The Food and Drug Administration (FDA) plays a primary role in setting public health policy in the United States. From drug approval to food safety to the regulation of medical devices, the agency has a hand in setting and enforcing a wide variety of policies. By the agency’s own estimate, twenty five cents of every consumer dollar spent in the United States is spent on products regulated by the FDA.
While the FDA has a variety of responsibilities in the policy areas it regulates, among the most important are its monitoring and enforcement activities. It is through these activities that the agency ensures that regulated firms comply with directives set down by the agency, Congress, and the president. The monitoring process begins when the agency inspects firms and takes and analyzes samples from these firms. If the agency finds, through its inspections and samples that a firm is acting illegally, or is in some other way violating regulatory rules or laws, it has a variety of enforcement options available to it, ranging from urging the firm to recall its products to seizing the firm’s products. Thus, the agency’s monitoring and enforcement powers are profound.
In this paper I analyze the monitoring actions of the FDA over time and examine the political influences on these actions. This analysis serves two purposes. First, it gives us greater insight into the public health activities of the Food and Drug Administration, into the level of activism of this agency, and into the effect that other political actors have on the FDA. Second, it provides us with insights into the nature of political influence on regulatory agencies and into how such political influence ought to be measured. It does so by using a spatial model that demonstrates that agency policymaking takes places within specific regulatory regimes, and that the nature of these regimes determines whether or not political actors will influence the actions of an agency.
While the analysis in this paper centers around measuring political influence generally, the more specific focus is on institutions internal to Congress and how they affect agency actions. Thus, this paper follows the lead of other studies that examine the impact of institutional structures on agency policy-making. In addition, as most studies now realize, political control of agencies is not perfect and bureaucracies do not operate autonomously (see, e.g., Hammond and Knott 1996, Krause 1999).[1] Thus, instead of looking for either perfect control or complete autonomy, and instead of arguing that one particular institution is dominant, this paper draws on a theoretical approach that allows for multiple principals and varying levels of political control.
The paper proceeds as follows. First, I briefly review some existing studies of political influence on agencies. Second, I use a spatial model to demonstrate how previous empirical estimations of political influence may be flawed. Third, guided by the spatial model, I undertake an empirical examination of the monitoring activities of the FDA. After presenting the results, I discuss the limitations of this study and suggest avenues for future research.
Previous Literature on Political Influence
In the United States today, the overwhelming majority of public policy decisions are made by political bureaucracies. This high level of agency involvement in policymaking leads to some serious questions about the nature of our democracy. Since at least the 1930s, the courts have allowed Congress to delegate broad powers to agencies, and delegate it has. Yet in a representative democracy, it is important, even essential, that the bureaucrats to whom policy-making power has been given are accountable to elected officials. This is a basic and essential normative concern in a political system that allows delegation.
While the extent to which bureaucratic decisions are affected by the preferences of political principals has been a topic of interest for decades (see, e.g., Cushman, 1941; Herring, 1936), this topic recently has received renewed attention. In particular, empirical studies have used increasingly sophisticated methods to attempt to determine the extent of political influence on political agencies. [2] These studies have been extremely valuable, providing a more complete and nuanced understanding of the amount of influence on agencies as well as the factors affecting such influence. At the same time, however, they generally ignore the different political regimes that exist, which can result in biased estimates of statistical parameters of interest (Amato and Shipan 1994). In the following section I discuss the concept of political regimes, show how ignoring these regimes can lead to misspecification, and then suggest a more appropriate estimation procedure.
Regimes and Agency Policymaking
While not commonly used in the study of American politics, the concept of a regime is invoked quite often in other subfields of political science. In comparative politics, for example, Fish (1994) has used the concept to examine the transition to democracy in Russia. In international relations, Krasner (1982) utilizes the concept of international regimes, where he defines a regime as "principles, norms, rules, and decision-making procedures around which actor expectations converge in a given issue-area" (see also Kaplan, 1966 and Keohane, 1984). In law and politics, Eskridge and Ferejohn (1994) and Shipan (1997, 2000) explore the influence of legal regimes on courts and agencies.
