Panel Data Models of New Firm Formation in New England

Parajuli, Jitendra; Haynes, Kingsley E.

doi:10.18335/region.v4i3.167

Cited by 9 publications

(3 citation statements)

References 43 publications

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“…Rupasingha and Goetz (2011) found that self-employment rates are associated with poverty reduction in nonmetropolitan counties but not in metropolitan areas. Average establishment size is often found to be negatively associated with regional start-up activities in that clustering of smaller establishments may signify a friendly environment for business entry (Acs & Armington, 2006; Chinitz, 1961; Glaeser et al, 2010; Parajuli & Haynes, 2017; Qian, 2017a, 2017b). Moreover, the presence of industrial clusters represents competitive advantage for regions (Porter, 1998) and contributes to the success of entrepreneurs (Delgado et al, 2010).…”

Section: Literature Reviewmentioning

confidence: 99%

Entrepreneurship in Small Cities: Evidence From U.S. Micropolitan Areas

Liu

Qian

Haynes

2020

Economic Development Quarterly

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This article presents the spatial patterns of general and high-tech start-up rates and explores regional factors associated with entrepreneurship in U.S. micropolitan areas. Regression results show that general entrepreneurship in these small cities is predicted by population growth, the middle-age population group, the presence of small businesses, and natural amenities. Additionally, high-tech start-up activities are positively associated with human capital, creative knowledge (instead of technological knowledge), high-tech clustering, and proximity to a large metropolitan area. These findings are compared with the patterns in larger metropolitan areas. This research sheds light on local entrepreneurship policy in the small-city context.

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Section: Literature Reviewmentioning

confidence: 99%

Entrepreneurship in Small Cities: Evidence From U.S. Micropolitan Areas

Liu

Qian

Haynes

2020

Economic Development Quarterly

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“…Following the notation of Parajuli and Haynes (2017) and based on the values of ρ and λ different models can be specified:…”

Section: Modelmentioning

confidence: 99%

A spatial analysis of precariousness and the gender wage gap in Mexico, 2005–2018

2019

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The North American Free Trade Agreement (NAFTA) between Canada, Mexico, and the USA (1994-2018) has led to a reorganization of the productive structure of Mexico's 32 metropolitan areas, affecting what jobs are offered in the country's regions. This includes tertiary activity, which determines most employment in metropolitan areas. Most employment in this sector, however, is more precarious than in the secondary sector. Additionally, women participate in this sector more than men, exacerbating wage inequality. Based on data from the National Survey of Occupation and Employment (ENOE in Spanish), we analyze the income gap in Mexico's metropolitan areas. We find that inequality is exacerbated in less developed areas in which the tertiary sector predominates. Moreover, the decrease in the labor gap is caused by the precariousness of working conditions.

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“…The range of variables included in recent logistics firm location models, such as gross domestic product (GDP) (Guerin et al 2021), population density and accessibility to residential population and manufacturing (Sakai et al 2020) and urban structure (Holl, Mariotti 2018), suggests that researchers are beginning to think beyond infrastructure density to broader factors that affect regional TW competitiveness. In the U.S. context, rural areas can be more attractive to certain types of firms because wages, property taxes, and land costs are all lower than in most metro areas (Parajuli, Haynes 2017, Wilkerson 2001.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating Determinants of Transportation and Warehousing Establishment Locations Using U.S. Administrative Data

Carpenter

Dudensing

Sandt

2022

REGION

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Interactions between transportation and warehousing and other industry clusters are not widely explored and the determinants of logistics locational determinants is limited in the U.S. context. These gaps in the literature, along with the U.S. transportation and warehousing sector's decentralization from urban areas and concentration in regions, highlight the importance of understanding the effects of place-based factors and inter-industry clusters on the locations and employment of transportation and warehousing industries. The analysis uses restricted-access U.S. Census Bureau data aggregated to the county level, along with secondary data sources, to estimate the locational determinants of transportation and warehousing (TW) industries based on transportation infrastructure as well as sociodemographic and institutional variables. The analysis takes a cross-sectional (non-causal) approach to focus on time-invariant location factors while testing and implementing zero-inflated count data distributions to model the data generation processes more accurately. Results indicate that subsectors are affected differently by infrastructure, sociodemographic, and institutional variables. Additionally, different factors are associated with industry presence versus size. Finally, we show that data using aggregated industries obscures locational factors' importance for individual sub-sectors and, further, that industrial aggregation obscures TW sectors' relationships to other clusters.

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Panel Data Models of New Firm Formation in New England

Cited by 9 publications

References 43 publications

Entrepreneurship in Small Cities: Evidence From U.S. Micropolitan Areas

Entrepreneurship in Small Cities: Evidence From U.S. Micropolitan Areas

A spatial analysis of precariousness and the gender wage gap in Mexico, 2005–2018

Estimating Determinants of Transportation and Warehousing Establishment Locations Using U.S. Administrative Data

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