J. Dawber, Nora Würz, Paul A. Smith, Tanya Flower, Heledd Thomas, T. Schmid, N. Tzavidis
Abstract Like many other countries, the United Kingdom (UK) produces a national consumer price index (CPI) to measure inflation. Presently, CPI measures are not produced for regions within the UK. It is believed that, using only available data sources, a regional CPI would not be precise or reliable enough as an official statistic, primarily because the regional partitioning of the data makes sample sizes too small. We investigate this claim by producing experimental regional CPIs using publicly available price data, and deriving expenditure weights from the Living Costs and Food survey. We detail the methods and challenges of developing a regional CPI and evaluate its reliability. We then assess whether model-based methods such as smoothing and small area estimation significantly improve the measures. We find that a regional CPI can be produced with available data sources, however it appears to be excessively volatile over time, mainly due to the weights. Smoothing and small area estimation improve the reliability of the regional CPI series to some extent but they remain too volatile for regional policy use. This research provides a valuable framework for the development of a more viable regional CPI measure for the UK in the future.
{"title":"Experimental UK Regional Consumer Price Inflation with Model-Based Expenditure Weights","authors":"J. Dawber, Nora Würz, Paul A. Smith, Tanya Flower, Heledd Thomas, T. Schmid, N. Tzavidis","doi":"10.2478/jos-2022-0010","DOIUrl":"https://doi.org/10.2478/jos-2022-0010","url":null,"abstract":"Abstract Like many other countries, the United Kingdom (UK) produces a national consumer price index (CPI) to measure inflation. Presently, CPI measures are not produced for regions within the UK. It is believed that, using only available data sources, a regional CPI would not be precise or reliable enough as an official statistic, primarily because the regional partitioning of the data makes sample sizes too small. We investigate this claim by producing experimental regional CPIs using publicly available price data, and deriving expenditure weights from the Living Costs and Food survey. We detail the methods and challenges of developing a regional CPI and evaluate its reliability. We then assess whether model-based methods such as smoothing and small area estimation significantly improve the measures. We find that a regional CPI can be produced with available data sources, however it appears to be excessively volatile over time, mainly due to the weights. Smoothing and small area estimation improve the reliability of the regional CPI series to some extent but they remain too volatile for regional policy use. This research provides a valuable framework for the development of a more viable regional CPI measure for the UK in the future.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42748707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The importance of constructing sub-national spatial price indexes (SPIs) has been acknowledged in the literature for over two decades. However, systematic attempts to compile sub-national SPIs on a regular basis have been hampered by the labour-intensive analyses required for processing traditional price data. In the case of household consumption expenditures, the increasing availability of big data may change the current approach for estimating sub-national SPIs by considering the use of weighted index formulae. The aim of this paper is twofold: firstly, to review previous literature on sub-national SPIs and secondly to estimate Italian consumer SPIs. To this aim we use scanner data referring to grocery products sold in a random sample of approximately 1,800 Italian outlets belonging to the most important retail chains and including information on prices, quantities and quality characteristics of products at barcode level. Various weighted index formulas are used for calculating consumer SPIs at detailed territorial level and at the lowest aggregation level. Our results show an interesting territorial variability of consumer prices of products sold in large-scale retail outlets across the Italian regions. Overall, the Southern regions appear to have price levels below the national average both for food and non-food products with some interesting exceptions.
