Corruption in America

We use a data set of federal corruption convictions in the U.S. to investigate the causes and consequences of corruption. More educated states, and to a less degree richer states, have less corruption. This relationship holds even when we use historical factors like education in 1928 or Congregationalism in 1890, as instruments for the level of schooling today. The level of corruption is weakly correlated with the level of income inequality and racial fractionalization, and uncorrelated with the size of government. There is a weak negative relationship between corruption and employment and income growth. These results echo the cross-country findings, and support the view that the correlation between development and good political outcomes occurs because more education improves political institutions.

Corruption in America

We use a data set of federal corruption convictions in the U. S. to investigate the causes and consequences of corruption. More educated states, and to a less degree richer states, have less corruption. This relationship holds even when we use historical factors like education in 1928 or Congregationalism in 1890, as instruments for the level of schooling today. The level of corruption is weakly correlated with the level of income inequality and racial fractionalization, and uncorrelated with the size of government. There is a weak negative relationship between corruption and employment and income growth. These results echo the cross-country findings, and support the view that the correlation between development and good political outcomes occurs because more education improves political institutions.

Why is Manhattan So Expensive? Regulation and the Rise in House Prices

In Manhattan and elsewhere, housing prices have soared over the 1990s. Rising incomes, lower interest rates, and other factors can explain the demand side of this increase, but some sluggishness on the supply of apartment buildings also is needed to account for the high and rising prices. In a market dominated by high rises, the marginal cost of supplying more space is reflected in the cost of adding an extra floor to any new building. Home building is a highly competitive industry with almost no natural barriers to entry, yet prices in Manhattan currently appear to be more than twice their supply costs. We argue that land use restrictions are the natural explanation of this gap. We also present evidence consistent with our hypothesis that regulation is constraining the supply of housing so that increased demand leads to much higher prices, not many more units, in a number of other high price housing markets across the country.

Why Have Housing Prices Gone Up?

Since 1950, housing prices have risen regularly by almost two percent per year. Between 1950 and 1970, this increase reflects rising housing quality and construction costs. Since 1970, this increase reflects the increasing difficulty of obtaining regulatory approval for building new homes. In this paper, we present a simple model of regulatory approval that suggests a number of explanations for this change including changing judicial tastes, decreasing ability to bribe regulators, rising incomes and greater tastes for amenities, and improvements in the ability of homeowners to organize and influence local decisions. Our preliminary evidence suggests that there was a significant increase in the ability of local residents to block new projects and a change of cities from urban growth machines to homeowners' cooperatives.

Why is Manhattan So Expensive? Regulation and the Rise in House Prices

In Manhattan and elsewhere, housing prices have soared over the 1990s. Rising incomes, lower interest rates, and other factors can explain the demand side of this increase, but some sluggishness on the supply of apartment buildings also is needed to account for the high and rising prices. In a market dominated by high rises, the marginal cost of supplying more space is reflected in the cost of adding an extra floor to any new building. Home building is a highly competitive industry with almost no natural barriers to entry, yet prices in Manhattan currently appear to be more than twice their supply costs. We argue that land use restrictions are the natural explanation of this gap. We also present evidence consistent with our hypothesis that regulation is constraining the supply of housing so that increased demand leads to much higher prices, not many more units, in a number of other high price housing markets across the country.

Why Have Housing Prices Gone Up?

Since 1950, housing prices have risen regularly by almost two percent per year. Between 1950 and 1970, this increase reflects rising housing quality and construction costs. Since 1970, this increase reflects the increasing difficulty of obtaining regulatory approval for building new homes. In this paper, we present a simple model of regulatory approval that suggests a number of explanations for this change including changing judicial tastes, decreasing ability to bribe regulators, rising incomes and greater tastes for amenities, and improvements in the ability of homeowners to organize and influence local decisions. Our preliminary evidence suggests that there was a significant increase in the ability of local residents to block new projects and a change of cities from urban growth machines to homeowners’ cooperatives.

Urban Growth and Housing Supply

Cities are physical structures, but the modern literature on urban economic development rarely acknowledges that fact. The elasticity of housing supply helps determine the extent to which increases in productivity will create bigger cities or just higher paid workers and more expensive homes. In this paper, we present a simple model that provides a framework for doing empirical work that integrates the heterogeneity of housing supply into urban development. Empirical analysis yields results consistent with the implications of the model that differences in the nature of house supply across space are not only responsible for higher housing prices, but also affect how cities respond to increases in productivity.

Urban Growth and Housing Supply

Cities are physical structures, but the modern literature on urban economic development rarely acknowledges that fact. The elasticity of housing supply helps determine the extent to which increases in productivity will create bigger cities or just higher paid workers and more expensive homes. In this paper, we present a simple model that provides a framework for doing empirical work that integrates the heterogeneity of housing supply into urban development. Empirical analysis yields results consistent with the implications of the model that differences in the nature of house supply across space are not only responsible for higher housing prices, but also affect how cities respond to increases in productivity.

Antibacterial properties of Devil’s Walking Stick and Winged Sumac extracts

Many native plants are used for the treatment of various diseases. Mainly those species in high chemical compound plant families can have antimicrobial properties. We selected two native plants in Arkansas, Devil’s walking stick (Aralia spinosa), and Winged sumac (Rhus copallinum), and tested them for antibacterial properties. We used three gram-positive bacteria (Bacillus cereus, Bacillus subtilis, and Staphylococcus epidermidis) and three gram-negative bacteria (Alcaligenes faecalis, Escherichia coli, and Serratia marcescens). The disc diffusion method is employed to identify any potential antibacterial properties for the two plant species. For this experiment, 6.50 g of dehydrated plant material (leaves of each plant species) was combined with 50 mL of 75% ethanol creating their respective tinctures which were processed to remove alcohol and make power samples. The antibacterial activity of the powders in sterile Milli-Q water was tested against 75% ethanol and hydrogen peroxide controls. After 24 and 48 hours of incubation at 37°C, the zones of inhibition were measured for each bacteria/plant sample combination. The plant samples were tested for inhibition of each bacterial species. We used nested ANOVA (analysis of variance) to examine the effect of different concentrations of plant samples and two different incubation times (24h and 48h) of each plant species on zones of inhibition for six different bacteria. Preliminary investigations showed antibacterial properties in the samples. This indicates that native plant species can have potential medicinal properties