Detecting Untruthful Answering in Randomized Response Sampling

This paper considers the problem of unbiasedly estimating the population proportion when the study variable is potential sensitive in nature. In order to protect the respondent’s privacy, various techniques of generating randomized response rather than direct response are available in the literature. But the theory concerning them is developed only under the hypothesis of completely truthful reporting. Actually, the occurrence of untruthful reporting is a prospect in dealing with highly sensitive matters such as abortion or socially deviant behaviors. Illustrating Warner’s [(1965), Journal of the American Statistical Association. 60: 63–69] randomized response technique we show how unbiased estimation of the population proportion can be extended to cover a case when some respondents may lie.     

Analysing Misleading Discrete Responses: A Logit Model Based on Misclassified Data*

This study presents an alternative to direct questioning and randomized response approaches to obtain survey information about sensitive issues. The approach used here is based on a logit model that can be used when survey data on the dependent variable are misclassified. The method is applied to a direct survey of undergraduate cheating behaviour. Student responses may not always be truthful. In particular, a student claiming to be a non‐cheater may actually be a cheater. The results indicate that the incidence of cheating in our sample is approximately 70% rather than the self‐reported value of 51%.     

Variance Estimation of Imputed Estimators of Change for Repeated Rotating Surveys

A common problem in survey sampling is to compare two cross‐sectional estimates for the same study variable taken from two different waves or occasions. These cross‐sectional estimates often include imputed values to compensate for item non‐response. The estimation of the sampling variance of the estimator of change is useful to judge whether the observed change is statistically significant. Estimating the variance of a change is not straightforward because of the rotation in repeated surveys and imputation. We propose using a multivariate linear regression approach and show how it can be used to accommodate the effect of rotation and imputation. The regression approach gives a design‐consistent estimation of the variance of change when the sampling fraction is small. We illustrate the proposed approach using random hot‐deck imputation, although the proposed estimator can be implemented with other imputation techniques.     

Bayesian Analysis of a Sensitive Proportion for a Small Area

Without accounting for sensitive items in sample surveys, sampled units may not respond (nonignorable nonresponse) or they respond untruthfully. There are several survey designs that address this problem and we will review some of them. In our study, we have binary data from clusters within small areas, obtained from a version of the unrelated‐question design, and the sensitive proportion is of interest for each area. A hierarchical Bayesian model is used to capture the variation in the observed binomial counts from the clusters within the small areas and to estimate the sensitive proportions for all areas. Both our example on college cheating and a simulation study show significant reductions in the posterior standard deviations of the sensitive proportions under the small‐area model as compared with an analogous individual‐area model. The simulation study also demonstrates that the estimates under the small‐area model are closer to the truth than for the corresponding estimates under the individual‐area model. Finally, for small areas, we discuss many extensions to accommodate covariates, finite population sampling, multiple sensitive items and optional designs.     

What does Variation in Survey Design Reveal about the Nature of Measurement Errors in Household Consumption?

We randomly assigned eight different consumption surveys to obtain evidence on the nature of measurement errors in estimates of household consumption. Regressions using data from more error‐prone designs are compared with results from a ‘gold standard’ survey. Measurement errors appear to have a mean‐reverting negative correlation with true consumption, especially for food and especially for rural households.     

Coastal Urban Planning in The ‘Green Republic’: Tourism Development and the Nature–Infrastructure Paradox in Costa Rica

This article examines coastal urban planning in Costa Rica vis‐à‐vis the country's values in the areas of sustainable tourism and community development, focusing on the city of Jacó. I argue that an anti‐urban tourism development strategy, swift coastal urban development and weak planning have nurtured a nature–infrastructure paradox: when people are brought closer to nature without proper urban and governmental infrastructure, this causes social and environmental damage. To assess this paradox and understand local perceptions of development, I analyzed lengthy semi‐structured interviews and survey responses in San José and Jacó in this study. Research methods also encompassed analysis of current tourism planning institutions and regulations, tourism media coverage and reports, real estate data, participant observation of planning and community meetings and activities, and observations of the built and natural environmental conditions in Jacó and its surroundings. The findings show jurisdictional fragmentation, regulatory weaknesses, complexity, poor coordination, slow action, and incoherent planning and development, leading to environmental degradation and socio‐spatial inequities. A more balanced approach to planning and development would seek to improve environmental health and socio‐spatial equity in tandem, by nurturing and advancing both nature and infrastructure development. Lessons from Jacó have global resonance, given the expansion of the worldwide tourism and second‐home/retirement‐housing industries, their recent concentration in urban coastal destinations of developing countries, and the fragility of these socio‐ecological systems.     

