changes in relationships between state characteristics and regulatory enforcement over time

polp_261 991..1014

Changes in Relationships between StateCharacteristics and Regulatory Enforcement

over Time

PETER SHROCKSoutheastern Louisiana University

This article investigates the stability of relationships between state-levelvariables and regulatory enforcement across short periods of time. Bycomparing multilevel models, it is demonstrated that the effects ofstate-level variables on fines imposed by the Occupational Safety andHealth Administration differ between 1989-91 and 1992-94. In a secondanalysis, a model where variables are averaged over the six years from1989 through 1994 is compared with a model in which they vary fromyear to year. The effects of state characteristics on fines differ betweenthe averaged-year model and the varying-year model, which suggeststhat even minor temporal changes can alter relationships between statevariables and enforcement. Based on these findings, it is recommendedthat researchers who are interested in the impact of state characteristicson regulation examine longer time frames or investigate variables thatcan explain this instability of relationships.

Keywords: Regulatory Enforcement, State-Level Variables,Temporal Variation, Occupational Safety and Health, United States.

Este artículo investiga la estabilidad en las relaciones entre variables anivel estatal y el reforzamiento regulatorio a través de periodos cortosde tiempo. Al comparar modelos multinivel se demuestra que los efectosde las variables a nivel estatal en multas impuestas por laAdministración de la Seguridad Ocupacional y de Salud (OSHA, porsus siglas en ingles) difieren entre los periodos 1989-91 y 1992-94. En unsegundo análisis, un modelo en el que las variables son promediadas a lolargo de seis años de 1989 hasta 1994 es comparado con un modelo enel que varían de año a año. Los efectos de las características de losestados en la multas difieren entre el modelo de promedio anual y el

Acknowledgements: The author thanks Ken Bolton, Marc Riedel, Michael Scicchitano, and theanonymous Politics & Policy reviewers for their critiques and suggestions in the development ofthis article.

Politics & Policy, Volume 38, No. 5 (2010): 991-1013. Published by Wiley Periodicals, Inc.© The Policy Studies Organization. All rights reserved.

modelo de datos variables anuales, lo que sugiere que aún cambiostemporales menores pueden alterar las relaciones entre las variablesestatales y su regulación. Basados en estos hallazgos, recomendamos ainvestigadores interesados en el impacto de las características de losestados sobre la regulación que examinen periodos de tiempo más largoso variables que puedan explicar esta inestabilidad en las relaciones.

The effects of state-level variables on enforcement depend on federalactivities (Hedge 1993; Hedge and Scicchitano 1994), but it is unclear whetherthese effects are stable over time, even after controlling for federal influences. Anegative answer would indicate that social scientists need to exercise care ingeneralizing about the impacts of state characteristics on enforcement, sincerelationships discovered for one span of years may not be evident in otherperiods. This article examines whether statistical relationships observed in oneperiod between state characteristics and regulatory enforcement outcomespersist over other periods of time. Two analyses are used to answer thisquestion. First, models that incorporate relationships between state variablesand fines imposed by the U.S. Occupational Safety and Health Administration(OSHA) are compared for adjacent periods. Second, models where state-levelvariables are averaged over several years are compared with models where theyare allowed to vary over time, in order to examine the impact of small temporalchanges on how such variables relate to enforcement outcomes.

This article employs multilevel modeling techniques, which permit moreaccurate tests of whether and how variables at different levels affectenforcement outcomes. Among social scientists, multilevel models are mostfrequently associated with education research (Raudenbush and Bryk 2002), butthey have gained acceptance among political scientists as well (Shor et al. 2007).Relationships are examined between establishment-level, year-level, and state-level independent variables and establishment-level outcomes in order toilluminate the central problem described above.

Literature Review

Earlier research into subnational influences on U.S. regulation largelyignored issues of temporal change. Authors examined relationships betweenregulatory outcomes and state-level variables (Marvel 1982) or region-levelvariables (Shover et al. 1984), but not how such relationships might themselveschange over time. Yet, since a substantial body of research exists concerning howtemporal changes in the U.S. federal government affect governmental agencies(e.g., Moe 1985; Vike 2007; Weingast and Moran 1983; Wood and Waterman1994), it was perhaps inevitable that studies examining the effects of subnationalvariables on U.S. regulation would begin to incorporate temporal change.

Thompson and Scicchitano (1985) averaged several variables over multiyearperiods, which suggests an inattention to temporal change; however, they

992 | POLITICS & POLICY / October 2010

included not only contemporaneous but also prior-year explanatory variablesin their investigation of influences on enforcement vigor. Later studies onenforcement employed time series cross-sectional data—cross-sectional datasets taken from multiple years—to examine variation over time as well as space(Hedge 1993; Hedge and Scicchitano 1994; Scholz, Twombly, and Headrick1991; Scholz and Wei 1986; Wood 1992).

