Local politics and police strength

Abstract

Numerous studies have explored variation in police employment across cities, usually focusing on public choice, conflict, or organizational explanations. Yet, few consider whether the local political context affects police employment. Recent research suggests that local politics affects criminal justice outcomes. Using insights from urban politics research, I develop testable hypotheses about the effects of local political arrangements on municipal police strength. WLS regression results suggest the value of considering local political context in models of police strength. Specifically, in a sample of 945 cities with 25,000 or more residents in 1990, net of other variables, cities with unreformed political systems (mayor‐council forms of government, district‐based city councils and partisan elections) had more police employees per 1,000 residents, and this effect varied by region. Additionally, the effect of minority populations and crime rates on police strength varied across municipal political contexts. Implications for theories of police strength are discussed.

Keywords:

• local politics
• police strength
• employment

Acknowledgments

This research was supported, in part, by a National Science Foundation Dissertation Improvement Award (#SES‐0002291), and the Center for Criminology and Socio‐Legal Studies at the University of Iowa. Police employment and crime data were obtained through the Inter‐University Consortium for Political and Social Research (study # 9028). Neither the Consortium nor the original collectors of the data bear any responsibility for the analysis or interpretations presented here. I am very grateful to Ann Holmes, Robert A. Brown, Roger Jarjoura, Karen Heimer, Kevin Leicht, Robert Baller, Peverill Squire, and Celesta Albonetti, and anonymous reviewers for helpful comments on earlier drafts of this paper.

Notes

Thomas D. Stucky is an Assistant Professor of Criminal Justice in the School of Public and Environmental Affairs at Indiana‐Purdue University at Indianapolis. His research interests are at the intersection of politics and criminal justice, specifically the relationship between politics and crime/policing at the city‐level, and state‐level trends in imprisonment and correctional spending.

The discussion of police strength here is limited to studies of police employment. For excellent examples of research on police expenditures, see Jackson (1986, 1989).

Of course, this assumes that African Americans would want fewer police, and therefore, as they reach numerical majority police employment would decline as their wishes are translated into policy. However, Sampson and Raudenbush (1999) note that the evidence suggests that poor, minority communities are often the most insistent on increasing police protection. Similarly, Velez (2001) finds that access to “public control” in the form of increased police protection reduces victimization, and this effect is most pronounced in the poorest neighborhoods.

For more on the history of progressive era reforms, see Banfield and Wilson (1963), Fox (1977), Griffith (1974), and Hofstadter (1955).

For a more extensive review of this urban politics research, see Stucky (2003).

This assumes that citizen perception of crime and the officially recorded crime rate closely track one another, which may not be the case. However, there are no measures of citizen fear of crime available for the large number of cities in the sample. Therefore, the officially recorded crime rate is used as a proxy for citizen concern regarding crime.

In practice, the choice of dependent variable is less problematic than it might appear. First, in the current sample, the measures are correlated at .96. Second, the pattern of results reported in Table 2 is substantively similar regardless of whether sworn officers or total police employment is used.

I am grateful to an anonymous reviewer for this observation.

As Loftin and McDowall (1982: 395) note, “… Granger [1969] argues that if one variable (X) causes another variable (Y), then X should provide a more accurate prediction of Y’s present value than could be obtained by using past values of Y alone.” Thus, if the independent variables included in the analysis are significantly related to the current value of the dependent variable, controlling for the lagged value of the dependent variable, then they can be said to Granger cause crime. Marvell and Moody (1996) also discuss Granger causality in a recent study of police employment and crime rates.

This refers to actual employees versus authorized employment for consistency across agencies because staffing issues could cause significant variation between actual and authorized employment.

For an excellent discussion of the measurement and meaning of the Gini coefficient, see Allison (1978).

This raises potential causal order issues, but it is quite unlikely that the number of police employed in a particular year will be an important factor in cities changing political structures. In addition, local institutional political structures tend to be relatively stable over time.

One of the assumptions of OLS regression is that error variance is constant. Violation of this assumption can produce inefficient parameter estimates and lead to misleading results from significance tests. A plot of residuals against city population for the models in Table 2 indicated heteroskedasticity.

Some might suggest that inclusion of the lagged dependent variable artificially inflates the explained variance. However, a model identical to equation 1 excluding the lagged dependent variable explained 78 percent of the variation in the dependent variable.

It is also interesting to note that the effect of inequality on total police employment becomes significant but is actually negative, which is opposite to the relationship one would expect based on an economic conflict perspective.

The effect of the traditional political structure index on police strength is unaffected by the inclusion or exclusion of the lagged dependent variable.

Because the predictors have been centered, coefficients refer to the effect of the variable when the other variables are at their mean. Therefore, the other variables all drop out of the prediction equation. Thus, Figure 1–4 predict deviation from mean values of police employment for Midwestern cities, when all other variables are at their means.

One reviewer suggested that the political effects shown in Table 2 might be an artifact of the relationship between police department age (see King, 1999) and city political system structure. To test this possibility, an alternative model including police department age was estimated. The political effects in Table 2 were unaffected.