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Table 1. Definition of Variables, 2000Note: All variables are based on year 2000, except one variable, Spn_dtrav. * All core variables are used for the GWR regression, and the unit for all variables is $million. ** Some independent variables in common variables set are selected for the GWR regressions *** Specific variables set is only for specified sector shown in NoteEstimation of State-by-State Trade Flows for Service Industries* JiYoung Park** Von Kleinsmid Center 382 School of Policy, Planning, and Development University of Southern California Los Angeles, CA 90089-0001 Email: [email protected] Phone: (213) 550-9979 * An earlier version of this paper was presented North American Meetings of the Regional Science Association International 53rd Annual Conference, Fairmont Royal York Hotel, Toronto, Canada, November 16-18, 2006. ** This research was supported by the United States Department of Homeland Security through the Center for Risk and Economic Analysis of Terrorism Events (CREATE) under grant number N00014-05-0630. However, any opinions, findings, and conclusions or recommendations in this document are those of the authors and do not necessarily reflect views of the United States Department of Homeland Security. Also, the author wishes to acknowledge the intellectual support of Profs. Peter Gordon, Harry W. Richardson, and James E. Moore II.Estimation of State-by-State Trade Flows for Service Industries Abstract. Multiregional input-output models have been discussed for many years, but their implementation has been rare. The limitations are mostly because of the difficulty of adding spatial detail representing trade flows between the 50 states. Since 1993, however, Commodity Flow Survey (CFS) data have been widely used, but these data have several inherent problems. The most serious ones are that the CFS does not report trade flows below the state level and also they are not the complete trade flows even between the states. To construct trade flows as the basic data set for a U.S. interstate MRIO, there have recently been various attempts to estimate interregional trade flows based on the 1997 CFS. However, the common problem with the all of these trials is that there has been too little attention paid to the problems of estimating trade flows among the service sectors. In the modern information economy, this is a serious omission. Therefore, this research addresses new approaches to relaxing the assumption of no interstate trade in services and, instead, proposes estimates of interstate trade flows for the service sectorss. Using Geographically Weighted Regressions (GWR) econometric analysis, this study proposes and implements a sequence of computational and spatial econometric steps for estimating inter-state trade flows for the major service sectors required for implementing a U.S. interstate MRIO model. Furthermore, the approach can be expanded to examine the economic relationships between sub-state level areas, as well as to forecast future trade flows. JEL Classification: C31, R12, R15, L8, L9 Key words: Service trades, geographically weighted regressions, multi-regional input-output 11 Introduction and Issues National economic models of the U.S. aggregate over large numbers of diverse regions. However, many regional scientists are interested in evaluating socioeconomic impacts that involve the states, especially in terms of their policy significance. A U.S. Multi-regional input-output model is an example of useful spatial disaggregation, but models like this are still difficult to construct because of the difficulty of developing detailed state-by-state trade data (Lahr, 1993). The U.S. Commodity Transportation Survey data on interregional trade flows have been available since 1977, but reporting was discontinued for some years. For the years since 1993, this data deficit can be met to some extent with the recent Commodity Flow Survey (CFS) data from the Bureau of Transportation Statistics (BTS). Since 1993, CFS data have been widely used, but the data have several inherent problems (Erlbaum and Holguin-Veras, 2005). The most serious one among them is that the CFS data do not include trade flows below the state level but also that they are not complete even between the states. Since Polenske (1980) and Faucett Associates (1983), there has been no comprehensive inventory of flows for probably these reasons. Furthermore, even though the commodity flow data between the states of the U.S. are published every five years, there is no inventory of trade flows for services. Recent approaches to estimating state-by-state trade flows of U.S. based on 1997 CFS, therefore, have included too little attention paid to the problems of estimating the trade flows among service sectors and maintained strong assumptions of no or small trades in these sectors. However, in the modern information economy, this is a serious omission. Therefore, this research addresses new approaches to relaxing these assumptions and, instead, proposes estimates of interstate trade flows for the service sectors. Using Geographically Weighted Regressions (GWR) econometric analysis, this study proposes and implements a sequence of computational and spatial econometric steps for estimating inter-state trade flows among all of the major service sectors, especially as required for implementing a U.S. interstate MRIO model. Furthermore, the approach can be expanded to examine the economic relationships between sub-state-level areas, as well as to forecast future trade flows. 2The next section of this paper develops the background for estimating state-by-state trade flows. In the following section, based on specially prepared data, the Geographically Weighted Regressions (GWR) econometric methodology and an application is explained. In the final section, conclusions and some remarks are elaborated. 2 Trade Flows Estimation and Service Industries The existence of many unreported values in trade flow data has required relying on other data sources for completeness. Harrigan et al (1981) compared several old methodologies for estimating interregional trade flows and showed ‘more information, better results’, based on 1973 Scotland data. This is because all techniques used as examples are simple ratio-based methodologies. Using the CFS, based on an approach of location quotients, Lie and Vilain (2004) estimated trade inflows of subregional levels below the states.


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