This paper discusses what determines the preferences of individuals for redistribution. We review the theoretical literature and provide a framework to incorporate various effects previously studied separately in the literature. We then examine empirical evidence for the US, using the General Social Survey, and for a large set of countries, using the World Values Survey. The paper reviews previously found results and provides several new ones. We emphasize, in particular, the role of historical experiences, cultural factors and personal history as determinants of preferences for equality or tolerance for inequality.

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Preferences for Redistribution

The last three decades have witnessed the rise of a pohtical gender gap in the United States wherein more women than men favor the Democratic party. We trace this development to the decline in marriage, which we posit has made men richer and women poorer. Data for the United States support this argument. First, there is a strong positive correlation between state divorce prevalence and the political gender gap—higher divorce prevalence reduces support for the Democrats among men but not women. Second, longitudinal data show that following marriage (divorce), women are less (more) likely to support the Democratic party.

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Why Have Women Become Left-Wing? The Political Gender Gap and the Decline in Marriage

Survey researchers are rightly concerned with measuring the level of potential bias in estimates generated from the surveys.2 Bias in estimates can result from measurement error, processing/editing error, coverage error, and nonresponse error (Federal Committee on Statistical Methodology [FCSM] 2001). It is nonresponse bias that is the focus of this editorial and it is also the subject of the paper by Halbesleben and Whitman (2013) that this editorial accompanies.

Nonresponse bias is a perennial concern for survey researchers as not everyone we attempt to include in our surveys responds. And to the extent that nonrespondents are different from respondents on the key variables the survey was designed to study these differences could bias the very estimates the survey was designed to make. Because we often have very little information about those who do not respond, survey researchers have long focused on the response rate as a key indicator of survey quality. The assumption is that the more nonresponse there is in a survey, the higher the potential for nonresponse bias. Unfortunately, this assumption has served as a problematic diversion for survey research from the real concern of how survey nonresponse potentially biases survey estimates.

Nonresponse rates are a problematic indicator of nonresponse bias in survey research

This paper discusses what determines the preferences of individuals for redistribution. We review the theoretical literature and provide a framework to incorporate various effects previously studied separately in the literature. We then examine empirical evidence for the US, using the General Social Survey, and for a large set of countries, using the World Values Survey. The paper reviews previously found results and provides several new ones. We emphasize, in particular, the role of historical experiences, cultural factors and personal history as determinants of preferences for equality or tolerance for inequality. JEL codes are: H10, Z1

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Preferences for redistribution

Measures of patient satisfaction have gained prominence in recent years as changes to the American health care system have led to the incorporation of such metrics into reimbursement models. The response rate for widely-used outpatient satisfaction metrics and variables influencing the probability of survey nonresponse remain largely unknown. We reviewed all unique adult patients (16,779) who completed an outpatient encounter in the Department of Orthopaedic surgery at our academic institution from 1/1/13 to 10/24/13. Survey data was linked to each clinic visit, and patient factors including age, sex, insurance type, zip code, and orthopaedic subspecialty visited were recorded. The overall survey response rate was calculated. Logistic regression was performed, and unadjusted and adjusted odds ratios of patients’ probability of responding to the Press-Ganey survey were calculated. Two thousand seven hundred sixty two (16.5 %) of individuals completed a Press-Ganey patient satisfaction survey and 14017 patients did not respond. For those patients considered responders, 906 patients (32.8 %) did not complete all the survey items. Among these 906 patients, the mean number of missing items was 2.24 (Standard Deviation SD: 2.19). Age, sex, insurance type, and orthopaedic subspecialty were all found to be associated with the odds of responding to our patient satisfaction survey. Advancing age increased the odds of responding to the survey (Adjusted Odds Ratio (OR) = 3.396 for ≥65 vs. 18–29, p < 0.001). Several variables were associated with a decreased odds of survey response, and included male sex (Adjusted OR = 0.782 for Males vs. Females, p < 0.001), insurance type (Adjusted OR = 0.311 for Medicaid/Self-Pay vs. Private), and subspecialty type (Adjusted OR = 0.623 for Trauma vs. Adult Reconstruction). The response rate to the Press-Ganey Medical Practice Survey of outpatient satisfaction is low in an orthopaedic outpatient population, and furthermore, is impacted by patient characteristics such as age, sex, insurance type, and type of orthopaedic subspecialist encountered. The findings of the present study should inform future non-response weighting procedures in this area. More research is needed to assess non-response bias—including follow-up studies of non-respondents—in order to more accurately measure of patient satisfaction.

