Short-term forecasting of crime

The major question investigated is whether it is possible to accurately forecast selected crimes 1 month ahead in small areas, such as police precincts. In a case study of Pittsburgh, PA, we contrast the forecast accuracy of univariate time series models with naïve methods commonly used by police. A major result, expected for the small-scale data of this problem, is that average crime count by precinct is the major determinant of forecast accuracy. A fixed-effects regression model of absolute percent forecast error shows that such counts need to be on the order of 30 or more to achieve accuracy of 20% absolute forecast error or less. A second major result is that practically any model-based forecasting approach is vastly more accurate than current police practices. Holt exponential smoothing with monthly seasonality estimated using city-wide data is the most accurate forecast model for precinct-level crime series.     

Predicting the geo-temporal variations of crime and disorder

Traditional police boundaries—precincts, patrol districts, etc.—often fail to reflect the true distribution of criminal activity and thus do little to assist in the optimal allocation of police resources. This paper introduces methods for crime incident forecasting by focusing upon geographical areas of concern that transcend traditional policing boundaries. The computerised procedure utilises a geographical crime incidence-scanning algorithm to identify clusters with relatively high levels of crime (hot spots). These clusters provide sufficient data for training artificial neural networks (ANNs) capable of modelling trends within them. The approach to ANN specification and estimation is enhanced by application of a novel and noteworthy approach, the Gamma test (GT).     

Criminal incident prediction using a point-pattern-based density model

Law enforcement agencies need crime forecasts to support their tactical operations; namely, predicted crime locations for next week based on data from the previous week. Current practice simply assumes that spatial clusters of crimes or “hot spots” observed in the previous week will persist to the next week. This paper introduces a multivariate prediction model for hot spots that relates the features in an area to the predicted occurrence of crimes through the preference structure of criminals. We use a point-pattern-based transition density model for space–time event prediction that relies on criminal preference discovery as observed in the features chosen for past crimes. The resultant model outperforms the current practices, as demonstrated statistically by an application to breaking and entering incidents in Richmond, VA.     

After, before and during: returns to education in Hungary (1986–1998)

How valuable are the skills acquired under socialism in a market economy? This paper throws light on this question using unique data covering the years before and during transition (1986–1998) for about 3 million Hungarian wage earners. We find that returns to a year of schooling increased by 75% from 6.4% in 1986 to 11.2% in 1998. We also find that the private sector rewards formal education more than the public and, in terms of gender, although in 1986 women had greater returns to schooling than men, by 1998 this difference had been eliminated.     

Duopoly locations and optimal zoning in a small open city

This paper analyzes a duopoly location model with an asymmetric zoning that prohibits firms from locating to a specific interval in a small open linear city. It is shown that the maximum differentiation principle presented in d'Aspremont et al. [Econometrica 47 (1979) 1145] is still valid under area zoning regulation. Moreover, a zoning regulation can be seen as a policy instrument to limit firms' excess profits, and a proper regulation may even reduce the distortion in total transportation costs, therefore enhancing social welfare. Specifically, the optimal zoning is about 29.5 percent of the city with no amenity effect. Finally, all the land rents raised by zoning are eventually confiscated by the absentee landowner.     

Russia’s and Kazakhstan’s agro-food sectors under liberalized agricultural trade: a case for national product differentiation

Russia is a major importer of agro-food products in the Commonwealth of Independent States (CIS), whereas Kazakhstan’s agro-food sector is more export-oriented. Based on a multi-market-model with bilateral trade, the paper investigates how a Common Agricultural Market (CAM) and World Trade Organization (WTO) accession would affect prices, quantities, and welfare on agro-food markets in both countries. Trade diversion effects are expected because of a changed tariff structure. Kazakhstan’s agro-food sector will loose price competitiveness on Russia’s markets if liberalization is too narrowly focused on import tariffs. Simulations also suggest that national product differentiation can be part of a strategy for Russian agro-food industries to cope with stronger international competition.