[
Alfons, A. and M. Templ. 2013. “Estimation of social exclusion indicators from complex surveys: The r package laeken.” Journalof Statistical Software 54 (15): 1–25. DOI: https://doi.org/10.18637/jss.v054.i15.10.18637/jss.v054.i15
]Search in Google Scholar
[
Battacharya, D. 2007. “Inference on inequality from household survey data.” Journal of Econometrics 137: 674–707. DOI: https://doi.org/10.1016/j.jeconom.2005.09.003.10.1016/j.jeconom.2005.09.003
]Search in Google Scholar
[
Bianchi, A., E. Fabrizi, N. Salvati, and N. Tzavidis. 2018. “Estimation and testing in m-quantile regression with applications to small area estimation.” International Statistical Review 86 (3): 541–570. DOI: https://doi.org/10.1111/insr.12267.10.1111/insr.12267
]Search in Google Scholar
[
Bourguignon, F. 1979. “Decomposable income inequality measures.” Econometrica 42: 27–41. DOI: https://doi.org/10.2307/1914138.10.2307/1914138
]Search in Google Scholar
[
Box, G., and D. Cox. 1964. “An analysis of transformations.” Journal of the Royal Statistical Society Series B 27 (2): 211–252. DOI: https://doi.org/10.1111/j.2517-6161.1964.tb00553.x.10.1111/j.2517-6161.1964.tb00553.x
]Search in Google Scholar
[
Breckling, J., and R. Chambers. 1988. “M-quantiles.” Biometrika 75 (4): 761–771. DOI: https://doi.org/10.1093/biomet/75.4.761.10.1093/biomet/75.4.761
]Search in Google Scholar
[
Chambers, R.L. 1986. “Outlier robust finite population estimation.” Journal of the American Statistical Associationtion 81 (396): 1063–1069. DOI: https://doi.org/10.1111/rssb.12019.10.1111/rssb.12019
]Search in Google Scholar
[
Chambers, R., H. Chandra, N. Salvati, and N. Tzavidis. 2014. “Outlier robust small area estimation.” Journal of the Royal Statistical Society Series B 76 (1): 47–69. DOI: https://doi.org/10.1111/rssb.12019.10.1111/rssb.12019
]Search in Google Scholar
[
Chambers, R., and Dunstan. 1986. “Estimating distribution function from survey data.” Biometrika 73: 597–604. DOI: https://doi.org/10.1093/biomet/73.3.597.10.1093/biomet/73.3.597
]Search in Google Scholar
[
Chambers, R., and N. Tzavidis. 2006. “M-quantile models for small area estimation.” Biometrika 93 (2): 255–268. DOI: https://doi.org/10.1093/biomet/93.2.255.10.1093/biomet/93.2.255
]Search in Google Scholar
[
Cowell, F., and K. Kuga. 1981. “Inequality measurement: An axiomatic approach.” Journal of Economic Theory 15: 287–305. DOI: https://doi.org/10.1016/S0014-2921(81)80003-7.10.1016/S0014-2921(81)80003-7
]Search in Google Scholar
[
Davidson, R. 2009. “Reliable inference for the gini index.” Journal of Econometrics 150: 30–40. DOI: https://doi.org/10.1016/j.jeconom.2008.11.004.10.1016/j.jeconom.2008.11.004
]Search in Google Scholar
[
Davidson, R., and E. Flachaire. 2007. “Asymptotic and bootstrap inference for inequality and poverty measures.” Journal of Econometrics 141 (1): 141–66. DOI: https://doi.org/10.1016/j.jeconom.2007.01.009.10.1016/j.jeconom.2007.01.009
]Search in Google Scholar
[
Deltas, G. 2003. “The small-samples bias of the gini coefficient: results and implications for empirical research.” The Review of Economics and Statistics 85: 226–34. DOI: https://doi.org/10.1162/rest.2003.85.1.226.10.1162/rest.2003.85.1.226
]Search in Google Scholar
[
Diallo, M.S., and J.N.K. Rao. 2018. “Small area estimation of complex parameters under unit-level models with skew-normal errors.” Scandinavian Journal of Statistics 45 (4): 1092–1116. DOI: https://doi.org/10.1111/sjos.12336.10.1111/sjos.12336
]Search in Google Scholar
[
Dongomo-Jiongo, V., and P. Nguimkeu. 2018. Bootstrapping mean squared errors of robust small-area estimators: Application to the method-of-payments data. Technical report, Staff Working Paper: 18–28, Bank of Canada. Available at: https://www.bankofcanada.ca/wp-content/uploads/2018/06/swp2018-28.pdf. (accessed November 2021).
