Respect in intergenerational relationships: Young adult women’s motivations

Care and respect are two sides of the same coin, yet very little is known about respect shown by younger women in relation to people aged 60 and older. This study sets out to explore what motivates young adult women to respect older people. Twenty-five post-graduate women from a tertiary education institution in South Africa participated in the Mmogo-method®, a projective visual data-collection method, to produce textual and visual data. These were analysed using sequential and visual analysis. Findings reported combinations of motivational types for respect of older people, on a continuum from extrinsic (socialised normative values) to intrinsic (personal choice and empathy), and from conditional to unconditional, namely: extrinsic/conditional; extrinsic/unconditional; self-motivated/conditional and self-motivated/unconditional. Respect as an impact on relational interactions between people is either constructive and encouraging or damaging and disparaging. Interpersonal risks are associated with motivation and this could affect future interactions and manifestations of intergenerational respect.     

Bayesian estimation of the network autocorrelation model

The network autocorrelation model has been extensively used by researchers interested modeling social influence effects in social networks. The most common inferential method in the model is classical maximum likelihood estimation. This approach, however, has known problems such as negative bias of the network autocorrelation parameter and poor coverage of confidence intervals. In this paper, we develop new Bayesian techniques for the network autocorrelation model that address the issues inherent to maximum likelihood estimation. A key ingredient of the Bayesian approach is the choice of the prior distribution. We derive two versions of Jeffreys prior, the Jeffreys rule prior and the Independence Jeffreys prior, which have not yet been developed for the network autocorrelation model. These priors can be used for Bayesian analyses of the model when prior information is completely unavailable. Moreover, we propose an informative as well as a weakly informative prior for the network autocorrelation parameter that are both based on an extensive literature review of empirical applications of the network autocorrelation model across many fields. Finally, we provide new and efficient Markov Chain Monte Carlo algorithms to sample from the resulting posterior distributions. Simulation results suggest that the considered Bayesian estimators outperform the maximum likelihood estimator with respect to bias and frequentist coverage of credible and confidence intervals.     

Alternative estimation methods for identifying contagion effects in dynamic social networks: A latent-space adjusted approach

Contagion effects, also known as peer effects or social influence process, have become more and more central to social science, especially with the availability of longitudinal social network data. However, contagion effects are usually difficult to identify, as they are often entangled with other factors, such as homophily in the selection process, the individual’s preference for the same social settings, etc. Methods currently available either do not solve these problems or require strong assumptions. Following Shalizi and Thomas (2011), I frame this difficulty as an omitted variable bias problem, and I propose several alternative estimation methods that have potentials to correctly identify contagion effects when there is an unobserved trait that co-determines the influence and the selection. The Monte-Carlo simulation results suggest that a latent-space adjusted estimator is especially promising. It outperforms other estimators that are traditionally used to deal with the unobserved variables, including a structural equation based estimator and an instrumental variable estimator.     

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.     

Is it really worse to have public health insurance than to have no insurance at all? Health insurance and adult health in the United States

Using prospective cohort data from the 1979 National Longitudinal Survey of Youth, this study examines the extent to which health insurance coverage and the source of that coverage affect adult health. While previous research has shown that privately insured nonelderly individuals enjoy better health outcomes than their uninsured counterparts, the same relationship does not hold for those publicly insured through programs such as Medicaid. Because it is unclear whether this finding reflects a true causal relationship or is in fact due to selection bias on socioeconomic status and health, previous estimates of the contribution of health insurance to inequities in health may have been biased. This study attempts to disentangle these competing hypotheses of causation or selection bias by using fixed effects models with sibling clusters to corroborate--or contradict--the results of a conventional OLS regression. By controlling for unobserved factors shared by siblings, such as parental genetic influences, sibling models estimate health insurance effects that are less affected by selection bias. Findings suggest that, among the US. birth cohorts of 1957 to 1961, the negative relationship between public health insurance and health is not causal, but rather due to prior health and socioeconomic status. Conversely, the lack of health insurance coverage has a strong cumulative negative impact on adult health.     

