The relationship between Corporate Social Performance (CSP) and Corporate Financial Performance (CFP)

Transcription

1 The relationship between Corporate Social Performance (CSP) and Corporate Financial Performance (CFP) Evidence from the US banking industry Master Thesis Student name: Gerben van Hemert Administration number: address: Date: November, 2014 Supervisor name: F. Castiglionesi

2 Table of contents 1. Introduction Literature review Possible relationships between CSP and CFP Negative relationship Neutral relationship Positive relationship Relationship for the banking industry Reverse causality Research design Data description Variables Regression model Hypotheses Samples Empirical results Correlation Regression analyses (samples one to three) Regression analyses (samples four to nine) Reverse causality analysis Summary of the results Conclusion References Appendix

3 Abstract This thesis has two major purposes. The first purpose of this thesis is to investigate whether there is a positive relationship between Corporate Social Performance (CSP) and Corporate Financial Performance (CFP). The second purpose of this study is to examine whether the relationship between CSP and CFP is higher after the subprime mortgage crisis than before this crisis. This research is based on data from banking corporations established in the United States. The CSP data is based on ratings from the KLD index and ratings from the Community Reinvestment Act (CRA). The CFP data and bank-specific data are obtained from Bankscope. Macro-economic data has been obtained from the website of the IMF. In this research nine samples were regressed. Only one sample showed a positive and significant relationship between CSP and CFP. However, in this sample only 15 of the 401 observations were negative, so this result could be biased. Six of the nine samples were used to investigate whether the relationship between CSP and CFP is higher after the subprime mortgage crisis than before this crisis. This cannot be concluded, because five of the six samples did have an insignificant CSP variable. On the basis of the results of this research, it can be concluded that there is no relationship between CSP and CFP. Also it can be concluded that there is no higher relationship between CSP and CFP after the subprime mortgage crisis than before this crisis. 3

4 1. Introduction Nowadays, the trust among people in the banking industry is very low. YouGov- Cambridge conducted three surveys under a total of 11,089 citizens of the United Kingdom and published their results in In these surveys the reputation of 26 industries were tested. The results for the banking industry were terrible. 73% of the participants described the reputation of the banking industry as bad. Just 4% of the participants considered the banks as high ethical and with high moral standards, which was an equal rating received by casinos, betting shops and the online gambling industry. Peter Kellner, president of YouGov, said in a response to these findings: The real challenge for the banking community is to show by the deeds and not just their words that they belong to the same world as the rest of us. The YouGov-Cambridge Survey is a survey conducted in the United Kingdom and therefore the survey results could be different from the global results. For that reason, also the results from the 2013 Edelman Trust Barometer will be given. The 2013 Edelman Trust Barometer is a survey conducted in the year 2012 across 26 countries with 31,000 participants. The results of this survey were for the banking industry comparable with the results of the YouGov-Cambridge survey. The least trusted sectors were the banking sector and financial sector with for both sectors a meager score of 50%. Nevertheless, the results for these sectors were higher than the results from the 2012 Edelman Trust Barometer. In this survey the scores for the banking sector and financial sector were 47% and 45%, respectively. Thus, the trust among people is rising in these sectors. The trust in the banking sector is lower than a few years ago. For example, in the 2008 Edelman Trust Barometer the trust in the banking sector among the people in the US was 69%. However, this trust declined a year later to 36%. This decline in trust was a result of the subprime mortgage crisis. Many people accused the banking corporations for this crisis. 4

5 Based on these findings this research examines whether a banking corporation benefits more from doing good deeds after the subprime mortgage crisis in 2007 than before. In other words, if there is a positive relationship between Corporate Social Performance (CSP) and Corporate Financial Performance (CFP) for the banking industry, whether this relationship is higher after this crisis than before this crisis. The findings of this research could help the banking corporations in their decision to allocate the scarce corporate resources. Two research questions will be answered in this thesis. The first research question in this thesis is if there is a positive relationship between CSP and CFP in the banking industry. The idea behind this question can be found in the paper from Bushman and Wittenberg-Moerman (2012). They found that higher bank reputation is associated with high profitability and credit quality. This means that banking corporations with high social reputation are able to find more creditworthy borrowers and therefore should have higher profit and higher asset quality. Hence, it is expected that there will be a positive relationship between CSP and CFP. The second research question that will be answered is whether there is a higher positive relationship between CSP and CFP after the subprime mortgage crisis in 2007 than before this crisis. As mentioned before, the trust in the banking industry is lower after the subprime mortgage crisis than before this crisis. This means that after this crisis the overall reputation of the banking industry is lower. Because the overall reputation of the banking industry is lower, banking corporations with high CSP could benefit even more from this high CSP. In this paper ratings from the Community Reinvestment Act (CRA) and ratings obtained from the KLD index will be used to get CSP data. The CFP data and control variables are obtained from different databases. Bankscope is used to acquire bankspecific data. Data for various control variables and the CFP data are obtained from this database. The macro-economic factors will be obtained from the website of the International Monetary Fund. The two macro-economic control variables obtained from this data source are the growth of the GDP and the annual rate of inflation. 5