Empirical Likelihood Approach for Aligning Information from Multiple Surveys

When two surveys carried out separately in the same population have common variables, it might be desirable to adjust each survey's weights so that they give equal estimates for the common variables. This problem has been studied extensively and has often been referred to as alignment or numerical consistency. We develop a design-based empirical likelihood approach for alignment and estimation of complex parameters defined by estimating equations. We focus on a general case when a single set of adjusted weights, which can be applied to both common and non-common variables, is produced for each survey. The main contribution of the paper is to show that the impirical log-likelihood ratio statistic is pivotal in the presence of alignment constraints. This pivotal statistic can be used to test hypotheses and derive confidence regions. Hence, the empirical likelihood approach proposed for alignment possesses the self-normalisation property, under a design-based approach. The proposed approach accommodates large sampling fractions, stratification and population level auxiliary information. It is particularly well suited for inference about small domains, when data are skewed. It includes implicit adjustments when the samples considerably differ in size. The confidence regions are constructed without the need for variance estimates, joint-inclusion probabilities, linearisation and re-sampling.     

Developments in Survey Research over the Past 60 Years: A Personal Perspective

Many developments have occurred in the practice of survey sampling and survey methodology in the past 60 years or so. These developments have been partly driven by the emergence of computers and the continuous growth in computer power over the years and partly by the increasingly sophisticated demands from the users of survey data. The paper reviews these developments with a main emphasis on survey sampling issues for the design and analysis of social surveys. Design‐based inference based on probability samples was the predominant approach in the early years, but over time, that predominance has been eroded by the need to employ model‐dependent methods to deal with missing data and to satisfy analysts' demands for survey estimates that cannot be met with design‐based methods. With the continuous decline in response rates that has occurred in recent years, much current research has focused on the use of non‐probability samples and data collected from administrative records and web surveys.     

Numerical Maximisation of Likelihood: A Neglected Alternative to EM?

There is by now a long tradition of using the EM algorithm to find maximum‐likelihood estimates (MLEs) when the data are incomplete in any of a wide range of ways, even when the observed‐data likelihood can easily be evaluated and numerical maximisation of that likelihood is available as a conceptually simple route to the MLEs. It is rare in the literature to see numerical maximisation employed if EM is possible. But with excellent general‐purpose numerical optimisers now available free, there is no longer any reason, as a matter of course, to avoid direct numerical maximisation of likelihood. In this tutorial, I present seven examples of models in which numerical maximisation of likelihood appears to have some advantages over the use of EM as a route to MLEs. The mathematical and coding effort is minimal, as there is no need to derive and code the E and M steps, only a likelihood evaluator. In all the examples, the unconstrained optimiser nlm available in R is used, and transformations are used to impose constraints on parameters. I suggest therefore that the following question be asked of proposed new applications of EM: Can the MLEs be found more simply and directly by using a general‐purpose numerical optimiser?     

Well-posedness of measurement error models for self-reported data

This paper considers the widely admitted ill-posed inverse problem for measurement error models: estimating the distribution of a latent variable $X^1$ from an observed sample of $X$, a contaminated measurement of $X^1$. We show that the inverse problem is well-posed for self-reporting data under the assumption that the probability of truthful reporting is nonzero, which is supported by empirical evidences. Comparing with ill-posedness, well-posedness generally can be translated into faster rates of convergence for the nonparametric estimators of the latent distribution. Therefore, our optimistic result on well-posedness is of importance in economic applications, and it suggests that researchers should not ignore the point mass at zero in the measurement error distribution when they model measurement errors with self-reported data. We also analyze the implications of our results on the estimation of classical measurement error models. Then by both a Monte Carlo study and an empirical application, we show that failing to account for the nonzero probability of truthful reporting can lead to significant bias on estimation of the latent distribution.     

