Community Attributes Influencing Local Food Processing Growth in the U.S. Corn Belt

Henderson, Jason; McNamara, Kevin T.

doi:10.1111/j.1744-7976.1997.tb00206.x

Cited by 27 publications

(56 citation statements)

References 20 publications

(45 reference statements)

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“…The cost and availability of energy for plant operation are also important inputs for ethanol firms, the expectation being that firms will seek out low-cost energy supplies ( Schmenner, Huber, and Cook (1987) Capps, Fuller, and Nichols (1998) Lopez and Henderson (1989) Leistritz (1992) Woodward (1992) Barkley and McNamara (1994) Vesecky and Lins (1995) Henderson and McNamara (1997) Henderson Holcomb, 2006). The availability of abundant water supplies for processing ethanol and for use in the cooking and cooling stages of production is also cited as a significant consideration (Higgins, Richardson, & Outlaw, 2008;Zeman, 2006).…”

Section: Conceptual Framework Ethanol Firm Location Factorsmentioning

confidence: 99%

“…Infrastructure is the set of public and private facilities that support industry activities (Henderson & McNamara, 1997). Important infrastructure factors for the ethanol industry include access to highway and rail lines, access to natural gas pipelines, and the electric power grid (Clean Fuels Development Coalition & The Nebraska Ethanol Board, 2006;Dhuyvetter et al, 2005;Kenkel & Holcomb, 2006).…”

Section: Conceptual Framework Ethanol Firm Location Factorsmentioning

confidence: 99%

“…We hypothesize that the biofuels industry is supply oriented because the cost of acquiring and transporting the raw agricultural product represents a significant share of total production costs (Barkley & McNamara, 1994;Henderson & McNamara, 1997;Lopez & Henderson, 1989;Sarmiento & Wilson, 2007). We also assume that the period of our analysis represents an early stage of industry development when firms are not severely constrained by the existence of competitors seeking the same inputs or supplying products to the same market areas (Greenhut, Norman, & Hung, 1987).…”

Section: Conceptual Framework Ethanol Firm Location Factorsmentioning

confidence: 99%

“…For example, Henderson and McNamara (1997) examined the factors that account for growth in food-processing industries to assess the potential for growth or expansion into rural communities. Using ordinary least squares methods, the authors explored the relationship between the change in the number of food-processing plants in 936 counties in the Corn Belt from 1987 to 1992 and a number of variables that measured product markets, product market access, labor, agglomeration, infrastructure, transportation, and local government fiscal policy.…”

Section: Agro-industry Location Factorsmentioning

confidence: 99%

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Locational Choices of the Ethanol Industry in the Midwest Corn Belt

Haddad

Taylor

Owusu

2009

Economic Development Quarterly

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The Corn Belt has experienced a rapid expansion of corn-based ethanol plants. This has provided researchers the opportunity to examine the relative importance to the renewable fuels industry of several location factors previously identified as important to agro-industries. Using probit regressions, this study identifies the factors significant to ethanol firms' location decisions in the four-state study area of Iowa, Illinois, Minnesota, and Nebraska. In Iowa and Illinois, where corn is largely ubiquitous, firms move beyond corn supply to consider other localized factors in their decision-making process. Factors such as rail access, population density, and proximity to blending terminals emerge as significant considerations. Probit regressions comparing states reveal the competitive advantages that each offers to ethanol firms. The importance of the findings to economic development professionals is discussed and areas for future research are suggested.

