[
Alfons, A., and M. Templ. 2013. “Estimation of social exclusion indicators from complex surveys: The R package laeken.” Journal of Statistical Software 54: 1–25. DOI: https://doi.org/10.18637/jss.v054.i15.
]Search in Google Scholar
[
Alfons, A., M. Templ, and P. Filzmoser. 2013. “Robust estimation of economic indicators from survey samples based on pareto tail modeling.” Journal of the Royal Statistical Society: Series C (Applied Statistics) 62: 271–286. DOI: https://doi.org/10.1111/j.1467-9876.2012.01063.x.
]Search in Google Scholar
[
Benavent, R., and D. Morales. 2016. “Multivariate Fay Herriot models for small area estimation.” Computational Statistics and Data Analysis 94: 372–390. DOI: https://doi.org/10.1016/j.csda.2015.07.013.
]Search in Google Scholar
[
Braml, M., and G. Felbermayr. 2018. Regionale Ungleichheit in Deutschland und der EU: Was sagen die Daten? ifo Schnelldienst 71: 37–49. Available at: https://www.ifo.de/publikationen/2018/aufsatz-zeitschrift/regionale-ungleichheit-deutschland-und-der-euwas-sagen-die (accessed August 2021).
]Search in Google Scholar
[
Bundesinstitut für Bau-, Stadt-, und Raumforschung. 2021. Indikatoren und Karten zur Raum- und Stadtentwicklung. Datenlizenz Deutschland – Namensnennung – Version 2.0. Available at: http://www.inkar.de/ (accessed May 2022).
]Search in Google Scholar
[
Casas-Cordero, C., J. Encina, and P. Lahiri. 2016. “Poverty mapping for the chilean comunas. In Analysis of Poverty Data by Small Area Estimation, edited by M. Pratesi: 379–404. John Wiley and Sons. DOI: https://doi.org/10.1002/9781118814963.ch20.
]Search in Google Scholar
[
Citro, C.F., and G. Kalton. 2000. Small-Area Estimates of School-Age Children in Poverty: Evaluation of Current Methodology. Washington, DC: The National Academies Press. DOI: https://doi.org/10.17226/10046.
]Search in Google Scholar
[
Cowell, F., and E. Flachaire 2007. “Income distribution and inequality measurement: The problem of extreme values.” Journal of Econometrics 141: 1044–1072. DOI: https://doi.org/10.1016/j.jeconom.2007.01.001.
]Search in Google Scholar
[
Elbers, C., J.O. Lanjouw, and P. Lanjouw. 2003. “Micro level estimation of poverty and inequality.” Econometrica 71: 355–364. DOI: https://doi.org/10.1111/1468-0262.00399.
]Search in Google Scholar
[
Esteban, M., D. Morales, A. Perez, and L. Santamaria. 2012. “Small area estimation of poverty proportions under area-level time models.” Computational Statistics and Data Analysis 56: 2840–2855. DOI: https://doi.org/10.1007/s11749-019-00688-w.
]Search in Google Scholar
[
Fabrizi, E., M.R. Ferrante, and C. Trivisano. 2016. “Bayesian beta regression models for the estimation of poverty and inequality parameters in small areas.” In Analysis of Poverty Data by Small Area Estimation,. 299–314. John Wiley and Sons, Ltd, Hoboken, NJ, USA.
]Search in Google Scholar
[
Fabrizi, E., and C. Trivisano. 2016. “Small area estimation of the Gini concentration coefficient.” Computational Statistics and Data Analysis 99: 223–234. DOI: https://doi.org/10.1016/j.csda.2016.01.010.
]Search in Google Scholar
[
Fay, R.E., and R. A Herriot. 1979. “Estimates of income for small places: An application of James Stein procedures to census data.” Journal of the American Statistical Association 74: 269–277. DOI: https://doi.org/10.2307/2286322.
]Search in Google Scholar
[
Furceri, D., and J.D. Ostry. 2019. “Robust determinants of income inequality.” Oxford Review of Economic Policy 35(3): 490–517. DOI: https://doi.org/10.1093/oxrep/grz014.
]Search in Google Scholar
[
Gini, C. 1912. Variabilita emutabilita: contributo allo studio delle distribuzioni e delle relazioni statistiche. Bologna: C. Cuppini. 3–159.
]Search in Google Scholar
[
Goebel, J. 2017. Informationen zu den SOEP-Geocodes (SOEP.V35). SOEP Survey Papers 407: Series D. Berlin: DIW/SOEP. Available at: https://www.diw.de/documents/publikationen/73/diw_01.c.571219.de/diw_ssp0407.pdf (accessed January 2021).
]Search in Google Scholar
[
Goebel, J., and J. Frick. 2005. “Regional income stratification in unified Germany using a Gini decomposition approach.” Regional Studies 42: 555–577. DOI: https://doi.org/10.1080/00343400701543181.
