Hundreds of articles have been written that include empirical estimates of the dis-employment effects of minimum wages; however, many of these articles find statistically insignificant effects, some find significant negative effects, and a few find significant positive effects. Most of these studies use multivariate analyses which can be criticized for omitting key variables. The omitted variables problem ruins all statistics and estimates. This paper uses reiterative truncated projected least squares (RTPLS), a solution to the omitted variables problem, to estimate the percentage increase in unemployment due to a one percent increase in the real minimum wage using monthly data for the 50 states of the USA from 1987 to 2021. RTPLS produces a separate elasticity for every observation where differences in these estimates are due to omitted variables. We argue that RTPLS solves most of the econometric problems that David Neumark identified in his keynote address at a minimum wage conference in Berlin in 2018. We find that the percentage change in the unemployment rate due to a one percent change in the minimum wage ranges between 1.156 and 3.389, that the elasticities for different states tend to move together over time, and that all these elasticities are statistically significant at a 95 percent confidence level.

Utilizing a 2021 nationally representative sample of 7,504 White-owned employer businesses (WOBs) in the United States of America (USA), the extent, and the determinants of WOB business registration are estimated. Business registration is employed as a proxy for business in/formality. Approximately one-fifth (22.5%) of all employer WOBs are unregistered or informal business concerns hidden from government purview. The primary determinants of employer WOB informality are business size (annual revenues under $500,000 and few paid employees), a business owner with less than a four-year college degree, and an upbringing in a lower- or middle-class environment, among other findings. Notably, this research reveals the white lie of the magnitude of White-owned employer business informality in the USA.

Economic growth rests on entrepreneurship and capitalism. Entrepreneurship is important because entrepreneurs work unceasingly to create, use, and disseminate new products and productive techniques to raise living standards. Capitalism is characterized by private property rights, a free market, and the pursuit of profit, which are driving forces for innovation and productivity and in turn economic growth. Using six proxies for economic growth, our results show that capitalism, measured by the Economic Freedom of the World Index, and entrepreneurship, measured by the Global Entrepreneurship Monitor data set, fostered economic growth in member states of the European Union between 2000 and 2021.

This paper describes two novel approaches to cost estimation of manufactured products where a data set of similar products have known manufactured costs. The methods use the notion of piecewise functions and are (1) clustering and (2) splines. Cost drivers are typically a mixture of categorical and numeric data which complicates cost estimation. Both clustering and splines approaches can accommodate this. Through four case studies, we compare our approaches with the often-used regression models. Our results show that clustering especially offers promise in improving the accuracy of cost estimation. While clustering and splines are slightly more complex to develop from both a user and a computational perspective, our approaches are packaged in an open-source software. This paper is the first known to adapt and apply these two well-known mathematical approaches to manufacturing cost estimation.

Bank capital requirements would entail large social costs if they made resource allocation suboptimal and banking services costly by unduly limiting the banks’ ability to lend. This paper considers three main factors that may make capital requirements relevant, namely, deposit insurance subsidies, stock valuation errors, and tax shields derived from debt financing. The theoretical model analyzes the combined effects of the three factors on the banks’ incentives to make fairly priced loans, which should also be socially optimal loans. A key finding is that the long-term cost of capital requirements is likely to be very small when deposit insurance is underpriced. Increased funding costs resulting from higher capital requirements are absorbed by shareholders of banks, rather than passed on to borrowers. Under some reasonable assumptions, higher capital requirements improve resource allocation by countervailing distortionary effects of deposit insurance subsidies. Short-term adjustment costs can still be large, but it should be relatively easy to mitigate the short-term effects.

College choice and choice of major are the most important decisions for future earnings. It is still unclear, however, what makes a greater difference—college or major—or whether a choice of college matters more for some majors, but not the others. Using cross-classified models and College Scorecard data, I show that a discipline is more consequential for future earnings than a college. The effect of STEM is substantial but is less pronounced at institutions with higher overall median earnings. The effect of college selectivity on earnings is more pronounced for non-STEM disciplines. Institutional characteristics—such as tuition, shares of graduates receiving different forms of financial aid, institutional size and location, and type of college—correlate with earnings of graduates. Racial and gender composition of an educational program correlate with expected earnings of its graduates even after control for other institutional and disciplinary characteristics. Models presented here provide a better understanding of the effect of college and major choices on future earnings.

Despite the status of the United States as one of the world’s economic superpowers, the country is plagued by child poverty. The issue of child poverty is most prominent in rural, majority-minority communities, where years of limited opportunity, often created by systematic oppression, have created a vicious cycle of economic despair. There are a number of policies that have been explored which could limit the effects of child poverty, most notably the Expanded Child Tax Credit (ECTC), which was introduced as part of the American Rescue Plan Act (ARPA) of 2021. This paper uses a combination of qualitative and quantitative methods to thoroughly examine this policy (ECTC) and its potential to both cut child poverty in the short term and increase educational attainment and economic mobility in the long term for recipients. The ECTC program ended in 2022 and only lasted in a short period. By examining the impacts of a similar and long-standing but less accessible and generous program, the Earned Income Tax Credit (EITC), on educational attainment in specific communities we can theorize and determine the significance the Expanded CTC would have. We use EITC program as a proxy of ECTC and reach inference of the long-term impact of ECTC program on children’s education attainment. The result of this research indicates a number of benefits of the Expanded Child Tax Credit, including its potential to cut child poverty and increase educational attainment, its impact on future economic prosperity in rural, majority-minority communities, and the policy’s ability to decrease economic disparities between non-metro and metropolitan areas.

