Abstract
Objectives
The current study examines the percentage of a state’s total expenditures that is allocated for corrections in an attempt to untangle how the influence of key determinants helped to shift state resources in ways likely to aid prison expansion. This study further attempts to address how the salience of crime, partisan politics, and racial and social threats may have shifted over time and across regions.
Methods
The study employs a pooled time-series analysis of 49 states from 1971 through 2008, with a final sample size of N = 1862 cases, to examine the factors that help to predict the log-transformed percent of total state expenditures allocated for the total direct expenditures on corrections.
Results
The current study finds that determinants of state-level corrections spending vary across time. Violent crime had a positive and significant influence on corrections spending during the 80s, while Republican strength was similarly associated with increases in spending from the 80s through the 90s. Further, beginning in the 80s, percentage African-American were negatively associated with the proportion of budget allocated for corrections.
Conclusions
The findings presented in this study emphasize the importance of time and place when trying to untangle trends in correctional budget decision-making.
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Notes
Campbell et al. (2015) found that the percentage of Hispanics and Republican strength influence state-level incarceration rates in Sunbelt states, while having no significant impact in non-Sunbelt states. Relatedly, the percentage of non-Hispanic blacks and political ideology significantly impact incarceration in non-Sunbelt states, while having no statistically significant relationship with incarceration rates in Sunbelt states.
Corrections expenditures were log-transformed to minimize skewness of the distribution across states.
Data were not available for state expenditures for the years 2002 and 2004, so the missing data were linearly interpolated. Supplementary analyses were conducted with the interpolated data excluded, and the results were nearly identical with all significant predictors remaining the same. These analyses are available upon request.
Electoral year and presidential party have been argued to influence incarceration rates (see Smith 2004); however, they have not been shown to influence spending on corrections (see Stucky et al. 2007). Both measures were included in preliminary analyses, but were excluded from the final models since they failed to reach significance and had no impact on other significant variables.
Since this measure is not very intuitive, a more nuanced computation is provided.
Citizen Ideologyd,t = (Proportion of electorate preferring incumbentd,t)(Ideology score of imcumbentd,t) + (Challenger proportion of the electorated,t)(Challenger ideology scored,t), in which d denotes the district and t denotes the year. Each district’s ideological score is than combined to generate the un-weighted average.
State-level Citizen Ideologyt = (Citizen Ideology(Sum)d)/d).
The quadratic term for Black Squared was excluded from the time-specific models after preliminary results found that this relationship was no longer significant and had no impact on other relevant significant predictors.
Many punishment scholars suggest that national level forces, such as court-ordered prison reform, help to shape state-level penal policy, in turn leading to increases in incarceration rates. For example, Heather Schoenfeld (2010) found that court-ordered prison reform to reduce prison populations in Florida led to the building of new prisons to reduce prison overcrowding, eventually backfiring and resulting in increased incarceration numbers. Taggart (1989) and Fliter (1996), on the contrary, argue that the federal judiciary has a limited role in shaping state correctional operating expenditures. It is suggested that “spending is shaped in large measure by forces much more compelling and forceful than a single discrete event such as a court order” (Taggart 1989, p. 268). Despite this argument, the current models include a measure of federal aid in an attempt to capture how national level forces, such as the Violent Crime Control and Law Enforcement Act of 1994, may have influenced state-level budgetary decisions. However, it is impossible to determine what the federal aid is spent on within the state, as it is just an aggregated measure of all funds given to the state.
Region fixed-effects, rather than state fixed-effects, were used to gain a better understanding of how state-level punishment decisions may group together by regional location, as argued to be the case by prior scholars (Zimring and Hawkin, 1991). However, alternative models were run including state-level fixed-effects. Statistically significant relationships remained stable, and state-level fixed-effects do not appear to be significant after controlling for the panel-specific correlation structure and region, and are therefore left out of the models. Supplemental analyses are available upon request.
Diagnostics show that the inclusion of an AR(1) term eliminated the serial correlation within the models, with rho estimates nearing zero.
A more detailed list of the sources is available upon request.
When the lagged dependent variable is removed from Model 1, for example, the R-squared drops from .95 to .37, suggesting that previous year spending on corrections is the strongest predictor of current year’s spending.
Percentage change in state GDP was also included in later models to measure the temporal changes, however, the results suggest that the impact of growth in state GDP in subsequent time periods was no different than in 1970. This result suggests that steady growth in GDP followed along with steady growth in correctional budgetary decisions. In other words, the two variables are highly correlated (p < .001).
The only variable that has an alternative interpretation is the log-transformed previous year’s spending. This variable is interpreted as a one percent increase in previous year’s spending is associated with a coefficient percent increase in current year spending.
Wald tests were conducted on the final Model (Model 2 of Table 3) to provide the F-statistic for the variables used, including the time-specific interaction variables. For the interaction terms, violent crime (F = 45.25***) and republican strength (F = 19.42***) were statistically significant, while percentage black (F = 4.84), unemployment (F = 8.84), and citizen ideology (F = 1.53) failed to reach significance. The remainder of the variables—property crime (F = 0.36), ages 18–24 (F = 2.08), federal aid (F = 2.67), percentage change in income (F = 0.21), determinate sentencing (F = 0.11), and population (F = 2.47)—failed to reach significance with the exception of percentage change GDP (F = 4.20*).
The authors analyzed region-specific models to gain better insight into how these factors may vary across region. Two key findings emerge. First, result suggest that the South is primarily responsible for the influx in corrections spending during the 1970s, with political partisanship having little effect. Following, republican strength across the remaining regions was significantly associated with increased spending on corrections, relative to the 1970s. These supplemental analyses are available from the corresponding author upon request.
It should be noted that supplemental models were conducted using more specific and shorter times periods (5-year blocks as compared to the current decade blocks), and results were little changed. The supplemental analysis is available from the corresponding author upon request.
From 1960 to 1975, many Southern states with large proportions of African Americans had double digit increases in incarceration rates. For example, Georgia experienced a 22 percent increase in incarceration over this time period, while North and South Carolina faced increases of 58 percent and 151 percent, respectively.
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Williams, J.H., Campbell, M. Exploring the Time-Varying Determinants of State Spending on Corrections. J Quant Criminol 37, 671–692 (2021). https://doi.org/10.1007/s10940-020-09460-y
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DOI: https://doi.org/10.1007/s10940-020-09460-y