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Article Excerpt
The Federal regulation-writing process is vital to understanding how laws are translated into policy. This paper re-examines data on human services interest groups active in lobbying the executive branch to determine what factors influence their effectiveness. Building on findings from Hoefer (2000), structural equation modeling is used to re-analyze the original regression model of interest group effectiveness (IGE) on a sample of 127 Washington D.C.-based interest groups. Results indicate that some of the previous findings are not supported and an alternative model is proposed. A group's position, context and access to information and policymakers emerge as significant determinants of IGE. Access also mediates the impact of a group's strategy and position on IGE. Implications for practice and future research are provided.
Keywords: human services, interest groups, regulations, policy, advocacy, structural equation modeling, policymaking Regulatory politics--the struggle for control over the administrative levers of power and policy shaped within government agencies--is central to government activity in the United States (Harris and Milkus, 1989, p. viii).
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Regulations control implementation of laws. This vital fact concerning the policy process can often be overlooked in the euphoria of winning a legislative battle or in the dispiriting crush of losing a vote. Either way, significant opportunities exist in the post-legislative process to affect policy, a fact noted in the scant social work literature on the subject (Albert 1983; Bell & Bell 1982; Haynes & Mickelson 1997; Hoefer 2000; Jansson 1999). Most of this social work literature, however, is concerned with explaining what the regulatory process is and how it fits into the policy process rather than with researching how best to affect the process, particularly for the interest groups that try on a daily basis to affect policy.
Moving the administrative levers of policy requires influence and power. Interest groups that understand the importance of regulatory policymaking want to be as effective as possible in their pursuit of this task. The study of interest group effectiveness (IGE) (a type of political power) is a subject of great interest and importance, yet one that is controversial because skeptics doubt that the concept can ever be measured. The search for interest group influence has been equated with "a blind man searching for a black cat in the coal bin at midnight" (Loomis 1983, p. 194). Less lyrically, Sloof (1998, p. 247) states: "... the prospects for a comprehensive model of the political influence of competing interest groups on government policy look rather dim."
The major problem with demonstrating interest group influence is connected with the "second face of power" argument (Bachrach & Baratz, 1962). This posits that truly influential groups are at work behind the scenes so that proposals contrary to their interests are never put on the decision agenda. If this were true, we would not need to study interest group effectiveness, for it would be clear that interest groups visible in the process were not key actors in the agenda-setting or decision-making processes of government. Their actions would be mere window-dressing to confuse the masses into believing that their voices and actions (as reflected in interest group activity) were meaningful.
While the power to control the agenda is clearly important, the second face of power argument is not the end of the story. Important issues of concern to various interests are publicly debated and acted on by elected officials. The literature on agenda setting notes that issues are put onto the decision agenda in various ways, including crises or prominent events, changes in widely respected indicators, a gradual accumulation of knowledge and perspectives among specialists, and the development of new technology (Kingdon, 1995, pp. 16-17). Baumgartner and Jones (1993) describe how shifts in the way an issue is viewed are instrumental in changing the political agenda. Thus, exceptions to a powerful actor's ability to control the agenda exist. Once a topic is on the agenda (whether by interest group activity or other means), interest groups then act to try to influence the outcome of the political battle. While interest groups may not control the agenda, Kingdon (1995, p. 50) argues that they "affect the alternatives considered", surely a significant type of power and one that might profitably be investigated.
For researchers not dissuaded from approaching this difficult subject, different approaches have been tried in an effort to measure interest group effectiveness. Similarly, review of the extant literature reveals a variety of different statistical methods used to identify the predictors of effectiveness and to understand the interrelationships among them. This paper presents research that adopts a different statistical technique to re-test Hoefer's (2000) ordinary least squares (OLS) model of interest group effectiveness in the Federal regulation-writing process using structural equation modeling (SEM).
Although a number of other types of analysis have been used in studying interest group power (notably, probit [Hojnacki & Kimball, 1998; Wiggins, Hamm & Bell, 1992], logit [Evans, 1996], and regression analysis [Grenzke, 1989; Haider-Markel, 1999], as well as modeling based on game theory [Sloof, 1998]), SEM has rarely, if ever, been used despite its potential advantages. SEM is better in handling a dependent variable that is continuous (not possible with probit and logit techniques) and is better able to handle the multiple, overlapping variables and indirect pathways involved in this analysis than is regression analysis. It also allows for confirmatory factor analysis to ensure that the measured variables are truly aspects of their respective theoretical constructs.
It is incumbent upon social work researchers to use the best theory and the most powerful tools available to test our understanding of phenomena of interest. Ordinary least squares regression techniques are powerful, but SEM is able to handle vexing issues that cannot be dealt with successfully using OLS. While Hoefer (2000) is a good example of the use of OLS, we believe the re-analysis of the data using structural equation modeling is justified because SEM is a more powerful technique for taking into account "the modeling of interactions, correlated independent variables, measurement error, correlated error terms, [and] multiple latent independent variables, each measured by multiple indicators ..." (Garson, 2002, p. 1). SEM thus appears useful both to determine how robust Hoefer's original results are and to facilitate a more precise measurement of determinants using latent structures and multiple indicators that theory posits are important.
After describing past research to understand current thinking about measuring the concept of IGE and identifying the oft-cited determinants, this paper describes the results of testing the original model with SEM. Drawing from the interest group literature over the past decade, an alternative model with increased fit is proposed, which introduces new predictors absent in the original model. Findings are then discussed in the context of the existing literature and suggestions for future directions of research are presented.
Measuring Interest Group Effectiveness (IGE)
Two major approaches to measuring IGE are well documented. The first is measuring a group's reputation and the second is...
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