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The Pass-through of Retail Crime
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Abstract
This paper shows that retailers increase prices in response to organized retail crime, revealing a substantial aspect of retail crime's social costs. We match detailed information on store-level crimes to administrative scanner data from the universe of transactions for cannabis retailers in Washington state. Exploiting quasi-experimental variation from the timing of store-level robberies and burglaries, we find that crimes cause a 1.8% increase in retail prices at victimized stores. Nearby rivals of victimized stores increase prices by a similar amount with a two-month lag. Retailers' price responses are not driven by demand effects, increased wholesale costs, or strategic price responses. Instead, they are consistent with precautionary security expenditures. We find the largest pass-through rates for independent stores and in less concentrated markets. We estimate that crime imposes a 1% "hidden" unit tax on affected stores, implying an annual negative welfare effect of approximately $30.6 million, with consumers bearing two-thirds of this burden.
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We provide generalizable results on the price and promotion tactics employed in the U.S. retail grocery industry. First, we document a large degree of price dispersion for UPCs and brands across stores, both nationally and at the local market level. Base price differences across stores and price promotions contribute to the overall price variance, and we show how to decompose the price variance into base price and promotion components. Second, we document that a large percentage of the variation in prices and promotion tactics across stores can be explained by retail chain and especially market/chain factors, whereas market factors explain only smaller percentage of the variation. Third, we show that the chain-level price and promotions similarity can be explained by similarity in demand. In particular, a large percentage of the variance in price elasticities and promotion effects can be explained by retail chain and especially market/retail chain factors. Further, price elasticities and promotion effects across stores of the same chain are hard to distinguish from the chain-market-level mean, and cross-price elasticities are typically imprecisely estimated. These findings suggest that retail managers may plausibly consider price discrimination across stores to be infeasible.
We estimate the effect of minimum wages on low-wage jobs using 138 prominent state-level minimum wage changes between 1979 and 2016 in the U.S using a difference-in-differences approach. We first estimate the effect of the minimum wage increase on employment changes by wage bins throughout the hourly wage distribution. We then focus on the bottom part of the wage distribution and compare the number of excess jobs paying at or slightly above the new minimum wage to the missing jobs paying below it to infer the employment effect. We find that the overall number of low-wage jobs remained essentially unchanged over the five years following the increase. At the same time, the direct effect of the minimum wage on average earnings was amplified by modest wage spillovers at the bottom of the wage distribution. Our estimates by detailed demographic groups show that the lack of job loss is not explained by labor-labor substitution at the bottom of the wage distribution. We also find no evidence of disemployment when we consider higher levels of minimum wages. However, we do find some evidence of reduced employment in tradable sectors. We also show how decomposing the overall employment effect by wage bins allows a transparent way of assessing the plausibility of estimates.
This article considers the rapid spread of chain firms in many industries. The conventional explanation is that chains generate economies of scale in costs. Alternatively, the structure of chains may enhance demand by helping firms develop reputations, among other reasons. I quantify the value of these explanations empirically with a large, detailed data set on the hotel industry, combining a reduced-form analysis of revenues with a structural estimation of firm costs. Revenue analysis shows substantial evidence of a large chain premium. Cost estimation shows that after accounting for unobserved heterogeneity, chain-affiliated firms receive no cost advantage relative to independent firms.
We investigate an underexplored externality of crime: the impact of violent crime on individuals’ participation in walking. For many adults walking is the only regular physical activity. We use a sample of nearly 1 million people in 323 small areas in England between 2005 and 2011 matched to quarterly crime data at the small area level. Within area variation identifies the causal effect of local violent crime on walking and a difference-in-difference analysis of two high-profile crimes corroborates our results. We find a significant deterrent effect of violent crime on walking that translates into a drop in overall physical activity.
