Geography and High-Tech Employment Growth in US Counties‡

Fallah, Belal; Partridge, Mark D.; Rickman, Dan S.

doi:10.1093/jeg/lbt030

Cited by 59 publications

(48 citation statements)

References 71 publications

(61 reference statements)

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“…(EMSI) with information on county employment and earnings disaggregated at 4--digit NAICS level. EMSI data have been successfully used in various studies in recent years (Betz et al, 2015;Dorfman et al, 2011;Fallah et al, 2011;Fallah et al, 2014;Nolan et al, 2011). This allows us to measure oil--and gas--extraction employment, as well as other sectors of interest, more precisely (especially in calculating our energy and industry mix terms).…”

Section: Estimation Approach Variables and Data Sourcesmentioning

confidence: 99%

Economics of modern energy boomtowns: Do oil and gas shocks differ from shocks in the rest of the economy?

Tsvetkova

Partridge

2016

Energy Economics

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The U.S. shale boom has intensified interest in how the expanding oil and gas sector affects local economic performance. Research has produced mixed results and has not compared how energy shocks differ from equal--sized shocks elsewhere in the economy. What emerges is that the estimated impacts of energy development vary by region, empirical methodology, as well as the time horizon that is considered. This paper captures these dimensions to present a more complete picture of energy boomtowns. Utilizing U.S. county data, we estimate the effects of changes in oil and gas extraction employment on total employment growth as well as growth by sector. We compare this to the effects of equal--sized shocks in the rest of the economy to assess whether energy booms are inherently different. The analysis is performed separately for nonmetropolitan and metropolitan counties using instrumental variables. We difference over 1--, 3--, 6--, and 10--year time periods to account for county fixed effects and to assess responses across different time horizons. The results show that in nonmetro counties, energy sector multiplier effects on total county employment first increase up to 6--year horizons and then decline for 10--year horizons. In metro counties, 1--year differences analysis suggests crowding out though the multipliers are insignificant in longer horizons. We also observe positive spillovers to the nontraded goods sector, while spillovers are small or negative for traded goods. Yet, equal--sized shocks in the rest of the economy produce more jobs on average than oil and gas shocks, suggesting that policymakers should seek more diversified development.

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Section: Estimation Approach Variables and Data Sourcesmentioning

confidence: 99%

Economics of modern energy boomtowns: Do oil and gas shocks differ from shocks in the rest of the economy?

Tsvetkova

Partridge

2016

Energy Economics

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“…The economic geography of these industries has been the subject of considerable research (Currid & Connolly, 2008), with the importance of tacit knowledge and the need to hire workers with highly specific and often unique skills meaning that firms in the high-tech sector tend to concentrate spatially in a limited number of cities (Bieri, 2010;Fallah et al, 2014). These are factors which have been known in academic and policy circles for some time and have triggered a race to create and attract high-tech firms as the formula for achieving local economic development and economic success.…”

Section: High-technology Growth and Povertymentioning

confidence: 99%

“…Knowledge spillovers may be particularly important in urban environments in the case of high-tech industries (Fallah et al, 2014). Echeverri-Carroll and Ayala (2009), in particular, have indicated that there is an overall 'tech-city wage premium' in cities with high shares of tech-employment.…”

Section: High-technology Growth and Povertymentioning

confidence: 99%

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Is There Trickle-Down from Tech? Poverty, Employment, and the High-Technology Multiplier in U.S. Cities

Lee

Rodríguez‐Pose

2016

Annals of the American Association of Geographers

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High-technology industries are seen as important in helping urban economies thrive, but at the same time they are often considered as potential drivers of poverty and social exclusion. However, little research has assessed how high-tech affects urban poverty and the wages of workers at the bottom of the pyramid. This paper addresses this gap in the literature and investigates the relationship between employment in high-tech industries, poverty and the labour market for non-degree educated workers using a panel of 295 Metropolitan Statistical Areas (MSAs) in the United States between 2005 and 2011. The results of the analysis show no real impact of the presence of high-technology industries on poverty. Yet there is strong evidence that tech-employment increases wages for nondegree educated workers and, to a lesser extent, employment for those without degrees. These results suggest that while tech employment has some role in improving welfare for non-degree educated workers, tech-employment alone is not be enough to reduce poverty.

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“…One might easily expect that there exist spatial differences because the economic activities are unevenly distributed. Building on this idea, many studies have shown that there exists a meaningful correlation between a firm's location and job creation (Act et al 2007;Loof and Nabavi 2013;Feser et al 2008;Fallah et al 2014;Kim et al 2013).…”

Section: Smes' Basic Factors and Job Creationmentioning

confidence: 99%

Association of SME and Government Policy Factors with the Creation of New Employment: Manufacturing Industry in Korea

Jang

2018

Social Sciences

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Abstract:The rise in unemployment is an important and urgent task to be tackled for the growth of economy in Korea. Although prior studies have revealed some factors leading to the creation of employment, studies simultaneously considering various factors, such as small and medium enterprises' (SMEs') basic factors, technology innovation activities, innovation capabilities, and the government policies, have been limited. This study seeks to design an integrated model that focuses on both SME and government policy factors associated with the creation of new employment. For this, we use the Korea Innovation Survey (KIS) data on 2828 firms in the manufacturing industry in Korea for 2013-2015. Using the multivariate regression model, we identified the association of SME and the government policy factors on the creation of employment. We show that sales, product innovation, organization innovation, and the support program by the government contributed to the job creation, whereas a firm's age had a negative correlation with the creation of employment. The statistical results and discussion of our findings facilitate a better understanding of the association between various SMEs' factors and the creation of employment, and therefore, should help in designing better policies and strategies for employment growth.

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Geography and High-Tech Employment Growth in US Counties‡

Cited by 59 publications

References 71 publications

Economics of modern energy boomtowns: Do oil and gas shocks differ from shocks in the rest of the economy?

Economics of modern energy boomtowns: Do oil and gas shocks differ from shocks in the rest of the economy?

Is There Trickle-Down from Tech? Poverty, Employment, and the High-Technology Multiplier in U.S. Cities

Association of SME and Government Policy Factors with the Creation of New Employment: Manufacturing Industry in Korea

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