{"title":"Using Scanner Data for Computing Consumer Spatial Price Indexes at Regional Level: An Empirical Application for Grocery Products in Italy","authors":"T. Laureti, F. Polidoro","doi":"10.2478/jos-2022-0003","DOIUrl":"https://doi.org/10.2478/jos-2022-0003","url":null,"abstract":"Abstract The importance of constructing sub-national spatial price indexes (SPIs) has been acknowledged in the literature for over two decades. However, systematic attempts to compile sub-national SPIs on a regular basis have been hampered by the labour-intensive analyses required for processing traditional price data. In the case of household consumption expenditures, the increasing availability of big data may change the current approach for estimating sub-national SPIs by considering the use of weighted index formulae. The aim of this paper is twofold: firstly, to review previous literature on sub-national SPIs and secondly to estimate Italian consumer SPIs. To this aim we use scanner data referring to grocery products sold in a random sample of approximately 1,800 Italian outlets belonging to the most important retail chains and including information on prices, quantities and quality characteristics of products at barcode level. Various weighted index formulas are used for calculating consumer SPIs at detailed territorial level and at the lowest aggregation level. Our results show an interesting territorial variability of consumer prices of products sold in large-scale retail outlets across the Italian regions. Overall, the Southern regions appear to have price levels below the national average both for food and non-food products with some interesting exceptions.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47638542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract In recent years, there has been much interest among national statistical agencies in using web-scraped data in consumer price indices, potentially supplementing or replacing manually collected price quotes. Yet one challenge that has received very little attention to date is the estimation of expenditure weights in the absence of quantity information, which would enable the construction of weighted item-level price indices. In this article we propose the novel approach of predicting sales quantities from their ranks (for example, when products are sorted ‘by popularity’ on consumer websites) via appropriate statistical distributions. Using historical transactional data supplied by a UK retailer for two consumer items, we assessed the out-of-sample accuracy of the Pareto, log-normal and truncated log-normal distributions, finding that the last of these resulted in an index series that most closely approximated an expenditure-weighted benchmark. Our results demonstrate the value of supplementing web-scraped price quotes with a simple set of retailer-supplied summary statistics relating to quantities, allowing statistical agencies to realise the benefits of freely available internet data whilst placing minimal burden on retailers. However, further research would need to be undertaken before the approach could be implemented in the compilation of official price indices.
{"title":"Estimating Weights for Web-Scraped Data in Consumer Price Indices","authors":"D. Ayoubkhani, Heledd Thomas","doi":"10.2478/jos-2022-0002","DOIUrl":"https://doi.org/10.2478/jos-2022-0002","url":null,"abstract":"Abstract In recent years, there has been much interest among national statistical agencies in using web-scraped data in consumer price indices, potentially supplementing or replacing manually collected price quotes. Yet one challenge that has received very little attention to date is the estimation of expenditure weights in the absence of quantity information, which would enable the construction of weighted item-level price indices. In this article we propose the novel approach of predicting sales quantities from their ranks (for example, when products are sorted ‘by popularity’ on consumer websites) via appropriate statistical distributions. Using historical transactional data supplied by a UK retailer for two consumer items, we assessed the out-of-sample accuracy of the Pareto, log-normal and truncated log-normal distributions, finding that the last of these resulted in an index series that most closely approximated an expenditure-weighted benchmark. Our results demonstrate the value of supplementing web-scraped price quotes with a simple set of retailer-supplied summary statistics relating to quantities, allowing statistical agencies to realise the benefits of freely available internet data whilst placing minimal burden on retailers. However, further research would need to be undertaken before the approach could be implemented in the compilation of official price indices.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48879756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract U.S. import and export price indexes replaced unit value indexes forty years ago, given quality concerns of mismeasurement due to unit value bias. The administrative trade data underlying the unit values have greatly improved since that time. The transaction records are now more detailed, available electronically, and compiled monthly with little delay. The data are used by academic researchers to calculate price measures, and unit value indexes based on trade data are used by other national statistical offices (NSOs). The U.S. Bureau of Labor Statistics is now evaluating whether replacing price indexes with unit value indexes for homogeneous products calculated from administrative trade data could expand the number of published official import and export price indexes. Using export transactions, the research calculates detailed unit value indexes from 200 + million trade records from 2012–2017 for 123 export product categories. Results show that 27 of the 123 unit value indexes are homogeneous and closely comparable to published official price indexes. This article presents the concepts and methods considered to calculate and evaluate the unit value indexes and to select the product categories that are homogeneous. Compared to official price indexes, export unit value indexes for the 27 5-digit BEA (U.S. Bureau of Economic Analysis) end-use product categories would deflate real exports of these goods by 13 percentage points less over the period. Incorporating these 27 indexes into the top-level XPI would increase the value of real exports of all merchandise goods by 2.6 percentage points at the end of 2017.