Hedge (1993) found that federal-level changes affect differentially the states,as measures of state political climates explained far less variation in enforcementunder the Reagan Administration than they had under President Carter. Hedgesuggested that Carter’s political vulnerability in the late 1970s gave statesmore license to determine regulatory policy, whereas Reagan’s popularity inthe early 1980s granted his administration greater control over regulatoryimplementation. Hedge and Scicchitano (1994) present other possible reasonswhy changes in the federal government might affect differentially the states.States represented on authorizing congressional committees might be able toblunt the effects of the federal government (e.g., to promote more severeregulation or to target regulation toward certain industries) better thanstates without such influence. Accordingly, new federal leadership (e.g., a newpresidential administration or a new Congress) that seeks to implementregulatory reforms might be able to do so more thoroughly in states withoutsuch committee representation. Similarly, new federal leadership inclined topromote stringent regulation may be able to do so more effectively in states withproregulatory political and economic climates than in antiregulatory states.

Possible Influences on State Variable Stability

These studies (Hedge 1993; Hedge and Scicchitano 1994) suggest thatpolitical changes at the federal level affect relationships between statecharacteristics and regulatory enforcement, but they do not address whether theeffects of state-level variables on enforcement change independently of federalinfluence. There are several reasons why this might be the case. Specific eventsthat attract state-level attention might alter the effects of states on enforcement,as for example when a strongly probusiness state becomes more supportive ofregulation in the aftermath of a scandal. An example would be the upgrading ofregulatory standards for child care in North Carolina. Despite the state’s effortsto attract business, the state government regulated child care more vigorouslyin the wake of a child abuse scandal at the Little Rascals Day Care Center(Gormley 1998, 373-4). Although the state’s overall attitude toward regulationand business had not changed, the scandal provoked a shift in this area ofregulation.

Changes in substate variables might also alter relationships between statecharacteristics and enforcement outcomes. Scholz, Twombly, and Headrick(1991) theorized that county-level leaders can influence OSHA enforcement byproviding or impeding access to resources that are important to enforcement

Shrock / CHANGES IN STATE CHARACTERISTICS AND REGULATION | 993

tasks (e.g., coordinating the activities of different parties concerned withregulation, leading business resistance to OSHA, or providing moral supportfor field inspectors and area office personnel). They found that local politicalparties and state politicians in New York state had independent statisticaleffects on OSHA penalties, which suggests that county-level phenomena operateas influences on enforcement independently of state-level political leadership.Finally, local supervisors in field offices influence inspectors (Hedge, Menzel,and Williams 1988), and they might persuade field personnel to alter theirregulatory approach for reasons unrelated to state-level phenomena. This mighthappen when a new area director assumes leadership in an office or when anold area director’s thinking on regulation changes over time. Similarly, fieldinspectors might alter their regulatory approach in response to localizeddevelopments; in particular, changes in interpersonal dealings and relationshipswith regulated businesses can affect inspectors’ overall regulatory approach(Carson 1970; Hutter 1989; Richardson, Ogus, and Burrows 1983).

It may be proposed, then, that once federal government activities andnational occurrences have been controlled, changes in relationships betweenstate characteristics and regulatory enforcement can be driven by developmentsat the state level or at local, substate levels. It is unknown how sensitive theserelationships are to developments at either level. Consequently, it is not knownto what extent inferences can be drawn concerning state–enforcementrelationships from studies done in prior or subsequent years.

The question is not so much whether subnational developments affect therelationships between state environments and enforcement over extendedtime periods, such as the era from the late 1960s to the present. Inasmuch asprofound changes have occurred during this time frame, including the economicintegration of the South into the larger United States and the correspondingdecline of industry in the northeastern states (Levy 1998), it would hardly besurprising if state–enforcement relationships had changed. What is uncertain,however, is whether state–enforcement relationships change over shorterperiods of time: whether, for example, state wealth had the same effect onregulatory enforcement in the 1990s that it had in the 1980s (assuming, ofcourse, that there is any effect to consider). This uncertainty reduces confidencein the extent to which generalizations about the nature of state environmentsand state influences on regulation can be safely made from even more recentstudies.

This article investigates how stable are statistical relationships between statecharacteristics and enforcement outcomes by examining such relationships inadjacent periods of time. A finding that these relationships differ betweendifferent periods would suggest that caution should be exercised in makinggeneralizations about influences on regulatory enforcement based on evenrecent studies. As a corroborative check, models where state-level variablesare averaged over several years will be compared with models where thosevariables are allowed to change from year to year. This will help determine how


sensitive the relationships between state characteristics and regulatoryenforcement are to small temporal changes in the values of state characteristics.

Data and Methods

Multilevel Modeling IssuesIn this article, models are compared to determine how small temporal

changes affect relationships between regulatory enforcement and variablestheorized to affect enforcement. These models employ time series cross-sectionaldata from 1989 through 1994 to examine effects of establishment-level, year-level (e.g., presidential administration), and state-level variables on outcomesfor OSHA inspections of business establishments. Establishment-levelobservations have therefore been drawn from larger, or higher-level, units ofanalysis, namely years and states. Another way of expressing this is to say thatestablishments are nested within years and states. Because of this,establishment-level observations are not independent but are correlated to adegree within higher-level units.1

Multilevel modeling techniques are used to address these problems.Multilevel techniques model the dependence of establishment-level observationswithin higher-level units. This is done by treating intercept and/or slopecoefficients of micro- or lower-level, models as dependent variables in higher-level models. The lower-level models represent the effects of micro-levelindependent variables on micro-level dependent variables. Then, in the higher-level models, independent variables in the lower-level models are treated asdependent variables influenced by macro-level independent variables. Lower-and higher-level models can be combined into a multilevel model throughstraightforward algebraic substitution.