6 The data in this paper will be based on data from banking corporations established in the United States. The United States has been chosen as country of research, because this country has the most social data available. For example, the KLD index has ratings for 3000 companies, but all those 3000 companies are established in the US. Two different datasets will be used. The first dataset is based on the CRA ratings and it contains 609 observations from 280 banking corporations for the years 2001 to The second dataset is based on the KLD index. This dataset contains 832 observations from 129 banking corporations for the years 2002 to The CFP variable in this paper is measured with the performance indicator Return on Assets (ROA), thus the dependent variable is ROA. To measure CSP one dummy variable has been created, the CSP dummy. The CSP dummy is based on the CRA ratings and the ratings obtained from KLD index. Various bank-specific and macroeconomic control variables are used to control for their influences on the dependent variable ROA. Nine samples are created from the CRA dataset and the KLD dataset. The first sample is based on the whole CRA dataset, the second sample is based on the entire KLD dataset and the third sample is based on both the CRA dataset and the KLD dataset. The remaining six samples are obtained by dividing the three samples mentioned above in two parts. The three samples based on the first part have a sample period until 2007 and the three samples based on the second part have a sample period beginning in All nine samples are used to examine whether there is a positive relationship between CSP and CFP in the banking industry. The last six samples are also used for the second research question. Two of the nine samples do have a significant coefficient in the univariate regression. The regression results obtained from the univariate regression of the whole CRA dataset shows a positive and a significant coefficient for the CSP dummy. The regression results from the sample based on the first part of the KLD dataset shows for the univariate regression a negative and a significant CSP dummy. Only one multivariate regression has a significant CSP dummy. The CSP dummy obtained from 6

7 the multivariate regression based on the first part of the CRA sample is positive and significant. However, this regression has a caveat. It could be that the results are biased, because only 15 of the 401 observations are negative. The relationship between CSP and CFP has been a topic of academic study the last decades. Some researchers have found a negative relationship between CSP and CFP (e.g., Cordeiro and Sarkis, 1997). Other researchers have found a neutral or inconclusive relationship between CSP and CFP (e.g., Cohen, Fenn, and Naimon, 1995; McWilliams and Siegel, 2000). But the most researchers have found a positive relationship between CSP and CFP (e.g., Waddock and Graves, 1997; Ruf, Muralidhar, Brown, Janney and Paul, 2001). That most research results showed a positive relationship between CSP and CFP has been confirmed by some researchers. The paper from Griffin and Mahon (1997), and the paper from Roman, Hayibor, and Agle (1999) confirmed that most studies found a positive relationship. A more recent paper, written by Endrikat, Guenther and Hoppe (2014), conducted a meta-analysis based on 245 bivariate correlations and 208 partial correlations derived from a total of 149 studies and found once again an overall positive relationship between CSP and CFP. Simpson and Kohers (2002) found in their study similar results for the banking industry in the US. Their research was based on data from a sample of commercial banks established in the US and they found also a positive relationship between CSP and CFP. Another more recent study written by Wu and Shen (2013) found similar results for the banking industry. CSP and CFP were also positive related in this research. When researching the relationship between CSP and CFP a possible problem can arise. The possible problem is reverse causality. Reverse causality means in this case that it seems that CSP does affect CFP because the CSP dummy is significant, while in reality CFP affects CSP. So, higher financial performances will result in more resources available to be invested in social factors. Various researchers investigated 7

