How can lobbyists impact the creation of the budget

It was and I was a summer intern with the Senate Budget Committee. The floor fight was unprecedented. The Senate Budget Committee had only just come into being—it, along with its House counterpart, was created by the sweeping Congressional Budget Act the year before. For a new committee to challenge the powerful Senate Armed Services Committee was shocking.

The Armed Services Committee chairman, John Stennis of Mississippi, was known for his legendary parliamentary skills, and the defense authorization bills reported out of his committee were seldom challenged. But Muskie wanted to make clear there was a new budget sheriff in town and he won. This was the moment that lobbyists realized they had to pay attention to the budget process.

Suddenly, lobbyists were interested—an interest that bordered on panic. And it would all be done using new concepts and terms and a very hard-to-understand process that most lobbyists never before had to grasp.

The calls, letters, and personal visits from lobbyists started the next morning. Budget committee hearings that up to that point had barely drawn a handful of Capitol Hill tourists were now so crowded with lobbyists from military contractors and government affairs liaison officers that it was often standing room only, with a line out the door of others waiting to get in.

That was the moment when lobbyists discovered the congressional budget process. It was also the moment when budget committee decisions free of lobbyist influence ended and action on the deficit, debt, and federal priorities started to be determined more by those who could devote resources to getting what they wanted than by national need and appropriate fiscal policy.

And researchers have found that donating to political campaigns can grant access to legislators that would not otherwise be given. Furthermore, if contributions can buy access to lobby, then lobbying victories would necessarily increase the incentive to engage in campaign funding. While not always successful, lobbying efforts have been found to affect legislative outcomes, especially in cases of preventing policy change.

A recent study of Wisconsin lawmakers found that lobbying has a significant effect on legislative outcomes, according to Daniel C. Lewis, an assistant professor of political science at Siena College. Wisconsin provides an excellent example for studying the effect of lobbying because state regulations there require lobbyists and interest groups to disclose which bill they are seeking to influence and their position on the bill.

By tracking the level of lobbying that occurs for and against a given bill and comparing it to the eventual outcome, the study found that lobbying efforts significantly affected legislative outcomes. One reason that lobbying cannot succeed in all instances is that lobbyists often are working against one another.

In those cases, the advantage goes to the lobbyist who is defending the status quo position, and stopping policy change from occurring is one area in which lobbying has proven especially effective. Maintaining the status quo is substantially advantaged, according to Amy McKay, a professor of political science at Georgia State University. In other words, a lobbyist seeking to maintain the status quo is more than three times as influential as one seeking change.

One explanation for the difficulty of enacting change comes from Stiglitz, who has noted that uncertainty and complexity play a large role in preventing policy changes that are good for all involved. One view of lobbying that is more generous than moneyed interests trying to persuade legislators to change their vote is the idea that lobbyists provide a legislative subsidy for their allies.

Whether convincing legislators directly or through their colleagues, however, the money spent on advancing policy preferences is still rent-seeking if the goal is special-interest favors. With billions spent on lobbying the federal government in , it is unlikely that businesses and other organizations are spending this amount of money without realizing some benefit. Indeed, one commentator has noted that if lobbying was not profitable, it is likely that more shareholder lawsuits would have emerged attacking the practice.

Instead, lobbying is conducted by firms looking to affect government policy and can be quite successful. Similar to other political activities, firms that have a greater stake in policy outcomes most commonly engage in lobbying. The effectiveness of lobbying shows up through the channels described above. It is associated with moving bills through committees, stopping policies from passing, and achieving ultimate legislative outcomes. Economic growth depends upon an efficient use of resources.

As this brief has outlined, however, rent-seeking is inherently inefficient because it diverts resources from potentially more-productive activities and thus imposes significant economic costs. Sadly, the evidence suggests that there is significant rent-seeking in the U.

Not only are large sums of money spent on campaign contributions and lobbying, the research indicates that these efforts can and do shape policy outcomes. To be sure, not all effort to influence policy is clearly rent-seeking and harmful to the economy, but at least some of the policy changes brought about by money in politics have been wasteful, inefficient, or directly harmful.

Additional research is needed to help clarify the scope of the harm that rent-seeking does to the U. Even worse, the economic costs of rent-seeking are likely to grow in the future. With the barriers that limit money in politics falling in the courts, it should be expected that even more money will be directed toward rent-seeking activities in the future.

