Do Firm’s Organisational Slacks Influence the Relationship between Corporate Lobbying and Corporate Financial Performance? More Is Not Always Better

Abstract

A political involvement in any organisation has often proved to be profitable for such firms that are seeking support and favourable regulatory conditions. Though many studies have investigated the effect of the corporate lobbying activities on the organisations, no clear results have been achieved. In this study, we have investigated the lobbying expenditure of some of the most famous United States (US)-based companies, which support the U.S. government during 2007–2016. Primarily, we tested the relationship between the corporate lobbying and the Corporate Financial Performance (CFP), with the help of a dynamic panel data analysis, which is based on the System Generalised Methods of the Moment (SYS-GMM). The results of this study indicated that the corporate lobbying did not increase the probability of gaining more support from the government in comparison to the firms that did not use any lobbying techniques. Furthermore, the findings showed that corporate lobbying is a component of the zero-sum political agenda that cannot be accurately evaluated and does not contribute towards the improvement of the CFP. This study introduced the important component of organisational slack and noted that the corporate lobbying could significantly destroy the CFP if the organisational slack was high.

1. Introduction

Lobbying, of any type, is always used for acquiring some political advantage in the United States (US). Even the Congressional historical records have stated that the American Revolutionary veteran groups lobbied the Congress during the 1790s, which was much before any primary lobbying-related regulations were implemented in 1876 (Byrd 1991). Currently, the growth and the scale of the lobbying practices are very substantial. In the USA, the political campaign costs have increased significantly. The report submitted by The Centre for Responsive Politics (https://www.opensecrets.org/lobby/) stated that the lobbying costs that were spent by the various organisations in 2016 were >$3.2 billion, where the Top-5 firms were responsible for 40% of the overall costs. Amongst the Top-5 companies, Gump et al. spent ≥$36 million, followed by the Podesta Group ($24 million), Van Scoyoc Associates ($21 million), Holland and Knight ($19 million), and the Squire Patton Boggs ($18 million). Many organisations partake in lobbying. In comparison to the alternative techniques of seeking a political influence, like the contributions made by the corporate Political Action Committees (PACs), many organisations used the lobbying activities to acquire a political advantage (Chen et al. 2015). The corporate lobbying involves the various political activities that are employed by the corporations to influence the legislators at different governmental levels. At the federal level, the corporate lobbying includes any form of communication which is made on the behalf of the client to the various members of the Congress, like the staffers, White House staff, The President, or other high-level employees regarding the modification, formulation, or the adoption of any legislative decision (The Centre for Public Integrity).1 This was controlled by the Lobbying Disclosure Act of 1995.

Many economists have stated that the firms that lobbied for political connections could acquire many economic benefits (Roberts 1990; Stratmann 2005; Fisman 2001; Jayachandran 2006; Faccio and Parsley 2009; Hochberg et al. 2009; Igan et al. 2009; Richter et al. 2009; Cooper et al. 2010; Yu and Yu 2011; Hill et al. 2013). Also, Richter et al. (2009) suggested that an important factor that led to corporate lobbying included the desire to acquire subsidies for the R and D sector, which was considered to be a very risky investment when compared to the capital investment. Earlier studies indicated that engaging in any political process could act as insurance against the economic crisis. Faccio et al. (2006) noted that the firms that had a political connection in 35 countries were very likely to derive government bailouts during economic distress as compared to the non-connected organisations. The rationale behind this observation was that, since the government actions and regulations directly affected the company’s business condition, political lobbying could help the companies to derive important information regarding the regulatory agenda, acquire political information for altering their business decisions in time, and encourage (or discourage) the regulatory decisions that could be beneficial (or detrimental) to the company, if possible. Due to regular communication, this information helps the government make better decisions. However, corporate lobbying acts as an instrument by the organisations for interfering in the policy formation process, which directly affects the lobbying interests. For instance, the various companies involved in the controversies, scandals, or lawsuits (like Phillip Morris, WorldCom, Enron, and Halliburton) had spent a lot of money on lobbying (Chen et al. 2015).

It is very surprising that the various empirical studies ignored the effect of corporate lobbying on the financial performance of the organisation. Lobbying was seen to be the biggest type of political involvement in corporate America. With regards to its financial effect, corporate lobbying was 20-times higher than the PAC or the soft-money contribution made every election cycle. Furthermore, many corporations, business-related groups, or trade associations are responsible for the huge amount of money spent on lobbying. Many individual organisations also spend a lot of money every year. Additionally, many researchers, investigating the corporate political activities, have investigated the different types of expenditures by the corporate-affiliated PACs. This also involved the problem of limited variation and a narrow political activity, i.e., sponsorship of the PACs (Andres 1985; Masters and Keim 1985; Grier and Munger 1991; Grier et al. 1994; Humphries 1991; McKeown 1994; Mitchell et al. 1997). In their study, Milyo et al. (2000) described the “inordinate attention to the PACs”, by stating that the data regarding the contribution was available and the PACs could be linked easily to their industrial or corporate sponsors. Furthermore, Sabato (Morris and Sabato 1984) noted that the PACs are formed by the groups that are not capable of establishing a governmental affair or a legislative lobbying department. On the other hand, many companies have a good lobbying division and no PAC. Brasher and Lowery (2006) warned that this type of investigation was narrow and suggested that the empirical studies regarding the PAC behaviour could not be generalised, but they should have focused more on corporate lobbying. Ansolabehere et al. (2003) observed that the major distinction was that since the PAC finances are because of individual contributions and not by corporations, the PAC expenditure cannot be considered as ‘corporate’. However, corporate lobbying is considered to be a corporate expense, which helps in improving the firm’s performance. Hence, many corporations indulge in corporate lobbying. Similar to the alternative techniques of acquiring political influence, like the PAC contribution, lobbying is another popular method that is used by the corporations for exercising the political strategies (Chen et al. 2015). Based on these parameters, in this study, we have focused on the effect of the corporate lobbying on the financial performance of the corporations.

Here, we have also determined the dominating effect amongst the above-mentioned countervailing forces, and the circumstances surrounding it. Firstly, we have analysed the corporate lobbying expenses and compared them to the PAC contributions. We attempted to determine the causal effects of the corporate lobbying on the Corporate Financial Performance (CFP). We analysed all of the firms from which they could obtain the financial data, including many firms that were not very politically active. This helped us to derive the benefits from the data’s panel structure, which helped us further control the various time-invariant and firm-specific variables after we had compared the firms that lobbied and those that did not. A few researchers (Nichols 2007; Angrist and Pischke 2010) stated that this technique was used for addressing the bias that occurred because of unobserved confounders, which were commonly seen in the studies that rely solely on the observational data. This study also used another identification strategy, wherein another moderating variable was introduced for examining the effect of the organisation’s slack on the association between the CFP and corporate lobbying. Here, we evaluated the impact of the lobbying activities on the CFP, while controlling the inherent endogeneity by using many alternative methods.

We have also postulated that the total costs (or benefits) of the corporate lobbying varied with regards to the organisational slack. The slack resources included the spare assets and the capabilities of the company, which could be reclaimable for the re-deployment. It also includes the hidden and underutilised spare resources that can be reused and recaptured for many tasks. Though several governmental policies and regulations affect the firms, the marginal advantage derived due to the lobbying activities can be determined by the different factors that affect the demand and supply of the firm’s products or services. Hence, every lobbying activity shows a different effect on the CFP. Very few studies used this approach. In this study, we examined the association between the corporate lobbying activities and the CFP based on organisational slack.

