The Robin Hood Index Adjusted for Negatives and Equivalised Incomes

Indisputable figures on income and wealth inequality are indispensable for politics, society and science. Although the Gini coefficient is the most common measure of inequality, the straightforward concept of the Robin Hood index (namely, the income share that has to be transferred from the rich to the poor to make everyone equally well off) makes it a more attractive measure for the general public. In a distribution with many negative values – particularly wealth distributions – the Robin Hood index can take on values larger than 1, indicating an intuitively impossible income transfer of more than 100%. This article proposes a method to normalise the Robin Hood index. In contrast to the original index, the normalised Robin Hood index always takes on values between 0 and 1 and ends up as the original index in a distribution without negatives. As inequality measures are commonly applied to equivalised income, we also introduce a method for adequately transferring equivalised incomes from the rich to the poor within the framework of the (normalised) Robin Hood index. An empirical application shows the effect of normalisation for the Robin Hood index, and compares it to the normalisation of the Gini coefficient from previous research.

Robust Estimation of the Theil Index and the Gini Coeffient for Small Areas

Small area estimation is receiving considerable attention due to the high demand for small area statistics. Small area estimators of means and totals have been widely studied in the literature. Moreover, in the last years also small area estimators of quantiles and poverty indicators have been studied. In contrast, small area estimators of inequality indicators, which are often used in socio-economic studies, have received less attention. In this article, we propose a robust method based on the M-quantile regression model for small area estimation of the Theil index and the Gini coefficient, two popular inequality measures. To estimate the mean squared error a non-parametric bootstrap is adopted. A robust approach is used because often inequality is measured using income or consumption data, which are often non-normal and affected by outliers. The proposed methodology is applied to income data to estimate the Theil index and the Gini coefficient for small domains in Tuscany (provinces by age groups), using survey and Census micro-data as auxiliary variables. In addition, a design-based simulation is carried out to study the behaviour of the proposed robust estimators. The performance of the bootstrap mean squared error estimator is also investigated in the simulation study.

Estimation of Domain Means from Business Surveys in the Presence of Stratum Jumpers and Nonresponse

Misclassified frame records (also called stratum jumpers) and low response rates are characteristic for business surveys. In the context of estimation of the domain parameters, jumpers may contribute to extreme variation in sample weights and skewed sampling distributions of the estimators, especially for domains with a small number of observations. There is limited literature about the extent to which these problems may affect the performance of the ratio estimators with nonresponse-adjusted weights. To address this gap, we designed a simulation study to explore the properties of the Horvitz-Thompson type ratio estimators, with and without smoothing of the weights, under different scenarios. The ratio estimator with propensity-adjusted weights showed satisfactory performance in all scenarios with a high response rate. For scenarios with a low response rate, the performance of this estimator improved with an increase in the proportion of jumpers in the domain. The smoothed estimators that we studied performed well in scenarios with non-informative weights, but can become markedly biased when the weights are informative, irrespective of response rate. We also studied the performance of the ’doubled half’ bootstrap method for variance estimation. We illustrated an application of the methods in a real business survey.

Occupation Coding During the Interview in a Web-First Sequential Mixed-Mode Survey

Coding respondent occupation is one of the most challenging aspects of survey data collection. Traditionally performed manually by office coders post-interview, previous research has acknowledged the advantages of coding occupation during the interview, including reducing costs, processing time and coding uncertainties that are more difficult to address post-interview. However, a number of concerns have been raised as well, including the potential for interviewer effects, the challenge of implementing the coding system in a web survey, in which respondents perform the coding procedure themselves, or the feasibility of implementing the same standardized coding system in a mixed-mode self- and interviewer-administered survey. This study sheds light on these issues by presenting an evaluation of a new occupation coding method administered during the interview in a large-scale sequential mixed-mode (web, telephone, face-to-face) cohort study of young adults in the UK. Specifically, we assess the take-up rates of this new coding method across the different modes and report on several other performance measures thought to impact the quality of the collected occupation data. Furthermore, we identify factors that affect the coding of occupation during the interview, including interviewer effects. The results carry several implications for survey practice and directions for future research.