摘要美国进出口价格指数在40年前取代了单位价值指数，考虑到单位价值偏差造成的计量错误的质量问题。从那时起，作为单位价值基础的行政贸易数据有了很大改善。交易记录现在更加详细，可以通过电子方式获得，并且每月编制一次，几乎没有延误。学术研究人员使用这些数据来计算价格指标，其他国家统计局(NSOs)使用基于贸易数据的单位价值指数。美国劳工统计局(Bureau of Labor Statistics)目前正在评估，用从行政贸易数据计算的同质产品的单位价值指数取代价格指数，是否可以扩大公布的官方进出口价格指数的数量。该研究利用出口交易，从2012-2017年的2亿多份贸易记录中计算出123个出口产品类别的详细单位价值指数。结果表明，123个单位价值指数中有27个与公布的官方价格指数具有同质性和可比性。本文介绍了单位价值指标的计算和评价以及同质产品品类的选择所考虑的概念和方法。与官方价格指数相比，27个5位数的BEA(美国经济分析局)最终用途产品类别的出口单位价值指数在此期间将使这些商品的实际出口减少13个百分点。将这27个指数纳入最高水平的XPI，到2017年底，所有商品的实际出口价值将增加2.6个百分点。
{"title":"Unit Value Indexes for Exports – New Developments Using Administrative Trade Data","authors":"Don Fast, S. Fleck, Dominic A Smith","doi":"10.2478/jos-2022-0005","DOIUrl":"https://doi.org/10.2478/jos-2022-0005","url":null,"abstract":"Abstract U.S. import and export price indexes replaced unit value indexes forty years ago, given quality concerns of mismeasurement due to unit value bias. The administrative trade data underlying the unit values have greatly improved since that time. The transaction records are now more detailed, available electronically, and compiled monthly with little delay. The data are used by academic researchers to calculate price measures, and unit value indexes based on trade data are used by other national statistical offices (NSOs). The U.S. Bureau of Labor Statistics is now evaluating whether replacing price indexes with unit value indexes for homogeneous products calculated from administrative trade data could expand the number of published official import and export price indexes. Using export transactions, the research calculates detailed unit value indexes from 200 + million trade records from 2012–2017 for 123 export product categories. Results show that 27 of the 123 unit value indexes are homogeneous and closely comparable to published official price indexes. This article presents the concepts and methods considered to calculate and evaluate the unit value indexes and to select the product categories that are homogeneous. Compared to official price indexes, export unit value indexes for the 27 5-digit BEA (U.S. Bureau of Economic Analysis) end-use product categories would deflate real exports of these goods by 13 percentage points less over the period. Incorporating these 27 indexes into the top-level XPI would increase the value of real exports of all merchandise goods by 2.6 percentage points at the end of 2017.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48907225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Hedonic regressions are widely used and recommended for property price index (PPI) measurement. Hedonic PPIs control for changes in the quality-mix of properties transacted that can confound measures of change in average property prices. The widespread adoption of the hedonic approach is primarily due to the increasing availability, in this digital age, of electronic data on advertised and transaction prices of properties and their price-determining characteristics. Yet hedonic PPIs are only as good as the underlying estimated hedonic regressions. Regression-based measures are unusual in official economic statistics. There is little technical support in the international Handbooks and Guides for diagnostic measures and graphical plots for estimated regression equations as applied to PPIs. These diagnostics are essential to the transparency and credibility of hedonic PPI measurement. This article seeks to remedy this.
{"title":"Econometric Issues in Hedonic Property Price Indices: Some Practical Help","authors":"M. Silver","doi":"10.2478/jos-2022-0008","DOIUrl":"https://doi.org/10.2478/jos-2022-0008","url":null,"abstract":"Abstract Hedonic regressions are widely used and recommended for property price index (PPI) measurement. Hedonic PPIs control for changes in the quality-mix of properties transacted that can confound measures of change in average property prices. The widespread adoption of the hedonic approach is primarily due to the increasing availability, in this digital age, of electronic data on advertised and transaction prices of properties and their price-determining characteristics. Yet hedonic PPIs are only as good as the underlying estimated hedonic regressions. Regression-based measures are unusual in official economic statistics. There is little technical support in the international Handbooks and Guides for diagnostic measures and graphical plots for estimated regression equations as applied to PPIs. These diagnostics are essential to the transparency and credibility of hedonic PPI measurement. This article seeks to remedy this.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49618497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract It is essential to measure within-country differences in housing costs in order to evaluate costs of living, assessing and comparing poverty levels, quantifying salaries and disposable income of families and finally for designing housing policies at local level. To the authors knowledge, no studies have yet been carried out on the computation of Space Price Indexes for Housing Rents (SPIHRs). In this article we computed preliminary estimates of sub-national SPIHRs by using hedonic regression model, which is an extension of the Country Product Dummy method, for all the Italian regions. The hedonic regression is generally used to obtain multilateral spatial indexes, thus allowing us to obtain multilateral SPIHRs for the Italian regions. The estimates have been done using 2017 data from the Real Estate Market Observatory which is a part of the Italian Agency of Revenue and Tax. This data source is the most comprehensive source of information on Italian houses price rents with a wide geographical coverage, including data for each Italian municipality. The obtained results show significant differences across the Italian regions, thus highlighting the importance of calculating SPIHR in Italy on a regular basis and the need to continue researches in this field.