Because multilevel models take into account the nesting of lower-levelobservations in higher-level units, they offer more trustworthy significance teststhan ordinary least squares, which can underestimate standard errors ofregression coefficients and thereby inflate significance levels (Kreft and deLeeuw 1998). In addition, these models incorporate parsimoniously slopes ofestablishment-level independent variables that differ across higher-level units(referred to in multilevel modeling as “random effects”), by providing anestimate of the variance of the different slopes, along with a point estimate foran overall average slope.

In addition to two-level models, there can be three or even higher-levelmodels. In this study establishments are nested within years and years are nestedwithin states, following the research design of DiPrete and Grusky (1990).

1 Admittedly, the extent of higher-level correlations in these models is not great. A total of 86percent of variation occurs between establishments within years within states. The converse ofthis statement, however, is that establishments exhibit a .14 correlation within year-statecombinations, and it is risky to ignore this dependency in the data.


Because there are few years involved in this analysis, modeling of year-leveleffects is kept simple: establishment-level slopes are assumed not to vary acrossyears, and the effects of year-level dummies are assumed not to vary acrossstates. By contrast, establishment-level slopes are allowed to vary across states.Preliminary analysis indicated that it was appropriate to model all inspection-level variables as random effects in this manner.

A disadvantage of multilevel analysis is that such complexity generatesinstability in models. In particular, including random effects can introducemodel instability (Kreft and de Leeuw 1998). Backward elimination (Fisher andFreudenburg 2004) was used to determine whether point estimates are stableenough to be reliable. In this process, a full model that contains all theoreticallyrelevant independent variables is fit. Then, nonsignificant variables are droppedfrom the model one by one, each time selecting the variable furthest fromsignificance, until a model with only significant variables is left. If the model isstable, dropping nonsignificant variables should not unduly affect the remainingcoefficients or standard errors.2

In using time series cross-sectional data, problems can arise withautoregressive error terms (Shor et al. 2007). This occurs when units of analysisat time t are affected by those same units at time t - 1. However, only about 5percent of establishments are inspected more than once, so inspections shouldnot be noticeably influenced by previous inspections. State-level variables thatinfluence inspection enforcement outcomes may have inertial effects beyondthose included in the model, but any such effects should be minimized bymodeling the conditional year-level variation after controlling for year-levelvariables.

Variables and Data SourcesFines per inspection, an inspection-level variable, is the dependent variable.

It is highly nonnormally distributed: over half of all observations are equal to$0, it has an average value of $1,316.95, but there are some extremely high-fineobservations of over $100,000. Because of its nonnormal distribution, thisvariable is treated as a categorical variable. The reference category is $0; theother category thresholds are located at $4,600 and $24,100. These thresholdswere chosen to create categories that represent roughly equivalent total amountsof dollars (each category comprises roughly $4.2 million in total fines). Thus,higher categories comprise fewer, but because of their higher fines, moresubstantively important inspections.3 This ordinal dependent variable isanalyzed using cumulative logit models.

2 Only full models are presented in this article. Reduced models obtained through backwardelimination are available from the author upon request.3 Before assigning fines to categories, they were deflated for inflation, with 1989 treated as the baseyear.


Fines per inspection indicates the regulatory approach of the ComplianceSafety and Health Officers (CSHOs, as OSHA refers to its inspectors) and theirsuperiors (i.e., toward greater or less severity). However, it also reflects thephysical conditions of the inspected workplace. Therefore, so that fines perinspection has more validity as an indicator of regulatory approach, sets ofdummy variables are included representing the number of other, serious, andwillful/repeat citations imposed per inspection.4 Including citations as a controlvariable presents a disadvantage in that it restricts citations’ influence on fines,inasmuch as CSHOs manipulate fines by altering how they record violations(Lloyd-Bostock 1988). However, it seems more important to control thoroughlyfor workplace conditions. Numbers of citations were converted into dummiesbecause, like fines per inspection, the numbers of other, serious, and willful/repeat citations are highly nonnormally distributed. There are three categorieseach of other, serious, and willful or repeat citations. The reference category iszero and the remaining categories were defined so that each would containroughly equal total numbers of citations, according to the same logic thatguided the creation of categories for fines per inspection.

Another inspection-level independent variable is the statewide injury andillness rate for the industry5 of the inspected establishment. This serves as a proxyfor the physical characteristics of the inspected workplace (Marlow 1981;McCaffrey 1983); it also influences CSHO decisions, as CSHOs use this rate toobtain a sense of the scope of the problems associated with various industries(Scholz and Wei 1986). The size of the inspected establishment, as indicated bythe number of employees, is another independent variable. Although largerestablishments tend naturally to contain more violations and receive higher totalfines, regulators have found larger businesses to be both more compliant and alsomore intimidating (Lynxwiler, Shover, and Clelland 1984). Once the number ofviolations is taken into account therefore fines may be lower for largerestablishments. Cranston (1979) nevertheless found that regulators believed thatlarger businesses needed more severe penalties to induce compliance. It is thusunclear how size can be expected to affect regulatory approach. Because ofprivacy concerns, the inspection records supplied by OSHA did not include theexact number of employees for each establishment. Therefore, dummy variablesare used to indicate establishments with 11-20 employees, 21-50 employees,51-100 employees, 101-250 employees, 251-500 employees, 501-1,000 employees,

