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The effect of a bonus cap on firm performance: a

case study in the Netherlands

Name: Koen Bosmans Student number: 11045574 Thesis supervisor: Patrick Stastra Date: 26 June 2018

Program: Bsc Economics and Business Specialization: Economics and Finance

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Abstract

This paper explores the influence of a bonus restriction on firm performance. More

specifically, it performs a case study examining the effect of the Dutch bonus cap as installed in 2015 on the performance of the Dutch financial sector. In this research, historical stock returns are used as indicator for performance and benchmarked against characteristics-based reference portfolios. By applying a long-run event study and the differences-in-difference method statistical inference regarding the effect is drawn. This research does not find a significant effect of the restriction on performance. The results indicate that both the level and the volatility of stock returns have not significantly changed after instalment of the law.

Statement of Originality

This document is written by Student Koen Bosmans who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Table of Contents

1 Introduction ... 4

2 Literature Review ... 5

2.1 Principal-Agent theory ... 5

2.2 Pay for performance studies ... 6

2.3 Hypothesis development ... 7 3 Methodology ... 8 3.1 Research method ... 8 3.2 Sample formation ... 11 3.3 Data ... 13 4 Results ... 13

5 Conclusion and Limitations ... 16

References ... 17

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1 Introduction

The recent remuneration proposal of ING for its CEO caused a lot of criticism in the Netherlands. The bank planned to increase its top executive’s salary by 50% making him the best-paid banker in the country. Dutch politicians were quickly to respond and expressed their discontent on the matter. The main reason for their criticism was the fact that the bank received state aid during the crisis and bankers’ salaries are perceived as one of the factors causing this recession (Turner, 2009). ING argued that it is essential for them to follow-up on the European remuneration trend and compensate their CEO accordingly. However, due to political pressure and public controversy they withdrew their proposal.

Since the economic crisis emerged, there has been a call for increased regulation on bankers’ bonuses in order to create a more responsible financial sector. In Europe, this resulted in a bonus cap for all bankers active in the European Union as per January 2014. More specifically, this law limits the ratio of variable to fixed remuneration to 100%, which could be increased to 200% if a vast majority of shareholders approves. Soon afterwards, the Netherlands took further efforts by installing their own more severe bonus cap. Since January 2015 financial institutions in the country are not allowed to remunerate new employees with more than 20% of their fixed salary. A year later, the law applied to all working in the Dutch financial sector. The only way to more generously compensate staff members is now to increase their base salary, which is what ING unsuccessfully tried.

The overall effect of variable pay on performance remains an unresolved issue. Traditional economic theory sees it as a great way to incentivize workers to exert effort and perform better (Jensen & Meckling, 1976). On the contrary, psychological literature highlights the negative effects bonuses can have on behavior, such as decreased intrinsic motivation and creativity (Glucksberg, 1962). With the bonus cap being such a new phenomenon, its effects have yet to be researched by science.

The Dutch government wants to reform its financial sector by regulating their remuneration. Banks, however, seem to struggle with this bonus cap and express their concerns about the lack of competitiveness they think it causes. If this law really distorts financial institutions’ ability to attract and retain talented employees, and incentivize their managers, it will decrease their performance. Since this could be harmful to the Dutch economy it is important to critically evaluate the law. Therefore, this research will investigate how the Dutch bonus cap influences the performance of the Dutch financial sector.

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In order to examine the effect of the law on firm performance both qualitative and quantitative research have been conducted. The following section consists of a literature review critically evaluating the theory and findings of existing research on how variable payments affect performance. The third section describes the methodology that is used in this study. In the fourth section the results of this research are presented and explained. The fifth and last section discusses the results of this research. In addition, it describes its limitations and provides directions for future research.

