Spatial characteristics: improving model accuracy and providing regional research insights
| dc.contributor.author | Crofton, Kevin, author | |
| dc.contributor.author | Cutler, Harvey, advisor | |
| dc.contributor.author | Weiler, Stephan, committee member | |
| dc.contributor.author | Shields, Martin, committee member | |
| dc.contributor.author | Manning, Dale, committee member | |
| dc.date.accessioned | 2024-09-09T20:52:13Z | |
| dc.date.available | 2024-09-09T20:52:13Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | The research presented in this dissertation began with an investigation of water transfers from rural Colorado to a growing urban region and how this would affect the rural economy. Chapter 1 focuses on the growing concern of water scarcity in the arid western region of the US. In this part of the country, it is widely known that water is limited, and as populations continue to increase, so will the demand for water, which is already in short supply. A multiregional Computable General Equilibrium (MRCGE) model using spatially detailed data was built to study the impact of urban growth on a rural community and is presented in Chapter 1. The construction of the MRCGE model led to consideration of how aggregation shapes output. This evolved into a comparison of a MRCGE model that utilized spatial details that explained the differences between a rural and urban economies with a single region CGE model that aggregated these regional differences. Chapter 2 discusses identical simulations in either model, demonstrates insights gained from refining the spatial details into a MRCGE model, and identifies specific elements lost when using a broader aggregated description blending different regions together. Different spatial qualities between locations are critical in expanding the understanding of skier behavior. Chapter 3 provides a skier behavior model of the US, which confirms the pull effect of destination snowfall shown in regional models of the ski industry. Additionally, this research demonstrates that skier origin weather also influences skier visitation by shifting the subjective interest of traveling to another region. The result of this model provides evidence of push and pull weather variables for a winter ski destination, filling a gap generally left by travel literature that often focuses on warm weather destinations. Chapter 1 describes a three-region CGE model that utilizes the unique spatial characteristics of urban, rural, and interface regions; the latter includes a blend of features of both the rural and urban regions. Using explicitly defined regions provides an enhanced analysis of each community's Ag and non-Ag sectors, while also describing the impact on households. This model connects the three regions via a water market, which allows for endogenous transfers of water to occur due to urban population growth. The model adds an interregional intermediate input market, allowing urban growth to demand greater domestic supply from the rural and interface industries. The interregional intermediate input market, which captures another link between the urban region and the rural region, is a new addition to the literature. These key modeling features refine the approach to investigate urban growth's influence on a rural economy by modeling multiple interregional markets and identifying regional specific characteristics. Chapter 1 allows for a more complete understanding of the dynamics between urban growth, water transfer from the rural region, and the resulting influence on the rural economy. Chapter 1 compares a model, which includes the water market and the intermediate input market, to two restricted models, either only trade water or only trade intermediate inputs, between regions to assess the impact these markets have on the rural economy. This comparison demonstrates that both markets can increase the cost of production due to greater urban demand, but when either is restricted, rural economies can expand in respond to urban growth. When water cannot be traded between regions factor prices become relatively cheaper in the rural region because there is not the greater urban demand causing higher water prices. With cheaper factors the rural economy can expand supply to meet the growing urban demand for intermediate inputs. When the water market is the only interregional market, the rural region transfers water (primarily from the agricultural sector resulting in Ag output decline), to the urban region. The rural agricultural sector subsequently reduces their demand for land and labor making it available to the rural non-Ag sector, thus expanding their output with these relatively cheaper factors. The greater output of the non-Ag sector offsets the Ag decline in output, resulting in a total domestic supply increase. The rural economy increases output in each alternative model due to different cross-regional effects but experiences a decline in output when all interregional markets are modeled. The inclusion of both markets generates higher cost of production that cannot be offset by substitution between sectors or by the greater urban demand for intermediate inputs. Past research has focused on how the rural region adjusts to water transfers with varied conclusions. For example, Berck et al. (1991) describes rural agriculture shifting to less water intensive production methods and benefitting from the combined payment for their water and the adjusted domestic supply. Alternatively, Seung et al. (2000) describes a decline in rural domestic supply in response to water transfers, relying on a Leontief factor substitution specification. Both research conclusions depended on the elasticity of factor substitution. The model used in Chapter 1 applies a constant elasticity of substitution that is similar to Berck et al. (1991) but describes a decline in rural domestic supply. This different economic outcome is due to the dynamics of markets that connect the multiple regions, rather than only modeling the rural region in isolation. The Watson and Davies (2009) model includes a water market that endogenously transfers water between sectors due to urban population growth. In their model, water is a factor input for all sectors of the economy, and as urban population expands, it shifts the demand for water to non-agricultural outputs, resulting in a higher water price. The higher price of water forces the agricultural sector to shift