Ruy Teixeira , John Halpin. Kristina Costa , Jitinder Kohli. Finally, federal contracts were more likely to be awarded to firms that have given federal campaigns higher contributions, even after controlling for previous contract awards. Rent-seeking Most economists agree that rent-seeking causes a net societal loss that harms the economy. The major economic concerns of rent-seeking can be categorized into three types of inefficiencies: Resources are wasted engaging in rent-seeking.

Policies sought by rent-seeking result in an inefficient use of resources. This grouping includes small business, pro-business, and international trade associations, as well as chambers of commerce. Business associations lobby on issues like labor regulations, intellectual property, product safety, and taxes, but mostly, lobbying efforts have focused on civil justice system reform.

Business associations want to make sure that damages awarded to plaintiffs involving torts or wrongful acts that led to legal liabilities are limited asbestos, medical malpractice, etc. Other important legal issues include business tax reform, including corporate tax policy and taxation of U. The top business association lobbyist in has been the U.

As you might imagine, the oil and gas lobbying sector is one of the most active lobbying groups. Lobbying efforts have historically focused on promoting legislators with pro-energy views in the areas of fossil fuel production.

This category includes all healthcare institutions : hospitals, nursing homes, hospice providers, and drug and alcohol inpatient centers. Lobbying in this industry was especially active in and again in with legislative actions involving health care and the Affordable Care Act. At present, lobbyist efforts in the sector are generally focused on fighting insurers over surprise medical bills and legislation to expand healthcare coverage with Medicaid and Medicare. As of , the companies that spent the most on lobbying were the National Association of Realtors, the U.

The main purpose of lobbying is to influence legislation in favor of a company or industry. No one individual would have the power to change or preserve legislation but through lobbying, industries can come together to pool their capital to make sure that the laws created by the government work in their favor.

Lobbying is a way for industries and companies to influence legislation in their favor. It is a big part of the U. The practice of lobbying has constantly come into question as many citizens believe that it changes legislation to favor big business as opposed to the average citizen. Open Secrets. Health Insurance. Your Privacy Rights.

To change or withdraw your consent choices for Investopedia. At any time, you can update your settings through the "EU Privacy" link at the bottom of any page. Furthermore, any observed bill characteristics related to the content of the bill are potentially correlated with the error term in the enactment equation because bill contents are endogenously determined.

In sum, the bill-level estimation results may be biased due to the combination of omitted variables such as lobbying expenditures on other related bills and endogenous selection of the bill content. To quantify the degree of such bias, I estimate the model using the bill-level data. In doing so, the specification of the model is slightly adjusted so that the initial enactment probability of a bill in equation 3.

Additionally, one could utilize the bill-level data to analyse the process by which policies are bundled into bills. Legislative bargaining and agenda setting have long been theoretically studied see, e. Unpacking the process by which lobbying efforts are converted into political outcomes in such a manner would allow one to gain a better understanding of the mechanisms at play.

This topic is left for future research. In this article, I have presented a unique approach to the empirical analysis of political influence by interest groups based on the specification and estimation of an all-pay contest with heterogeneous interest groups over policies considered in the US Congress.

One of the main contributions of this article is that I debut a novel unit of analysis: policies, which are parts of bills, rather than bills themselves as in previous works. This is particularly relevant for the study of lobbying behaviours because the content of a bill can and often does change throughout the entirety of the legislative process. I show that bill-level analyses which take the content of a bill as exogenously given can generate biased estimates of the effects of lobbying expenditures on policy changes.

Using a newly-constructed dataset that contains information on policies and lobbying activities, I have quantified the effect of lobbying expenditures on the probability that a policy is enacted, and estimated the average returns to lobbying expenditures for or against a policy. I find that the effect of lobbying expenditures on a policy's equilibrium enactment probability is very small.

In this study, I focus on energy policies and lobbying activities targeting these policies by energy firms. The approach developed in this article can, however, be applied to study the effects of lobbying in other policy domains.

The findings are closely related to the puzzle that the total amount of lobbying expenditures is relatively small when compared to the value of the government policies they are intended to influence. A similar observation regarding campaign contributions was made by Tullock and Ansolabehere et al.

