Regional workforce dynamics in West Virginia: Insights from shift-share and location quotient analysis

Bandara, Saman Janaranjana Herath Article Regional workforce dynamics in West Virginia: Insights from shift-share and location quotient analysis Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Bandara, Saman Janaranjana Herath (2024) : Regional workforce dynamics in West Virginia: Insights from shift-share and location quotient analysis, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 12, Iss. 11, pp. 1-26, https://doi.org/10.3390/economies12110290 This Version is available at: https://hdl.handle.net/10419/329217 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. 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Economies 12: 290. https://doi.org/10.3390/ economies12110290 Academic Editor: Kostas Rontos Received: 26 August 2024 Revised: 9 October 2024 Accepted: 16 October 2024 Published: 28 October 2024 Copyright: © 2024 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article Regional Workforce Dynamics in West Virginia: Insights from Shift-Share and Location Quotient Analysis Saman Janaranjana Herath Bandara Department of Economics, Finance and Marketing, College of Business and Social Sciences, West Virginia State University, Institute, WV 25112, USA; [email protected] Abstract: West Virginia, home to approximately 1.77 million residents, has been grappling with significant economic challenges characterized by persistent poverty and sluggish growth. Despite ongoing development efforts, the state’s Gross State Product (GSP) has seen only a modest increase of 0.1% over the past five years, reaching USD 71.7 billion, while the unemployment rate remains at 4.0%. The annualized employment growth rate of 0.7% lags behind the national average, and only about 54% of West Virginia’s adult population is either employed or actively seeking employment, resulting in one of the lowest labor force participation rates in the nation. In contrast, certain industrial sectors, such as healthcare, social assistance, retail trade, and accommodation and food services, have shown intermittent growth at the county and regional levels. To explore the unique characteristics and significance of these regions in relation to employment growth, this study examines regional employment patterns in West Virginia from 2001 to 2020, focusing on the main regions of the state: Metro Valley, Mid-Ohio Valley, New River/Greenbrier Valley, Mountain Lakes, and Potomac Highlands. Utilizing shift-share and location quotient ( LQ ) analyses, this research identifies the sectors driving regional employment and assesses their performance. Key findings reveal strong sectoral performance in mining, manufacturing, and finance in the Mid-Ohio Valley; wholesale trade, transportation, and utilities in the Metro Valley; agriculture and administrative services in the New River/Greenbrier Valley; agriculture and manufacturing in the Potomac Highlands; and scientific services, healthcare, and utilities in the Mountain Lakes region. Based on these insights, this study recommends targeted policy interventions to address regional disparities, enhance sectors with significant shortand long-term benefits, and foster balanced economic development across the state. Keywords: shift-share analysis; location quotient analysis; economic growth 1. Introduction West Virginia, nestled in the Appalachian region of the United States, is one of the least populous states, with a population of 1,775,513. The state faces negative annualized growth and ranks 50th among U.S. states in terms of growth rate. Its gross state product (GSP) has reached USD 71.7 billion, reflecting a modest 0.1% increase over the past five years. Despite continuous efforts to stimulate economic development, the unemployment rate remains at 4.0%, underscoring ongoing economic challenges (IBISWorld 2024). West Virginia has long struggled with economic diversification and population decline, exacerbated by the fact that a significant portion of its residents are aged 65 and older. Given the state’s weaker economic performance compared to neighboring states and the broader U.S. economy, net migration has increasingly contributed to its population decline. The state’s annualized employment growth rate of 0.7% lags behind the national average, and only about 54% of West Virginia’s adult population is either employed or actively seeking employment, making it one of the lowest labor force participation rates in the nation. This presents a considerable obstacle to long-term economic prosperity (Bureau of Business and Economic Research, John Chambers College of Business and Economics, West Virginia University 2020). Additionally, factors such as poor health outcomes and Economies 2024,12, 290. https://doi.org/10.3390/economies12110290 https://www.mdpi.com/journal/economies Economies 2024,12, 290 2 of 26 limited human capital have further hindered labor force participation, affecting the state’s capacity to expand its workforce and improve economic prospects. West Virginia’s unemployment rate has fluctuated in recent years, reflecting both economic difficulties and demographic trends (IBISWorld 2024). However, certain industrial sectors, such as the service sector, have experienced intermittent growth at the county and regional levels. The declining unemployment rate in regions like North Central and the Eastern Panhandle is partly attributed to relatively healthy economic conditions and growth in industries beyond traditional sectors, bolstered by infrastructure development. In addition to these, emerging sectors like healthcare, social assistance, retail trade, and accommodation and food services have contributed to employment growth in various regions of the state. When examining West Virginia’s five key regions—Metro Valley, Mid-Ohio Valley, New River/Greenbrier Valley, Potomac Highlands, and Mountain Lakes—specific industrial sectors emerge as potential drivers of future growth. A regional economy is composed of diverse firms and industries, each with distinct growth patterns and economic potential. Sectoral expansions or contractions can significantly impact overall economic performance (McNamara 1991;Bartik 2004;Herath et al. 2013). Therefore, understanding the competitive advantages of different regions and sectors is critical for shaping effective policy and investment strategies (Melachroinos 2002). This is particularly important for less-developed regions like West Virginia, where strategic investments could have a disproportionately positive effect on economic development. Given the limited studies addressing industrial sectors, employment shifts, and their impacts on the regions of West Virginia, this study aims to answer the following research question: • How have various industrial sectors contributed to West Virginia’s economic progress over the past two decades, considering the state’s economic challenges and demographic shifts, and what policy measures can be identified to enhance future economic growth? With the significance of the above research questions, this study has the following objectives: •To analyze industrial growth patterns in key sectors of West Virginia. •To evaluate the regional economic impacts of sectoral changes on employment. • To identify policy recommendations that promote economic growth and address challenges in West Virginia’s economy. The remainder of this paper is structured as follows: Section 2reviews the relevant literature, providing a foundation for the study. Section 3outlines the data and methods used in the research. Section 4presents the empirical results along with the analysis. Finally, Section 5concludes with key findings and policy recommendations. 