Within the study of American politics, the concept of a regime has been applied most commonly (and most fruitfully) to the study of regulation.[3] For example, although he does not explicitly use the term regime, Rabin (1986), in what is probably the best historical overview and analysis of regulation, implicitly uses the notion of a regime throughout his discussion of various regulatory eras (i.e., regimes).[4] Similarly, Harris and Milkis (1989) discuss changes in regulatory regimes in their examination of two agencies.
These studies, however, have been largely historical and sweeping in their conception of a regime. In this analysis I define a regime simply as a given configuration of institutions and preferences. The concept takes on importance because, as I demonstrate in the spatial model presented below, an agency will choose a policy within a certain regime, or within a given configuration of institutions and preferences. And when statistically assessing political influence on agencies, one must take into account the possibility that at different times an agency will be making decisions in different regimes.
A Spatial Model of Policymaking and Regulatory Regimes
Spatial models can provide a good illustration of how an agency may find itself in different regimes. These models can be used to recognize that agencies make decisions within a political context and that internal institutions, such as the committee system in Congress, may have important political repercussions. They also can be used to acknowledge that there are several political principals that might influence the agency.[5] In addition, they incorporate the knowledge that policymaking is sequential and that because the sequence of policymaking is fixed, when the preferences of elected political actors change, an agency may be faced with a different regime.
A simple spatial model can be used to illustrate how agency policymaking can occur in different regimes. The model incorporates the following key assumptions. First, a unicameral legislature has delegated responsibility over some policy to an agency, A.[6] Second, F, the floor of this legislature has delegated primary responsibility within the legislature to a committee, C. Third, the committee has gatekeeping powers. This is admittedly a controversial assumption. Yet numerous theoretical support exists for adopting such an assumption (e.g., Denzau and Mackay 1981, Shepsle and Weingast 1987, Epstein 1997). And more importantly, it comports with the empirical regularity of how Congress functions – numerous studies have documented the extent to which the vast majority of bills referred to committees die in committee (e.g., Deering and Smith 1997).[7] Fourth, action proceeds under an open rule. Finally, I assume that the policy and the actors’ preferences can be aligned along a single dimension.
Figure One presents an array of preferences in which A < C < F. The figure also includes an additional point -- the indifference point for the committee with respect to F, labeled C(F), where C(F) = 2C - F. In this figure, A is placed to the left of C(F). The situation shown in Figure One, in which A < C(F) < C < F, will be called Regime 1.
|
A C(F) C F |
The committee’s indifference point is of particular importance for the agency. If the agency selects a policy corresponding to A, its most preferred policy, the committee will open the legislative gates and propose legislation.[8] Then, under an open rule, the floor will amend the policy and F will be the result. If, on the other hand, the agency thinks carefully and strategically about the situation, it will choose a policy corresponding to C(F). The agency realizes that if it locates at this point, the committee will be indifferent between inaction, which would result in C(F) becoming policy, and opening the gates and introducing legislation, which, under an open rule, would result in a policy of F. Because the agency does not prefer F to C(F), it will allow the agency’s policy choice to stand.
What do we learn from this simple example? First, the policy outcome -- the subgame perfect equilibrium -- is generated by the combination of the actors' preferences and the sequence of the game. The legislature’s ability to respond to the decision of the agency causes the agency to choose a policy away from its most preferred point in order to avoid being saddled with a policy that is even less desirable. The order of choosing is: agency, committee, floor. Each actor, when choosing what to do, will take into account the likely actions of all actors who still have a chance to play the game.
Second, as the preferences of the legislature change, so will (at least in this regime) the agency's outputs. The model shows that as the committee moves to the right, so will the agency’s output. Counterintuitively, it shows that as the floor moves to the right, the agency can locate farther to the left.[9] Put somewhat differently, if we were to regress some measure of agency output on the preferences of the committee and the floor in Regime 1 (i.e., where A < C(F) < C < F), we would expect the coefficient for the committee to be positive but the coefficient for the floor to be negative.