We used the data from various Fortune magazines’ American Most Admired Corporation (AMAC) firms for a 10-year period (2007–2016) and evaluated whether the corporate lobbying was advantageous to the shareholders. We also determined if the organisational slack controlled the association between the CFP and the corporate lobbying. This study showed no significant relationship between the CFP and the corporate lobbying activities. Furthermore, this neutral association could also be partially due to the fact that the lobbying expenditure by the company was unable to provide a few tangible benefits or helped the firm acquire additional governmental contracts and improved the probability of getting the bill passed in the Congress. This indicated that agency expenditure (i.e., an inefficient use of the corporate funds) for the lobbying activities dominated the strategic benefits of the lobbying. Secondly, we also noted the positive relationship between the lobbying activities and the organisational financial slack on the CFP that firms’ organisational slack acts as a moderator of the impacts of corporate lobbying on performance; firms that are efficient in utilizing their available resources will be able to register better links between corporate lobbying and firm performance.

These findings showed significant implications. Firstly, the results indicated that the lobbying costs were higher than the benefits, thereby supporting the agency’s perspective in comparison to the stewardship viewpoint. Secondly, the results highlighted the endogenous and dynamic nature of the association between the CFP and the lobbying activities. These results suggested that the control of the endogeneity was important for investigating the effect of the various lobbying activities. Thirdly, we noted that the impact of the lobbying activities was heterogeneous for the firms and that the moderating variable of organisational slack could enhance the relationship between the CFP and the lobbying activities.

The remaining paper is organised in the following manner: Section 2 includes the literature review and the hypothesis development, whereas Section 3 discusses all the acquired data and the methodology used. Section 4 and Section 5 describes and analyses all of the empirical results, while the discussions and conclusions are presented in Section 6 and Section 7.

2. Literature Review and the Hypotheses Development

2.1. Literature Review

Many lobbying activities help in seeking protection against the foreign competition. In his study, Schuler (1996) has stated that the lobbying activities in the U.S. steel industry helped to shape the trade policy. Also, Gawande and Bandyopadhyay (2000) mentioned that the U.S. protection pattern was influenced by the lobbying expenditure and the lobbying competition, thus, the protection was ‘sold’. Lee and Baik (2010) made a similar argument where they stated that the companies that were engaged in the lobbying activities could buy trade protection against the foreign competition. Many academic institutions also engaged in lobbying activities. De Figueiredo and Silverman (2006) noted that, during particular circumstances, the academic institutions acquired an average return of $11–36 for every dollar that was spent for lobbying. Lastly, many firms (and/or the associated individuals) that contributed to the various political campaigns and acquired significant subsidies from the successful administration had to face several allegations (i.e., Solyndra incident). On the other hand, no conclusive empirical evidence is present, which shows the relationship between the lobbying activities and the CFP. Many studies have shown a negative/positive or neutral relationship between the lobbying activities and the CFP.

2.1.1. Positive Association

According to Chen et al. (2015) and Brown (2016), lobbying could positively affect the CFP, wherein the organisations engaging in the lobbying activities could derive better returns in comparison to the firms that did not. Hill et al. (2013)noted that the shareholders incorporated the lobbying activities pursued by the managers in the share prices of the company. Alexander et al. (2009) showed that the firms that lobbied for the American Jobs Creation Act (2004) acquired $11–$36 for every dollar that was spent for lobbying. De Figueiredo and Silverman (2006) provided an empirical evidence for this scenario, wherein a majority of the educational institutions did not receive any return for their corporate lobbying activities. Fisman (2001) used a novel event study-based approach for investigating the 1997 Indonesian financial crisis and estimating the effect of the political connections on the Indonesian firms. The researcher noted that the politically-connected companies could derive benefits due to their connections. Many studies have indicated that the political connections helped the companies to acquire additional benefits, like regulatory reforms (Stigler 1971), a better tax regime (De Soto 1989), and a preferential treatment by the various government-owned organisations (Backman 1999).

2.1.2. Negative Association

Many studies provided empirical evidence that supported this negative association. Furthermore, the political connections and lobbying activities also have many detrimental effects on the companies, especially during political turmoil. For instance, Gul (2006) noted that, after the occurrence of the Asian financial crisis, the politically connected companies in Malaysia had to pay a higher audit fee in comparison to their non-connected associates. The researcher stated that the Malaysian government could not financially support the politically-connected firms after this financial crisis. Aggarwal et al. (2012) observed that the companies which donated funds to the political candidates had to face many cash-flow issues, which showed long-term negative effects. The researchers also noted that these donating companies engage in a higher acquisition, which was characterised by a lower return in comparison to other acquisitions that were made by the non-donating companies. Hadani and Schuler (2013) also observed that the company’s political investments were negatively associated with the market value of the company and the returns on the sales. The researchers claimed that the personal managerial bias and imperatives, in addition to the firm-level profit-maximising objectives, drove the company’s political behaviour. Aggarwal et al. (2012) measured the political activities of the companies, but excluded the lobbying activities, whereas Hadani and Schuler (2013) combined all political activities, like the lobbying and the different PACs. The lobbying expenditure and the political donations are different forms of political investment with regards to their influence and scale of expenditure (Yu and Yu 2011; Aggarwal et al. 2012). Coates (2012) also observed a negative effect of the lobbying activities on the corporate value.

2.1.3. No Impact or a Mixed Outcome

Two studies did not observe any relationship between the CFP and the corporate lobbying. In one study, Ansolabehere et al. (2004) did not observe any benefit from the various campaign donations, whereas Hersch et al. (2008) stated that the lobbying activities did not generate any financial capital. Two studies determined multiple outcomes and noted a mixed outcome. Faccio et al. (2006) mentioned that the politically connected firms could pursue the bailout policies in their favour, but they showed a poor operational performance. Tu et al. (2013) observed that, although the connected firms had to a pay a lower premium than the other firms during the privatisation, they showed a lower stock and operating performance during the post-privatisation period.

2.2. Organisational Slack

The basic organisational slack definition presented by Bourgeois (1981) and which has been used by many other studies (Meyer 1982; Sharfman et al. 1988), stated that:

‘Slack includes the potential or actual resources which help any organisation successfully adapt to changes. It also provides a means for the organisations to adopt certain strategies to the external environments’.

(p. 30)

These resources comprise a surplus of inputs, such as labour and equipment, which are not functioning at maximum level due to unwise investments in technology know-hows that could have generated better profits and incomes (Meyer 1982; Tan 2003). The definition by Bourgeois (1981) indicates that companies may amass slack resources for future use in case of any environmental changes. Furthermore, these slack resources help organisations to waive short-term returns in support of longer-term profits by generating avenues for chasing current and future competitive prospects (Sharfman et al. 1988). This is because slack resources can be used in an elective fashion for determining competitive and market prospects, although some researchers consider these slack resources unnecessary (Dimick and Murray 1978). However, some organisational slack could be beneficial to the company, wherein a company can develop a financial cushion that can shield the company against any cash flow deficiency or unexpected losses (Rajagopalan and Spreitzer 1997).

