Skip to main content
Top
Published in: Social Indicators Research 2/2021

14-10-2020 | Original Research

Estimating Household Consumption Expenditure at Local Level In Italy: The Potential of the Cokriging Spatial Predictor

Author: Luca Secondi

Published in: Social Indicators Research | Issue 2/2021

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Measuring well-being and living conditions at local level is essential for policy makers who wish to study inequalities and formulate targeted and effective economic and social intervention policies. In Italy, the available official statistics in this field are usually provided at regional level and several studies have been carried out in order to obtain the estimates of those measures at disaggregated level, which is done in order to obtain estimations at provincial level. However, due the heterogeneity of these phenomena within each province, it is important to possess data at micro-territorial level, that is at the municipality level, in order to study and monitor territorial development and inequalities in depth.This paper proposes an estimation of household consumption expenditure, one of the most important indicator of the economic material well-being of an area, for the 7893 Italian municipalities.To this end, the cokriging spatial interpolation technique was employed in order to explore its potentialities. This method is normally used in natural sciences to predict variables of interest at micro territorial level, using available sample data or population aggregates, analysing their spatial dependence and introducing information on auxiliary correlated variables available at micro level. In this study, the available information on household consumption expenditure at provincial level was combined with the data on taxable income at municipality level as auxiliary variable.The evaluation of model performance enabled us to confirm the validity of this approach to obtain a more detailed picture of the local systems for which intervention policies are important.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Appendix
Available only for authorised users
Footnotes
1
This general term also includes the Living Standards and Food Survey in the United Kingdom (former Family Expenditure Survey up to 2001) as well as the Consumer Expenditure Survey (CES) in the United States. Similarly in Italy, this specific survey, carried out by ISTAT, from 2014 is called Consumer Expenditure Survey.
 
2
In the research by Bollino and Polinori (2005), Table 2.1 shows applied research focusing on the estimation of economic local-level measures, whose foundations can be found in the Marbach’s research.
 
4
The description of the project, the related indicators and the methodological approach are available at: https://​www.​istat.​it/​it/​benessere-e-sostenibilit%C3%A0/​la-misurazione-del-benessere-(bes)/​il-bes-dei-territori.
 
5
The estimates at provincial level are available in the G. Tagliacarne website section “Numbers and Territory”. It is worth noting that the data available from Tagliacarne institute—for the year from 2012 to 2017—refer for all the years to the Italian partition in 110 provinces. For these reasons in the text we specified the existence of n = 110 provinces.
 
6
The point estimates for all the 7983 municipalities are available on request. In assessing the results, it is important to note that estimates for the municipalities located on the administrative borders of two provinces may be affected by spatial (and economic) proximity of the provinces.
 