If lobbying is a part of the economic activities of interest groups, one potential explanation for the puzzle is that the average returns to lobbying are small.

However, I find that the average returns are much larger than normal market returns. Furthermore, articles that look at lobbying expenditures and stock returns, such as Hill et al. This implies that lobbyists could charge the interest groups much more, but they do not. This suggests that significant frictions may exist in the market for policy influence.

One such friction is limited access to the market. Granted, in this article, I impose very minimal market frictions on the four energy lobbying coalitions. The only friction in the model is that the coalitions are supposed to incur the minimum initial lobbying costs. However, this almost unrestricted access to the market may be available only to certain firms and trade associations. Another friction in the market is related to political organization as described by Olson A further study on these potential frictions in the market can be very important to our understanding of the policy-making process and the welfare implications of the regulation of lobbying.

The dataset covers all bill sections that create, modify, or repeal a federal financial intervention or regulation whose main statutory subject is coal, oil, nuclear or renewable energy companies, or electric and gas utilities.

The challenge is to effectively winnow out all relevant bill sections from the pool of over 11, bills and joint resolutions that were introduced during the th Congress. By employing the following procedure, I select 2, bill sections that are contained in bills and joint resolutions. First, I divide all versions of bills and joint resolutions into sections as defined in the text.

With a program I coded for this specific purpose, I check each section to determine if its title includes at least one word related to the energy industry. The number of the words I include in my search is over ; all words are related to various energy sources coal, oil, natural gas, nuclear, and renewable energy , electricity, and environmental regulations.

Finally, I read each section in order to exclude the sections whose main statuary subjects are not coal, oil, nuclear, or renewable energy companies, or electric and gas utilities. Here, I describe the procedures to determine the unit of analysis—a policy—and its final legislative status, namely, whether or not it was enacted.

First, based on a vector space model, I represent the sections by corresponding vectors based on word frequency, and measure the distance between the vectors by calculating the cosine of the angle between them. When the cosine measure is 0, the sections have no similarity because it means that there are no words that exist in both sections.

On the other hand, when the measure is 1, the sections are equal because it means that all words used in one section are also used in the other section with the same frequency. Although the ordering of the words may be different, this is of less concern because bills are written in a formulaic manner. Second, I group the bill sections based on the measured distances.

I consider two texts whose distance is greater than or equal to 0. With this cutoff, it is reasonable to consider that the two connected texts are essentially the same. Third, using a Matlab routine to find connected components in graph graphconncomp. On average and based on the metric, 2. For example, creating a production tax credit for electricity produced from marine renewable resources appeared in thirty-two different bill sections in the exact same terms. The distribution of the number of bill sections that are categorized as one component is shown in Figure A1.

Finally, I combine some components if 1 they address the same policy issue and 2 they affect each of the lobbying coalitions in the same direction, either positively or negatively.

Two different policy proposals or bill sections are considered to address the same policy issue if they either amend the same section s of the US Code, or create a new section with the same or a very similar title. After this procedure, the components are re-grouped into groups, with each group representing a policy in the analysis.

On average, each policy appeared in about three different bills. The distribution of the number of bill sections that are categorized as one policy is shown in Figure A1. Table A1 shows the average and the standard deviation of the number of bills across which a policy moved during the two-year term of the Congress, conditional on the legislative status of the first bill and the last bill.

The legislative status of the last bill determines the final status of the policy. Most policies policies began with a bill when it was introduced to the Congress, while the remainder were inserted into bills after they were initially introduced.

Typically, a new section can be added to an original bill as the bill goes through the committee s and the floor of House and Senate. Most policies policies did not pass or were not reported by the committee s , although they were often reintroduced as a part of another bill. It can also be seen that those policies which were finally enacted were included in about 6 bills on average. In total, there are firms and associations in the energy sector which filed at least one lobbying report during — To overcome this challenge, I merge my dataset with the dataset compiled and cleaned by the Center for Responsive Politics CRP to determine the industry in which a lobbying client is involved and to figure out parent—subsidiary relationships and the changes in the names of companies, due for example to mergers and acquisitions.

I also did my own research on firms and trade associations by checking their websites and the website of Bloomberg Businessweek when the information in the CRP dataset was not sufficient. In the analysis, I designate certain firms and trade associations as strategic or major in lobbying the legislature on energy policies, and assume that they lobby cooperatively as lobbying coalitions.