4 OSHA classifies violations as serious, other than serious, willful, or repeat. A serious violationcreates a substantial probability of death or serious physical harm. An other (i.e., an other thanserious) violation does not create such a probability but does have a direct and immediaterelationship to employees’ safety and health. A willful violation results from deliberate, voluntary,or intentional action, as opposed to mere accident or negligence; a repeat violation is one forwhich the employer has previously been cited. Willful and repeat violations constitute the mostgrievous violations, and both expose employers to possible criminal charges.5 The industry of the inspected firm is determined by the applicable two-digit Standard IndustrialClassification (SIC) number.


and over 1,000 employees. (The reference category, of course, is establishmentswith one to ten employees.) Another dummy indicates whether an inspection wasconducted under the auspices of federal OSHA or one of the 23 state plans activein 1989-94.6 This controls for any overall difference in regulatory approachbetween state governments and the federal U.S. government.

The year-level variables are dummies. Presidential administrations have alarge impact on federal agencies like OSHA (Fisher 1991; Moe 1985; Scholz,Twombly, and Headrick 1991; Scholz and Wei 1986; Vike 2007; Wood andWaterman 1994). Presidential administration is indicated by dummies forPresidents George H. W. Bush and Clinton. Political variables in these analysesare lagged by one year (Scholz, Twombly, and Headrick 1991) to account fordelays inherent in the processes of political actors communicating their wishesto regulators and regulatory agencies making the institutional adjustmentsnecessary to respond to those wishes. In keeping with this assumption, 1989is assumed to be influenced by the Reagan Administration (Reagan is thereference category for presidents). The Bush Administration’s influence isassumed to run from 1990 through 1993, and Clinton’s influence in the year1994. Because both Bush and Clinton were less strongly opposed to severeregulation than was President Reagan, both dummies are expected to havepositive effects. A 1990 amendment to the Occupational Safety and Health Actthat increased the ceiling on fines sevenfold was modeled with a dummy thatequals one for 1991 through 1994.

Two state-level variables address relations between regulators andbusinesses. Regulatory inspectors may impose more and higher fines whereviolations are less visible to their immediate surveillance, relying on regulatoryseverity to compensate for low certainty of detection of violations (Kagan 1994).Visibility of violations, accordingly, should be negatively associated with fines perinspection. This variable is measured with the number of OSHA inspectionsprompted by complaints or referrals, standardized by the number of businessestablishments within that state. Regulatory inspectors are also inclined to besevere when they have less contact with businesses and have less opportunity todevelop cooperative relationships with such businesses (Ayres and Braithwaite1992; Hutter 1989). A proxy measure of contact was calculated by dividing thenumber of CSHOs in a state by the number of business establishments within thatstate. Unfortunately, data for these two variables are only available for one year,1992. Both variables are logged in order to make their distributions more normaland improve the normality of state-level residuals.

6 OSHA allows states to operate their own occupational safety and health plans, subject to federalapproval. One might suppose that a dummy for state plans should be a state-level variable.However, even in states with their own OSHA plans, a nontrivial proportion of inspections wereconducted under federal-OSHA auspices in 1989-94. This is because federal OSHA continues toinspect certain issues that are federal only, such as maritime, construction of federal buildings,Indian reservations, and construction on federal lands. Therefore, this is treated as an inspection-level variable.


Two variables capture economic conditions. Annual state per capita incomeis included to measure state wealth, which has been associated with more severeenforcement, possibly because wealthier states that operate their own OSHAplans can afford more vigorous enforcement of regulatory codes (Thompsonand Scicchitano 1985). Research indicates that OSHA regulators are morelenient during economic slowdowns, since they are more concerned aboutinflicting economic harm when businesses’ profit margins are likely to be thin(Kagan 1994; Scholz, Twombly, and Headrick 1991; Scholz and Wei 1986).Such slowdowns are measured by the percent change in state unemployment,with OSHA fines expected to be negatively associated with this variable.

Four main political variables are employed. Unionized percentage of stateworkforce (McCallion 1994) measures the strength of labor unions in a state.This variable should be associated positively with higher fines, since laborunions advocate typically regulatory severity and are involved highly inworkplace safety and health issues (Scholz, Twombly, and Headrick 1991;Scholz and Wei 1986; Thompson and Scicchitano 1985). The remaining threepolitical variables indicate the proportions of politicians at different levels ofgovernment who can be expected to promote either regulatory severity orleniency. All three variables are expected to be positively associated with higherfines (Hedge 1993; Scholz, Twombly, and Headrick 1991; Scholz and Wei 1986;Thompson and Scicchitano 1985). Prolabor Congresspersons is an ideologicalindex of a state’s Congresspersons, which was calculated by subtracting theratings given by the U.S. Chamber of Commerce from those given by theAmerican Federation of Labor–Congress of Industrial Organizations forall U.S. Congresspersons from that state over the 1988-93 period. PercentDemocrats in state legislature refers to the proportion of state legislative seatsoccupied by Democrats in 1988-93. Democratic governor is the number of yearsin 1988-93 for which the governorship in a state is controlled by Democrats.Data for these three variables came from Congressional Quarterly’s publicationPolitics in America (Duncan 1989, 1991, 1993, 1995). A variable indicatingwhether a state’s Congresspersons were on committees with oversight overOSHA was considered (based on Hedge and Scicchitano 1994) but eventuallynot included because in no model did it attain statistical significance.