Literature
go back to reference Aguiar, M., & Bils, M. (2015). Has consumption inequality mirrored income inequality? American Economic Review, 105(9), 2725–2756.CrossRef Aguiar, M., & Bils, M. (2015). Has consumption inequality mirrored income inequality? American Economic Review, 105(9), 2725–2756.CrossRef
go back to reference Alaimo, L. S., Arcagni, A., Fattore, M., Maggino, F., & Quondamstefano, V. (2020). Measuring equitable and sustainable well-being in italian regions: the non-aggregative approach. Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement. https://doi.org/10.1007/s11205-020-02388-7CrossRef Alaimo, L. S., Arcagni, A., Fattore, M., Maggino, F., & Quondamstefano, V. (2020). Measuring equitable and sustainable well-being in italian regions: the non-aggregative approach. Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement. https://​doi.​org/​10.​1007/​s11205-020-02388-7CrossRef
go back to reference Ansenlin, L. (2003). GeoDa 0.9 User’s Guide: Spatial Analysis Laboratory (SAL). Department of Agricultural and Consumer Economics, University of Illinois, Urbana-Champaign, IL. Ansenlin, L. (2003). GeoDa 0.9 User’s Guide: Spatial Analysis Laboratory (SAL). Department of Agricultural and Consumer Economics, University of Illinois, Urbana-Champaign, IL.
go back to reference Ardilly, P., Audric, S., de Bellefon, M. P., Buron, M. L., Durieux, E., Eusebio, P., & Le Gleut, R. (2018). Manuel d’analyse spatiale. Montrouge Cedex (France): Insee-Eurostat. Ardilly, P., Audric, S., de Bellefon, M. P., Buron, M. L., Durieux, E., Eusebio, P., & Le Gleut, R. (2018). Manuel d’analyse spatiale. Montrouge Cedex (France): Insee-Eurostat.
go back to reference Attanasio, O. P., & Pistaferri, L. (2016). Consumption inequality. Journal of Economic Perspectives, 30(2), 3–28.CrossRef Attanasio, O. P., & Pistaferri, L. (2016). Consumption inequality. Journal of Economic Perspectives, 30(2), 3–28.CrossRef
go back to reference Biggeri, L., Laureti, T., & Secondi, L. (2014). Well-being and quality of life in Italy: Assessing and selecting indicators for local policy making. Italian Journal of Applied Statistics, 24(2), 125–152. Biggeri, L., Laureti, T., & Secondi, L. (2014). Well-being and quality of life in Italy: Assessing and selecting indicators for local policy making. Italian Journal of Applied Statistics, 24(2), 125–152.
go back to reference Bollino, C. A., & Polinori, P. (2005). Il valore aggiunto su scala comunale: la Regione Umbria 2001–2003. Dipartimento di economia, finanza e statistica, Università degli studi di Perugia. Bollino, C. A., & Polinori, P. (2005). Il valore aggiunto su scala comunale: la Regione Umbria 2001–2003. Dipartimento di economia, finanza e statistica, Università degli studi di Perugia.
go back to reference Brown, H., Abdallah, S., & Townsley, R. (2017). Understanding local needs for wellbeing data: Measures and indicators (pp. 1–62). Scoping Report - Happy City. London, UK: What Works Centre for wellbeing. Brown, H., Abdallah, S., & Townsley, R. (2017). Understanding local needs for wellbeing data: Measures and indicators (pp. 1–62). Scoping Report - Happy City. London, UK: What Works Centre for wellbeing.
go back to reference Calderón, G. F. A. (2009). Spatial regression analysis vs. kriging methods for spatial estimation. International Advances in Economic Research, 15(1), 44–58.CrossRef Calderón, G. F. A. (2009). Spatial regression analysis vs. kriging methods for spatial estimation. International Advances in Economic Research, 15(1), 44–58.CrossRef
go back to reference Cao, J., Ho, M. S., Hu, W., & Jorgenson, D. (2020). Estimating flexible consumption functions for urban and rural households in China. China Economic Review, 61, 101453.CrossRef Cao, J., Ho, M. S., Hu, W., & Jorgenson, D. (2020). Estimating flexible consumption functions for urban and rural households in China. China Economic Review, 61, 101453.CrossRef
go back to reference Cao, J., Li, C., Wu, Q., & Qiao, J. (2020). Improved mapping of soil heavy metals using a vis-nir spectroscopy index in an agricultural area of Eastern China. IEEE Access, 8, 42584–42594.CrossRef Cao, J., Li, C., Wu, Q., & Qiao, J. (2020). Improved mapping of soil heavy metals using a vis-nir spectroscopy index in an agricultural area of Eastern China. IEEE Access, 8, 42584–42594.CrossRef
