Population Forecasting with Nonstationary Multiregional Growth Matrices

Though the mathematics of multiregional population projections were defined over twenty years ago, and the methodology has seen some adoption internationally, most practitioners in the United States still use rudimentary cohort component projections techniques. Both the stationarity assumption and the implicit five‐year retrospective time scale imposed by the census migration data have probably contributed to the limited use of multiregional projections methods. This paper reviews previous attempts to overcome the stationarity assumption and proposes a decompositional approach using log linear models estimated via the ECM algorithm. The paper discusses the advantages of the decompositional approach and implements the model for intrastate migration in California.     

Iran's new strategic entity

This article focuses on the two political factions in Iran, the Jihadi (traditionalist combative) and the Ijithadi (creatively interpretive) and their competition and accommodation since the Revolution. The author argues that US-policy and developments in the region have favoured the Jihadis and enabled President Ahmadinejad to act more intransigently and assertively than would otherwise been the case. At a time of profound shift in the sectarian and strategic balance in the region, the challenge for the US and its allies is to widen the arena for Ijithadis within Iranian politics.     

A Descriptive Analysis of Discrete U.S. Industrial Complexes*

Abstract. We use the Getis/Ord local G statistic and detailed county‐level industry employment data from the U.S. Bureau of Labor Statistics to isolate discrete industrial complexes—or groups of nominally linked industries clustered in particular locations—for two recent years: 1989 and 1997. We describe the characteristics of the complexes in terms of their number, spatial extent, broad regional distribution, and other factors. Data from the two periods help illustrate key shifts in industrial locations, including the continuing concentration of the apparel industry in the Southeast and the ongoing southern shift in U.S. vehicle production.     

A Geostatistical Linear Regression Model for Small Area Data. 一种适用于小区域数据的地统计线性回归模型

We present a new linear regression model for use with aggregated, small area data that are spatially autocorrelated. Because these data are aggregates of individual‐level data, we choose to model the spatial autocorrelation using a geostatistical model specified at the scale of the individual. The autocovariance of observed small area data is determined via the natural aggregation over the population. Unlike lattice‐based autoregressive approaches, the geostatistical approach is invariant to the scale of data aggregation. We establish that this geostatistical approach also is a valid autoregressive model; thus, we call this approach the geostatistical autoregressive (GAR) model. An asymptotically consistent and efficient maximum likelihood estimator is derived for the GAR model. Finite sample evidence from simulation experiments demonstrates the relative efficiency properties of the GAR model. Furthermore, while aggregation results in less efficient estimates than disaggregated data, the GAR model provides the most efficient estimates from the data that are available. These results suggest that the GAR model should be considered as part of a spatial analyst's toolbox when aggregated, small area data are analyzed. More important, we believe that the GAR model's attention to the individual‐level scale allows for a more flexible and theory‐informed specification than the existing autoregressive approaches based on an area‐level spatial weights matrix. Because many spatial process models, both in geography and in other disciplines, are specified at the individual level, we hope that the GAR covariance specification will provide a vehicle for a better informed and more interdisciplinary use of spatial regression models with area‐aggregated data.     

Selling Maize in Mexico: The Persistence of Peasant Farming in an Era of Global Markets

The last decade of the twentieth century was heralded as the ‘end of agrarian reform’ in Mexico and the initiation of a new era of market‐led agricultural policy and practice. The impact of neoliberalism and the North American Free Trade Agreement on smallholder maize production has been widely conceived as negative, associated with ecological degradation, rural emigration and cultural erosion. Yet, some twenty years later, all evidence suggests that smallholder maize production is continuing in Mexico, albeit in evolving structures and forms. This article uses a farm‐level survey implemented in three Mexican states to assess the current condition of maize farming in Mexico. The authors revisit past categorizations of Mexican farmers and apply similar approaches to explore what maize‐producing households are doing with their maize, and what current patterns of production imply for future Mexican maize policy. They find evidence of greater persistence and adaptability in Mexican maize farming than is often presented. On the basis of their analysis, they advocate for a reconsideration of the underlying assumptions of public policy, highlighting the heterogeneity of the maize landscape and the unrealized and generally unrecognized potential this heterogeneity represents.     

Plant Size and Clustering of Manufacturing Activity

Although a growing number of studies emphasize the advantages small firms gain by co-locating in space, there is little empirical work that directly examines clustering trends by firm size. This paper presents an empirical analysis of the isolated effects of size on clustering patterns of producers in a major manufacturing state in the southeast United States. Recent developments in point process modeling allow us to control for the critical fact that economic activity is, in general, concentrated in space. Our findings suggest that the relationship between establishment size and clustering in North Carolina is roughly characterized by an inverted u-shape, that is, clustering increases up to some size threshold and then decreases again.