But the relationship between public a number of phases or stages, each of which is vital for opinion and the public policy-making process is a difficult the overall. This paper reports the relationship between state policy and public . tation that state opinion would predict these policy decisions made decades later .. and Luttbeg, ), the "consensus" model (Sullivan, ), or the process of. policy structure. Federalism may make public responsiveness more difficult, and, by relationship between public opinion and public policy, a model which we believe captures much of . The process of amendment and veto is thus crucial.
Here, we look at the timing and frequency of states' grant applications, rather than the amount of money states requested from the federal government, because we believe that these decisions signal willingness to implement the ACA see Rigby for a similar argument.
Additionally, information regarding the timing of both policy decisions is readily available for us to code. Substantively, these two policy decisions vary on visibility and saliency, allowing us to test H1 and H3. Gubernatorial announcements and the implementation of the health insurance marketplaces received high media attention at both the national and local levels Gollust et al. Unlike decisions about the marketplace, grant activity received relatively less media coverage, likely because state health departments rather than politicians applied for these grants.
This may also explain why less visible policy decisions are not as responsive to public opinion. Because bureaucrats are often not elected, they may feel less beholden to public preferences. We anticipate the policy feedback mechanism and the opinion learning mechanism to be particularly influential for the timing of marketplace decisions and less so for the timing of grant applications.
Given the salience and media coverage of the ACA, we anticipate that gubernatorial announcements will influence policy preferences in nearby states H1. Similarly, because governors are sensitive to preferences in like-minded states, we expect changes in policy preferences toward the ACA in nearby states to influence gubernatorial announcements in the home state H3.
While both decisions are likely to be related to public opinion in the home state, we suspect that grant applications will be less related to changing preferences since these decisions are largely hidden from the public H2. Finally, while much is known about the determinants of state-level decision making regarding the types of marketplaces federal, state-based, or partnership e.
Thus, our article not only contributes to the extensive literature on policy diffusion, but also has implications for health policy scholars. Measuring State ACA Policy Decisions We rely on policy briefs from the Kaiser Family Foundation to measure the month and year in which governors announced their marketplace decisions starting in States differ significantly in both the type of marketplace exchanges as well as the timing of gubernatorial announcements as shown in table 1.
California was the first state to announce the structure of its marketplace in September By Mayall fifty states had announced their marketplace structure.
Data on the timing decisions of grant applications come from various reports by the Centers for Medicare and Medicaid Services.
Table 2 shows differences across the states in the type of grants and timing of applications. States were eligible to apply for grants roughly once every quarter, but were allowed to apply in multiple funding cycles.
While the majority of states applied for one grant during each cycle, table 2 shows that Massachusetts applied for two L1 grants during the last quarter of Additionally, more states applied for L1 grants compared to L2 grants. Given that L2 grants were relatively rare, the analyses below uses a measure that combines grant activity for L1 and L2 grants.
MRP produces accurate estimates of public opinion by state Lax and Phillips and congressional district Warshaw and Roddenand over time Pacheco ; We use the population frequencies obtained from the public use micro data samples in supplied by the census bureau for post-stratification. Adding a Time Component We add a time component by pooling surveys across a small time frame. We use a three-quarter moving average to estimate quarterly opinion toward the ACA. For instance, to get point estimates for Q1 in using a three-quarter pooled window, we combine estimates from Q4 inQ1 inand Q2 inand then perform the MRP technique on this pooled dataset.
The MRP process is repeated for each quarter after moving the time frame up a quarter at a time.
By pooling and taking the median estimate, the first and last quarters are missing. Pacheco shows that while there is a trade-off between the reliability of estimates and sensitivity to very short-term shocks, the efficiency benefits of pooling over a small time period outweigh the costs of biasedness.
Public opinion can play a positive role in policy making
The use of multilevel modeling and post-stratification overcomes two major problems that arise when trying to measure state opinion from national surveys. Post-stratification corrects for non-representativeness due to sampling designs by adjusting estimates so that they are more representative of state populations.
State favorability toward the ACA is generally low; on average across the United States and during this time period, only 46 percent of residents view the ACA favorably, which corroborates with previous research.
This national estimate, however, ignores significant variation across and within states; 73 percent of the variance in ACA favorability is across states and 28 percent is within states. In some states e. As shown in figure 1there is also movement in ACA favorability with some states declining in support and others experiencing bouts of increased support.
Recall that we expect state policy decisions that are highly visible to influence policy preferences in nearby states; more specifically, we expect the timing of gubernatorial announcements, but not the timing of ACA grant applications, to influence shifts in support elsewhere. To test for spillover policy effects, we employ traditional time series methods.
More specifically, we use an error correction model ECM. An ECM allows for the estimation of both short- and long-term effects of independent variables and tells us how quickly the system returns to equilibrium or the overall mean after being disrupted.The (public) policy making process
The dependent variable captures the changes in opinion toward the ACA. A lagged dependent variable is included to account for time dependence.
