (Burgess et al., 2020; Lee et al., 2020).
Environment: General resources
The reading lists for MIT’s Environmental policy and economics (Allcott, Spring 2011) and Environmental economics and government responses to market failure (Greenstone, Spring 2005) are good primers for measurement issues in environmental economics. Though the measurement issues are more focused on cost benefit analysis, the Energy economics (Joskow, Spring 2007) course also has a reading list that may be helpful.
- Book section: Environment modules, by Dale Whittington, in Designing household survey questionnaires for developing countries; World Bank, Volume 2, 5-30 (2000) -- An overview of measurement issues surrounding indicators relevant for environmental policy, including sections on contingent valuation, measuring resource use, and capturing environmental priorities. Has a particular focus on LSMS methods and includes example questionnaire modules.
Environment: Measuring benefits
- Paper: Nonmarket valuation of environmental resources: An interpretive appraisal, by V. Kerry Smith (1993) -- A literature review of the pros and cons of various methods of nonmarket valuation of environmental resources; covers both indirect (e.g., revealed preference) and direct (e.g., WTP surveys) methods. [Gated]
- Book: The measurement of environmental and resource values, by A. Myrick Freeman III, Joseph A. Herriges, and Catherine L. Kling; RFF Press, Vol. 3 (2014) [direct download link] -- A graduate-school level textbook that covers the valuation of environmental resources. Includes chapters on topics including, but not limited to, hedonic pricing, environmental quality as a factor input, and valuing longevity and health.
- Paper: Hedonic prices, property values and measuring environmental benefits: A survey of the issues, by A. Myrick Freeman III (1979) -- A review of the theoretical and empirical literatures on using hedonic pricing models to measure environmental benefits and demand functions.
- Paper: Can markets value air quality? A meta-analysis of hedonic property value models, by V. Kerry Smith and Ju-Chin Huang (1995) -- A meta-analysis of empirical estimates of air-quality demand from hedonic property value models.
- Paper: Wages, rents, and the quality of life, by Jennifer Roback (1982) -- Introduces a city-level measure of quality of life determined by wage rent values.
- For a relatively recent example of using housing-price data to measure air quality demand, see Chay and Greenstone (2005). [Gated published version]
Environment: Measuring bads and costs
Measuring pollution using air quality sensors
- Broad overview, Data: The EPA’s Air Emission Measurement Center (EMC) and Daily Air Quality Tracker -- The Air Emission Measurement Center compiles resources, including a comprehensive list of available tests, for measuring pollutant emissions from smokestacks and other industrial sources. The Daily Air Quality Tracker provides current and historical data on air quality at different levels of geographic aggregation.
- Paper: Measuring environmental quality: an index of pollution, by Neha Khanna (2000) -- Introduces a new method for creating an air quality/pollution index, which uses the epidemiological dose-response functions of pollutants to aggregate by welfare loss. Compares the index to the EPA’s Pollutant Standards Index (PSI). [Gated]
- Paper: Using unobtrusive sensors to measure and minimize Hawthorne effects: Evidence from cookstoves, by Andrew M. Simons, Theresa Beltramo, Garrick Blalock, and David I. Levine (2017) -- The Data in brief has a more detailed description of methods, including using kitchen performance tests, stove use monitors, and UCB Particulate and Temperature Sensors. [Gated published version]
- Paper: Ozone exposure among Mexico City outdoor workers, by Marie S. O’Neill et al. (2003) -- Compares air pollution data gathered from both fixed and personal monitors; ozone measurement from fixed monitors is generally higher than that measured from personal monitors.
- Paper: Effortless perfection: Do Chinese cities manipulate air pollution data?, by Dalia Ghanem and Junjie Zhang (2014) -- Introduces a set of tests for whether reported air pollution data has been manipulated, and tests the method on data from Chinese cities, finding suggestive evidence of manipulation.
