Reconsidering union wage effects: Surveying new evidence on an old topic

I examine evidence on private sector union wage gaps in the United States. The consensus opinion among labor economists of an average union premium of roughly 15 percent is called into question. Two forms of measurement error bias downward standard wage gap estimates. Match bias results from Census earnings imputation procedures that do not include union status as a match criterion. Downward bias is roughly equal to the proportion of workers with imputed earnings, currently about 30 percent. Misclassification of union status causes additional attenuation in union gap measures. This bias has worsened as private sector density has declined, since an increasing proportion of workers designated as union are instead nonunion workers. Corrections for misclassification and match bias lead to estimated union gaps substantially higher than standard estimates, but with less of a downward trend since the mid 1980s. Private sector union gaps corrected for these biases are estimated from the CPS for 1973–2001. The uncorrected estimate for 2001 is .13 log points. Correction for match bias increases the gap to .18 log points; further correction for misclassification bias, based on an assumed 2 percent error rate, increases the gap to .24. Reexamination of the skill-upgrading hypothesis leads to the conclusion that higher union gap estimates are plausible. The conventional wisdom of a 15 percent union wage premium warrants reexamination.     

Membership and wage impact of right-to-work laws

This paper examines the effects of “Right-to-Work” laws on union membership and on the earnings of union and nonunion members. Using regression analysis, we find that once the simultaneous equations bias between the degree of unionization and RTW laws is eliminated, RTW laws have no statistically significant influence on union membership. Similarly, using a human-capital earnings model, we find that RTW laws have no significant influence on the wages of all workers, union workers, or nonunion workers. However, we did find evidence that such laws may promote aggressive union wage policies resulting in a larger union/nonunion relative wages advantage in RTW states than in non-RTW states.     

Unemployment classifications and subjective well-being

Further research on the impact of unemployment on workers and their families requires greater refinement and more precise measurement of the concept. While previous studies have indicated the effects of unemployment on a wide range of variables, such as health and well-being, many of these studies suffer from imprecision by conceptualizing unemployment too narrowly and by using too strict a dichotomy between currently employed and currently unemployed workers. This research effort attempts to broaden and refine the concept of unemployment by defining it in terms of two job dimensions: current employment status and previous job loss. Using this conceptualization, the authors find that current unemployment after other job losses may have more devastating effects on well-being than losing one's job for the first time. Similarly, among the currently employed, those who have lost jobs in the past may experience more emotional difficulties than those who have never lost their jobs.     

The reliability of recall measurement in assessing migrant reintegration: Evidence from Ethiopia,Somalia, and Sudan

This article asks whether and under what conditions the use of retrospective or “recall” measurement is reliable. Migration researchers are often forced to retrospectively measure baselines when evaluating the impact of interventions due to the transitory nature of migration, developing country contexts, and hastily assembled policy programmes, a situation exacerbated by Covid-19. This article first theoretically considers the extent to which this approach is reliable and likely to result in biased estimates, as well as its broader advantages, disadvantages, and recommendations for best practice. It then considers the case of the “IMPACT” evaluation of the EU-IOM Joint Initiative for Migrant Protection and Reintegration in which 1774 Ethiopian, Somalia, and Sudanese migrant returnees in 2021 were assessed on a range of reintegration measures, 1095 of whom were measured retrospectively. Regression analyses demonstrate that those measured retrospectively give more negative scores on several “Reintegration Sustainability Scores” in line with some theoretical expectations but contrary to others. However, this—mostly non-statistically significant—effect is largely diminished when the small minority who report finding it difficult to remember the baseline period are removed—suggesting that any retrospective measurement effect results from memory bias rather than, for example, consistency bias. No evidence is found to support several theoretically derived interaction effects. Determinants of self-reported memory are demonstrated and recommendations for usage of retrospective measurement are provided, based on these findings.     

Immigration and the Wages of Native Workers: Spatial versus Occupational Approaches

Using data from the Current Population Survey, we study the impact of the increasing proportion of immigrants on the wages of native workers. Two different approaches are contrasted. The most common method has been the spatial approach that uses some geographic unit of analysis to investigate the multivariate association between the proportion of immigrants and the wages of native workers. Previous studies using the spatial approach have generally found little evidence of a significant negative effect. We propose, however, a contrasting method that represents an occupational approach in which occupations are the unit of analysis to investigate the impact of the proportion immigrant. This occupational approach avoids the bias that is inherent in the spatial approach due to the endogenous nature of immigrants' decisions about where to reside and the economic opportunities of local areas. In contrast to the spatial approach, our results using the same data but employing the occupational approach yield consistently negative net effects of the proportion immigrant on the wages of native workers during the period from 1994 to 2006.     

