Consolidated Metropolitan Statistical Area Research Articles

DOES MACHINE LEARNING PREDICTION DAMPEN THE INFORMATION ASYMMETRY FOR NON-LOCAL INVESTORS?

In this study, we examine the prediction accuracy of machine learning methods to estimate commercial real estate transaction prices. Using machine learning methods, including Random Forest (RF), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Deep Neural Networks (DNN), we estimate the commercial real estate transaction price by comparing relative prediction accuracy. Data consist of 19,640 transaction-based office properties provided by Costar corresponding to the 2004–2017 period for 10 major U.S. CMSA (Consolidated Metropolitan Statistical Area). We conduct each machine learning method and compare the performance to identify a critical determinant model for each office market. Furthermore, we depict a partial dependence plot (PD) to verify the impact of research variables on predicted commercial office property value. In general, we expect that results from machine learning will provide a set of critical determinants to commercial office price with more predictive power overcoming the limitation of the traditional valuation model. The result for 10 CMSA will provide critical implications for the out-of-state investors to understand regional commercial real estate market.

Transit Service, Parking Charges, and Mode Choice for the Journey to Work: An Analysis of the 1990 NPTS

In this paper, the effects of transit service and parking charges on the choice of commuters to drive alone, carpool, or use transit are estimated. The analysis is based on data for 20 Consolidated Metropolitan Statistical Areas from the 1990 Nationwide Personal Transportation Survey, FTA’s Section 15, and the Texas Transportation Institute s estimates of traffic congestion. Both the level of transit service and the likelihood of being charged for parking are found to have significant positive effects on the likelihood of choosing transit for the commute. The results also indicate that improving transit access leads to a very small increase in transit’s mode share, while improving the level of service produces a much greater mode share increase.

Identifying elderly travel time disparities using a correlated grouped random parameters hazard-based duration approach

Populations in countries throughout the world are ageing. Within the United States, baby boomers – those born between the years 1946 and 1964 – are living longer and are desiring more active and mobile lifestyles than prior generations. It is well established that the onset of chronic diseases and mobility impairments increase with age. Unmitigated mobility gaps threaten well-being, social interaction, and overall quality of life. As a result, transportation policy makers and planners should anticipate, identify, plan, and address transport disadvantages impacting increasingly vulnerable and possibly underserved population segments such as the elderly. Our study reveals potential mobility gaps by quantifying travel time disparities associated with the various household, traveler, travel mode, and trip purpose characteristics. In doing so, business opportunities with respect to unmet transportation needs may be uncovered.To analyze elderly trip durations, this paper extracts data from the 2009 National Household Travel Survey for the New York Consolidated Metropolitan Statistical Area. Specifically, the elderly are divided into two cohorts; those 65 through 74 years of age and those 75 and older. We apply a correlated grouped random parameters hazard-based duration model. This specification accounts for unobserved heterogeneity in the underlying hazard function and across observations, as well as unobserved effects due to the correlation between random parameters. To the authors' knowledge, this is the first study to use this statistical modeling framework to analyze travel times. Results suggest that the use of a correlated grouped random parameters provides a superior statistical fit to several established comparison models. The findings also reveal that the elderly population is not a homogeneous group and that the underlying distribution characterizing the hazard function for each age group is different. To that end, separate models are estimated for each age cohort. Furthermore, the study reveals apparent disparities in elderly travel times associated with birth nationality, ethnicity, education level, and public/private travel modes.

Knowledge Spillovers and Local Industry Growth: A Patent Citation Approach

We create unique patent-based measures of Marshall–Arrow–Romer (MAR) and Jacobs knowledge spillovers using patent citations data and use them to test the Glaeser et al. model of local industry employment growth on three emerging technology categories, namely, computing and communications, drugs and medical, and electrics and electronics. We test growth in 45 U.S. metropolitan statistical areas (MSAs) and consolidated metropolitan statistical areas (CMSAs) for eight two-digit industries over the period 1994 to 2000. We find strong evidence for MAR spillovers from specialization, but little for Jacobs spillovers from diversity. Our results suggest that regional specialization, but not diversity, boosts local industry employment growth in these knowledge fields. JEL classifications: J21, L16

Structural Breaks: A Shift of Roles in the Relationship between Real Estate and Regional Industry

We conduct an analysis of connectivity between regional housing markets and regional industry by examining 10 consolidated metropolitan statistical areas (CMSAs) in the Case-Shiller Index and stock portfolios representing the economic base for each region. We test for cointegrating relationships between real estate markets and industry performance and find that, prior to the recent real estate market collapse, prices in the housing market are dependent on industry performance; post-collapse, however, this relationship changes. Our results suggest that the recent financial crisis was so severe that it resulted in a structural break in the causal relationship between regional industry performance and real estate markets.