This paper analyzes the link between rising city crime rates and urban flight. Each additional reported crime is associated with a roughly one-person decline in city population. Almost all of the crime-related population decline is attributable to increased out-migration rather than a decrease in new arrivals. Households that leave the city because of crime are much more likely to remain within the Standard Metropolitan Statistical Area (SMSA) than those that leave the city for other reasons. Migration decisions of highly educated households and those with children are particularly responsive to changes in crime. Causality appears to run from rising crime rates to city depopulation. © 2000 by the President and Fellows of Harvard College and the Massachusetts Institute of Technolog
Multi‐outlet firms, or chains, account for most US retail spending. This article quantifies the welfare and profit effects of standardized chains in the restaurant industry: chains face higher demand than independents, but are less flexible in customizing product selection or prices across locations. I find that on average chains could earn 19% higher variable profits if they could customize their product optimally, but they would lose 28% of their variable profits if they were to lose their demand advantages. Local policies that ban chains would decrease consumer welfare by 1.5% to 5.4% of restaurant spending and disproportionately impact lower income consumers.
In recent years, the literature has seen a surge of interest in pass‐through as an economic tool. At the same time, widespread concerns have emerged about the rising market power of firms. How does competition affect pass‐through? A standard intuition is that more competition makes prices more cost‐reflective and hence raises the rate of cost pass‐through. This article shows this conclusion is sensitive to the routine assumption that firms' marginal costs are constant. With modestly convex costs, market power can raise pass‐through (even when it lies below 1). These results have implications for antitrust policy, environmental regulation, and welfare analysis.
We develop a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects. We show that conventional regression-based estimators fail to provide unbiased estimates of relevant estimands absent strong restrictions on treatment-effect homogeneity. We then derive the efficient estimator addressing this challenge, which takes an intuitive “imputation” form when treatment-effect heterogeneity is unrestricted. We characterize the asymptotic behavior of the estimator, propose tools for inference, and develop tests for identifying assumptions. Our method applies with time-varying controls, in triple-difference designs, and with certain non-binary treatments. We show the practical relevance of our results in a simulation study and an application. Studying the consumption response to tax rebates in the United States, we find that the notional marginal propensity to consume is between 8 and 11 percent in the first quarter — about half as large as benchmark estimates used to calibrate macroeconomic models — and predominantly occurs in the first month after the rebate.
We discuss properties and pitfalls of panel‐data event study designs. We derive three main results. First, assuming constant treatment effects before and/or after some event time, also known as binning, is a natural restriction imposed on theoretically infinite effect windows. Binning identifies dynamic treatment effects in the absence of never‐treated units and is particularly suitable in case of multiple events. Second, event study designs with binned endpoints and distributed‐lag models are numerically identical leading to the same parameter estimates after correct reparametrization. Third, classic dummy variable event study designs can be generalized to models that account for multiple treatments of different signs and varying intensities, which are common in public and labor economics. We demonstrate the practical relevance of our methodological points in an application studying the effects of unemployment benefit duration on job search effort.
We measure how pass-through varies with competition in isolated oligopolistic markets with captive consumers. Using daily pricing data from gas stations on small Greek islands, we study how unanticipated and exogenous changes in excise duties are passed through to consumers in markets with different numbers of retailers. We find that pass-through increases from 0.4 in monopoly markets to 1 in markets with four or more competitors and remains constant thereafter. The speed of price adjustment is about 60 percent higher in more competitive markets. Finally, we show that geographic market definitions based on measures of distance across sellers often result in signifi-cant overestimation of the pass-through. (JEL H22, H25, L13, L81)
Recent research shows prices are insensitive to local demand conditions because national chains charge geographically uniform prices. We examine the price response to local cost shocks, including 68 excise tax changes, 76 sales tax changes, and other geographically based cost differences, using data on 35,151 retail stores in 96 multi- state chains. We find local cost shocks are passed through to local prices, with no spillovers to unaffected stores in otherwise affected chains, and at similar rates for national and local chains. Firms adjust local prices according to local cost changes, suggesting retailers respond asymmetrically to local cost and demand shocks.