Neighborhood and community effects in East and Southeast Asia: A systematic review and meta-analytical exploration of publication bias

Neighborhood and community effects studies have informed urban policies in the West for three decades. Since about ten years ago, this research line is seen increasingly in East and Southeast Asia as well. As an emerging field, the literature has yet to be critically reviewed and its body of literature provides a unique opportunity to study the effects that different research communities might have on its development. This systematic review collects 165 studies and gives a critical appraisal of this literature, specifically focusing on publication bias. Findings show that “true” neighborhood effects might be overestimated. Health research shows greater publication bias than human geography and general social science. Studies by only local scholars are more prone to bias than studies from collaborative teams or only nonlocal scholars, suggesting that this field is relatively early in its life-cycle or that publication pressure is much higher in Asia compared to the West.     

W/age bias in worker displacement: how industrial structure shapes the job loss and earnings decline of older American workers

This paper examines age differences in patterns of worker displacement and employment and earnings changes of displaced workers. Data from the 1998 Displaced Worker Supplement (DWS) of the Current Population Survey (CPS) indicate that older displaced workers possessed higher weekly wages than did younger workers at the time of their displacement. Moreover, older displaced workers suffered greater amounts of earnings loss than did younger displaced workers. Older workers, more often than younger workers, were displaced from jobs in the goods-producing sector. We attribute this age and industry difference in displacement to the higher earnings premium of older workers in the goods producing sector relative to the earnings premium of older workers in the service-producing sector. The age bias against older displaced workers may be viewed as part of social structural changes in the economy that have reduced the wage premium of the more expensive age segment of the workforce within industries that may economically benefit the most.     

Self-esteem and earnings

Recent research in economics suggests a positive association between self-esteem and earnings. Using the National Longitudinal Survey of Youth (NLSY), which administered the Rosenberg Self-Esteem Scale during its 1980 and 1987 interviews, I provide further evidence for the existence of a self-esteem premium by exploiting variation in these measures between the 2 years. I show that self-esteem in 1980 has a sizeable impact on wages 8 years later, controlling for a wide set of individual characteristics and addressing problems of omitted variable bias and reverse causality. The instrumental variables estimate of the effect of self-esteem in 1987 on earnings is about two times greater than previous OLS estimates would imply. The main explanation for this discrepancy is that the previous OLS estimates are biased downward as a result of measurement error in the reported self-esteem measure.     

I can’t smile without you: Spousal correlation in life satisfaction

This paper tests whether one partner’s happiness significantly influences the happiness of the other partner. Using 10 waves of the British Household Panel Survey, it utilizes a panel-based GMM methodology to estimate a dynamic model of life satisfaction. The use of the GMM-system estimator corrects for correlated effects of partner’s life satisfaction and solves the problem of measurement error bias. The results show that, for both genders, there is a positive and statistically significant spillover effect of life satisfaction that runs from one partner to the other partner in a couple. The positive bias on the estimated spillover effect coming from assortative mating and shared social environment at cross-section is almost offset by the negative bias coming from systematic measurement errors in the way people report their life satisfaction. Moreover, consistent with the spillover effect model, couple dissolution at t + 1 is negatively correlated with partners’ life satisfaction at t.     

Scaling bias in pooled exponential random graph models

Researchers often use pooled exponential random graph models (ERGM) to analyze samples of networks. However, pooled ERGM—here, understood to include both meta-regression and combined estimation on a stacked adjacency matrix—may be biased if there is heterogeneity in the latent error variance (‘scaling’) of each lower-level model. This study explores the implications of scaling for pooled ERGM analysis. We illustrate that scaling can produce bias in pooled ERGM coefficients that is more severe than in single-network ERGM and we introduce two methods for reducing this bias. Simulations suggest that scaling bias can be large enough to alter conclusions about pooled ERGM coefficient size, significance, and direction, but can be substantially reduced by estimating the marginal effect within a block diagonal or random effects meta-regression framework. We illustrate each method in an empirical example using Add Health data on 15 in-school friendship networks. Results from the application illustrate that many substantive conclusions vary depending on choice of pooling method and interpretational quantity.     