Post-treatment surveillance for ovarian carcinoma patients: The effect of Consolidated Metropolitan Statistical Area (CMSA).

e16538 Background: In the USA, ovarian cancer is the eighth most common cancer in women; about 21,000 new cases are diagnosed each year. Many women experience recurrence after curative-intent treat...

The U.S. Housing Market and the Pricing of Risk: Fundamental Analysis and Market Sentiment

We explore the pricing patterns of the residential real estate market in the United States in the context of the recent housing bubble and subsequent deflation. We examine 10 consolidated metropolitan statistical areas and calculate excess residential market return per risk. Then, using an error correction model, we regress excess residential market return per risk on fundamental market risk factors from a range of demand- and supply-side variables together with a non-fundamental-based sentiment variable. Our long-run findings reveal that non-fundamental-based (irrational) consumer sentiment is a significant exogenous variable in the pricing pattern of U.S. residential real estate.

Spatial Patterns of Breast Cancer Incidence and Uninsured Women of Mammography Screening Age

Breast cancer is the most common cancer and second leading cause of cancer mortality in women in the United States. Women who lack insurance have mammography screening rates that are suboptimal. Our objective was to spatially correlate incidence rates of breast cancer and uninsured women aged 40-64 years and identify outliers-areas where women may be underscreened due to poor access. The eight-county consolidated metropolitan statistical area centered on Harris County, Texas was selected as the study region. Breast cancer incidence data from 1995 to 2004 were acquired from the State of Texas Cancer Registry as individual case data geocoded at the census tract level. A bivariate local indicator of spatial autocorrelation was used to evaluate the spatial pattern of breast cancer incidence and uninsured. Statistically significant negative spatial autocorrelation was observed between breast cancer incidence and uninsured status in women aged 40-64 (Moran's I -0.2065, p < 0.001), indicating that as breast cancer incidence increased, uninsured rates decreased globally. Statistically significant local clusters of low breast cancer incidence and high incidence of uninsured were found. Future research is needed to assess mammography screening behaviors and barriers to screening at the local level.

Entrepreneurship and urban growth: dimensions and empirical models

PurposeThe purpose of this paper is to look at various dimensions of entrepreneurship and the empirical models that try to explain the relationship between entrepreneurship and growth in cities for both developed (USA and Europe) and developing countries.Design/methodology/approachThis paper provides an in‐depth and extensive review of the existing literature on entrepreneurship and economic growth in cities. In most empirical studies, the growth rate of employment or unemployment rate is used as the dependent variable to analyze the effect of entrepreneurship on development. The important independent variables other than entrepreneurship (new start‐ups) are localization, urbanization, level of education, age, industry structure (specialization vs competition), monopoly or competition. The economic units considered for cities are labor market areas (LMAs), standard metropolitan areas (SMAs) and consolidated metropolitan statistical areas (CMSAs). The majority of studies have utilized discrete dependent variable models such as Tobit or Probit to calculate the probability of the effect of entrepreneurship on economic growth. Other studies have applied ordinary least squares estimation to find the cross‐sectional variation of employment growth that accounts for entrepreneurial activities. Panel data are employed in a number of models to control for region‐specific and country‐specific fixed effects.FindingsIn this paper, four important dimensions of entrepreneurship are identified. First, for entrepreneurial studies on economic growth, cities are considered to be appropriate economic units rather than states or countries. Second, there are several definitions and measurements of entrepreneurship available in the literature. Hence, empirical models and their results may vary depending on the model specification. Third, the relationship between employment growth (a proxy for economic growth) and innovative activity is dynamic in nature and thus the problem of endogeneity needs to be addressed. And, finally, entrepreneurship has a spatial dimension and that characteristic must be incorporated into the urban and regional models of entrepreneurship. Three different types of urban models are chosen to reflect these four central dimensions of entrepreneurship. All three urban models confirm the hypothesis that there exists a statistically significant and positive relationship between entrepreneurship and growth in cities. However, the causality of the relationship is not well established.Originality/valueA critical and in‐depth summary of existing quantitative work on entrepreneurship and economic growth in different cities is the original contribution of the paper.

Exploring the Dual Dichotomy within Urban Geography: An Application of Fuzzy Urban Sets

This study argues that the concept of fuzzy urban sets is particularly well suited to analyzing aspects of a dual dichotomy that characterizes much of the literature on urban geography. By treating individual urban areas ("cities," or in this case "Consolidated Metropolitan Statistical Areas") as subsets of one aggregate fuzzy urban set, the method can address the dichotomy pertaining to the structure of urban systems within cities versus those among cities. By defining the membership criterion by which individual elements ("districts," or in this case "census places") are included in the fuzzy urban set, one can determine what kind of fuzzy urban set one is dealing with. In our empirical example, we use service employment as the key criterion. We argue that the method employed is more broadly applicable than the specific example used here, and indeed is well suited generally to addressing the dual dichotomy within urban geography.