Slack resources can help firms to implement strategies for improving efficiency and profitability and to prosper in the long run (Nystrom et al. 2002; Tan and Peng 2003; Adams and Lamont 2003; Bogetoft and Hougaard 2004), because such resources, although not optimally deployed, can provide the mechanisms for effective learning, thereby providing the potential for further enhancements of alignment skills (Levinthal and March 1993). Sharfman et al. (1988) point out that slack resources are especially important during turbulent times since slack resources can buffer the technical cores of corporations during such turbulence (Moreno et al. 2009). This is because slack resources offer organisations a surplus of time as well as the possibility of experimenting with these resources (Geppert 1996). Together, these studies support the notion of Bourgeois (1981), who states that slack represents resource cushions that can be used to counteract threats as well as develop opportunities.

The emerging concept of slack in financial resources was introduced in Barnard’s (Barnard 1938) ‘inducement-contribution ratio’ scheme. Nevertheless, the model failed to secure recognition, until Cyert and March (1963) applied the financial resource slack concept to management models and practices. Organisational slack was utilised to describe the strategic and political behaviours of top management teams (Bourgeois and Singh 1983), their risk-taking behaviours (Bromiley 1991; Palmer and Wiseman 1999; Singh 1986; Wiseman and Bromiley 1996), environmental initiatives (Bowen 2002), and the performances of firms (Greenley and Oktemgil 1998). Discretionary organisational resources enable firms to develop slack (Seifert et al. 2004) and organise uncommitted resources into outputs (Nohria and Gulati 1996). For an organisation with various slack resources (i.e., excess labour and raw material, excessive work-in-process inventories, surplus productive or plant capacity), the most discretionary of slack resource types would be the excess or slack in financial resources (Austin et al. 1996). Slack in financial resources is defined in this research as utilisable financial capital that could be diverted or else allocated by an organisation in pursuit of its goals (George 2005). Such capital is typically denoted by measures of the capital at hand (namely, net profits after optional expenses and taxes), which is normally treated as a key corporate monetary resource (Austin et al. 1996). The available literature on societal engagement contends that measures of profitability remain the strongest indicators of the “availability of resources for the potential funding of social and environmental investments” (Julian and Ofori-dankwa 2013, p. 1321). Nonetheless, there is a continuing argument on the ideal level of organisational slack, since some academics believe that a ‘surplus of slack’ is linked to a lack of competence (Wu and Tu 2007).

The idea of the organisational slack was based on the concept of the inducement-contribution ratio, which was initially proposed by Barnard (1938). Cyert and March (1963) were the first to apply this concept to the management-related theories and practices. Organisational slack can be defined as the resources that are acquired by an organisation and are not included in the necessary expenditure. On the other hand, some resources have to be used in an unrestricted manner. This also includes many techniques in which the energy and the resources are used for pursuing some organisational objectives that can be channelled into many other things. If the resources have to be considered as slack, then they must be easily visible to the company manager and should be deployable during need. However, other resources are more recoverable and visible, while some are not. Also, different slacks require a different level of flexibility and discretion. As the discretionary level of the resources increases, they are used in many scenarios and contingencies, and they also provide a broader range of tools that can be used by the managers (Sharfman et al. 1988). Conversely, the less discretionary resources are used as a protection in some scenarios, i.e., during rising demands or an excess capacity. Finally, organisational slack is a capital-based firm resource (i.e., human resource, physical, financial, and organisational), which is used by the firms for implementing strategies and improving the organisational efficiency and/or the effectiveness. It is also used as a mechanism for effective learning, which provides a potential for enhancing the alignment skills.

Many studies showed that the firms having higher organisational slacks could engage in some political activities (Schuler and Rehbein 1997) and derive the associated benefits (Hillman et al. 2004). Also, the slack resources were seen to significantly affect the corporate political activities, like lobbying. Furthermore, studies observed that the organisations were more probable to follow the legitimate practices if they had higher financial resources (Seifert et al. 2004). If the organisations aim to influence the different legislatures and lobby for acquiring legitimacy from the administration, then, the firms with a better performance and/or higher slack resources were very likely to mobilise the resources and use them for lobbying.

The organisational slack resources can be measured using a ratio of free cash flow (FCF) to revenue, for approximating the organisation’s liquidity (Griffin and Mahon 1997). Cash flow was considered to be more suitable to measure organisational slack as compared to accounting return in this study, since cash flow denotes the uncommitted money that is accessible for corporate lobbying and other discretionary activities. Numerous companies have profit but lack cash flow (George 2005). Here, this study converted the FCF value to its log value. This variable was generally skewed and it could violate the assumption of normality. This study used the DataStream database for determining the FCF value and the formula is as follows:

FCF = (net profit + interest expenses + non-cash expenses) − increase in the working capital − capital expenditure

Organisational slack = FCF/total of net sales

2.3. Hypotheses Development

2.3.1. Corporate Lobbying and the Firm Performance

According to the stewardship theory, corporate lobbying is an integral component of the organisational strategy. This theory also states that the corporate political activities must positively affect the company’s profits. Based on this theory, the lobbying expenditure has a higher Return on Investment (ROI). In their study, Alexander et al. (2009) mentioned that, under specific circumstances, lobbying generated a 22% ROI. Empirical evidence showed that firms, which conduct lobbying-related activities for achieving a specific goal, are generally successful. Many individuals or groups who are affected by the current or the pending governmental activities or legislations provide vital information to the government about the various lobbying activities. This information helps the government to make better and informed decisions. Some studies noted this relationship in other scenarios. Richter et al. (2009) observed that the lobbying was related with a low corporate tax rate in a majority of the publicly-traded companies. Lee and Baik (2010) noted that the disbursements provided by the U.S. government to the domestic firms in the anti-dumping petition were related to the lobbying activities. Bonardi et al. (2006) investigated some U.S. electric utilities and noted that the firm’s lobbying activities were related to the permitted rate of returns by the Public Utilities Commission (PUCs). According to Brown (2016), the lobbying activities were associated with a high Return on Assets (ROA), Return on Invested Capital (ROIC), and a Return on Equity (ROE) in some of the Fortune-500 firms. Hence, we proposed that:

Hypothesis 1.

Corporate lobbying has a positive effect on the CFP.

2.3.2. Corporate Lobbying, Organisational Slack, and CFP

In this study, we also investigated the effect of the organisational slack on the association between the CFP and the lobbying activities. As mentioned above, the slack resources are described as excessive resources that generate some organisational output (Nohria and Gulati 1996). The buffer argument stated that the presence of organisational slack is related to the increase in the corporate lobbying. The proponents of the slack theory argued that this encouraged the strategic behaviour, helped the companies adapt to the new environment, fostered a long-term thinking, and helped in the exploration of many uncertain investment opportunities, which would otherwise not be used (Burgeois and Singh 1983; Rajagopalan and Spreitzer 1997). A higher organisational slack is related to high data acquisition costs and difficulty in monitoring, while economists agreed that lobbying protected the company’s benefits. Hence, many companies conduct lobbying for acquiring the governmental support and surviving in the market (Melitz 2005). Some researchers (Richter 2010; Houston et al. 2014) stated that the political influence increases the firm’s access to the debt and the equity finance. Hence, many firms lobby for improving their access to the capital. The availability of the organisational slack is an important criterion for developing a corporate political strategy. The bigger firms have the necessary slack resources for conducting corporate lobbying and influencing the legislators at different governmental levels. Also, firms with a higher organisational slack can exploit the scale economy-related benefits and generate a significant effect on the CFP. Hence, we proposed that:

Hypothesis 2.

Corporate lobbying is more positively related to the CFP when the firms have a high organisation slack and less positively when organisation slack levels are low.