The main independent variables are the proportion of neighboring states in which the governor has announced an ACA decision and applied for grant applications. We control for a number of other factors that may influence changes in ACA preferences. First, we control for policy decisions in the home state with the expectation that there may be some policy feedback effects.
Specifically, we include measures of whether the governor in the home state has already announced the ACA decision and grant activity. We also include a measure of the type of exchange that a state announced since those that defaulted to the federal government are generally less supportive of the ACA Jones, Bradley, and Oberlander Finally, we include fixed unit effects e.
We also include panel corrected standard errors as suggested by Beck and Katz Results are shown in table 3. The coefficient on the lagged dependent variable gives the error correction rate with a value closer to zero, indicating a slow return to equilibrium. Consistent with the first part of the policy feedback mechanism, the model suggests that, as the proportion of neighboring states announce their ACA decisions, ACA support in the home state increases in the short run, but not the long term.
The coefficient on the differenced proportion of neighboring states variable gives the short-term effect of policy adoption on state public opinion. To get the estimated effect of a unit change in X, we simply multiply this effect with the coefficient. Although this effect is small, it is large if changes in neighboring policies occur in consecutive quarters. Surprisingly, neighboring grant activity also has a statistically significant effect on public support for the ACA in the short term.
For instance, the model predicts that if the number of grants applied for by neighboring states increased by two which is roughly two standard deviations above the mean changepublic support for the ACA also increased by about 2 percent e. If the policy feedback mechanism is true, then opinion should influence the probability of state ACA decisions; state officials should respond to the preferences of state residents.
To test the second component of the policy feedback mechanism, we employ event history analysis. The dependent variable in these models is the probability that state i will either announce their marketplace structure or apply for a federal grant in quarter t. For gubernatorial announcements, this variable takes a value of one in the quarter that the governor in state i announces the state's marketplace structure and a zero in all quarters prior to announcement.
For grant activity, the dependent variable takes a value of one in all quarters that a state applied for either an L1 or L2 grant and a zero otherwise. In the case of multiple or repeated events, it is important to control for a state's previous decisions Beck, Katz, and Tucker Accordingly, we include a count of the previous number of grant applications for each state.
To account for potential problems of non-independence of observations and of heteroskedasticity, we rely on the cluster procedure where observations are clustered by state. The main independent variable is state support for the ACA, as described in the previous section.
According to H2, as support increases, the probability for announcement and grant activity should also increase. We include the proportion of neighbors that have announced their ACA decisions and neighboring grant activity to account for the influence of other states.
Some states are highly involved in grant activity, and we expect for states with more resources to be particularly well suited to apply for federal grants. We control for gubernatorial partisanship using a binary measure which takes the value of one if the governor is a Republican and zero otherwise. The ACA is a highly partisan issue and Republican-led states may take longer to announce their marketplace structures or be less likely to apply for grants.
States with a larger uninsured population may be more proactive in implementing the ACA by announcing their marketplace structure early or may have greater need for federal funding assistance. To control for this, we include a measure of the percentage of uninsured state residents. We include several demographic measures that are often used in diffusion studies such as the natural log of the state's population size and the median income in the state.
We also include time and time squared. As shown in table 4ACA support influences gubernatorial announcements, but not state grant activity. More specifically, the model predicts that a state that has the highest level of support for the ACA at time t-1 has a probability of announcing the ACA decision that is twenty points higher than states with the lowest level of support for the ACA in the previous year.
It is interesting to note that including the public opinion measures does not completely account for the influence of neighboring states on gubernatorial announcements; states are also more likely to announce their decisions if neighboring states have already announced. This suggests the possibility that additional mechanisms of policy diffusion, besides the policy feedback mechanism, may be present. Turning to state-level grant activity, the majority of variables do not have a statistically significant influence.
We do find that states with a federal exchange were less likely to apply for grants. Consistent with our expectations, however, public opinion does not influence state-level grant activity. Overall, our results suggest modest support for the policy learning mechanism of diffusion.
While we find that gubernatorial announcements and grant activity exhibit spillover effects that increased support for the ACA in neighboring states, public opinion is only significantly related to gubernatorial announcements in the home state.
This generally conforms to our expectations that public opinion matters more for the diffusion of highly visible policy decisions, such as gubernatorial announcements of the ACA, compared to less salient policies, such as state-level grant activity.
For gubernatorial announcements, we use a dichotomous measure that is coded one if the governor in State A announces that it will adopt the same marketplace that has already been announced by State B's governor in a previous quarter, and zero otherwise. For the time periods after State A has announced its marketplace structure, the dependent variable is set to missing since State A is no longer at risk of moving closer to State B's policy decision. For grant applications, our dependent variable takes a value of one if State A applies for a grant in quarter t that moves it closer to the number of grants that State B has applied for by quarter t—1.