Comparing the Response Burden between Paper and Web Modes in Establishment Surveys

Previous research is inconclusive regarding the effects of paper and web surveys on response burdens. We conducted an establishment survey with random assignment to paper and web modes to examine this issue. We compare how the actual and perceived response burdens differ when respondents complete a survey in the paper mode, in the web mode and when they are allowed to choose between the two modes. Our results show that in the web mode, respondents have a lower estimated time to complete the questionnaire, while we do not find differences between paper and the web on the perceived response time and perceived burden. Even though the response burden in the web mode is lower, our study finds no evidence of an increased response burden when moving an establishment survey from paper to the web.

Trends in Establishment Survey Nonresponse Rates and Nonresponse Bias: Evidence from the 2001-2017 IAB Establishment Panel

Evidence from the household survey literature shows a declining response rate trend in recent decades, but whether a similar trend exists for voluntary establishment surveys is an understudied issue. This article examines trends in nonresponse rates and nonresponse bias over a period of 17 years in the annual cross-sectional refreshment samples of the IAB Establishment Panel in Germany. In addition, rich administrative data about the establishment and employee composition are used to examine changes in nonresponse bias and its two main components, refusal and noncontact, over time. Our findings show that response rates dropped by nearly a third: from 50.2% in 2001 to 34.5% in 2017. Simultaneously, nonresponse bias increased over this period, which was mainly driven by increasing refusal bias whereas noncontact bias fluctuated relatively evenly over the same period. Nonresponse biases for individual establishment and employee characteristics did not show a distinct pattern over time with few exceptions. Notably, larger establishments participated less frequently than smaller establishments over the entire period. This implies that survey organizations may need to put more effort into recruiting larger establishments to counteract nonresponse bias.

Combining Cluster Sampling and Link-Tracing Sampling to Estimate Totals and Means of Hidden Populations in Presence of Heterogeneous Probabilities of Links

We propose Horvitz-Thompson-like and Hájek-like estimators of the total and mean of a response variable associated with the elements of a hard-to-reach population, such as drug users and sex workers. A portion of the population is assumed to be covered by a frame of venues where the members of the population tend to gather. An initial cluster sample of elements is selected from the frame, where the clusters are the venues, and the elements in the sample are asked to name their contacts who belong to the population. The sample size is increased by including in the sample the named elements who are not in the initial sample. The proposed estimators do not use design-based inclusion probabilities, but model-based inclusion probabilities which are derived from a Rasch model and are estimated by maximum likelihood estimators. The inclusion probabilities are assumed to be heterogeneous, that is, they depend on the sampled people. Variance estimates are obtained by bootstrap and are used to construct confidence intervals. The performance of the proposed estimators and confidence intervals is evaluated by two numerical studies, one of them based on real data, and the results show that their performance is acceptable.

Freedom of Information and Personal Confidentiality in Spatial COVID-19 Data

We draw attention to how, in the name of protecting the confidentiality of personal data, national statistical agencies have limited public access to spatial data on COVID-19. We also draw attention to large disparities in the way that access has been limited. In doing so, we distinguish between absolute confidentiality in which the probability of detection is 1, relative confidentiality where this probability is less than 1, and collective confidentiality, which refers to the probability of detection of at least one person. In spatial data, the probability of personal detection is less than 1, and the probability of collective detection varies directly with this probability and COVID-19 morbidity. Statistical agencies have been concerned with relative and collective confidentiality, which they implement using the techniques of truncation, where spatial data are not made public for zones with small populations, and censoring, where exact data are not made public for zones where morbidity is small. Granular spatial data are essential for epidemiological research into COVID-19. We argue that in their reluctance to make these data available to the public, data security officers (DSO) have unreasonably prioritized data protection over freedom of information. We also argue that by attaching importance to relative and collective confidentiality, they have over-indulged in data truncation and censoring. We highlight the need for legislation concerning relative and collective confidentiality, and regulation of DSO practices regarding data truncation and censoring.