The Under-Reporting of Transfers in Household Surveys: Its Nature and Consequences

Meyer, Bruce D.; Mok, Wallace K. C.; Sullivan, James X.

doi:10.3386/w15181

Cited by 280 publications

(310 citation statements)

References 27 publications

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“…25 In addition, prior research suggests that individuals do not reliably disclose whether they participate in SNAP on surveys, meaning our analysis likely underestimates true rates of food assistance coverage. 26,27 Those receiving WIC, or both SNAP and WIC, had similar odds of food insecurity to those receiving no food assistance. There are several possible interpretations of this finding.…”

Section: Discussionmentioning

confidence: 95%

Food Insecurity, Neighborhood Food Access, and Food Assistance in Philadelphia

et al. 2014

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An estimated 17.6 million American households were food insecure in 2012, meaning they were unable to obtain enough food for an active and healthy life. Programs to augment local access to healthy foods are increasingly widespread, with unclear effects on food security. At the same time, the US government has recently enacted major cuts to federal food assistance programs. In this study, we examined the association between food insecurity (skipping or reducing meal size because of budget), neighborhood food access (self-reported access to fruits and vegetables and quality of grocery stores), and receipt of food assistance using the 2008, 2010, and 2012 waves of the Southeastern Pennsylvania Household Health Survey. Of 11,599 respondents, 16.7 % reported food insecurity; 79.4 % of the food insecure found it easy or very easy to find fruits and vegetables, and 60.6 % reported excellent or good quality neighborhood grocery stores. In our regression models adjusting for individualand neighborhood-level covariates, compared to those who reported very difficult access to fruits and vegetables, those who reported difficult, easy or very easy access were less likely to report food insecurity (OR 0.62: 95 % CI 0.43-0.90, 0.33: 95 % CI 0.23-0.47, and 0.28: 95 % CI 0.20-0.40). Compared to those who reported poor stores, those who reported fair, good, and excellent quality stores were also less likely to report food insecurity (OR 0.81: 95 % CI 0.60-1.08, 0.58: 95 % CI 0.43-0.78, and 0.43: 95 % CI 0.31-0.59). Compared to individuals not receiving food assistance, those receiving Supplemental Nutrition Assistance Program (SNAP) benefits were significantly more likely to be food insecure (OR 1.36: 95 % CI 1.11-1.67), while those receiving benefits from the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) (OR 1.17: 95 % CI 0.77-1.78) and those receiving both SNAP and WIC (OR 0.84: 95 % CI 0.61-1.17) did not have significantly different odds of food insecurity. In conclusion, better neighborhood food access is associated with lower risk of food insecurity. However, most food insecure individuals reported good access. Improving diet in communities with high rates of food insecurity likely requires not only improved access but also greater affordability.

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Section: Discussionmentioning

confidence: 95%

Food Insecurity, Neighborhood Food Access, and Food Assistance in Philadelphia

et al. 2014

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“…Shortfalls in reported receipt of means-tested welfare benefits compared with administrative information are also found in US surveys, on a larger scale (Meyer et al, 2009). Wheaton (2007 uses microsimulation to calculate entitlement and then to calibrate the numbers of recipients so that they match administrative statistics.…”

Section: Box 2: Benefit Recipients In the Uk: Comparing Microsimulatimentioning

confidence: 99%

“…An obvious example is cross-checking whether gross and net income values (if both are reported) correspond to each other (see section 2.1.1). As benefit income tends to be underreported in survey data (Lynn et al, 2012;Meyer et al 2009), use of simulated benefits has the potential to improve the accuracy of income information (see more in section 4.1).…”

