An Analysis of Nonignorable Nonresponse to Income in a Survey with a Rotating Panel Design

In a rotating panel survey, individuals are interviewed in some waves of the survey but are not interviewed in others. We consider the treatment of missing income data in the labor force survey of the Municipality of Florence in Italy, a survey with a rotating panel design where recipiency and amount of income are missing for waves where individuals are not interviewed, and amount of income is missing for waves where individuals are interviewed but refuse to answer the income amount question. It is thus a question of a multivariate missing data problem with two missing-data mechanisms, one by design and one by refusal, and varying sets of covariates for imputation depending on the wave of the survey. Existing methods for multivariate imputation such as sequential regression multiple imputation (SRMI) can be applied, but assume that the missing income values are missing at random (MAR). This assumption is reasonable when missing data arise from the rotating panel design, but less reasonable when the missing data arise from refusal to answer the income question, since in this case missingness of income is generally thought to be related to the value of income itself, after conditioning on available covariates. In this article we describe a sensitivity analysis to assess the impact of departures from MAR for refusals, based on SRMI for a pattern-mixture model. The sensitivity analysis avoids the well-known problems of underidentification of parameters of missing not at random models, is easy to carry out using existing sequential multiple imputation software, and takes into account the different mechanisms that lead to missing data

AN ANALYSIS OF NONIGNORABLE NONRESPONSE IN A SURVEY WITH A ROTATING PANEL DESIGN

Missing values to income questions are common in survey data. When the probabilities of nonresponse are assumed to depend on the observed information and not on the underlining unobserved amounts, the missing income values are missing at random (MAR), and methods such as sequential multiple imputation can be applied. However, the MAR assumption is often considered questionable in this context, since missingness of income is thought to be related to the value of income itself, after conditioning on available covariates. In this article we describe a sensitivity analysis based on a pattern-mixture model for deviations from MAR, in the context of missing income values in a rotating panel survey. The sensitivity analysis avoids the well-known problems of underidentification of parameters of non-MAR models, is easy to carry out using existing sequential multiple imputation software and has a number of novel features

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

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

Integrating Survey and Administrative Data on Local Social Protection

Welfare systems can be observed according to two different perspectives. The former deals with the supply of social protection, i.e. with the funding and provision of social benefits and the production of social services and goods. The latter focuses on the demand of social protection, and particularly on the characteristics of people benefiting from social protection or asking for it. Typically, data on the supply of social benefits have an administrative nature (registers and budgets data) whereas data on beneficiaries come from sample surveys. In theory, administrative data, being census data, can be detailed by territory. On the contrary, sample surveys are usually planned to provide accurate estimates at the national level or for large sub-national areas. This chapter provides an example on the use of different data sets for the Old age and Family/children functions at the province level (LAU 1 in the EU nomenclature). Data on the supply of benefits derive from the SISSIM (Istat Survey on Interventions and Social Services of Individual and associated Municipalities) and from municipalities' budgets. Data on the demand of social protection come from EU-SILC (European Union - Statistics on Income and Living Conditions), a survey that is annually conducted by Istat in a comparable European framework. Earned benefits are estimated applying small area estimation methods, given that the sample size of the EU-SILC survey at the province level is small, so the traditional design-based estimators usually are unreliable. Results are analysed to understand whether administrative and sample survey data can be used to to compose a coherent picture of social protection delivered at the provincial level

Local Comparisons of Small Area Estimates of Poverty: An Application Within the Tuscany Region in Italy

The aim of this paper is to highlight some key issues and challenges in the analysis of poverty at the local level using survey data. In the last years there was a worldwide increase in the demand for poverty and living conditions estimates at the local level, since these quantities can help in planning local policies aimed at decreasing poverty and social exclusion. In many countries various sample surveys on income and living conditions are currently conducted, but their sample size is not enough to obtain reliable estimates at local level. When this happens, small area estimation (SAE) methods can be used. In this paper, a SAE model is used to compute the mean household equivalised income and the head count ratio for the 57 Labor Local Systems of the Tuscany region in Italy for the year 2011. The caveats of the analysis of poverty at the local level using small area methods are many, and some are still not so well explored in the literature, starting from the definition of the target indicators to the relevant dimensions of their measurement. We suggest in this paper that together with the universally recognized multidimensional, longitudinal and local dimensions of poverty, a new dimension must be considered: the price dimension, which should take into account local purchasing power parities to cor- rectly compare the poverty indicators based on income measures

Small area estimation based on M-quantile models in presence of outliers in auxiliary variables