- For examples of research using air quality data, see Lavy, Ebenstein, and Roth (2014), Chang et al. (2016), and Ebenstein et al. (2017). For more technical papers on the variability of sensor measurement, see Blanchard and Tanenbaum (2003) [gated], who demonstrate the within-week variability of pollution measures, and Braniš et al. (2005) [gated], who examine the impact of local human activity on pollution measurement. For an example of research using continuous emissions monitoring systems, see Greenstone et al. (forthcoming).
Measuring pollution using audits
- Paper: Truth-telling by third-party auditors and the response of polluting firms: Experimental evidence from India, by Esther Duflo, Michael Greenstone, Rohini Pande, and Nicholas Ryan (2013) -- Compares air and water pollution data gathered from audits and audit back-checks, pollution sampling, and administrative reports on regulatory action.
- Paper: EIA practice in India and its evaluation using SWOT analysis, by Ritu Paliwal (2006) -- A discussion of the strengths and weaknesses of data taken from Environmental Impact Assessments (EIA) in India, as well as recommendations for improving that system. [Gated]
- Paper: Does severe air pollution affect audit judgment?: Evidence from China, by Feng Chen, Xiaofeng Peng, and Jianguang Zeng (2017) -- Provides evidence that local air pollution can negatively affect the quality of audits.
Measuring deforestation using satellite imagery
- Paper: Humid tropical forest disturbance alerts using Landsat data, by Matthew C. Hansen et al. (2015) -- Introduces and tests a methodology for measuring tropical forest disturbances using Landsat data. The tests were performed in Peru, the Republic of the Congo, and Indonesia, and the methodology is currently used by MAAP (the Monitoring of the Andean Amazon Project), among others.
- Paper: Cash for carbon: A randomized trial of payments for ecosystem services to reduce deforestation, by Seema Jayachandran et al. (2017) -- Contains a description of using satellite imagery to measure deforestation in small areas.
Anthropometric measurement of costs
- Paper: Up in smoke: The influence of household behavior on the long-run impact of improved cooking stoves, by Rema Hanna, Esther Duflo, and Michael Greenstone -- Measuring health effects of smoke exposure through carbon monoxide exhalation measurements, health recall data, biometric tests, and spirometry readings. [Gated published version]
Energy: General resources
- Book section: Environment modules by Dale Whittington, in Designing household survey questionnaires for developing countries; World Bank, Volume 2, 5-30 (2000) -- An overview of measurement issues surrounding indicators relevant for environmental policy, including sections on contingent valuation, measuring resource use, and capturing environmental priorities. Has a particular focus on LSMS methods and includes example questionnaire modules.
- Paper: Does household electrification supercharge economic development?, by Kenneth Lee, Edward Miguel, and Catherine Wolfram (2020) -- Challenges to measuring electrification (and combining measures of electrification).
Energy: Access and use
- Data: The World Bank’s Sustainable Energy for All Databank -- Contains country-level indicators of energy consumption and production, with a particular focus on renewable energy.
- Paper: Energy poverty: What you measure matters, by Lauren C. Culver (2017) -- A review of the current methods of measuring energy poverty, as well as a discussion of their strengths and weaknesses and of indices combining different pieces of them.
- Paper: The U.S. Department of Energy’s Review of selected home energy auditing tools (2010) -- A review of home energy audit tools, including a literature review of past tests of audit tools as well as a comparison of ten selected tools.
- Survey guide: The International Atomic Energy Agency’s Energy indicators for sustainable development: Guidelines and methodologies (2005) -- Guidelines for constructing macroeconomic energy indicators.
- Paper: Measuring energy access: Supporting a global target, by Morgan Bazilian et al. (2015) -- A review of available measures of energy access, a discussion of how to combine them into indices, and a proposed framework for improving the measurement of energy access and poverty.
