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This paper aims to use statistical analyses to understand the trends on housing liquidity and pricing by accounting for macroeconomic factors that vary at national level and at metropolitan statistical area level for all metropolitan statistical areas in Texas. In addition, the authors test for seasonality in all the metropolitan statistical areas in Texas.

Using publicly available data from Zillow a listing website, the authors conduct an analysis of all housing markets at metropolitan statistical area level in Texas to understand the factors that drive the liquidity and pricing. The authors use two measures for liquidity, namely, time to sell the house and sales to list ratio. The authors also try to understand the decision to lower the price of the listed houses. In addition, the authors conduct a test for seasonality within the year in these housing markets.

The analyses conclude that there is a significant impact of listing prices, unemployment rates, 30-year mortgage rates, consumer sentiment and oil price changes on the liquidity of the housing markets and decisions of sellers to adjust the prices down. In addition, the authors provide evidence of the existence of seasonality in most metropolitan statistical areas in Texas both for pricing and volume of transactions.

This is the first study to look at housing liquidity and pricing trends for about 25 markets in Texas. In addition, the authors provide evidence of the importance of oil prices for the housing markets in Texas metropolitan statistical areas.

Spatial bibliometrics and scientometrics have traditionally focused on examining both country and regional levels; however, in recent years, numerous spatial analyses on the city level have been carried out. While city-level scientometric analyses have gained popularity among policymakers and statistical/economic research organizations, researchers in the field of bibliometrics are divided regarding whether it is possible to observe the spatial unit “city” through bibliometric and scientometric tools. The purpose of this paper is to reveal the most significant challenges ahead of spatial scientometrics focusing on the city level by examining relevant scientometric studies.

This analysis involves the most significant spatial scientometric studies focusing on the city level and carefully examines how they collect bibliometric and/or scientometric data, what methodologies they employ to process bibliometric data and most importantly, how they approach the spatial unit “city”.

After systematically scrutinizing relevant studies in the field, three major problems have been identified: there is no standardized method of how cities should be defined and how metropolitan areas should be delineated; there is no standardized method of how bibliometric and scientometric data on the city level should be collected and processed; and it is not clearly defined how cities can profit from the results of bibliometric and scientometric analysis focusing on them.

This is the first study that compiles a “database” of scientometric studies focusing on the city level. The paper not only reveals major challenges ahead of city level spatial analysis but recommends some possible solution as well.

The purpose of the study is to examine the long-run convergence properties of condominium prices based on the ripple effect for five major US metropolitan areas (Boston, Chicago, Los Angeles, New York and San Francisco). Specifically, we test for both overall convergence in condominium prices and the possibility of distinct convergence clubs to ascertain the interdependence of geographically dispersed metropolitan condominium markets.

Our analysis uses two approaches to identify the convergence properties of condominium prices: the Lee and Strazicich (2003) unit root test with endogenous structural breaks and the Phillips and Sul (2007, 2009) time-varying nonlinear club convergence tests.

The Lee and Strazicich (2003) unit root tests identify two structural breaks in 2006 and 2008 with the rejection of the null hypothesis of a unit root and long-run convergence in condominium prices in the cases of Boston and New York. The Phillips and Sul (2007, 2009) club convergence test reveals the absence of overall convergence in condominium prices across all metropolitan areas, but the emergence of two distinct convergence clubs with clear geographical segmentation: on the east coast with Boston and New York and the west coast with Los Angeles and San Francisco while Chicago exhibits a non-converging path.

The results highlight the distinct geographical segmentation of metropolitan condominium markets, which provides useful information to local policymakers, financial institutions, real estate developers and real estate portfolio managers. The limitations of the research are the identification of the underlying sources for the convergence clubs identified due to the availability of monthly data for a number of potential variables.

The absence of overall convergence in condominium prices, but the emergence of distinct convergence clubs that reflects the geographical segmentation of metropolitan condominium markets raises the potential for portfolio diversification.