8 and confirmed this reverse relationship (e.g., Stanwick and Stanwick, 1997; Waddock and Graves, 1997). Based on the results of this paper it can be concluded that the relationship between CSP and CFP for the banking industry in the United States is inconclusive. Only one of the nine multivariate regressions showed a positive and significant relationship between CSP and CFP. This result is based on the first part of the CRA dataset. But, it could be that this result is significant due to reverse causality. Therefore, this sample is once again regressed, only this time with the CSP dummy as dependent variable, ROA as independent variable and firm size as control variable. The independent variable ROA is insignificant, so it can be concluded that the positive and significant result for the CSP dummy is not the result of reverse causality. However, this significant result could be biased, because only 15 of the 401 observations are negative. For that reason, the overall conclusion is that there is no relationship between CSP and CFP. Based on these findings the second research question must also be answered negatively. So, there is no higher positive relationship between CSP and CFP after the subprime mortgage crisis in 2007 than before the crisis, because there is no significant relationship between CSP and CFP for a sample with a sample period from 2001 to 2007 (or 2002 to 2007) and for a sample from 2008 to 2013 (or 2008 to 2011). The results from Simpson and Kohers (2002), and Wu and Shen (2013) are different from the findings of this paper. The papers from Simpson and Kohers, and Wu and Shen showed a positive relationship between CSP and CFP, while in this paper no significant relationship between CSP and CFP has been found. The differences between the results obtained from the samples based on the CRA dataset and Simpson and Kohers research results could be the result of the low amount of negative ratings in the CRA dataset. The low amount of negative ratings could be caused by using different sample years and different criteria for selecting banking corporations in the sample. The differences between the results of this paper and the results of the paper from Wu and Shen could also be the result of using different sample years and different criteria for selecting banking corporations in the sample, but it could also be 8

9 the result of using different CSP databanks and different countries. At last, the difference in the results of this paper and the results of these two papers could also be caused by using different control variables. A limitation of this paper is that the results from the regressions based on the CRA dataset could be incorrect due to the low amount of negative ratings. A recommendation for further research is to do a global research about the relationship between CFP and CSP for the banking industry and for other industries, because it could be that the result of the relationship between CSP and CFP for the banking corporations in the US differ with the results from other countries and other industries. Another recommendation for further research is to investigate which control variables do affect the CFP variable. In this research ROA is used as CFP variable and firm size had no significant affect on ROA for all the nine samples. But, the variable firm size has been used as control variable for ROA in many researches. Therefore, further research is needed to find the correct control variables. The rest of this thesis is organized as follows: Chapter 2 will cover the literature review. In 2.1 the different findings about the relationship between CSP and CFP will be present. In 2.2 two papers with findings about the relationship between CSP and CFP for the banking industry will be given. Reverse causality will be covered in paragraph 2.3. Chapter 3 contains information about the research design. The results of this empirical research can be found in chapter 4. At last, in chapter 5, the main conclusion and an answer on the research questions will be given. 9

10 2. Literature review In this chapter some of the papers that are mentioned in the introduction will be further reviewed. In the first paragraph the possible findings about the relationship between CSP and CFP will be given. The papers with conclusions based on the relationship between CSP and CFP for the banking industry will be covered in second paragraph. In the third paragraph a possible problem mentioned by other researchers, which is the problem of reverse causality, will be covered. 2.1 Possible relationships between CSP and CFP First, the paper with the result of a negative relationship between CSP and CFP will be covered. Second, the papers that have found a neutral or an insignificant relationship between CSP and CFP will be covered. Third, the papers that show a positive relationship between CSP and CFP will be covered in this paragraph Negative relationship The result that there is a negative relationship between CSP and CFP is not found often. Griffin and Mahon (1997) categorized 62 research results; 33 research results showed a positive relationship, 9 research results were insignificant and 20 research results showed negative relationship. This categorizing was redone by Roman, Hayibor and Agle (1999). They modified and extended the paper of Griffin and Mahon and found 33 research results with a positive relationship, 14 research results that showed an inconclusive relationship and only 5 research results with a negative relationship. The modifications were based on different interpretations of some results and the modifications were also a result of using the standards available in 1999 to check the validity and relevance, instead of assuming that the standards of the used papers were correct. Therefore, the paper from Roman et al. gives a better overview of the different relationships between CSP and CFP than the paper of Griffin and Mahon. So, only 5 of the 52 research results showed a negative relationship between CSP and CFP. 10