- Paper: Measuring energy poverty: Focusing on what matters, by Patrick Nussbaumer, Morgan Bazilian, Vijay Modi, and Kandeh K. Yumkella (2012) -- Includes a review of past literature on measuring energy poverty and introduces a new energy poverty index, the Multidimensional Energy Poverty Index. [Gated published version]
- Paper: Towards a new measurement of energy poverty: A cross-community analysis of rural Pakistan, by Bilal Mirza and Adam Szirmai (2010) -- Provides an overview of a specially-designed energy poverty instrument (the Energy Poverty Survey) and descriptive statistics from its use in rural Pakistan, and introduces a new energy poverty index that takes energy inconveniences, household size, and energy shortfalls into account.
- For an example of research using administrative data to measure energy use, see Jack and Smith (2016).
- Paper: Demand for electricity on the global electrification frontier, by Robin Burgess, Michael Greenstone, Nicholas Ryan, and Anant Sudarshan (2020) -- Revealed preference approach to measuring demand and estimation of substitution between electricity sources; structural model to study policy counterfactuals.
- See also Welfare benefits of decentralized solar energy for the rural poor in India by the same authors.
Financial inclusion, or access by individuals and businesses to quality and affordable financial products and services that meet their needs, is an increasingly common goal among policymakers. Measuring financial inclusion can help policymakers assess the current state of financial inclusion, set goals, and monitor progress towards achieving them. Moreover, research on these topics helps build our understanding of how interventions can support financial inclusion efforts. Data on financial inclusion has grown and improved in quality over the past decade.
To measure financial inclusion, researchers usually collect data from users (demand-side) or providers (supply-side). On the demand-side, surveys can capture an individual’s, household’s, or businesses' access to and use of financial services. One challenge with surveys is that it relies on self-reported data: respondents may not remember some financial decisions and may not want to provide accurate responses given the sensitivities of talking about one’s finances. On the supply-side, researchers can use administrative data from financial service providers or regulators. However, one limitation is that data from a single financial institution does not provide a full picture of an individual’s financial behavior, particularly if they use multiple institutions or do not use one at all, a case which is particularly common for low-income individuals. Finally, there are efforts to develop indices that capture the many dimensions of financial inclusion and aggregate measures of financial inclusion at a macro-level.
This section discusses various approaches to measuring financial inclusion, including by using survey data, using administrative or non-survey data, constructing indices of financial inclusion, and by using aggregate/macroeconomic measures of financial inclusion.
- Data: The IPA Dataverse contains survey data and instruments from their series of Consumer Protection in Digital Finance Users Surveys, as well as survey instruments and data from many finance-related RCTs.
- Brief: IPA’s Building resilience through financial inclusion: A review of existing evidence and knowledge gaps, by Danielle Moore, Zahra Niazi, Rebecca Rouse, and Berber Kramer (2019) -- A research brief that provides a review of research on financial inclusion, as well as areas for further research.
- Paper: Financial inclusion – measuring progress and progress in measuring, by Thorsten Beck (2016) -- An overview of issues in measuring financial inclusion as well as a review of recent advances in methods and data availability. Discusses indicators at the individual, household, firm, and aggregate levels.
- Blog post: 10 useful data sources for measuring financial inclusion, by Karina Broens Nielsen -- A short list of data sources for measuring financial inclusion.
Using survey/administrative data
Using survey data
- Data: Measuring financial inclusion: The Global Findex Database, by Asli Demirgüç-Kunt and Leora Klapper (2012) -- An introduction to and first analysis of the World Bank’s Findex Database, which contains survey data on financial inclusion.
- See also the Global Findex Database
- Paper: Measuring household usage of financial services: Does it matter how or whom you ask?, by Robert Cull and Kinnon Scott (2010) -- A randomized test of respondent effects on measures of financial inclusion; measures tabulated from a survey of the head of household are compared to those taken from all members of a household and those taken from a randomly-selected member of the household. [Gated published version]
- Survey guide & questionnaire: IPA’s Financial health survey manual -- A guide to implementing IPA’s Financial Health Survey, which measures individuals’ access-to-funds, access-to-finance, and financial behavior. Includes the instrument itself.