Consider now a different configuration of preferences, one where A is located in the interval [C(F), F], which will be called Regime 2. In this case the agency will be able to implement its most preferred policy, A, unconstrained by the preferences of the legislature. It is able to do so because the committee prefers A, the agency’s choice, to F, which would be the policy outcome under an open rule. In this instance the agency receives a considerable amount of autonomy from the legislature, and will be unaffected by changes in congressional preferences. Finally, consider the situation where C < F < A, denoted Regime 3. In this case, if the agency chooses a policy outcome equal to A, then the committee would introduce legislation to overturn the agency’s action. In fact, since the committee prefers F, the legislative outcome under an open rule, to any point to the right of F, the only way the agency can avoid being overturned is to locate at F. Thus, in this regime we would expect the influence of the floor to be significant and positive – as the floor moves to the right, so will the agency. However, we would not expect to find a significant relationship between the committee and the agency. As long as the agency and committee are located on opposite sides of the floor, a shift in the committee’s preferences will not result in a shift in the agency’s preferences.
The most important result from this model is that whether an agency is affected by the legislature, and by a committee within the legislature, depends on the configuration of these actors’ preferences. Figure 2 sums up the results discussed in the previous paragraphs, where Regime 1 is the situation in which A < C(F) < C < F, Regime 2 is where C(F) < A < F, and Regime 3 is where F < A. In Regime 1, an agency will be positively affected by changes in the committee’s preferences but negatively affected by changes in the floor’s preferences. In Regime 2, the agency will be unaffected by both the floor and the committee. And in Regime 3, the agency will be positively affected by changes in the floor’s preferences but will not be influenced by changes in the committee’s preferences.
|
Regime 1 Regime 2 Regime 3 C(F) C F |
The importance of this simple theoretical model for empirical estimations of political influence on agency policymaking should be clear. Existing models typically regress some measure of agency output on the preferences of political actors. Some of these models find that the preferences of political actors exert a significant effect on the agency, while others do not. But what this model shows is that whether or not legislative institutions have a significant effect on agency actions depends on the nature of the political regime. The floor, for example, should have a negative influence in Regime 1, a positive influence in Regime 3, and no influence at all in Regime 2. The committee, on other hand, should have a positive influence in Regime 1 and no influence in the other two regimes. Thus, in order to achieve meaningful results – or even to expect to achieve meaningful results – the nature of the regulatory regime must be taken into account in any empirical analysis of political influence on agencies.
Complications
Unfortunately for political analysts, the world is never as neat and clean as a formal model. At least three complicating factors need to be taken into account before any empirical analysis can be done. First, in the U.S. system there are two houses, not one. Second, we must consider how to take the president into account. And third, we do not have any independent measures of the agency’s preferences.[10]
The spatial model can be expanded to account for other political actors. For example, in a bicameral legislature like the U.S. Congress, both the House and the Senate and their respective committees will have an interest in the agency's decisions. Such a complication obviously needs to be taken into account. At the same time, however, it complicates the analysis tremendously. Instead of having three distinct regimes – committee-floor influence, agency autonomy, and floor influence – we now have five. The agency might be influenced by the House committee and floor, the Senate committee and floor, the House floor alone, or the Senate floor alone, or it might operate entirely autonomously from the legislature.
However, since both houses would have to pass legislation in order for a law overriding the agency to be passed, the agency need only pay attention to one house. Once that house is satisfied by the agency’s action, the agency no longer needs to worry about override legislation being passed and it can disregard the preferences of the other chamber. Thus, while in principal the agency needs to worry about both floors and both committees, in reality it needs to worry about pleasing only one committee-floor pair (in Regime 1) or one floor (in Regime 3).
An example can illustrate this point. In terms of the model presented above, if the House committee’s indifference point with respect to the House floor is CH(FH) and the Senate committee’s indifference point with respect to the Senate floor is CS(FS), then when A < CH(FH) < CS(FS), as in Figure 3 below, the agency needs to worry about satisfying only the House committee.[11] In this case, the House committee and floor would be expected to have significant coefficients, but the Senate committee and floor would not. However, if the positions of the House and the Senate were reversed, then the Senate committee and floor would be influential. Similarly, if either CS(FS) < A < FS or CH(FH) < A < FH, then the situation would be classified as Regime 2 and the agency would be able to act independently of Congress. Finally, if FH < A and FS < A, then Regime 3 exists and the agency would be influenced by FH if FS < FH < A and will be influenced by FS if FH < FS < A.