Firm Ownership and Rent Sharing

In this paper we analyse—theoretically and empirically—how the degree of private versus public ownership of firms affects the degree of rent sharing between firms and their workers. Using a particularly rich linked employer-employee dataset from Portugal, covering a large number of corporate ownership changes across a wide spectrum of economic sectors over more than 20 years, we find that rent sharing is significantly higher in firms with a larger share of private ownership. Estimates from our most preferred empirical specification suggest that an increase in the private ownership share of 10 percentage points increases (on average) the rent-sharing elasticity by 0.0002. Based on a theoretical analysis that incorporates union-firm wage bargaining and efficiency wage effects within the same modelling framework, this result cannot be explained by private firms being more profit oriented than public ones. However, the result is consistent with a scenario whereby privatisation leads to less job security for workers, implying stronger efficiency wage effects.     

SWITCHING REGRESSION ESTIMATES OF THE INTERGENERATIONAL PERSISTENCE OF CONSUMPTION

The influential economic theory of intergenerational transfers predicts a negative connection between credit constraints and intergenerational mobility of consumption. Existing work has used bequest receipt to signal a parent's access to credit markets when investing in his children's human capital. However, measurement error in bequest receipt generates misclassification error and, in turn, attenuation bias. Employing switching regressions with imperfect sample separation to deal with this error, we show that the intergenerational persistence of consumption in the United States for credit constrained families is much higher than that for unconstrained families, contrary to what the theory implies. This means that children from constrained families are more likely to have consumption levels similar to those of their parents than children from unconstrained families. Our results are robust to the choice of bequest variables and other predictive variables in the switching equation. (JEL C13, D12, E21, J62)     

Longitudinal measurement models in evaluation research: Examining stability and change

This article provides an introduction to the use of Confirmatory Factor Analysis to test measurement invariance and stability in longitudinal research. The approach is illustrated through examples representing: (a) one construct, two measurement waves; (b) one construct, three waves; (c) two constructs, two waves; and (d) comparison of treatment and control groups in pre-post designs. Basic issues in establishing measurement invariance over time, across treatment groups, and within measurement waves are discussed. Estimates of the stability coefficients that are corrected for measurement error and method variance associated with each specific measured variable are provided. Establishing measurement invariance is a critical requirement for making inferences about treatment effects and changes in constructs over time.     

Measurement quality issues in dyadic models of relationships

Interpersonal relationships are an important and integral part of numerous social science research agendas. Analytical tools have been created in the last 10 years that model dyadic interactions. In particular, this article focuses on the dyadic models of Fienberg and Wasserman [Fienberg, S.E., Wasserman, S., 1981. Categorical data analysis of single sociometric relations. In: Leinhardt, S. (Ed.), Sociological Methodology. Jossey-Bass, San Francisco.], Holland and Leinhardt [Holland, P.W., Leinhardt, S., 1981. An exponential family of probability densities for directed graphs. Journal of the American Statistical Association 76 (1981) 33–51.], Iacobucci and Wasserman [Iacobucci, D., Wasserman, S., 1988. A general framework for the statistical analysis of sequential dyadic interaction data. Psychological Bulletin 103 (1988) 379–390.] and Wasserman and Iacobucci [Wasserman, S., Iacobucci, D., 1986. Statistical analysis of discrete relational data. British Journal of Mathematical and Statistical Psychology 39 (1986) 41–64.]. However, measurement issues like reliability and validity, as discussed by Allen and Yen [Allen, M.J., Yen, W.M., 1979. Introduction to Measurement Theory. Brooks/Cole, Monterey, CA, 1979.], Nunnally [Nunnally, J., 1978. Psychometric Theory, 2nd edn. McGraw-Hill, New York, NY, 1978.] and Uebersax [Uebersax, J.S., 1988. Validity inferences from interobserver agreement. Psychological Bulletin 104 (1988) 405–416.], have not been considered in conjunction with these models, and little is known about the empirical performance of the dyadic models under sub-optimal measurement quality conditions. We offer two essential approaches to ascertaining the level of measurement error in the observed indicators of social ties and relationships. The first approach combines latent class and social network models in one integrated framework and allows for the simultaneous study of measurement and dyadic structural issues. The second approach is an alternative that may be more useful to social science researchers, both because the method is more accessible and because researchers could apply the techniques to data they have already partially analyzed. This approach is a two-staged procedure whereby in the first stage, a probability model based on latent class analysis is estimated which provides an indication of the measurement quality in the data. In the second stage, traditional social network models are estimated. To investigate the implications of different levels of measurement error for interpreting the nature of the network ties and the dyadic parametric performance, we also designed a Monte Carlo experiment. Measurement error is simulated as the likelihood of a binary relational choice (for simplicity) being inaccurately classified, where incorrect diagnoses can result from poor interitem agreement (i.e., unreliability) or poor interrater agreement. The simulation can be used by researchers in combination with the two-stage approach. The results of the simulation provide guidelines for situations when social network models can withstand a reasonable degree of sub-optimal measurement quality and highlight adverse conditions which can significantly affect the performance of the modeling approach. Further, the simulation shows that sample size assists in reducing the chances of making Type II errors, but it does not compensate for biases in parameter estimates in the presence of increasing error. Finally, the measurement and dyadic analytical methods are applied to a real dataset describing interorganizational relational activity using multiple raters. Recommendations are offered to guide the researcher in making decisions about research design when using dyadic models.     

