Measuring Nonresponse Bias in a Cross-Country Enterprise Survey
Nonresponse is a common issue affecting the vast majority of surveys. Efforts to convince those unwilling to participate in a survey might not necessary result in a better picture of the target population and can lead to higher, not lower, nonresponse bias. We investigate the impact of non-response...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Austrian Statistical Society
2015-04-01
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| Series: | Austrian Journal of Statistics |
| Online Access: | http://www.ajs.or.at/index.php/ajs/article/view/60 |
| _version_ | 1818774520845041664 |
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| author | Katarzyna Bańkowska Malgorzata Osiewicz Sébastien Pérez-Duarte |
| author_facet | Katarzyna Bańkowska Malgorzata Osiewicz Sébastien Pérez-Duarte |
| author_sort | Katarzyna Bańkowska |
| collection | DOAJ |
| description | Nonresponse is a common issue affecting the vast majority of surveys. Efforts to convince those unwilling to participate in a survey might not necessary result in a better picture of the target population and can lead to higher, not lower, nonresponse bias.
We investigate the impact of non-response in the European Commission & European Central Bank Survey on the Access to Finance of Enterprises (SAFE), which collects evidence on the financing conditions faced by European SMEs compared with those of large firms. This survey, conducted by telephone bi-annually since 2009 by the ECB and the European Commission, provides a valuable means to search for this kind of bias, given the high heterogeneity of response propensities across countries.
The study relies on so-called “Representativity Indicators” developed within the Representativity Indicators of Survey Quality (RISQ) project, which measure the distance to a fully representative response. On this basis, we examine the quality of the SAFE Survey at different stages of the fieldwork as well as across different survey waves and countries. The RISQ methodology relies on rich sampling frame information, which is however partly limited in the case of the SAFE. We also assess the representativeness of the SAFE particular subsample created by linking the survey responses with the companies’ financial information from a business register; this sub-sampling is another potential source of bias which we also attempt to quantify. Finally, we suggest possible ways how to improve monitoring of the possible nonresponse bias in the future rounds of the survey. |
| first_indexed | 2024-12-18T10:42:27Z |
| format | Article |
| id | doaj.art-1fb4a15bd2dd4507b3eb339cb5e66864 |
| institution | Directory Open Access Journal |
| issn | 1026-597X |
| language | English |
| last_indexed | 2024-12-18T10:42:27Z |
| publishDate | 2015-04-01 |
| publisher | Austrian Statistical Society |
| record_format | Article |
| series | Austrian Journal of Statistics |
| spelling | doaj.art-1fb4a15bd2dd4507b3eb339cb5e668642022-12-21T21:10:37ZengAustrian Statistical SocietyAustrian Journal of Statistics1026-597X2015-04-0144210.17713/ajs.v44i2.6032Measuring Nonresponse Bias in a Cross-Country Enterprise SurveyKatarzyna BańkowskaMalgorzata Osiewicz0Sébastien Pérez-DuarteECBNonresponse is a common issue affecting the vast majority of surveys. Efforts to convince those unwilling to participate in a survey might not necessary result in a better picture of the target population and can lead to higher, not lower, nonresponse bias. We investigate the impact of non-response in the European Commission & European Central Bank Survey on the Access to Finance of Enterprises (SAFE), which collects evidence on the financing conditions faced by European SMEs compared with those of large firms. This survey, conducted by telephone bi-annually since 2009 by the ECB and the European Commission, provides a valuable means to search for this kind of bias, given the high heterogeneity of response propensities across countries. The study relies on so-called “Representativity Indicators” developed within the Representativity Indicators of Survey Quality (RISQ) project, which measure the distance to a fully representative response. On this basis, we examine the quality of the SAFE Survey at different stages of the fieldwork as well as across different survey waves and countries. The RISQ methodology relies on rich sampling frame information, which is however partly limited in the case of the SAFE. We also assess the representativeness of the SAFE particular subsample created by linking the survey responses with the companies’ financial information from a business register; this sub-sampling is another potential source of bias which we also attempt to quantify. Finally, we suggest possible ways how to improve monitoring of the possible nonresponse bias in the future rounds of the survey.http://www.ajs.or.at/index.php/ajs/article/view/60 |
| spellingShingle | Katarzyna Bańkowska Malgorzata Osiewicz Sébastien Pérez-Duarte Measuring Nonresponse Bias in a Cross-Country Enterprise Survey Austrian Journal of Statistics |
| title | Measuring Nonresponse Bias in a Cross-Country Enterprise Survey |
| title_full | Measuring Nonresponse Bias in a Cross-Country Enterprise Survey |
| title_fullStr | Measuring Nonresponse Bias in a Cross-Country Enterprise Survey |
| title_full_unstemmed | Measuring Nonresponse Bias in a Cross-Country Enterprise Survey |
| title_short | Measuring Nonresponse Bias in a Cross-Country Enterprise Survey |
| title_sort | measuring nonresponse bias in a cross country enterprise survey |
| url | http://www.ajs.or.at/index.php/ajs/article/view/60 |
| work_keys_str_mv | AT katarzynabankowska measuringnonresponsebiasinacrosscountryenterprisesurvey AT malgorzataosiewicz measuringnonresponsebiasinacrosscountryenterprisesurvey AT sebastienperezduarte measuringnonresponsebiasinacrosscountryenterprisesurvey |