We explain when and how staggered difference-in-differences regression estimators, commonly applied to assess the impact of policy changes, are biased. These biases are likely to be relevant for a large portion of research settings in finance, accounting, and law that rely on staggered treatment timing, and can result in Type-I and Type-II errors. We summarize three alternative estimators developed in the econometrics and applied literature for addressing these biases, including their differences and tradeoffs. We apply these estimators to re-examine prior published results and show, in many cases, the alternative causal estimates or inferences differ substantially from prior papers.
This paper uses lottery allocation of retail store entry licenses as a natural experiment to understand the size and source of returns to scale in retail.
The efficiency of any police action depends on the relative magnitude of its crime-reducing benefits and legitimacy costs. Policing strategies that are socially efficient at the city level may be harmful at the local level, because the distribution of direct costs and benefits of police actions that reduce victimization is not the same as the distribution of indirect benefits of feeling safe. In the United States, the local misallocation of police resources is disproportionately borne by Black and Hispanic individuals. Despite the complexity of this particular problem, the incentives facing both police departments and police officers tend to be structured as if the goals of policing were simple—to reduce crime by as much as possible. Formal data collection on the crime reducing-benefits of policing, and not the legitimacy costs, produce s further incentives to provide more engagement than may be efficient in any specific encounter, at both the officer and departmental level. There is currently little evidence as to what screening, training, or monitoring strategies are most effective at encouraging individual officers to balance the crime reducing benefits and legitimacy costs of their actions.
The canonical difference-in-differences (DD) estimator contains two time periods, ”pre” and ”post”, and two groups, ”treatment” and ”control”. Most DD applications, however, exploit variation across groups of units that receive treatment at different times. This paper shows that the two-way fixed effects estimator equals a weighted average of all possible two-group/two-period DD estimators in the data. A causal interpretation of two-way fixed effects DD estimates requires both a parallel trends assumption and treatment effects that are constant over time. I show how to decompose the difference between two specifications, and provide a new analysis of models that include time-varying controls.
In imperfectly competitive settings, a firm's price depends on its own costs as well as those of its competitors. We demonstrate that this has important implications for the estimation and interpretation of pass-through. Leveraging a large input cost shock resulting from the fracking boom, we isolate price responses to firm-specific, regional and industry-wide input cost shocks in the US oil refining industry. The pass-through of these components vary from near zero to full pass-through, reconciling seemingly disparate results from the literature. We illustrate the policy implications of rival cost pass-through in the context of a tax on refinery carbon emissions.
We investigate whether adult marijuana use in Washington responds to increased local access as measured by drive time to the nearest legal marijuana retailer as well as measures of retail density. Using survey data from the Behavioral Risk Factor Surveillance System, we find that as retailers open closer to where they live, more individuals use marijuana and more frequently. These effects are concentrated among young adults (ages 18–26), women, and rural residents. Controlling for distance to the nearest retailer, we find that whether retail density affects marijuana use depends on how it is measured.
In this article, we consider identification, estimation, and inference procedures for treatment effect parameters using Difference-in-Differences (DiD) with (i) multiple time periods, (ii) variation in treatment timing, and (iii) when the “parallel trends assumption” holds potentially only after conditioning on observed covariates. We show that a family of causal effect parameters are identified in staggered DiD setups, even if differences in observed characteristics create non-parallel outcome dynamics between groups. Our identification results allow one to use outcome regression, inverse probability weighting, or doubly-robust estimands. We also propose different aggregation schemes that can be used to highlight treatment effect heterogeneity across different dimensions as well as to summarize the overall effect of participating in the treatment. We establish the asymptotic properties of the proposed estimators and prove the validity of a computationally convenient bootstrap procedure to conduct asymptotically valid simultaneous (instead of pointwise) inference. Finally, we illustrate the relevance of our proposed tools by analyzing the effect of the minimum wage on teen employment from 2001–2007. Open-source software is available for implementing the proposed methods.
To estimate the dynamic effects of an absorbing treatment, researchers often use two-way fixed effects regressions that include leads and lags of the treatment. We show that in settings with variation in treatment timing across units, the coefficient on a given lead or lag can be contaminated by effects from other periods, and apparent pretrends can arise solely from treatment effects heterogeneity. We propose an alternative estimator that is free of contamination, and illustrate the relative shortcomings of two-way fixed effects regressions with leads and lags through an empirical application.