{"title":"Sub-National Spatial Price Indexes for Housing: Methodological Issues and Computation for Italy","authors":"Ilaria Benedetti, L. Biggeri, T. Laureti","doi":"10.2478/jos-2022-0004","DOIUrl":"https://doi.org/10.2478/jos-2022-0004","url":null,"abstract":"Abstract It is essential to measure within-country differences in housing costs in order to evaluate costs of living, assessing and comparing poverty levels, quantifying salaries and disposable income of families and finally for designing housing policies at local level. To the authors knowledge, no studies have yet been carried out on the computation of Space Price Indexes for Housing Rents (SPIHRs). In this article we computed preliminary estimates of sub-national SPIHRs by using hedonic regression model, which is an extension of the Country Product Dummy method, for all the Italian regions. The hedonic regression is generally used to obtain multilateral spatial indexes, thus allowing us to obtain multilateral SPIHRs for the Italian regions. The estimates have been done using 2017 data from the Real Estate Market Observatory which is a part of the Italian Agency of Revenue and Tax. This data source is the most comprehensive source of information on Italian houses price rents with a wide geographical coverage, including data for each Italian municipality. The obtained results show significant differences across the Italian regions, thus highlighting the importance of calculating SPIHR in Italy on a regular basis and the need to continue researches in this field.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41602626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The article by Diewert and Fox provides a comprehensive overview of challenges that NSOs face in producing the CPI in pandemic times by touching on many different fields. A focus is on the treatment of missing prices, where they propose different methods depending on the resources available to the NSO. However, some of the procedures proposed can be seen as being less practical like the use of reservation prices (which is also debatable from a theoretical point of view) and of alternative data sources for weights whose implementation supposedly takes longer than the pandemic itself. Overall, the article provides an important contribution for making CPI production more robust for similar crises in the future.
{"title":"Creative and Exhaustive, but Less Practical – a Comment on the Article by Diewert and Fox","authors":"B. Goldhammer","doi":"10.2478/jos-2022-0014","DOIUrl":"https://doi.org/10.2478/jos-2022-0014","url":null,"abstract":"Abstract The article by Diewert and Fox provides a comprehensive overview of challenges that NSOs face in producing the CPI in pandemic times by touching on many different fields. A focus is on the treatment of missing prices, where they propose different methods depending on the resources available to the NSO. However, some of the procedures proposed can be seen as being less practical like the use of reservation prices (which is also debatable from a theoretical point of view) and of alternative data sources for weights whose implementation supposedly takes longer than the pandemic itself. Overall, the article provides an important contribution for making CPI production more robust for similar crises in the future.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48496485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Official rentals for housing (rent) price inflation statistics are of considerable public interest. Matched-sample estimators, such as that used for nearly two-decades in New Zealand (2000–2019), require an unrealistic assumption of a static universe of rental properties. This article investigates (1) a property fixed-effects estimator that better reflects the dynamic universe of rental properties by implicitly imputing for price change associated with new and disappearing rental properties; (2) length-alignment simulations and property life-cycle metrics to inform the choice of data window length (eight years) and preferred splice methodology (mean-splice); and (3) stock-imputation to convert administrative data from a ‘flow’ (new tenancy price) to ‘stock’ (currently paid rent) concept. The derived window-length sensitivity findings have important implications for inflation measurement. It was found that the longer the data window used to fit the model, the greater the estimated rate of inflation. Using administrative data, a range of estimates from 55% (window length: three-quarters) to 127% (window of 90-quarters) were found for total inflation, over the 25-years to 2017 Q4.