Colorado, Idaho, Mississippi, Nebraska, New Hampshire, North Dakota,Ohio, Pennsylvania, and South Dakota are excluded from these analyses, andWyoming is excluded from the comparisons between the 1989-91 and 1992-94periods, because of missing data on injury and illness rates. North Carolina andNew York were excluded because of missing data on unionization rates. Thelack of the major manufacturing states of New York, Ohio, Pennsylvania, andNorth Carolina should not be critical, given that the data do include suchmanufacturing states as Indiana, Illinois, Michigan, and Wisconsin. However,their absence is regrettable and raises the issue that the data may underrepresentthe influence of the manufacturing sector on occupational safety and healthenforcement practices. Otherwise, the major absence in the data is of the Great


Plains states. These are generally smaller states with largely agriculturaleconomies, and their loss should be less consequential.

Analysis and Results

It is of course worthwhile to consider what these analyses substantivelyindicate about how establishment, year, and state-level variables affectregulatory enforcement. However, since this study is mainly concerned withhow stable are the relationships between state-level variables and enforcementoutcomes over time, the presentation of results in this section initially focuses onhow models differ because of short-term temporal changes and small temporalvariation. At the end of this section, a model that takes full advantage ofavailable data is presented and the effects that variables at establishment, year,and state levels have on regulatory enforcement are discussed.

Comparing Influences on Fines in Adjacent Time PeriodsTable 1 compares models using data from 1989 to 1991 and from 1992 to

1994. There is little evidence that injury and illness rate affects fines per inspectionin 1989-91. After all nonsignificant state-level variables had been deleted in theprocess of backward elimination, the coefficient for injury and illness ratebecame significant, but in all other models it was nonsignificant. In 1992-94, bycontrast, injury and illness rate is consistently significant throughout backwardelimination of nonsignificant variables. In both periods, most dummy variablesfor establishment size have significant effects. The dummy indicating 51-100employees is nonsignificant in 1989-91 but significant in 1992-94; also, the effectof 1,000 and over employees is negative in 1989-91 but positive in 1992-94.

Among the year-level variables, the 1990 increase in OSHA fines isassociated with higher levels of fines in the 1989-92 period. The presidentialdummies are generally nonsignificant, although in both periods the dummiesbecame significant through backward elimination. The evidence that BushAdministration affected OSHA fines is very weak, inasmuch as it was significantonly in one model and became nonsignificant again when unionized percentageof state workforce was dropped. Evidence for an effect of Clinton Administrationis somewhat stronger, inasmuch as it stayed significant after change in stateunemployment was dropped from the model. Overall, however, neither modelprovides firm evidence that presidential administration affects OSHA fines.

Turning to state-level variables, one sees that Democratic governor has asignificant positive effect on fines per inspection in 1992-94 but not in 1989-91.The effects of visibility of violations and contact with businesses are harder tojudge. In both periods, these variables are highly correlated (.74), and backwardelimination reveals that in both periods they seem to affect one another, whetherthey attain statistical significance or not. In 1989-91, contact with businessesbecomes nonsignificant when visibility of violations is dropped from the model,despite the fact that the latter variable in this period is consistently


Table 1. Effects of Variables on OSHA Fines per Inspection for 1989-91 and 1992-94(Full Models, before Backward Elimination)

1989-91 1992-94

Cumulative logit interceptsIntercept 3 -13.620 (.267)† -12.265 (.191)†

Intercept 2 -11.217 (.265)† -9.400 (.190)†

Intercept 1 -4.375 (.263)† -3.781 (.188)†

Establishment-level variablesInjury / illness rate .023 (.013) .017 (.006)†

Establishment size11-20 employees .010 (.019) .015 (.018)21-50 employees .044 (.019)† .216 (.017)†

51-100 employees .011 (.024) .329 (.021)†

101-250 employees .090 (.026)† .589 (.021)†

251-500 employees .176 (.037)† .748 (.030)†

501-1,000 employees .170 (.050)† .645 (.043)†

1,000 and over employees -.363 (.045)† .236 (.039)†

Other violations1-3 other violations .475 (.039)† .615 (.093)†

4-6 other violations .599 (.056)† .708 (.099)†

Over 6 other violations .813 (.074)† .803 (.102)†

Serious violations1-3 serious violations 5.814 (.109)† 5.234 (.102)†

4-8 serious violations 6.585 (.122)† 6.666 (.135)†

Over 8 serious violations 8.172 (.152)† 8.125 (.153)†

Willful/repeat violations1 willful/repeat violation 3.509 (.114)† 3.348 (.112)†

2-4 willful/repeat violations 3.733 (.114)† 3.758 (.126)†

Over 4 willful/repeat violations 4.624 (.191)† 5.105 (.315)†

State plan–OSHA inspection 1.519 (.297)† 1.361 (.279)†

Year-level variablesBush Administration‡ .124 (.097)Increase in OSHA fines‡ 1.070 (.094)†