2 Literature Review

2.1 Principal-Agent theory

A common way to analyze how incentive pay influences performance is by using the principal-agent framework. This model is centered around the problem that arises when one party (the agent) has to make decisions and/or take actions on behalf of the other party (the principal) (Ross, 1973). The principal cannot perfectly observe the agent’s actions causing information asymmetry. Since the agent is motivated to increase his own well-being this often leads to a conflict of interest (Jensen & Meckling, 1976).

In business, a prominent example of this problem can be found in the interaction between shareholders acting as principal and corporate management in the role of the agent. The shareholders, who want to maximize shareholder value, delegate the task of daily management to the executives who act in their own interest (Lucian & Fried, 2003). Moreover, the shareholders have incomplete information about all the investment opportunities and other managerial decisions the executives face (Jensen & Murphy, 1990). With the use of a contract shareholders try to overcome this problem by aligning both parties’ interests. By tying executives’ pay to certain from shareholders’ perspective desirable outcomes they hope to decrease the conflict of interest (Jensen & Meckling, 1976). Examples of this are paying a bonus based on earnings or remunerating management with stock options.

Research has tried to find which incentives work best under different circumstances and what consequences these incentives have for human behavior using agency theory. Jensen and Meckling (1976) found that increasing firm ownership for managers incentivizes them to behave in the interest of the shareholders. In other terms, an outcome-based contract inspires management to engage in more desirable activities. Fama (1980) added that information effects can decrease managerial opportunism. He described that when shareholders are better able to

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monitor their executives, they will be more likely to act in their interest. However, both of these findings were perceived as minimalistic and lacking at explaining reality (Perrow, 1986). For this reason, Eisenhardt (1989) added more sophisticated propositions that can be derived from the framework. She stated that outcome is a function of more than just management’s behavior. When managers are risk-averse, paying them on the basis of such an uncertain outcome could potentially harm both parties (Eisenhardt, 1989).

More recent research on the topic provides insight into a broad range of other factors that are relevant to how incentive pay can affect human behavior. Dohmen and Falk (2011) state that the traditional view of incentives underestimates the effect variable pay has on self-selection. They found that characteristics such as gender, risk attitude and self-assessment have a significant effect on how individuals respond to different incentive schemes. Ariely, Gneezy, Loewenstein and Mazar (2009) approve of the link between variable pay and effort, but show that variable pay - depending on the task at hand - does not necessarily lead to increased performance. They show that excessive bonuses can decrease an individual’s performance. This can be explained by the bonus imposing stress on a person, which can eventually overwhelm the motivational influence.

2.2 Pay for performance studies

Empirical results on how variable pay influences performance on both the individual and the firm level are divided. In addition, research is often performed in a controlled environment making it difficult to generalize findings.

Traditional economic theory predicts a positive relationship between monetary incentives and performance. However, Ariely et al. (2009) provide evidence that excessive pay can have a detrimental effect on the performance of an individual due to increased stress. Moreover, Glucksberg (1962) showed that attaching a bonus to a task can crowd out intrinsic motivation and decrease creativity. According to Dohmen and Falk (2011), variable pay does increase productivity with the effect being mainly due to self-selection. They also highlight the importance of human characteristics for the optimal incentive contract. Lazear (2000) found that installing a pay-for-performance scheme in a company increases productivity of current employees and helps attracting skilled workers. The firm that was being researched also showed increased profitability from the new method. However, these results were obtained in a windshield installation firm making them hard to generalize to firms with more complex jobs.

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Financial research has also investigated the effect of variable pay on performance, particularly focusing on the link between remuneration policy and stock prices. Murphy (1985) found a strong positive relation between managerial compensation and corporate performance in the US. In his research, corporate performance was measured by both shareholder return and sales. A similar relation was found by Gao and Li (2005) who expanded the research scope by including private firms using a unique dataset. Since private firms are not listed on a stock exchange they measured firm performance using accounting accruals. Core, Holthausen and Larcker (1999) used a different approach by testing whether predicted excess compensation has an effect on future performance. Their results showed that overpaying a CEO is linked with a decrease of future stock returns because it is probably a sign of poor corporate governance. Moreover, Holmstrom and Milgrom (1994) suggest that despite low-powered incentives sometimes being labelled as performance decreasing, they can actually inspire corporation and coordination within the organization.