3. Data and Empirical Design

3.1. Data and Sample Selection

In this study, we have focused solely on America’s Most Admired Companies (AMAC), since the data for such companies is very easily available. A new list for the Fortune companies is established every year in March (in 2017, 341 companies listed) and the data was collected for a period between 2007 and 2016. We collected lobbying expenditure data for the period between 2007 and 2016 for the firms from the Centre for Responsive Politics (CRP). The CRP is a non-profit group that operates in Washington D.C. and studies the effect of lobbying activities on the legislative procedure. This group maintains a public access database for the lobbying expenditure and campaign contribution at www.opensecrets.org. The CRP also provides the lobbying expenditure of the publicly-traded firms. This study manually compared the data for all the firms for determining which of the firms were included in the Fortune list for 2007–2016. In order to avoid from the error of sample selection bias, this study does not need a balanced panel. Therefore, the number of firms in our sample dataset varies from year to year and the estimation approach uses as many observations as available. Besides, to exploit the dynamic dimension of our database (i.e., to include the lagged dependent variable) we have to observe firms over at least five consecutive years. We combined the data with the DataStream for extracting the public firm-related data. As the CRP did not use any company identifiers, we manually verified the names of all public firms for ensuring that the CRP lobbying and DataStream data matched and excluded those firms that were without provide complete information. Finally, due to a lack of relevant data for the CFP and lobbying expenditure, eventually, this decreased the sample population to 134 firms. Our final unbalanced panel sample comprises 2494 observations over the 2007–2016 periods.

3.2. Empirical Design

Here, we developed a dynamic panel model of the lagged levels for the dependent variables and have used the two-step Generalised Methods of the Moment (GMM) technique (Blundell and Bond 1998; Blundell et al. 2001) along with the error correction system (Windmeijer 2005). The use of this methodology could be justified based on the fact that the conventional fixed effects estimator was biased when the lagged dependent variable was used as a regressor. Furthermore, this technique also takes into consideration the probable endogeneity of a few dependent variables. The researchers stated that the alternative consistent estimators having a lagged dependent variable are valid only if the explanatory variables are exogenous.

They also used a novel methodology, i.e., system-GMM, for two main reasons. First, it is possible that the CFP is endogenous and it affects the corporate lobbying activities that are conducted and it is also simultaneously affected by the corporate lobbying. Second, many relevant factors motivate the corporate lobbying; however, they are not included or observed in the model. All these factors include various intangibles like the management quality and the company attitude. In this model, we considered that the ROA, ROE, and ROA were endogenous, since these factors responded to the changes in the corporate lobbying variable, which was pre-determined. Furthermore, the system-GMM method shows the minimal bias and the highest precision when the N factor in the data (in our study refers to the number of the firms) is high and the series is moderately or very persistent, in comparison to other commonly used estimators, i.e., a fixed or differing effects (Soto 2009).

Corporate lobbying requires a lot of expenditure within a short period of time, while the results with respect to governmental support are realised later (Chen et al. 2015). This delay in the gain of the lobbying activities indicates that the lobbying initiatives require significant internal or external changes with regards to their restructuring and reorganising, to help the firms become more familiar with the novel corporate political strategies. The dynamic effects can be incorporated in the panel data model by including the lagged dependent variable. This was seen to be a common strategy that was used by several researchers who investigated the firm performance in the industrial organisations (Chen et al. 2015; Keele and Kelly 2006). The introduction of a lagged dependent variable indicates that the standard estimators are highly inconsistent. The consistent estimators are determined with the help of the GMM approach, which was described earlier (Arellano and Bond 1991). It converts the equation into the differences and then applying a lagged value of the endogenous variables as tools, wherein the instrument number differs for every time period. This procedure was used for deriving the estimates of the dynamic model, with the help of the Stata ver. 15.0. These GMM estimates are seen to be inconsistent if no serial correlation exists. This problem is also noted in the model that was based on the second-order serial correlation seen in the first difference model. Furthermore, we have also reported a diagnostic test for the first and the second order serial correlation, along with the Hansen test for instrumental validity.

3.3. Corporate Lobbying and CFP

This study used the empirical model, described below, for investigating the relationship between the CFP and corporate lobbying.

CFP = αPrio_CFP_it + β₁Corporate_Lobbying_it + β₂Firm_Size_it + β₃Leverage_it + β₄Advertisment_Intensity_it + β₅Revenue_it + Year_Effects_it + ε_it

Corporate lobbying is the variable of interest, which is present on the right-hand side of the model. The ln Lobbying refers to the natural logarithm of the total expenditure (in dollars) of the firm’s lobbying activities in the given year. Firm performance is the dependent variable. For understanding the behaviour between a dependent (i.e., CFP) and an independent variable (i.e., corporate lobbying), it is important to apply appropriate techniques for measuring the dependent variable and determining its actual effect. If it is not measured properly, then the effect will not be clear. CFP can be measured using two techniques, i.e., accounting (market-based) or economic performance measures. Here, this study has used accounting-based measures, like the Return on Assets (ROA) and the Return on Equity (ROE), to measure CFP.

The first measure included the ROA, which was commonly used in other studies (Coles et al. 2008; Adams and Ferreira 2009; Masulis and Mobbs 2011). ROA was defined as the company’s operating income divided by the total assets. The main dependent variable includes the natural logarithm of the ROA. It also describes the organisation’s profitability and assesses the management’s ability to create revenue by using the firm’s assets. ROA represents the competent manner in which the company resources are used for generating income. It also presents the efficiency of the company’s management in generating an income from its resources (Khrawish 2011). Wen (2010) stated that a high ROA indicates the efficiency of a company in utilising its funds.

ROE is a different financial value that indicates the amount of the income that was generated by the company in comparison to a total of the shareholder equity that was capitalised or recorded on the company’s financial statement. It indicates the rate of the return made on the various resources invested by the stakeholders in the bank. Furthermore, ROE is a way to determine the efficient manner in which the company management utilises the shareholders’ resources. Hence, a better ROE value indicates the efficient utilisation of the shareholders’ investment. For measuring the robustness, this study used a second measure of the CFP, i.e., ROIC, which was defined as the company’s operating income divided by the total equity.

This study used a comprehensive set of various control variables noted in the literature that were associated with the CFP. The firm size could be defined as a natural logarithm of the total assets. We also calculated the leverage as a ratio of the book value of the long-term debt to the total assets. The earlier firm profitability was measured based on the lagged CFP (i.e., lagged ROA, lagged ROE, or a lagged ROIC). The adverting intensity refers to the expense scaled by the total assets. We also used the reputation index as a control variable, as the sample was derived from the AMAC, which could significantly affect the CFP (Sánchez and Sotorrío 2007).

3.4. Effect of Organisational Slack on the Relationship between the Corporate Lobbying and CFP

The researchers used another model for testing the effect of the different company characteristics on the association between the CFP and the corporate lobbying.