This paper estimates the pass-through of minimum wage increases into the prices of US grocery and drug stores. We use high-frequency scanner data and leverage a large number of state-level increases in minimum wages between 2001 and 2012. We find that a 10% minimum wage hike translates into a 0.36% increase in the prices of grocery products. This magnitude is consistent with a full pass-through of cost increases into consumer prices. We show that price adjustments occur mostly in the three months following the passage of minimum wage legislation rather than after implementation, suggesting that pricing of groceries is forward-looking.
This paper uses UPC-level data to examine the relationship between excise taxes, retail prices, and consumer welfare in the distilled spirits market. We document a nominal rigidity in retail prices that arises because firms largely choose prices that end in 99 cents and change prices in whole-dollar increments. A correctly specified model, like an ordered logit, takes this discreteness into account when predicting the effects of alternative taxes. Explicitly accounting for price points substantially impacts estimates of tax incidence and the excess burden cost of tax revenue. Meaningful nonmonotonicities in these quantities expand the potential considerations in setting excise taxes. (JEL H22, H71, L11, L66)
Since the 1972 U.S. Clean Water Act, government and industry have invested over 1 trillion to abate water pollution, or 100 per person-year. Over half of U.S. stream and river miles, however, still violate pollution standards. We use the most comprehensive set of files ever compiled on water pollution and its determinants, including 50 million pollution readings from 240,000 monitoring sites and a network model of all U.S. rivers, to study water pollution's trends, causes, and welfare consequences. We have three main findings. First, water pollution concentrations have fallen substantially. Between 1972 and 2001, for example, the share of waters safe for fishing grew by 12 percentage points. Second, the Clean Water Act's grants to municipal wastewater treatment plants, which account for 650 billion in expenditure, caused some of these declines. Through these grants, it cost around 1.5 million (2014 dollars) to make one river-mile fishable for a year. We find little displacement of municipal expenditure due to a federal grant. Third, the grants' estimated effects on housing values are smaller than the grants' costs; we carefully discuss welfare implications.
Linear regressions with period and group fixed effects are widely used to estimate treatment effects. We show that they estimate weighted sums of the average treatment effects (ATE ) in each group and period, with weights that may be negative. Due to the negative weights, the linear regression coefficient may for instance be negative while all the ATEs are positive. We propose another estimator that solves this issue. In the two applications we revisit, it is significantly different from the linear regression estimator. (JEL C21, C23, D72, J31, J51, L82)
We study how changes in energy input costs for US manufacturers affect the relative welfare of manufacturing producers and consumers (i.e., incidence). We also develop a methodology to estimate the incidence of input taxes that accounts for incomplete pass-through, imperfect competition, and substitution among inputs. For the several industries we study, 70 percent of energy price-driven changes in input costs get passed through to consumers in the short to medium run. The share of the welfare cost that consumers bear is 25–75 percent smaller (and the share producers bear is larger) than models featuring complete pass-through and perfect competition would suggest. (JEL H22, H23, L60, Q48,Q54, Q58)
This paper considers the impact of the minimum wage on both labor and product markets using detailed store-level scanner data. I provide empirical evidence that a 10% increase in the minimum wage raises grocery store prices by 0.6%-0.8%, and suggest that the minimum wage not only raises labor costs but also affects product demand, especially in poorer regions. This points to novel channels of heterogeneity in pass-through that have distributional consequences, with key implications for real wage inequality. I also find that price rigidity within retail chains ameliorates these effects, reducing the pass-through elasticity for retail prices by about 60%.
This paper provides a comprehensive assessment of the margins along which firms responded to a large and persistent minimum wage increase in Hungary. We show that employment elasticities are negative but small even four years after the reform; that around 75 percent of the minimum wage increase was paid by consumers and 25 percent by firm owners; that firms responded to the minimum wage by substituting labor with capital; and that dis-employment effects were greater in industries where passing the wage costs to consumers is more difficult. We estimate a model with monopolistic competition to explain these findings.