In the Netherlands, there is not much research examining the Dutch pay for performance relation. Mertens, Knop and Strootman (2007) found a small but positivize relation between the level of short-term bonus and firm performance. Their research applied a similar method as used by Jensen and Murphy (1990) in order to put their results in an international perspective. Research performed by Dubbheus and Kabir (2008), however, did not find a significant relation between the level of executive pay and firm performance.

2.3 Hypothesis development

The purpose of this research is to explore the effect of the Dutch bonus cap on financial institutions’ performance. Since bonus caps are relatively new, there has not been conducted any scientific research that specifically focuses on the topic. Pay for performance studies provide insight in the way bonuses affect individual and firm performance, and could help to understand the consequences of such a restriction. However, results in the area are divided and hard to generalize to different settings (Dohmen & Falk, 2011). In addition, there has been little research conducted on the Dutch pay for performance relation also without providing a clear answer (Dubbheus & Kabir, 2008).

Many financial institutions express their concerns about the way this law interferes with their ability to operate. The government seems to ignore their complaints, but of course does not wish to harm their national economy. Since present research offers insufficient knowledge to obtain clear insight in the matter the following hypothesis is formed:

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Hypothesis 1: The Dutch Bonus cap has a no effect on the performance of the Dutch financial sector.

The motive behind implementing the law is not to generate profits, but to reform the country’s financial sector in a responsible way. By severely restricting bonuses the government hopes to reduce perverse incentives and build towards a more stable financial sector. Dohmen and Falk (2011) show that a compensation plan with a high level of fixed to variable pay is likely to attract people with higher risk aversion. This could in turn lower the risks of financial institutions. However, Murphy (2013) predicts the opposite will happen. He argues that restricting bonus payments will most likely lead to an increase in fixed salary. This makes it harder for banks to adapt to business cycle fluctuations making them more vulnerable. The following hypothesis will test whether the law enhanced stability of the sector:

Hypothesis 2: The Dutch Bonus cap has not affected the stability of the Dutch financial sector

3 Methodology

3.1 Research method

In order to test the effect of the Dutch bonus cap on firm performance a long-run event study will be performed. An event study attempts to measure the effect of a certain event on stock returns of a firm (Brown and Warner, 1985). More specifically, it tries to find out whether there are abnormal returns that can be explained by the event. Traditional event studies focus on corporate events, such as earning announcements or equity offerings, but the last decade a wide variety of events have been analyzed using the method.

Calculating abnormal returns can be done in many different ways. For a short-term event study, it is common to calculate abnormal returns by subtracting expected return as predicted by a market-model from actual returns (Brown and Warner, 1985). However, Barber and Lyon (1997) demonstrated that this can lead to severe biases in the t-statistic when applied to a longer horizon. They offer a solution by showing that calculating abnormal returns by matching sample firms to control firms eliminates these biases. Moreover, in this way any bias resulting from variables common to the event group and non-event group is implicitly

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controlled for. In addition, they demonstrate that calculating abnormal returns should be done using the buy-and-hold return instead of the cumulative return. In this research, the recommendations made by Barber and Lyon (1997) are followed and abnormal returns are defined in the following way:

𝐵𝐻𝐴𝑅%,' = 1 + 𝑅%,' − 1 + 𝑅,-./01234,' ' '56 ' '56 Where:

BHARi,t : Abnormal buy-and-hold return

Ri,t : Buy-and-hold return of Dutch financial sector

RBenchmark,t : Buy-and-hold return of appropriate benchmark portfolio

Three benchmarks are considered for calculating the abnormal returns: (i) A size-matched sample, (ii) an industry-and-size-matched sample and (iii) the AMX index. Since size is known to influence stock returns it is included as matching criterion (Fama and French, 1992). Unfortunately, it is not possible to construct a book-to-market matched portfolio since firms in the financial sector typically have a higher book to market ratio than firms in other industries. However, the industry-and-size matched sample does control for this ratio since it consists of financial institutions only. The firms in this particular benchmark will have to be found outside of the Netherlands since all Dutch financial institutions are subject to the bonus cap. To control for the influence of national economies on firm performance, their returns will be corrected for GDP differences. For this benchmark, the abnormal return will be calculated as follows:

𝐵𝐻𝐴𝑅%,' = 1 + 𝑅%,' − (1 + 𝑅,-./01234,') ∙ (1 + 𝑔;<'/0 >;?,') (1 + 𝑔@2'%A.2B >;?,') ' '56 ' '56 Where:

gDutch GDP,t : GDP growth factor of the Netherlands

gNational GDP,t : GDP growth factor of control firm’s country

The monthly abnormal returns are calculated for 41 months before and 41 months after instalment of the Dutch bonus cap for each of the three benchmarks. Afterwards, the mean BHAR is computed for the two periods as for the whole window. A t-test will show whether these are significantly different from zero, which would indicate under or over performance to

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a certain benchmark in that period. Finally, the differences between the mean BHAR before and after the law will be examined using a two-means t-test. The formula of the two test-statistics is as follows: 𝑡 = ,DEFG H ,DEFG/ . , 𝑡 = ,DEF J3- K,DEF(JAL') H ,DEFG / .

It is possible that the law only started significantly influencing the performance of the financial sector as time passed. To test this effect over time the difference-in-differences (DID) technique is applied. This econometric method attempts to mimic an experimental design by looking at observational data before and after an intervention, such as a policy change (Angrist & Pischke, 2008). Furthermore, it compares the changes in a certain outcome over time between a ‘treatment group’ that is affected by the policy and a ‘control group’ that is not. By performing a regression on this difference, it tests whether the policy has a significant effect on the outcome variable. Since an interaction term between time and the intervention is introduced, it also tests the effect over time. In this research, the following regression model will be used:

𝐵𝐻𝐴𝑅%,' = 𝛽N+ 𝛽6∗ 𝑇𝐼𝑀𝐸 + 𝛽T∗ 𝐵𝐶 + 𝛽V𝑇𝐼𝑀𝐸 ∗ 𝐵𝐶 + 𝜀

Where,

TIME : Period of time

BC : Dummy variable for the bonus cap

TIME*BC : Interaction term between time and the bonus cap

Lastly, the volatility effects of the bonus cap will be tested. First, the standard deviation of the monthly returns in the financial sector is calculated both before and after the bonus cap. Then, a F-test will be performed to examine whether significant difference in volatility has occurred since the law. Calculation of the test statistic is as follows:

𝐹 =𝜎T(01/08/2011 − 01/01/2015)

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After performing these tests, statistical inferences on the hypotheses is found, which provides an answer to the research question. For clarifying purposes, the mathematical formulation of the hypotheses is listed below:

H10: 𝐵𝐻𝐴𝑅 2011 − 2015 = 𝐵𝐻𝐴𝑅 (2015 − 2018)

H11: 𝐵𝐻𝐴𝑅 (2011 − 2015) ≠ 𝐵𝐻𝐴𝑅 (2015 − 2018)

H20: 𝜎 2010 − 2015 = 𝜎 (2015 − 2018)

H21: 𝜎 2010 − 2015 ≠ 𝜎 (2015 − 2018)

3.2 Sample formation

The analysis of this research begun by constructing a proxy for the Dutch financial sector, followed by composing the reference portfolios. Each of these portfolio is composed on an equally weighted basis. The exact steps that were taken during the sampling process are clearly structured below. In the appendix, additional results of the sampling process can be found. The construction of a proxy for the Dutch financial sector:

1. Collection of all Dutch financial institutions that are listed on AEX, AMX or AscX 2. Omit all firms that have no or interrupted stock data for the period 01/08/2011 –

01/06/2018

These steps yielded the following firms as proxy for the Dutch financial sector: - ING GROEP

- AEGON

- VAN LANSCHOT KEMPEN - BINCKBANK

- KASBANK

- NN GROUP1

The construction of a size-matched sample:

1. Calculate market capitalization by multiplying number of shares outstanding with the share price for all listed firms in the Netherlands on 01/01/2015

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3. Pick for each Dutch financial institution a control firm that has the closest size These steps yielded the following control firms:

- ASML HOLDING

- KONINKLIJKE AHOLD DELHAIZE - RANSTAD

- KONINKLIJKE BAM GROEP - AMSTERDAM COMMODITIES - NEDAP

The construction of a size-and-industry based sample:

1. Calculate market capitalization by multiplying number of shares outstanding with the share price for all listed firms in Belgium, France and Germany operating in the financial industry on 01/01/2015

2. Rank all firms based on their size

3. Pick for each Dutch financial institution a control firm that has a similar industry code and size

These steps yielded the following control firms: - SOCIETE GENERALE GROUP

- CNP ASSURANCES SA - HANNOVER RUECK SE

- OLDENBURGISCHE LANDESBANK AG - MLP AG

- OVB HOLDING AG

The last reference portfolio existed of the Amsterdam Midkap Index (AMX). This index was chosen since it includes more firms than the control groups and has a similar median size as the firms representing the financial sector. In addition, it is favorable to the other two Dutch indices, the AEX and the AscX, since they both already include three financial institutions in their index. Using them as benchmark would yield high cross-sectional dependence.

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3.3 Data

As mentioned before, firm performance is measured using stock returns. Calculation of these stock returns has been done by taking the relative difference of the adjusted closing price. This value corrects for possible redistributions and corporate actions, and provides a good estimate of historical returns. For constructing the reference portfolios, the market capitalization was calculated by multiplying the stock price with the number of shares outstanding. In addition, the industry codes were obtained from the financial institutions in Belgium, Germany and France and used as matching criterion. All of this financial data has been retrieved from Thomson Reuters Datastream. This database has also been consulted for data on quarterly GDP growth with the exception of the second quarter of 2018. Since no data was available at the time of this research, an estimate of quarterly GDP growth was taken from the IMF. These quarterly figures were converted to monthly growth rates in order to correct the foreign companies’ stock returns.

4 Results

This research draws inference from carefully investigating the stock returns of the Dutch financial sector. The following figure displays the behavior of the sector’s stock returns and its relative performance to the different benchmarks. It consists the holding period returns (HPR) of investing in the Dutch financial sector as well as investing in the different reference portfolios for the period 01/08/2011 – 01/06/2018.

Figure 1: Holding period return of investing in Dutch financial sector, size-matched portfolio, industry-and-size

matched portfolio and the AMX

-50% 0% 50% 100% 150% 200% 2011 2012 2013 2014 2015 2016 2017

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Investing in the value-weighted portfolio of the Dutch financial sector on 01/08/2011 until 01/06/2018 yields a HPR of 26% (pre-bonus cap: 13%, post-bonus cap: 10%). For the size-matched portfolio, industry-and-size matched portfolio and the AMX these returns are 158% (63%; 46%), 56% (13%; 39%) and 42% (15%; 18%) respectively. Although some of these differences in returns seem quite substantial, it is important to notice that the entire investment window exists of 82 months.