Section: Adding Informationmentioning

confidence: 99%

Microsimulation and Policy Analysis

Figari

Paulus

Sutherland

2015

Handbook of Income Distribution

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ISER Working Paper Series www.iser.essex.ac.uk Microsimulation and Policy AnalysisNon-technical summary This paper is prepared as a chapter forthcoming in the Handbook of Income Distribution, Volume 2 (edited by A. B. Atkinson and F. Bourguignon, Elsevier-North Holland). Microsimulation methods are increasingly used to study the effects of policies on the income distribution contributing both to policy debates and academic literature. In general terms, microsimulation refers to modelling changes in the state or behaviour of micro-level units. We provide an overview of approaches that address questions related to the impact of taxbenefit policies on household income distribution. We first discuss how microsimulation modelling contributes to the analysis of the income distribution more broadly, pointing out that it can enrich survey and administrative data by checking its consistency, adding further details and enhancing analytical flexibility. In addition, it provides the basis for calculating indicators that cannot otherwise be estimated directly from micro-data such as measures of work incentives, net support for children and automatic stabilisers embedded in tax-benefit systems. We then focus more specifically on the role of microsimulation in policy evaluation. Taxbenefit models can provide information on household disposable income and its components under various policy scenarios and as such constitute a unique tool for ex ante policy analysis, though equally can be used for ex post analysis. We review the formal framework underlying such microsimulation-based decomposition analysis of income distribution and suggest a broader perspective to accommodate a wider range of applications. Our main emphasis is on static modelling methods, though we also consider extensions accounting for behavioural reactions and links with macroeconomic models. As microsimulation models are often concerned with current and future policies, we discuss various approaches to predict the income distribution given that data providing information on the current situation becomes available with a time lag, distinguishing between nowcasting and forecasting. We emphasise the role of comparative analysis as cross-country differences in tax-benefit systems and population structures provide a basis to assess the robustness of results and generalise conclusions. We provide empirical illustrations drawing mainly on analysis using the EU-wide tax-benefit model EUROMOD. Covering 27 countries and made generally accessible this is now one of the most widely used models. There are many remaining challenges to providing estimates of the effects of policy and policy changes that can be used with confidence within policy analysis and we consider three major issues: reconciling the simulated income distribution and that measured using the original micro-data; accounting for benefit non take-up and tax non-compliance, as well as validating and assessing the statistical reliability of microsimulation estimates. While our main focus is on the distributio...

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“…A straightforward approach to measuring net misreporting is to compare counts of program recipients (possibly by geographic or demographic group) from public survey data to corresponding counts released by the agency administering the program. 6 This is the approach of Meyer et al (2009), who compare survey estimates of total SNAP recipients from five surveys to aggregates provided by the FNS. They find that net national underreporting rates for participation in the SNAP were as high as 34 percent in the 1980 Current Population Survey (CPS) and increased to 47 percent in the 2006 CPS.…”

Section: Literaturementioning

confidence: 99%

“…1 Effective 1 outreach depends, though, on the availability of reliable data regarding the characteristics of people who do and do not participate in SNAP. Responses to survey questions about SNAP participation, however, are known to suffer from measurement error due to non-random misreporting by survey respondents (see, for example, Bound et al, 2001;Oberheu and Ono, 1975;Meyer et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Within and Across County Variation in SNAP Misreporting: Evidence from Linked ACS and Administrative Records

Cerf¹

2017

Preprint

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This paper examines sub-state spatial and temporal variation in misreporting of participation in the Supplemental Nutrition Assistance Program (SNAP) using several years of the American Community Survey linked to SNAP administrative records from New York (2008York ( -2010 and Texas (2006Texas ( -2009. I calculate county false-negative (FN) and false-positive (FP) rates for each year of observation and find that, within a given state and year, there is substantial heterogeneity in FN rates across counties. In addition, I find evidence that FN rates (but not FP rates) persist over time within counties. This persistence in FN rates is strongest among more populous counties, suggesting that when noise from sampling variation is not an issue, some counties have consistently high FN rates while others have consistently low FN rates. This finding is important for understanding how misreporting might bias estimates of sub-state SNAP participation rates, changes in those participation rates, and effects of program participation.

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The Under-Reporting of Transfers in Household Surveys: Its Nature and Consequences

Cited by 280 publications

References 27 publications

Food Insecurity, Neighborhood Food Access, and Food Assistance in Philadelphia

Food Insecurity, Neighborhood Food Access, and Food Assistance in Philadelphia

Microsimulation and Policy Analysis

Within and Across County Variation in SNAP Misreporting: Evidence from Linked ACS and Administrative Records

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