2. Literature Review 2.1. Shift-Share and LQ Applications In regional economic analysis, various methods such as shift-share analysis and location quotient ( LQ ) have been extensively used to understand economic dynamics and competitive advantages. Shift-share analysis, developed in the 1940s by Daniel Creamer and later popularized by Dunn in 1960, decomposes regional economic changes into three components: national, industry, and regional effects (Dunn 1960). This method has been widely applied to examine geographical shifts in economic activity, making it valuable for assessing regional competitiveness over time and across various fields, including regional and political economy, urban studies, and marketing (Knudsen 2000; Shi and Yang 2008). Research has demonstrated the utility of shift-share analysis in diverse contexts. For instance, the technique has been employed to forecast regional growth, analyze policy effects, and support strategic community planning (Selting and Loveridge 1994). It has Economies 2024,12, 290 3 of 26 also been used to predict regional investment decisions (Ireland and Moomaw 1981), measure employment growth (Barff and Knight 1988), and assess economic impacts across different regions (Herath et al. 2013;Herath Bandara 2024). Notably, recent applications include evaluating resource regions for mineral exploitation (Sablin et al. 2018), analyzing international trade sensitivity (Markusen et al. 1991), and studying regional resilience during economic crises (Giannakis and Bruggeman 2015). Shift-share analysis continues to provide valuable insights into regional economic dynamics across various sectors and geographic scales (Dembi´nska et al. 2022). In recent years, it has also been utilized to assess sustainable development in Polish regions (Cie´slak et al. 2019), examine regional growth in Romania following EU accession (Goschin 2014), and explore green investment trends in China (Sheng et al. 2021). Location quotient ( LQ ) is another crucial tool used to assess regional economic specialization. It compares a region’s concentration in a particular industry to that of a broader reference area, typically on a national scale, to highlight sectors where the region may have a comparative advantage (Morrissey 2014). LQ is widely used in regional economic analysis and policymaking. Recent studies have applied LQ to identify hot spots for industrial reshoring (Sarder et al. 2018), differentiate between urban and rural labor markets (Franconi et al. 2024), and evaluate regional production multipliers (Morrissey 2014). LQ has also been used to determine dominant economic sectors in specific regions, such as those connected to infrastructure projects (Sampe et al. 2023), and to track changes in industrial concentration over time (Prats and Ramirez 2018). While LQ is effective in highlighting regional strengths and sectoral advantages, it does have limitations. These include sensitivity to extreme values, sparsity, and size effects in smaller regions (Franconi et al. 2024). Adjusted LQ values have been proposed to address these issues and improve classification robustness. Overall, both shift-share analysis and LQ remain essential tools for understanding and enhancing regional economic performance, each offering unique insights into regional growth, competitiveness, and specialization. 2.2. Context and Relevance of the Study Area 2.2.1. Agricultural Sector The agricultural sector in West Virginia significantly contributes to the state’s economy, generating an average of USD 800 million annually. Despite this substantial input, the sector has seen a slight decline in employment numbers in recent years. This trend is concerning, especially in counties such as Greenbrier, Pendleton, Hardy, Monroe, and Preston, which collectively hold the most farmland by acreage in the state (Sperow 2023; West Virginia Agriculture 2023). This decline mirrors broader rural economic patterns, where high agricultural productivity persists despite decreasing workforce participation. Historically, West Virginia’s farmland was dedicated to corn and small grains, covering 1.2 million acres by the late 19th century. Today, about 40% of corn is harvested for silage, and soybean production has shifted to a cash market. Fruit production, including apples and peaches, has notably decreased; most apples are now processed, and peaches are mostly sold fresh (Sperow 2023). Currently, hay is the leading crop, contributing 2% to the state’s agricultural receipts. The state also grows apples, corn, wheat, soybeans, peaches, and tobacco (Atitwa 2020). Grass, primarily used for permanent pastures, covers nearly one-third of the 3.6 million acres of farmland. Livestock production, including broilers, cattle, turkeys, sheep, hogs, chickens, and farm-raised fish, dominates, accounting for 82% of agricultural output (Atitwa 2020;Sperow 2023). In response to industry challenges, such as those faced by the dairy sector, West Virginia has streamlined regulations for small producers by transferring authority to the WVDA. This regulatory shift supports new market opportunities for non-hazardous foods and milk, addressing sector-specific issues and fostering growth (Leonhardt 2021). Economies 2024,12, 290 4 of 26 2.2.2. Manufacturing Sector West Virginia’s manufacturing sector presents a complex landscape, marked by both positive growth and significant challenges. Chemical manufacturing, a dominant force within this sector, accounts for 20% of manufacturing jobs and nearly 40% of the sector’s economic output. This industry includes the production of adhesives, plastics, pharmaceuticals, and industrial chemicals. However, the manufacturing sector in West Virginia is diversified, encompassing motor vehicles, primary metals, petroleum and coal products, and more. Despite its challenges, the sector constituted 10.25% of the state’s total output and employed 6.48% of the workforce in 2018, generating USD 7.94 billion in total output (Fauber 2022;Miller 2022). Small businesses are also a vital component, representing 76% of total manufacturing exporters in the state (Atitwa 2020). These data underscore the importance of manufacturing to the state’s economy, aligning with national trends that highlight the sector’s role in economic resilience and job creation. Recent developments in West Virginia’s manufacturing sector highlight both growth and challenges. Procter & Gamble has significantly expanded in Berkeley County with a USD 500 million facility, boosting local employment to over 1400 and advancing the production of soaps and cleaning products (Bureau of Business and Economic Research 2022). Meanwhile, the sector faces setbacks, such as Viatris’ closure of the Mylan facility in Morgantown, leading to anticipated job losses of over 1400 and heightened uncertainty (Bureau of Business and Economic Research 2022). The state is also beginning to see growth in clean-tech manufacturing. GreenPower is setting up a plant for electric buses near Charleston, and Sparkz plans to build a Gigafactory in Taylor County for next-gen EV batteries. Additionally, Berkshire Hathaway Energy’s renewable energy microgrid at the former Century Aluminum site could attract more clean-tech manufacturers seeking cost-effective energy solutions (Bureau of Business and Economic Research 2022). 2.2.3. Construction Sector The construction sector in West Virginia is crucial for job creation, income, and tax revenue, affecting related industries like manufacturing, warehousing, transportation, and real estate. Over the past two decades, the sector has experienced significant fluctuations. Notably, workforce levels declined sharply in late 2018 due to various industry challenges (West Virginia Construction and Building Trends 2023). Between mid-2012 and mid-2013, the state started over 2000 new single-family homes, a 19% increase from the previous year but still below 2006 levels. Apartment construction peaked in 2006–2007 with over 2000 units but dropped to fewer than 300 units by mid-2013. Non-building construction, including infrastructure projects, showed a 46% decrease in value in early 2013 compared to 2012, with the exception of waterway projects. In 2022, West Virginia’s non-residential construction totaled USD 500.6 million (45th among U.S. states), while residential construction reached USD 18.1 million (41st). The state issued 3929 building permits in 2022, reflecting a 7.3% annualized growth rate from 2017 to 2022 (West Virginia Construction and Building Trends 2023). These fluctuations highlight broader economic shifts, reflecting both cyclical and long-term structural changes in the state. 2.2.4. Mining Sector The mining sector, once a cornerstone of West Virginia’s economy, has seen a dramatic decline in workforce numbers, particularly in coal mining. Historical data reveal a sharp drop from 131,700 miners in 1948 to just 20,100 in 2006, a five-fold decrease even after accounting for population decline (Bell and York 2010). This decline continued with coal mine employment falling from nearly 14,500 workers in early 2018 to just over 11,000 in late 2020 (Witt and Fletcher 2005;Witt and Leguizamon 2007). Despite these challenges, there have been some improvements due to increased global coal demand and temporary boosts in the domestic steam coal market. In 2021, production averaged nearly 80 million short Economies 2024,12, 290 5 of 26 tons. Although mining only comprises 3% of statewide employment, it accounted for 15% of the state’s GDP in 2021 (Bureau of Business and Economic Research 2022). This decline reflects national trends in the coal industry, as market dynamics shift toward alternative energy sources and automation reduces the need for labor. 2.2.5. Healthcare Sector The healthcare sector has emerged as a crucial component of West Virginia’s economy. Hospitals alone contribute nearly USD 10.5 billion annually and support approximately 46,000 jobs. The state’s hospitals serve around 227,000 inpatients and over 7 million outpatients each year, underscoring the sector’s significance (Young 2019). The sector has experienced substantial growth, particularly with the expansion of WVU Medicine, which added more than 550 doctors nationwide over four and a half years. This growth is reflected in the broader trend of an increasing healthcare workforce over the past two decades, driven by significant changes in health-related activities (HRSA 2022). These developments align with national trends, where healthcare continues to expand due to an aging population and increasing demand for services. 