A Methodological Comparison of Age‐Period‐Cohort Models: The Intrinsic Estimator and Conventional Generalized Linear Models

Age‐period‐cohort (APC) accounting models have long been objects of attention in statistical studies of human populations. It is well known that the identification problem created by the linear dependency of age, period, and cohort (Period = Age + Cohort or P = A + C) presents a major methodological challenge to APC analysis, a problem that has been widely addressed in demography, epidemiology, and statistics. This paper compares parameter estimates and model fit statistics produced by two solutions to the identification problem in age‐period‐cohort models—namely, the conventional demographic approach of constrained generalized linear models (Fienberg and Mason 1978, 1985; Mason and Smith 1985) and the intrinsic estimator method recently developed by Fu (2000; Knight and Fu 2000; Fu, Hall, and Rohan 2004). We report empirical analyses of applications of these two methods to population data on U.S. female mortality rates. Comparisons of parameter estimates suggest that both constrained generalized linear models and the intrinsic estimator method can yield similar estimates of age, period, and cohort effects, but estimates obtained by the intrinsic estimator are more direct and do not require prior information to select appropriate model identifying constraints. We also describe three statistical properties of the estimators: (1) finite‐time‐period bias, (2) relative statistical efficiency, and (3) consistency as the number of periods of observed data increases. These empirical analyses and theoretical results suggest that the intrinsic estimator may well provide a useful alternative to conventional methods for the APC analysis of demographic rates .     

Lifecycle-consistent female labor supply with nonlinear taxes: evidence from unobserved effects panel data models with censoring,selection and endogeneity

This paper uses the Panel Study of Income Dynamics (PSID) from 1979 to 2007 to estimate within-period lifecycle-consistent labor supply elasticities of US females in a two-stage budgeting framework. The paper combines a variety of econometric approaches to estimate unobserved effects panel data models with censoring, selection and endogeneity. The paper finds evidence of substantial upward bias in estimated wage elasticities from pooled panel models which do not account for unobserved effects, as fixed effects and correlated random effects (CRE) specifications yield smaller elasticities. Estimates are also somewhat sensitive to using a lifecycle-consistent specification versus a standard static model. The lifecycle-consistent wage elasticity from a CRE model with instrumental variables is 0.56 on the extensive margin and 0.31 on the intensive margin for an overall wage elasticity of 0.87. The standard static model, on the other hand, yields a wage elasticity of 0.46 on the extensive margin and 0.13 on the intensive margin for an overall elasticity of 0.59.     

Predicting Children's Asthma Hospitalizations: Rural and Urban Differences in Texas*

Abstract Asthma is the number one chronic health condition facing children today; however, little is known about rural‐urban inequalities in asthma. This “area effects on health” study examines rural‐urban differences in childhood asthma hospitalizations within the state of Texas using negative binomial regression models. Effects associated with residence on the Texas‐Mexico border, where the majority of rural Texas children live, are also considered. Neighborhood‐level social class and proportion of the population that was native‐born Hispanic were significant predictors of asthma hospitalizations, independent of rural‐urban location. Conversely, proportion African American, Native American, and foreign‐born Hispanic, not a citizen; median year of home construction; and neighborhood location on the Texas‐Mexico border were conditional on urban‐rural location, and the strength of these predictors was stronger in rural areas. This suggests that locational and social factors intersect in marginalized spaces (like the rural and border regions of Texas) to create vulnerability to asthma hospitalizations.     