U.S. Latino Population Composition Change and Comprehensive Cancer Centers

We describe a typology characterizing population trends of U.S. Latinos/Hispanics from 1990 to 2000 with respect to National Cancer Institute-designated comprehensive cancer centers (CCCs) and corresponding consolidated metropolitan statistical or metropolitan statistical areas (CMSA/MSAs). Using U.S. Census Bureau data, we constructed population pyramids to analyze population growth and composition for each CMSA/MSA with a CCC. We identified four types of population growth and composition: Type I--Very Fast and Unstable; Type II--Fast and Unstable; Type III--Somewhat Fast and Stable; Type IV--Slow and Stable. The CCCs in areas with Types I and II population growth face the greatest challenges because of the lack of infrastructure for reaching medically underserved Latinos. In contrast, CCCs in areas with Types III and IV population growth may have significant infrastructure but must quickly develop interventions to reach and provide access to aging Latinos to reduce health disparities in cancer mortality and morbidity.

Model-Based Approach to Synthesize Household Travel Characteristics across Neighborhood Types and Geographic Areas

Household travel survey data are crucial in regional travel demand analysis. However, good quality data are not always available owing to financial constraints, privacy concerns, poorly designed sampling schemes, and/or low response rates. Thus, various data synthesis techniques have been proposed in the past. In this paper, we identify the limitations of the existing data updating/synthesis methods and propose a two-level random coefficient model to synthesis household travel characteristics across geographic areas. Then the two-level structure was applied to the sampled households in the 2001 National Household Travel Survey across (consolidated) metropolitan statistical areas of various population sizes. One particular travel characteristic, journey to work vehicle trip rate, is investigated. The study findings confirm the effect of neighborhood (defined at the census tract level) attributes (e.g., intersection density, average auto mobile work trip travel time) on household number of journey to work vehicle trips. This effect is significant on the urban households of study, whereas the suburban counterparts across the country do not seem to be affected by their living environments after controlling for neighborhood type. In general, the two-level structure is shown statistically superior to the one level.

Measuring Ethnic Labour Market Concentration and Segmentation

With the huge influx of immigrants into the United States in recent years, considerable efforts have been devoted to describing the extent and variation of labour market concentration across ethnic groups within or between regions. However, there is no consensus among social scientists on how to measure and identify ethnic labour market concentration patterns. The issues mainly include, firstly, how to define an employment sector—as an industrial or an occupational sector; secondly, how to break down employment categories; and thirdly the extent to which a job sector can be identified as an ethnic-concentrated sector, that is, what index and what threshold value should be used to define a ‘niche’ sector? Using the case of Chinese in the San Francisco Consolidated Metropolitan Statistical Area, this paper demonstrates how different choices could encourage different evaluation and understanding of multi-ethnic urban labour market segmentation processes.

Fat content of ground beef: comparison of actual (analytical) to label claim

Sampling Plan Retail outlets were identified through a probability sampling plan developed for the National Food and Nutrient Analysis Program (Pehrsson et al.,2000) and consisted of two retail outlets (locations) per Consolidated Metropolitan Statistical Area (CMSA), three (3) CMSAs per Region, and four (4) regions nationwide. Ground beef products varying in fat content were purchased at each of the 24 retail outlets nationwide; Products were purchased in each of three (3) fat categories, based on label declaration: Low fat <12% fat

REVENUE, POPULATION, AND COMPETITIVE BALANCE IN MAJOR LEAGUE BASEBALL

This article examines the impact of hometown market size on competitive balance in Major League Baseball. We use a four‐equation simultaneous model of win percent, team payroll, team total revenue, and team local revenue in order to avoid simultaneity bias in the estimates. Using two‐stage least squares, our results show that consolidated metropolitan statistical area population does have a statistically significant positive impact on local revenue. This leads to increased payroll, which has a significantly positive, but small, impact on win percent. Specifically, the estimated impact of an additional one million in population ranges from 0.233 to 1.126 additional wins per season. (JEL L38)

Urban spatial pattern as self-organizing system: An empirical evaluation of firm location decisions in Cleveland–Akron PMSA, Ohio

Economic models of urban spatial patterns have largely ignored complexity as an attribute of urban systems. Complexity theorists on the other hand have not made sufficiently serious and sustained attempts to verify empirically the relevance of complex systems models for urban spatial patterns. This research bridges this gap by simulating the evolution of an urban employment pattern as a self-organizing complex system and seeking its empirical validation. It estimates the model’s parameters by using firm data aggregated to the level of municipalities in Cleveland-Akron Consolidated Metropolitan Statistical Area in Ohio. The interaction among four parameters, forces of attraction and dispersion and their respective rates of dissipation with distance, are modeled as a two-dimensional complex system. The research compares the states of the modeled system with empirical data to present viable methods for verification, calibration and validation of such models.