The high price of energy due to green energy policy will cause adjustments across the US economy predicted in the present general equilibrium model that includes energy Btu input with capital and labor to produce manufactures and services. This same model in trade theory examines the effects of a tariff on an imported factor of production such as a natural resource or capital.  Corresponding error correction estimates of the reduced form equations of the model in annual 1970-2018 data prove robust and suggest model modifications. A parametric approach to noncompetitive pricing based on unit cost diminishing with output brings the model much closer to the estimates. Manufactures are revealed to have a higher degree of noncompetitive pricing than services. Assuming constant elasticity of substitution production, a weak degree is revealed to provide the best fit to the error correction estimates. The high price of green energy will cause an inelastic decrease in energy input resulting in increased energy revenue. Outputs of both sectors fall, more in manufactures due to its energy intensity. The capital return and wage also fall given the weak substitution of capital and labor relative to the price of energy. The wage rises slightly in the model but falls considerably in the error correction estimate. The only clear winner is energy input with increased revenue. The government owns a large share of hydrocarbon reserves and will benefit from a higher price of energy.

Income and population growth are key determinants of housing demand, while land use regulations are designed to affect housing supply. Previous studies of housing price determinants focus either on specific regulations in particular cities/regions, or on selective subsets of major cities and regulations. This study examines the impact of land use regulations on housing prices from 1989 to 2006 in an unusually large sample of 250 major US cities. Aside from factors that are commonly associated with housing demand (income, population growth and density), housing prices are found to be associated with local cost-increasing land use regulations (approval delays) and with statewide regulations. Since statewide regulations factor prominently into the results, specific examples of the impact of different types of land use regulations are provided for 5 cities in the state of Washington. The estimated increase in housing prices associated with regulations is, on average (over 250 cities), substantially larger than the effects of income and population growth. While the estimated dollar costs associated with regulations may be sizable at times, the results are remarkably consistent with previous studies that were based on smaller cross sections.

Consideration of optimal commodity storage with different discount rates. Finding that, even with a lower discount rate than private storage, optimal government-financed storage may not narrow price fluctuations compared with optimal privately financed storage because a government has to choose a probability of buffer stock failure greater than zero to economize on storage costs that could conceivably become very large. There is no presumption that government financed storage would be larger, with narrower intervention bands and more stable prices, than with privately-financed storage. The welfare enhancing effect of government financed storage compared to private storage is therefore indeterminate.

This paper presents a model of bank runs and evaluates relevant policy tools. The model is founded on the historical pattern of banking panics, involving an economic boom, an adverse shock, prominent bank failures, and runs on both insolvent and solvent banks. The model analyzes various ways in which solvency information affects the likelihood of systemwide bank runs. An interesting result is that partial bank-specific information can be worse than no bank-specific information. The model can also explain runs driven by liquidity concern based on incomplete solvency information. The main policy implication derived from the model and the evaluation of policy tools is that policy actions to contain a financial crisis should incorporate weeding out insolvent institutions and assuring the solvency of remaining institutions.

In this study, I analyze the impact of the aggregate, income, corporate and social security tax revenues on both the U.S. output and the stock market return in a structural vector autoregression (SVAR) framework between 1960:Q1 and 2015:Q4. Unlike some of the other studies, I use aggregate and disaggregated tax revenue variables to examine the impact of scal policy. Results show that an increase in aggregate tax revenue reduces output and market return. In addition, an increase in income, corporate, and social security tax revenues reduces output and the market return signi cantly at varying degrees.

This paper examines whether sell-side analyst-affiliated investors trade stocks before their analysts release material information. Using class action lawsuits, I explore the pre-lawsuit periods and investigate how potentially informed analysts trade their covered firms. The event study finds that analyst-affiliated investors reduce their stockholdings of firms prior to their own analyst downgrades. The findings are more pronounced among investors employed by investment banks. The post-trading performance of analyst-affiliated investors suggests that they have superior information and front-run to maximize benefits.

