The development of an intervening opportunities model with spatial dominance effects

Akwawua, Siaw; Pooler, James A.

doi:10.1007/pl00011468

Cited by 24 publications

(21 citation statements)

References 30 publications

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“…In Latvia results give more support to the IOSD (intervening opportunities with spatial dominance, see Akwawua and Pooler (2001)) model than to spatial mismatch: commuting is directed predominantly toward the capital city; the likelihood of commuting increases with education both in urban and rural areas and falls when one moves further away from the capital; the occupational structure of commuters' flows is closer to host than to source demand structure; and the capital city-countryside gap in educational attainment of employees widens when measured by job location rather than residence, in contrast with Lithuania, where it narrows.…”

Section: Discussionmentioning

confidence: 82%

Does Commuting Reduce Wage Disparities?

Hazans

2004

Growth and Change

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This paper shows that in the Baltic countries, commuting reduces urban-rural wage and employment disparities and increases national output. To quantify the effect of commuting on wage differentials, two sets of earnings functions are estimated (based on Estonian, Latvian, and Lithuanian Labor Force Surveys) with location variables (capital city, rural, etc.) measured at the workplace and at the place of residence. We find that the "ceteris paribus" wage gap between capital city and rural areas, as well as between capital and other cities is significantly narrowed by commuting in some cases but remains almost unchanged in others. Different outcomes are explained by country-specific spatial patterns of commuting, educational and occupational composition of commuting flows, and presence or absence of wage discrimination against rural residents in urban markets. A treatment effects model is used to estimate individual wage gains to rural-urban or inter-city commuting; these gains are substantial in most but not all cases. Wage effects of commuting distance, as well as impact of education, gender, ethnicity, and local labor market conditions on the commuting decision are also explored. Copyright 2004 Gatton College of Business and Economics, University of Kentucky..

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Section: Discussionmentioning

confidence: 82%

Does Commuting Reduce Wage Disparities?

Hazans

2004

Growth and Change

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“…For this purpose, I construct origin‐specific centrality indices , defined here as , where the summation is over all states, including the reference state i , in order to avoid ending up with “donut holes.” The intra‐state distance for the reference state i is calculated as , where Area i is state i 's surface area in square kilometers (Head and Mayer, 2002, p. 11). The pattern depicted in Figure 1 suggests the presence of strong spatial structure, or “spatial dominance” (Akwawua and Pooler, 2001). (Since the distribution of the centrality indices is heavily (positively) skewed, their log values have been used in drawing the map in Figure 1.)…”

Section: Empirical Case‐study Analysismentioning

confidence: 99%

“…The intra-state distance for the reference state i is calculated as D ii = .67 √ Area i /, where Area i is state i's surface area in square kilometers (Head and Mayer, 2002, p. 11). The pattern depicted in Figure 1 suggests the presence of strong spatial structure, or "spatial dominance" (Akwawua and Pooler, 2001). (Since the distribution of the centrality indices is heavily (positively) skewed, their log values have been used in drawing the map in Figure 1.)…”

Section: Variables Included In Empirical Gravity Modelmentioning

confidence: 99%

“…Several attempts have been made in earlier migration studies to account for the spatial structure of migration. The most prominent ones are the intervening‐opportunities (Stouffer, 1960; Akwawua and Pooler, 2001) and competing‐destinations models (Fotheringham, 1991; Hu and Pooler, 2002). These models extend the gravity equation by including some centrality or accessibility indicator to capture the “hierarchical structure of space” emanating from the spatially uneven distribution of opportunities (usually proxied by population counts) within the migration setting studied.…”

Section: Introductionmentioning

confidence: 99%

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Gravity and Spatial Structure: The Case of Interstate Migration in Mexico*

Peeters

2012

Journal of Regional Science

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The estimation of gravity models of internal (aggregate) place‐to‐place migration is plagued with endogeneity (omitted‐variable) biases if the unobserved effects of spatial structure are not accounted for. To address this econometric problem, this paper presents a more general specification of the gravity model, which allows for (bilateral) parameter heterogeneity across individual migration paths—along with (unilateral) origin‐ and destination‐specific effects. The resultant “three‐way fixed‐effects” (3FE) model is applied for an analysis of interstate migration in Mexico based on cross‐sectional data. To overcome parameter‐dimensionality problems (due to limited or incomplete information), the 3FE model is estimated using the Generalized Maximum Entropy (GME) estimator. The empirical implications of this new modeling strategy are illustrated by contrasting the 3FE‐GME estimates with those for the traditional and two‐way fixed‐effects (2FE) models. The former are far more plausible and intuitively interpretable than their traditional and 2FE counterparts, with parameter estimates changing in expected directions. The (average) effect of the migrant stock is markedly smaller than usually estimated, providing a more realistic measure of network‐induced migration. Migration outflows from centrally located origins have significantly steeper distance decay. Path‐specific distance effects exhibit directional asymmetries and spatial similarities.

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“…The difference between the models arises from the approach to determine the disutility. Within the TG model the disutility is described by travel costs, while in the IO model the disutility depends on the number of intervening opportunities (Cascetta et al 2007;Akwawua and Pooler 2001). IO models are especially useful for trip purposes, in which the opportunities are not homogeneously distributed, but form discrete attraction points in the urban environment.…”

Section: Introductionmentioning

confidence: 99%

Trip distribution for limited destinations: a case study for grocery shopping trips in the Netherlands

2010

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In this paper, we introduce a new trip distribution model for destinations that are not homogeneously distributed. The model is a gravity model in which the spatial configuration of destinations is incorporated in the modeling process. The performance was tested on a survey with reported grocery shopping trips in the Dutch city of Almelo. The results show that the new model outperforms the traditional gravity model. It is also superior to the intervening opportunities model, because the distribution can be described as a function of travel costs, without increasing the computational time. In this study, the distribution was described by a simple function of Euclidean distance, which provides a good fit to the survey data. The slope of the distribution is quite steep. This shows that most trips are made to nearby supermarkets. However, a significant fraction of trips, mainly made by car, still goes to supermarkets further away. We argue that modeling of these trips by the new method will improve traffic flow predictions.

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The development of an intervening opportunities model with spatial dominance effects

Cited by 24 publications

References 30 publications

Does Commuting Reduce Wage Disparities?

Does Commuting Reduce Wage Disparities?

Gravity and Spatial Structure: The Case of Interstate Migration in Mexico*

Trip distribution for limited destinations: a case study for grocery shopping trips in the Netherlands

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