Is There Really an Export Wage Premium? A Case Study of Los Angeles Using Matched Employee-Employer Data

This article investigates the effects of exporting on wages, specifically the claim that workers are paid higher wages if they are employed in manufacturing plants that export vis-à-vis plants that do not. Past research on U.S. plants has supported the existence of an export wage premium, though European studies dispute those results, calling for more care in econometric investigation to control for worker characteristics. The authors answer this call by developing a matched employee-employer data set linking worker characteristics from the one-in-six long form of the Decennial Household Census to manufacturing establishment data from the Longitudinal Research Database. Analysis focuses on 1990 and 2000 data for the Los Angeles Consolidated Metropolitan Statistical Area. Results confirm that the average wage in manufacturing plants that export is larger. However, after controlling for worker characteristics such as age, gender, education, race, and nationality, the export wage premium vanishes. That is, when comparing workers with similar characteristics, there is no wage difference between exporting and nonexporting plants. These results concord with recent findings from Europe and elsewhere.

Housing as a Heuristic Condition in the Simultaneous Projection of Population and Households

Conventional population projections regard individuals, rather than households, as population units of reference. Such an approach has been questioned on both methodological and empirical grounds. Furthermore, in applications to smaller populations, conventional population projections have repeatedly yielded poor results. The simultaneous projection of population and households, on the other hand, regards households as population units of reference, but, in applications based on the notion of the household composition matrix, it has occasionally yielded analytically infeasible results. In the present study I examine the simultaneous projection of population and households in a etropolitan area, under feasibility constraints. A housing-market specification is expressed as a feasibility condition against multipliers of the household composition matrix, extracted here for the Cleveland Consolidated Metropolitan Statistical Area (CMSA), 1990. The feasibility condition is shown to function as a gateway to exogenous considerations regarding the transfer of headship in households, and is exemplified in a forecast of population and households for the Cleveland CMSA.

Geographic variation in patient follow-up after curative-intent treatment for rectal carcinoma

3713 Background: In wealthy societies, most patients with rectal cancer treated with curative intent enter some type of follow-up regimen. A recent survey of the members of the American Society of Colon and Rectal Surgeons (ASCRS) disclosed appreciable variation in follow-up practices. We sought to determine whether geographic factors could account for this variation. Methods: The surveys were sent to all 1,782 members of the ASCRS in 2002. Details have been published elsewhere. Four potential predictors of variation in follow-up intensity were tested (US census region, consolidated metropolitan statistical area (MSA), primary MSA, and managed care organization (MCO) penetration rate) using repeated-measures analysis of variance. Results: There were 566 responses (32% response rate), of which 346 provided evaluable data. There was a significant (p < 0.05) main effect of census region on frequency of use of office visit, CBC, CEA level, colonoscopy, flexible sigmoidoscopy, chest x-ray, intra-rectal ultrasound, and CT of abdomen-pelvis; there was no significant main effect of census region on frequency of use of liver function tests. Five other infrequently used modalities were not evaluable. There were significant main effects of MCO penetration rate on frequency of use of office visit and CT of abdomen-pelvis, but no other modalities. Consolidated MSA and primary MSA were not significant predictors of follow-up intensity for any modality. Conclusions: Census region and MCO penetration rate account for some, but not most, of the variability in rectal cancer patient follow-up by ASCRS surgeons. These data should be valuable in designing controlled trials of post-treatment follow-up regimens for rectal cancer patients. No significant financial relationships to disclose.

Immigration, Union Density, and Brown-Collar Wage Penalties

Author(s): Catanzarite, Lisa | Abstract: Previous research focusing on the impact of immigration on native-born workers demonstrates that workers experience wage penalties when they are employed in local occupations with a large share of immigrants. Does unionization mediate such pay penalties? Lisa Catanzarite utilizes the 2000 5% Census Public Use Microdata Sample in conjunction with pooled unionization data from the 1998-2002 Current Population Surveys to investigate the impact of union density on pay penalties in brown-collar occupations (with overrepresentations of recent-immigrant Latinos). The results indicate that unionization, particularly in the private sector, significantly eases the downward pressure on wages in brown-collar fields for both native workers and earlier-immigrant Latinos, net of individual and occupational characteristics. The analyses focus on the greater Los Angeles and San Francisco Bay Areas (California’s primary immigrant destinations) and also use data on immigrant-receiving Consolidated Metropolitan Statistical Areas nationally. The finding that union density lessens brown-collar wage penalties indicates that policies to address immigrant wage competition can be mutually beneficial to newcomers and to the more established groups with whom they may compete. Strengthening the position of marginal workers may, indeed, protect those higher in the employment hierarchy.