A growing body of research has documented the consequences of neighborhood crime for a myriad of individual, household, and community outcomes. Given that neighborhood businesses figure into the link between neighborhood structure and crime as sources of employment or sites for neighbor interaction, the present study examines the extent to which neighborhood crime is associated with the survival, mobility, and destination locations of businesses in the subsequent year. Using business data from Reference USA (Infogroup, 2015)and crime data from the Southern California Crime Study (SCCS)we assess this question for neighborhoods across cities in the Southern California region. We find that in general, higher violent and property crime are significantly associated with both business failure and mobility, and that higher crime in a destination neighborhood reduces the likelihood that a business locates there. We also present findings specific to industries, and discuss the implications of our findings for future research.
We study commodity taxation and characterize the Laffer curve, a trade-off between tax rates and revenue, in noncompetitive markets. Pricing in these markets leads to incomplete tax pass-through and agents re optimize their purchase and pricing decisions in response to any tax change. We use detailed data from Pennsylvania, a state that monopolizes retail sales of alcoholic beverages, to estimate a model of demand for horizontally differentiated products that ties consumers' demographic characteristics to heterogeneous preferences for spirits. We find that under the state's current tax policy, spirits are overpriced. Distillers respond to decreases in the tax rate by increasing wholesale prices, which limits the state's revenue gain to only 13% of the incremental tax revenue predicted under the common assumption of perfect competition. The strategic response of noncompetitive firms to changes in taxation therefore flattens the Laffer curve significantly.
We document a causal response of local retail prices to changes in local house prices, with elasticities of 15–20 percent across housing cycles. These price responses are largest in zip codes with many homeowners and are driven by changes in markups rather than local costs. We argue that markups rise with house prices because greater housing wealth reduces homeowners’ demand elasticity, and firms raise markups in response. Shopping data confirm that house price changes affect the price sensitivity of homeowners, but not that of renters. Our evidence suggests a new source of business cycle markup variation.
A central question in the debate over privatized Medicare is whether increased government payments to private Medicare Advantage (MA) plans generate lower premiums for consumers or higher profits for producers. Using difference-in-differences variation brought about by a sharp legislative change, we find that MA insurers pass through 45 percent of increased payments in lower premiums and an additional 9 percent in more generous benefits. We show that advantageous selection into MA cannot explain this incomplete pass-through. Instead, our evidence suggests that market power is important, with premium pass-through rates of 13 percent in the least competitive markets and 74 percent in the most competitive. (JEL G22, H51, I11, I13, I18).
We estimate pass-through with 30 years of data from the portland cement industry. Robust econometric evidence supports that fuel cost changes are more than fully transmitted downstream in the form of price changes. This validates an implicit pass-through assumption made in recent academic research and regulatory analyses. We combine the econometric results with estimates of competitive conduct obtained from the literature to evaluate the incidence of market-based CO2 regulation. Producers bear roughly 11% of the regulatory burden and could be compensated with 16% of the revenues obtained.
Decentralization can improve service delivery, but it can also generate externalities across jurisdictional boundaries. We examine the nature and size of water pollution externalities as rivers flow across jurisdictions. Panel data on water pollution in Brazilian rivers coupled with county splits that change the locations of borders allow us to identify the spatial patterns of pollution as rivers approach and cross borders, controlling for fixed effects and trends specific to each location. The theory of externalities predicts that pollution should increase at an increasing rate as the river approaches the downstream exit border, that there should be a structural break in the slope of the pollution function at the border, and that a larger number of managing jurisdictions should exacerbate pollution externalities. We find support for all four predictions in the data. Satellite data on growth in night-time lights along rivers show that local authorities allow more settlements to develop close to rivers in the downstream portions of counties, which is the likely underlying mechanism. The border effects on pollution are not as pronounced when the cost of inter-jurisdictional coordination is lower.