to less water intensive production methods resulting in less output. However, the large urban population demands greater agricultural output offsetting negative supply shift. One weakness of this model is that households and industries are not regionally identified, making it unclear how rural and urban regional economic outcomes differ. The model in Chapter 1 applies a similar endogenous water market driven by urban growth but concludes that rural domestic supply and household income both decline due to regional price variations derived from the multiregional approach. Chapter 2 compares a spatial disaggregated MRCGE model to a single region model that uses the same data to expose the importance of spatial aggregation in CGE modeling. This analysis isolates the influence of how aggregation shapes output, revealing the differences between the two models with identical simulations. The performance of the two models highlights that spatial data must be correctly leveraged by a disaggregated structure to explain unique regional output. This comparison demonstrates that qualities in the analysis are lost when CGE models use larger aggregated regions rather than a spatially sensitive MRCGE approach, which illustrates the importance of modeling spatial details. The water model from Chapter 1 is transformed into a single region model and compared to the original specification. The same data is used in each model, leaving only the regional specification as the structural difference between them. The data used in this model is based on PUMS data that describe community level labor and household characteristics, refining the disaggregated descriptions of regional economies. Additionally, county assessor's data provide parcel level descriptions of land and building values, thus improving the descriptions of residential and business in the model. Each of these data sets refine spatial details, which enhance the multiregional specification. When these details are aggregated into a single region, many of the county level insights are obscured. For example, rural labor markets have greater wage gains compared to similar labor groups in different regions in response to a total factor productivity increase. This outcome is unique to the rural region, and under the aggregated single region model, this labor group does not experience an increase in wages. The model comparison demonstrates how spatial characteristics represented by the MRCGE structure can shape output by preserving spatial characteristics, which determine unique model behaviors that enhance economic analysis. The literature has recognized the benefits of the MRCGE specification. However, the justification for the MRCGE improvements over past aggregated approaches is due to adjusting the older aggregated structure with spatially descriptive data and revealing new outputs. The problem with this approach is that the new model has both a change in structure from a single region to a multi-region, and it includes additional spatial descriptive data. This method cannot determine whether improved spatial data or the spatial disaggregation is responsible for the improvement. To address this, the model presented in Chapter 2 aggregates a disaggregated model so that the spatial data values are the same but have been transformed from spatial to aspatial. This approach allows the spatial structure to be analyzed without the influence of additional data, revealing how this uniquely impacts and shapes the output. Chapter 3 describes how weather motivates skiers to travel from one region to another in the US. It is widely known that skiing benefits from winter conditions and that skiers are willing to travel to other locations to pursue better quality ski experiences. This model considers seasonal weather variations of destination ski areas and the origin weather variables that could impact their decision-making process. Destination and origin weather variables are significant determinants of skier visits, as confirmed by the research. Snow accumulation at a destination can pull greater visits, and colder weather at the origin makes ski trips more appealing. Chapter 3 fits within the broad literature on travel that has focused on weather's influences on travel decisions. The travel literature describes the desirable weather variables that pull travelers towards those destinations. Additionally, this literature provides push variables where the weather of a traveler's origin changes their subjective demand for leaving their home for vacation. The push and pull effects of destination and origin weather have been applied extensively to warm weather destinations, but there are many fewer applications for the winter season and cold-weather destinations. This model tests the push and pull effect for ski areas in the US and confirms that colder, snowy destination weather pulls a greater number of skier visits and that colder weather at a skier origin further pushes them to ski in any region. The gap in the travel literature is also shared by the ski industry studies that have exclusively focused on how destination temperature and snowfall contribute to skier attendance. The ski industry research has not examined how the skier's origin influences attendance. This model addresses the smaller scope of the ski industry research that focuses on one ski area instead of skier behavior across the whole country. Across each of the chapters in this dissertation, spatial details motivate unique economic activity. From western water markets to the differences between rural and urban labor markets to the impact of destination snowfall on skier visits, the inclusion of spatial characteristics improves model analysis and provides key insights into regional research. The diverse application in the following chapters provides strong evidence of the importance of spatial characteristics, highlighting how they shape individual behavior and drive unique research and economic outcomes. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier | Crofton_colostate_0053A_18525.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/239278 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.title | Spatial characteristics: improving model accuracy and providing regional research insights | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Economics | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) |
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