11 In this literature review one paper that suggests a negative relationship between CSP and CFP will be covered. The paper is from Cordeiro and Sarkis (1997). Their sample consists of data from 523 corporations established in the United States in the year As dependent variable they used 1- and 5-year earnings-per-share forecasts. The variable environmental pro-activism was used as measure for CSP. As control variable only firm size and firm leverage were used. They separated their regressions in a regression with adjustments for industry means and a regression without adjustments for industry means. For both regressions the independent variable for CSP was found significant and negative Neutral relationship Another relationship between CSP and CFP is the neutral or inconclusive relationship. This means that the independent variable for CSP has a positive or negative sign, but is insignificant. If this is the case it cannot be said that CSP affects CFP, but that the coefficient is a result based on chance. Some researchers found this relationship and two papers that have this relationship as research result will be covered. The first paper is from Cohen, Fenn and Naimon (1995). In this paper environmental performance variables (e.g. volume of oil spills) were used to determine whether a portfolio with low pollution get higher returns than a portfolio with high pollution. Three dependent financial variables were used: risk-adjusted market returns, ROA and ROE. They found for the environmental performance variables that either the results were insignificant or that the results were positive. So, the relationships between some environmental performance variables and financial performance variables were inconclusive, while for other environmental performance variables the relationships were positive. Therefore, they concluded that overall they found no penalty for investing in a green portfolio or a positive return from green investing. The second paper has been written by McWilliams and Siegel (2000). The paper investigated the control variable research and development. Most prior researches did not control for the investments in research and development, so the regression model which was used in previous researches could be flawed. As dependent variable they used the long-run financial performance measure ROA. The independent variable for 11

12 CSP was a dummy variable based on results from the KLD index. The dummy variable had two values: 1 when the rating was positive and 0 when the rating was negative. As control variable they used size, risk, advertising intensity, R&D investments and in one regression the type of industry was added as control variable. They found that the control variable R&D investments had a positive, significant effect on the financial performance and that the coefficient for CSP was insignificant Positive relationship A positive relationship between CSP and CFP has been found often by researchers. Two papers that have as research result a positive relationship between CSP and CFP will be reviewed briefly. The first paper was written by Waddock and Graves (1997). Waddock and Graves researched the relationship between CSP and CFP with a sample of 469 corporations. Most of these corporations were S&P 500 corporations. The variable they used for CSP was based on KLD ratings for the years 1990 and Instead of using CFP and control data for the years 1990 and 1991, they used lagged CFP and control data. CFP was measured with three accounting variables: ROA, ROE and return on sales (ROS). As control variable they used firm size, risk and industry. They found that CSP has a positive and a significant coefficient for all three dependent variable. Further, Waddock and Graves researched whether there is a reverse causality between CSP and CFP. So, whether a higher CFP leads to a higher CSP. They found that this relationship was also positive and significant. Therefore, they concluded that firms may do good by doing well, but that firms may also do well by doing good. The second paper is from Ruf, Muralidhar, Brown, Janney and Paul (2001). They examined whether shareholders got short-term benefits from an improving CSP. Thus, whether shareholders benefit from an improving CSP in the year where the CSP was improved and in the subsequent year. Their sample consists of 496 firms for the years 1991 to They used the growth in sales, the change in ROE and the change in ROS as measures for CFP. They developed an own measure for the change in CSP based on the categories of the KLD index. As control variables they used industry, size and when the dependent variable was the change in ROE or the change in ROS 12

13 they also used the change in ROE from the previous year or the change in ROS from the previous year as control variable, respectively. The coefficient for CSP was positive and significant for growth in sales for the year 0 ( ), for the change in ROE and the change in ROS for year 3 ( ). Even though, in this paper not all the years have a positive and significant relationship between CSP and CFP, the results give some support for a positive relationship between CSP and CFP. 2.2 Relationship for the banking industry The industry of research in this paper is the banking industry as mentioned in the introduction. Therefore, the research results of two papers, which investigated the relationship between CSP and CFP for the banking industry, will be reviewed. The first paper is from Simpson and Kohers (2002). In this paper the variable for CSP was a dummy variable based on the Community Reinvestment Act (CRA). The dummy variable was one when the CRA rating was outstanding. The dummy variable was zero when the CRA rating needed to be improved or when the CRA rating was substantial noncompliance. The CRA rating could also be two, which was satisfactory, but they omitted this rating to provide a clear separation between banks with high social performance and low social performance. They used two variables to measure the CFP: ROA and loan losses. The variable loan losses was calculated by dividing the net charged-off loans by the average total assets. For the regression with ROA as dependent variable firm size, risk, asset portfolio composition, local economic environment, holding company affiliation, level of investment in branch offices, costs of funds and overhead expenses were used as control variables The control variables for the regression with loan losses as dependent variable were firm size, risk of the loan portfolio, size of the loan portfolio, and economic conditions in the local loan market. For the ROA regression they found a positive and significant result for the CRA dummy. The regression with loan losses as dependent variable had a negative and significant result for the CRA dummy, which means that high social performance banking corporations did have low loan losses. Based on these results they concluded that there is a positive relationship between CSP and CFP for the banking industry. 13