- Paper: Measuring financial health around the globe, by Lasse Brune, Dean Karlan, and Rebecca Rouse (2020) -- A companion paper to the above survey guide, it provides a brief literature review of measuring financial health, describes the creation of IPA’s Financial Health Survey, and provides results from a validation of the tool and advice for use.
- For an example of research using financial inclusion measured through survey data, see Beck, Demirgüç-Kunt and Peria (2008), who measure barriers to financial inclusion [Gated].
Using administrative/non-survey data
- Data & survey guide: Measuring financial access: 10 years of the IMF Financial Access Survey, by Marco Espinosa-Vega et al. (2020) -- An overview of the IMF’s Financial Access Survey (FAS), including a short literature review of measuring financial inclusion, a discussion of and guide to the instrument, and an introduction to future areas of research in financial inclusion measurement.
- See also the Financial Access Survey
- Brief: IPA’s Social media usage by digital finance consumers: Analysis of consumer complaints in Kenya, Nigeria, and Uganda from July 2019 - July 2020, (2021) -- Demonstrates the feasibility of using AI to extract and analyze social media information on digital finance consumers.
- For examples of research using financial inclusion measured through administrative data, see Mian (2006) [gated], who uses administrative data on loans, and Beck, Ioannidou, and Schäfer (2017), who use administrative data on lending policies and practices. [gated published version]
Constructing indices or using aggregate measures
- Paper: Index of financial inclusion – A measure of financial sector inclusiveness, by Mandira Sarma (2012) -- Introduces a new index for measuring financial inclusion by aggregating measures taken from banking sector indicators.
- Paper: Assessing countries’ financial inclusion standing— A new composite index, by Goran Amidžić, Alexander Massara, and André Mialou (2014) -- Constructing index weights using factor analysis.
- Paper: Measuring financial inclusion: A multidimensional index, by Noelia Cámara and David Tuesta (2014) -- Constructing index weights using principal components analysis.
- Paper: Household financial assets in the process of development, by Patrick Honohan (2006) -- Discusses various sources of data on financial inclusion and introduces an econometric method for estimating a country’s level of financial inclusion.
- Data: Section IV of the World Bank’s Financial inclusion strategies reference framework, by Douglas Pearce and Claudia Ruiz Ortega (2012) -- Discussion of measuring financial inclusion followed by a list of available surveys and indicators.
- Paper: Discussing measures of financial inclusion for the main euro area countries, by Giorgio Nuzzo & Stefano Piermattei (2019) -- A discussion of various indices of financial inclusion and their applicability for euro area countries; further introduces the diffusion of electronic cards into the indices and tests the results using survey data. [Gated]
Field experiments in health economics help to answer a variety of questions related to the take-up and delivery of health products and services. From helping to better determine the factors that motivate individuals to adopt healthy behaviors to identifying innovations that improve the delivery of health services, this type of research is an important input to strengthening health systems and improving health outcomes around the world. Accurately measuring baseline, intermediate, and final health outcomes is a critical component of determining whether a given policy or program was effective. Some metrics, such as HIV prevalence, can be measured through relatively straightforward tests. But other outcomes are trickier to measure. For instance, child malnutrition is a key predictor of mortality. What is the best measure of malnutrition rates? Height-for-age, weight-for-height, mid-arm circumference, iron deficiency anemia, and more can all be appropriate in certain situations. Which one should a researcher choose given the context and their research questions? Use of modern contraceptives is an important measure of fertility, but respondents may be tempted to report regular use, even if this is untrue, if they feel they should be using them. How can researchers avoid this type of desirability bias?
This section, categorized according to outcomes and health conditions, compiles resources to guide researchers through these and other health measurement challenges. Produced by experts including the World Health Organization, UNICEF, and pioneering researchers in the field, these resources range from survey design guides to best practices for measuring tricky outcomes. In instances where multiple metrics may be appropriate, they also provide suggestions on how to help determine the best indicator(s).