Equity and fairness perceptions in the child welfare workforce

The role ethnic identification plays in worker perceptions of workplace dynamics in child welfare services (CWS) organizations has not often been a topic of inquiry. The current study follows the unexpected finding from a previous study in which African American (AA) social workers were significantly less likely to report that they would remain in their CWS agencies than European American (EA) workers. Utilizing a mixed methods approach, the authors explored whether inequity from bias in CWS agencies related to ethnicity was a contributor to intentions to stay/leave. The results revealed no significant relationships between ethnicity and job satisfaction or intentions to stay in CWS agencies among EA, AA, or Hispanic/Latino (HL) workers. However, striking findings emerged related to worker perceptions of court duties concerning inequitable workloads and pay.     

Revisiting multi-tier wage structures: Equity,employment mobility,and tier effects

This study considers the competing conclusions reached by Martin and Peterson (1987) and Cappelli and Sherer (1990) regarding two-tier wage systems and examines how worker perceptions of their own employment mobility affect attitudes about two-tier systems. Findings from a hybrid multi-tier wage system in the retail food industry containing both permanent and merging or temporary tiers indicates that workers on the permanently lower B tier possessed more negative attitudes than their A-tier counterparts, with some evidence that the effects of permanent and temporary plans differ. Employment mobility moderates these attitudes, for low mobility workers report more positive attitudes than do high mobility workers.     

Structural effects of network sampling coverage I: Nodes missing at random

Network measures assume a census of a well-bounded population. This level of coverage is rarely achieved in practice, however, and we have only limited information on the robustness of network measures to incomplete coverage. This paper examines the effect of node-level missingness on 4 classes of network measures: centrality, centralization, topology and homophily across a diverse sample of 12 empirical networks. We use a Monte Carlo simulation process to generate data with known levels of missingness and compare the resulting network scores to their known starting values. As with past studies (0035 and 0135), we find that measurement bias generally increases with more missing data. The exact rate and nature of this increase, however, varies systematically across network measures. For example, betweenness and Bonacich centralization are quite sensitive to missing data while closeness and in-degree are robust. Similarly, while the tau statistic and distance are difficult to capture with missing data, transitivity shows little bias even with very high levels of missingness. The results are also clearly dependent on the features of the network. Larger, more centralized networks are generally more robust to missing data, but this is especially true for centrality and centralization measures. More cohesive networks are robust to missing data when measuring topological features but not when measuring centralization. Overall, the results suggest that missing data may have quite large or quite small effects on network measurement, depending on the type of network and the question being posed.     

Measurement error in network data: A re-classification

Research on measurement error in network data has typically focused on missing data. We embed missing data, which we term false negative nodes and edges, in a broader classification of error scenarios. This includes false positive nodes and edges and falsely aggregated and disaggregated nodes. We simulate these six measurement errors using an online social network and a publication citation network, reporting their effects on four node-level measures – degree centrality, clustering coefficient, network constraint, and eigenvector centrality. Our results suggest that in networks with more positively-skewed degree distributions and higher average clustering, these measures tend to be less resistant to most forms of measurement error. In addition, we argue that the sensitivity of a given measure to an error scenario depends on the idiosyncracies of the measure's calculation, thus revising the general claim from past research that the more ‘global’ a measure, the less resistant it is to measurement error. Finally, we anchor our discussion to commonly-used networks in past research that suffer from these different forms of measurement error and make recommendations for correction strategies.     

Patterns of union membership and relative wages

This paper, using the Panel Study of Income Dynamics, examines the effects of union membership on the wages of white males. The empirical relationship between current wage and union status is estimated controlling for union status in years before and after the current year. The resulting status profiles are four years long in contrast to one or two years used previously. Results indicate that wage changes experienced when workers join or leave unions vary significantly and systematically across these profiles. For example, a status change that appears to be long-term is associated with larger absolute wage changes than short-term changes in status. The authors thank Jeff Moore for comments and suggestions on an early draft of this paper and express special appreciation to John Raisian for his painstaking and valuable review of a recent draft. We are, of course, responsible for remaining errors.