Age at migration, language proficiency, and socioeconomic outcomes: evidence from Australia

This study estimates the causal effects of language proficiency on the economic and social integration of Australian immigrants. Identifying the effects of languages on socioeconomic outcomes is inherently difficult owing to the endogeneity of language skills. Using the phenomenon that younger children learn languages more easily than older children, we construct an instrumental variable for language proficiency. To achieve this, we consider the age at arrival of immigrants who came as children from Anglophone and non-Anglophone countries. We find a significant positive effect of English proficiency on wages and promotions among adults who immigrated to Australia as children. Higher levels of English proficiency are associated with increased risk-taking, more smoking, and more exercise for men, but have considerable health benefits for women. English language proficiency has a significant influence on partner choice and a number of social outcomes, as well as on children's outcomes, including their levels of academic achievement. The results are robust to alternative specifications, including accounting for between-sibling differences and alternative measures of English skills.

NONFARM ACTIVITY AND POVERTY REDUCTION IN RURAL VIETNAM: 2002-2008

This paper uses the Vietnamese Households Living Standards Surveys of 2002, 2004, 2006and 2008 to examine the effects of nonfarm activity on poverty in rural Vietnam. We show that nonfarm activity helps the poor, but not the poorest, given that households need to own a minimum level of endowment to partake in nonfarm activity. We find an inverted-U shaped relationship between households’ endowment levels and the probability of participating in nonfarm activity. Also, in contrast to previous studies which largely by-passed the endogeneity of nonfarm activity, we instrument it with nonfarm networks, and find that nonfarm activity plays an important role in poverty reduction, however, this impact decreases over time.

Social capital and hygiene practices among the extreme poor in rural Bangladesh

We investigate the effect of social capital on hygiene practices pertaining to lives of the extreme poor in rural Bangladesh. Analysing a unique survey dataset for 5,600 extreme poor households, we document a significant positive effect of social capital on sanitary latrine use and wearing shoes/sandals at home for hygiene. We account for the endogeneity of social capital by instrumental variable estimation. Our findings emphasise the role of social capital in preventing common diseases through improving hygiene practices for the extreme poor, who usually lack access to medical services in the event of illness, which has important policy implications.

Corporate opacity and cost of debt for family firms

This paper uses a sample of Chinese firms to examine the impact of corporate opacity on the relationship between family control and firms’ cost of debt. We find that family control is associated with a lower cost of debt on average, and a negative impact exists mainly in firms with relatively low corporate opacity. We further provide evidence that the moderating effect of corporate opacity becomes more pronounced when investors’ perception of controlling families’ moral hazard of expropriation is higher. Our results are robust to alternative opacity proxies and controlling for endogeneity of family control using the instrumental variable method. Our study highlights that controlling families are heterogeneous in their impact on the shareholder–debtholder relationship in family firms, and debtholders view corporate opacity as an important reference in assessing the extent of potential agency conflicts in China.

Determinants of non-life insurance expenditure in developed and developing countries: an empirical investigation

The determinants of non-life insurance expenditure in a panel data set covering 36 developed countries and 31 developing countries for the period 2000–2011 are analysed. Results of our instrumental variable analysis indicate that economic freedom, income, bank development, urbanization, culture and law systems are the key drivers of the non-life insurance expenditure across countries. However, their impacts differ significantly between the groups of developed and developing countries, suggesting that the heterogeneity among countries in terms of the level of development plays an important role. The global financial crisis is also found to influence the direction of those effects, especially in developed countries. The article yields useful policy and economic implications for governments and multinational non-life insurance companies with regard to the development of the non-life insurance sector, an important engine for economic growth and prosperity.

Bureaucratic corruption and income : evidence from the land sector in Bangladesh

We examine, for the first time, the effects of corruption on income using household survey data from a developing country. Estimating the effects of corruption on income is challenging because of the simultaneous relationship between the two variables. We use a two-step instrumental variable approach to identify the effects of corruption on income. We find that after adjusting for simultaneity bias the act of bribery reduces income and that higher bribes have a negative effect on income. Taken together, our results provide a possible explanation why a vicious cycle between corruption and income inequality does not exist in the land sector in Bangladesh.