The members of lobbying coalitions are listed in Table A2. This analysis is complementary to the policy tracking because I record whether or not a bill contains a similar policy issue discussed in the th Congress by actually reading the text, not by calculating the numerical distance between texts. Because this analysis requires careful reading of bill texts, not all bills were studied; only those that satisfy the aforementioned conditions were read and compared to the policies discussed during the th Congress.

Examples of these bills are S. Alternatively, one can use a generalized method of moments GMM estimator, as suggested by Imbens and Lancaster , based on the moment conditions that 1 the expectation of the first derivative of log-likelihood, or the score, is zero; and 2 the expectation of the difference between the observed total lobbying expenditures and the model-predicted total lobbying expenditures by each player is zero.

While there exists a theoretical guidance for an optimal weighting matrix for the GMM estimator so that the efficiency of the estimator is guaranteed, I do not have a counterpart for the estimator used in this article. As can be seen in the section on empirical results, the key parameters of the model are estimated with a high degree of precision. Furthermore, compared to this GMM estimator, the estimator used in this article is computationally less intensive.

If there are L interest groups, the total number of the profiles is 2 L. For each lobbying participation profile, I solve for the equilibrium lobbying spending profile. When solving the equilibrium, I use an algorithm derived from the proof for the existence and uniqueness of the second-stage equilibrium in Appendix B. There is no closed-form solution, but the proof is instrumental to compute the equilibrium. For each lobbying participation profile, I calculate the sum of the equilibrium payoffs of all interest groups.

Then I find the participation profile with the largest sum of the payoffs. In sections D. In section D. Finally, two sets of sensitivity analyses regarding the assumptions that I impose when constructing the data are presented in sections D. The key part of Assumption 1 is that the lobbying activities regarding one policy are assumed not to affect the enactment of another policy.

To effect this grouping, I rely on 1 the broad issue and 2 the positions of each energy lobbying coalition. There are fifty-eight unique broad issues, ranging from air pollution regulation of stationary sources to oil spill management. Based on this grouping exercise, I identify policy groups. I assume that each lobbying coalition decides its lobbying decisions on a policy group as a whole. Furthermore, if any of the policies within a group is enacted, the policy group itself is recorded to be enacted.

As a result, the enactment probability Using these policy groups, instead of policies in the main estimation, I estimate the model, and present the results in the third column in Table A4.

As can be seen in the table, the estimated effectiveness of lobbying is is proportionately much larger but still relatively small in magnitude.

In Alternative 1 , I use a different categorization of policies as described in section D. In Alternative 2 , I use a different policy enactment production function, whose specification can be found in section D. In Alternative 3 , a different equilibrium selection rule, as explained in section D. See section D. In the specification considered in the main text, the marginal benefit of lobbying is monotone in the initial enactment probability. Unlike the specification considered in the main text, the relationship between the benefit of lobbying participation and the initial enactment probability is the same for both supporters and opponents.

As a result, given this alternative specification, the initial enactment probability when there was only supporting lobbying is predicted to be similar to that when there was only opposing lobbying, if the effect parameters are symmetric.

In the main estimation, I assume a specific equilibrium selection rule, where the equilibrium that maximizes the sum of the payoffs of all players is chosen if there exist multiple equilibria.

In Table A4 , I show the estimation results based on a different equilibrium selection rule, where the equilibrium that minimizes the sum of the payoffs is chosen. As can be seen in the table, the results are very similar to those in the main estimation. I assume that the entry cost is observed by the econometrician. This estimate of the entry cost may not be a consistent estimate for two reasons.

First, the data are potentially truncated because an entity with small lobbying expenditures or revenues is not required to register and report to the government if certain conditions are met. However, this problem is mitigated by the fact that once registered, an entity is supposed to report its lobbying activities regardless of the amount of its total lobbying costs or revenues.

Second, the lobbying entry cost for a player in the analysis may be different from that of an entity. For these reasons, I show how the results may change as I change the value of the entry cost. First, the estimates of the parameters of the enactment production function are larger as the entry cost is set to have larger values. This is an expected result because to maintain the same participation rate given higher entry costs, the marginal benefit of lobbying should be larger.

