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AStA Wirtschafts- und Sozialstatistisches Archiv
Erschienen in: AStA Wirtschafts- und Sozialstatistisches Archiv 2-3/2016

26.07.2016 | Originalveröffentlichung

The use of Twitter data to improve small area estimates of households’ share of food consumption expenditure in Italy

verfasst von: Stefano Marchetti, Caterina Giusti, Monica Pratesi

Erschienen in: AStA Wirtschafts- und Sozialstatistisches Archiv | Ausgabe 2-3/2016

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Abstract

The use of big data in many socio-economic studies has received a growing interest in the last few years. In this work we use emotional data coming from Twitter as auxiliary variable in a small area model to estimate Italian households’ share of food consumption expenditure (the proportion of food consumption expenditure on the total consumption expenditure) at provincial level. We show that the use of Twitter data has a potential in predicting our target variable. Moreover, the use of these data as auxiliary variable in the small area working model reduces the estimated mean squared error in comparison with what obtained by the same working model without the Twitter data.
Vorheriger Artikel Big Data und was nun? Neue Datenbestände und ihre Auswirkungen
Nächster Artikel Schätzung von Holzvorräten unter Verwendung von Fernerkundungsdaten

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Fußnoten
1
The equivalence scale is the Carbonaro scale used by ISTAT, according to which the expenditure of a family is divided by a specific coefficient depending on the household size (for example equal to 0.66 for a household with 1 member, 1.33 for a household with 3 members and up to 2.40 for a household with 7 members or more). In this way the expenditures of households of any size can be directly compared with those of households composed by two members.
 
2
This survey is a census survey, although some nonresponses can occur. Here we ignore the nonresponses and we use these data as census data.
 
3
Intended as households consisting of two or more individuals who are related by birth, marriage or adoption, although they also may include other unrelated people.
 
4
In other social studies content analysis of the texts posted using social network seems to provide acceptable predictions of the behavior of the whole population, not only of those using Twitter. Particularly, the debate is lively on the electoral predictions (Ceron et al 2015).
 
Literatur
Barigozzi M, Alessi L, Capasso M, Fagiolo G (2009) The distribution of households consumption-expenditure budget shares. Working papers series. European Central Bank, Frankfurt a.M. (Tech. rep.)MATH
Blumenstock J, Cadamuro G, On R (2015) Predicting poverty and wealth from mobile phone metadata. Science 350:1073–1076CrossRef
Ceron A, Curini L, Iacus S (2015) Using social media to fore- cast electoral results: A review of state-of-the-art. Italian J Appl Stat 25(3):237–259
Cordero C, Encina J, Lahiri P (2016) Analysis of poverty data by small area estimation. Wiley, Chichester (chap Poverty mapping for the Chilean comunas)
Curini L, Iacus S, Canova L (2015) Measuring idiosyncratic happiness through the analysis of twitter: An application to the italian case. Soc Indic Res 121(2):525–542CrossRef
Datta G, Lahiri P (2000) A unified measure of uncertainty of estimated best linear unbiased predictors in small area estimation problems. Stat Sin 10:613–627MathSciNetMATH
Datta G, Hall P, Mandal A (2011) Model selection and testing for the presence of small area effects, and application to area-level data. J Am Stat Assoc 106:362–374MathSciNetCrossRefMATH
Deaton A (2003) Household surveys, consumption, and the measurement of poverty. Econ Syst Res 15(2):135–159CrossRef
Decuyper A, Rutherford A, Wadhwa A, Bauer J, Krings G, Gutierrez T, Blondel V, Luengo-Oroz M (2014) Estimating food consumption and poverty indices with mobile phone data. United Nations Global Pulse, New York (Tech. rep.)
Fay R, Herriot R (1979) Estimation of icome from small places: An application of James-Stein procedures to census data. J Am Stat Assoc 74:269–277MathSciNetCrossRef
Giusti C, Marchetti S, Pratesi M, Salvati N (2012) Robust small area estimation and oversampling in the estimation of poverty indicators. Surv Res Methods 6(3):155–163
Giusti C, Masserini L, Pratesi M (2016) Local comparisons of small area estimates of poverty: an application within the tuscany region in italy. Soc Indic Res. doi:10.1007/s11205-015-1193-1
Hastie T, Pregibon D (1992) Generalized linear models. Wadsworth and Brooks/Cole, Edmonton (chap 6)
Hidiroglou M, Singh A, Hamel M (2007) Some thoughts on small area estimation for the canadian community health survey. Statistics Canada (Internal statistics canada document)
Horvitz D, Thompson D (1952) A generalization of sampling without replacement from a finite universe. J Am Stat Assoc 47:663–685MathSciNetCrossRefMATH
ISTAT (2015) Statistiche report, la spesa per consumi delle famiglie, anno 2014. roma, 8 luglio 2015. ISTAT, Roma (Tech. rep.)
Jiang J, Lahiri P, Wan S, Wu C (2001) Jackknifing in the fay-herriot model with an example (unpublished manuscript)
Lechene V (2000) National Food Survey: 2000. Department for Environment Food and Rural Affairs, London (chap Income and price elasticities of demand for foods consumed in the home)
Marchetti S, Secondi L (2016) Estimates of household consumption expenditure at provincial level in italy by using small area estimation methods: Real comparisons using purchasing power parities. Soc Indic Res. doi:10.1007/s11205-016-1230-8
Marchetti S, Giusti C, Pratesi M, Salvati N, Giannotti F, Pedreschi D, Rinzivillo S, Pappalardo L, Gabrielli L (2015) Small area model-based estimators using big data sources. J Off Stat 31:263–281
Meyer BD, James XS (2003) Measuring the well-being of the poor using income and consumption. J Hum Resour v38(Supplement):1180–1220CrossRef
Porter A, Holan S, Wikle C, Cressie N (2014) Spatial fay-herriot models for small area estimation with functional covariates. Spat Stat 10:27–42MathSciNetCrossRef
Pratesi M, Giusti C, Marchetti S (2012) Small area estimation of poverty indicators. In: Davino C, Fabbris L (eds) Survey data collection and integration. Springer, Berlin Heidelberg
Regmi A, Deepak M, Seale J, Bernstein J (2001) Changing structure of global food consumption and trade. USDA (United States Department of Agriculture) – Economic Research Service, Washington (chap Cross-country analysis of food consumption patterns)
Salvati N, Giusti C, Pratesi M (2014) The use of spatial information for the estimation of poverty indicators at the small area level. Routledge, London, New York
Shapiro S, Wilk M (1965) An analysis of variance test for normality (complete samples). Biometrika 67:215–216MathSciNetMATH
Tzavidis N, Marchetti S, Chambers R (2010) Robust estimation of small area means and quantiles. Aust New Zel J Stat 52(2):167–186MathSciNetCrossRefMATH
Metadaten
Titel
The use of Twitter data to improve small area estimates of households’ share of food consumption expenditure in Italy
verfasst von
Stefano Marchetti
Caterina Giusti
Monica Pratesi
Publikationsdatum
26.07.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
AStA Wirtschafts- und Sozialstatistisches Archiv / Ausgabe 2-3/2016
Print ISSN: 1863-8155
Elektronische ISSN: 1863-8163
DOI
https://doi.org/10.1007/s11943-016-0190-4

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