go back to reference Casini-Benvenuti, S., Tortolini, V., & Viviani, A. (2007) Stima dei consumi interni nei comuni di Lombardia, Toscana e Sicilia. Documenti di discussione dell’ufficio studi—2007/2, Agenzia delle Entrate. Casini-Benvenuti, S., Tortolini, V., & Viviani, A. (2007) Stima dei consumi interni nei comuni di Lombardia, Toscana e Sicilia. Documenti di discussione dell’ufficio studi—2007/2, Agenzia delle Entrate.
go back to reference Chun, Y., & Griffith, D. A. (2013). Spatial Statistics & Geostatistics. Thousand Oaks, CA: SAGE Publications Ltd. Chun, Y., & Griffith, D. A. (2013). Spatial Statistics & Geostatistics. Thousand Oaks, CA: SAGE Publications Ltd.
go back to reference Corona, P., Fattorini, L., Franceschi, S., Chirici, G., Maselli, F., & Secondi, L. (2014). Mapping by spatial predictors exploiting remotely sensed and ground data: A comparative design-based perspective. Remote sensing of Environment, 152, 29–37.CrossRef Corona, P., Fattorini, L., Franceschi, S., Chirici, G., Maselli, F., & Secondi, L. (2014). Mapping by spatial predictors exploiting remotely sensed and ground data: A comparative design-based perspective. Remote sensing of Environment, 152, 29–37.CrossRef
go back to reference Cressie, N. A. (1993). Statistics for spatial data (Revised). New York: John Wiley. Cressie, N. A. (1993). Statistics for spatial data (Revised). New York: John Wiley.
go back to reference Cutillo, A., & Scanu, M. (2020). A Mixed Approach for Data Fusion of HBS and SILC. Social Indicators Research, 150, 1–27.CrossRef Cutillo, A., & Scanu, M. (2020). A Mixed Approach for Data Fusion of HBS and SILC. Social Indicators Research, 150, 1–27.CrossRef
go back to reference Di Biase, R. M., Fattorini, L., & Marchi, M. (2018). Statistical inferential techniques for approaching forest mapping. A review of methods. Annals of Silvicultural Research, 42(2), 46–58. Di Biase, R. M., Fattorini, L., & Marchi, M. (2018). Statistical inferential techniques for approaching forest mapping. A review of methods. Annals of Silvicultural Research, 42(2), 46–58.
go back to reference European Commission (2015). Household Budget Survey 2010 Wave. EU Quality Report. DOC HBAS/2015/01/EN. EC, Eurostat. Directorate F: Social Statistics, Unit F-4 Quality of Life. European Commission (2015). Household Budget Survey 2010 Wave. EU Quality Report. DOC HBAS/2015/01/EN. EC, Eurostat. Directorate F: Social Statistics, Unit F-4 Quality of Life.
go back to reference Fisher, M., & Getis, A. (2009). Handbook of applied spatial analysis: software tools, methods and applications. Berlin: Springer. Fisher, M., & Getis, A. (2009). Handbook of applied spatial analysis: software tools, methods and applications. Berlin: Springer.
go back to reference Gillis, M., Shoup, C., & Sicat, G. P. (2001). World development report 2000/2001-attacking poverty. The World Bank. Gillis, M., Shoup, C., & Sicat, G. P. (2001). World development report 2000/2001-attacking poverty. The World Bank.
go back to reference Giusti, C., Masserini, L., & Pratesi, M. (2017). Local comparisons of small area estimates of poverty: An application within the Tuscany region in Italy. Social Indicators Research, 131(1), 235–254.CrossRef Giusti, C., Masserini, L., & Pratesi, M. (2017). Local comparisons of small area estimates of poverty: An application within the Tuscany region in Italy. Social Indicators Research, 131(1), 235–254.CrossRef
go back to reference Golden, N., Zhang, C., Potito, A., Gibson, P. J., Bargary, N., & Morrison, L. (2020). Use of ordinary cokriging with magnetic susceptibility for mapping lead concentrations in soils of an urban contaminated site. Journal of Soils and Sediments, 20(3), 1357–1370.CrossRef Golden, N., Zhang, C., Potito, A., Gibson, P. J., Bargary, N., & Morrison, L. (2020). Use of ordinary cokriging with magnetic susceptibility for mapping lead concentrations in soils of an urban contaminated site. Journal of Soils and Sediments, 20(3), 1357–1370.CrossRef
go back to reference Günlü, A., Bulut, S., Keleş, S., & Ercanlı, İ. (2020). Evaluating different spatial interpolation methods and modeling techniques for estimating spatial forest site index in pure beech forests: a case study from Turkey. Environmental Monitoring and Assessment, 192(1), 53.CrossRef Günlü, A., Bulut, S., Keleş, S., & Ercanlı, İ. (2020). Evaluating different spatial interpolation methods and modeling techniques for estimating spatial forest site index in pure beech forests: a case study from Turkey. Environmental Monitoring and Assessment, 192(1), 53.CrossRef
go back to reference ISTAT. (2019). Indagine sulle spese delle famiglie. Istituto Nazionale di Statistica, Roma: Aspetti metodologici dell’indagine. ISTAT. (2019). Indagine sulle spese delle famiglie. Istituto Nazionale di Statistica, Roma: Aspetti metodologici dell’indagine.