]Search in Google Scholar
[
Elbers, C., J.O. Lanjouw, and P. Lanjouw. 2003. “Micro-level estimation of poverty and inequality.” Econometrica 71 (1): 355–364. DOI: https://www.jstor.org/stable/3082050.10.1111/1468-0262.00399
]Search in Google Scholar
[
Elbers, C., and R. van der Weide. 2014. Estimation of Normal Mixtures in a Nested Error Model with an Application to Small Area Estimation of Poverty and Inequality. The World Bank. Available at: https://openknowledge.worldbank.org/handle/10986/19362. (accessed November 2021).10.1596/1813-9450-6962
]Search in Google Scholar
[
Foster, J. 1983. “An axiomatic characteriazation of the Theil measure of income inequality.” Journal of Economic Theory 31: 105–121. DOI: https://doi.org/10.1016/0022-0531(83)90023-6.10.1016/0022-0531(83)90023-6
]Search in Google Scholar
[
Foster, J., J. Greer, and E. Thorbecke. 1984. “A class of decomposable poverty measures.” Econometrica 52: 761–766. DOI: https://doi.org/10.2307/1913475.10.2307/1913475
]Search in Google Scholar
[
Gershunskaya, J., and P. Lahiri. 2010. “Robust small area estimation using a mixture model.” In Proceedings of the Joint Statistical Meeting 2010, 1 July to 5 August 2010, Vancouver, British Columbia, Canada. Available at: https://ww2.amstat.org/meetings/jsm/2010/onlineprogram/AbstractDetails.cfm?abstractid=307425 (accessed November 2021).
]Search in Google Scholar
[
Gini, C. 1914. “Sulla misura della concentrazione e della variabilita‘ dei caratteri.” In Atti del Regio Istituto Veneto di Scienze Lettere ed Arti. Available at: https://www.hetweb-site.net/het/texts/gini/gini_1914.pdf.
]Search in Google Scholar
[
Graf, M., J.M. Marín, and I. Molina. 2019. “A generalized mixed model for skewed distributions applied to small area estimation.” TEST 28 (2): 565–597. DOI: https://doi.org/10.1007/s11749-018-0594-2.10.1007/s11749-018-0594-2
]Search in Google Scholar
[
Istat Siqual. 2008. “Information on EU-SILC survey.” Available at: http://siqual.istat.it/SIQual/visualizza.do?id=5000170&refresh=true&language=IT.
]Search in Google Scholar
[
Istat. 2017. “Occupati e disoccupati.” Available at: https://www.istat.it/it/files/2017/07/CS_Occupati-e-disoccupati_giugno_2017.pdf.
]Search in Google Scholar
[
Kreutzmann, A.-K., S. Pannier, N. Rojas-Perilla, T. Schmid, M. Templ, and N. Tzavidis. 2019. “The R package emdi for estimating and mapping regionally disaggregated indicators.” Journal of Statistical Software 91 (7): 1–33. DOI: https://doi.org/10.17169/refubium-25826.10.18637/jss.v091.i07
]Search in Google Scholar
[
Langel, M., and Y. Tillè. 2013. “Variance estimation of the gini index: revisiting a result several time published.” Journal of the Royal Statistical Society A 7: 521–40. DOI: https://doi.org/10.1111/j.1467-985X.2012.01048.x.10.1111/j.1467-985X.2012.01048.x
]Search in Google Scholar
[
Lombardía, M., W. González-Manteiga, and J. Prada-Sánchez 2003. “Bootstrapping the chambers-dunstan estimate of finite population distribution function.” Journal of Statistical Planning and Inference 116: 367–388. DOI: https://doi.org/10.1016/S0378-3758(02)00240-9.10.1016/S0378-3758(02)00240-9
]Search in Google Scholar
[
Maasoumi, E. 1986. “The measurement and decomposition of multi-dimensional inequality.” Econometrica 54: 991–97. DOI: https://doi.org/10.2307/1912849.10.2307/1912849
]Search in Google Scholar
[
Marchetti, S., N. Tzavidis, and M. Pratesi. 2012. “Non-parametric bootstrap mean squared error estimation for m-quantile estimators of small area averages, quantiles and poverty indicators.” Computational Statistics and Data Analysis 56 (10): 2889–2902. DOI: https://doi.org/10.1016/j.csda.2012.01.023.10.1016/j.csda.2012.01.023
]Search in Google Scholar
[
Mills, J., and S. Zandvakili. 1997. “Statistical inference via bootstrapping for measures of inequality.” Journal of Applied Econometrics 12 (2): 133–50. DOI: https://doi.org/10.1002/(SICI)1099-1255(199703)12:2,133::AID-JAE433.3.0.CO;2-H.10.1002/(SICI)1099-1255(199703)12:2<133::AID-JAE433>3.0.CO;2-H
]Search in Google Scholar
[
Molina, I., and J. Rao. 2010. “Small area estimation of poverty indicators.” Canadian Journal of Statistics 38 (3): 369–385. DOI: https://doi.org/10.1002/cjs.10051.10.1002/cjs.10051
]Search in Google Scholar
[
R Development Core Team. 2013. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available at: https://www.yumpu.com/en/document/view/6853895/r-a-language-and-environment-for-statistical-computing. (accessed November 2021).
