Crime in the
Jerome L. McElroy
Professor of Economics
Department of Business Administration and Economics
Fellow, Center for Women’s Intercultural Leadership
Saint Mary’s College
Andrea J. Roccanti
Senior Client Service Associate
Within the space of a generation following the advent of commercial
jet travel in the early 1960s, the
However, in an industry where safety
is paramount, a rising crime wave also threatens tourism’s long-term
viability. Evidence is mounting that
crime is increasing across the region and that visitation is being negatively
affected. In the first case, Harriott
(2002, p. 4) states that since 1970 there have been “significant increases in
the rate of violent crimes in every
In the second case, rising crime is
damaging the region’s reputation. In a
survey of major
The vast literature on crime in LDCs
suggests one major conclusion: development is positively associated with
property crime, especially theft (Leggett, 2000). In the
Two other related theories of
Finally, many authors implicate the growth of drug (cocaine mainly) running through the island chain from Colombian producers to North American consumers. Since trafficking was deflected by the tightening of the Mexican border by
study provides a very provisional cross-national test of the major determinants
of crime in the
The independent variables were selected to correspond to major determinants identified in the four crime theories. First, to test the opportunity cost influence, the average daily visitor density (per 1,000 population) was employed to represent the presence of visitors or lucrative targets (benefits), and overall population density was selected to represent degree of anonymity or probability of detection (costs). Second, because of its common usage, the unemployment rate was selected to test the absolute deprivation or poverty hypothesis. Third, the percent of the population 0-14 years was used loosely as a surrogate measure for the presence of juvenile offenders, and more indirectly the presence of a hard-core subculture of disenfranchised youth. The variable was chosen over 15-64 years for two reasons: (1) ready availability, and (2) its narrowly defined and less inclusive character.
political status was used as a dichotomous proxy indicator for crime
enforcement capacity. It was assumed
that independent countries (scored as 1.0), which tend to be larger, would have
more resources and stronger capabilities while dependent islands (scored as
0.0), which tend to be smaller, would have less. In terms of the narco thesis, it was further
assumed that traffickers would avoid high-enforcement islands in favor of the
weaker, low-enforcement territories.
Likewise it was argued that traffickers would have a natural preference
for transiting drugs through dependencies because of their preferential access
to metropolitan markets in
independent variables were tested against the three crime rates using OLS
multivariate regression analysis. To
avoid possible distortions introduced by the 2001 terrorist attacks—since
Table 1 records results of the provisional cross-national analysis. According to Equation 1, across the sixteen-country sample none of the independent influences affected levels of violent crime as measured by the murder rate. This is not unexpected since most previous research has largely been unable to scientifically predict violent crime or has uncovered only weak relationships. On the other hand, Equation 2 shows that over a third of the variation in property crime, as measured by theft, could be explained by the multivariate model. However, only unemployment and visitor density were statistically significant predictors of theft. The other influences—population density measuring anonymity, political status measuring enforcement capacity, and percent of the population 0-14 years measuring the potential pool of youthful offenders—had no impact.
Further experiments omitting these insignificant variables revealed Equation 3 as the best-fit model to “explain” property crime. It indicates that 35 percent of theft is associated with changes in unemployment and visitor density in the same direction. Higher levels of visitation and higher rates of unemployment are conducive to higher levels of theft. These results tend to partially confirm the opportunity-cost and absolute deprivation theories, i.e. that the presence of visitors provides jobless residents with relatively low-cost chances to steal from persons unfamiliar with their surroundings, and/or their hotel rooms or rental cars. Of the two influences, visitor density is the more important, accounting for 85 percent of the theft variation explained by the model and 30 percent of total theft variation. For the sixteen-country sample, the results clearly demonstrate the link the between visitor presence and property crime often noted in the literature.
(Table 1 about here)
As an indirect test of the presence of the narcoeconomy, the independent variables were tested again the rate of drug offenses. According to Equation 4, the most important influence on drug offenses is visitor density, which is highly statistically significant (see Table 1). The close linking of these two variables tends to corroborate the observed evidence of rising visitor harassment and drug peddling among street vendors around shopping areas, beaches and nightclubs (WTTC, 2004, p. 58). Further experiments with various combinations of factors yielded the best-fit model in Equation 5. Accordingly over 70 percent of the variation in drug offenses is “explained” by the combined interaction of visitor presence and population density. This suggests a fairly strong confirmation of the opportunity cost theory. Popular tourist destinations provide not only abundant opportunities for criminally related drug activity in terms of lucrative targets (visitor density), but also they product the kind of crowding and anonymity (population density) that obstruct detection. On the other hand, in this sample of countries drug enforcement capacity, poverty and the share of the youthful population had no impact on the rate of drug offenses.
least three conclusions flow from this analysis. First, in this limited sample of countries
during the late 1990s,
sheer size of drug trafficking across the islands has become a major
concern. Its value approaches $5 billion
(McElroy, 2005). This figure exceeds the
value of all merchandise exports (petroleum, aluminum ore, rum etc.) of all the
islands combined. It also dwarfs the GDP
levels of three fourths of the islands taken separately, as well as the total
tourism economic impact of all but the three largest: Cuba, Dominican Republic
and Puerto Rico. In addition, it
generates an estimated $60 billion every year in organized crime money
laundering through the islands’ offshore finance sector. Finally, according to Harriott (2002, p. 12)
illegal payoffs to public officials and cooperating functionaries to look away
criminality is increasing across the
These two pillars
of contemporary Caribbean economy are under threat during an era of
intensifying tourism competition from growing markets in Eastern Europe and
Asia as well as during a period of macroeconomic instability as the Caribbean
grapples with falling terms of trade and export preferences for traditional
staples (sugar, bananas) and diplomatic, the loss of manufacturing jobs to
Mexico through NAFTA, and the loss of aid with diplomatic downgrading since the
fall of Communism. For these reasons, more
comprehensive and systematic research is warranted on the determinants of
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on jobs and the economy.
Equation Depend. Var. Independ. Var. Reg. Coeff. t-value R2 F-value
1 Murder Unemployment -0.194 -0.32 0% 0.62
Population Dens. -0.002 -0.20
Visitor Density -0.035 -0.69
Political Status 7.726 1.00
Pop. 0-14 years -0.014 -0.02
2 Theft Unemployment 164.99 2.91** 39.2% 2.94
Population Dens. 1.206 1.24
Visitor Density 12.231 2.53*
Political Status -712.3 -0.97
Pop. 0-14 years 2.12 0.03
3. Theft Unemployment 132.47 2.50* 35.0% 5.03
Visitor Density 13.44 2.93**
4 Drugs Unemployment 13.030 0.76 71.3% 8.45
Population Dens. 0.431 1.40
Visitor Density 7.040 4.81***
Political Status 28.700 0.13
Pop. 0-14 years -32.32 -1.53
5 Drugs Population Dens. 0.625 2.42* 72.4% 20.7
Visitor Density 6.775 5.91***
Sources: For crime rates, Interpol (2004); for independent variables, World Factbook
(CIA, 2002); for visitor density, Padilla & McElroy (2004).
Notes: 1. The critical t-values at the 0.05 level of statistical significance for 10 and 13
degrees of freedom are 1.81 and 1.77 respectively.
2. Asterisks represent levels of statistical significance: (*) = 0.025, (**) = 0.01
and (***) = 0.005.