go back to reference Krige, D. G. (1951). A statistical approach to some basic mine valuation problems on the Witwatersrand. Journal of the Southern African Institute of Mining and Metallurgy, 52(6), 119–139. Krige, D. G. (1951). A statistical approach to some basic mine valuation problems on the Witwatersrand. Journal of the Southern African Institute of Mining and Metallurgy, 52(6), 119–139.
go back to reference Krueger, D., & Perri, F. (2006). Does income inequality lead to consumption inequality? Evidence and theory. The Review of Economic Studies, 73(1), 163–193.CrossRef Krueger, D., & Perri, F. (2006). Does income inequality lead to consumption inequality? Evidence and theory. The Review of Economic Studies, 73(1), 163–193.CrossRef
go back to reference Lloyd, C. D. (2010). Local Models for Spatial Analysis (2nd ed.). Florida: CRC Press.CrossRef Lloyd, C. D. (2010). Local Models for Spatial Analysis (2nd ed.). Florida: CRC Press.CrossRef
go back to reference Marbach (a cura di), G. (1985). Il reddito nei Comuni italiani 1982. Utet, Torino: Quaderni del Banco di Santo Spirito. Marbach (a cura di), G. (1985). Il reddito nei Comuni italiani 1982. Utet, Torino: Quaderni del Banco di Santo Spirito.
go back to reference Marchetti, S., & Secondi, L. (2017). Estimates of household consumption expenditure at provincial level in Italy by using small area estimation methods: “Real” Comparisons using purchasing power parities. Social Indicators Research, 131(1), 215–234.CrossRef Marchetti, S., & Secondi, L. (2017). Estimates of household consumption expenditure at provincial level in Italy by using small area estimation methods: “Real” Comparisons using purchasing power parities. Social Indicators Research, 131(1), 215–234.CrossRef
go back to reference Maroufpoor, S., Bozorg-Haddad, O., & Chu, X. (2020). Geostatistics: principles and methods. Butterworth-Heinemann, Portsmouth: In Handbook of Probabilistic Models. Maroufpoor, S., Bozorg-Haddad, O., & Chu, X. (2020). Geostatistics: principles and methods. Butterworth-Heinemann, Portsmouth: In Handbook of Probabilistic Models.
go back to reference Matern, B., 1960. Spatial Variation. Meddelanden fran StatensSkogsforskningsinstitut, 49, no. 5 (second ed. 1986, LectureNotes in Statistics, no. 36). Springer, New York. Matern, B., 1960. Spatial Variation. Meddelanden fran StatensSkogsforskningsinstitut, 49, no. 5 (second ed. 1986, LectureNotes in Statistics, no. 36). Springer, New York.
go back to reference Matheron, G. (1963). Principles of geostatistics. Economic geology, 58(8), 1246–1266.CrossRef Matheron, G. (1963). Principles of geostatistics. Economic geology, 58(8), 1246–1266.CrossRef
go back to reference Meyer, B. D., & Sullivan, J. X. (2003). Measuring the well-being of the poor using income and consumption (No. w9760). National Bureau of Economic Research. Meyer, B. D., & Sullivan, J. X. (2003). Measuring the well-being of the poor using income and consumption (No. w9760). National Bureau of Economic Research.
go back to reference Misaka, T., Herwan, J., Ryabov, O., Kano, S., Sawada, H., Kasashima, N., & Furukawa, Y. (2020). Prediction of surface roughness in CNC turning by model-assisted response surface method. Precision Engineering, 62, 196–203.CrossRef Misaka, T., Herwan, J., Ryabov, O., Kano, S., Sawada, H., Kasashima, N., & Furukawa, Y. (2020). Prediction of surface roughness in CNC turning by model-assisted response surface method. Precision Engineering, 62, 196–203.CrossRef
go back to reference OECD. (2013). OECD framework for statistics on the distribution of household income, consumption and wealth. Paris: OECD Publishing.CrossRef OECD. (2013). OECD framework for statistics on the distribution of household income, consumption and wealth. Paris: OECD Publishing.CrossRef
go back to reference OECD. (2014). How’s Life in Your Region?: Measuring Regional and Local Well-being for Policy Making. Paris: OECD Publishing.CrossRef OECD. (2014). How’s Life in Your Region?: Measuring Regional and Local Well-being for Policy Making. Paris: OECD Publishing.CrossRef
go back to reference Pardo-Iguzquiza, E., & Chica-Olmo, M. (2008). Geostatistics with the matern semivariogram model: A library of computer programs for inference, kriging and simulation. Computers & Geosciences, 34(9), 1073–1079.CrossRef Pardo-Iguzquiza, E., & Chica-Olmo, M. (2008). Geostatistics with the matern semivariogram model: A library of computer programs for inference, kriging and simulation. Computers & Geosciences, 34(9), 1073–1079.CrossRef