2.2.6. Education Sector West Virginia’s education workforce shows a slight decline from 2001 to 2020, though it temporarily increased between 2010 and 2012 before stabilizing. In 2020, the state employed 283,044 teachers, achieving a favorable student-to-teacher ratio of 1:14 compared to the national average of 1:16. Per-pupil spending averaged USD 12,697, and the graduation rate improved significantly to 91% in the 2018–2019 school year, up from 81.4% in 2012–2013 (U.S. Census Bureau 2022;National Center for Education Statistics 2022). The public high school event dropout rate in West Virginia improved from 3.4% in 2010–2011 to 2.7% in 2011–2012, compared to a stable national rate of 3.3% during these years. However, college enrollment among high school graduates fell from 56.3% in 2011 to 48.3% in 2021 (WVHEPC (West Virginia Higher Education Policy Commission) 2023). West Virginia’s higher education sector has shown growth in recent years, with a 7% increase in degrees conferred from 2017 to 2021, totaling 32,051 degrees (WVHEPC (West Virginia Higher Education Policy Commission) 2023). The state has 36 degree-granting institutions, including 14 private colleges with about 50,000 undergraduate students, where 17,610 degrees were awarded in 2021, largely through online programs. Public colleges, comprising 13 institutions, conferred 14,116 degrees with around 44,500 students enrolled. Additionally, nine community colleges enrolled 9136 students and awarded 2144 associate degrees in 2021. To advance its education, economy, and workforce, West Virginia should leverage its strengths in AI, machine learning, big data, and supercomputing. With 42 colleges and universities, state leaders are crucial in managing educational resources and tackling challenges like budget cuts and outmigration (Cart 2016;Williams 2012). 2.2.7. Government Sector The government sector in West Virginia has seen a notable decline in workforce numbers, particularly since 2019. However, the federal government remains a crucial source of employment in the state, with agencies such as the FBI, US Treasury, and National Park Service expanding their staffing levels (Bureau of Business and Economic Research 2022). The long-term impact of the state’s declining population on federal government decisions remains uncertain, but the sector’s influence on job creation in West Virginia is undeniable. This decline reflects broader national trends, where state and local government employment has faced cuts due to budgetary pressures, while federal employment remains more stable. Economies 2024,12, 290 6 of 26 2.2.8. Transportation Sector The transportation sector in West Virginia has experienced a slight decline in workforce levels over the past two decades, with a marginal decrease particularly evident in recent years, exacerbated by the challenges posed by the COVID-19 pandemic. The state’s transportation network, which includes highways, local roads, streets, bridges, airports, transit and rail, freight railroads, and ports and waterways, plays a vital role in facilitating the movement of travelers, supporting businesses, transporting freight, and driving economic growth (West Virginia Transportation by the Numbers 2021). The sector’s performance aligns with national trends where transportation infrastructure remains critical but faces challenges related to funding, maintenance, and adaptation to new technologies. West Virginia’s transportation equipment sector includes a burgeoning auto parts supply chain in the Kanawha and Mid-Ohio River valleys and a diverse mix of civilian and defense aerospace production. Auto part manufacturing has been the fastest-growing segment, with a 2.5% average annual job growth since 2008. Despite past challenges, the aerospace industry in North Central West Virginia has expanded, supported by increased commercial travel efforts and new contracts at the Applied Ballistics Laboratory (ABL), which is set to grow further with upcoming Northrop Grumman hires (Bureau of Business and Economic Research 2022). Looking ahead, emerging technologies are poised to further revolutionize West Virginia’s transportation sector. The impact of these technologies will be influenced by the state’s unique terrain and rural road network. To navigate these changes effectively, the West Virginia Department of Transportation (WVDOT) must proactively prepare by incorporating insights from peer states and integrating new technologies into the 2050 Long-Range Transportation Plan (West Virginia Transportation by the Numbers 2021). 2.2.9. Finance Sector The finance sector in West Virginia has shown a declining trend of employees as in the other sectors. From 2001 to 2020, the finance sector in West Virginia experienced a decline, encompassing roles in securities, commodity contracts, financial investments, real estate, and leasing services. This reduction aligns with national trends influenced by consolidation, technological advancements, and regulatory changes, which have diminished traditional finance roles while increasing demand for fintech and data analysis skills. Currently, approximately 18,000 employees are in the state’s financial sector, including banks, insurance companies, investment firms, real estate agencies, and property management services (Bureau of Business and Economic Research 2022). This represents a loss of 4000 jobs compared to the year 2000. 2.2.10. Utilities Sector The utilities sector has exhibited a general decline over time, with a slight increase observed after 2016. The sector indicates a decrease in workforce numbers, particularly during the pandemic. Utility services in West Virginia encompass the provision and distribution of electricity, natural gas, water, and wastewater treatment, all of which are essential for supporting residential, commercial, and industrial activities across the state. While West Virginia’s coal plants suggest increased utility spending and ratepayer costs, a shift toward clean energy could create an affordable and reliable energy supply for ratepayers, accompanied by additional long-term community benefits (Massie et al. 2023). This transition reflects a broader national and global trend toward sustainable energy practices, as environmental concerns and economic incentives drive the shift away from fossil fuels. 2.2.11. Service Sector West Virginia’s service industry is diverse, encompassing sectors such as health and education, biometrics and government, hospitality, media and telecommunications, printing, retail, tourism, and banking. The community, personal, and business service sectors Economies 2024,12, 290 7 of 26 hold the largest share of the gross product within this industry, with tourism and healthcare playing pivotal roles in driving growth and creating numerous job opportunities. Employment in entertainment and accommodation services indicates a substantial decline during the onset of the COVID-19 pandemic but has rebounded significantly since spring 2021 (Bureau of Business and Economic Research 2022). This recovery is consistent with national trends in the service industry, where the pandemic caused widespread disruption, followed by a gradual but uneven recovery as restrictions eased and consumer confidence returned. 3. Data and Methods 3.1. Data The primary source of data for this study was the Bureau of Labor Statistics (BLS). Employment changes in West Virginia over a 20-year period, from 2000 to 2020, were carefully examined to gain insights into workforce trends within the state. In addition to the BLS data, census data and other relevant information pertaining to West Virginia were incorporated to provide a comprehensive view of the economic and demographic factors influencing employment patterns. To conduct the analysis, a detailed panel dataset spanning the last two decades was carefully compiled. This dataset allowed for a thorough examination of employment trends over time, enabling a deeper understanding of the shifts and dynamics within West Virginia’s labor market. By integrating various sources of data, this study aimed to present a robust and nuanced analysis of the employment landscape in the region. To effectively achieve its main objectives, this study utilized a combination of general descriptive analysis, shift-share analysis, location quotient analysis, and Boudeville’s classification following the shift-share analysis. 