Input-Output Analysis: A Primer, 2nd ed.

Input-output (IO) analysis is a modeling framework that records the business transactions in an economy over a given time period. It is used in any number of ways, all of which are intended to improve our understanding of how industries in an economy are interrelated. The economy under study can be a national economy, a multi-state, state, or multi-county regional economy. As its name suggests, the IO accounting framework describes and depicts the input and output relationships of all industries in an economy. The utility of the IO framework is manifold. Most immediately, the inter-industry transactions table, or input-output matrix, describes the direct sales and purchases relationships among industries. The framework, in its several forms, is useful for assessing the impacts of changes in economic activity within or outside a region and for targeting industries for retention or recruitment policies. This brief monograph introduces the IO framework and addresses these and related concepts and applications.https://researchrepository.wvu.edu/rri-web-book/1012/thumbnail.jp

THE ROLE OF SMALL BUSINESS IN ECONOMIC GROWTH AND POVERTY ALLEVIATION IN WEST VIRGINIA: AN EMPIRICAL ANALYSIS

In OLS and 2SLS regression analysis a positive relationship exists between small business and economic growth. A strong inverse relationship also exists between the incidence of poverty and small business and economic growth. Thus, the empirical result establishes the linkage between small business, economic growth and the incidence of povertyResearch Methods/ Statistical Methods,

Assessing Demographic Changes and Income Inequalities: A Case Study of West Virginia

This study investigates demographic change and income inequalities, and relationship between economic growth and income inequality in West Virginia. Income growth was positively related with population and employment growth, but is significantly and negatively related with income inequality. This indicates that higher income inequality is associated with slower economic growth.Labor and Human Capital,

Commodities are Not Industries! A Value Chain Example

Leontief and Stone both received Nobel Prizes in Economics for development and extension of input-output (IO) analysis, a framework that has gained little traction in mainstream U.S. economics. Although IO modeling has gained renewed focus in several problem domains, many contemporary economists eschew Stone\u27s enhancements, resulting in inconsistent analytics, even in top economics journals. In this paper, we use an increasingly common approach to value chain analysis as one example that demonstrates such conceptual misunderstandings and by presenting properly formulated alternatives, we demonstrate the extent of the consequences of neglecting the Stone enhancements and important role of reproducing results

Is Income Inequality Endogenous in Regional Growth?

This study focuses on testing the relationship between income inequality and growth within U.S. counties, and the channels through which such effects are observed. The study tests three hypotheses: (1) income inequality has an inverse relationship with growth; (2) regional growth adjustments are the channels through which the inequality and growth are equilibrated; and (3) income inequality is endogenous to regional growth and its adjustment. Results, based on a system of equations estimation, confirm the hypotheses that income inequality has a growth dampening effect; income inequality is endogenous to regional growth and growth adjustment; and the channels through which income inequality determines growth are regional growth adjustments, such as migration and regional adjustment in job and income growth. Results have numerous policy implications: (1) to the extent that income inequality is endogenous, its equilibrium level can be internally determined within a regional growth process; (2) to the extent that traditional income inequality mitigating policies have indirect effect on overall regional growth, they may have unintended indirect effects on income inequality; and (3) to the extent that regional growth adjustment also equilibrates income inequality, such forces can be utilized as policy instruments to mitigate income inequality, and its growth dampening effects hence forth.Income inequality, economic growth, Gini coefficient, growth modeling, population change, per capita income, Community/Rural/Urban Development, Public Economics, I32, J15, O18, P25, R11, R23, R25, R51, R53, R58,

A SPATIAL MODEL OF REGIONAL VARIATIONS IN EMPLOYMENT GROWTH IN APPALACHIA

In this study, a spatial equilibrium model of employment growth is developed and empirically estimated by Generalized Spatial Two-Stage Least Squares (GS2SLS) estimator using cross-sectional data from Appalachian counties for 1990-2000. Besides the existence of spatial spillover effects, the results suggest that agglomerative effects that arise from the demand and the supply side contribute to employment growth in the study area during the study period. The policy implications of the findings are: (1) Regional cooperation of counties and communities is advisable and may in fact be necessary to design effective policies to encourage employment growth; and (2) Policy makers at the county level may need to design policies that can attract people with high endowments of human capital and higher income into their respective counties.APPALACHIA, EMPLOYMENT GROWTH, SPATIAL MODEL

