Thursday, June 30, 2016

Anti-Crime Laws and Retail Prices



One sentence summary: The anti-panhandling ordinance has resulted in lower gasoline prices in the County of Sacramento.

The corresponding paper by Hakan Yilmazkuday has been accepted for publication at Review of Law and Economics.

The working paper version is available here.
Abstract
The fear of becoming a victim of crime acts like barriers to retail trade for consumers, where retailers attempt to reduce such barriers by enduring additional costs such as insurance or security/surveillance costs; as a result, retail prices are affected by the possibility of crime. This paper attempts to measure such effects by considering the recent experience of the County of Sacramento, where an anti-panhandling ordinance has been issued to protect the retailers. As an application, a difference-in-difference approach is employed to identify the effects of the ordinance on Sacramento gasoline prices at the retail level, by considering the gasoline prices in neighbor counties as the control group of a natural experiment. The results show that the anti-panhandling ordinance has resulted in lower gasoline prices in the County of Sacramento.


Non-technical Summary
The fear of victimization imposes indirect costs to society through its negative impact on local business establishments, especially retailers that make a neighborhood a convenient and stable place to live and shop. It has been shown that individuals perceive crime as highly visible signs of disorderly and disreputable behavior in the community, which affect a community's social and economic vitality. Therefore, crime is perceived as one of the most serious urban problems where high-crime neighborhoods discourage individuals from living, shopping, conducting business or seeking entertainment. Although the fear of victimization has shown to contribute to neighborhood decline and deterioration, policy makers have given more importance to residential crime, fear of crime, and various disorders such as homelessness, prostitution, and abandoned buildings. However, the same attention has not been provided for the neighborhood businesses until recently. Realizing this lack of attention, given the social and economic effects of crime on the local business establishments, many jurisdictions have started special programs to prevent crime in the last two decades, including a recent case by The County of Sacramento in 2015 to prohibit aggressive panhandling.
In the U.S., the Supreme Court has held that panhandling/begging is a form of speech that is protected by the Constitution, but political divisions have successfully outlawed "aggressive" forms of panhandling. Therefore, aggressive panhandling has been started being defined as a crime in certain neighborhoods. The County of Sacramento is one of these divisions that has recently passed an ordinance prohibiting panhandling that has become effective on January 14th, 2015 as announced by the Sacramento County Sheriff's Department. In particular, the ordinance prohibits soliciting for cash in "an aggressive or intrusive manner in any public place," including within 35 feet from an automated teller machine (ATM), within 200 feet of a vehicle at an intersection, within any vehicle stopped at a gas station, on any traffic median strip and on buses and city 
This paper investigates the short-run effects of this ordinance on the equilibrium gasoline retail price in Sacramento County by using data at the station level. Since the equilibrium price depends on both demand and supply conditions, the effects of the ordinance may be either through (i) the consumer side where customers may stop shying away from gas stations due to the fear of meeting aggressive panhandlers, or (ii) the producer side where gas stations may stop facing additional costs (because of aggressive panhandlers) such as insurance premiums to cover losses, security/surveillance costs, lower profits due to shorter operating hours, replacing and repairing property, or higher labor costs in order to compensate employees for higher risks of working. Within this context, the ordinance would result in a higher demand when customers stop shying away from gas stations, and it would result in a higher supply when gas stations face fewer costs. Accordingly, the effects of the ordinance on the equilibrium gasoline price depends on the relative magnitude of such changes/shifts in demand and supply conditions as well as their corresponding initial positions (i.e., the price elasticities of demand and supply). In other words, the theory is silent, and we need an empirical investigation in order to figure out such effects.


In terms of the methodology, we achieve our investigation by using a difference-in-difference approach where the gas stations located in the County of Sacramento experiencing the policy change on January 14th, 2015 are analyzed as the treatment group of a natural policy experiment, and the control group consists of gas stations in the neighbor counties with no policy changes. Since the ordinance restricting panhandling near gas stations is due to the Sacramento County law (rather than market conditions), using a difference-in-difference approach is a compelling way to study the effects of the ordinance on retail prices, and it is robust to any identification/endogeneity problem.