]Search in Google Scholar
[
Rao, J., and I. Molina. 2015. Small Area Estimation. Wiley Series in Survey Methodology. Wiley.10.1002/9781118735855
]Search in Google Scholar
[
Rojas-Perilla, N., S. Pannier, T. Schmid, and N. Tzavidis. 2020. “Data-driven transformations in small area estimation.” Journal of the Royal Statistical Series A 183 (1): 121–148. DOI: https://doi.org/10.1111/rssa.12488.10.1111/rssa.12488
]Search in Google Scholar
[
SAMPLE (Small Area Methods for Poverty and Living Conditions). Project founded by the 7th Framwork Programme of the EU. Grant SSH - CT - 2007 – 217565. Available at: http://www.sample-project.eu.
]Search in Google Scholar
[
Schmid, T., N. Tzavidis, R. Münnich, and R.L. Chambers. 2016. “Outlier robust small area estimation under spatial correlation.” Scandinavian Journal of Statistics 43 (3): 806–826. DOI: https://doi.org/10.1111/sjos.12205.10.1111/sjos.12205
]Search in Google Scholar
[
Shapiro, S., and M. Wilk. 1965. “An analysis of variance test for normality (complete samples).” Biometrika 67: 215–216. DOI: https://doi.org/10.2307/2333709.10.2307/2333709
]Search in Google Scholar
[
Shorrocks, A. 1980. “The class of additively decomposable inequality measures.” Econometrica 48: 613–625. DOI: https://doi.org/10.2307/1913126.10.2307/1913126
]Search in Google Scholar
[
Sinha, S., and J. Rao. 2009. “Robust small area estimation.” The Canadian Journal of Statistics 37 (3): 381–399. DOI: https://doi.org/10.1002/cjs.10029.10.1002/cjs.10029
]Search in Google Scholar
[
Theil, H. 1967. Economics and Information Theory. Chicago: Rand McNally and Company.
]Search in Google Scholar
[
Tzavidis, N., S. Marchetti, and R. Chambers. 2010. “Robust estimation of small area means and quantiles.” Australian and New Zeland Journal of Statistics 52 (2): 167–186. DOI: https://doi.org/10.1111/j.1467-842X.2010.00572.x.10.1111/j.1467-842X.2010.00572.x
]Search in Google Scholar
[
Tzavidis, N., L.-C. Zhang, A. Luna, T. Schmid, and N. Rojas-Perilla. 2018. “From start to finish: a framework for the production of small area official statistics.” Journal of the Royal Statistical Society Series A 181 (4): 927–979. DOI: https://doi.org/10.1111/rssa.12364.10.1111/rssa.12364
]Search in Google Scholar
[
Wu, C., and R. Sitter. 2001. “Variance estimator for the finite population distribution function with complete auxiliary information.” The Canadian Journal of Statistics 29. DOI: https://doi.org/10.2307/3316078.10.2307/3316078
]Search in Google Scholar
[
Zenga, M. 2007. “Inequality curve and inequality index based on the ratios between lower and upper arithmetic means.” Statistica e Applicazioni 4: 3–27. DOI: https://doi.org/10.1400/209575.
]Search in Google Scholar