Less-Lethal Weapons, including electric Tasers, velocity-reduction bullet-capture projectiles, rubber bullets, beanbag rounds, and other less-lethal projectiles used in police departments, federal and local law enforcement agencies, and military policing operations, as well as for private use, are topics of significant debate in recent years. These weapons can reduce collateral damage, reputational risk, and legal exposure while preventing wrongful and unnecessary deaths caused by conventional bullet wounds. Yet, opponents have highlighted concerns regarding the safety and effectiveness of these weapons, as well as their potential to cause injury or even death. While the primary duty of law enforcement officials is to preserve order and execute the law while protecting individual rights, they ultimately achieve these goals by exercising their coercive authority. Society expects police officers to rely on their professional training and judgment when using physical force to defend citizens and themselves or to apprehend criminal suspects. Because police officers must occasionally participate in physical acts of coercion, it is unavoidable that some of these interactions may result in bodily injuries to individuals, including the officers themselves. These injuries have real costs, such as medical treatment, lost wages, municipal liability, court claims and settlements, riots, and legal or even criminal exposure, as well as less-tangible collateral costs, such as the erosion of police legitimacy and public trust, which are more difficult to quantify but arguably more influential in shaping long-term public perceptions than personal injuries alone. Less lethal technology eliminates some of the concerns raised by lethal weaponry. This study reviews the advantages and disadvantages of these less-lethal weapon technologies versus traditional firearms for use by the police force, federal agencies, and military police while also performing advanced analytics to model the valuation and return on investment for a law enforcement organization implementing such technologies.

The construction and optimization of a portfolio is a complex process that has been a historically active research area in finance. For portfolios with highly correlated assets, the performance of traditional risk-based asset allocations methods such as the mean-variance (MV) model is limited when numerous assets are correlated. A novel clustering-based asset allocation method, called Hierarchical Risk Parity (HRP), provides an opportunity to mitigate these limitations in portfolio construction. HRP utilizes the hierarchical relationships between the covariance of assets in a portfolio to determine weight distributions, eliminating the need for the inversion of the covariance matrix that is required by most traditional risk-based asset allocation methods. This research examines the viability of Hierarchical Risk Parity (HRP) method in portfolio construction of a US equity portfolio and compares the performances of HRP to traditional asset allocation methods exemplified by the mean-variance (MV) method. The results of this research show that the performance of the HRP method is comparable to the performance of the MV method. Given these findings, HRP provides an advantageous approach for portfolio construction in practical scenarios where correlated assets are present in the portfolios.

We present two approaches to forecasting parameters in the SABR model. The first approach is the vector autoregressive moving-average model (VARMA) for the time series of the in-sample calibrated parameters, and the second is based on machine learning techniques called epsilon-support vector regression (ε-SVR). Using daily data of S&P 500 ETF option prices from January 1, 2014, to December 31, 2018, we first calibrate the daily values of the model parameters from the training sample, then conduct out-of-sample forecasting of parameters and pricing of options. Both approaches produce good fits between the forecasted and calibrated parameters for out-of-sample dates. A comparison study shows that using forecasted parameters as inputs, the SABR model generates better pricing results than assuming constant parameters or using lag parameters. We also discuss the market conditions under which one approach outperforms the other.

This study investigates the impact of institutions and public agricultural Research and Development (R&D) on agricultural productivity in low- and middle-income countries. The paper employs panel data with fixed-effects models, spanning 12 years and including 49 low-and middle-income countries. Agricultural productivity is measured by cereal yield, while institutional factors are assessed through four key indicators: judicial independence, property rights protection, contract enforcement, and impartial public administration. Public agricultural R&D investments serve as the measure for agricultural R&D. The findings show that property rights protection positively influences agricultural productivity, whereas legal contract enforcement has a negative impact across these countries. Additionally, public agricultural R&D significantly and positively affect productivity in Asia and the LAC regions, but not in sub-Saharan Africa.

To make an assessment of ratemaking proposals, this article proposes that regulators should consider applying a logical decision-making process, such as multi-criteria decision analysis (MCDA), that selects or modifies those rate mechanisms that come closest to achieving the public interest, as defined by regulators. MCDA seems like an fitting tool to improve regulatory decisions by making more explicit the relationship between different rate mechanisms and the public interest. This article provides a simplified version of MCDA to demonstrate how regulators can apply this tool to evaluate new rate mechanisms in terms of the public interest. As far as the author knows, the MCDA methodology applied to utility ratemaking has not appeared in the literature. While regulators may not want to or lack the resources to execute all the steps presented in this article, MCDA can provide direction to regulators in evaluating different rate mechanisms and ultimately reaching decisions that are more rational and aligned with the public interest. After all, evaluating rate mechanisms is one of the major functions of utility regulators. Doing it wrongly can have a consequential effect on society’s welfare.

This paper quantifies the impact of horizontal mergers on the prices of drugs. We study all mergers between two publicly traded pharmaceutical companies working in the US between 2010 and 2015 and their effect on their drugs prices. Using a differences-in-differences approach, we estimate that drugs marketed by the merging firms experience an AWP increase of about between 8% and 15% during the five years following the merger. These price increases are present even in markets with more than five competitors.

Advanced Air Mobility (AAM) may, in the coming decades, result in tens of thousands of new jobs and billions of dollars in additional economic activity. While much of the discussion surrounding AAM focuses on the technical aspects of the nascent industry, estimates of the potential economic impact vary significantly. Much less attention has been paid in the literature to potential externalities, positive and negative, and how these externalities may impact estimates of economic impact. We argue that much work remains to be done before policy advisors and decisions makers can formulate and implement strategies based on the projections of future economic impact of AAM.