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Section: Conceptual Framework Ethanol Firm Location Factorsmentioning

confidence: 99%

Section: Conceptual Framework Ethanol Firm Location Factorsmentioning

confidence: 99%

Section: Conceptual Framework Ethanol Firm Location Factorsmentioning

confidence: 99%

Section: Agro-industry Location Factorsmentioning

confidence: 99%

See 2 more Smart Citations

Locational Choices of the Ethanol Industry in the Midwest Corn Belt

Haddad

Taylor

Owusu

2009

Economic Development Quarterly

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“…Manufacturing plant decision is a two-stage process (82,84,(87)(88)(89)(90)(91)(92)(93)(94). In the first stage, companies choose a region based on the firm's overall goals, such as product markets, access to raw materials, or other objectives.…”

Section: Manufacturing Location Decisionmentioning

confidence: 99%

Socioeconomic Forecasting

Xiong¹,

Fricker²,

McNamara³

2012

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JOINT TRANSPORTATION RESEARCH PROGRAMThe Joint Transportation Research Program serves as a vehicle for INDOT collaboration with higher education institutions and industry in Indiana to facilitate innovation that results in continuous improvement in the planning, design, construction, operation, management and economic efficiency of the Indiana transportation infrastructure. https://engineering.purdue.edu/JTRP/index_html Published reports of the Joint Transportation Research Program are available at: http://docs.lib.purdue.edu/jtrp/ NOTICEThe contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views and policies of the Indiana Department of Transportation or the Federal Highway Administration. The report does not constitute a standard, specification or regulation. AbstractThe role of the REMI Policy Insight+ model in socioeconomic forecasting and economic impact analysis of transportation projects was assessed. The REMI PI+ model is consistent with the state of the practice in forecasting and impact analysis. REMI PI+, like its competitors, is vulnerable to the trends contained in the historical data it uses, especially recent trends. After the most recent periodic update in data, the performance of the REMI PI+ model improved, that is, it produced long-term forecasts that were more credible. Zonal-level population and employment forecasts for direct input to the Indiana Statewide Travel Demand Model (ISTDM) can be achieved by applying disaggregation regression methods. Indiana University's Center for Econometric Model Research (CEMR) model is also a sound forecasting model. Because of the knowledge of in-state economists, the CEMR-IBRC model could provide forecasts of the Indiana economy that reflect characteristics not known to out-of-state forecasters. The researchers also examined economic impact analysis models that are possible alternatives to REMI PI+. Acquiring a new economic impact analysis package does not seem necessary for INDOT, if REMI forecasts can be adjusted to (a) accommodate recent and reasonable expected trends in the Indiana economy, and (b) meet the geographic (TAZ) needs of the ISTDM. MCIBAS-which is currently used by the Indiana Department of Transportation-is a good hybrid system to use in the economic impacts analysis of transportation projects. Indiana University's CEMR is capable of conducting economic impact analyses, with local knowledge of the Indiana economy, at a cost lower than REMI's. However, INDOT would have to decide whether these potential advantages justify changing the present relationship with REMI. In cases where the credibility of data, forecasts, and/or impact analyses needs to be verified, an Because the traffic forecasts produced by the Indiana Statewide Travel Demand Model (ISTDM) are driven by the demographic and socioeconomic inputs to the model, particular attention must be given to obtaining the most accurate demographic an...

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Meat processing plant survival: The role of plant and regional characteristics

Isley

Low

2023

J of Agr & App Econ Assoc

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Federal and state governments are making major investments in expanded meat processor capacity and improved industry resilience. To improve decision‐making, this research identifies characteristics related to the probability of meat processing plant survival using hazard analysis and establishment‐level data on US meat processors (including beef, pork, goat, lamb and mutton, and large game processors) 1997–2020. We find plant survival is associated with both plant characteristics and local context, though specific factors related to survival vary with plant size and rurality. Smaller plants are less likely to survive than larger plants, and for smaller plants survival is most strongly related to business diversification. For larger plants, local context, including workforce variables, has the strongest relationship with survival. Our analysis shows little relationship between meat processing industry concentration and plant survival, though we find weak evidence of a positive relationship between industry concentration and large nonmetro plant survival.

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Community Attributes Influencing Local Food Processing Growth in the U.S. Corn Belt

Cited by 27 publications

References 20 publications

Locational Choices of the Ethanol Industry in the Midwest Corn Belt

Locational Choices of the Ethanol Industry in the Midwest Corn Belt

Socioeconomic Forecasting

Meat processing plant survival: The role of plant and regional characteristics

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