- Questionnaire: Model surveys from the Demographic and Health Surveys program -- Provides a high-level overview of the DHS’s four main questionnaires (Man, Woman, Household, and Biomarker), and provides links to current and past modules.
- Book section: Health modules by Paul Gertler, Elaina Rose, and Paul Glewwe, in Designing household survey questionnaires for developing countries; World Bank, Volume 1: 177-216 (2000) -- An overview of indicators relevant for health policy, a discussion of survey methods used to capture those indicators, and annotated example questionnaire modules. Has a particular focus on LSMS methods.
- Online course: J-PAL’s Measuring health outcomes in field surveys course -- Contains lectures and interactive material on all aspects of measuring health outcomes in field surveys: measuring individual and population health, selecting health indicators and measurement tools, questionnaire development, and practical and ethical issues for data collection.
- Paper: The impact of recall periods on reported morbidity and health seeking behavior, by Jishnu Das, Jeffrey Hammer, and Carolina Sánchez-Paramo (2012). -- An experimental comparison of different recall periods on different reported health outcomes, including morbidity, doctor visits, time spent sick, and use of self-medication. Includes an exploration of the effects among different subgroups of the sample. [Gated published version]
- Blog post: Quantifying the Hawthorne effect, by Jed Friedman and Brinda Gokul (2014) -- A compilation and short literature review of papers attempting to quantify the Hawthorne effect in health studies.
- Broad overview: The World Health Organization’s Global reference list of 100 core health Indicators (plus health-related SDGs), 2018 -- A list of 100 core health indicators compiled by the WHO, with a broad classification, a definition, possible disaggregations, preferred measurement technique, and preferred data source listed for each indicator.
- Survey guide: Disability measurement in household surveys: A guidebook for designing household survey questionnaires, by Marco Tiberti and Valentina Costa (2020) -- An LSMS guidebook on measuring disabilities. Includes an exploration of the definition of a disability, a framework for measurement, and a comparison of three sets of modules: those by the Washington Group, by DHS, and by the WHO. Includes sample modules.
- Paper: Measuring the health of populations: explaining composite indicators, by Adnan A. Hyder, Prasanthi Puvanachandra, and Richard H. Morrow (2012) -- A review of the main composite health measures, including DALYs, HeaLYs, and QALYs, their construction, and their advantages and limitations.
- Paper: The National Academy of Sciences’ Biological and clinical data collection in population surveys in less developed countries (2000) -- A discussion of the logistics, ethics, and usefulness of adding anthropometry measures to large-scale household surveys and censuses; contains disease-specific discussion and guidance.
- Book section: Anthropometry modules by Harold Alderman, in Designing household survey questionnaires for developing countries; World Bank, Volume 1: 251-272 (2000) -- Sample anthropometry modules, along with a discussion of when to include one in a larger health module, and for which members of the household.
- Survey guide: The FANTA project’s Guide to anthropometry: A practical tool for program planners, managers, and implementers, by Kristen Cashin and Lesley Oot (2018) -- A comprehensive practical guide to anthropometry for four groups: children 0-5 years of age, children and adolescents 5 to 19, pregnant and postpartum women and girls, and Adults 18 years and older. Contains example protocols and recommended equipment.
- Paper: Methodologic issues in measuring physical activity and physical fitness when evaluating the role of dietary supplements for physically active people, by William L. Haskell and Michaela Kiernan (2000) -- The “Measurement Of Physical Activity And Physical Fitness” section provides an introduction to a number of indicators used to measure physical activity and fitness.
Early childhood development (general)
- Questionnaire: The Caregiver Reported Early Childhood Development Instruments (CREDI) -- a set of instruments for measuring early childhood development validated specifically for children ages 0 to 3 across developing countries that rely heavily on caregiver reporting. The site contains the instrument, an overview, and a user guide.
