The impact of unionization,right-to-work laws,and female labor force participation on earnings inequality across states

This study examines the effect of unionization, right-to-work laws, and participation of women in the labor force on income inequality. Two distinct models are developed using 1970 and 1980 census data on the 50 states in the U.S. First, an income inequality model is specified as a beta distribution of the second kind to estimate Gini measures of income inequality. Second, these Gini estimates are used in a simultaneous equations model. The 1970 results indicate that higher unionization rates decreased inequality while right-to-work laws increased inequality. In 1980, the measure of inequality was lower in states with higher female labor force participation. We thank an unknown referee and the editor for comments and criticisms that greatly improved the paper. The usual caveat applies.     

UNDERSTANDING MAIL SURVEY RESPONSE BEHAVIOR A META-ANALYSIS

A meta-analysis of prior studies of techniques designed to inducemail survey response rates was conducted. Research encompassing184 effects (study outcomes) in 115 studies (articles) for 17predictors of response rate was examined. The average effectsize across all manipulations was r=.065, indicating an averageincrease of about 6.5 percent in response rates for manipulations.Effect sizes for specific predictors and two potential moderatorsof effects were examined. Results indicated that repeated contactsin the form of preliminary notification and follow-ups, appeals,inclusion of a return envelope, postage, and monetary incentives,were effective in increasing survey response rates. Significanteffect sizes for the predictors ranged from an increase in responseof 2 percent to 31 percent. Implications of the results forthe conduct of mail surveys and future research on mail surveyresponse behavior are discussed.     

Grounded Neural Networking: Modeling Complex Quantitative Data

The latest advances in artificial intelligence software (neural networking) have finally made it possible for qualitative researchers to apply the grounded theory method to the study of complex quantitative databases in a manner consistent with the postpositivistic, neopragmatic assumptions of most symbolic interactionists. The strength of neural networking for the study of quantitative data is twofold: it blurs the boundaries between qualitative and quantitative analysis, and it allows grounded theorists to embrace the complexity of quantitative data. The specific technique most useful to grounded theory is the Self‐Organizing Map (SOM). To demonstrate the utility of the SOM we (1) provide a brief review of grounded theory, focusing on how it was originally intended as a comparative method applicable to both quantitative and qualitative data; (2) examine how the SOM is compatible with the traditional techniques of grounded theory; and (3) demonstrate how the SOM assists grounded theory by applying it to an example based on our research.     

The validity of survey responses on abortion: Evidence from Estonia

This paper presents results of a validation survey of abortion conducted in Tallinn, Estonia in April and May 1992. The sample was drawn from patient records in a maternity hospital. Women who had an abortion in that hospital in 1991 were asked about recent abortions as part of a survey about women’s health. More than 80% of the respondents reported having a recent abortion. Some respondents misreported their abortion as a miscarriage. Moreover, some variation in reporting was associated with respondents’ characteristics. Ethnic Estonians were less likely to report their abortion than were Russians, women over age 40 were less likely to report the abortion than younger women, and women who had the abortion late in the first trimester were less likely to report that abortion. There was some evidence that unmarried women were less likely than married women to report their abortion, and that women who had borne three or more children were less likely to report their abortion than women who had borne fewer children. These differences probably stem from the extent to which pregnancy or abortion is considered stigmatizing for women in different situations.     

Nonresponse weighting adjustment using estimated response probability

To reduce nonresponse bias in sample surveys, a method of nonresponse weighting adjustment is often used which consists of multiplying the sampling weight of the respondent by the inverse of the estimated response probability. The authors examine the asymptotic properties of this estimator. They prove that it is generally more efficient than an estimator which uses the true response probability, provided that the parameters which govern this probability are estimated by maximum likelihood. The authors discuss variance estimation methods that account for the effect of using the estimated response probability; they compare their performances in a small simulation study. They also discuss extensions to the regression estimator.