3.2. Shift-Share Analysis The shift-share analysis technique, developed by Daniel Creamer in the 1940s and summarized by Dunn in 1960 (Shi and Yang 2008), is designed to evaluate changes in economic activity across different regions. This method is widely used for analyzing regional employment growth by breaking down total growth into three components: the National Growth Effect (NGE), the Industry Mix Effect (IME), and the competitive effect (CE) (Barff and Knight 1988;Knudsen 2000;Wilson and Chern 2005;Herath et al. 2010, 2013;Herath Bandara 2024). Shift-share analysis can be conducted using various methods, each offering distinct insights into regional economic growth. These methods include standard shift-share analysis, dynamic shift-share analysis, and spatial shift-share analysis. Each method differs in terms of its data requirements and the depth of analysis it provides. Standard shift-share analysis is the most straightforward and commonly used approach, breaking down employment changes into three components: national growth, industry mix, and regional competitive effects. It requires only basic employment data at national and regional levels, making it ideal for situations where data availability is limited. Dynamic shift-share analysis introduces a temporal dimension, focusing on how regional competitiveness and industry performance evolve over time. While this method offers more in-depth insights, it requires complex time-series data, making it more challenging to implement, especially in regions lacking comprehensive historical data. Spatial shift-share analysis considers the influence of neighboring regions on a region’s economic performance. This method demands detailed data on both the target region and its surrounding areas, significantly increasing data complexity. Its application is often constrained by the unavailability of spatially detailed datasets. In this study, the standard shift-share analysis method is employed due to its clear and straightforward breakdown of employment changes into national growth, industry mix, and regional competitive effects. Limited data availability for the study period of 20 years made it impractical to conduct dynamic or spatial analyses. On the other hand, the focus of this study was on evaluating broad employment trends and sectoral shifts, Economies 2024,12, 290 8 of 26 which the standard method captures effectively. Its simplicity ensures that the findings are easily interpretable and actionable for policymakers, highlighting competitive industries and offering valuable insights for developing targeted economic strategies. The shift-share analysis compares regional employment growth with national trends, providing insights into whether changes are driven by broader national trends, specific industry dynamics, or regional competitive advantages. By calculating these effects, shiftshare analysis helps determine the actual change in regional employment and sheds light on the underlying factors influencing economic performance (Richardson 1978). Following the notation of Richardson (1978), the three growth effects for a specific region and industrial sector are expressed as follows: National growth effect for sector iin region r=Eir ×Gn. Industrial mix effect for sector iin region r=Eir ×(Gin −Gn). Competitive effect for sector iin region r=Eir ×(Gir −Gin). where Eir = employment in sector iin region rat the beginning of the time period. Gn= growth rate for total employment for the nation over the time period. Gin = growth rate in sector ifor the nation for the time period. Gir = growth rate in sector iin region rfor the time period. In this specific investigation, the utilization of shift-share analysis is employed to evaluate employment growth in five significant regions of West Virginia: Mid-Ohio Valley, Metro Valley, New River/Greenbrier Valley, Potomac Highlands, and Mountain Lakes. Figure 1illustrates the regional sectors of West Virginia, while Table 1lists the counties within each sector out of the state’s 55 counties. This assessment is made in relation to the overall employment growth within the state, with state employment growth acting as a benchmark akin to national growth. The primary focus lies in comparing the average regional employment growth to that of the state, highlighting the influence of the state growth effect (SGE), industrial mix effect (IME), and competitive effect (CE). The cumulative impact of these factors reveals the actual change in total employment within each region during the specified study period. Economies 2024, 12, x FOR PEER REVIEW 8 of 28 In this study, the standard shift-share analysis method is employed due to its clear and straightforward breakdown of employment changes into national growth, industry mix, and regional competitive effects. Limited data availability for the study period of 20 years made it impractical to conduct dynamic or spatial analyses. On the other hand, the focus of this study was on evaluating broad employment trends and sectoral shifts, which the standard method captures effectively. Its simplicity ensures that the findings are easily interpretable and actionable for policymakers, highlighting competitive industries and offering valuable insights for developing targeted economic strategies. The shift-share analysis compares regional employment growth with national trends, providing insights into whether changes are driven by broader national trends, specific industry dynamics, or regional competitive advantages. By calculating these effects, shiftshare analysis helps determine the actual change in regional employment and sheds light on the underlying factors influencing economic performance (Richardson 1978). Following the notation of Richardson (1978), the three growth effects for a specific region and industrial sector are expressed as follows: National growth effect for sector i in region r = Eᵢᵣ × G ₙ . Industrial mix effect for sector i in region r = Eᵢᵣ × (Gᵢ ₙ − G ₙ ). Competitive effect for sector i in region r = Eᵢᵣ × (Gᵢᵣ − Gᵢ ₙ ). where Eᵢᵣ = employment in sector i in region r at the beginning of the time period. G ₙ = growth rate for total employment for the nation over the time period. Gᵢ ₙ = growth rate in sector i for the nation for the time period. Gᵢᵣ = growth rate in sector i in region r for the time period. In this specific investigation, the utilization of shift-share analysis is employed to evaluate employment growth in five significant regions of West Virginia: Mid-Ohio Valley, Metro Valley, New River/Greenbrier Valley, Potomac Highlands, and Mountain Lakes. Figure 1 illustrates the regional sectors of West Virginia, while Table 1 lists the counties within each sector out of the state’s 55 counties. This assessment is made in relation to the overall employment growth within the state, with state employment growth acting as a benchmark akin to national growth. The primary focus lies in comparing the average regional employment growth to that of the state, highlighting the influence of the state growth effect (SGE), industrial mix effect (IME), and competitive effect (CE). The cumulative impact of these factors reveals the actual change in total employment within each region during the specified study period. Figure 1. Regional map of West Virginia. Source: Virginia-map.com (2022). Figure 1. Regional map of West Virginia. Source: Virginia-map.com (2022). Economies 2024,12, 290 15 of 26 Table 3. Employment growth in Metro Valley in West Virginia: 2001–2020. Sectors/Industries SGE IME CE Actual Growth Total Industries −23,953.63 0.00 −13,381.37 −37,335.00 Information −615.22 −2265.51 −294.27 −3175.00 Agriculture −37.87 −104.73 −60.40 −203.00 Mining −1130.12 −1978.32 −2881.56 −5990.00 Construction −1333.23 −828.28 −1041.49 −3203.00 Manufacturing −2064.54 −5164.40 920.94 −6308.00 Wholesale Trade −1091.43 −1501.40 −744.17 −3337.00 Retail Trade −3377.78 −1065.93 −1930.30 −6374.00 Transportation −769.06 −1.42 −1292.52 −2063.00 Finance −1080.72 −1276.33 −841.95 −3199.00 Scientific −1086.49 1572.26 −1755.78 −1270.00 Utilities −311.55 −476.47 −214.98 −1003.00 Entertainment −215.82 145.38 199.44 129.00 Rental −334.37 54.14 −276.78 −557.00 Administrative −1453.78 −3.92 −790.30 −2248.00 Healthcare −4075.44 12,337.52 1028.92 9291.00 Accommodation −1994.56 2032.81 −1700.25 −1662.00 Educational Services −2068.53 −736.36 −292.10 −3097.00 Other Services (except public admin.) −913.13 −1382.90 −769.98 −3066.00 Table 4highlights employment growth in the New River/Greenbrier Valley region over the past two decades, with notable gains in healthcare, rental, transportation, and administrative services, and minimal losses in agriculture and utilities. Table 4. Employment growth in New River/Greenbrier Valley in West Virginia: 2001–2020. Sectors/Industries SGE IME CE Actual Growth Total Industries −9784.49 0.00 −2726.51 −12,511.00 Information −234.52 −863.59 −194.90 −1293.00 Agriculture −73.98 −204.59 77.56 −201.00 Mining −471.38 −825.17 898.56 −398.00 Construction −505.02 −313.75 −888.24 −1707.00 Manufacturing −555.12 −1388.63 −39.25 −1983.00 Wholesale Trade −348.36 −479.22 −10.42 −838.00 Retail Trade −1688.42 −532.82 −907.77 −3129.00 Transportation −151.25 −0.28 290.53 139.00 Finance −318.61 −376.27 −569.12 −1264.00 Scientific −265.09 383.62 −378.53 −260.00 Utilities −69.51 −106.30 62.81 −113.00 Entertainment −174.30 117.41 −283.11 −340.00 Rental −91.27 14.78 197.49 121.00 Administrative −342.01 −0.92 897.93 555.00 Healthcare −1995.38 6040.58 −1756.20 2289.00 Accommodation −1089.07 1109.96 −1083.89 −1063.00 Educational Services −1002.63 −356.92 −306.45 −1666.00 Other Services (except public admin.) −408.58 −618.78 −332.64 −1360.00 The healthcare sector added 2289 jobs, driven by a favorable industrial mix effect (IME), despite unfavorable state trends (SGE) and comparative effect (CE). Similarly, administrative services grew by 555 jobs, benefiting from positive competitive and industry composition effects. The rental sector also highlights modest growth, adding 121 jobs, with its performance bolstered by strong CE and IME, despite negative state trends. Conversely, the retail trade sector lost 3129 jobs, facing declines across all components— state trends, industry composition, and competitiveness—highlighting challenges like changing consumer behavior. The educational services sector lost 1666 jobs, hindered by weak competitive performance and unfavorable industry and state trends. Manufacturing also faced significant losses, with 1983 jobs lost due to negative impacts across all components, reflecting broader industry challenges and reduced regional competitiveness. The agriculture sector in the New River/Greenbrier Valley region indicates a modest decline of 201 jobs from 2001 to 2020. Negative state growth and industrial mix effects indicate Economies 2024,12, 290 16 of 26 unfavorable state trends and sector composition. However, a positive competitive effect suggests that the sector performed relatively well compared to other regions, showing some resilience despite broader challenges. Table 5presents the shift-share analysis for the Potomac Highlands region from 2001 to 2020, revealing mixed employment trends across various sectors. Growth was observed in healthcare, scientific services, retail trade, education, and accommodation, while sectors such as manufacturing, construction, entertainment, and administrative services experienced significant declines. Table 5. Employment growth in Potomac Highlands in West Virginia: 2001–2020. Sectors/Industries SGE IME CE Actual Growth Total Industries −8447.03 0.00 7345.03 −1102.00 Information −280.97 −1034.66 285.63 −1030.00 Agriculture −83.62 −231.26 51.88 −263.00 Mining −25.29 −44.26 230.55 161.00 Construction −496.90 −308.70 −403.39 −1209.00 Manufacturing −1351.46 −3380.66 692.13 −4040.00 Wholesale Trade −294.73 −405.44 −73.82 −774.00 Retail Trade −1207.15 −380.94 2007.10 419.00 Transportation −187.24 −0.35 0.58 −187.00 Finance −234.63 −277.10 537.73 26.00 Scientific −216.40 313.16 662.24 759.00 Utilities −21.88 −33.46 79.33 24.00 Entertainment −169.95 114.48 −587.53 −643.00 Rental −91.27 14.78 −20.51 −97.00 Administrative −340.25 −0.92 −383.84 −725.00 Healthcare −1327.47 4018.63 2641.84 5333.00 Accommodation −913.36 930.88 911.48 929.00 Educational Services −966.05 −343.90 1498.95 189.00 Other Services (except public admin.) −238.40 −361.04 625.44 26.00 The healthcare sector led the region’s growth, adding 5333 jobs. This growth was primarily driven by a strong industrial mix effect (IME) and competitive effect (CE), suggesting that the sector benefited from favorable changes in its industry composition compared to state trends. Despite a negative state growth effect (SGE), the healthcare sector’s positive competitive effect (CE) indicates it outperformed its competitors, reflecting robust regional demand and expansion in healthcare services. The scientific sector also experienced growth, adding 759 jobs. This increase was largely supported by a favorable IME, which highlights the sector’s benefit from advantageous changes in industry composition. The positive CE further underscores the sector’s strong performance relative to its competitors, despite broader state trends not being supportive. The accommodation sector added 929 jobs, driven by a positive IME and strong regional competitiveness. Despite unfavorable state trends, the sector leveraged its advantages to outperform competitors. On the other hand, the manufacturing sector experienced a significant decline, losing 4040 jobs. This downturn was driven by negative effects across all components: an unfavorable SGE, a declining IME, and a poor CE, highlighting the sector’s struggles with competitiveness and broader industry challenges. Similarly, the entertainment sector experienced a decline of 643 jobs, primarily due to a negative CE, indicating underperformance compared to competitors. Despite some favorable changes in the sector’s composition, as reflected by a positive IME, the overall decline was exacerbated by unfavorable state trends. Administrative services also faced a reduction of 725 jobs. The sector was negatively impacted across all components, with the SGE, IME, and CE all reflecting broader regional challenges and difficulties in maintaining competitiveness. Table 6presents the shift-share analysis results for the Mountain Lakes region from 2001 to 2020, showing significant employment growth in the healthcare, scientific, administrative, and accommodation sectors. Conversely, the mining, manufacturing, and educational services sectors experienced declines. Economies 2024,12, 290 17 of 26 Table 6. Employment growth in Mountain Lakes in West Virginia: 2001–2020. Sectors/Industries SGE IME CE Actual Growth Total Industries −14,463.28 0.00 14,217.28 −246.00 Information −259.10 −954.10 353.20 −860.00 Agriculture −58.22 −161.00 −35.78 −255.00 Mining −672.26 −1176.82 −768.92 −2618.00 Construction −802.93 −498.82 1893.75 592.00 Manufacturing −1067.67 −2670.76 −485.58 −4224.00 Wholesale Trade −352.36 −484.72 898.08 61.00 Retail Trade −2185.20 −689.59 1467.79 −1407.00 Transportation −346.48 −0.64 734.12 387.00 Finance −351.07 −414.61 198.68 −567.00 Scientific −503.73 728.95 1509.78 1735.00 Utilities −220.17 −336.72 218.88 −338.00 Entertainment −76.92 51.81 505.11 480.00 Rental −151.13 24.47 174.66 48.00 Administrative −550.18 −1.48 1645.67 1094.00 Healthcare −2875.11 8703.77 2572.34 8401.00 Accommodation −1301.60 1326.56 989.04 1014.00 Educational Services −2200.14 −783.21 −203.65 −3187.00 Other Services (except public admin.) −489.02 −740.61 627.63 −602.00 The healthcare sector indicates significant growth, adding 8401 jobs. This increase was largely driven by a strong industrial mix effect (IME), indicating favorable changes in the sector’s composition despite an unfavorable state growth effect (SGE). The positive competitive effect (CE) further underscores the sector’s outperformance relative to competitors, reflecting a strong regional demand. The scientific sector added 1735 jobs, supported by a favorable IME and positive CE, demonstrating strong performance despite a negative SGE. Similarly, administrative services grew by 1094 jobs, driven by positive CE and IME, showing resilience and competitiveness despite unfavorable state trends. Conversely, the mining sector experienced a decline of 2618 jobs, influenced by negative effects across all components: an unfavorable SGE, a declining IME, and a poor CE. This downturn highlights broader challenges and reduced competitiveness in the mining industry. The manufacturing sector also faced a significant decline, losing 4224 jobs. Negative effects across the SGE, IME, and CE reflect broader industry challenges and diminished regional competitiveness. The educational services sector experienced a decline of 3187 jobs, with negative effects across all components, including an unfavorable SGE, a declining IME, and poor performance relative to the competitors. This decline suggests challenges such as funding issues and declining enrollment amid broader state trends. 