As with the previous grant application model, there is a possibility for repeated events whereby State A may apply for multiple grants which move its total number of grant applications closer to State B's total number of grant applications.
It is important in cases of multiple or repeated events to control for the number of prior events, so we include a count of the previous instances of State A emulating State B's grant activity as suggested by Beck, Katz, and Tucker Accordingly, in the gubernatorial announcement models, we exclude observations where State B has not yet declared their marketplace structure. In the grant application models, we exclude cases if State B has never applied for any grants or if State B has applied for the same number or fewer grants than State A.
According to the opinion learning mechanism, policy makers will consider the level of policy support in another state, at least in relation to their own citizens' policy support, when making policy decisions.
Therefore, our independent variable of interest is the similarity in public support for the ACA in the dyad. Since ACA decisions are more salient than grant activity, however, we do not expect to find the same effect for the timing of grant applications. We also control for several factors that may affect the similarity in state decisions. Of course, policy responsiveness is an institutional outcome. In parliamentary systems, this is fairly straightforward—the government can change policy directly, assuming that it does not face a realistic threat of a vote of no confidence.
In presidential systems, agreement across institutions usually is required, as in the United States. Presidential responsiveness to public preferences is conceptually quite simple: The president represents a national constituency and is expected to follow national preferences.
Congressional responsiveness is more complex, even putting aside bicameralism, as members of the legislature represent districts.
To the extent that they are responsive to public preferences, then, both the president and Congress should move in tandem, and predictable policy change is the logical consequence, even in the presence of divided government.
Here we have a good amount of evidence, as we have seen. How exactly do politicians know what public preferences are? Elections likely provide a good deal of information, but direct representation between elections requires something further.
Politicians may learn about preferences through interactions with constituents; they may just have a good intuition for public preferences Fenno, Polls likely also play a critical role.
This work is critical: Of course, politicians have other, more direct sources of information as well. The characteristics of domains appear to matter, for instance. Let us briefly trace the logic. Issue Salience In its simplest sense, a salient issue is politically important to the public.
People care about the issue and have meaningful opinions that structure party support and candidate evaluation. Candidates are likely to take positions on the issue and it is likely to form the subject of political debate. Politicians, meanwhile, are likely to pay attention to public opinion on the issue—it is in their self-interest to do so, after all.
Public opinion can play a positive role in policy making | Public Leaders Network | The Guardian
There are many different and clear expressions of this conception of importance. This reflects a now classic perspective see, e. When an issue is not very salient to the public, politicians are expected to be less responsive. As salience increases, however, the relationship should increase.
That is, to the extent that salience varies over time, the relationship between opinion and policy itself may vary. Though the expectation is clear, there is little research on the subject.
We simply do not know whether representation varies much over time. Indeed, we still do not know much about the variation in issue importance see Wlezien, This patterned movement in preferences is well documented in the United States Erikson et al. The pattern has led some scholars to conclude that the public does not have preferences for policy in different areas, but rather a single, very general preference for government activity e. From this perspective, measured preferences in various domains largely represent multiple indicators of a single, underlying preference for government action.
When compared with the more traditional perspective, this characterization of public opinion implies a very different, global pattern of representation. Some research shows that, although preferences in different areas do move together over time, the movement is not entirely common Wlezien, Preferences in some domains share little in common with preferences in others; these preferences often move quite independently over time.
In short, the work indicates that preferences are some combination of the global and specific—moving together to some degree, but exhibiting some independent variation as well. Not surprisingly, these domains tend to be highly salient to voters, the ones on which they pay close attention to what policymakers do. In other less salient domains, policy only follows the general global signal. In yet other, very low salience domains, policy seemingly does not follow preferences at all.
Institutions and Representation Polities differ in many ways, and some of these differences should have significant implications for the nature and degree of representation. Of fundamental importance are media openness and political competition.
Without some degree of media openness, people cannot easily receive information about what government actors do, and thus cannot effectively hold politicians accountable for their actions.
Without some level of political competition, governments have less incentive to respond to public opinion.
This essay critically assesses the role of public opinion in the policy-making process in Nigeria, and while it is acknowledged that responsive and genuinely democratic governments are hugely sensitive to the opinions of the citizens on issues of public policy, it is argued that this is not the case in Nigeria.
It is concluded that the extreme poverty and illiteracy which pervade the Nigerian society have emasculated and disempowered the majority of the people and made them inconsequential observers in the policy-making process in the country.
An approach to the study of organization of government 2nd ed. Pigasiann and Grace International. The subject matter of political science 2nd ed. College Press and Publishers Ltd. Foundation of political science. Eminue, Okon Effiong Public policy analysis and decision-making. Fundamentals of public administration revised ed. Internal politics and foreign policy