]Search in Google Scholar
[
Gonzalez-Manteiga, W., M. J. Lombardia, I. Molina, D. Morales, and L. Santamaria. 2005. “Analytic and bootstrap approximations of prediction errors under a multivariate Fay-Herriot model.” Computational Statistics and Data Analysis 52: 5242–5252. DOI: https://doi.org/10.1016/j.csda.2008.04.031.
]Search in Google Scholar
[
Hadam, S., N. Wuerz, and A.-K. Kreutzmann. 2020. Estimating regional unemployment with mobile network data for functional urban areas in Germany. DOI: https://doi.org/10.17169/refubium-26791.
]Search in Google Scholar
[
Hagenaars, A., K. de Vos, and M.A. Zaidi. 1994. Poverty Statistics in the Late 1980s: Research Based on Micro-data. Office for Official Publications of the European Communities. Luxembourg.
]Search in Google Scholar
[
Immel, L., and A. Peichl 2020. “Regionale Ungleichheit in Deutschland: Wo leben die Reichen und wo die Armen.” ifo Schnelldienst 73: 43–47. Available at: https://www.ifo.de/DocDL/sd-2020-05-immel-peichl-regionale-ungleichheit.pdf (accessed August 2021).
]Search in Google Scholar
[
Janicki, R. 2020. “Properties of the beta regression model for small area estimation of proportions and application to estimation of poverty rates.” Communications in Statistics-Theory and Methods 49: 2264–2284. DOI: https://doi.org/10.1080/03610926.2019.1570266.
]Search in Google Scholar
[
Jiang, J., and J. Rao. 2020. “Robust small area estimation: An overview.” Annual Review of Statistics and Its Application 7: 337–360. DOI: https://doi.org/10.1146/annurev-statistics-031219-041212.
]Search in Google Scholar
[
Kara, S., S. Zimmermann, and SOEP Group. 2019. SOEPcompanion (v34), v.2. SOEP Survey Papers 743: SeriesG. Berlin: DIW/SOEP. Available at: https://www.diw.de/-documents/publikationen/73/diw_01.c.611577.de/diw_ssp0588.pdf (accessed December 2020).
]Search in Google Scholar
[
Koehler, E., E. Brown, and S.J.P.A. Haneuse. 2009. “On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses.” The American Statistician 63(2), 155–162. DOI: https://doi.org/10.1198/tast.2009.0030.
]Search in Google Scholar
[
Kreutzmann, A.-K., P. Marek, M. Runge, N. Salvati, and T. Schmid. 2022. “The Fay-Herriot model for multiply imputed data with an application to regional wealth estimation in Germany.” Journal of Applied Statistics 49(13): 3278–3299. DOI: https://doi.org/10.1080/02664763.2021.1941805.
]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: 1–33. DOI: https://doi.org/10.18637/jss.v091.i07.
]Search in Google Scholar
[
Lahiri, P., and J.B. Suntornchost. 2015. “Variable selection for linear mixed models with applications in small area estimation.” The Indian Journal of Statistics 77: 312–320. DOI: https://doi.org/10.1007/s13571-015-0096-0.
]Search in Google Scholar
[
Liu, B., P. Lahiri, and G. Kalton. 2014. “Hierarchical bayes modeling of survey-weighted small area proportions.” Survey methodology 40: 1–13. Available at: https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2014001/article/14030-eng.pdf?st=0WhqkvP4 (accessed June 2022).
]Search in Google Scholar
[
Marhuenda, Y., D. Morales, and M. Pardo. 2014. “Information criteria for Fay-Herriot model selection.” Computational Statistics and Data Analysis 70: 268–280. DOI: https://doi.org/10.1016/j.csda.2013.09.016.
]Search in Google Scholar
[
Molina, I. and J.N.K. Rao. 2010. “Small area estimation of poverty indicators.” The Canadian Journal of Statistics / La Revue Canadienne de Statistique 38: 369–385. Available at: http://www.jstor.org/stable/27896031 (accessed July2020).
]Search in Google Scholar
[
Neves, A., D. Silva, and S. Correa. 2013. “Small domain estimation for the Brazilian service sector survey.” Estadistica 65: 13–37. Available at: https://www.statistics.-gov.hk/wsc/CPS003-P7-S.pdf (accessed November 2019).
]Search in Google Scholar
[
OECD. 2011. Income distribution database: by country - inequality. Available at: https://stats.oecd.org. (accessed June 2021).
]Search in Google Scholar
[
OECD. 2013. Regional well-being: Regional income distribution and poverty. Available at: https://stats.oecd.org (accessed June 2021).
]Search in Google Scholar
[
Perugini, C., and G. Martino. 2008. “Income inequality within european regions: Determinants and effects on growth.” Review of Income and Wealth 54: 373–406. DOI: https://doi.org/10.1111/j.14754991.2008.00280.x.
]Search in Google Scholar
[
Pfeffermann, D. 2013. “New important developments in small area estimation.” Statistical Science 28(1): 40–68. DOI: https://doi.org/10.1214/12-STS395.