The regional impact of cultural diversity on wages: evidence from Australia

This paper investigates the impact of cultural diversity on labour market outcomes, particularly on wages across regions using a large longitudinal data. We apply an instrumental variable approach and account for individual and time fixed effects. Our findings indicate that the current level of cultural diversity positively affected current regional weekly wages; however, the positive effect holds only partially when the diversity is lagged. The results appear to be robust in all estimations controlling for heterogeneity factors and accounting for the self-selection of individuals into places with better economic opportunities. Our findings concerning the effect of lagging on the effect of diversity may explain the variation in the literature where some studies report that cultural diversity increases wages across time while others do not.

Sequential fixed-width confidence bands for kernel regression estimation

We consider a random design model based on independent and identically distributed (iid) pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the unknown function m(x) when we have no precise information about the form of the true density, f(x) of X. We describe an estimation procedure of non-parametric regression model at a given point by some appropriately constructed fixed-width (2d) confidence interval with the confidence coefficient of at least 1−. Here, d(> 0) and 2 (0, 1) are two preassigned values. Fixed-width confidence intervals are developed using both Nadaraya-Watson and local linear kernel estimators of nonparametric regression with data-driven bandwidths.

The sample size was optimized using the purely and two-stage sequential procedure together with asymptotic properties of the Nadaraya-Watson and local linear estimators. A large scale simulation study was performed to compare their coverage accuracy. The numerical results indicate that the confidence bands based on the local linear estimator have the best performance than those constructed by using Nadaraya-Watson estimator. However both estimators are shown to have asymptotically correct coverage properties.

Two-stage sequential procedure for nonparametric regression estimation

In nonparametric statistics the functional form of the relationship between the response variable and its associated predictor variables is unspecified but it is assumed to be a smooth function. We develop a procedure for constructing a fixed width confidence interval for the predicted value at a specified point of the independent variable. The optimal sample size for constructing this interval is obtained using a two stage sequential procedure which relies on some asymptotic properties of the Nadaraya--Watson and local linear estimators. Finally, a large scale simulation study demonstrates the applicability of the developed procedure for small and moderate sample sizes. The procedure developed here should find wide applicability since many practical problems which arise in industry involve estimating an unknown function.

Sample size determination for kernel regression estimation using sequential fixed-width confidence bands

We consider a random design model based on independent and identically distributed pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the unknown function m(x) when we have no precise information about the form of the true density, f(x) of X. We describe an estimation procedure of non-parametric regression model at a given point by some appropriately constructed fixed-width (2d) confidence interval with the confidence coefficient of at least 1−. Here, d(> 0) and 2 (0, 1) are two preassigned values. Fixed-width confidence intervals are developed using both Nadaraya-Watson and local linear kernel estimators of nonparametric regression with data-driven bandwidths. The sample size was optimized using the purely and two-stage sequential procedures together with asymptotic properties of the Nadaraya-Watson and local linear estimators. A large scale simulation study was performed to compare their coverage accuracy. The numerical results indicate that the confi dence bands based on the local linear estimator have the better performance than those constructed by using Nadaraya-Watson estimator. However both estimators are shown to have asymptotically correct coverage properties.

A commentary on current practice in mediating variable analyses in behavioural nutrition and physical activity

Objective : To critique current practice in, and provide recommendations for, mediating variable analyses (MVA) of nutrition and physical activity behaviour change.

Strategy : Theory-based behavioural nutrition and physical activity interventions aim at changing mediating variables that are hypothesized to be responsible for changes in the outcome of interest. MVA are useful because they help to identify the most promising theoretical approaches, mediators and intervention components for behaviour change. However, the current literature suggests that MVA are often inappropriately conducted, poorly understood and inadequately presented. Main problems encountered in the published literature are explained and suggestions for overcoming weaknesses of current practice are proposed.

Conclusion : The use of the most appropriate, currently available methods of MVA, and a correct, comprehensive presentation and interpretation of their findings, is of paramount importance for understanding how obesity can be treated and prevented.

Disease mapping and regression with count data in the presence of overdispersion and spatial autocorrelation: a Bayesian model averaging approach

This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present. These considerations lead to nine regression models derived from using three probability distributions for count data: Poisson, generalised Poisson and negative binomial, and three different autocorrelation structures. We employ the framework of Bayesian variable selection and a Gibbs sampling based technique to identify significant cancer risk factors. The framework deals with situations where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. The evidence from applying the modelling methodology suggests that modelling strategies based on the use of generalised Poisson and negative binomial with spatial autocorrelation work well and provide a robust basis for inference.