Analysis of the Role of Commercial Real Estate in the Economic Development of the Northeastern United States

Research on the relationship between real estate investment and economic growth has a long history in the economic development literature. This study adds to the literature by focusing on the largest segment of commercial real estate, retail establishments. Not only quantifying the economic impacts of retail establishments, but also understanding how the economic and so-cio-demographic characteristics of an area affect investment location decisions, contributes to the understanding of regional growth process. A 3SLS simultaneous equations model derived from is used to analyze the role of “small” and “large” retail establishments in the economic development of the Northeast region of the United States. The study concludes that growth in retail establishments indeed plays a significant role in the economic growth process in the study region. Counties’ government expenditure and housing values play a strong role in attracting retail businesses. Population density has different effects on “small” and “large” retail establishments; high population density areas are found to be attractive, particularly to “small” retail establishments, whereas “large establishments” prefer areas with low population densities.

in 2012, which is about one out of every 10 workers. The industry is also projected to lead real output growth among all industries by 2020, and to show the second largest employment growth (Bureau of Labor Statistics, 2012) [2].
Traditionally, retail was considered a non-basic industry, meaning that it recycles money within an area and doesn't bring new money from the outside, based on the observation that retailing is largely dependent on local and regional customers and businesses (Ducatel and Blomley, 1990) [3], Lowe, M., and Crewe, L. (1991) [4]. A local economy is assumed to be strong if it relies on industries that primarily depend on external markets and purchases, which are called basic industries.
Today's regional development professionals recognize the dual roles of retail sector, as a basic as well as non-basic industry. As a basic industry, the retail sector contributes to the regional economy by attracting customers from elsewhere. This started a long time ago with the invention of mail order stores and improved transportation and shipping. More recently, the growth of online shopping and selling has increased the geographical reach of retailers and the location of stores is no longer the constraint it used to be. The mail order and online retailer, Lands' End, located in a Wisconsin town of less than 5000 inhabitants, illustrates the possibilities. Additionally, shopping habits have also changed, and customers seem willing to travel farther than in the past. Apart from this, some retailers locate strategically, for example at the intersection of major interstate highways. For example, Tamarack Square ("A Taste of West Virginia") offers high quality crafts, and a restaurant, within a short of its construction, has become a major tourist attraction in Beckley in southern West Virginia. A survey by Price, Kent, Springer, and Al (2008) [5] indicated that some 75 percent of those who stopped at Tamarack were non-West Virginia residents.
In addition to helping attract money from outside their home region, retail businesses contribute directly to the quality of life by offering services and goods. Their presence signals a healthy economy and, to other stakeholders, reduced investment risk. High quality of life and a sound economy promote the growth and development of existing and new businesses, further enhancing employment opportunities and attracting income to a region. The retail sector also plays a major role in reducing expenditures that would otherwise "leak" out of an area (Williams, 1997) [6], (Gibson, Albrecht, and Evans, 2003) [7]; this increases the multiplier effect of all new spending in the region.
Understanding and quantifying the economic impacts of retail businesses and their relationship to other economic factors and demographic characteristics in a region, is crucial information to many stakeholders. Exploring how economic and demographic disparities within a region affect the level of retail business and related economic benefits helps improve policies aimed at attracting investors to an area. To analyze the interdependent relationship between retail businesses and economic and socio-demographic variables, a three-stage least square simultaneous equation model was used. The model consists of a system of inter-related equations. It is a form of instrumental variable estimation that permits correlations of the observed disturbance across several equations and puts restrictions on coefficients of different equations. The model is known for its consistent estimation and improved efficiency since it estimates coefficients simultaneously by accounting for correlations across equations.
In summary, the main objective of this study is to empirically analyze the impact of investment in retail establishments, the largest segment of commercial real estate, on the economic development of the Northeast region. The remainder of this article is organized into four sections, starting with a literature review (Section 2), followed by the description and development of the empirical model (Section 3). Section 4 presents the results and discusses their implications for economic development policy. The final section provides a summary if the conclusions and recommendations.