|
A CH(FH) CS(FS) CS CH FS FH |
Thus, even when there are two houses, configurations can still be classified into the three regimes discussed earlier. There is either one committee-floor pair that matters; one floor that matters; or the agency can act autonomously from the legislature. Collapsing the five potential regimes into three regimes has both benefits and costs. The benefit is that by focusing on only the committee and the floor that might matter, regardless of chamber, we are able to limit the empirical analysis to three regimes. This is important for two reasons: first, to preserve degrees of freedom; and second, because it is possible that there might be very few observations in any given regime, a situation that would be exacerbated by using five regimes. In addition, such an approach still gives us leverage on whether congressional institutions influence an agency. The cost, however, is that we lose the ability to determine whether this influence is coming from the House or the Senate.
We can use the stylized fact of presidential influence over executive branch agencies to allow us to determine whether an agency is making policy in Regime 1, 2, or 3. If the president’s ideal point is to the left of CH(FH) and CS(FS), then the agency makes policy in Regime 1; if either CS(FS) < P < FS or CH(FH) < P < FH, then the agency makes policy in Regime 2; and if FH < P and FS < P, then the agency makes policy within Regime 3.[12] This is admittedly a strong assumption, and in the conclusion to this paper I explore some potential alternative ways to deal with this problem. However, this assumption seems defensible in light of convincing empirical and descriptive accounts of presidential powers over executive agencies (e.g., Meier 1993). Furthermore, I should be clear that I am not assuming that P = A. Rather, I am assuming that the agency’s preference are similar to those of the president. If the agency’s preferences are not similar, the president can choose to put a new agency head in place, moving the agency closer to his preferences. Finally, the main effect of assuming that the agency’s ideal point is located close to the president’s is to bias coefficients toward zero and to make it more difficult to find significant results for congressional variables in Regimes 1 and 3.[13]
Finally, including the president in the empirical analysis also presents a challenge. Because incorporating the president into a spatial model by allowing his veto to affect the actions of the legislature and the agency quickly complicates the model (see, e.g., Ferejohn and Shipan 1990, Spitzer 1990, Steunenberg 1992, Hammond and Knott 1996), I instead allow the president to be included in the empirical analysis in two different ways. First, I examine whether the president is able to influence the location of the agency in Regime 2. The logic here is that in this regime he does not need to compete with the legislature in order to influence the agency, as the agency can operate autonomously of the legislature. Consequently, it is in this regime that he is most able to use his powers to exert control over the agency.[14] Second, I take into account the ideology of the president who appointed the current agency head. Even if current political actors wish to influence the agency, it is possibly that the agency’s course has already been set, at least in part, by the president who appointed the sitting agency chair.
Thus, by utilizing a few assumptions and the theoretical model spelled out above, we can empirically estimate political influence on agencies. Once we know how to classify each observation, then we know which variables should matter. Table 1 spells out the conditions under which each regime occurs and offers predictions about the direction and significance of the congressional variables. Based on these results, the theoretical model calls for the following empirical specification:
Yt = b0
+ b1D1Committeet
+ b2D1Floort
+ b3D2Presidentt
+ b4D3Floort
+ biXi,t
+ et
where D1, D2, and D3 = 1 for Regimes 1, 2, and 3, respectively, and are equal to zero otherwise and where Xt represents a vector of control variables. Yt represents the level of agency outputs. As discussed in the next section, a greater value of Yt indicates a higher level of agency activity, and thus a more liberal level of activity. The predictions for the model are: b1 > 0, b2 < 0, b3 > 0, and b4 > 0.[15]
Table 1 about here
Estimating Political Influence on the FDA
The FDA describes itself as “[f]irst and foremost, a public health agency, charged with protecting American consumers by enforcing the Federal Food, Drug, and Cosmetic Act.”[16]
In order to pursue this mandate, the agency views its monitoring and enforcement activities as central to its mission. In fact, a former FDA commissioner characterized the agency as a “law enforcement agency” whose personnel “work long and hard to make sure that the companies that produce products under the agency’s jurisdiction comply with the federal Food, Drug and Cosmetic (FD&C) Act and other statutes enforced by FDA” (Benson 1991).
