Kamar Ali

Voting with Their Feet: Jobs versus Amenities

The determinants of rural and urban community population change over the period 1991-2001 are investigated at a very fine level of disaggregation for Canada. The study examines the influence of local amenities, economic factors, and agglomeration economies on population growth for age cohorts starting from the very young to the elderly. Motivated by the objective of assessing the overall jobs versus people question in economic development, the emphasis is on estimating the relative contribution of groupings of variables in explaining the variations in population change rather than the contribution of individual variables. Results indicate that rural and urban populations are influenced to differing degrees by amenity, economic, and urban scale groupings of variables and that there are variations among age cohorts in both urban and rural areas. While economic variables are the most influential in population change for all rural cohorts, their contribution somewhat diminishes with age. In urban areas, amenity, and economic variable groupings have approximately equal importance across all cohorts. For the key young adult cohort, the economic grouping is clearly the most influential in rural areas, while it is a close second to amenities in urban areas. Copyright 2007 Blackwell Publishing.

The Geographic Diversity of U.S. Nonmetropolitan Growth Dynamics: A Geographically Weighted Regression Approach

Spatial heterogeneity is introduced as an explanation for local-area growth mechanisms, especially employment growth. As these effects are difficult to detect using conventional regression approaches, we use Geographically Weighted Regressions (GWR) for non-metropolitan U.S. counties. We test for geographic heterogeneity in the growth parameters and compare them to global regression estimates. The results indicate significant heterogeneity in the regression coefficients across the country, most notably for amenities and college graduate shares. Using GWR also exposes significant local variations that are masked by global estimates suggesting limitations of a one-size-fits-all approach to describe growth and to inform public policy. (JEL R11, R23)

CAN GEOGRAPHICALLY WEIGHTED REGRESSIONS IMPROVE REGIONAL ANALYSIS AND POLICY MAKING

Policy design in a regional context requires explicit recognition of spatial heterogeneity in community characteristics as well as in the heterogeneity of how these characteristics impact the target variables. By providing only a “global” measure for the entire space, standard approaches such as ordinary least squares or (most) spatial econometric models tend to compromise spatial heterogeneity in favor of average estimates and efficiency. More assessment is needed of whether the gains of simplicity and statistical efficiency offset the losses from ignoring spatial heterogeneity. Using data for about 1,900 rural Canadian communities as a backdrop, the authors address this issue using a geographically weighted regression approach. The authors find that for about two-thirds of the variables, standard approaches would have significantly understated the spatial differences in the impact of selected variables. Standard analysis would not have uncovered this information, suggesting that subsequent policy inferences would be poorly suited to many local settings.

Improving Allocative Efficiency of Scarce Water in Southern Alberta

Using a positive mathematical programming (PMP) model with improved ‘wide-scope’ calibration, this study demonstrates how allocative efficiency of scarce water could be improved in the Bow- and Oldman River Sub Basins (BRSB and ORSB) of Southern Alberta, where 12 irrigation districts, two cities and three major industrial/commercial users withdraw bulk of the surface water for irrigation, municipal, industrial and commercial needs. Earlier studies ironically neglected the larger ORSB even though it is subject to the same water licensing and regulation policies as the BRSB. The inclusion of nine irrigation districts and non-irrigation users of ORSB enables this model to estimate allocative efficiency gains in a more comprehensive manner than before. Results indicate that ORSB has a relatively less elastic water demand curve primarily due to its more reliance on irrigation and less water saving/supply options. It is also less responsive to allocations with alternative policies as reflected in net returns, land use and cropping pattern changes due to its less elastic water demand.