Literature Review
There is a growing number of studies examining the economic contributions of retail establishments. Some of the research has shown that retail establishments contribute to economic development by attracting new businesses to a region (Ling and Naranjo, 1997) [8], (Vias, 2004) [9]. They enhance the multiplier effect of household expenditures and create linkages up and down the supply chain (Ling and Naranjo, 1997) [8]; Hongyu, Park, and Siqi, 2002) [10]. Some researchers have examined the role of retail establishments in enhancing the physical environment and its influence on residential location decisions and found positive impacts on housing values. (Seidman, 2006) [11], (Colwell, Gujral, and Coley, 1985) [12], (Porter, 1997) [13]. Retail establishments have also been found to positively contribute to residents' perception of their neighborhood and lower perceived investment risks, which further helps attract new residents and businesses (West and Orr, 2003) [14].
Despite the many positive contributions of retailing to an area, business and household location decisions depend importantly on the quality of the existing infrastructure, general economic conditions, and demographic characteristics of a region (Nair, 2011) [15], (Wincott and Mueller, 1995) [16], (Thilmany, McKenney, Mushinski, and Weiler, 2005) [17], (Lieser and Groh, 2011) [18], (Jackson, 2001) [19], (Alwitt and Donley, 19970) [20], (Boon, 2005) [21], (Colavolpe, 2010) [22], (Malizia, 1991) [23], (Fickes, 2007) [24]. Access to the regional and national transportation network is particularly important for businesses and influences retail business location decisions. In general, more urbanized regions with higher per capita income, industrial diversity, and public transportation, are more likely to attract investors than less urbanized, less affluent, and economically less diversified regions. In line with the results obtained by Miller and Weber's (2014) [25], most rural US counties show persistently slow population growth, a high unemployment rate, a growing poverty rate, and an increasing gap in per capita income compared to their urban counterparts. These trends in rural communi-American Journal of Industrial and Business Management ties make attracting investors and retaining existing businesses comparatively more challenging. These challenges also pertain to commercial real-estate investors and retail businesses. Alwitt and Donley (1997) [20] investigated the hypothesis that poor neighborhoods are at the disadvantage in attracting to retail establishments, but they found no statistically significant difference in the number of retail outlets per million dollars of purchasing power. However, they detected a significant difference in retail establishment sizes. Chapple and Jacobus (2009) [26] studied the relationship between growth of chain stores and economic status of neighborhoods in San Francisco. Their analysis revealed a positive relationship of chain stores with "middle class income becoming" neighborhoods as opposed to economically "stratifying" or "gentrifying" areas, indicating that some sector development is closely related to a growing number of middle-income residents, rather than other forms of neighborhood change. Thilmany suggesting that retail establishments depend on local and regional demand conditions, including population size.
To understand the economic contributions of retail establishments in the regional growth process and identify the socio-economic factors that affect investments, this study first differentiates retail establishments from other forms of commercial and non-commercial real-estate and explores the potential economic contributions of the sector.
Second, to consider possible differences in production technologies and economies of scale between retail establishments of different size, this study distinguishes between establishments with 1 -19 employees to represent relatively small retail establishments, and those with at least 20 employees to represent relatively large ones. Employment between 1 and 19 is used for small retailers because they capture most locally owned stores. A second reason is that a cutoff point of more than 20 employees would eliminate most counties from having even one large retailer, which would cause an imbalance that would make comparisons difficult. It should be noted that the terms "small" and "large" are relative and generally depend on industry type.
Third, this study also examines how socio-economic and demographic disparities within a region affect the growth of establishments. The result will provide information for government officials for developing policies for attracting businesses and stimulating the regional economy.
The 3SLS simultaneous equation model accounts for simultaneous interactions among interdependent variables. All such models must content with indigeneity, which can only be addressed by applying starting conditions. With this caveat in mind, the model helps overcome inconsistency and bias, and produces efficient estimators. The 3SLS estimation also overcomes the problem of correlation among error terms across equations and helps to accounts all restrictions on parameters in the system of simultaneous equations.

The Model
The model specifies the interaction between population density (POP), employment density (EMP), per capita income (PCI), and investment in commercial real estate (CRE), where investment in commercial real estate later will be replaced by small and large retail establishments. The interactions between the four variables are explained as follows: The speed with which variables move towards equilibrium is given by the pa-  [29] explained that the speed of adjust- The model allows the estimation of the structural relationships in the short-term adjustments in population density, employment density, per capita income, and retail establishment density, and the long-term equilibria * POP , * EMP , * PCI , and * CRE , while simultaneously separating the effects of growth in retail establishments on regional economic growth.