Bias decomposition and estimator performance in respondent-driven sampling

Respondent-Driven Sampling (RDS) is a method of network sampling that is used to sample hard-to-reach populations. The resultant sample is non-random, but different weighting methods can account for the over-sampling of (1) high-degree individuals and (2) homophilous groups that recruit members more effectively. While accounting for degree-bias is almost universally agreed upon, accounting for recruitment-bias has been debated as it can further increase estimate variance without substantially reducing bias. Simulation-based research has examined which weighting procedures perform best given underlying population network structures, group recruitment differences, and sampling processes. Yet, in the field, analysts do not have a priori knowledge of the network they are sampling. We show that the RDS sample data itself can determine whether a degree-based estimator is sufficient. Formulas derived from the decomposition of a ‘dual-component’ estimator can approximate the ‘recruitment component’ (RC) and ‘degree component’ (DC) of a sample’s bias. Simulations show that RC and DC values can predict the performance of different classes of estimators. Samples with extreme ‘RC’ values, a consequence of network homophily and differential recruitment, are better served by a classical estimator. The use of sample data to improve estimator selection is a promising innovation for RDS, as the population network features that should guide estimator selection are typically unknown.     

Compositional or Contextual Effects? Neighbourhoods and Teenage Parenthood in Stockholm,Sweden

Selection bias constitutes a major problem in neighbourhood effect research but perhaps especially so for studies of effects on teenage birth rates. Results from both the U.S. and Europe are highly inconsistent: where some find substantial effects and others no effects. This inconsistency in combination with the clear correlations between neighbourhood environment and teenage birth rates where teenage mothers and mothers-to-be are overrepresented in low SES neighbourhoods makes it unclear if and to what extent neighbourhoods exerts a causal influence on teenage birth rates or whether correlations are mere reflections of differences in choices on the housing markets. This study adds to the (mainly American and British) body of literature on neighbourhood effects on teenage birth but focuses on a context—Stockholm, Sweden—where teenage birth rates are substantially lower. It offers a discussion on the potential impacts of selection bias on estimates of neighbourhood effects on teenage birth rates and empirically tests the existence of such neighbourhood effects. To account for selection bias I use a random effects model with a Mundlak correction (a hybrid model), a model that corrects for selection similarly to a fixed-effects model but also allows the inclusion of fixed parameters. The hybrid model produces coefficients that are substantially smaller compared to an OLS model that does not control for selection; selection thus biases results upwards. The neighbourhood effects estimated by the hybrid model are so small that they can be ignored.     

Households’ preferences for attributes of Conditional Cash Transfer programmes: A choice experiment in Ghana

Conditional cash transfer (CCT) is increasingly being integrated into poverty reduction strategies in developing countries. However, debate remains over key elements of CCT programme design. Using a discrete choice model, this article examines Ghanaian households’ preferences regarding key CCT programme design elements including conditionality, targeting, and payment method. The results revealed a preference for CCT designs that target beneficiaries with limited or no productive capacity and CCT designs that are conditional on beneficiaries either investing in children's human capital or performing communal service, relative to unconditional programmes. Also, direct bank deposit was revealed as the preferred payment mode relative to receipt of cash payments or use of mobile money systems.     

Mutual gains? labor and management evaluate their employee involvement programs

This study examines the impact of employee involvement programs on social, psychological, production, and economic issues. The findings are based on a diverse group of 236 EI programs in Pennsylvania, where data were gathered from both management and labor in the same workplace. The majority of the programs are based on a quality circle model; few contain innovative pay systems; and less than one third could be classified as intensive. Management indicates that these programs have a positive impact on a variety of factors, especially social and psychological effects. Unions are less enthusiastic about EI but are generally positive or neutral, with few indicating any negative effects. Despite these generally positive reports by both labor and management, direct comparison yields little agreement between labor and management on the effects of EI. Our results question the assumption of “mutual gains,” i.e., that the effects of EI programs are clear, public, and shared equally by both labor and management. Alternative explanations are discussed. This research was conducted with grants from the Pennsylvania Department of Labor and Industry and the Pennsylvania MILRITE Committee. The views expressed, however, are solely those of the authors.     