4.3. Location Quotient Analysis Location quotients ( LQ s) are ratios used to compare employment distribution across different industries within a specific area relative to a reference area, typically the overall industry total (Richardson 1973). According to the Bureau of Labor Statistics, an LQ of 1 indicates that the industry’s share of local employment is proportional to its share in the reference area. An LQ greater than 1 signifies that the industry has a higher proportion of local employment compared to the reference area. Table 7presents the LQ s for all regions of West Virginia for the year 2020. The first column of the table lists each sector or industry, while the remaining columns show the regions. The table illustrates changes in employment numbers over the 20-year period across all regions, highlighting the critical importance of certain industries to specific regions of West Virginia in terms of employment. This underscores the need to understand and prioritize key industries within the regions. Additionally, LQ analysis identifies emerging industries and emphasizes the need for focused development efforts. This insight helps policymakers and stakeholders make informed decisions about resource allocation and economic development strategies. Economies 2024,12, 290 18 of 26 Table 7. Location quotient analysis for all regions in West Virginia, 2020. Mid-Ohio Valley Metro Valley New River/Greenbrier Potomac Highlands Mountain Lakes Total Industries 11111 Information 0.6438 1.0497 0.8422 1.6319 0.9292 Agriculture 0 0.2886 2.4434 2.5563 0.7891 Mining 1.4640 0.7875 1.8487 0.1924 0.9137 Construction 1.0454 0.9853 0.7375 0.8594 1.2182 Manufacturing 1.5515 0.9755 0.5587 1.5202 0.5706 Wholesale Tde 1.0493 1.1837 0.9956 0.8114 0.8252 Retail Trade 1.0614 0.9290 1.0984 1.0446 0.9676 Transportation 0.8915 1.1144 0.8349 0.8227 1.1246 Finance 1.3911 1.1955 0.6787 0.9487 0.6540 Scientific 0.7242 1.1760 0.6926 0.90227 1.2038 Utilities 0.5246 1.2143 0.8298 0.3644 1.5338 Entertainment 1.2222 0.9599 1.3134 0.9219 0.7511 Rental 0.7333 1.2044 1.1121 0.8414 0.9518 Administrative 0.7777 1.2748 1.0273 0.6428 0.9861 Healthcare 0.8807 1.0355 1.0677 0.9211 1.0490 Accommodation 1.0708 0.8807 1.1105 1.1774 0.9426 Edu. Services 0.8044 0.8634 0.9617 1.1775 1.2532 Other Services 1.0885 0.9602 1.0168 0.9869 0.9850 4.3.1. Mid-Ohio Region Table 7presents the location quotients ( LQ s) for employment concentration in the Mid-Ohio Valley region, highlighting several sectors with positive LQ s that exceed the state average. Among these, mining, manufacturing, and finance stand out as the key sectors with the highest LQs, indicating their significant role in the region’s economy in 2020. Notably, the mining sector, with an LQ of 1.4640, stands out for its significant job concentration, underscoring its critical role in the regional economy. This elevated LQ highlights the sector’s reliance on local natural resources and active mining operations, indicating that the Mid-Ohio Valley has maintained a competitive advantage in mining. This advantage is likely due to key facilities and a skilled workforce. The positive competitive effect observed in the shift-share analysis for the region (Table 2) further underscores the importance of continued investment in mining for the region’s economic stability. Similarly, the manufacturing sector stands out with an LQ of 1.5515, indicating a strong concentration of manufacturing jobs in the Mid-Ohio Valley region. This high LQ suggests a sustained competitive advantage in manufacturing. However, the competitive effect (CE) from the shift-share analysis for the period 2000–2020 (Table 2) reveals that the region is underperforming relative to the state average. This underperformance implies that further investments in manufacturing may not yield long-term benefits for the region. The finance sector also demonstrates a strong LQ of 1.3911, indicating a higher concentration of financial services employment compared to the state average. This is reinforced by the competitive effect in shift-share analysis, underscoring the sector’s potential for driving regional economic growth in the future. Similarly, the entertainment sector reflects this trend, with both its LQ value and shift-share analysis suggesting its growing importance in the region. 4.3.2. Metro Valley Region In the Metro Valley region, several industries displayed notable location quotient ( LQ ) values in 2020 (Table 7). Among these, the Wholesale Trade, Transportation, and Utilities sectors are particularly significant, reflecting the region’s diverse and strategically important economic activities. The Wholesale Trade sector, with an LQ of 1.1837, indicates a higher concentration of jobs compared to the state average, highlighting the Metro Valley region’s pivotal role in distribution and trade. This advantage is likely supported by the region’s strategic location and infrastructure, which bolster wholesale activities in 2020. However, the competitive Economies 2024,12, 290 19 of 26 effects (CE) from the shift-share analysis for the region (Table 3) suggest that this sector may not provide long-term regional benefits, as the growth may not be driven by local advantages. Comparing LQ s with CE helps identify whether concentrated sectors are expanding due to regional strengths (positive CE) or facing local challenges (negative CE). Similarly, the Transportation sector, with an LQ of 1.1144, and the Utilities sector, with an LQ of 1.2143, also show significant employment concentrations, yet both lack positive competitive effects in the shift-share analysis, indicating potential challenges in sustaining these sectors’ growth. However, the healthcare sector ( LQ 1.0355), along with manufacturing ( LQ 0.9755) and entertainment ( LQ 0.9555), all exhibit LQ s close to 1 and demonstrate positive competitive effects in the shift-share analysis. This indicates that these sectors are benefiting from local advantages, suggesting that future investments in these areas could drive regional employment growth. These findings point to the Metro Valley region’s strong potential in sectors like healthcare, manufacturing, and entertainment, underscoring the importance of strategic investments to capitalize on these local strengths. 4.3.3. New River/Greenbrier Valley Region In the New River/Greenbrier Valley region, several industries exhibited notable location quotient ( LQ ) values in 2020, particularly in agriculture, mining, entertainment, rental, and administrative services (see Table 7). For example, the agriculture sector stands out with a high LQ of 2.4434, indicating that employment in this industry is more than twice as concentrated in this region compared to the state average. This underscores agriculture’s vital role in the local economy, likely driven by the region’s rural character and extensive farming activities. The positive competitive effects in the shift-share analysis (Table 4) further support this advantage. Similarly, the mining sector, with an LQ of 1.8487, administrative services with an LQ of 1.1105, and rental services with an LQ of 1.1101, demonstrate strong regional concentrations and positive competitive effects, highlighting the local benefits in these industries. However, while the entertainment sector shows a notable LQ of 1.3234, its competitive effects are less favorable, with negative values (see Table 4). This suggests that the sector’s concentration may not be driven by local advantages but rather by other factors. 4.3.4. Potomac Highlands Region In the Potomac Highlands region, several industries exhibited notable location quotient ( LQ ) values in 2020, emphasizing their significant concentration compared to state averages (see Table 7). The agriculture sector stands out with an exceptionally high LQ of 2.5563, indicating that employment in agriculture is more than twice as concentrated in the Potomac Highlands as it is statewide. Despite this, the competitive effect from the shift-share analysis (Table 5) is less favorable, suggesting a lower level of local competitiveness in this sector. The manufacturing sector also shows a strong presence with an LQ of 1.5202, and the shift-share analysis indicates positive competitive effects. This reflects a robust concentration of manufacturing jobs in the region, signifying that manufacturing continues to be a crucial part of the Potomac Highlands’ economy, supported by local industries and production facilities. The education sector, with an LQ of 1.1775, also demonstrates significant competitive effects according to the shift-share analysis. This suggests that educational services are notably more concentrated in the Potomac Highlands compared to the state average, likely due to the presence of educational institutions and support services catering to the local population. Additionally, the rental and accommodation sectors, based on LQ and shift-share analysis results, could play a significant role in shaping the region’s economic landscape moving forward. Economies 2024,12, 290 20 of 26 4.3.5. Mountain Lakes Region In the Mountain Lakes region, several industries exhibited notable location quotient ( LQ ) values in 2020, highlighting their significant concentration compared to state averages (see Table 7). Key sectors with high LQ s include education, scientific services, finance, mining, healthcare, and utilities. The scientific, healthcare, finance, and utilities sectors all show positive LQ s and favorable competitive effects in the shift-share analysis (Table 6), indicating a strong regional advantage in these industries. Investing further in these sectors could yield substantial long-term benefits. For example, the scientific sector has a robust LQ of 1.2038, reflecting a high concentration of employment in research and development or specialized technical consulting within the region. This suggests a strong presence of scientific and technical services that contribute significantly to the local economy. The utilities sector boasts an impressive LQ of 1.5338, underscoring its critical role in the region’s economy. This high LQ indicates that energy production, distribution, and related services are notably more concentrated in the Mountain Lakes region compared to the state averages, likely due to the region’s natural resources and infrastructure supporting local and regional energy needs. While the education sector also stands out with an LQ of 1.2532, indicating a significant presence, the shift-share analysis suggests that it may not be as competitive in generating additional economic benefits or employment compared to other sectors. 