Survey Response and Survey Characteristics: Microlevel Evidence from the European Community Household Panel

Nicoletti, Cheti; Peracchi, Franco

doi:10.1111/j.1467-985x.2005.00369.x

Cited by 91 publications

(84 citation statements)

References 15 publications

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“…It has been shown that individuals that participate in the first wave of survey panels are more likely to continue participating. It is also reasonable to believe that those individuals answering a self-administered questionnaire, a telephone or a proxy interview as opposed to a face-to-face personal interview might be less interested in the survey project or have less time for it and therefore may be less likely to be retained in the future (see, Nicoletti and Peracchi 2005, and references therein). Coefficients in Table 6 show this is actually the case.…”

Section: The Modelmentioning

confidence: 99%

Understanding poverty persistence in Spain

Ayllón

2012

SERIEs

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The aim of this paper is to study the mechanisms behind poverty persistence in Spain. We examine the importance of past poverty experiences for explaining current poverty as opposed to observed and unobserved individual heterogeneity. Our results are based on the model proposed by Cappellari and Jenkins (J Appl Econometr 19:593-610, 2004a) that estimates poverty transitions while simultaneously controlling for attrition and initial conditions. We find that about 50 % of aggregate state dependence is genuine: poverty in a given year increases in itself the chances of experiencing poverty again in the future. The remainder is explained, among other characteristics, by living with a head of household who has no educational qualifications, being an immigrant or cohabiting with teenagers. Our findings call for a comprehensive and coordinated strategy against poverty that should focus equally on income-support policies and on enhancing those characteristics that best protect against economic hardship. From a methodological point of view, we learn that unobservables affecting initial conditions and sample retention are exogenous to those related to poverty transience. However, results prove to be sensitive to the choice of poverty line.

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Section: The Modelmentioning

confidence: 99%

Understanding poverty persistence in Spain

Ayllón

2012

SERIEs

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“…This evidence strongly suggests that mortality and worsening health conditions are likely to be the main cause of nonresponses. For a similar approach on the attrition weighting procedure see, among others, Raab et al (2005), Nicoletti and Peracchi (2005) and Vandecasteele and Debels (2007).…”

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confidence: 99%

Education and health in Europe

2017

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The productive and allocative theories predict that education has positive impact on health: the more educated adopt healthier life styles and use more efficiently health inputs and this explains why they live longer. We find partial support for these theories with an econometric analysis on a large sample of Europeans aged above 50 documenting a significant and positive correlation among education years, life styles, health outputs and functionalities. We however find confirmation for an anomaly already observed in the US, namely the more educated are more likely to contract cancer. Our results are robust when controlling for endogeneity and reverse causality in IV estimates with instrumental variables related to quarter of birth and neighbours' cultural norms.

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“…Often, logistic regression models are used to relate covariates to nonresponse and to compare response rates across subgroups (Axinn et al 2011;Johansson and Klevmarken 2008;Johnson et al 2006;Abraham et al 2006;Little and Vartivarian 2005;Nicoletti and Peracchi 2005;Lepkowsi and Couper 2002;Little 1986;Rosenbaum and Rubin 1983). In many applications, however, classification trees are considered easier to specify and interpret, specifically with regard to interaction effects (Phipps and Toth 2012;Schouten 2007;Schouten and de Nooij 2005).…”

Section: Classification and Ensemble Treesmentioning

confidence: 99%

Modeling Nonresponse in Establishment Surveys: Using an Ensemble Tree Model to Create Nonresponse Propensity Scores and Detect Potential Bias in an Agricultural Survey

Earp

Mitchell

McCarthy

et al. 2014

Journal of Official Statistics

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Increasing nonresponse rates in federal surveys and potentially biased survey estimates are a growing concern, especially with regard to establishment surveys. Unlike household surveys, not all establishments contribute equally to survey estimates. With regard to agricultural surveys, if an extremely large farm fails to complete a survey, the United States Department of Agriculture (USDA) could potentially underestimate average acres operated among other things. In order to identify likely nonrespondents prior to data collection, the USDA’s National Agricultural Statistics Service (NASS) began modeling nonresponse using Census of Agriculture data and prior Agricultural Resource Management Survey (ARMS) response history. Using an ensemble of classification trees, NASS has estimated nonresponse propensities for ARMS that can be used to predict nonresponse and are correlated with key ARMS estimates.

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Survey Response and Survey Characteristics: Microlevel Evidence from the European Community Household Panel

Cited by 91 publications

References 15 publications

Understanding poverty persistence in Spain

Understanding poverty persistence in Spain

Education and health in Europe

Modeling Nonresponse in Establishment Surveys: Using an Ensemble Tree Model to Create Nonresponse Propensity Scores and Detect Potential Bias in an Agricultural Survey

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