Clinton Administration‡ .124 (.069)State-level variables

Visibility of violations 1.030 (.676) 1.345 (.536)†

Contact with businesses -1.703 (.642)† -1.606 (.493)†

State per capita income .054 (.078) -.059 (.072)Change in state unemployment -.005 (.003) -.001 (.002)Unionized % of state workforce‡ .033 (.034) .004 (.030)Prolabor Congresspersons‡ -.004 (.004) -.002 (.003)% Democrats in state legislature‡ .018 (.012) .002 (.007)Democratic governor‡ .157 (.246) .308 (.141)†

Inspection-level N 272,617 241,213Year-level N 3 3State-level N 38 38

Notes: † a = .05.‡ Variables lagged one year to reflect delayed impact of political variables.


nonsignificant. This may indicate that visibility of violations has actually asignificant positive effect on OSHA fines, but this is hidden because highcorrelation with contact with businesses inflates the variance of visibility(Gujarati 1995). In 1992-94 both of these variables are significant throughoutthe backward elimination process. Given the ambiguity surrounding therelationship between visibility of violations and contact with businesses, one cannot say for certain that the effects of these variables differ substantively from1989-91 to 1992-94. However, it remains clear that Democratic governor has asignificant positive effect on fines per inspection in the latter period but not theformer.

Time-Varying versus Time-Averaged DataIt was stated earlier that it would not be surprising to see the effects of state

variables on enforcement outcomes change over long periods of time. One couldalso criticize the preceding analysis for relying on an excessively short period oftime. Researchers minimize generally the impact of unique, nonrepeating eventsand developments by examining a number of years in order to smooth out thequirks of any given exceptional years. Other research on enforcement hasexamined from five to ten years of data (Hedge 1993; Hedge and Scicchitano1994; Scholz, Twombly, and Headrick 1991; Scholz and Wei 1986; Wood 1992),but in the previous section each period under examination was only three years.Accordingly, this section features an analysis that uses all six years of data. Atime series cross-sectional model is contrasted with one for which cross-year,within-state averages have been calculated for state-level variables. This willcompare the effects of state variables that do not change over time with statevariables that do, and therefore help determine how sensitive relationshipsbetween enforcement outcomes and state characteristics are to small temporalchanges in the latter.

A preliminary examination of the correlations between the variables andtheir mean averages generally suggests that most variation exists between statesrather than between years (Table 2). Overall, it is plausible to expect thatsubstituting time-averaged for time-varying data will not affect the conclusionsif this model is not overly sensitive to temporal change. However, thecorrelations between Democratic governor and change in state unemploymentand their respective mean averages are considerably lower and suggest that thesetwo variables bear special attention. Therefore, Table 3 displays three models.One uses time-averaged state-level variables (model a). The second (model b)allows governor and change in unemployment to vary from year to year butconstrains other state-level variables to their by-year averages. The last modelallows state-level variables to vary by year (model c).

Results for establishment-level variables are essentially similar. Thecoefficient for injury and illness rates in models a and b are both close tosignificance at conventional levels: it is significant at a = .0552 in model a anda = .0591 in model b. During backward elimination the standard error for this


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variable decreased from .009 to .008 in both models. One can probably concludethat this variable has a similar positive effect on fines per inspection in all threemodels. All other inspection-level variables have identical effects in all threemodels.

Initially, it seems as if the models differ in terms of year-level variables.Although Bush Administration is nonsignificant and increase in OSHA fines issignificant in all models, Clinton Administration is nonsignificant only in modelc because its point estimate is lower in that model. However, the presidentialadministration dummies are highly correlated (–.64), and when BushAdministration was dropped from all three models during backwardelimination, the standard error for Clinton Administration decreased to .061 inmodel a, .066 in model b, and 0.67 in model c. It is probably safest, therefore, toconclude that Clinton Administration is significant and positive in all threemodels, a finding that was hidden only because Bush Administration inflatedClinton’s standard error.

In models a and b, percent Democrats in state legislature has a positiverelationship with fines per inspection. However, backward elimination revealedthat this variable is significant in models a and b only when certain complexcombinations of other variables are also included. Because these other variablesfail to attain significance in either model a or b, and because such complexrelationships between variables are unlikely to reflect a theoretically plausiblerelationship, it is probable that the significant coefficient for percent Democratsin state legislature results from accidental interactions between variouseffects in the models and does not reveal a genuine influence on fines perinspection. Among the other state-level variables, visibility of violations andcontact with businesses are significant in all three models. However, prolaborCongresspersons is significant in model c, whereas it is not in models a or b. Theeffect of this variable survives backward elimination, which indicates that eventhe minor temporal differences between models a and b and model c lead tosubstantively different results.