CFP = αPrio_CFP_it + β₁Corporate_Lobbying_it + β₃Slack_it + β₂(Corporate_Lobbying*Slack)_it + β₃Firm_Size_it + β₄Leverage_it + β₅Advertisment_Intensity_it + β₆Revenue_it + Year_Effects_it + ε_it

We used the factor analysis for characterising the major firm attributes, i.e., organisational slack. Thereafter, we included the interaction between the variables for corporate lobbying and organisational slack in the empirical analysis for determining the differential effect of the lobbying on the CFP. We also calculated the organisational slack using the free cash flow and the sale amount. Many other studies used the CFP as a precursor of the slack resources, instead of the measure of the slack alone (Singh 1986; Sharfman et al. 1988). Buchholtz et al. (1999) disapproved of the use of the accounting returns for measuring the slack resources based on the fact that the income and the profit margin cannot reveal the number of the firm’s resources, which were committed, and how much was available for discretionary purposes. We agreed that the profits are not indicative of the firm’s slack resources, and hence, we have proposed that the cash flow must capture the concept of the resources that are available for the discretionary purpose. The cash flow can be defined as the firm’s operative income, which is determined before the depreciation and minus the nondiscretionary expenditure, like the interest on the debt, taxes, or dividends (Lehn and Poulsen 1989). Thereafter, we derived the measure of the operational complexity based on the advertisement intensity, firm size, and leverage (Coles et al. 2008). Subsequently, we used the reputation index as a control variable, as the sample was collected from the AMAC, and this could significantly affect the CFP.

4. Empirical Results

Table 1. Descriptive statistics.

Variable	Unit of Measurement	Obs	Mean	Std. Dev	Min	Max
lnroa	Log Net income before extraordinary items to total asset of firm	1294	3.762	0.170	0.000	4.404
lnroe	Log Net income before extraordinary items to total equity of firm	1294	5.488	0.189	0.000	6.620
lnroic	Log Net income before extraordinary items to total equity and debt of firm	1294	4.147	0.203	0.000	4.814
lncpa	Log of total amount of lobbying to total net sales of firm	1294	7.419	1.834	−2.303	10.635
lnfreecash	Log Free cash flow to total of net sales	1294	5.383	0.167	0.000	6.216
lnlev	Long term debt of firms to total equity	1294	1.135	0.660	0.113	6.899
lntotalasset	Log of total asset of firm	1294	10.460	1.267	7.282	13.632
lnadv	Log advertising expenses to total net sales of firm	1245	2.816	0.788	−0.713	4.775

Notes: All statistics are based on original data values.

and

Table 2. Correlation.

	lnroa	lnroe	lnroic	lncpa	lnfreecash	lnlev	lntotalasset	lnadv
lnroa	1
lnroe	0.332 ***	1
lnroic	0.726	0.400 ***	1
lncpa	0.153 ***	0.088 ***	0.162 ***	1
lnfreecash	0.099 ***	0.038	0.080 ***	0.005	1
lnlev	−0.265 ***	−0.112	−0.117 ***	0.101 ***	−0.088 ***	1
lntotalasset	−0.072 ***	−0.027	−0.093 ***	0.315 ***	0.009	0.205 ***	1
lnadv	0.039	0.002	0.021	−0.076 ***	0.074	0.096 ***	−0.005	1

Notes: All statistics are based on original data values. The p-values ***, ** and * indicate significance at 1%, 5%, and 10% levels, respectively.

present the descriptive statistics for the variables used in this study. The researchers noted that the relationship between these variables had a statistical significance (based on the t-value) and fulfilled the expectations of this study. This could be seen from the control variables that were associated with the other variables. Moreover, the correlation matrix showed that all of the study variables did not show the problem of multicollinearity as they showed a value ≥0.80. Also, the analysis of the Variance Inflation Factors (VIF) (1.48) showed no multicollinearity issue in this data, as the VIF values were further away from the maximal threshold value, i.e., 10.

4.1. Testing the Effect of the Corporate Lobbying on the CFP

Here, this study has presented the empirical results of the association between the CFP and corporate lobbying in 2 steps. In Step 1, we have provided a direct evidence regarding the dynamic relationship of the corporate lobbying and CFP (i.e., ROA, ROE, and ROIC); and, in Step 2, they directly tested the interactive effect of the organisational slack and corporate lobbying on the CFP.

Table 3. Corporate lobbying and Corporate Financial Performance (CFP) (Return on Assets (ROA)) (N = 134 firms; T = 10; period = 2007–2016).

	Static			Dynamic
	OLS	Fixed Effect	OLS	Fixed Effect	Syetem GMM
Variables	Model 1	Model 2	Model 3	Model 4	Model 5
lnroat−1			0.326 **	0.257 **	0.131 ***
			(0.146)	(0.129)	(0.0329)
lncpa	0.0202 ***	0.0262	0.00911 ***	0.00952 ***	−0.00583
	(0.00617)	(0.0164)	(0.00301)	(0.00339)	(0.00383)
lnlev	−0.0710 ***	−0.0605 ***	−0.0468 ***	−0.0513 ***	−0.0290 ***
	(0.00819)	(0.0131)	(0.0138)	(0.0140)	(0.0109)
lntotalasset	−0.0117 ***	−0.0176 **	−0.00964 ***	−0.0101 *	−0.0164
	(0.00330)	(0.00826)	(0.00345)	(0.00551)	(0.0202)
lnadv	0.0182 ***	0.00534	0.0166 ***	0.0158 **	−0.0287
	(0.00561)	(0.00902)	(0.00549)	(0.00687)	(0.0225)
Year	Yes	Yes	Yes	Yes	Yes
Constant	3.806 ***	3.838 ***	2.558 ***	2.853 ***	3.599 ***
	(0.0455)	(0.0983)	(0.561)	(0.493)	(0.261)
Observations	1245	1245	1114	1114	1114
R-squared	0.132		0.334
Number of code		134		134	134
AR1					−3.420 (0.001)
AR2					0.300 (0.761)
Hansen Test					15.680 (0.547)
Difference Hansen Test					1.600 (0.809)

Notes: The standard errors are reported in parentheses, except for Hansen test, AR (1), AR (2), and Difference-in-Hansen which are p-values. ***, ** and * indicate significance at 1%, 5%, and 10% levels, respectively. Time dummies are included in the model specification, but the results are not reported to save space. System GMM model is estimated by using the Blundell and Bond (1998) dynamic panel system GMM estimations and Roodman (2009)—Stata xtabond2 command.

,

Table 4. Corporate lobbying and CFP (ROE) (N = 134 firms; T = 10; period = 2007–2016).

	Static			Dynamic
	OLS	Fixed Effect	OLS	Fixed Effect	Syetem GMM
Variables	Model 1	Model 2	Model 3	Model 4	Model 5
lnroet−1			0.316	0.316 ***	0.339 ***
			(0.303)	(0.0878)	(0.0215)
lncpa	0.0112 ***	0.0107	0.00725 **	0.00725 ***	0.00135
	(0.00176)	(0.00327)	(0.00366)	(0.00242)	(0.00361)
lnlev	0.0113	−0.00178	0.0103	0.0103	−0.0540
	(0.0450)	(0.0656)	(0.0456)	(0.0411)	(0.0380)
lntotalasset	−0.0117 ***	−0.0114	−0.00920	−0.00920 **	0.00238
	(0.00435)	(0.00749)	(0.00615)	(0.00452)	(0.0152)
lnadv	0.00193	−0.000505	0.00131	0.00131	−0.0837 **
	(0.00381)	(0.00755)	(0.00420)	(0.00572)	(0.0388)
Year	Yes	Yes	Yes	Yes	Yes
Constant	5.524 ***	5.541 ***	3.762 **	3.796 ***	3.881 ***
	(0.0284)	(0.0512)	(1.653)	(0.510)	(0.210)
Observations	1245	1245	1114	1114	1114
R-squared	0.026		0.117
Number of code		134		134	134
AR1					−1.11 (0.268)
AR2					0.970 (0.330)
Hansen Test					17.85 (0.598)
Difference Hansen Test					2.39 (0.664)

Notes: The standard errors are reported in parentheses, except for Hansen test, AR (1), AR (2), and Difference-in-Hansen which are p-values. ***, ** and * indicate significance at 1%, 5%, and 10% levels, respectively. Time dummies are included in the model specification, but the results are not reported to save space. System GMM model is estimated by using the Blundell and Bond (1998) dynamic panel system GMM estimations and Roodman (2009)—Stata xtabond2 command.

and

Table 5. Corporate lobbying and CFP (Return on Invested Capital (ROIC)) (N = 134 firms; T = 10; period = 2007–2016).

