Farming Patterns,Rural Restructuring,and Poverty: A Comparative Regional Analysis1

Abstract This study examines the contentions of two recent perspectives on rural economic organization and their implications for poverty. Building from (1) agrarian political economy and (2) the rural restructuring literatures, we present a comparative regional analysis of how farming patterns and other aspects of economic organization differentially affect poverty in rural areas. Data are based on 2,349 nonmetropolitan U.S. counties for the 1970–1980 period. Nonhired labor-dependent, family-operated farming (smaller and larger family farming) has relatively similar cross-regional effects on rural poverty. The effects of industrialized farming are more spatially variant, suggesting that this type of farming is integrated into regional political economies in different ways than are simple commodity units. However, farming patterns have only a small effect on rural poverty relative to other factors, such as the local employment structure, characteristics of the population, and geographic location. The results of this study highlight the need to move beyond the farm sector to understand both the dynamics of this sector and the socioeconomic consequences of rural restructuring. More broadly, the study underscores the importance of testing general sociological relationships under different spatial (e.g., regional) contexts.     

The gambling market in the Federal Republic of Germany and the helpseeking of pathological gamblers

The development of the gambling market in the Federal Republic of Germany since the middle of the seventies and data on pathological gamblers seeking help are described. The continuously increasing supply, together with increases in turnover, was followed-with some temporal delay-by an increase in the number of gamblers who sought advice and treatment. Preliminary measures taken by the health authorities as well as some consequences of pathological gambling are discussed.This article is a translation of a German article published in: Deutsche Hauptstelle gegen die Suchtgefahren (1990): Jahrbuch '91 zur Frage der Suchtgefahren. Hamburg: Neuland-Verlag.     

Toward a formal theory of interactive resocialization: The case of adoptive parenthood

Resocialization is pulled out of its structuralist roots and recast into an interactionist framework. Building on a substantive theory of the resocialization experience of becoming an adoptive parent, three theoretical properties of interactionist resocialization are presented. These can be summarized as involving unlearning and relearning some aspects of a core identity; the interpretation and active reconstruction of that identity which has reciprocal effects on all actors involved; and finally, the effects of both situational and trans-situational norms that give shape to the resocialization experience. Suggestions are made for other substantive areas of research that could be used for comparative analysis in order to further refine a formal theory of interactionist resocialization.     

Integrating Career and Personal Counseling

Career and personal counseling are inextricably intertwined. Career problems have a strong emotional component. Career indecision and procrastination may be better interpreted as zeteophobia, the fear of career exploration. Trait and factor theory pictures career counseling as unrealistically simplistic and leads neophytes to view it as boring. Training all counselors to integrate career and personal counseling would lead to better service for clients.     

The analysis of incomplete data using stochastic covariates

In the development of many diseases there are often associated random variables which continuously reflect the progress of a subject towards the final expression of the disease (failure). At any given time these processes, which we call stochastic covariates, may provide information about the current hazard and the remaining time to failure. Likewise, in situations when the specific times of key prior events are not known, such as the time of onset of an occult tumour or the time of infection with HIV-1, it may be possible to identify a stochastic covariate which reveals, indirectly, when the event of interest occurred. The analysis of carcinogenicity trials which involve occult tumours is usually based on the time of death or sacrifice and an indicator of tumour presence for each animal in the experiment. However, the size of an occult tumour observed at the endpoint represents data concerning tumour development which may convey additional information concerning both the tumour incidence rate and the rate of death to which tumour-bearing animals are subject. We develop a stochastic model for tumour growth and suggest different ways in which the effect of this growth on the hazard of failure might be modelled. Using a combined model for tumour growth and additive competing risks of death, we show that if this tumour size information is used, assumptions concerning tumour lethality, the context of observation or multiple sacrifice times are no longer necessary in order to estimate the tumour incidence rate. Parametric estimation based on the method of maximum likelihood is outlined and is applied to simulated data from the combined model. The results of this limited study confirm that use of the stochastic covariate tumour size results in more precise estimation of the incidence rate for occult tumours.     

Estimating the Rayleigh distribution scale parameter from doubly censored samples—Some comments

In a recent paper in this journal, Lee, Kapadia and Brock (1980) developed maximum likelihood (ML) methods for estimating the scale parameter of the Rayleigh distribution from doubly censored samples. They reported convergence difficulties in attempting to solve numerically the nonlinear likelihood equation (LE). To mitigate these difficulties, they employed approximations to simplify the LE, but found that the solution of the resulting simplified equation can give rise to parameter estimates of erratic accuracy. We show that the use of approximations to simplify the LE is unnecessary. In fact, under suitable parametric transformation, the log-likelihood function is strictly concave, the ML estimate always exists, is unique and finite. Furthermore, the LE is easy to solve numerically. A numerical example is given to illustrate the computations involved.