The agency has several options it can pursue when it finds that a firm is not complying with the FD&C Act or related laws. It can issue citations or criminal penalties, seek injunctions, seize products, engage in criminal prosecutions, issue citations, or safety alerts. Alternatively, the agency can persuade the company to voluntarily correct or recall a faulty product. The agency does engage in all of these activities, and the mix of these activities changes over time (Olson 1996a).
Prior to engaging in any of these enforcement activities, however, the FDA first must monitor the activities of the firms that it regulates. This is a large undertaking, as the agency regulates almost 95,000 firms across the country. To accomplish this task the agency uses two primary tools: it engages in on-site inspections and it collects and analyzes samples from regulated firms. Because these two monitoring tools can lead to penalties and sanctions, they are extremely important to the firms being regulated. These factors combine to make these activities a subject of political interest, as has been demonstrated by both qualitative (e.g., Quirk 1980) and quantitative (e.g., Carpenter 1996, Olson 1996a) analyses.[17]
To analyze the influence of political factors on the FDA’s monitoring activities over time, I have collected data on the number of inspections and samples taken from 1947 to 1995.[18] Initially I combine these activities into a single variable called “monitoring activity.” While a plausible case could be made for treating these variables separately (see Carpenter 1996), there is no theoretical reason to separate them. In addition, it is possible that these two variables can act as substitutes for each other. Thus, looking at each individually could produce biased estimates. Finally, it is worth adding that samples are not a subset of inspections. Although some samples result from inspections, many others are taken due to requests issues by firms, other executive agencies, or the public. The agency gets to decide which sort of monitoring activity to use; and absent any theoretical explanation of why we might expect the agency to engage in one rather that the other, it makes sense to combine them.
What factors might influence the quantity of monitoring activities in each year? First, there are the political variables discussed in the previous section: the preferences of the president and the committees and floors in each chamber. For estimates of these preferences, I relied on Common Space Nominate scores.[19] To determine the primary oversight committee in each house, I relied on interviews with FDA staff members and historians and an examination of committee hearings over time. In the Senate, primary oversight responsibility was held by the Agriculture Committee until 1954, by the Labor and Public Welfare Committee from 1954 to 1976, and by the Labor and Human Resources committee from 1977 to the present. In the House, primary oversight responsibility was held by the Energy and Commerce Committee.[20]
Once the ideology scores were obtained for the president, the median member of each committee, and the median member of each floor in each year, I was able to classify each year as belonging to a particular regime. For example, in 1990, the Nominate scores for the median members of the Senate floor, Senate committee, House floor, and House committee were 0.092, 0.352, 0.055, and 0.140, respectively.[21] Because the Nominate score for President Bush was -0.462, the preferences were arrayed such that P < FH,FS < CS(FS),CH(FH). This means that conditions were met for Regime 3, and the observation was classified accordingly (i.e., D1 = 0, D2 = 0, and D3 = 1). Overall, from 1947 to 1990, twenty years were classified as belonging to Regime 1, thirteen were classified as belonging to Regime 2, and the remaining sixteen were classified as belonging to Regime 3. Table 2 presents a list of the regime type by year, along with the aggregate number of monitoring activities per year.
Table 2 about here
Of course, in addition to these political and institutional variables, a number of other factors need to be controlled for. Olson (1995, 1996a), using an external signals model, identifies several factors that influence the activities of the FDA.[22] First, because monitoring activities are relatively high-cost activities, the agency’s budget should influence them. Second, she contends that industry size should be an important determinant of agency actions. Carpenter also argues that the budget and industry size need to be accounted for, and that an additional factor that should enter into the empirical specification is the 1962 passage of the Kefauver-Harris Amendments, which increased the agency’s regulatory authority and led to an increased level of activism.[23]
I controlled for these variables as follows. First, I included the agency’s budget in real dollars (using 1983 as the baseline). Second, I measured industry employment by controlling for the number of workers in the food and health industry in each year.[24] Third, to control for the Kefauver-Harris Amendments I created a dummy variable that was set equal to zero prior to 1962 and one from 1962 on.[25]
In addition to these variables, I included separate control variables for inspections and samples to account for different reporting methods used by the FDA at different points in time. Prior to 1975 the agency reported the count of all samples that it collected, whereas after 1975 the agency reported only the count of samples that were physically analyzed. Thus, in the analysis of samples I included a dummy variable that took on the value of one beginning in 1975, which I expect to have a negative and significant coefficient. Prior to 1955 the agency reported factory inspections, while from 1955 on the agency has reported established inspections. Thus, a dummy variable was included for inspections, set equal to zero from 1950 to 1954 and one from 1955 to the present.