- Survey guide: UNICEF’s Multiple indicator cluster surveys: Delivering robust data on children and women across the globe, by Shane Khan and Attila Hancioglu (2019) -- An overview of UNICEF’s MICS, including a list of available modules and discussions of sample selection and data quality.
- Survey guide: A first initiative to create regionally comparative data on child development in four Latin American countries: Technical annex, by Aimee Verdisco et al. (2015) -- A retrospective on the PRIDI initiative of the IADB: includes a discussion of the development of the initiative and its implementation, as well as the final instrument used in gathering the data.
Early childhood development (cognitive)
- Journal issue: ScienceDirect’s collection of articles on the Bayley-III Scale -- A collection of journal articles on the Bayley-III Scale, an instrument designed to assess the developmental functioning of infants, toddlers, and young children aged between 1 and 42 months; contains articles on the individual scales that make up the Bayley-III, as well as an international review of research that employs it and reviews of similar developmental tools.
- Questionnaire: Tools from the U.S. Bureau of Labor Statistics:
- The Peabody Picture Vocabulary Test -- An overview of the Peabody Picture Vocabulary Test, which measures verbal ability and scholastic aptitude for individuals 2.5-40 years of age. Includes links to the instrument itself, its technical report, and similar cognitive development tools.
- The Home Observation for Measurement of the Environment (HOME) -- An overview of the Home Observation for Measurement of the Environment module, which measures the quality of a child’s home environment. Includes links to the instrument itself, its technical report, and similar tools from the BLS.
Early childhood development (physical)
- Journal issue: WHO child growth standards, by Mercedes de Onis, Cutberto Garza, Adelheid W. Onyango, and Reynaldo Martorell (2006) -- A collection of articles that describe the WHO Child Growth Reference Study and the standards that came out of it; includes the standards themselves as well as multiple articles discussing the development and validity of the study.
- Paper: Using height-for-age differences (HAD) instead of height-for-age z-scores (HAZ) for the meaningful measurement of population-level catch-up in linear growth in children less than 5 years of age, by Jef L. Leroy et al. (2015) -- Argues for the use of height-for-age differences (HAD) instead of height-for-age z-scores (HAZ) when examining child growth in the same population over time because of the cross-sectional nature of HAZ; tests both HAD and HAZ using DHS and COHORTS data.
- Survey guide: The World Health Organization’s Indicators for assessing infant and young child feeding practices: definitions and measurement methods (2021) -- A set of 17 recommended indicators for measuring IYCF and their definitions and measurement tips; contains example questionnaire modules, example surveyor instructions, and tips for adapting the instruments to specific contexts.
- Survey guide: The Food Insecurity Experience Scale: Development of a global standard for monitoring hunger worldwide, by Terri J. Ballard, Anne W. Kepple, and Carlo Cafiero (2013) -- A discussion of the FAO’s Food Insecurity Experience Scale (FIES) and its development; includes a review of other food insecurity and hunger indicators, the questions that form the scale, and advice for translation and implementation.
- Paper: What do we really know? Metrics for food insecurity and undernutrition, by Hartwig de Haen, Stephan Klasen, and Matin Qaim. (2011) -- A comparison of three of the main ways to measure chronic food insecurity: the FAO indicator of undernourishment, childhood anthropometrics, and household food consumption surveys. Tests the correlation between the three measures and discusses each of their strengths and pitfalls. [Gated published version]
- Paper: Development, validation and utilisation of food-frequency questionnaires – a review, by Janet Cade, Rachel Thompson, Victoria Burley, and Daniel Warm (2002) -- A literature review of food-frequency questionnaires and their development and validation.
