The determinants of union membership growth in Taiwan

This paper examines the determinants of intertemporal variation in the growth rate of trade union membership in Taiwan during 1960–1987. The empirical results indicate that the widely used empirical model of growth in trade union membership based largely on cyclical variables applies to the case of a newly industrialized Asian country—Taiwan.     

TESTING FOR INDEPEDENCE IN MULTIVARIATE EXPONENTIAL DISTRIBUTIONS2

In this paper the work of Weier & Basu (1980) is extended to a special case of the trivariate exponential distributions and to the general k-variate case. In the trivariate case several statistics are derived including one based on the likelihood ratio approach and the locally most powerful rank statistic, and power studies are carried out. The general k-variate model is derived, and testing for independence is shown to reduce to a solved problem.     

INTERMARRIAGE AND THE LABOR MARKET OUTCOMES OF ASIAN WOMEN

The impact of intermarriage with natives, on labor market outcomes of immigrants, is not homogeneous across ethnic groups. Wages of Asian women are compared with non‐Asians. Both ordinary least squares and instrumental variables estimates of the effects of intermarriage on the wages of Asian women are negative and significant. Non‐Asian women earn a wage premium that becomes insignificant when controls for selection into marriage are introduced. One possible explanation for the intermarriage penalty for Asians is an income effect of having a high‐earning native husband. Intermarriage penalties rise with husband's education. Assimilation patterns of intermarried Asians indicate that they have lower initial wages, market hours, and employment, but exhibit faster rates of growth over their years of stay. The results are robust across Asian subgroups and husband's ethnicity. (JEL J16, J12, J31, J61)     

Tests for statistical significance of a treatment effect in the presence of hidden sub-populations

For testing the statistical significance of a treatment effect, we often compare between two parts of a population; one is exposed to the treatment, and the other is not exposed to it. Standard parametric or nonparametric two-sample tests are commonly used for this comparison. But direct applications of these tests can yield misleading results, especially when the population has some hidden sub-populations, and the effect of this sub-population difference on the response dominates the treatment effect. This problem becomes more evident if these sub-populations have widely different proportions of representatives in the samples obtained from these two parts. In this article, we propose some simple methods to overcome these limitations. These proposed methods first use a suitable clustering algorithm to find the hidden sub-populations, and then they eliminate the sub-population effect by using a suitable transformation of the data. Standard two-sample tests, when they are applied on the transformed data, usually yield better results. We analyze some simulated and real data sets to demonstrate the utility of these proposed methods.     

Cross-country disparities in skill premium and skill acquisition

Skilled individuals are rewarded more in poor than in rich countries. Why aren't more individuals acquiring skills in poor countries? We document that the unemployment rate of the skilled net of that of the unskilled decreases with a country's level of development. Using a matching model of occupational choice and skill acquisition, we quantify the role of barriers to enter entrepreneurship for these unemployment rates, skill premium and acquisition. The cross-country correlation between skill premium and acquisition decreases by 45% when each country's gap to the US in the entrepreneurship barrier is decreased enough to even the unemployment differential.     

Testing whether survival function is bivariate new better than used

We present statistical procedures to test that a life distribution is bivariate exponential against the alternative that it is bivariate new better than used (BNBU).     

A joint model for incomplete data in crossover trials

Crossover designs are popular in early phases of clinical trials and in bioavailability and bioequivalence studies. Assessment of carryover effects, in addition to the treatment effects, is a critical issue in crossover trails. The observed data from a crossover trial can be incomplete because of potential dropouts. A joint model for analyzing incomplete data from crossover trials is proposed in this article; the model includes a measurement model and an outcome dependent informative model for the dropout process. The informative-dropout model is compared with the ignorable-dropout model as specific cases of the latter are nested subcases of the proposed joint model. Markov chain sampling methods are used for Bayesian analysis of this model. The joint model is used to analyze depression score data from a clinical trial in women with late luteal phase dysphoric disorder. Interestingly, carryover effect is found to have a strong effect in the informative dropout model, but it is less significant when dropout is considered ignorable.