Results
To estimate the empirical model I use the Prais-Winsten autoregressive technique. Because the data is time-series, autocorrelation is a potential problem. When the model is run using OLS, a Durbin-Watson test reveals that there is a high level of autocorrelation. Prais-Winsten produces more accurate estimates in such cases. It is worth noting that including a lagged dependent variable with OLS produces results very similar to those obtained using Prais-Winsten.
The results are presented in Table 3. Column 1 presents the results from the basic model, where the number of monitoring activities is a function of congressional committee and floor preferences in Regime 1, the president’s preferences in Regime 2, and the floor’s preferences in Regime 3. In this equation I also control for the agency’s budget, the Kefauver-Harris Amendments, the size of the industry, and the change in the FDA’s reporting methods as discussed above. The results provide general support for the theory’s prediction. Most strikingly, the floor has a negative and significant influence in Regime 1; but a positive and significant influence in Regime 3. The president in Regime 2 also has the expected, positive influence. The committee variable in Regime 1, however, is not significant. Finally, all control variables are significant.
Table 3 about here
In Column 2 I investigate whether the ideology of the president who appointed the current agency head is significant. The results of this equation are similar to those presented in Column 1. Once again we find general support for the model, with the exception of the committee variable in Regime 1. Column 3, in which I include dummy variables for each regime, thereby allowing the intercepts to vary by regime, provides additional support. In general, the results are robust with respect to the congressional variables and indicate that these variables influence the agency’s actions in the manner predicted by the model.
The analysis so far has used the aggregated number of monitoring activities as the dependent variable. The argument for doing so is that the agency, when deciding what to do, can choose to make inspections, take samples, both, or neither. Nothing in the theory presented here allows us to distinguish between these monitoring activities. Thus, I grouped them together.
Although there is no theory that allows us to predict which method of monitoring will be used under which conditions, there is an econometric technique that allows us to disaggregate the data. Seemingly unrelated regression (SUR) is appropriate when the error terms of two separate equations are correlated. Each equation can be estimated separately; however, doing so, in effect, throws away the information that comes from knowing that the two equations are related – and in particular that the error terms in the two equations are related (Greene 1990). In this case, the FDA can substitute inspections and samples for each other. Thus, if we were to regress each of these variables separately on the set of independent variables included in the previous table, we would be ignoring this possible substitution effect. SUR can be used to estimate the two equations jointly; and further, it provides a way to test whether the two equations are related.
I use the variables presented in Column 1 of Table 3. However, I must add one additional variable before proceeding. Table 3 uses an autoregressive technique, Prais-Winsten, to account for autocorrelation. Since I cannot use an autoregressive technique with SUR, I include a lagged version of the dependent variable for each equation to account for autocorrelation.[26] In addition, I use only the relevant dummy variable for each dependent variable.
The results using SUR are presented in Table 4. Once again we see that the variable for the floor switches signs, depending on the regime. In Regime 1, the sign of the floor is negative; in Regime 3, it is positive (although for inspections this variable is not significant). We also again find that the president exerts a positive and significant influence in Regime 2. Unfortunately, we also see once again that the committee variable is insignificant in the predicted direction. Finally, the correlation between the residuals of the two equations is 0.36. A Breusch-Pagan test of independence allows us to reject the hypothesis that the two equations are independent (p<.05). Thus, it is appropriate to estimate the two equations jointly, rather than independently.
Table 4 about here
Discussion and Conclusion
Several interesting findings emerged from the empirical analysis in this study. First, each equation proved to be an excellent fit, yielding high R2 values. Second, the models showed support for the notion that elected politicians affect the monitoring actions of the FDA. Third, the results are robust when we control for the ideology of the appointing president or allow the intercept to vary by regime.
