SHALE INDUSTRY’S ECONOMIC CONTRIBUTION IN OHIO, USA: IMPLICATIONS FOR FUTURE ACTIVITY IN THE STATE

Gilbert MICHAUD

PhD. Assistant Professor of Practice, Ohio University, Athens, Ohio, United States of America

michaudg@ohio.edu

Abstract

Ohio’s shale industry serves as a significant facet of the state’s economy, employing nearly 150,000 and contributing over $22 billion of positive impacts as of 2015.  With advancements in hydraulic fracturing techniques, and access to the Marcellus and Utica shale plays in the eastern part of the state, Ohio has noteworthy potential for future shale development despite anecdotal discussion of a potential bust of the industry.  This research employed a multi-industry economic contribution analysis using IMPLAN and an input-output methodology with 2015 data to quantify the economic contribution of the shale industry across the entire State of Ohio, as well as a 26-county Appalachian Ohio region where most shale extraction activity is taking place.  Strong economic impact metrics are found for shale activity, including robust multiplier effects relative to other industries in the state.  Out of the six modeled shale-related sectors, Pipeline Transportation, by far, pays the highest wages.  Further, in order, the top five counties by total economic contribution per capita are Noble, Monroe, Belmont, Guernsey, and Washington.  In fact, roughly 90% of the gross regional product in Monroe and Noble counties is attributable to the shale industry.  With these findings, economic development and policy implications are highlighted, which are important as no other shale-play region in the U.S. is so disproportionally affected by resource extraction which contributes to regional poverty and negative pollution effects.  Retaining wealth in this region with the legacy of boom-and-bust resource extraction is ever important, and this paper provides a baseline for analysis when looking how the shale industry changes over time.

Keywords: Energy, Natural Resources, Rural Economics, Resource Policy

JEL classification: J68, O13, P48
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CHARACTERIZATION OF AGRICULTURAL SYSTEMS IN THE EUROPEAN UNION REGIONS: A FARM DIMENSION- COMPETITIVENESS-TECHNOLOGY INDEX AS BASE

Vítor João Pereira Domingues MARTINHO

Coordinator Professor with Aggregation, Agricultural School, Polytechnic Institute of Viseu, 3500 Viseu, Portugal

vdmartinho@esav.ipv.pt

Abstract

The agricultural realities across the several regions belonging to the European Union (EU) present some significant differences, in terms of the socioeconomics, cultural, structural and environmental dimensions. In general, because it is difficult to consider all the realities, within each country, sometimes the public decision makers, in Europe, need to design common approaches for all countries and regions, despite there being some decentralization of decisions. In this scenario, this study aims to identify and characterize the main agricultural systems in the European Union, using statistical information available in the FADN (2017), for the periods 2007-2009 and 2012-2013. This was done, considering the utilized agricultural area (farm dimension), the machinery (farm technology and innovation) and the farm net value added (farm competitiveness) as principal indicators. From these variables the European Union countries and regions were grouped into agricultural systems through cluster approaches and based on a farm dimension-competitiveness-technology index obtained with factor analysis. This approach was complemented by spatial analysis, through the observation of spatial autocorrelation between European Union countries, as determining the farming characteristics in neighboring countries. Of stressing, the relevant differences in the farm characteristics, not only, among European countries, but also, inside each member state between regions. On the other hand, of highlighting the adequacy of the index considered as representative of the farming particularities.

Keywords: European Union regions, Farm Accountancy Data Network (FADN), Spatial approaches, Factor and clusters analysis

JEL classification: C21, C38, O13, O52, Q10
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