Regional disparities in the spatial correlation of state income growth, 1977–2002

Garrett, Thomas A.; Wagner, Gary A.; Wheelock, David C.

doi:10.1007/s00168-007-0114-x

Cited by 52 publications

(13 citation statements)

References 36 publications

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“…As Anselin (1988) points out, accounting for spatial relationships using the spatial error model will improve the efficiency of resulting hypothesis tests. This model has been estimated in a variety of contexts, including U.S. state income convergence by Garrett et al (2007) and Rey and Montouri (1999).…”

Section: Empirical Methods and Datamentioning

confidence: 99%

The Impact of Local Decentralization on Economic Growth: Evidence From U.S. Counties*

Hammond

Tosun

2011

Journal of Regional Science

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. www.econstor.eu The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. Terms of use: Documents in D I S C U S S I O N P A P E R S E R I E SIZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. S. CountiesWe analyze the impact of fiscal decentralization on U.S. county population, employment, and real income growth. Our findings suggest that government organization matters for local economic growth, but that the impacts vary by government unit and by economic indicator. We find that single-purpose governments per square mile have a positive impact on metropolitan population and employment growth, but no significant impact on nonmetropolitan counties. In contrast, the fragmentation of general-purpose governments per capita has a negative impact on employment and population growth in nonmetropolitan counties. Our results suggest that local government decentralization matters differently for metropolitan and nonmetropolitan counties.JEL Classification: E62, H7, R11

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Section: Empirical Methods and Datamentioning

confidence: 99%

The Impact of Local Decentralization on Economic Growth: Evidence From U.S. Counties*

Hammond

Tosun

2011

Journal of Regional Science

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“…(2005) for the Italian regions. Garrett et al. (2007) use a time‐space pooled model for the United States and find a better fit with the SL model as well.…”

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confidence: 99%

Variations on economic convergence: The case of the United States

Checherita¹

2009

Papers in Regional Science

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This paper tests the hypothesis of "conditional &bgr;-convergence" in per capita income across the United States by extending the neoclassical growth model to incorporate public capital, government taxation, and human capital, and controlling empirically for technology growth. We expand the period of analysis from the late 1980s when studies using public capital stock have stopped, investigate spatial variation across the United States under various cross-sectional and panel spatial models, and tackle the issue of nonlinearities. All model "variations" provide evidence of economic convergence across the United States over the period 1960-2005. Copyright (c) 2008 the author(s). Journal compilation (c) 2008 RSAI.

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“…It may well be the case that spatial correlations within groups encompassing more or less homogeneous provinces can be different. Based on this hypothesis Garrett et al (2007) suggest a model with group‐specific spatial dependence. As usual, both pooled and fixed‐effects models can be defined, which are estimated using MLE.…”

Section: Alternative Modelsmentioning

confidence: 99%

How helpful are spatial effects in forecasting the growth of Chinese provinces?

Girardin

Kholodilin

2010

Journal of Forecasting

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In this paper, we make multi-step forecasts of the annual growth rates of the real gross regional product (GRP) for each of the 31 Chinese provinces simultaneously. Beside the usual panel data models, we use panel models that explicitly account for spatial dependence between the GRP growth rates. In addition, the possibility of spatial effects being different for different groups of provinces (Interior and Coast) is allowed for. We fi nd that both pooling and accounting for spatial effects help substantially to improve the forecast performance compared to the benchmark models estimated for each of the provinces separately. It is also shown that the effect of accounting for spatial dependence is even more pronounced at longer forecasting horizons (the forecast accuracy gain as measured by the root mean squared forecast error is about 8% at the 1-year horizon and exceeds 25% at the 13-and 14-year horizons).and Baltagi et al. (2003) for gasoline demand, Baltagi et al. (2000) for cigarette demand, Baltagi et al. (2002) for electricity and natural gas consumption, Baltagi et al. (2004) for Tobin's q estimation, and Brücker and Siliverstovs (2006) for international migration, among others.In addition to pooling, accounting for spatial interdependence between regions may prove beneficial for the purposes of forecasting. Spatial dependence implies that, due to spillover effects (e.g. migration and trade fows), neighboring regions may have similar economic performance and hence location matters. However, the number of studies that illustrate the usefulness of accounting for (possible) spatial dependence effects across cross sections in the forecasting exercise is still limited. For example, Elhorst (2005), Baltagi and Li (2006), and Longhi and Nijkamp (2007) demonstrate the forecast superiority of models accounting for spatial dependence across regions using data on demand for cigarettes from states of the USA, demand for liquor in the American states, and German regional labor markets, respectively. However, only Longhi and Nijkamp (2007) conduct quasi realtime forecasts for period t + h (h > 0) based on the information available in period t. On the other hand, the forecasts made in Elhorst (2005) and Baltagi and Li (2006) are not real-time forecasts, since they take advantage of the whole information set that is available in the forecast period, t + h.Applications of panel data models accounting for spatial effects for the forecasting of regional GDP are even more limited. To our knowledge, there are only two papers treating this issue, namely that of Polasek et al. (2007), who make long-term forecasts of the GDP of 99 Austrian regions, but do not evaluate their accuracy in a formal way, and Kholodilin et al. (2008), who forecast the GDP of German Länder at horizons varying from 1 to 5 years and evaluate them in terms of the root mean square forecast error (RMSFE).Structural type predictions of future trend output growth for China are made by Holz (2008) and Perkins and Rawski (2008). Existing work on forecasting Chine...

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Regional disparities in the spatial correlation of state income growth, 1977–2002

Abstract: G28, C23, R10,

Cited by 52 publications

References 36 publications

The Impact of Local Decentralization on Economic Growth: Evidence From U.S. Counties*

The Impact of Local Decentralization on Economic Growth: Evidence From U.S. Counties*

Variations on economic convergence: The case of the United States

How helpful are spatial effects in forecasting the growth of Chinese provinces?

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