{"title":"Rentals for Housing: A Property Fixed-Effects Estimator of Inflation from Administrative Data","authors":"A. Bentley","doi":"10.2478/jos-2022-0009","DOIUrl":"https://doi.org/10.2478/jos-2022-0009","url":null,"abstract":"Abstract Official rentals for housing (rent) price inflation statistics are of considerable public interest. Matched-sample estimators, such as that used for nearly two-decades in New Zealand (2000–2019), require an unrealistic assumption of a static universe of rental properties. This article investigates (1) a property fixed-effects estimator that better reflects the dynamic universe of rental properties by implicitly imputing for price change associated with new and disappearing rental properties; (2) length-alignment simulations and property life-cycle metrics to inform the choice of data window length (eight years) and preferred splice methodology (mean-splice); and (3) stock-imputation to convert administrative data from a ‘flow’ (new tenancy price) to ‘stock’ (currently paid rent) concept. The derived window-length sensitivity findings have important implications for inflation measurement. It was found that the longer the data window used to fit the model, the greater the estimated rate of inflation. Using administrative data, a range of estimates from 55% (window length: three-quarters) to 127% (window of 90-quarters) were found for total inflation, over the 25-years to 2017 Q4.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43762649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The import and export price indices of an economy are usually compiled by some Laspeyres type index. It is well known that such an index formula is prone to substitution bias. Therefore, also the terms of trade (ratio of export and import price index) are likely to be distorted. The underlying substitution bias accumulates over time. The present article introduces a simple and transparent retroactive correction approach that addresses the source of the substitution bias and produces meaningful long-run time series of import and export price levels and, therefore, of the terms of trade. Furthermore, an empirical case study is conducted that demonstrates the efficacy and versatility of the correction approach.
{"title":"Substitution Bias in the Measurement of Import and Export Price Indices: Causes and Correction","authors":"Ludwig von Auer, Alena Shumskikh","doi":"10.2478/jos-2022-0006","DOIUrl":"https://doi.org/10.2478/jos-2022-0006","url":null,"abstract":"Abstract The import and export price indices of an economy are usually compiled by some Laspeyres type index. It is well known that such an index formula is prone to substitution bias. Therefore, also the terms of trade (ratio of export and import price index) are likely to be distorted. The underlying substitution bias accumulates over time. The present article introduces a simple and transparent retroactive correction approach that addresses the source of the substitution bias and produces meaningful long-run time series of import and export price levels and, therefore, of the terms of trade. Furthermore, an empirical case study is conducted that demonstrates the efficacy and versatility of the correction approach.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46496467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Response burden has long been a concern for data producers. In this article, we investigate the relationship between some measures of actual and perceived burden and we provide empirical evidence of their association with data quality. We draw on two business surveys conducted by Banca d’Italia since 1970, which provide a very rich and unique source of information. We find evidence that the perceived burden is affected by actual burden but the latter is not the only driver. Our results also show a clear link between a respondent’s perceived effort and the probability of not answering some important questions (such as those relating to expectations of future investments and turnover) or of dropping out of the survey. On the contrary, we do not find significant effects on the quality of answers to quantitative questions such as business turnover and investments. Overall, these findings have implications for data producers that should target the perceived burden, besides the actual burden, to increase data quality.
{"title":"Response Burden and Data Quality in Business Surveys","authors":"Marco Bottone, Lucia Modugno, A. Neri","doi":"10.2478/jos-2021-0036","DOIUrl":"https://doi.org/10.2478/jos-2021-0036","url":null,"abstract":"Abstract Response burden has long been a concern for data producers. In this article, we investigate the relationship between some measures of actual and perceived burden and we provide empirical evidence of their association with data quality. We draw on two business surveys conducted by Banca d’Italia since 1970, which provide a very rich and unique source of information. We find evidence that the perceived burden is affected by actual burden but the latter is not the only driver. Our results also show a clear link between a respondent’s perceived effort and the probability of not answering some important questions (such as those relating to expectations of future investments and turnover) or of dropping out of the survey. On the contrary, we do not find significant effects on the quality of answers to quantitative questions such as business turnover and investments. Overall, these findings have implications for data producers that should target the perceived burden, besides the actual burden, to increase data quality.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48371412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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