]Search in Google Scholar
[
Prasad, N., and J. Rao. 1990. “The estimation of the mean squared error of small-area estimators.” Journal of the American Statistical Association 85(409): 163–171. DOI: https://doi.org/10.2307/2289539.
]Search in Google Scholar
[
Pratesi, M. 2016. Analysis of Poverty Data by Small Area Estimation. John Wiley and Sons, Inc, Hoboken, NJ, USA. DOI: https://doi.org/10.1002/9781118814963.
]Search in Google Scholar
[
Rao, J., and I. Molina. 2015. Small Area Estimation. John Wiley and Sons, Inc, Hoboken, NJ, USA.
]Search in Google Scholar
[
Rivest, L.-P., and N. Vandal. 2002. “Mean squared error estimation for small areas when the small area variances are estimated. In Proceedings of International Conference of Recent Advanced Survey Sampling, edited by J. Rao: 197–206. Ottawa, July 10-13, 2002. Available at: https://www.mat.ulaval.ca/fileadmin/mat/documents/lrivest/Publications/64-RivestVandal2003.pdf.
]Search in Google Scholar
[
Rubin, D.B. 1987. Multiple imputation for nonresponse in surveys. John Wiley and Sons, Hoboken.
]Search in Google Scholar
[
Schmid, T., F. Bruckschen, N. Salvati, and T. Zbiranski. 2017. “Constructing sociodemographic indicators for national statistical institutes by using mobile phone data: estimating literacy rates in Senegal.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 180(4), 1163–1190. DOI: https://doi.org/10.1111/rssa.12305.
]Search in Google Scholar
[
Siegers, R., V. Belcheva, and T. Silbermann. 2020. SOEP-core v35 documentation of sample sizes and panel attrition in the German socio-economic panel (SOEP), 1984 until 2018. SOEP Survey Papers 826: Series C. Berlin: DIW/SOEP. Available at: https://www.diw.de/documents/publikationen/73/diw_01.c.745900.de/diw_ssp0826.pdf (accessed December 2020).
]Search in Google Scholar
[
Slud, E., and T. Maiti. 2006. “Mean-squared error estimation in transformed Fay-Herriot models.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 68: 239–257. DOI: https://doi.org/10.1111/j.1467-9868.2006.00542.x.
]Search in Google Scholar
[
Socio-Economic Panel. 2019. Data for years 1984–2017, version 34, SOEP. Socio-Economic Panel, Berlin. DOI: https://doi.org/10.5684/soep.v34.
]Search in Google Scholar
[
Statistische Ämter der Länder. 2021. Volkswirtschaftliche gesamtrechnungen der länder – zusammenhänge, bedeutung, ergebnisse. Available at: https://www.statistikportal.de/-sites/default/files/2020-11/vgrdl_brochure_2020.pdf (accessed June 2022).
]Search in Google Scholar
[
Statistische Ämter des Bundes und der Länder. 2011a. Available at: https://ergebnisse2011.zensus2022.de/datenbank/online/. (accessed: January 2021).
]Search in Google Scholar
[
Statistische Ämter des Bundes und der Länder. 2011b. Regionaldatenbank Deutschland. Available at: https://www.regionalstatistik.de/genesis/online (accessed January 2021).
]Search in Google Scholar
[
Sugasawa, S., and T. Kubokawa. 2017. “Transforming response values in small area prediction.” Computational Statistics and Data Analysis 114: 47–60. DOI: https://doi.org/10.1016/j.csda.2017.03.017.
]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 (Statistics in Society) 181(4), 927–979. DOI: https://doi.org/10.1111/rssa.12364.
]Search in Google Scholar
[
UN General Assembly. 2015. Transforming our world: the 2030 agenda for sustainable development, Available at: https://www.refworld.org/docid/57b6e3e44.html (accessed April 2023).
]Search in Google Scholar
[
Van Kerm, P. 2007. Extreme incomes and the estimation of poverty and inequality indicators from EUSILC. Working Paper Series 2007-01. Centre 39 dEtudes de Populations, de Pauvrete et de Politiques Socio-Economiques International Network for Studies in Technology, Environment, Alternatives, 40 Development. Available at: https://liser.elsevierpure.com/en/publications/extreme-incomes-and-the-estimation-of-poverty-and-inequality-indi (accessed December 2020).
]Search in Google Scholar
[
Wang, J., and W.A. Fuller. 2003. “The mean squared error of small area predictors constructed with estimated area variances.” Journal of the American Statistical Association 98(463): 716–723. DOI: https://doi.org/10.1198/016214503000000620.
]Search in Google Scholar
[
You, Y., and B. Chapman. 2006. “Small area estimation using area level models and estimated sampling variances.” Survey Methodology 32(3): 97–103. Available at: https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X20060019263 (accessed March 2020).
]Search in Google Scholar