4.4. Boudeville’s Framework: Insights into West Virginia’s Economic Sectors Boudeville’s (1966) classification is employed in this study to provide a structured framework for understanding the economic dynamics of different regions within West Virginia. By categorizing regions based on their economic structure, this classification aids in identifying specific challenges and opportunities for each region, particularly when analyzed alongside shift-share analysis. Boudeville’s classification categorizes regions into three types: Specialized, Diversified, and Peripheral Regions. Specialized Regions rely heavily on one or a few key industries for their economic activity, benefiting from the strong performance of these sectors while also becoming vulnerable to sector-specific shocks, such as fluctuations in commodity prices. Diversified Regions, in contrast, feature a balanced mix of industries across various sectors, including primary (like agriculture), secondary (such as manufacturing), and tertiary (like services), which enhances their resilience to economic downturns. Peripheral Regions are characterized by weaker economic structures and often face challenges like high unemployment and declining industries, necessitating targeted policy interventions to stimulate growth. When combined with shift-share analysis, Boudeville’s framework provides deeper insights into how specific industries drive regional economic changes, revealing the competitive effects, structural changes, and industry mix that contribute to overall employment growth or decline in a region. 4.4.1. Mid-Ohio Valley Region The employment growth data presented in Table 2illustrate the findings of the shiftshare analysis for the Mid-Ohio Valley region. This information can be further examined through the framework of Boudeville’s (1966) classification, which offers valuable insights into the region’s economic structure. The mining sector demonstrates a significant positive competitive effect (CE) of 2521.37, indicating its dominant role in the region’s economy. Despite negative trends in other sectors, mining’s strong performance suggests specialization in this industry. However, such dependence on mining makes the region vulnerable to sector-specific shocks, particularly given the volatility of the mining market. In the healthcare sector, there is notable structural growth reflected by a large industrial mix effect (IME) of 7288.51, but its negative CE of -4486.90 indicates that other factors are hindering its full growth potential. While sectors like entertainment, accommodation, and Economies 2024,12, 290 21 of 26 scientific services exhibit small positive competitive effects, they are insufficient to classify the region as fully diversified. The overall decline in most sectors, including critical industries like manufacturing, retail trade, and educational services, points to characteristics of a peripheral economy. The total industries category shows a significant employment decline of − 18,498 jobs, highlighting an underdeveloped or declining economic structure. Peripheral regions often require policy intervention to stimulate growth and lessen reliance on declining sectors. 4.4.2. Metro Valley Region The employment growth data presented in Table 3illustrates the findings of the shift-share analysis for the Metro Valley region. This information can be further examined through the framework of Boudeville’s (1966) classification, which offers valuable insights into the region’s economic structure. The mining sector exhibits a negative competitive effect (CE) of − 2881.56, leading to an actual job loss of − 5990.00. This suggests that the region is specialized in mining, but such dependence on a declining industry has resulted in significant economic challenges. The healthcare sector shows a positive industrial mix effect (IME) of 12,337.52 and a competitive effect (CE) of 1028.92, indicating growth potential. However, this is offset by declines in manufacturing, retail, and educational services. The mixed performance of sectors like entertainment and accommodation is insufficient to classify the region as diversified. Overall, the Metro Valley region exhibits characteristics of a peripheral economy, with total industries experiencing an alarming employment decline of − 37,335 jobs. Most sectors, especially manufacturing and retail, are in decline, highlighting the need for policy interventions to stimulate growth. 4.4.3. New River/Greenbrier Valley Region The employment growth data in Table 4highlight the results of the shift-share analysis conducted for the New River/Greenbrier Valley region. This analysis can be further interpreted using Boudeville’s (1966) classification, which provides significant insights into the economic structure of the region. The mining sector shows a competitive effect (CE) of 898.56, indicating its role as a key industry in the region’s economy. However, despite this positive competitive effect, the overall negative growth in other sectors highlights a concerning dependence on mining. The significant loss of employment across various industries suggests that while mining may provide some stability, the region remains vulnerable to sector-specific shocks. The healthcare sector demonstrates substantial potential with a high industrial mix effect (IME) of 6040.58, but it also has a negative CE of − 1756.20. This indicates that while healthcare has growth potential, it is not currently fully realized. Other sectors, such as transportation and administrative services, show small positive competitive effects, suggesting a degree of diversity; however, these contributions are insufficient in establishing the region as a fully diversified economy. The overall decline in most sectors, including agriculture, manufacturing, retail trade, and educational services, characterizes the New River/Greenbrier Valley as a peripheral economy. The total industries category reflects a significant employment decline of −12,511 jobs , indicating a deteriorating economic structure. This scenario underscores the need for policy interventions aimed at stimulating growth and reducing reliance on declining industries. 4.4.4. Potomac Highlands Region The employment growth data in Table 5highlights the results of the shift-share analysis conducted for the Potomac Highlands region. This analysis can be further interpreted using Boudeville’s (1966) classification, which provides significant insights into the economic structure of the region. Economies 2024,12, 290 22 of 26 The mining sector shows a competitive effect (CE) of 230.55, indicating its role in the region’s economy, albeit with modest growth. However, the overall negative growth in the total industries category ( − 1102 jobs) suggests that while mining may be a key industry, it is not strong enough to classify the region as specialized. The presence of declining sectors raises concerns about economic stability. The healthcare sector exhibits a notable industrial mix effect (IME) of 4018.63, coupled with a positive CE of 2641.84, indicating significant potential for growth. Additionally, sectors like scientific services, retail trade, and accommodation show small but positive competitive effects, which suggests a degree of economic diversity. However, the overall performance of many sectors is still weak, preventing the region from being classified as fully diversified. The Potomac Highlands region displays characteristics of a peripheral region, evidenced by significant declines in critical industries such as manufacturing, retail trade, and educational services. The overall decline in employment across many sectors contributes to the region’s underdeveloped economic structure. The total industries category reflects a notable employment loss of − 8447.03 jobs, highlighting the need for policy interventions to stimulate growth and improve competitiveness. 