户籍制度改革对流动人口收入的影响研究

以往关于户籍制度对收入的影响的诸多研究基本上遵循的是工资差异分解模型的思路。然而,将外来人口和本地市民的工资收入差异进行分解,无法克服样本选择性与遗失关键变量带来的偏误。本文基于中国家庭收入调查数据,采用新近发展的倾向得分匹配方法来控制样本选择偏误,并采用自抽样法(Bootstrap)法进行统计推断,以克服小样本偏误,试图回答户籍限制对流动人口收入的影响。研究结果发现:(1)户籍制度对流动人口收入影响非常显著,克服选择性偏误之后的负向效应要高于普通回归结果和代理变量回归结果;(2)户籍限制对倾向得分较低的流动人口的收入效应是正向的,对倾向得分较高(能力较高)的流动人口产生影响为负;(3)这一不利影响随着市场化进程推进变得更显著。     

Contrasts in Infant Classical Eyeblink Conditioning as a Function of Premature Birth

The impact of premature birth on associative learning was evaluated using simple delay eyeblink conditioning in which a tone conditional stimulus was paired with an air puff unconditional stimulus. Fourteen preterm (28–31 weeks gestation) and 11 full‐term infants completed at least 3 conditioning sessions, 1 week apart, at 5 months of age (corrected age). Preterm and full‐term groups demonstrated associative learning, as confirmed by comparison with an unpaired control group. Preterm infants, however, exhibited more variability in their learning rates. The majority of full‐term infants and half the preterm infants exhibited rapid acquisition and gradual extinction of conditional responding. A greater proportion of preterm than full‐term infants failed to acquire conditional responding within 2 training sessions. Differences in associative learning rates were not the result of differences in arousal or attentional processes. Diversity in acquisition rates exposed an increased risk for disrupted infant learning due to premature birth.     

Effects of Macroeconomic Uncertainty on Investment and Economic Growth: Evidence from Pakistan

The relationship between macroeconomic uncertainty, investment and economic growth is an empirical issue in developing countries. This paper investigates the effects of macroeconomic uncertainty on investment and economic growth in Pakistan for the period 1975–2008 by using the accelerator model of investment and endogenous growth model. The conditional variances, directly estimated through the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is utilized for erecting the uncertainty variables related to fiscal policy, openness and foreign capital inflows. The results clearly indicate that the macroeconomic uncertainty have significant negative effects on investment and per capita income of Pakistan. We conclude that a reduction in macroeconomic uncertainty through appropriate fiscal and monetary policy, stability in capital inflows and improved trade performance could result in high investment and sustainable economic growth in the country.     

Native‐Migrant Wage Differential Across Occupations: Evidence from Australia

We investigate wage differential by migrant status across white‐collar and blue‐collar occupations in Australia. Migrants are observed to have a higher wage; this difference, however, does not exist once we control for covariates. The unconditional wage differential varies over wage distribution as well as by occupation. Significant wage differentials are found above the median: positive for white‐collar workers and negative for blue‐collar workers. Using recently developed decomposition methods based on Firpo, Fortin, and Lemieux (2009) we decompose wage differentials across their distribution. Overall, the wage advantage of migrants reflects their superior labour market characteristics, and in particular, their levels of education. We find that English language proficiency plays an important role in wage differences among immigrants from non‐English speaking countries.     