	Static			Dynamic
	OLS	Fixed Effect	OLS	Fixed Effect	Syetem GMM
Variables	Model 1	Model 2	Model 3	Model 4	Model 5
lnroict−1			0.395 ***	0.352 **	0.119 ***
			(0.144)	(0.143)	(0.0351)
lncpa	0.0249 ***	0.0312	0.0104 ***	0.0109 **	−0.00384
	(0.00702)	(0.0209)	(0.00390)	(0.00480)	(0.00407)
lnlev	−0.0342 ***	−0.0401 *	−0.0201	−0.0233	−0.0373 ***
	(0.0129)	(0.0239)	(0.0140)	(0.0174)	(0.0140)
lntotalasset	−0.0228 ***	−0.0281 ***	−0.0169 ***	−0.0176 ***	−0.0758 **
	(0.00401)	(0.0105)	(0.00505)	(0.00659)	(0.0298)
lnadv	0.0127 *	−0.00302	0.0125 **	0.0122	−0.0507 **
	(0.00693)	(0.0123)	(0.00621)	(0.00864)	(0.0234)
Year	Yes	Yes	Yes	Yes	Yes
Constant	4.253 ***	4.299 ***	2.569 ***	2.789 ***	4.672 ***
	(0.0538)	(0.122)	(0.619)	(0.605)	(0.374)
Observations	1245	1245	1114	1114	1114
R-squared	0.076		0.302
Number of code		134		134	134
AR1					−1.11 (0.268)
AR2					0.970 (0.330)
Hansen Test					17.85 (0.598)
Difference Hansen Test					2.39 (0.664)

Notes: The standard errors are reported in parentheses, except for Hansen test, AR (1), AR (2), and Difference-in-Hansen which are p-values. ***, ** and * indicate significance at 1%, 5%, and 10% levels, respectively. Time dummies are included in the model specification, but the results are not reported to save space. System GMM model is estimated by using the Blundell and Bond (1998) dynamic panel system GMM estimations and Roodman (2009)—Stata xtabond2 command.

present the static and dynamic results, along with the fixed effect model and the system-GMM results for the ROA, ROE, and the ROIC, which were estimated using the corporate lobbying for evaluating the effect after overlooking the dynamic corporate lobbying–CFP relation and heterogeneity. The results were validated for the misspecification tests (i.e., Hansen test for other restrictions along with a second-order serial correlation test, i.e., AR 2 test), which helped in confirming the appropriateness of using the system-GMM model specification. Furthermore, the positive and significant coefficient for the lagged dependent variable validated the persistence of the CFP, which was seen to significantly rely on its past value. As shown in Table 3, Table 4 and Table 5, the positive and significant coefficient for the lagged dependent variable for the system-GMM was present between the fixed effects and the pooled OLS. Similar results were noted earlier (Bond et al. 2001), where the researchers applied an effective and unbiased system GMM. Hence, in this study, we used the system-GMM two-step2 estimator for all calculations and the Hansen test for the other restrictions. Thus, we concluded that the system-GMM was an effective model specification. Irrespective of the estimation technique used, the control variables showed the following effect on the CFP (i.e., ROA, ROE, and ROIC). The total revenue of firms showed a positive effect on the CFP. We observed a negative effect of the total asset and the leverage on the CFP. We also noted a mixed relationship between the advertising intensity and the CFP.

The model that was used in the study defines the measures for corporate lobbying. As seen in the study, a few biases can arise if the researchers overlooked the dynamic corporate lobbying-CFP relationship (i.e., fixed-effects model) and the unobservable heterogeneity (i.e., pooled OLS model). As shown in Table 3, a positive relationship (based on the OLS estimate—model 1 and 3, based on fixed-effects estimate—model 2) was noted between the corporate lobbying and ROA, while a neutral relationship was seen between the system-GMM (model 5) and the dynamic fixed-effects model (model 4). On the other hand, Table 4 and Table 5 also showed same results, with a significant relationship between corporate lobbying and ROE and ROIC for all models. Hence, the dynamics must be considered before determining the CFP and the corporate lobbying relationship. As biased results could be generated with the help of the OLS estimate and the fixed-effects model, the researchers have highlighted these system-GMM results. With regards to the relationship between the CFP and the corporate lobbying, the ROA, ROE, and ROIC were unaffected by corporate lobbying. The resultant data did not show any effect on the H1, indicating that there was no effect of the corporate lobbying on the CFP. Thus, the researchers verified the presence of a neutral relationship between the CFP and the corporate lobbying activities. Similar results were noted in the earlier empirical studies (Ansolabehere et al. 2004; Cao et al. 2018; Faccio et al. 2006; Hersch et al. 2008).

4.2. Testing the Interactive Effect of Corporate Lobbying and the Organisational Slack on CFP

This study has also investigated the interactive effects of the corporate lobbying and the organisational slack on the CFP.

Table 6. Effects of corporate lobbying and organisational slack on the variable financial performance (ROA) (N = 134 firms; T = 10; period = 2007–2016).

		System GMM
Variables	Model 1	Model 2	Model 3
lnroat−1	0.337 ***	0.333 ***	0.230 ***
	(0.0519)	(0.0505)	(0.0642)
lncpa		−0.00227	3.043 *
		(0.00308)	(1.613)
lnfreecash	−0.00568	−0.00528	4.512 *
	(0.00613)	(0.00620)	(2.400)
(lncpa*lnfreecash)			−0.567 *
			(0.300)
lnlev	−0.0281 ***	−0.0277 ***	−0.0396 **
	(0.0103)	(0.0103)	(0.0164)
lntotalasset	0.00432	0.00412	0.00473
	(0.0222)	(0.0221)	(0.0233)
lnadv	−0.0476 **	−0.0455 **	−0.0693 ***
	(0.0204)	(0.0208)	(0.0252)
Year	−0.00609	−0.00660	−0.0113
	(0.0162)	(0.0162)	(0.0201)
Constant	2.642 ***	2.664 ***	−21.15
	(0.343)	(0.342)	(12.89)
Observations	1114	1114	1114
Number of code	134	134	134
AR1	−4.13 (0.000)	−4.10 (0.000)	−4.16 (0.000)
AR2	0.72 (0.472)	0.71 (0.480)	0.23 (0.818)
Hansen Test	31.59 (0.064)	30.90 (0.480)	21.35 (0.318)
Difference Hansen Test	6.76 (0.239)	8.30 (0.140)	4.12 (0.532)
Number of instrument	35	35	35

Notes: All models are estimated by using the Blundell and Bond (1998) dynamic panel data system GMM estimations and Roodman (2009)—Stata xtabond2 command. The standard errors are reported in parentheses, except for Hansen test, AR (1), AR (2), and Difference-in-Hansen which are p-values. ***, ** and * indicate significance at 1%, 5%, and 10% levels, respectively. Time dummies are included in the model specification, but the results are not reported to save space. The instruments employed in the first-differenced equation are two or more lags of the levels of the endogenous variables, while one lag of the first-difference of the endogenous variables is used as instrument in the difference equation.