Unlike previous studies that have focused on single-family housing, this is the first study to examine the convergence of metropolitan area condominium prices.

The purpose of this paper is to focus upon the financial performance of firms that maintain headquarters in the largest cities and firms based in smaller metropolitan areas. Big city locations offer numerous opportunities. On the other hand, maintaining headquarters in big cities is more costly than in less congested locations and the opportunities for distractions tend to be higher. A third alternative is that location does not matter.

The study, the first of its type, applies a multivariate analysis to a large sample of Compustat firms. The analysis tests for the industry-adjusted return on investment as a function of population density.

The results, which are both statistically and economically significant, show that firms headquartered in smaller cities tend to outperform those located in major business centers.

These results suggest at least two implications for financial managers. One is that headquarters location should be considered as a key element of financial management strategy. The second is that businesses should very carefully consider decisions to move headquarters to the very largest cities.

Theory suggests that business success should increase with the size of the city. This paper, the first large-sample examination of major US firms, shows that businesses with headquarters in smaller locations tend to enjoy greater financial success.

The following classified, annotated list of titles is intended to provide reference librarians with a current checklist of new reference books, and is designed to supplement the RSR review column, “Recent Reference Books,” by Frances Neel Cheney. “Reference Books in Print” includes all additional books received prior to the inclusion deadline established for this issue. Appearance in this column does not preclude a later review in RSR. Publishers are urged to send a copy of all new reference books directly to RSR as soon as published, for immediate listing in “Reference Books in Print.” Reference books with imprints older than two years will not be included (with the exception of current reprints or older books newly acquired for distribution by another publisher). The column shall also occasionally include library science or other library related publications of other than a reference character.

The aim of this paper is to identify factors (i.e. age, gender, ethnicity, type of medical facility, geographical location, etc.) associated with physicians' prescribing behavior when treating influenza in the USA. The study aims to examine why the number of antiviral prescriptions remains substandard.

Data were obtained from the National Ambulatory Medical Care Survey for each influenza season between the years of 2005-2008. Bivariate analyses and two models of multivariate logistic regression analyses (one with no fixed effect and the other including year as a fixed effect) were used to analyze the data.

The results from this study revealed that among family practice physicians, 40.5 percent prescribed antiviral medications to patients presenting with influenza while 59.5 percent prescribed another form of medication. Antibiotics comprised 41.3 percent of the prescriptions for treatment of influenza. Multivariable logistic regression analyses revealed that race (White; p=0.023), type of health setting (private solo/group practice; p=0.041), employment status (owner; p=0.046), and metropolitan location (metropolitan statistical area; p=0.032) were all significantly associated with prescribing antivirals. Patients' expected source of payment (private insurance) and geographical location (Midwest) of health facility were marginally associated with prescribing antivirals.

By identifying factors associated with physicians' prescribing practices of antiviral medications, a more timely diagnosis and treatment of influenza can occur. Efforts should be targeted to improve physician education and awareness of the illness. Interventions may be implemented to improve the prescribing of antiviral medications and potentially inappropriate prescribing.

This paper studies the dynamics and elasticities of house prices in Spain and Portugal (Iberia) at the Metropolitan Statistical Area (MSA) level, addressing panel regression problems such as heterogeneity and cross-sectional dependence between MSA.

The authors develop a two steps study. First, five distinct estimation methodologies are applied to estimate the long-term house price equilibrium of the Iberian MSA house market: Mean Group (MG), Fully Modified Ordinary Least Square (FMOLS) MG (FMOLS-MG), FMOLS Augmented MG (FMOLS-AMG), Common Correlated Effects MG (CCEMG) and Dynamic CCEMG (DCCEMG). FMOLS-AMG is found to be the best estimator for the long-term model. Second, an additional five distinct estimation methodologies are applied to estimate the short-term house price dynamics using the long-term FMOLS-AMG estimated price in the error-correction term of the short-term dynamic house price model: OLS Fixed Effects (FE), OLS Random Effects (RE), MG, CCEMG and DCCEMG. DCCEMG is found to be the best estimator for the short-term model.