4.4.5. Mountain Lakes Region The employment growth data in Table 6highlight the results of the shift-share analysis conducted for the Mountain Lakes region. This analysis can be further interpreted using Boudeville’s (1966) classification, which provides significant insights into the economic structure of the region. The Mountain Lakes region does not exhibit clear specialization in any single industry. Although mining has historically been a key sector, it shows a significant competitive effect (CE) of − 768.92, indicating a decline in its economic contribution. The total employment loss across all industries amounts to 246 jobs, underscoring a broader economic downturn. The healthcare sector demonstrates a significant industrial mix effect (IME) of 8703.77 and a positive CE of 2572.34, indicating strong growth potential. Additionally, sectors like scientific services and accommodation show positive competitive effects, reflecting some level of diversification within the economy. Nonetheless, the overall performance of many critical sectors remains weak, preventing classification as fully diversified. The Mountain Lakes region largely displays characteristics of a peripheral economy, as evidenced by substantial declines across key industries such as manufacturing, retail trade, and educational services. The total industries category reflects a significant employment loss of − 14,463.28 jobs, highlighting the need for policy interventions to stimulate growth and enhance economic resilience. 5. Conclusions and Policy Suggestions The primary aim of this study is to analyze employment growth patterns across various sectors in West Virginia over the past two decades and assess their implications for long-term economic development. By employing shift-share and location quotient ( LQ ) analyses, this study identifies the key sectors driving regional employment and evaluates their current performance. Additionally, it applies Boudeville’s framework to better understand regional economic structures, highlighting the importance of industry specialization and diversification. This approach provides guidance for policymakers in crafting strategies that address the unique challenges faced by different regions. The findings indicate a critical need for targeted policy interventions to promote economic stability and growth tailored to the diverse conditions across West Virginia. In the Mid-Ohio Valley, location quotient ( LQ ) values indicate that mining, manufacturing, and finance are the most concentrated industries. However, a subsequent shift-share analysis, informed by Boudeville’s classification, reveals that the region is primarily specialized in mining, with some diversification evident in healthcare and other service sectors. Despite this, a general decline across most industries, along with significant losses in Economies 2024,12, 290 23 of 26 manufacturing and retail, suggests that the Mid-Ohio Valley exhibits characteristics of a peripheral region. To stabilize and promote long-term growth, economic diversification and targeted policy interventions are crucial. Specifically, in the mining sector, implementing sustainable practices and diversifying operations—such as transitioning to renewable energy or valueadded processing—can enhance economic stability and reduce vulnerability to market fluctuations. This approach not only secures long-term employment opportunities but also benefits the local community. In the Metro Valley region, location quotient ( LQ ) values indicate that wholesale trade, transportation, and utilities are the most concentrated sectors. However, shiftshare analysis reveals that healthcare and entertainment are significant contributors to employment growth. Informed by Boudeville’s classification, the analysis also shows that while the region is specialized in mining, it possesses potential in healthcare. Nonetheless, the decline across critical sectors suggests that the Metro Valley exhibits characteristics of a peripheral region. To stabilize the economy and promote long-term growth, economic diversification strategies and targeted policy interventions are essential. Policies should prioritize enhancing infrastructure and logistics. Strategic investments in transportation networks would improve connectivity, reduce operational costs for businesses, and increase efficiency in trade and distribution. In the New River/Greenbrier Valley region, location quotient ( LQ ) values indicate that agriculture and mining are the most concentrated industries. However, shift-share analysis reveals that the healthcare sector is a significant contributor to employment growth. Utilizing Boudeville’s classification alongside shift-share analysis, it becomes clear that while the region specializes in mining, it faces challenges in diversification and has experienced substantial employment losses across various sectors, reflecting the characteristics of a peripheral region. To foster long-term economic stability and growth, it is crucial to implement economic diversification strategies and targeted policy interventions. Therefore, to promote long-term economic stability and growth in the New River/ Greenbrier Valley region, establishing grant programs or low-interest loans for small business development is crucial to encourage entrepreneurship and diversify the economy. Additionally, investing in tailored workforce development programs, particularly in healthcare and technology, will equip the local workforce with essential skills, enhancing employability and fostering a more resilient economy. In the Potomac Highlands region, location quotient ( LQ ) values indicate that agriculture and manufacturing are the most concentrated industries. Shift-share analysis reveals that employment growth has primarily been driven by healthcare, accommodation, and scientific sectors. Boudeville’s classification highlights that while the region demonstrates some strengths in healthcare, overall employment declines across various sectors suggest characteristics of a peripheral economy. The combination of specialized activities in mining and the potential for growth in healthcare underscores the urgent need for economic diversification strategies and targeted policy measures to stabilize the economy and promote long-term growth. To enhance resilience, modernizing agricultural practices through technology and sustainability can increase productivity and create more stable job opportunities for farmers. Additionally, supporting manufacturing by investing in infrastructure and advanced technologies would foster highly skilled jobs, boosting economic output and decreasing reliance on a limited number of sectors. Lastly, improving educational opportunities is crucial for equipping the workforce with the skills needed to thrive in these evolving industries. In the Mountain Lakes region, location quotient ( LQ ) values indicate that scientific services, healthcare, and utilities are the most concentrated industries. Shift-share analysis reveals that healthcare, administrative services, scientific sectors, and accommodation have significantly contributed to employment growth. According to Boudeville’s classification, while the Mountain Lakes region shows promise in healthcare and some construction Economies 2024,12, 290 24 of 26 activities, the overall decline in critical sectors suggests it exhibits characteristics of a peripheral economy. This combination of specialized activities and potential growth in healthcare underscores the need for targeted economic diversification strategies and policy measures to stabilize the economy and foster long-term growth. To achieve these goals, policymakers should focus on enhancing infrastructure and promoting workforce development. Investments in healthcare facilities and technological advancements can stimulate job creation and improve service delivery. Furthermore, developing training programs tailored to the needs of emerging industries will equip the local workforce with the skills necessary for future job opportunities, ultimately contributing to a more resilient and diversified economy. This study has certain limitations. Its analysis is primarily based on location quotient ( LQ ) and shift-share methodologies, which may not fully reflect the dynamic nature of regional economies. Additionally, LQ and shift-share analyses do not account for external factors, such as market fluctuations or policy changes, that can significantly impact sector performance. The emphasis on specific sectors may also overlook the interconnectedness of the industries, potentially resulting in a fragmented understanding of the regional economy. Future research could strengthen the findings by integrating additional economic indicators and qualitative assessments, providing a more comprehensive view of regional dynamics. Incorporating spatial analysis techniques and examining sector interactions could offer deeper insights into the drivers of economic growth. Moreover, conducting longitudinal studies that track changes over time would enhance our understanding of how policies and external conditions affect employment patterns, leading to more effective, tailored policy recommendations for each region. Funding: This research was supported in part by the Intramural Research Program of the U.S. Department of Agriculture, National Institute of Food and Agriculture, Evans-Allen, accession #7003783. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: All data are included in the article. Conflicts of Interest: The author declares no conflict of interest. References Atitwa, Sundra C. 2020. What Are the Biggest Industries in West Virginia? São Paulo: World Atlas. 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