A new privacy-protecting survey design for multichotomous sensitive variables

In this paper, we propose the diagonal model (DM), a survey technique for multicategorical sensitive variables. The DM is a nonrandomized response method; that is, the DM avoids the use of any randomization device. Thus, both survey complexity and study costs are reduced. The DM does not require that at least one outcome of the sensitive variable is nonsensitive. Thus, the model can even be applied to characteristics like income which are sensitive as a whole. We describe the maximum likelihood estimation for the distribution of the sensitive variable and show that the EM algorithm is beneficial to calculate the estimates. Subsequently, we present asymptotic as well as bootstrap confidence intervals. Applying properties of circulant matrices, we show the connection between efficiency loss and the degree of privacy protection (DPP). Here, we prove that the efficiency loss has a lower bound that depends on the DPP. Moreover, for any desired DPP, we derive model parameters that ensure the largest possible efficiency.     

Alternative Indicators for the Risk of Non‐response Bias: A Simulation Study

The growth of non‐response rates for social science surveys has led to increased concern about the risk of non‐response bias. Unfortunately, the non‐response rate is a poor indicator of when non‐response bias is likely to occur. We consider in this paper a set of alternative indicators. A large‐scale simulation study is used to explore how each of these indicators performs in a variety of circumstances. Although, as expected, none of the indicators fully depict the impact of non‐response in survey estimates, we discuss how they can be used when creating a plausible account of the risks for non‐response bias for a survey. We also describe an interesting characteristic of the fraction of missing information that may be helpful in diagnosing not‐missing‐at‐random mechanisms in certain situations.     

Covariate Measurement Error in Quadratic Regression

We consider quadratic regression models where the explanatory variable is measured with error. The effect of classical measurement error is to flatten the curvature of the estimated function. The effect on the observed turning point depends on the location of the true turning point relative to the population mean of the true predictor. Two methods for adjusting parameter estimates for the measurement error are compared. First, two versions of regression calibration estimation are considered. This approximates the model between the observed variables using the moments of the true explanatory variable given its surrogate measurement. For certain models an expanded regression calibration approximation is exact. The second approach uses moment-based methods which require no assumptions about the distribution of the covariates measured with error. The estimates are compared in a simulation study, and used to examine the sensitivity to measurement error in models relating income inequality to the level of economic development. The simulations indicate that the expanded regression calibration estimator dominates the other estimators when its distributional assumptions are satisfied. When they fail, a small-sample modification of the method-of-moments estimator performs best. Both estimators are sensitive to misspecification of the measurement error model.     

Optimal Adaptive Designs with Inverse Ordinary Differential Equations

Many industrial and engineering applications are built on the basis of differential equations. In some cases, parameters of these equations are not known and are estimated from measurements leading to an inverse problem. Unlike many other papers, we suggest to construct new designs in the adaptive fashion ‘on the go’ using the A‐optimality criterion. This approach is demonstrated on determination of optimal locations of measurements and temperature sensors in several engineering applications: (1) determination of the optimal location to measure the height of a hanging wire in order to estimate the sagging parameter with minimum variance (toy example), (2) adaptive determination of optimal locations of temperature sensors in a one‐dimensional inverse heat transfer problem and (3) adaptive design in the framework of a one‐dimensional diffusion problem when the solution is found numerically using the finite difference approach. In all these problems, statistical criteria for parameter identification and optimal design of experiments are applied. Statistical simulations confirm that estimates derived from the adaptive optimal design converge to the true parameter values with minimum sum of variances when the number of measurements increases. We deliberately chose technically uncomplicated industrial problems to transparently introduce principal ideas of statistical adaptive design.     

Estimation for sensitive characteristics using optional randomized response technique

This paper considers the problem of procuring honest responses to estimate the population mean of sensitive quantitative characteristics. It aims at pointing out that potential difference between sub-populations seems to be unnoticed under the usual optional randomized response technique. To make an extension to cover all the situations, we propose two alternative survey procedures that enable us to estimate the mean and the variance unbiasedly. Estimation for the sensitivity level of a question is under consideration as well. Efficiency comparisons have also been carried out to study the performances of the proposed procedures.     