The results show that in the long run Iberian house prices are inelastic to aggregate income (0.227). This is a much lower elasticity than what was previously found in US MSA house price studies, suggesting that there are other factors explaining Iberian house prices. According to our study, coastal MSA presents an inelastic housing supply and a price to income elasticity close to one, whereas inland MSA are shown to have an elastic supply and a non-significant price to income elasticity. Spatial differences are important and cross-section dependence is prevalent, affecting estimates in conventional methodologies that do not account for these limitations, such as OLS-FE and OLS-RE. Momentum and mean reversion are the main determinants of short-term dynamics.

Recent econometric advances that account for slope heterogeneity and cross-section dependence produce more accurate estimates than conventional panel estimation methodologies. The results suggest that house markets should be analyzed at the metropolitan level, not at the national level and that there are significant differences between short-term and long-term house price determinants.

To the best of the authors' knowledge, this is the first study applying recent econometric advances to the Iberian MSA house market.

The United States government is widely acknowledged as an inveterate collector and disseminator of statistical information. The various agencies of the government gather statistics on virtually every aspect of American life, as well as world affairs. The impact and use of these data are immeasurable, since U.S. government statistics on such topics as economics, society, and labor supply the bases for all forms of statistical research and analysis.

Researchers have identified the “vibrancy” of living in a spatially defined area as having a positive impact on entrepreneurial activity, measured as new business start-ups. Vibrant areas attract and facilitate would-be entrepreneurs, and attract other entrepreneurs and the skilled people and other resources who can take advantage of the opportunities other entrepreneurs create. A vibrant locale, then, can trigger a virtuous cycle of entrepreneurially led economic and social development. The purpose of this paper is to test this hypothesis.

The unit of observation is the county of the US Census-designated central city of metropolitan statistical areas. The authors use principal components analysis to recover scores from a variety of measures of urban vibrancy. The authors embed these scores in an OLS model of new business venturing.

Within a standard model of new business launches that is designed to be comparable across time and space, the inclusion of principal components scores based on urban vibrancy measures adds little explanatory power.

Policies designed to make an urban area “more vibrant” as a means to the end of attracting new establishment launches may be less successful than policymakers hope.

To the best of our knowledge, this is the first paper to apply principal components analysis to measures of urban vibrancy within a general model of new business venturing.

Some US counties are more likely to generate entrepreneurial opportunities. This paper aims to determine whether US micropolitan counties with disproportionately high nonfarm proprietorship (NFP) employment levels are systematically linked to specific attributes of the entrepreneurial ecosystem. A limited amount of research has been conducted on the geography of entrepreneurship in small to medium-sized micropolitan counties where rates of growth and change can be quite dramatic.

NFP employment data from the US Bureau of Economic Analysis (BEA) is used as a dependent variable proxy for entrepreneurship. NFP data are widely used in the entrepreneurship literature. Data on all independent variables were obtained from the US Census Bureau’s American Community Survey and BEA by county and subject to stepwise linear regression.

Results revealed a strong positive relationship between the percent of NFP employment by micropolitan county and percent construction employment, percent real estate, and rental and leasing employment, and the percent elderly. It is argued that the combination of predictors captures primarily a self-employment of opportunity (e.g., thriving land and real estate markets).

In attempting to encourage NFP employment, policymakers should be more alert to the key predictors that shape micropolitan entrepreneurial ecosystems when attempting to enhance competitive advantage in small- to medium-sized communities. Better understanding how micropolitan counties function relative to larger metropolitan places can help local policymakers more efficiently enhance the overall quality of life in smaller communities.

The focus on smaller micropolitan communities and the explicit spatial context of this paper has sometimes been overlooked in the traditional entrepreneurship literature and this research helps to fill that gap.