Sexual and Reproductive Health
- Paper: Sex, lies, and measurement: Consistency tests for indirect response survey methods, by Erica Chuang, Pascaline Dupas, Elise Huillery, and Juliette Seban (2021) -- Introduces internal consistency tests for list randomization and randomized response technique, and applies them in data collection on sexual and reproductive health in Côte d’Ivoire and Cameroon. [Gated published version]
- Book section: Fertility modules by Indu Bhushan and Raylynn Oliver in Designing household survey questionnaires for developing countries; World Bank, Volume 2: 31-48 (2000) -- Example and annotated short and long questionnaires for gathering fertility data in household (particularly LSMS) surveys, as well as recommendations for implementation.
- For an example of research measuring sexual and reproductive health, see Fertility decline and missing women, by Seema Jayachandran (2017).
Healthcare quality/patient satisfaction
- Blog post: Pitfalls of patient satisfaction surveys and how to avoid them, by David Evans (2018) -- A summary of two papers studying the validity and reliability of patient satisfaction surveys; discusses the results from the papers and provides advice on how to improve patient satisfaction surveys, including links to two recommended instruments.
- Paper: Bias in patient satisfaction surveys: a threat to measuring healthcare quality, by Felipe Dunsch, David K. Evans, Mario Macis, and Qiao Wang (2018) -- A test of whether positive framing in agree/disagree questions in patient satisfaction surveys biases estimates upward.
- Paper: Which doctor? Combining vignettes and item response to measure doctor quality, by Jishnu Das and Jeffrey Hammer (2005) -- Introduces a new method for measuring doctor quality--combining survey vignettes and Item Response Theory (IRT)--and tests the method using data from urban India. [Gated published version]
Using audits and mystery shoppers
- Blog post: Mystery clients in development research, by David Evans (2015) -- A compilation and review of papers using mystery clients in development research, with the majority focusing on health.
- Paper: Missing in action: Teacher and health worker absence in developing countries, by Nazmul Chaudhury, Jeffrey Hammer, Michael Kremer, Karthik Muralidharan, and F. Halsey Rogers (2006) -- A review of surveys recording teachers’ and health worker absence during unannounced visits in six developing countries.
Housing stability and homelessness
Housing instability is both a function of and a catalyst for poverty. Maintaining stable housing is a necessary prerequisite in many cases for health, employment, education, and a host of other fundamental needs. The scope and complexity of housing instability and homelessness highlight the need for rigorous evidence on the effectiveness of strategies to prevent and reduce homelessness. A first step in generating this evidence is defining and measuring homelessness and housing instability adequately.
Unfortunately, the measurement of housing instability is complicated by the existence of a variety of definitions and no widely established measurement system of it. For instance, in the United States, children who share housing with others (living “doubled up”) qualify for assistance under some programs, but not others. Moreover, the scope of people experiencing homelessness can vary by orders of magnitude depending on which definition one uses; including children who are living in doubled up conditions increases estimates of the number of children experiencing homelessness by a factor of 10 from the standard “point in time” (PIT) count. An emerging literature looks at how to measure housing stability using techniques for reaching mobile populations and consumer reference data (e.g. Phillips (2020); Kalton (2001))
Further challenges to measurement come with measuring people who are unsheltered (those sleeping outside or in places not meant for human habitation), typically part of the PIT count in the United States; some studies have found that PIT counts can understate the rate of unsheltered homelessness by as much as 50 percent (e.g. Evans, Phillips, and Ruffini (2019))
The resources included below cover survey and administrative data methods for counting people experiencing housing instability and homelessness, covering topics from oversampling to ensure adequate representation of minority groups through methods for including hard-to-reach subpopulations. A reflection of J-PAL’s internal expertise, the resources below center around housing instability and homelessness in the United States; we welcome suggestions for additional resources to include, particularly those based in or relevant to other countries.
- Broad overview: Measuring homelessness and the extent of the problem (2019), in Reducing and preventing homelessness: A review of the evidence and charting a research agenda by William N. Evans, David C. Phillips and Krista Ruffini (2019) -- Section 2 provides an overview of the different methods for measuring homelessness and associated challenges. It also defines homelessness and discusses methods to reduce it.