Sensitive Survey Questions: Measuring Attitudes Regarding Female Genital Cutting Through a List Experiment

Potential bias in survey responses is higher if sensitive outcomes are measured. This study analyses attitudes towards female genital cutting (FGC) in Ethiopia. A list experiment is designed to elicit truthful answers about FGC support and compares these outcomes with the answers given to a direct question. Our results confirm that the average bias is substantial as answers to direct questions underestimate the FGC support by about 10 percentage points. Moreover, our results provide suggestive but not statistically significant evidence that this bias is more pronounced among uneducated women and women targeted by an NGO intervention (not randomly assigned).     

The Zero Lower Bound and Parameter Bias in an Estimated DSGE Model

This paper examines how and to what extent parameter estimates can be biased in a dynamic stochastic general equilibrium (DSGE) model that omits the zero lower bound (ZLB) constraint on the nominal interest rate. Our Monte Carlo experiments using a standard sticky‐price DSGE model show that no significant bias is detected in parameter estimates and that the estimated impulse response functions are quite similar to the true ones. However, as the frequency of being at the ZLB or the duration of ZLB spells increases, the parameter bias becomes larger and therefore leads to substantial differences between the estimated and true impulse responses. It is also demonstrated that the model missing the ZLB causes biased estimates of structural shocks even with the virtually unbiased parameters. Copyright © 2015 John Wiley & Sons, Ltd.     

THE MULTIDIMENSIONAL EFFECTS OF RELIGION ON SOCIOECONOMIC DEVELOPMENT: A REVIEW OF THE EMPIRICAL LITERATURE

Religion plays a fundamental role in most people's lives with profound implications for socioeconomic development. This survey provides a comprehensive and systematic overview of the causal mechanisms between religion and development discussed and tested in the economics literature, and reviews quantitative empirical evidence on the actual effects of religion on economic and social dimensions of development. We start by disaggregating the concept of religion into four religious dimensions and propose a framework to conceptualize causal mechanisms. Numerous mechanisms are possible but only a few uncontested findings exist. Religion is ambivalent vis‐à‐vis development: although religious ideas can foster certain forms of human capital acquisition and labor market participation, scholars have found a negative relationship between religious dimensions and both income and gender equality as well as innovation activities. Religious identity is also a source of labor market discrimination and has ambivalent effects on economic growth and social cohesion. Methodological challenges refer to the availability of fine‐grained data, especially for developing countries, the use of concepts and definitions, and the lack of causal inference.     

What are the macroeconomic effects of high‐frequency uncertainty shocks?

This paper evaluates the effects of high‐frequency uncertainty shocks on a set of low‐frequency macroeconomic variables representative of the US economy. Rather than estimating models at the same common low frequency, we use recently developed econometric models, which allow us to deal with data of different sampling frequencies. We find that credit and labor market variables react the most to uncertainty shocks in that they exhibit a prolonged negative response to such shocks. When looking at detailed investment subcategories, our estimates suggest that the most irreversible investment projects are the most affected by uncertainty shocks. We also find that the responses of macroeconomic variables to uncertainty shocks are relatively similar across single‐frequency and mixed‐frequency data models, suggesting that the temporal aggregation bias is not acute in this context.     

An optional scrambled randomized response technique for practical surveys

Eichhorn and Hayre (J Stat Plan Inference 7:307–316, 1983) introduced the scrambled response technique to gather information on sensitive quantitative variables. Singh and Joarder (Metron 15:151–157, 1997), Gupta et al. (J Stat Plan Inference 100:239–247, 2002) and Bar-Lev et al. (Metrika 60:255–260, 2004) permitted the respondents either to report their true values on the sensitive quantitative variable or the scrambled response and developed the optional randomized response (ORR) technique based on simple random sample with replacement (SRSWR). While developing the ORR procedure, these authors made the assumption that the probability of disclosing the true response or the randomized response (RR) is the same for all the individuals in a population. This is not a very realistic assumption as in practical survey situations the probability of reporting the true value or the RR generally varies from unit to unit. Moreover, if one generalizes the ORR method as developed by these authors relaxing the ‘constant probability’ assumption, the variance of an unbiased estimator for the population total or mean can not be estimated as this involves the unknown parameter, ‘the probability of revealing the true response’. Here we propose a modified ORR procedure for stratified unequal probability sampling after relaxing the assumption of ‘constant probability’ of providing the true response. It is also demonstrated with a numerical exercise that our procedure produces better estimator for a population total than that provided by the method suggested by the earlier authors.