The benchmark results show that the gasoline prices have decreased in Sacramento County right after panhandling is prohibited compared to the neighbor counties. These short-run results are robust to the consideration of daily and hourly price changes that are common across all gas stations in the sample. Since the equilibrium retail prices may also depend on retail characteristics such as the brand of the gas station, having a car wash or a convenience store, or the exact location of the gas station within the neighborhood, the benchmark investigation also considers brand fixed effects or station fixed effects. Therefore, there is strong evidence for lower gasoline prices right after the ordinance. It is implied that the changes in supply conditions have been more effective than the changes in demand conditions in the determination of equilibrium gasoline prices. These benchmark results are further supported by longer-term before-and-after analyses, and robustness tests considering outliers or gas stations that are closer to the county border, which all suggest lower gasoline prices in Sacramento County after the ordinance.
More details can be found in the working paper version that is available here.


Posted by

Tuesday, June 14, 2016

Research teams and research fields of North American economics PhDs, 1980-2014


 

Since North American PhDs are so present and dominant within economics journal publications, it is important to understand the inherent as well as changing publication patterns of their representation.

Together with Ali Sina Onder, we present three main findings:
      
  1.     A trend towards heavier co-authorship;
  2.     Improving teams of women; and
  3.     Increasing share of different fields in publications by North American PhDs.

Share of fields in North American PhDs’ publications








Non-technical Summary
We employ complete publication records from 1980 to 2014 of the population of North American PhDs who graduated between 1970 and 2009. We summarize our observations and contributions in six bullet points: 
  1. Share of single-author papers diminish over time in North American PhDs’ total publications. We further document qualitative shares of different sizes of author-teams and find that coauthored papers get published significantly better than single-author papers throughout the whole sample period. While two-author papers were published better than three-author papers during 1980-1999, three-author papers get published better than two-author papers on average after 2000.
  2. Author-teams containing at least one author who is a graduate of a top thirty department publish significantly better in terms of quantity and quality compared to author-teams of non-top thirty graduates.
  3. Female authors and author-teams including at least one female have an increasing share in total publications over time. We further investigate all-male and female-or-mixed author-teams’ publication quality and compare their output before and after 2000. All-female and mixed-gender author-teams publish significantly less in terms of quantity and quality compared to all-male author-teams during 1980-1999, but we find no significant difference between publication qualities of all-male author-teams and female-or-mixed author-teams after 2000.
  4. Investigating different fields’ shares in total publications, we observe rather little variation in their shares over the course of three decades.
  5. In addition to documenting publication quality differences between all-male and female-or-mixed author-teams, we further document distributions of their publications over different fields. Male authors are proportionally over-represented in microeconomics and macroeconomics, whereas this is the case for female authors in labor economics and development economics. Nevertheless, the average quality of male and female authors’ publications are fairly similar to one another in all fields.
  6. Microeconomics, econometrics, and experimental economics research gets published in higher quality journals, whereas macroeconomics, public economics, industrial organization, finance, health and urban economics, development economics get published in journals that have lower quality weights than the average. Labor and economic history get published significantly better after 2000.





Posted by

Monday, June 13, 2016

Individual Tax Rates and Regional Tax Revenues: A Cross-State Analysis



One sentence summary: State tax revenues in the U.S. would benefit from a reduction in dividend-income taxes, while a reduction in wage–income, sales or property taxes would result in lower tax revenues.




The working paper version is available here, where you can find state-specific elasticities of tax revenues with respect to alternative tax rates.

Abstract
This paper analyzes the effects of state-level personal tax rates on state tax revenue and individual welfare. The policy analysis based on a general equilibrium model suggests that tax revenues would benefit from higher wage-income, sales or property taxes, while any increase in dividend-income tax would result in a reduction of revenues. It is also shown that individuals would suffer from an increase in state-level wage-income tax, dividend-tax or sales tax, while they would benefit from an increase in property taxes. The heterogeneity across states is determined by a TaxIndex, a weighted average of initial taxes at the state level.



Non-technical Summary
The Great Recession of 2007-2009 had a devastating effect on state finances in the U.S. when states took in $87 billion less in tax revenue from October 2008 through September 2009 than they collected in the previous 12 months; this corresponds to a decline of 11 percent, the steepest on record, resulted from the impact on tax collections of reduced wages and lowered economic activity. The requirement that states have balanced budgets has increased the pressure on states to deal with the unprecedented revenue shortfalls in a variety of ways; to recoup lost revenue, states have taken actions such as increasing tax rates. For example, according to the U.S. Census of Governments, the share of tax revenue in overall state revenue has increased from 38% to 42% between 2007 and 2012, on average across states. However, when it comes to the policy details, which type of tax should be modified in each state to improve the state budget? We answer this question by introducing an economic model that is matched by the U.S. data.