Theoretical and empirical arguments suggest that fear of violence will cause consumers, employees and entrepreneurs to alter their routine activities in areas that experience a surge in violent activity. This paper argues that understanding how businesses respond to violence has important implications for understanding community crime cycles and offers further evidence of how crime impacts the choices individuals make with regard to where they live, shop and work. Using newly available longitudinal business data and homicide data disaggregated to the ZIP code level, an examination is made of the impact of violence surges on the creation, destruction and growth of business establishments in five large US cities between 1987 and 1994. Controlling for pre-existing levels of violence, it is found that increased violence has the greatest consequences for service-related establishments in low-crime neighbourhoods. This finding is consistent with the notion that the fear of victimisation imposes additional indirect costs to society through its negative impact on local business establishments.
Crime is predominantly a local issue. The majority of both violent and nonviolent offenses takes place less than one mile from victims' homes, and most government expenditures on police protection are local (Bureau of Justice Statistics 2004; Census of Governments 2003). In response to crime risk, residents generally have two options: they can vote for anti-crime poli-cies, or they can vote with their feet. When individuals exercise the latter option, local response to crime will be observed in the housing market. This may be particularly salient for crime, since individuals can reduce their exposure without moving great distances, and empirical evidence on urban flight supports this notion (Julie B. Cullen and Steven D. Levitt 1999). Understanding the relationship between property values and local crime risk is useful for measuring the willingness of individuals to pay to reduce their exposure to crime risk. This, in turn, can help determine the appropriate level of public expenditures that reduce crime, such as police services. A number of papers have documented an inverse relationship between property values and local crime rates. In one of the earliest studies, Richard Thaler (1978) finds a negative relation between property crimes per capita and property values. His estimates imply that a one-standard-deviation increase in the incidence of property crime reduces home values by about 3 percent. A more recent study by Steve Gibbons (2004) finds a decrease in property values of 10 percent for a one-standard-deviation increase in property crime. These studies, however, face potential omitted variable problems in both the cross section and time series. In the cross section, crime rates are likely to covary with other geographic ameni-ties for which researchers cannot adequately control. Over time, crime rates may change as the composition and characteristics of neighborhoods change. Reductions in crime levels may corre-spond to other changes that increase the value of property located in a particular neighborhood. In this paper, we combine data from the housing market with data from sex offender registra-tions to estimate individuals' valuation of living in close proximity to a convicted criminal. By exploiting both the timing of move-in and the exact locations of sex offenders, we can improve on past estimates of the impact of crime risk on property values. The exact location of these offenders allows us to exploit variation in the threat of crime within small, relatively homogenous groupings of homes. The timing of a sex offender's arrival allows us to confirm the absence of substantive preexisting differences in property values and to control for the remaining minor differences.
This paper develops an incomplete information model wherein individuals face a trade-off between status and security when deciding the optimal amount of conspicuous consumption. On the one hand, we assume that individuals derive utility from status, which is obtained by signaling wealth through the consumption of an observable good. On the other hand, the increased consumption of observable goods also signals wealth to a criminal audience, thus increasing the chance of becoming target for criminal activities. The paper proposes an information channel through which crime distorts consumption decisions; this channel is different in nature from the channel whereby crime acts as a direct tax on observable and stealable consumption goods. More precisely, we argue that, in the presence of crime, individuals reduce their consumption of observable goods, not only because criminals may steal these goods, but also because it reveals information that could be used by criminals to target individuals’ wealth. We test our model’s predictions using U.S. data, and find that crime has a negative and significant impact on conspicuous consumption; also that this effect cannot be explained by the fact that some of these goods tend to be stolen by criminals. Finally, we show that this result is robust to different specifications and alternative measures of conspicuous consumption and crime.
We extend five principles of tax incidence under perfect competition to a general model of imperfect competition. The principles cover 1) the independence of physical and economic incidence, the 2) qualitative and 3) quantitative manner in which taxes are split between consumers and producers, 4) the determinants of tax pass-through and 5) the integration of local incidence to determine the overall division of surplus. We show how these principles can be used to simplify and generalize the analysis of a range of economic questions such as the optimal procurement of new markets and the welfare effects of third-degree price discrimination.