To examine the effect of the Dutch bonus cap on firm performance different tests were used. First, a t-test was done to detect the existence of significant abnormal returns relative to the reference portfolios. The following table summarizes the outcomes of these tests:

Table 1. Buy-and-hold abnormal returns of Dutch financial sector against certain benchmark

Size-matched

Industry-and-size

matched AMX

Time 𝐵𝐻𝐴𝑅 t-test 𝐵𝐻𝐴𝑅 t-test 𝐵𝐻𝐴𝑅 t-test

Pre-Bonus cap -0.89% -1.24 0.02% 0.03 0.04% 0.07

Post-Bonus cap -0.85% -1.49 -0.51% -0.87 -0.23% -0.40

Entire period -0.87% -1.90 -0.24% -0.56 -0.09% -0.24

The results of the t-tests indicate that the mean monthly buy-and-hold abnormal returns of the Dutch financial sector to its benchmarks have been significantly different from zero. In other terms, the Dutch financial sector did not significantly under or over perform its reference portfolios over the period 01/08/2011 – 01/06/2018. Although benchmarking against the size-matched sample does show signs of underperformance, it is not found significant. Reason for this insignificance is mainly due to the high volatility that characterizes stock returns. In addition, sampling is relatively small, which further reduces the power of the test.

The differences between the mean buy-and-hold returns have also been tested. A two-means t-test has been performed to examine the difference in 𝐵𝐻𝐴𝑅𝑠 before and after the Dutch bonus cap was installed. Table 2 depicts these results:

Table 2. Difference in mean buy-and-hold abnormal returns of financial sector to three benchmarks

∆𝐵𝐻𝐴𝑅 t-test

Size-matched-sample 0.04% 0.07

Industry-and-size-matched sample -0.53% -0.86

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The outcomes show that the abnormal returns have become slightly more negative after the introduction of the cap. However, these results are also statistically insignificant for similar reasons as described earlier.

To further investigate the effect of the bonus cap on performance the difference-in-differences technique was used. This method allows for testing the effect of an intervention over time using a regression. The regression results of the DID as performed in this research are presented in table 3:

Table 3. Difference-in-differences regression results

BHARsize BHARind BHARamx

cons -0.0270* -0.0165 -0.0077 (0.0129) (0.0135) (0.0101) Time 0.0009 0.0008 0.0004 (0.0006) (0.0005) (0.0004) BC dummy -0.0123 0.0332 0.0039 (0.0376) (0.0320) (0.0340) Time * BC -0.0004 -0.0011 -0.0004 (0.0008) (0.0006) (0.0007)

BHARsize: Buy-and-hold abnormal return of investing in Dutch financial sector against size-matched sample BHARind: Buy-and-hold abnormal return of investing in Dutch financial sector against industry-and-size-matched sample

BHARamx: Buy-and-hold abnormal return of investing in Dutch financial sector against AMX-index

*: significant effect at 5%

Output of the regression shows no significant effect of the bonus cap on stock returns, which is in line with the findings in table 2. Moreover, there is no statistical evidence of the existence of an effect that occurred over time. The regression does show that the Dutch financial sector significantly underperformed its size-matched control group over the whole research period. However, the regular t-test as depicted in table 1 does not find a significant result at a significance level of 5%. Together with the results in table 2 this provides evidence that the first hypothesis cannot be rejected. Hence, there is no significant effect of the bonus cap on the performance of the financial sector.

Lastly, the volatility effects of the bonus cap on firm performance were examined. This was done using a F-test on the volatility of the financial sector’s returns pre- and post-instalment of the law. For the period prior to the bonus cap volatility of these returns was

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0.0539. The period following the cap yielded a volatility of 0.0482. Although a small decrease has occurred, this result is not significant. Therefore, the second hypothesis also is not rejected.