The empirical results show that state tax revenue increases with wage-income tax for any state with an average (across states) elasticity of 0.88. When we make a comparison across states, Louisiana, Hawaii and Alabama would benefit most in terms of their tax revenue out of a wage-income tax increase, while states of Iowa, South Dakota, New Hampshire and Texas would benefit less. When we search for a systematic explanation for this result, the economic model introduced in the paper implies that TaxIndex, which is a weighted average of initial taxes at the state level, is the main determinant of the difference across regions. As is evident in Figure 1, the states that have benefited more in relative terms are the ones with a lower TaxIndex.


Regarding the short-run effects of an increase in sales taxes, the average (across states) elasticity of tax revenue with respect to sales taxes is about 0.20; the tax revenue of any state would increase after an increase in its sales tax rate. The elasticity of tax revenue with respect to sales taxes goes down with the TaxIndex; therefore, states with low TaxIndex such as Louisiana, Hawaii and Alabama would benefit most from an increase in sales taxes, while New Jersey, Wisconsin, Vermont and Nebraska would not have any significant changes in their tax revenues. An increase in state-level property taxes would also increase tax revenue of any state; once again, the TaxIndex seems to be the main determinant of tax revenue differences across states in Figure 1. A reduction in dividend-income tax is the only tax type that results in higher state tax revenue.

 




This paper has been mentioned, among others, in Carolina Journal, Bladen Journal, NC Spin The Robesonian, The Mountaineer, New Bern Sun Journal, and The Daily Dispatch (Henderson, NC).









Posted by

Understanding Gasoline Price Dispersion



One sentence summary: Refinery-specific costs, which have been ignored in the literature due to using local data sets, contribute up to 33% to the gasoline price dispersion within the U.S..



Abstract
This paper models and estimates the gasoline price dispersion across time and space by using a unique data set at the gas-station level within the U.S.. Nationwide effects (measured by time fixed effects or crude oil prices) explain up to about 51% of the gasoline price dispersion across stations. Refinery-specific costs, which have been ignored in the literature due to using local data sets within the U.S., contribute up to another 33% to the price dispersion. While state taxes explain about 12% of the price dispersion, spatial factors such as local agglomeration externalities, land prices, distribution costs of gasoline explain up to about 4%. The contribution of brand-specific factors is relatively minor.


Non-technical Summary
Retail prices of gasoline are considerably different across gas stations within the U.S. where consumer expenditure share of gasoline is about 5%. Since such price differences may be reflecting the frictions in the economy, understanding the reasons behind them is the key to an optimal policy that would improve the level and distribution of individual welfare.

For example, consider a typical day in 2010-2011 when the retail-level gasoline price difference between any two gas stations within the U.S. was as high as $2.25 (followed by $2.19) per gallon of regular gas. The gasoline price differences of both $2.25 and $2.19 were between Washington D.C. and Michigan on October 23rd, 2010 and December 16th, 2010, respectively. If you think that this price dispersion was due to differences in state-taxes per gallon, which ranged between 46.6 cents (for California) and 8 cents (for Alaska) in 2010 and between 49.6 cents (for Connecticut) and 8 cents (in Alaska) in 2011, you are only partially right, because, for a typical day of the very same sample period, the price difference between any two gas stations within any given state/district of the U.S. was as high as $1.57 (for Washington D.C. on October 15th, 2010) followed by $0.99 (for Iowa for October 10th, 2010).

The gasoline price dispersion was not due to outliers, either; because, according to Figure 2, for a typical day, the median price difference between any two gas stations within any given state/district of the U.S. was as high as $0.60 (for Hawaii) followed by $0.44 (for Washington D.C.) and by $0.41 (for Wyoming):

 

Therefore, gasoline price differences not only exist across states but also within states; accordingly, a detailed spatial analysis (rather than a state-level analysis) is required to understand the details behind gasoline price dispersion.

We use a daily gasoline price data set covering the period between September 10th, 2010 and January 31st, 2011 that involves brand information of gas stations as well as their location information at the exact address level. Combining this data set with the exact (address-level) location information of oil refineries, state-level taxes, crude oil prices, land prices (at the zip-code level), and local agglomeration externalities of spatial gasoline demand (namely, the distribution of nighttime lights data across space), we decompose the effects of crude oil prices, refinery costs, distribution costs, brand-specific costs, state taxes, land prices, and local agglomeration externalities (i.e., spatial demand and number of competitors) through a spatial analysis that models and estimates the transportation needs of individuals and distribution of gasoline from refineries to gas stations. While the crude oil prices (or any other time-varying effect that is common across gas stations) are considered to capture time-varying effects that are common across all gas stations within the U.S., other cross-sectional variables are considered to capture the effects due to spatial factors.