While the results are generally strong, there are several obvious directions that future research can take. First, I assumed that the agency, while not necessarily sharing the same exact preferences as the president, was at least located in the immediate vicinity of the president. In other words, the agency was classified as belonging to the same regime it would be in if its preferences were equal to those of the president. Several alternatives to this assumption need to be examined. First, one potential way to deal with this empirically would be to allow the agency’s position to be a weighted average of the current president and the previous president, and to allow the agency to slowly converge to the current president’s preferred position. Another possibility would be to incorporate whether a president has installed a new agency chair.[27] Still others would be to incorporate public opinion or other factors affecting the likelihood of being in one regime or another.
Alternatively, a potential way of dealing with this statistically is to make use of switching regimes regressions (see, e.g., Hamilton 1990, Lee and Porter 1984). A growing literature in economics has used this technique to obtain unbiased and consistent estimates in situations where alternative regimes exist.[28] In particular, stochastic switching regimes models would seem to be especially appropriate here, where our inability to specific exactly the nature of the regime would benefit from the ability of such techniques to endogenously determine the nature of the regime.[29]
Similarly, we need to consider two elaborations of the model that would have implications for the empirical analysis. One of these elaborations would be to allow five regimes: one where the agency is autonomous (equivalent to the current Regime 2); one where the Senate floor and committee influence the agency’s actions (similar to the current Regime 1); one whether the House floor and committee influence the agency’s actions (also similar to the current Regime 1); one where the Senate floor alone influences the agency’s actions (similar to the current Regime 3); and one whether the House floor alone influences the agency’s actions (also similar to the current Regime 1). A second elaboration would be to include a presidential veto, and see whether doing so produces clean empirical predictions.
It is possible that one of these elaborations will help us identify why the committee variables are never significant in the results presented in this paper. This would be important for a number of reasons, one of which related to the debate in recent years about whether or not congressional committees are representative of the floor. A related question is whether the floor is made better off by committees that are representative of the floor. The theoretical model presented here indicates that if a committee and an agency are on the same side of the floor, then the floor has an incentive to make sure that the committee is as close to the floor as possible. The farther the committee is from the floor, the more likely it is that the agency will be able to locate far from the floor. Thus, in order to obtain an agency output in line with its own preferences, the floor should prefer committees to be inliers. Furthermore, the model shows that floors should have a strong preference for committees that are located on the opposite side of the floor from the agency. Only when this is the case – when committees and agencies are countervailing – can the floor exert a positive influence on the actions of the agency, or expect the agency policy outcomes to be in line with the floor’s preferences. Interestingly, although the model used in this paper assumes the absence of transaction costs, this finding is consistent with the results of Epstein and O’Halloran’s (1999) transaction cost model.
