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Using data on a sample of small Iowa towns consistently collected over two decades, we investigate how agglomeration economies, social capital, human capital, local fiscal policy, and natural amenities affect new firm entry. We find that human capital and agglomeration are more conducive to new firm entry than are natural amenities, local fiscal policy, or social capital. The impact of local fiscal policy is too small to overcome the locational disadvantages from insufficient endowment of human capital and agglomeration. A rural development approach that encourages firm entry in rural towns with the largest endowments of human capital and market agglomeration would be more successful than trying to raise firm entry in every town.

Economic Development in Ghana and Malaysia investigates why two countries that appeared to be at more or less the same stage of economic development at one point in time have diverged so substantially. At the time of their independence from the UK in 1957, both Ghana and Malaysia were at roughly the same stage of economic development; in fact, Ghana's real per capita income was slightly ahead of Malaysia's. Since then, Ghana's development has been sluggish, while Malaysia's economy has taken off into sustained growth and today, the real per capita income of Malaysia is about five times that of Ghana. This volume examines the pre-colonial and colonial economies of both countries, and the economic policies pursued after independence. In doing so, it aims to identify policies which might have contributed to Malaysia's development and those which might have slowed Ghana's. The authors ask whether lessons can be learned from the successes of countries such as Malaysia. This detailed comparative analysis will be useful to students and researchers of development economics as well as public policy makers in developing countries. It is written in language which makes it accessible to the general reader. © 2020 Samuel K. Andoh, Bernice J. deGannes Scott and Grace Ofori-Abebrese. All rights reserved.

This paper presents a study on 80 countries that evaluates the socioeconomic factors in containing the spread and mortality of COVID-19. Our results show that the long-term social factors such as lower personal freedom, better education in science, and past coronavirus outbreak experience are more effective than the economic factors such as higher healthcare-associated factors per 1000 population and larger GDP. However, using GDP per capita as the instrumental variable, we also find that the richer countries with a high degree of personal freedom have a higher number of infection or death cases per million population because they would be less likely to adhere to and implement the policy of the movement restrictions to restrict their access to goods and services. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

Following the 2007–2008 financial crisis, there is widespread interest in understanding how derivative use drives bank lending behavior. Our paper examines the impact of bank ownership structure on the relationship between derivative use and lending activities of U.S. banks. We find that lending recovered faster in larger banks than smaller banks post-crisis and in line with Diamond’s (Diamond DW 1984 Financial intermediation and delegated monitoring. Rev Econ Stud 51:393–414) systemic risk reduction theory, derivative use is positively associated with lending growth. Ownership is significant in explaining the magnitude of the relationship even after controlling for alternative specifications of the derivative use variable. In both normal and crisis periods, the speed of adjustment of lending to derivatives use by stock banks lags that of mutual banks. We suggest that speculative trading in derivatives substitutes for lending growth to a larger extent for stock banks compared to mutual banks. These findings may have important implications for investors and bank regulators. © 2020, Academy of Economics and Finance.

Since the first COVID-19 case was discovered in December 2019, over 12.1 million cases have been reported in more than 188 countries and territories. In the USA, the Centers for Disease Control and Prevention has confirmed almost 3.05 million COVID-19 cases, with more than 132 000 deaths. The COVID-19 pandemic has had a particularly dramatic impact on the elderly and those with chronic underlying medical disorders. Before the second outbreak in July, long-term care facilities were the most severely affected in terms of case numbers, especially nursing homes. This article provides information and insight into the potential changes in consumer preferences toward long-term care facility selection and the possible structural change of the long-term care industry in three aspects; structure, conduct and performance. © 2020 MA Healthcare Ltd. All rights reserved.

A methodology is developed for combining mean value forecasts using not only all the important statistics related to the past performance and the dependence of the individual forecasts, but also a rank ordering of the individual forecasts representing the belief of a decision maker about the future performance of the forecasts. The maximum likelihood combination of the forecasts turns out to be a weighted linear combination of the individual forecasts, where the weights are a function of the rank order of the forecasts, correlation coefficients between the forecasts, and relative entropy information measures between the individual forecasts and the actual values. These weights are assessed once in the most general case and once in a special case where the forecasts are normally distributed. The sensitivity of the weights is also investigated. A sample application of this method for predicting U.S. hog prices is also presented.

This paper presents a methodology for producing a probability forecast of a turning point in U.S. economy using Composite Leading Indicators. This methodology is based on classical statistical decision theory and uses information-theoretic measurement to produce a probability. The methodology is flexible using as many historical data points as desired. This methodology is applied to producing probability forecasts of a downturn in U.S. economy in the 1970-1990 period. Four probability forecasts are produced using different amounts of information. The performance of these forecasts is evaluated using the actual downturn points and the scores measuring accuracy, calibration, and resolution. An indirect comparison of these forecasts with Diebold and Rudebusch's sequential probability recursion is also presented. It is shown that the performances of our best two models are statistically different from the performance of the three-consecutive-month decline model and are the same as the one for the best probit model. The probit model, however, is more conservative in its predictions than our two models.

Ordinary least squares (OLS) regression is relatively sensitive to the presence of outliers in a data set. In this paper, a robust estimation method, least median of squares (LMS) is used to identify outliers in land value data. Once the outliers are identified, are the land value equations re-estimated. The results show that a few observations can have a significant effect on the estimated coefficients. Finally, the observations which were identified as outliers were examined in more detail. One cause of outliers is an omitted variable. In this case, a large fraction of the outliers were found to be observations with high development potential.

This paper examines the effect of a zoning change on the land market in McHenry County, Illinois. One question addressed is whether zoning `'follows the market.” It is found that, for agricultural land, zoning does tend to follow the market. In addition, the effect of land prices on land use is examined. The results here, however, are mixed. In the initial years after the zoning change, a high relative price of residential land increases the probability that a parcel will be zoned residential. However, several years later, a high relative price of residential land decreases the probability that a parcel is zoned residential. This result suggests that it may take some time for a zoning change to have a significant impact on the local land market.