An analytical paradigm for the analysis of national inmigration patterns

This study proposes and demonstrates an analytic paradigm based upon a substantive categorization of a set of inmigration correlates. It exemplifies the notion of categorizing, analyzing according to the categorization, and subsequently discussing the phenomenon in more depth. The paradigm has 2 steps: 1) the variables are categorized according to the cells resulting from the intersection of a preferably small number of nominal dimensions and 2) the data are analyzed, directly anchored in the prior categorization. The data used is Israel's 1983 census macro-data gathered from the Central Bureau of Statistics for the Israeli towns with populations of at least 5000. The authors defined 6 variables as push variables and 4 as pull variables. Results of the regression employing push variables show that 4 variables accounting for 72% of inmigration were found to significantly predict inmigration: 1) unemployment, 2) percentage of Asians-Africans, 3) town size, and 4) religiosity. Within the pull classification, the regression analysis reveals that 2 of the 4 variables explain 31% of the inmigration variance: 1) educational level (26%) and 2) income (5%). The 1st regression analysis on the 2nd dimension shows that the percentage of Asian-African origin and town population size account for 32% of the immigration variance. In the 2nd regression analysis, unemployment explains 48% of the inmigration variance and educational level explains 8%. In the 3rd regression, only home crowding explains a significant amount of the immigration variance (19%). Results of a multiple regression analysis show that unemployment level, percentage of Asian-Africans, population size, and level of religiosity account for 72% of the inmigration variance. Thus, the characteristics of a town inmigrating (push variables) are demographic, economic, and social. However, the attractive features of a town are only economic. Among all economic factors, unemployment is primary. In addition, not only are both percentage of Asians-Africans and population size significantly deflective of inmigration, but each also plays a separate and independent role.     

Objective Testing Procedures in Linear Models: Calibration of the p-values

Abstract.  An optimal Bayesian decision procedure for testing hypothesis in normal linear models based on intrinsic model posterior probabilities is considered. It is proven that these posterior probabilities are simple functions of the classical F -statistic, thus the evaluation of the procedure can be carried out analytically through the frequentist analysis of the posterior probability of the null. An asymptotic analysis proves that, under mild conditions on the design matrix, the procedure is consistent. For any testing hypothesis it is also seen that there is a one-to-one mapping – which we call calibration curve – between the posterior probability of the null hypothesis and the classical bi p -value. This curve adds substantial knowledge about the possible discrepancies between the Bayesian and the p -value measures of evidence for testing hypothesis. It permits a better understanding of the serious difficulties that are encountered in linear models for interpreting the p -values. A specific illustration of the variable selection problem is given.     

A formula for determining social worker positions based upon the Pardes method

An alternative approach to traditionalad hoc methods of determining social worker positions is presented. It is based upon the collection of objective data and their inclusion in a mathematical formula. This apprach assures the employment of identical criteria and administrative procedures for the assignment of social workers' positions. It guarantees that for a given number of social work bureaux the assignment is equitable. The particular example presented is that of regional councils in Israel. The formula includes the variables: number of residents within each council's jurisdiction, and of community distress and distance travelled by social workers between the settlements of a council.     

Social factors as predictors of internal migration patterns in Israel

The influence of 1975 crime and poverty-dependence factor scores on 1976–1980 internal migration rates in Israel was studied. Lag correlations between the two factor scores and in and out migration revealed that poverty-dependence was strongly and negatively related to in-migration and moderately and negatively related to out-migration. No relationship was found between crime and in-migration over the five year period, but a strong positive relationship was indicated between crime and outmigration. It was thus shown that social indicators such as crime and poverty-dependence revealed lasting effects on migration rates, and may be an aid to social planners.     

Long distance intimacy: class,gender and intergenerational relations between mothers and children in Filipino transnational families

Abstract In this article I address transnational intergenerational relations between Filipino migrant mothers and their young adult children and examine how families achieve intimacy across great distances. I do this by identifying and examining the transnational communication methods Filipino migrant families use to develop intimacy, in other words familiarity, across borders. In my analysis, I address how political economy and gender shape the dynamics of transnational communication. By showing how economic conditions and gender shape transnational family communication, I provide a socially thick lens through which to understand the formation of transnational intimacy and emphasize how larger systems of inequality shape the lives of the children left behind by the global migration of women.     

Differential components of urban in-and outmobility

This study tested three hypotheses concerning the differential association of social and economic factors with locational in- and outmobility. The results suggest that inmobility is predominantly affected by the economic milieu of towns, while outmobility is predominantly influenced by extant social conditions. A multivariate examination of locational outmobility identified high divorce rate, distance from a major city and large family size as significantly explaining 45.8% of the outmobility variance. Similar analysis of locational inmobility identified the unemployment rate and the ethnic composition as the variables signiciantly explaining 73.9% of the inmobility variance. The results, which were congruent to the first two hypotheses submitted, were discussed and interpreted. The hypothesis concerning the relationship between in- and outmobility was not confirmed. Within successful locations no significant, positive correlation between in- and outmobility rates was obtained, while among unsuccessful locations positive, significant correlations were obtained.     

Long-term multivariate prediction of migration patterns

In- and out-migration rates of 56 Israeli cities with a population of over 5000 were predicted for a five-year period (1977–1981), on the basis of four 1976 social indicators: crime rate, percent unemployment, population size and distance from a major metropolitan center. Rather than employ independent correlation coefficients, the four indicators were entered as predictors into regression equations with in- and out-migration rates serving as the dependent variable. The analytic methods are compared and the clear advantages of the regression method emerge. None of the indicators reliably predicted in-migration. Crime consistently predicted out-migration. Distance and unemployment each entered into three of the prediction models.