[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-113016-en":3,"doc-seo-113016-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},113016,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Understanding Nonresponse Rates - Insights from 600,000 Opinion Surveys","Survey response rates critically shape the credibility of empirical findings, yet research has paid insufficient attention to design factors that influence whether respondents answer. Response rates for household and opinion surveys have declined over time, raising concern because nonrandom nonresponse can generate biased estimations when certain groups skip particular sections. The study examines how survey length and the day of the week of fieldwork, among other design elements, affect nonresponse rates using large public opinion databases.","Pub lic Disclosure Authorized Pub lic Disclosure Authorized  \nThe World Bank Economic Review, 34(Supplement), 2020, S98–S102 doi: 10.1093/wber/lhz040 Supplementary Article  \nUnderstanding Nonresponse Rates: Insights from  \n600,000 Opinion Surveys  \nGermán Reyes  \nAbstract  \nDespite the central role of surveys in empirical research, academics have not paid enough attention to the factors that affect response rates. This is especially concerning since survey response rates—of both household and opinion surveys—have been declining over time (Meyer, Mok, and Sullivan, 2015), which might lead to distorted survey results. This paper explores how the underlying design of each survey—such as its length and the day of the week in which the survey was conducted—affects the response rates of the survey.  \nJEL classification: C83, D12  \nKeywords: response rates, nonrandom nonresponses, biased estimations, underlying design, nonresponse rate  \nSurveys are among the most important tools of social science research, and one of the main sources of data for economists. Despite the central role of surveys in empirical research, academics have focused mostly on the economic questions that can be answered from survey responses, giving far less attention to the factors that affect such responses. How response rates are affected by factors such as the length of the survey, the day when the interview was conducted, and the characteristics of the interviewer are questions that for the most part have been relegated to a second plane. At the same time, the need to understand the drivers of response rates is perhaps higher than ever. Recent evidence suggests survey response rates—of both household and opinion surveys—have been declining over time.1 These trends are particularly worrisome considering nonrandom nonresponses can lead to biased estimations. If a subgroup of the population (e.g., females) does not to answer particular sections of a survey (e.g., labor market questions), any indicator calculated from those questions would not be representative of that subgroup, and the conclusions derived from those data would be biased towards the answers of the other groups (see Groves 2006 ) . This paper contributes to a better understanding of the drivers of nonresponse rates by exploring how the underlying design of each survey affects the response rates of the survey itself.  \n1. Data and Empirical Strategy  \nTo assess the relationship between survey design and response rates, this paper relies on two complementary public opinion surveys. The first one has been conducted by Latinobarómetro in 18 countries of Latin America and the Caribbean plus Spain annually since 1995 . The second database comes from the Latin American Public Opinion Project (LAPOP), which has information from a similar set of countries since  \nGermán Reyes is a PhD candidate in the Department of Economics at Cornell University, Ithaca, NY, USA; his email address is [gjr66@cornell.edu](gjr66@cornell.edu).  \n1 See, for example, Meyer, Mok, and Sullivan (2015) for household surveys and Pew (2012) for opinion surveys.  \n© The Author(s) 2019 . Published by Oxford University Press on behalf of the International Bank for Reconstruction and Development / THE WORLD BANK.  \nAll rights reserved. For permissions, please e-mail: [journals.permissions@oup.com](journals.permissions@oup.com)  \nDownloaded from [https://academic.oup.com/wber/article/34/Supplement_1/S98/5651314 by guest on 23 June 2021](https://academic.oup.com/wber/article/34/Supplement_1/S98/5651314 by guest on 23 June 2021)  \n2004. The distinguishing feature of these databases is that they include the administrative information regarding the survey design behind each interview. For instance, it is possible to know which particular day each interview was conducted and how many questions the respondents did not answer. At the sametime, each dataset has unique information useful to answer particular questions regarding response rates. 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The administrative design information in the datasets enables this analysis.\"},{\"question\":\"What is the main dependent variable used to measure nonresponse?\",\"answer\":\"The paper uses the item nonresponse rate for each interviewee, defined as the ratio between the number of questions a respondent did not answer and the number of questions asked.\"}]",1784498974,13,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"understanding-nonresponse-rates-insights-from-600000-opinion-surveys","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/understanding-nonresponse-rates-insights-from-600000-opinion-surveys/113016/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-19",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why do nonresponse rates matter in survey-based research?","Question",{"text":74,"@type":75},"Nonresponse rates can bias results when missing answers are not randomly distributed across population subgroups. This can distort estimates derived from questions that some groups systematically avoid.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the paper link survey design to response behavior?",{"text":79,"@type":75},"It studies whether design features—such as survey length and the day interviews are conducted—affect item nonresponse rates. 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