Estimation results show that local agglomeration externalities of spatial gasoline demand (measured by the standard deviation of nighttime lights) and the number of local competitors have negative and significant effects on gasoline prices as expected by the model. In terms of their magnitudes, one percent of an increase in local externalities can lower gasoline prices as much as 1.92%, while one percent of an increase in the number of competitors can lower gasoline prices as much as 0.19%. Distribution costs of gasoline from the nearest refiner to the gas station have positive and significant effects, also as expected; one percent of an increase in distribution costs leads to an increase up to about 0.41% in gasoline prices. The effects of land prices are also positive and significant (as expected) where one percent of an increase in land prices leads to an increase up to about 0.79% in gasoline prices. Also considering the unit coefficients in front of state and federal taxes (through restricted least squares), the R-squared values are all about 0.90, which is promising.

The corresponding variance decomposition of gasoline prices across time and space suggests that the highest contribution is by time fixed effects (or crude oil prices) capturing up to 51% (or 39%) of the price dispersion (i.e., nationwide effects are almost half of the overall effects), followed by refinery-specific costs capturing up to 33% of the price dispersion, state taxes capturing up to 12% of price dispersion, and spatial factors (such as local agglomeration externalities, land prices, distribution costs of gasoline) capturing up to 4% of price dispersion. The contribution of brand-specific costs is relatively minor.


The main contribution to the existing literature has been to show that refinery-specific costs explain a big part of the price dispersion across gas stations, which has been mostly ignored in the literature due to using local data sets covering either a couple of cities or states within the U.S..



Posted by

Friday, June 10, 2016

Forecasting the Great Trade Collapse



One sentence summary: International trade can be forecast by only using data on prices and quantities, without any formal estimation; an application using data on U.S. imports supports this methodology by successfully forecasting the Great Trade Collapse and the corresponding recovery period.

The corresponding paper by Hakan Yilmazkuday titled "Forecasting the Great Trade Collapse" has been published at International Economics...

The working paper version is available here.

Abstract
This paper introduces a simple methodology to forecast international trade. The main innovation is to calculate non-unitary expenditure elasticities of import demand implied by non-homothetic preferences in the previous year to be further combined with the current change in expenditure to forecast the current imports. Using U.S. data on aggregate expenditure and good-level imports, we test the performance of the methodology in forecasting international imports. The methodology is successful in forecasting not only the Great Trade Collapse and the corresponding recovery period but also the other periods in the sample.
















Non-technical Summary
During the recent financial crisis of 2008-2009, the decline in international trade has been more than the decline in domestic expenditure/income, the so-called Great Trade Collapse. Figure 1 shows the corresponding experience of the U.S. economy where the decline in U.S. imports is more than three times the decline in U.S. expenditure.


This has been a surprise for the trade literature, since trade and expenditure/income are supposed to move together, mostly according to the unitary expenditure/income elasticity of demand implication based on constant elasticity of substitution (CES) preferences that are commonly used in the literature. This may cause a problem, especially while conducting policy, because the policy makers simply would like to know how trade responds to changes in overall economic activity.

Using data on U.S. imports (including information on both quantities and unit prices) covering the quarterly period over 2000q1-2012q4, we calculate expenditure elasticities of demand for U.S. imports at the good level. Second, for each period, calculated expenditure elasticities of the previous year (at the good level) are multiplied with the current annual percentage change in U.S. expenditure to obtain current (ex post) forecasted annual percentage change in U.S. imports (at the good level). Therefore, two pieces of information are enough for forecasting (the annual percentage change in international trade): the expenditure elasticity measures coming from the previous year and the annual percentage change in expenditure in the current period.

The good-level results show that our methodology is highly successful in forecasting the Great Trade Collapse and the corresponding recovery period for the U.S.:


Our methodology is also successful in forecasting the overall U.S. imports:


as well as periods of the Great Trade Collapse and the corresponding recovery:


Regarding implications for policy makers, it is important to emphasize that this paper has considered an ex post forecasting strategy rather than an ex ante one, where the expenditure elasticity of import demand coming from the previous year has been considered as the estimation/historical segment, while the annual percentage change in overall expenditure in the current period has been considered as the exogenous variable in the out-of-estimation sample. Although an ex ante forecast has not been the objective of this paper, it can easily be achieved by combining expenditure elasticities of demand with any forecast/expectation regarding the future economic activity.




Posted by
Subscribe to: Posts (Atom)