Several other features of the political environment might also be accounted for. Does the agency’s structure or location influence the ability of the current Congress to influence it? How should ex ante control be taken into account? While these and questions will no doubt lead to improvement in our understanding of contemporaneous influence, they can build on the analysis in this paper. In particular, this paper shows that the proper question is not whether Congress controls the agency, but rather when, and under what conditions, does control exist? Furthermore, it demonstrates that the notion of regimes can be used to shed light on questions of political influence.
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Table 1
Conditions for Regime Classifications
|
Regime |
Conditions |
Congressional variables expected to be
significant |
|
1 |
If P < CH(FH) < FH and P < CS(FS) < FS |
Committee (positive) Floor (negative) |
|
2 |
If P < FH < CH(FH) or P < FS < CS(FS) |
None |
|
3 |
If FH < P and FS < P |
Floor (positive) |
Note: Regimes are classified the same way for mirror images (e.g., if FH > P and FS > P, then Regime 3 exists)
Table
2
Regime
Type by Year
|
61112 |
109474 |
|||||
|
61336 |
120681 |
|||||
|
70639 |
180398 |
|||||
|
58891 |
180428 |
|||||
|
54210 |
89129 |
|||||
|
50969 |
84957 |
|||||
|
49022 |
8822 |
|||||
|
46204 |
81497 |
|||||
|
30630 |
76362 |
|||||
|
45935 |
94780 |
|||||
|
48114 |
90432 |
|||||
|
60532 |
78440 |
|||||
|
68533 |
69243 |
|||||
|
66518 |
78398 |
|||||
|
94582 |
58263 |
|||||
|
125532 |
72934 |
|||||
|
146532 |
78991 |
|||||
|
185521 |
74379 |
|||||
|
181540 |
75046 |
|||||
|
148804 |
79592 |
|||||
|
149034 |
76621 |
|||||
|
123907 |
93447 |
|||||
|
123245 |
84372 |
|||||
|
105858 |
74267 |
|||||
|
|
60436 |
Table 3: FDA Monitoring Activities (in thousands) |
|||
|
Variables |
Column 1 |
Column 2 |
Column3 |
|
Constant |
898.0*** (183.0) |
618.4*** (160.1) |
-- |
|
Committee (Regime 1) |
-18.3 (31.2) |
26.5 (32.8) |
-11.1 (28.6) |
|
Floor (Regime 1) |
-78.6** (44.0) |
-91.3** (38.2) |
-90.1** (44.4) |
|
President (Regime 2) |
59.2*** (15.4) |
81.6*** (25.3) |
31.2* (19.6) |
|
Floor (Regime 3) |
219.9** (103.6) |
178.3* (105.8) |
164.5* (118.8) |
|
FDA
Appropriations ($ million) |
0.29*** (0.072) |
0.35*** (0.072) |
0.289*** (0.068) |
|
Kefauver-Harris Amendment |
36.6*** (14.3) |
33.6** (17.8) |
28.8** (13.6) |
|
Food
Industry Employment |
-0.455*** (0.100) |
-0.291*** (0.088) |
-0.485*** (0.109) |
|
Health
Industry Employment |
-0.012*** (0.004) |
-0.016*** (0.004) |
-0.016*** (0.006) |
|
Samples
dummy |
-114.7*** (15.3) |
108.6*** (13.4) |
-119.4*** (16.6) |
|
Inspections
dummy |
-18.8** (10.6) |
-43.7*** (14.4) |
-6.02 (11.7) |
|
Nominating
President |
-- |
-43.0** (17.5) |
-- |
|
Regime
1 dummy |
-- |
-- |
944.0*** (197.1) |
|
Regime
2 dummy |
-- |
-- |
962.3*** (200.9) |
|
Regime
3 dummy |
-- |
-- |
957.2*** (199.6) |
|
n |
45 |
45 |
45 |
|
R2 |
.82 |
.82 |
.96 |
|
Rho |
0.32 |
0.41 |
0.34 |
|
Durbin
Watson (original) |
1.48 |
1.33 |
1.44 |
|
Durbin
Watson (transformed) |
1.81 |
1.71 |
1.75 |
|
Notes:
Prais-Winsten estimates. Numbers in
parentheses are robust standard errors.
*** denotes p < .01, ** denotes p < .05, and * denotes p <
.10 (one-tailed test) |
|||
|
Table 4: SUR Estimate of FDA Samples and Inspections (in thousands) |
||
|
Variables |
Samples |
Inspections |
|
Constant |
469.6*** (107.0) |
169.2** (73.1) |
|
Committee (Regime 1) |
-18.0 (19.4) |
-30.9*** (13.0) |
|
Floor (Regime 1) |
-71.0** (31.1) |
-45.1** (22.1) |
|
President (Regime 2) |
30.1** (8.2) |
24.1*** (6.0) |
|
Floor (Regime 3) |
90.9* (66.7) |
27.1 (48.7) |
|
FDA
Appropriations ($ million) |
0.145*** (0.038) |
0.039* (0.026) |
|
Kefauver-Harris Amendment |
10.7 (8.57) |
4.57 (5.68) |
|
Food
Industry Employment |
-0.246*** (0.058) |
-0.084** (0.039) |
|
Health
Industry Employment |
-0.002 (0.003) |
-0.005*** (0.002) |
|
Samples
dummy |
-83.8*** (9.80) |
-- |
|
Inspections
dummy |
-- |
1.51 (4.59) |
|
Samples
(t-1) |
0.291*** (0.075) |
-- |
|
Inspections
(t-1) |
-- |
0.346*** (0.116) |
|
n |
41 |
41 |
|
R2 |
.92 |
.74 |
|
Breusch-Pagan
test of independence (chi-square) |
5.167 (p=.023) |
|
|
Notes: Seemingly unrelated regression estimates. Numbers in parentheses are standard errors. *** denotes p < .01, ** denotes p < .05, and * denotes p < .10 (one-tailed test) |
||