Influences on Regulatory EnforcementThe question remains of what these models tell us about establishment, year,

and state-level variables. Because model c of Table 3 includes the greatestamount of information, it is the focus of the following discussion. Thecumulative logit models used here present coefficients as log odds ratios of beingin a higher rather than a lower category.7 Thus, for example, a unit increasein injury and illness rate is associated with an increase of .016 in the log oddsfor total fines per inspection being in a higher category. In order to interpretthis coefficient, one must take the antilog. Doing so yields the finding that fines

7 Normally, cumulative logit models yield log odds of being in lower categories rather than highercategories. Because this can be nonintuitive and difficult to comprehend, the order of thecategories was reversed when the models were run.


are 1.016 times as likely to be in a higher category (e.g., 1, 2, or 3 as opposed to0, or 2 or 3 as opposed to 0 or 1) for every unit increase in the injury and illnessrate (holding all other variables constant). This leads to the expected conclusionthat fines per inspection will be higher where the industry of the inspectedworkplace presents more hazards to workers. The interpretationof the coefficient for dummy variables, such as state plan–OSHA inspection,is similar: by taking the antilog of 1.532, it appears that fines are 4.627 timesas likely to be in a higher category for state-OSHA inspections as for federal-OSHA inspections.8 This indicates that CSHOs in programs operated by stategovernments are, on average and all else being equal, more likely to imposehigher fines than CSHOs who work for federal OSHA. The finding contradictsthe general assumption of proregulation activists that the federal governmentregulates more stringently than the states, but it is not inconsistent with previousresearch that has indicated that states may not adopt OSHA plans in order toregulate more leniently (Thompson and Scicchitano 1985).

The effects of establishment size on level of OSHA fines are more surprising.The coefficients for these dummy variables indicate that fines per inspectionincreases with larger establishments, reaching a peak at places with 251-500employees. Then, surprisingly, fines per inspection begins to decrease; forestablishments with over 1,000 employees, there is no significant difference fromthe reference category of establishments with one to ten employees. Anexamination of cross-tabulations revealed a possible cause of these results.Within most categories of serious citations (i.e., zero citations, one to threecitations, four to eight, nine or over), fines are likely to be both higher and lowerin large establishments than in establishments with under eleven employees.That is, among large establishments there are not only more inspections withhigher levels of fines,but also more inspections with lower levels of fines. This isespecially true for establishments of over 1,000 employees. A similar pattern isseen for willful/repeat citations. An examination of inspection records with largenumbers of citations and no fines revealed that they occurred in government-associated workplaces: universities, post offices, army bases, and so on.Although there are few such workplaces overall, one must remember that thereare also few workplaces with over 500 employees: under 4 percent of the entiresample. Therefore, it takes comparatively few observations to affect the results.The net result is to make the effect of establishment size on likelihood of higherfines more complicated than might at first be assumed.

8 This point estimate is only an average, however. Because state plan–OSHA inspection wasmodeled as a random effect, the variance of its slopes across the 38 states in the sample wasdetermined. For this variable, the variance was estimated to be 1.5156. On the assumption that theslopes are normally distributed, one can state that 95 percent of state-OSHA slopes fall between-0.881 and 3.945. The random effects in the models discussed in this paper are not included inTables 1 and 2 because although modeling random effects was appropriate for obtaining thebest-fitting models, presenting the random effects themselves was not necessary to address thequestions investigated in this article.


Unsurprisingly, higher numbers of citations are positively associated withgreater likelihood of higher levels of fines. Serious citations have a strongerrelationship to fines than not only other citations but also willful and repeatcitations despite the fact that fines for willful and repeat violations typically arehigher. This is probably because serious citations are far more common: willfulor repeat violations were uncovered in only 5 percent of inspections, as opposedto 43 percent of inspections for serious citations.

Turning to year-level variables, one observes that the 1990 increase inOSHA fines has the largest effect of any year-level variable on OSHA fines. Asdescribed earlier, the Clinton Administration appears to be associated with moresevere regulation when Bush Administration is removed from the model. Inmodel c, the effect of the Clinton Administration on OSHA fines is considerablyless than that of the 1990 increase.

Among state-level variables, contact with businesses appears to reduceOSHA fines, as predicted. By contrast, visibility of violations has an unexpectedpositive association with fines. The positive coefficient may indicate that thisvariable should be interpreted more as an indicator of union activity inpromoting complaints and referrals (Scholz and Wei 1986) than as an indicatorthat CSHOs have information about what is happening in their jurisdiction.State per capita income, the measure of state wealth, has no effect. Change instate unemployment has a modest effect on fines; the negative coefficient isconsistent with past research (Kagan 1994; Scholz and Wei 1986; Thompsonand Scicchitano 1985). Finally, although most political variables exhibit noeffect on fines per inspection, there is a surprising negative effect of prolaborCongresspersons. By taking antilogs, one discovers that fines are .995 timesas likely to be in a higher category for each unit increase in prolaborCongresspersons. This is a small effect, but it may become fairly consequentialwhen comparing states with extremely prolabor to extremely antilaborCongressional contingents. The values of prolabor Congresspersons in the dataused here ranged from lows of -23 to -85 (for the lowest 10 percent) to highs of33 to 86 (for the highest 10 percent). The difference in ranking on the prolaborCongresspersons scale for these extreme congressional contingents therefore liesbetween 56 and 171. Thus, the results here suggest that all else being equal, finescould be from .756 to .425 times as likely to be in a higher category in a statewith extremely prolabor Congresspersons as compared with a state withextremely antilabor Congresspersons.