,

Table 7. Effects of corporate lobbying and organisational slack on the variable financial performance (ROE) (N = 134 firms; T = 10; period = 2007–2016).

		System GMM
Variables	Model 1	Model 2	Model 3
lnroet−1	0.117 ***	0.104 ***	0.0671 **
	(0.0123)	(0.0124)	(0.0270)
lncpa		−0.000254	37.16 *
		(0.00826)	(21.35)
lnfreecash	−0.00106	−0.00140	55.16 *
	(0.00982)	(0.00945)	(31.70)
(lncpa*lnfreecash)			−6.905 *
			(3.973)
lnlev	−0.180 ***	−0.203 ***	−0.296 ***
	(0.0194)	(0.0230)	(0.0646)
lntotalasset	−0.155 *	−0.125	−0.0240
	(0.0837)	(0.0885)	(0.141)
lnadv	−0.190 ***	−0.143 *	−0.145
	(0.0661)	(0.0772)	(0.133)
Year	Yes	Yes	Yes
Constant	7.169 ***	6.829 ***	−290.1 *
	(0.983)	(1.023)	(170.2)
Observations	1114	1114	1114
Number of code	134	134	134
AR1	−1.10 (0.270)	−1.10 (0.2.73)	−1.74 (0.081)
AR2	0.48 (0.630)	0.33 (0.740)	−1.29 (0.196)
Hansen Test	21.02 (0.458)	18.66 (0.544)	7.51 (0.991)
Difference Hansen Test	8.30 (0.414)	6.47 (0.264)	1.74 (0.884)
Number of instrument	35	35	35

Notes: All models are estimated by using the Blundell and Bond (1998) dynamic panel data system GMM estimations and Roodman (2009)—Stata xtabond2 command. The standard errors are reported in parentheses, except for Hansen test, AR (1), AR (2), and Difference-in-Hansen which are p-values. ***, ** and * indicate significance at 1%, 5%, and 10% levels, respectively. Time dummies are included in the model specification, but the results are not reported to save space. The instruments employed in the first-differenced equation are two or more lags of the levels of the endogenous variables, while one lag of the first-difference of the endogenous variables is used as instrument in the difference equation.

and

Table 8. Effects of corporate lobbying and organisational slack on the variable financial performance (ROIC) (N = 134 firms; T = 10; period = 2007–2016).

		System GMM
Variables	Model 1	Model 2	Model 3
lnroict−1	0.307 ***	0.305 ***	0.106 **
	(0.058)	(0.061)	(0.064)
lncpa		−0.0002	7.289 **
		(0.003)	(2.987)
lnfreecash	−0.003	−0.003	10.810 **
	(0.007)	(0.007)	(4.439)
(lncpa*lnfreecash)			−1.356 **
			(0.556)
lnlev	−0.031 *	−0.032 **	−0.076 ***
	(0.016)	(0.0159)	(0.0195)
lntotalasset	−0.0495	−0.042	−0.022
	(0.031)	(0.031)	(0.038)
lnadv	−0.061 ***	−0.059 ***	−0.075 **
	(0.020)	(0.023)	(0.032)
Constant	No	3.550 ***	−53.840 **
		(0.493)	(23.900)
Year	Yes	Yes	Yes
Observations	1114	1114	1114
Number of code	134	134	134
AR1	−3.89 (0.000)	−3.92 (0.000)	−4.34 (0.000)
AR2	−0.48 (0.634)	−0.48 (0.635)	−1.60 (0.110)
Hansen Test	26.87 (0.108)	27.04 (0.0.078)	19.46 (0.303)
Difference Hansen Test	6.17 (0.290)	8.06 (0.153)	2.18 (0.824)
Number of instrument	35	35	35

Notes: All models are estimated by using the Blundell and Bond (1998) dynamic panel data system GMM estimations and Roodman (2009)—Stata xtabond2 command. The standard errors are reported in parentheses, except for Hansen test, AR (1), AR (2), and Difference-in-Hansen which are p-values. ***, ** and * indicate significance at 1%, 5%, and 10% levels, respectively. Time dummies are included in the model specification, but the results are not reported to save space. The instruments employed in the first-differenced equation are two or more lags of the levels of the endogenous variables, while one lag of the first-difference of the endogenous variables is used as instrument in the difference equation.

show the regression analysis for the system-GMM estimator. The Arellano and Bond test for autocorrelation supported the overall model validity, as it provided evidence for the presence of the 1st-order autocorrelation (ar1) and the absence of the second-order autocorrelation (AR2), whereas the Hansen test supported the consistency of all GMM instruments. It could be seen that this estimation controlled the potential correlation between the regressors and the unobserved factors. Table 6, Table 7 and Table 8 present the results for the conventional determinants of the financial performance (i.e., ROA, ROE, and ROIC). Furthermore, the CFP was seen to be unaffected by the corporate lobbying.

Table 6, Table 7 and Table 8 present the results of the regression model that was used for investigating the proposed hypotheses. In both the tables, the interactions occurring between the organisational slack and the corporate lobbying along with their effect on the CFP were determined using Model 3 in Table 6, Table 7 and Table 8. We relied on the Model 3 for testing Hypothesis 2, since it was a completely specified model that accurately depicted the effects of every variable. The model includes the interaction term between corporate lobbying and organisational slack. According to Brambor et al. (2006), it is not necessary to interpret the individual term of the corporate lobbying and organisational slack, since these coefficients are not our main concern. Hypothesis 2 was proposed for predicting whether the organisational slack could positively moderate the association between the corporate lobbying and the CFP. When the organisational slack increased, the positive relationship between the CFP and the corporate lobbying also became stronger. However, the findings of this study showed an opposite result that the organisational slack negatively moderated this relationship and do not support the Hypothesis 2. As seen in the Model 3 in Table 6, Table 7 and Table 8 for the ROA, ROE, and ROIC, a significant and negatively coefficient was noted for the interaction between the organisational slack and the corporate lobbying (β_ROA = −0.567, p value < 0.10; β_ROE = −6.905, p value < 0.10; β_ROIC = −1.356, p value < 0.05). These results showed that the detrimental effect of the corporate lobbying on the CFP when interacting with organisational slack. This finding is inconsistent with Hypothesis 2.

Furthermore, a significant interaction was noted between the financial slack and corporate lobbying. A further exploration of this effect has been described in Figure 1 and Figure 2. Based on the Model 3 results shown in Table 6, Table 7 and Table 8, we applied the Aiken et al. (1991) strategy for plotting all significant interactive effects (p-value lower than 0.05) for depicting the moderating effects properly. Figure 1, Figure 2 and Figure 3 illustrate the effect of the corporate lobbying on the corporate lobbying for the 2 levels (i.e., low and high) of organisational financial slack. A standard deviation value that was greater or lesser than the mean could indicate the higher or lower level of all moderating variables, respectively.

As shown in Figure 1, when the corporate lobbying increased by a value of 1 standard deviation lower than the mean to 1 value above the mean, the ROA decreased from 3.31 to 2.93 in the case of the firms with a high level of organisational slack, whereas the ROA increased from 2.86 to 3.18 for the firms with a low level of organisational slack. In other words, when the corporate lobbying expenditure increased from 1 standard deviation value below the mean to 1 value above the mean, the ROA for the firms with a higher slack showed an 16.06% decrease, whereas, in the case of the firms with a lower slack, the ROA increased by 15.32%. Hence, Figure 1 showed that the effect of the corporate lobbying varied from low to high, while the corporate lobbying and ROA relationship shifted from negative to positive. The organisational slack moderated the effect of the corporate lobbying on the CFP, and a strong negative relationship could be seen between the parameters in the case of the companies with a higher level of organisational slack.

