This book is the classic source of empirical information on the patterns of occupational achievement in American society. Based on an unusually comprehensive set of data, it is renowned for its pioneering methods of statistical analysis as well as for its far-reaching conclusions about social stratification and occupational mobility in the United States. The American Occupational Structure received the Sorokin Award of the American Sociological Association in recognition of its significant contribution to the social sciences.

The American Occupational Structure

We compare and contrast two methods for fitting probability models to data which arise when animals are marked in batches, without individual identification, and live in several different sites or states. The methods are suitable for populations in which animals are marked at birth and then resighted over several sites/states, for small animals going through several growth stages (insects, amphibiae, etc.), as well as for the follow-up of animals released after laboratory colour-marking, for example. The methods we consider include a multi-state model for resightings of batch-marked animals, allowing us to estimate survival, transitions, and sighting probabilities. One method is based on the EM algorithm, and the second uses the Kalman filter for computing likelihoods. The methods are illustrated on real data from a cohort of Great Cormorants Phalacrocorax carbo, and their performance is evaluated using simulation. We recommend identifying the batches, for instance in the case of sites, by using a different colour on each site at the time of marking, and in general the use of the Kalman filter rather than the EM-based approach.

Estimating survival and transition probabilities from aggregate sightings of animals

A society is divided into classes on the basis of social status or occupation and the changing structure of society can be studied with the aid of the model. Stochastic probability processes are considered as models of social mob ility. Such processes are extremely similar to, and hence useful in the study of human mobility. For examp le, sons do not always follow in their fathers' footsteps and even if they do, the varying members of offspring in different classes would lead to fluctuations in the class sizes. Thus, any stochastic model of mobility can give a probabilistic description of how movement takes place fro m one class to another. In this paper, the celebrated Markov chain model has been used to analyse the mobility of the inhabitants of the district Go laghat in the state of Assam, India. Here, in addition to equilibriu m d istributions for various classes; actual distributions of fathers and sons for different classes based on the sample observations have been computed. Also, the average number of generations spent for the classes has been computed. Moreover, in this paper, an attempt is made to examine the immobility ratios for the first four generations for each social class.

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Stochastic Modelling of Social Mobility: A Case Study in Golaghat, Assam

Though various studies on spatial mobility are available, little is known about occupational mobility in Canada. This study is concerned with the estimation of Canadian occupational mobility from aggregate census data. The process of occupational mobility is characterized as a stationary Markov chain and certain meaningful conditions are imposed on the transition probabilities to convert the estimation strategy into a linear programming problem. Cross-section data for 1951 and 1961 are employed for estimation purposes. The study reveals that in 1951–61, the Canadian females had higher rates of mobility than the males.

A. Sangadasa

Papers

Stochastic indicators of occupational mobility, Canada: 1951–1961