Second, the average effect of lobbying expenditures on the enactment probability of a policy is very small in all cases, while on average, higher entry costs lead to higher effects. In the base estimation, the weight is set at 50—see section D. In Alternative 10 , I use a different rule for determining the policy positions for each lobbying coalition, which I explain in detail in subsection D.

Note : The last four rows show the difference between the total expenditures in the data and those predicted by the model at the estimated parameters for each coalition. To estimate the model, the sum of lobbying expenditures by each player on all energy policies is needed. However, in the data, I observe the sum of lobbying expenditures on all policies for each player. To determine the fraction of lobbying expenditures spent on energy policies, I use information on lobbying participation at the bill level.

First, for each entity that belongs to a player, I multiply its total lobbying expenditures by the ratio of the number of energy bills that the entity lobbied to the total number of bills that it lobbied. Then, I sum the obtained energy lobbying expenditures over all entities that belong to the player.

For each lobbying coalition or player, Table A7 shows both the total lobbying expenditures and the calculated total energy lobbying expenditures. This is consistent with the size of firms and organizations in each coalition: The bigger the firm or the organization is, the more likely that it is involved in lobbying a variety of issues such as general taxation and labor issues.

Although the bill-level lobbying information is the best available information for inferring energy-specific lobbying expenditures, it does not provide the exact amount of money spent on energy lobbying. In Table A5 , the estimation results based on both methods are shown, and they are very similar.

The position of a lobbying coalition on a specific policy is not always observed in the data, so I construct the position variable based on a variety of auxiliary sources of information.

As discussed earlier, this variable may contain a misclassification error. To address this issue, I construct an alternative position variable such that the estimated effect of lobbying can be maximal, and then re-estimate the model using this variable instead of the originally constructed position variable. It can be seen that the effect of lobbying would be estimated to be the largest if all participating players' lobbying was successful. Table A8 shows the frequency of taking a supportive position regarding a policy for each player, under both methods of constructing the position variable.

Since most of the policies in the dataset failed to be enacted, the frequency is lower under the alternative method. In Table A5 , the estimation results based on both policy position variables are shown.

Furthermore, the aggregate effect of lobbying is estimated to be 0. However, the difference between the estimates under both scenarios is not statistically significant. In addition, the extent of the estimates of the lobbying effects is relatively small even if I estimate the model with data where the policy positions are recorded such that the lobbying effects would be estimated to be the largest. To understand the extent to which we can generalize the estimation results to other Congresses, I simulate the model using the estimated parameters in Table 7 and the data on the policies that were considered during the th Congress and then re-considered during either of the subsequent two Congresses.

There are policies that appeared during both the th and the th Congresses, and sixty-six policies that were considered during both the th and the th.

While the observable characteristics of these policies are invariant between Congresses, data on public opinion does change from year to year and is incorporated here. For some policies, polling data changes dramatically over the period of the study. For example, there are no polling questions on oil spill regulation during the th Congress in my data, while there are many questions on that policy issue during the th.

Table A9 represents the simulated moments as well as the corresponding observed moments. Overall, the model fits the data well. For example, the model predicts the level of lobbying participation by the coal and the oil and gas coalitions reasonably well.

Although the rates of lobbying participation by the nuclear and the renewable energy coalitions are over-predicted, the lobbying participation patterns broken down by policy positions match well both in terms of the level and the trend.

Note : The moments are calculated over policies for the th Congress, and 66 policies for the th. This article is based on my Ph.

I am greatly indebted to my advisor, Antonio Merlo, and to Kenneth I. Wolpin, Hanming Fang, and Flavio Cunha for their guidance, support, and insight. Louis, and the Yale School of Management. Google Scholar. Google Preview. BAIK K. HONG H. RYAN S. BAYE M. CHE Y. GALE I. HALL R. HILL M. NESS R. IGAN D. JIA P. KATZ E. MIAN A. SUFI A. WONG S. RIAZ K. WONG A. YANG C. For example, de Figueiredo and Silverman estimate the elasticities of the amount of academic earmarks to universities with respect to lobbying expenditures, implicitly assuming that if a university does not lobby, it will receive no earmarks.

Having this assumption may result in overestimating the returns from lobbying. They also assume that there is no competition between universities for earmarks, which may further bias the results.