EXPLOITING SPATIAL DEPENDENCE TO IMPROVE MEASUREMENT OF NEIGHBORHOOD SOCIAL PROCESSES

A number of recent studies have used surveys of neighborhood informants and direct observation of city streets to assess aspects of community life such as collective efficacy, the density of kin networks, and social disorder. Raudenbush and Sampson (1999a) have coined the term "ecometrics" to denote the study of the reliability and validity of such assessments. Random errors of measurement will attenuate the associations between these assessments and key outcomes. To address this problem, some studies have used empirical Bayes methods to reduce such biases, while assuming that neighborhood random effects are statistically independent. In this paper we show that the precision and validity of ecometric measures can be considerably improved by exploiting the spatial dependence of neighborhood social processes within the framework of empirical Bayes shrinkage. We compare three estimators of a neighborhood social process: the ordinary least squares estimator (OLS), an empirical Bayes estimator based on the independence assumption (EBE), and an empirical Bayes estimator that exploits spatial dependence (EBS). Under our model assumptions, EBS performs better than EBE and OLS in terms of expected mean squared error loss. The benefits of EBS relative to EBE and OLS depend on the magnitude of spatial dependence, the degree of neighborhood heterogeneity, as well as neighborhood's sample size. A cross-validation study using the original 1995 data from the Project on Human Development in Chicago Neighborhoods and a replication of that survey in 2002 show that the empirical benefits of EBS approximate those expected under our model assumptions; EBS is more internally consistent and temporally stable and demonstrates higher concurrent and predictive validity. A fully Bayes approach has the same properties as does the empirical Bayes approach, but it is preferable when the number of neighborhoods is small.     

A comparison of various approaches to the exponential random graph model: A reanalysis of 102 student networks in school classes

This paper describes an empirical comparison of four specifications of the exponential family of random graph models (ERGM), distinguished by model specification (dyadic independence, Markov, partial conditional dependence) and, for the Markov model, by estimation method (Maximum Pseudolikelihood, Maximum Likelihood). This was done by reanalyzing 102 student networks in 57 junior high school classes. At the level of all classes combined, earlier substantive conclusions were supported by all specifications. However, the different specifications led to different conclusions for individual classes. PL produced unreliable estimates (when ML is regarded as the standard) and had more convergence problems than ML. Furthermore, the estimates of covariate effects were affected considerably by controlling for network structure, although the precise specification of the structural part (Markov or partial conditional dependence) mattered less.     

A PARTIAL INDEPENDENCE ITEM RESPONSE MODEL FOR SURVEYS WITH FILTER QUESTIONS

In many surveys, responses to earlier questions determine whether later questions are asked. The probability of an affirmative response to a given item is therefore nonzero only if the participant responded affirmatively to some set of logically prior items, known as "filter items." In such surveys, the usual conditional independence assumption of standard item response models fails. A weaker "partial independence" assumption may hold, however, if an individual's responses to different items are independent conditional on the item parameters, the individual's latent trait, and the participant's affirmative responses to each of a set of filter items. In this paper, we propose an item response model for such "partially independent" item response data. We model such item response patterns as a function of a person-specific latent trait and a set of item parameters. Our model can be seen as a generalized hybrid of a discrete-time hazard model and a Rasch model. The proposed procedure yields estimates of (1) person-specific, interval-scale measures of a latent trait (or traits), along with person-specific standard errors of measurement; (2) conditional and marginal item severities for each item in a protocol; (3) person-specific conditional and marginal probabilities of an affirmative response to each item in a protocol; and (4) item information and total survey information. In addition, we show here how to investigate and test alternative conceptions of the dimensionality of the latent trait(s) being measured. Finally, we compare our procedure with a simpler alternative approach to summarizing data of this type.     

EXTENSIONS OF RESPONDENT-DRIVEN SAMPLING: ANALYZING CONTINUOUS VARIABLES AND CONTROLLING FOR DIFFERENTIAL RECRUITMENT

Respondent-driven sampling (RDS) is a network-based method for sampling hidden and hard-to-reach populations that has been shown to produce asymptotically unbiased population estimates when its assumptions are satisfied. This includes resolving a major concern regarding bias in chain-referral samples—that is, producing a population estimate that is independent of the seeds (initial subjects) with which sampling began. However, RDS estimates are limited to nominal variables, and one of the assumptions required for the proof of lack of bias is the absence of differential recruitment. One aim of this paper is to analyze the role of differential recruitment, quantify the bias it produces, and propose a new estimator that controls for it. The second aim is to extend RDS so that it can be employed to analyze continuous variables in a manner that controls for differential recruitment. The third aim is to describe means for carrying out multivariate analyses using RDS data. The analyses employ data from an RDS sample of 264 jazz musicians in the greater New York metropolitan area, taken in 2002.