5 Conclusion and Limitations

The objective of this study was to explore the effect of the Dutch bonus cap on firm performance. By performing a critical literature review and quantitative analysis an answer to the research question has been found. The findings suggest that no significant effect exists between the introduction of the Dutch bonus cap and the performance of the Dutch financial sector. Both the absolute level and the volatility of their stock returns have not significantly changed after the law got installed. A possible explanation of these results is that people are motivated by more than just money. As described by Glucksberg (1962) bonus payments can crowd out intrinsic motivation and lower creativity, which distort variable pay’s positive effects. Alternatively, it could be that the law only has a long-term effect since people do not switch jobs on such a short notice. As for now, this result could be considered to be in line with research done by Dubbheus and Kabir (2008) who did not find a positive pay for performance relation to exist in the Netherlands

There are some limitations to this research that should be kept in mind when interpreting its results. First, the dataset used in this research is relatively small. Since there are few Dutch financial institutions that have been continually listed for a long time, the sample representing the sector exists of 6 firms only. Second, long-term event studies are known for their controversial methodology and remain a subject of discussion till today. Measuring an effect over a long horizon has a lot of noise to it and even when controlling for many factors test-statistics will still be slightly biased (Kothari & Warner, 2004). Third, human characteristics are known to affect the way in which variable pay influences performance (Dohmen & Falk, 2011). Since the results are obtained in one specific industry within a single country it is hard to generalize them to different settings.

For future research, the following recommendations have been made to overcome some of the issues experienced in this study. It might be interesting to include private firms in the dataset and test firm performance using a financial ratio. This provides a better proxy of the financial sector and higher statistical power. Furthermore, future research could examine the effect of the European bonus cap on banking performance. This also increases the number of observations and yields better external validity since it focuses on multiple countries.

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Appendix

Table 4. Firm characteristics on 01/01/2015

Size (in bln. €) Industry code Dutch Financials ING 41.90 6199 Aegon 13.43 6311 NN-Group 8.70 6311 V Lanschot Kempen 0.71 6020 Binckbank 0.50 6211 Kasbank 0.16 6200 Size Matched ASML 39.16

Koninklijke Ahold Delhaize 13.20

Randstad 7.22

Koninklijke Bam groep 0.70

Amsterdam commodities 0.45

Nedap 0.18

Industry-and-size matched

Societe Generale Group 28.17 6020

CNP Assurances SA 10.11 6311

Hannover Rueck SE 9.04 6311

Oldenburgische Landesbank AG 0.47 6020

MLP SE 0.40 6211

(20)

Table 5. Quarterly GDP growth Quarter NL GDP GER GDP FR GDP 2011-Q3 0.0002 0.0047 0.0021 2011-Q4 -0.0074 0.0002 0.0025 2012-Q1 -0.0017 0.0032 0.0008 2012-Q2 0.0009 0.0010 -0.0008 2012-Q3 -0.0039 0.0023 0.0014 2012-Q4 -0.0077 -0.0045 -0.0006 2013-Q1 0.0035 -0.0022 -0.0002 2013-Q2 -0.0024 0.0089 0.0067 2013-Q3 0.0063 0.0051 -0.0001 2013-Q4 0.0060 0.0041 0.0042 2014-Q1 -0.0017 0.0088 0.0009 2014-Q2 0.0053 -0.0017 0.0024 2014-Q3 0.0039 0.0032 0.0044 2014-Q4 0.0113 0.0089 0.0011 2015-Q1 0.0076 0.0011 0.0043 2015-Q2 0.0002 0.0044 -0.0003 2015-Q3 0.0043 0.0032 0.0038 2015-Q4 0.0033 0.0042 0.0025 2016-Q1 0.0076 0.0063 0.0067 2016-Q2 0.0034 0.0046 -0.0023 2016-Q3 0.0096 0.0033 0.0019 2016-Q4 0.0065 0.0042 0.0061 2017-Q1 0.0073 0.0090 0.0078 2017-Q2 0.0145 0.0063 0.0065 2017-Q3 0.0041 0.0073 0.0066 2017-Q4 0.0072 0.0061 0.0071 2018-Q1 0.0053 0.0030 0.0017 2018-Q2 0.0066 0.0046 0.0034

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