go back to reference Pratesi M. (2015). “Spatial Disaggregation and Small Area Estimation Methods for Agricultural Surveys: Solutions and Perspectives”, Technical Report in the Global Strategy Publications. Pratesi M. (2015). “Spatial Disaggregation and Small Area Estimation Methods for Agricultural Surveys: Solutions and Perspectives”, Technical Report in the Global Strategy Publications.
go back to reference Pratesi, M. (Ed.). (2016). Analysis of Poverty Data by Small Area Estimation. UK: John Wiley. Pratesi, M. (Ed.). (2016). Analysis of Poverty Data by Small Area Estimation. UK: John Wiley.
go back to reference Pratesi, M. (2014). M-Quantile small area models for measuring poverty at a local level. Springer, Cham: In Contributions to Sampling Statistics.CrossRef Pratesi, M. (2014). M-Quantile small area models for measuring poverty at a local level. Springer, Cham: In Contributions to Sampling Statistics.CrossRef
go back to reference Pratesi, M., Giusti, C., & Marchetti, S. (2012). Small area estimation of poverty indicators. In C. Davino & L. Fabbris (Eds.), Survey data collection and integration. Berlin: Springer. Pratesi, M., Giusti, C., & Marchetti, S. (2012). Small area estimation of poverty indicators. In C. Davino & L. Fabbris (Eds.), Survey data collection and integration. Berlin: Springer.
go back to reference Qin, Q., Wang, H., Lei, X., Li, X., Xie, Y., & Zheng, Y. (2020). Spatial variability in the amount of forest litter at the local scale in northeastern China: Kriging and cokriging approaches to interpolation. Ecology and Evolution, 10(2), 778–790.CrossRef Qin, Q., Wang, H., Lei, X., Li, X., Xie, Y., & Zheng, Y. (2020). Spatial variability in the amount of forest litter at the local scale in northeastern China: Kriging and cokriging approaches to interpolation. Ecology and Evolution, 10(2), 778–790.CrossRef
go back to reference Rinaldi, A. (2002). Fonti informative e indicatori statistici per l’analisi socio-economica territoriale. Rome: Istituto Guglielmo Tagliacarne. Rinaldi, A. (2002). Fonti informative e indicatori statistici per l’analisi socio-economica territoriale. Rome: Istituto Guglielmo Tagliacarne.
go back to reference Serafino, P., & Tonkin, R. (2017). Statistical matching of European Union statistics on income and living conditions (EU-SILC) and the household budget survey. Eurostat: Statistical Working Papers. Luxembourg: Publications Office of the European Union. Doi: 10.2785/933 Serafino, P., & Tonkin, R. (2017). Statistical matching of European Union statistics on income and living conditions (EU-SILC) and the household budget survey. Eurostat: Statistical Working Papers. Luxembourg: Publications Office of the European Union. Doi: 10.2785/933
go back to reference Steuer, N., & Marks, N. (2008). Local Wellbeing: Can We Measure It? London: The Young Foundation. Steuer, N., & Marks, N. (2008). Local Wellbeing: Can We Measure It? London: The Young Foundation.
go back to reference Vessia, G., Di Curzio, D., Chiaudani, A., & Rusi, S. (2020). Regional rainfall threshold maps drawn through multivariate geostatistical techniques for shallow landslide hazard zonation. Science of the total environment, 705, 135815.CrossRef Vessia, G., Di Curzio, D., Chiaudani, A., & Rusi, S. (2020). Regional rainfall threshold maps drawn through multivariate geostatistical techniques for shallow landslide hazard zonation. Science of the total environment, 705, 135815.CrossRef
go back to reference Wackernagel, H. (2013). Multivariate geostatistics: an introduction with applications. Berlin: Springer Science & Business Media. Wackernagel, H. (2013). Multivariate geostatistics: an introduction with applications. Berlin: Springer Science & Business Media.
go back to reference Waller, L. A., & Gotway, C. A. (2004). Applied spatial statistics for public health data. New York: John Wiley.CrossRef Waller, L. A., & Gotway, C. A. (2004). Applied spatial statistics for public health data. New York: John Wiley.CrossRef
Metadata
Title
Estimating Household Consumption Expenditure at Local Level In Italy: The Potential of the Cokriging Spatial Predictor
Author
Luca Secondi
Publication date
14-10-2020
Publisher
Springer Netherlands
Published in
Social Indicators Research / Issue 2/2021
Print ISSN: 0303-8300
Electronic ISSN: 1573-0921
DOI
https://doi.org/10.1007/s11205-020-02510-9

Other articles of this Issue 2/2021

Social Indicators Research 2/2021 Go to the issue

Premium Partner