It is of course surprising to find that prolabor Congresspersons are negativelyassociated with level of fines. However, an examination of the mean dollaramount of fines per inspection, divided by quartiles for prolaborCongresspersons (Table 4), indicates that this is not implausible. It is actually thequartile with the lowest values for prolabor Congresspersons that has the highestaverage dollar value of OSHA fines per inspection. Further investigation isneeded to better understand this unexpected relationship. An examinationof the lowest and third quartiles for prolabor Congresspersons (i.e., the quartiles


with the lowest and highest fines) did not reveal any striking patterns in terms ofgeographic location or population size, and both groups were about equallydivided between state plan– and federal-OSHA states. The only characteristicthat is immediately evident is that the high-fine states tend to be conservativestates outside of the Northeast, whereas the low-fine states mostly exclude theSouth. Tentatively, however, it may be suggested that these findings reflect adynamic similar to that noted by Shover and others (1984), where mineregulators tended to be more severe in areas that were more resistant toregulation, as a way of asserting their authority, while conversely, they were lesssevere where mines seemed more willing to grant respect. Perhaps CSHOs aresimilarly imposing higher fines in states that have traditionally resistedgovernment regulation and being relatively lenient where regulation is moreaccepted as a normal part of doing business.

Conclusion

Of course, the curious effect of prolabor Congresspersons on fines perinspection may well reflect a short-lived, temporary phenomenon with littleapplication to the years before 1989 or after 1994. The major implication of thisarticle is that one must be cautious in generalizing from research into regulatoryenforcement, especially as regards how enforcement is affected by statecharacteristics. In Table 1, although most variables had similar relationships tothe dependent variables of fines, Democratic governor went from nonsignificantin 1989-91 to significant in 1992-94. Similarly, the time-varying model ofTable 3 revealed an impact of prolabor Congresspersons on OSHA fines that thetime-averaged models had not indicated. Although most of the time-varyingvariables exhibited correlations in excess of .93 with their by-year averages, thedisparities between the variables and their averages were large enough to altersubstantively the conclusions indicated by the models.

This is less of a problem for researchers interested in how federal-levelgovernment affects U.S. regulatory enforcement: such questions are almostinevitably historically focused because they are concerned with how changes infederal politics and government over time affect regulatory outcomes. However,

Table 4. Mean Dollar Value of Fines per Inspection, by Quartile of ProlaborCongresspersons Index

Prolabor CongresspersonsScore

Mean Dollar Value of FinesImposed per WorkplaceInspection

-85 to -7 (lowest quartile) $1,641.51-7 to 8 (second quartile) $1,155.078 to 25 (third quartile) $990.2125 to 86 (highest quartile) $1,482.36


researchers who are concerned centrally with state-level influence on regulatoryenforcement need to confront this issue, which calls into question theapplicability of past research to subsequent situations.

One approach might be to incorporate such large-scale temporal variationby seeking out and using the longest time series available. As noted above,researchers already employ multiple-year periods when examining politicalphenomena to smooth out idiosyncrasies that might characterize any particularyear. One might extend this strategy to smooth out the idiosyncrasies ofparticular decades or eras by using data sets that incorporate 20 or 30 years ofstate data. This would effectively be a strategy of controlling temporal variationby incorporating it as much as possible. Clearly, it is not a great revelation thatlarger data sets and more information is preferable to smaller data sets and lessinformation; however, the current research underscores the risks of trying togeneralize from too-short time frames. The major obstacle to such a strategy, ofcourse, has to do with the practicality of obtaining such large data sets, as wellas the resources necessary to work with them.

An alternative might be to understand better what factors influencerelationships between state characteristics and enforcement outcomes and tocontrol for those variables. It was mentioned above that these relationshipsmight be affected not only by federal-level developments (Hedge 1993; Hedgeand Scicchitano 1994), but by state or substate phenomena. County-levelpolitical leadership (Scholz, Twombly, and Headrick 1991), field officeleadership (Hedge, Menzel, and Williams 1988), and interpersonal relationshipsbetween regulators and business owners (Hutter 1989) were suggested here aspossible influences on regulation, and there may be others. In the statisticalanalyses presented in this article, visibility of violations and contact withbusinesses were used to capture something of the quality of relationshipsbetween CSHOs and regulated businesses; but as state-level measures, they arecrude proxies at best for such relationships. Surveys of regulators and theirsupervisors, state and county politicians, and businesses could be used to obtaingenuinely micro-level data, and the inclusion of such variables in models couldenrich our understanding of enforcement decisions.

In a best-case scenario, researchers may find that including substate, micro-level considerations in models of regulatory enforcement helps to reveal moregenuinely stable relationships between state characteristics and enforcementoutcomes. In this way it may be possible to reform or improve regulatorypractices by targeting policy changes with respect to state or substate variables,or both. The article suggests that researchers face the challenge of identifyingand incorporating into quantitative models such variables in order tounderstand better which relationships between state characteristics andregulatory outcomes are unstable and which, if any, endure. However, it is farfrom clear what factors drive the relationships between state characteristics andregulatory enforcement. Given that, this study constitutes at most a preliminarypresentation of evidence that these are questions that need to be addressed.


About the Author

Dr. Peter Shrock is assistant professor of criminal justice at SoutheasternLouisiana University. He received his Ph.D. in sociology from the University atAlbany, State University of New York, and his research focuses on regulatoryenforcement and organizational crime.

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changes in relationships between state characteristics and regulatory enforcement over time

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