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Evidence of non-response bias in the Press-Ganey patient satisfaction survey

Random-digit-dial telephone surveys are experiencing both declining response rates and increasing under-coverage due to the prevalence of households that substitute a wireless telephone for their residential landline telephone. These changes increase the potential for bias in survey estimates and heighten the need for survey researchers to evaluate the sources and magnitudes of potential bias. We apply a Monte Carlo simulation-based approach to assess bias in the NIS, a land-line telephone survey of 19–35 month-old children used to obtain national vaccination coverage estimates. We develop a model describing the survey stages at which component nonsampling error may be introduced due to nonresponse and under-coverage. We use that model and components of error estimated in special studies to quantify the extent to which noncoverage and nonresponse may bias the vaccination coverage estimates obtained from the NIS and present a distribution of the total survey error. Results indicated that the total error followed a normal distribution with mean of 1.72 per cent (95 per cent CI: 1.71, 1.74 per cent) and final adjusted survey weights corrected for this error. Although small, the largest contributor to error in terms of magnitude was nonresponse of immunization providers. The total error was most sensitive to declines in coverage due to cell phone only households. These results indicate that, while response rates and coverage may be declining, total survey error is quite small. Since response rates have historically been used to proxy for total survey error, the finding that these rates do not accurately reflect bias is important for evaluation of survey data. Published in 2011 by John Wiley & Sons, Ltd.

Quantifying bias in a health survey: modeling total survey error in the national immunization survey.

This paper reports an experimental investigation on two mechanisms for the so-called King Solomon Dilemma, where one of them fails to implement the social choice rule dynamically. We compare the two mechanisms in terms of their welfare, incentive and learning properties.

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Solomon's dilemma: an experimental study on dynamic implementation

Cell phone surveys have become increasingly popular and researchers have noted major challenges in conducting cost-effective surveys while achieving high response rates. Previous work has shown that calling strategies that maximize both respondent contact and completed interviews for landline surveys may not be the most cost-effective for cell phone surveys. For example, Montgomery, et al. (2011) found important differences between landline and cell samples for best times to call and declines in contact rates after repeated dialing. Using paradata from the 2010 and 2011 National Flu Surveys (sponsored by the Centers for Disease Control and Prevention), we investigate differences in calling outcomes between landline and cell surveys. Specifically, we predict respondent contact and interview completion using logistic regression models that examine the impact of calling on particular days of the week, certain times of the day, number of previous calls, outcomes of previous calls and length of time between calls. We discuss how these differences can be used to increase the likelihood of contacting cooperative respondents and completing interviews for both sample types.

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Optimizing Call Patterns for Landline and Cell Phone Surveys.

Women may want greater taxes and redistributional spending than men if malein comes exceed female incomes and men and women are not always married. Cross-section evidence from opinion polls provides indirect support for this view.Women express greater favorability toward redistributional spending than do men; the difference is explained largely but not entirely by income differences. The empirical analysis provides partialsupport for Meltzer and Richard (1981) , and suggests that increases in singleness after World WarII may have acted to increase differences by sex in desired redistributional spending.

Sex and Fiscal Desire

More on the Extent of Undercoverage in RDD Telephone Surveys Due to the Omission of 0-Banks

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Robert Montgomery

Papers

Quantifying bias in a health survey: modeling total survey error in the national immunization survey.

Solomon's dilemma: an experimental study on dynamic implementation

Optimizing Call Patterns for Landline and Cell Phone Surveys.

Sex and Fiscal Desire

More on the Extent of Undercoverage in RDD Telephone Surveys Due to the Omission of 0-Banks