A Spatial Panel Simultaneous-Equations Model of Business Growth, Migration Behavior, Local Public Services and Household Income in Appalachia

In this paper we develop a spatial panel simultaneous-equations model of business growth, migration behavior, local public services and median household income in a partial lag-adjustment growth-equilibrium framework and utilizing a one-way error component model for the disturbances. This model is an extension of the jobs follow people or people follow jobs literature and it improved previous models in the growth-equilibrium tradition by: (1) explicitly modeling local government and regional income in the growth process; (2) explicitly modeling gross in-migration and gross out-migration separately in order to spell out the differential effects, which used to be glossed over under net population change in previous studies; (3) explicitly incorporating both spatially lagged dependent variables and spatially lagged error terms to account for spatial spillover effects in the data set; and (4) extending and generalizing the modeling and estimation of simultaneous systems of spatially interrelated cross sectional equations into a panel data setting. To estimate the model, we develop a five-step new estimation strategy by generalizing the Generalized Spatial Three-Stage Least Squares (GS3SLS) approach outlined in Kelejian and Prucha (2004) into a panel data setting. The empirical implementation of the model uses county-level data from the 418 Appalachian counties for 1980-2000. Generally, the results from these model estimations are consistent with the theoretical expectations and empirical findings in the equilibrium growth literature and provide support to the basic hypotheses of this study. First, the estimates show the existence of feedback simultaneities among the endogenous variables of the model. Second, the results also show the existence of conditional convergence with respect to the respective endogenous variable of each equation of the model and the speed of adjustment parameters are generally comparable to those in literature. Third, the results from the parameter estimation of the model indicate the existence of spatial autoregressive lag effects and spatial cross-regressive lag effects with respect to the endogenous variables of the model. One of the key conclusions is that sector specific policies should be integrated and harmonized in order to give the desirable outcome. Besides, regionally focusing resources for development policy may yield greater returns than treating all locations the same.Community/Rural/Urban Development,

Economic Structure in Appalachia’s Urban Regions: Clustering and Diversification Strategies

In support of economic development practitioners’ efforts to devise strategies that can align with both industrial clustering and industrial diversification, this report provides a wide range of relevant measures and metrics. In addition to standard regional analysis tools like coefficients of specialization, location quotients, and growth rates, we introduce two fundamentally new measures for understanding the nature of regional clusters. These measures focus on the industries that anchor the clusters and characterize their strength and regional dominance. The former measures the share of the anchor industry’s direct and indirect requirements that could be satisfied by regional industries, and the latter measures the share of the regional economy that is potentially oriented to the cluster anchor. We then apply an algorithm that identifies anchors and industries that might be further developed to strengthen the region’s industrial clusters. The design of the analysis commonly leads to the identification of different clusters, and thereby points to opportunities to strengthen within and diversify across clusters. Results of these analyses for all 120 micro- and metropolitan regions wholly within the Appalachian region are reported in the supplements to this methodological overview

Regional Development: Challenges, Methods, and Models

This Web Book reviews the challenges that the consideration of regions brings into economic analysis and provide an overview of some of the key methods and tools that can be used to gain a better understanding of how regional economies work, and through that, identify both the challenges and opportunities that they face. The exploration of these challenges begins with some consideration of the ways in which regional economies work to set the stage for subsequent sections that summarize a toolbox of methods and strategies that might be considered for evaluation of regional development initiatives. In contrast to past reviews of this field, this report presents an integration of more traditional regional macroeconomic modeling with new developments in spatial data analysis.https://researchrepository.wvu.edu/rri-web-book/1001/thumbnail.jp