When using small area estimation models, the presence of outlying observations in the response and/or in the auxiliary variables can severely affect the estimates of the model parameters, which can in turn affect the small area estimates produced using these models. In this paper we propose an M-quantile estimator of the small area mean that is robust to the presence of outliers in the response variable and in the continuous auxiliary variables. To estimate the variability of this estimator we propose a non-parametric bootstrap estimator. The performance of the proposed estimator is evaluated by means of model- and design-based simulations and by an application to real data. In these comparisons we also include the extension of the Robust EBLUP able to down-weight the outliers in the auxiliary variables. The results show that in the presence of outliers in the auxiliary variables the proposed estimator outperforms its traditional version that takes into account the presence of outliers only in the response variable

Does uncertainty in single indicators affect the reliability of composite indexes? An application to the measurement of environmental performances of Italian regions

In recent decades, the measurement and evaluation of important social and natural phenomena has significantly evolved, with many traditional measurements based on single variables increasingly being replaced by multi- dimensional approaches. One key aspect of these approaches is the development of composite indexes, usually real-value functions of multiple achievements of a group of units. The achievements in each of the selected dimensions are generally synthesised through one or more variables, often referred to as indicators. When in- dicators are obtained through an estimation process, it is crucial to understand if and how their estimation error – for example, sampling error – affects the resulting composite index. This paper presents a methodology based on a parametric bootstrap technique that evaluates to what extent uncertainty in indicators affects the reliability of the aggregate composite index. The method is applied to four composite indexes measuring the environmental performances of Italian regions based on real population and survey data. To our knowledge, this is the first attempt to measure the impact of indicators’ sampling error on composite indexes. If adequately generalised, our methodology could be used in the presence of measurement errors, non- response issues, or other kinds of non-sampling errors

Poverty Indicators at Local Level: Definitions, Comparisons in Real Terms and Small Area Estimation Methods

The importance of computing poverty measures at sub-national level is nowadays widely attested. Local poverty indicators are relevant both for a detailed planning of the policy actions against poverty and social exclusion, and for the citizens to evaluate their effects. However, there are still open problems to compute adequate sub-national poverty indicators. They refer to: 1) the definition of poverty lines; 2) the methods for accounting the spatial variation of the cost of living to make comparisons in ‘real terms’ between different areas; 3) the use of Small Area Estimation methods when the sample size is not enough to obtain accurate estimates of the indicators at local level. In this paper, we discuss the issues above by presenting some analyses on the impact of using different poverty lines on the value of the poverty rate for the 20 Italian Regions, which represent a planned domain of study in Italy. Then, we estimate the poverty rate for the 110 Italian Provinces, unplanned domains in Italy, by using specific parametric models and SAE methods. The key results highlight strong differences in the territorial distribution of the poverty rate by using national versus sub-national specific poverty lines. The effect of the heterogeneity of the general spatial price indexes on the poverty rates seems instead less important in comparison with the relevant territorial differences in the cost of housing. Moreover, the different methods of estimation of poverty rates at local level provides interesting first results and indicates the route for further research to improve the methods of estimation of poverty at the sub-regional level

Meat intake and non-Hodgkin lymphoma: a meta-analysis of observational studies

Purpose: High intake of meat has been inconsistently associated with increased risk of NonHodgkin Lymphoma (NHL). We carried out a meta-analysis to summarise the evidence of published observational studies reporting association between red meat and processed meat intake and NHL risk. Methods: Analytical studies reporting relative risks with 95% confidence intervals (95% CI) for the association between intake of red and/or processed meat and NHL or major histological subtypes were eligible. We conducted random-effects meta-analysis comparing lowest and highest intake categories and dose-response meta-analysis when risk estimates and intake levels were available for more than three exposure classes. Results: Fourteen studies (4 cohort and 10 case-control) were included in the meta-analysis, involving a total of 10121 NHL cases. The overall relative risks of NHL for the highest versus the lowest category of consumption were 1.14 (95%CI: 1.03, 1.26) for red meat and 1.06 (95%CI: 0.98, 1.15) for processed meat. Significant associations were present when the analysis was restricted to case-control studies but not when restricted to cohort studies. No significant associations were found for major NHL etiological subtypes. Dose response meta-analysis could be based only on 8 studies that provided sufficient data and, compared to no meat consumption, the overall NHL relative risk increased not linearly with increased daily intake of red meat. Conclusion: The observed positive association between red meat consumption and NHL is mainly supported by the effect estimates coming from case-control studies and is affected by multiple sources of heterogeneity. This meta-analysis provided mixed and inconclusive evidences on the supposed relationship between red and processed meat consumption and NHL