Data
The empirical model is used to analyze the effects of retail establishments on regional economic growth. The study uses change of population density (GPOP), In Table 1, all density calculations are relative to county population, except in the case of population density, where people per square mile is used. Serious crimes include any violent and felony charges. Violent crimes are defined as offenses that involve face-to-face confrontation between the victim and the perpetrator, including homicide, rape, robbery, aggravated assaults, etc.

Population Density Growth Equation
The results of the population density growth equations are presented in Table 2.

Employment Growth Equation
The results of the employment growth estimation are presented in Table 3

Per Capita Income Growth Equation
The results of the per capita income growth equations are presented in Table 4.  Note: ***, **, and * represent significance at 1%, 5%, and 10%, respectively.  Table 4. The initial condition of LRE investment is negatively related to per capita income growth.
This could be related to the negative relationship between initial retail establishment investment and growth in retail establishments (GRE) in the retail establishment equation in Table 4. relationship between per capita income growth and the per capita income tax implies that an increase in per capita income tax revenue funds government services such as education, healthcare, highways, railroads, crime prevention etc., that stimulate the economy. The percentage of retired population showed a negative and significant relationship with per capita income growth in both models. Since the main sources of income for most retired people are pensions and government benefits, this result was expected.
The dummy variable for metro (METRO) is significant with an unexpected negative sign in relation to growth in per capita income in both models; however, the magnitude is small.

Retail Establishment Equation
The results of the retail establishment growth equation for the Northeast region using 3SLS are presented in Table 5 Growth in population density (GPOP) has a positive and significant relationship with growth in the density of SRE. This implies that population is an important pull factor that represents the aggregate demand for goods and services.
Growth in population density unexpectedly has a negative and significant relationship with growth in the density of large retail establishments, which implies, counter-intuitively, that large retail establishments are more likely to locate where population density is lower. One possible explanation for this result could be that the large space and land requirements of large retailers are more easily met in less densely populated areas. In addition, such a location need not be disadvantageous if the transportation infrastructure makes it readily accessible.
The boost in online retailing may also make retailers less location dependent than in the past. This assumption finds some supported by the work of Kim and Orazem (2012), who show that broadband availability has a significant positive firm entry effect in rural areas. The initial population density also showed a negative relationship with growth of large retail establishments. Growth in employment density (GEMP) showed the expected positive and significant relationship with large retail establishments (GLRE). The initial condition of retail establishments showed a negative and significant relationship with growth in retail establishments. This result implies that counties that had low level of retail establishments in 2000 had higher retail establishment growth rate compared to counties that had a higher level. This could indicate the existence of previously untapped market opportunities. Note: ***, **, and * represent significance at 1%, 5%, and 10%, respectively.
The relationship between county median housing value (CMHV) and density of large retail establishments (GRE) is positive and significant. This result is expected because the median housing value is an indicator of the economic health of a county.
Density of number of firms (NFIRM) has a positive and significant relationship with growth in SRE investment and a negative and significant relationship with LRE investments. It is plausible that large retail establishments are less dependent on local and regional suppliers than small retail establishments because they are more likely to obtain most of their supplies from a regional warehouse, while small retailers may reap greater benefits from the agglomeration econo- showed an insignificant relationship with growth of retail establishments.

Conclusions and Recommendations
The empirical analysis indicated that growth in retail establishments played a measurable role in the economic growth process in the Northeast region of the United States from 2000 to 2010. This implies that local governments should include retail development as part of their economic development strategies. Though small local governments generally have limited abilities to encourage business development, they can support retail development when by locating or relocating government offices that frequently interact with members of the public proximate to retail businesses. They could also lobby county, state, and federal officials to give preference to co-locating services with retail businesses. In this respect, the relocation of many US Post Offices from in rural counties from downtown to outside of town is an unwelcome trend. Incentives such as using revenue bonds, tax increment financing (TIF), reduced tax rates, subsidies, and one-stop shopping for construction and other permits, help reduce the cost of making an investment.
The empirical results also showed a strong positive relationship between county median house values (CMHV) and growth in the density of retail establishments. Therefore, local governments should try strategies to maintain and improve neighborhoods and downtown areas, for example through consistent code enforcement and maintaining public infrastructure to avoid deterioration leading to blight. Such a strategy would benefit established retailers as well as newcomers. A periodical reassessment of parking policies may also be called for.