With regards to the moderation effect, Figure 1 showed the negative effect of the corporate lobbying on the ROA when the organisational slack was high. On the other hand, this relationship was positive when the level of organisational slack was low. These results verified the moderating effect of the organisational slack; while the interaction pattern between the parameters was seen to be inconsistent when compared to the prediction. Thus, the Hypothesis 2 could not be proved. Similar results are explained in Figure 2 and Figure 3 for ROE and ROIC, respectively, thus support this view on corporate lobbying by demonstrating that the effect of corporate lobbying is negative when organisational slack is high, but positive when organisational slack is low. In conclusion, based on the meaning of ROA, ROE, and ROIC, our findings indicate that firms with high corporate lobbying are expected to achieve high profit when organisational slack is low.

5. Selection Bias

In order to correct for potential selection bias, a Heckman Two-Step procedure was estimated (Heckman 1979). The Heckman model is warranted when samples are non-random and there is a chance that omitted variables from a broader sample of firms will bias results. In the two-step procedure, the first equation is a selection equation where the control variables are regressed on the selection variable, in this case, corporate lobbying, the result is a correction factor, known as the Inverse Mills Ratio (Bascle 2008). The Mills coefficient is then added to the original equation as an additional right-hand variable to adjust for the selection bias. However, if the Inverse Mills Ratio coefficient is insignificant (p > 0.05), then there is no selection bias, and the original results are unbiased due to selection. In both Heckman equations, the Inverse Mills Ratio was insignificant (Lamda = −4.707, p = 0.071 for corporate lobbying), and therefore, selection bias is not present in the included sample.

6. Discussion

There has been an increasing interest in the lobbying and other political activities of the various organisations. Till date, the academics and the financial press have focused primarily on the weaker measure of the political activities, i.e., PAC contributions. However, the PAC contributions are a very small percentage of the total corporate political activities. Furthermore, as they are made by the individuals, they cannot be included as corporate expenses. This study has noted that the lobbying expenses are the biggest type of corporate political activity in America. This is determined by the number of the companies that are engaged and the amount of money spent. These factors showed that the lobbying activities were significantly higher than the corporate PACs and the soft money donations (banned). Therefore, this study used the publicly-accessible data about the corporate lobbying activities for investigating the financial effect of the corporate political activities.

This study examined the relationship between the CFP and the corporate lobbying activities with an aim to determine the moderating role of organisational slack. Many recent studies have indicated that the relationship between the corporate lobbying and the CFP was slightly positive, however, some researchers stated that further investigation was necessary for clarifying the role played by the omitted variables that could interfere in the relationship. This debate is still open and new models are required, which can address these issues. This study has tried to explain the lobbying activity interface after analysing the role played by organisational slack.

This study has presented several results describing the relationship between the corporate lobbying and the CFP. Firstly, while taking into consideration the dynamic data for the system-GMM estimation and regression, the researchers noted that the lobbying expenses were not significantly correlated with the CFP. Secondly, this study investigated the two-way interaction between the lobbying and the organisational slack, after using the CFP as a dependent variable. The results showed that organisational slack negatively interacts with the corporate lobbying, which indicates the synergy. Also, the interaction between the corporate lobbying and logistic slack generated a detrimental effect on the CFP, thereby rejecting H2. These results were robust to the different measures of the CFP, i.e., ROA or ROIC.

All of the results in this study have contributed to the theory in many ways. We noted that the corporate lobbying does not necessarily improve the CFP, which was explained using the political market theory, as it emphasised on the political competition, suggesting that the corporate lobbying activities could be an important component of the zero-sum political game, which cannot be accurately evaluated. This political marketplace theory regards corporate lobbying as an exchange in the policy arena that occurs between the policy demanders, like various interest groups or firms, on one hand, and the legislators, policy suppliers, regulators, etc. on the other hand (Bonardi et al. 2006). Many firms compete with one another for limited access to public officials (Hillman and Hitt 1999). Due to the increasing number of political rivals, the specific firms find it difficult to forward a particular public policy agenda successfully (Hall and Wayman 1990; Baumgartner et al. 2009). Similar results were noted by Baumgartner et al. (2009), who stated that the lobbying expenditure was not related to the public policy success. They also concluded that they could not observe any relationship between the resources and the policy results.

To explain further, though some managers believe that they can alter the political scenario, in actual reality, the firms cannot successfully predict the decisions of the policymakers, since policymaking involves personal, party, state, national, or media influences (Keim 2001). This factor, along with the rational decisions of the corporate decision-makers, increases the strategic uncertainty related to the corporate lobbying or its ability to produce the desired outcome, like an improved CFP (Hart 2010). Due to this uncertain influence, the corporate lobbying was seen to be less successful than what is presumed.

In this paper, we have extended the contingency theory to include corporate lobbying and the CFP. The contingency theory claims that external and internal factors have an impact on organisational structure and management. In this perspective, we applied this theory to demonstrate that corporate lobbying is highly situational determined firm performance and organisational slack as influential factor. The contingency theory has highlighted the significance of obtaining a fit or a congruency between the corporate lobbying and the level of organisational slack of the firm. The idea of organisational slack as an important driver of corporate lobbying originates from behavioural theory. As shown by this study, the amount of organisational benefits that can be obtained from corporate lobbying is contingent on the organisational slack when the resources level is low. Firms with low slack might see political activities as the only solution to rectify their financial woes (Hillman et al. 2004). We argued that firms with limited organisational slack have the potential of increasing their profits through lobbying on public policy protection, for instance through high tariffs for imported goods using the “infant industry protection” argument, but do not have the purchasing power. The level of organisational slack of a firm may influence its decision to seek political favours. The lesser a firm is in slack, the more sensitive it is to government’s policies. Firms may seek political favor or intervention when trying to secure a government project and may ask for a subsidy.

Limitations and Future Research

Similar to other studies, this paper also has some limitations. Firstly, as the data used here was historical, the causalities could not be easily surmised. Though we have tested all of the equations within the model that showed a delayed effect on the political activity (reverse of the proposed hypothesis), they noted no evidence regarding this relationship. This raises a few questions regarding the causal relationships described in the empirical studies without experimentation. Secondly, since this field is very concentrated, the number of companies included was very small (N = 134). Though the estimations can be improved in this panel data set (N = 1294), increasing the sample size would be better. The future studies will have many techniques at their disposal for improving the published literature and determining the perfect category in which this study can be fit. This could be done by qualitatively studying the relationship that exists between the firm-level lobbying and the CFP. For instance, a massive case study can be carried out in many industries for expanding the scope of the corporate lobbying literature. This project would be empirical and also based on a theory. This phenomenon has not been researched in this area.

7. Conclusions

In this study, we investigated organisational slack as a main contingency that controls the effects of corporate lobbying on CFP. We argued that firms with low organisational slack will seek a political solution to their limited resources. We find that the relationship between corporate lobbying and CFP is negatively moderated by organisational slack. With these results, this study emphasizes the importance of examining organisational slack in understanding the firm’s performance implications of corporate lobbying and covers the way for a comprehensive understanding of contingencies when corporate lobbying is financially beneficial.