This paper estimates the impact of violent crime on the location of business activity and entrepreneurship in five US cities. Central to our analysis is the idea that different sectors of the economy will sort into high- and low-crime areas depending on their relative sensitivity to crime. We illustrate this by comparing retail industries to their wholesale counterparts, and high-end restaurants to low-end eateries. Because retail industries are dependent on pedestrian shoppers, they are expected to be especially sensitive to violent crime. Because high-end restaurants are dependent on evening business, they are expected to be especially sensitive to violent crime over the prime dinner hours. Findings indicate that retail, wholesale, high- and low-end restaurants are all more active in areas with higher local rates of violent crime, even after conditioning on an extensive set of model controls. This could arise because violent crime is attracted to our target industries. This also likely reflects that other sectors of the economy outbid our target industries for safer locations (e.g. residential). Further analysis confirms such sorting behavior. Retailers are more likely to locate in safer locations as compared to wholesalers in the same industry. Among restaurants, an increase in violent crime during the prime dinner hours equivalent to the sample max/min range would decrease the high-end share of local restaurants by roughly 40 percentage points. These findings indicate that entrepreneurs take violent crime into account when bidding for locations within a city. These finding also indicate that efforts to make distressed portions of cities more vibrant must give consideration to the need to ensure that such areas are safe.
I. Introduction Since the turn of the century, legislation in Western countries has expanded rapidly to reverse the brief dominance of laissez faire during the nineteenth century. The state no longer merely protects against violations of person and property through murder, rape, or burglary but also restricts "dis crimination" against certain minorities, collusive business arrangements, "jaywalking," travel, the materials used in construction, and thousands of other activities. The activities restricted not only are numerous but also range widely, affecting persons in very different pursuits and of diverse social backgrounds, education levels, ages, races, etc. Moreover, the likeli hood that an offender will be discovered and convicted and the nature and extent of punishments differ greatly from person to person and activity to activity. Yet, in spite of such diversity, some common properties are shared by practically all legislation, and these properties form the subject matter of this essay. In the first place, obedience to law is not taken for granted, and public and private resources are generally spent in order both to prevent offenses and to apprehend offenders. In the second place, conviction is not generally considered sufficient punishment in itself; additional and sometimes severe punishments are meted out to those convicted. What determines the amount and type of resources and punishments used to enforce a piece of legislation? In particular, why does enforcement differ so greatly among different kinds of legislation?
This paper uses data on over 2800 house sales in Jacksonville, FL to estimate the impact of crime on house prices. A GIS programme is used to develop neighbourhood characteristics that are unique to each observation. Crime data, available for 89 police beats are assigned to each observation. Weighting the seriousness of offences by the cost of crime to victims is used as an alternative to the customary measures of the number of index crimes. The cost of crime has virtually no impact on house prices overall, but homes are highly discounted in high crime areas. Copyright 2001 by Taylor and Francis Group
This paper estimates the impact of recorded domestic property crime on property prices in the London area. Crimes in the Criminal Damage category have a significant negative impact on prices. A one-tenth standard deviation decrease in the local density of criminal damage adds 1% to the price of an average Inner London property. Burglaries have no measurable impact on prices, even after allowing for the potential dependence of burglary rates on unobserved property characteristics. One explanation we offer here is that vandalism, graffiti and other forms of criminal damage motivate fear of crime in the community and may be taken as signals or symptoms of community instability and neighbourhood deterioration in general. Copyright 2004 Royal Economic Society.
Two striking facts describe work timing in the United States: a lower propensity to work evenings and nights in large metropolitan areas, and a secular decline in such work since 1973. One explanation is higher and possibly increasing crime in large areas. I link Current Population Survey data on work timing to FBI crime reports. Neither fact is explained by changes in nor inter-area differences in crime rates, but higher homicide rates do reduce such work. This reduction implicitly costs the economy between 10 billion. This negative externality illustrates a larger class of previously unmeasured costs of social pathologies.