- Survey guide: U.S. Department of Housing and Urban Development’s A guide to counting unsheltered homeless people (2008) -- Defines homelessness, reviews some common methods for measuring it and presents some common issues in the US context.
- Paper: Defining and measuring homelessness, by Volker Busch-Geertsema (2014) -- A review of the definitions, measurement and prevalence of homelessness in the EU.
Using administrative data
- Paper: The potential of linked administrative data for advancing homelessness research and policy, by Dennis P. Culhane (2016) -- Discusses how linked administrative data can help identify welfare programs used by homeless populations and uncover gaps that need to be addressed to reduce homelessness.
- Paper: Measuring housing stability with consumer reference data, by David C. Phillips (2020) -- Uses home addresses in consumer data to track changes in housing for low-income groups. [Gated published version]
- Broad overview: 2016 AHAR: Part 2 - Estimates of homelessness in the U.S., by Larry Buron, Tom McCall, and Claudia D. Solari (2017) -- Measuring homelessness using administrative data (in this case the U.S.’s Homeless Management Information System (HMIS)).
- Broad overview: Sampling and weighting a survey of homeless persons, by Pascal Ardilly and David Le Blanc (2001) -- Uses weight sharing to correct for duplication.
- Broad overview: Methods for oversampling rare subpopulations in social surveys, by Graham Kalton (2009) -- Statistical framework for oversampling rare subgroups in broad surveys.
- Broad overview: Practical methods for sampling rare and mobile populations, by Graham Kalton (2001) -- The statistical framework for sampling methods that can be used for measuring homelessness.
- Book section: Towards a strategy for counting the homeless, by Tracy Peressini, Lynn Mcdonald, and J. David Hulchanski in Finding home: Policy options for addressing homelessness in Canada; Canadian Observatory on Homelessness, Toronto (2010). -- Arguments in favor of service-based methods, compares it with other methods, and presents an example.
Capture-recapture methods (plant capture, mark-recapture etc.)
- Broad overview: Can we measure homelessness? A critical evaluation of ‘Capture-Recapture’, by Malcolm Williams (2010) -- Discusses the possibilities and limitations of the “capture-recapture” method for measuring homelessness.
- Paper: Estimating numbers of unsheltered homeless people through plant-capture and postcount survey methods, by Kim Hopper, Marybeth Shinn, Eugene Laska, Morris Meisner, and Joseph Wanderling (2008) -- Testing PIT estimates against two other counting methods.
- For a similar paper on the 1990 Census, see Issues in the use of a plant-capture method for estimating the size of the street dwelling population by Elizabeth Martin, Eugene Laska, Kim Hopper, Morris Meisner, and Joe Wanderling (1997).
- Paper: Bayesian estimation of the size of a street-dwelling homeless population, by Lawrence C. McCandless, Michelle L. Patterson, Lauren B. Currie, Akm Moniruzzaman, and Julian M. Somers (2016) -- Creating confidence intervals for homeless populations using plant-recapture techniques in Edmonton.
- Paper: Estimating the prevalence of hard‐to‐reach populations: The illustration of mark‐recapture methods in the study of homelessness, by Ian Shaw, Michael Bloor, Richard Cormack, and Howard Williamson (1996) -- Using mark-recapture methods to estimate the size of the homeless population. [Gated]
Last updated August 2021. These resources are a collaborative effort. If you notice a bug or have a suggestion for additional content, please fill out this form.
We thank Aimee Barnes, Sarah Baum, Sam Carter, Anupama Dathan, Maya Duru, Sarah Gault, Nilmini Herath, Eliza Keller, Tithee Mukhopadhyay, Rohit Naimpally, William Pariente, Maike Pfeiffer, Mikaela Rabb, Andrea Cristina Ruiz, and Caroline Tangoren for helpful review and comments, and Manvi Govil and Ximena Mercado Garcia for their help copy-editing the resource. Any errors are our own.