14 The second paper was written by Wu and Shen (2013). Wu and Shen investigated the relationship between Corporate Social Responsibility, which is a part of CSP, and CFP. Their sample contained 162 banks from 22 countries with observations for the years 2003 to They created an own index for CSR, which was based on the EIRIS database. Wu and Shen stated about the EIRIS database the following: EIRIS gathers data on the basis of a questionnaire. It specializes in the measurement of CSP against a consistent and objective set of criteria. They used five performance variables: ROA, ROE, Non-Performing Loans (NPL), Net Interest Income (NII) and Non-Interest Income (NonII). The control variables were divided in three categories. The first category was based on the bank characteristics (size, leverage, liquidity, overhead, coverage for non-performing loans). Because their study was conducted across different countries, the second category consisted of institutional factors (restrictions for securities, insurance and real estate, corruption, financial sector development, and product market structure). The third category controlled for macroeconomic variables (GDP per capita, GDP growth). They found that CSR was positively associated with CFP in terms of ROA, ROE, NII and NonII. CSR was negatively associated with NPL. Just as in the paper of Simpson and Kohers (2002), this means that a bank with high social performance has low loan losses. Thus, all five findings showed a positive relationship between CSR and CFP. 2.3 Reverse causality There is one problem when researching the relationship between CSP and CFP, which is shortly mentioned in the introduction and at the review of the paper from Waddock and Graves (1997). The problem is reverse causality. Even though, there is a significant positive relationship between CSP and CFP, it could be that CSP does not affect CFP, but that CFP affects CSP. Thus, a corporation with high CFP has more resources available to spend on CSP, which causes a higher CSP. This is the reason that the company with high CFP has high CSP and not the other way around. This reverse causality was not only examined by Waddock and Graves, but, for example, also by Stanwick and Stanwick (1997). Stanwick and Stanwick examined the relationship between CSP and the three independent variables CFP, size and environmental performances. For the dependent variable CSP they used data from the 14

15 Fortune Corporate Reputation Index. The independent variable CFP was measured by dividing the profits by the annual sales level. Their sample was based on 111 firms in 1987; 102 firms in 1988; 120 firms in 1989; 125 firms in 1990; 118 firms in 1991; and 121firms in They found for all the years a positive and significant relationship between CFP and CSP. Thus, they found that a higher CFP leads to a higher CSP. Therefore, it is important to check if there is reverse causality, because when there is reverse causality the results only show correlations and not causality. 15

16 3. Research design The first paragraph of this chapter describes which databases are used and gives information about the two datasets used in this paper. In the second paragraph the variables used for this research and their data sources can be found. The main regression model used in this paper can be found in the third paragraph. The hypotheses of this research are given in the fourth paragraph. In the fifth paragraph information will be given about the nine different samples which are used to measure the hypotheses. 3.1 Data description It is a difficult task to obtain reliable quantitative data for CSP, because no overall accepted database for CSP data exists. Simpson and Kohers (2002) mentioned that individual facets of CSP have been measured with the EPA Toxics Release Inventory, Corporate 500 Directory of Corporate Philanthropy data, product recalls and illegal acts. Broader measures of CSP are Fortune (a reputation survey), the KLD index and the Domini 400 Social Index. Simpson and Kohers also mentioned in their paper a problem with these broader measures for CSP. The problem is that the databases of these broader measures were not big enough to provide a large sample for one specific industry. Therefore, they used the CRA ratings as CSP measure for their study. In this paper both the ratings based on the CRA and the KLD index will be used. The reason why both CRA ratings and the KLD index are used as measures for CSP will be explained further in this paragraph. For this research various databases are used. Two databases, Bankscope and the KLD index, were accessed via the website of Wharton Research Data Services (WRDS). WRDS is a web-based business data research service since Bankscope was used to obtain data for the bank-specific control variables and the dependent variable ROA. The variables used to measure CSP are obtained from the KLD index and from the CRA database which was accessed via the Federal Financial Institutions Examination Council s (FFIEC) website. Marco-economic economic control variables were obtained from the website of the International Monetary Fund (IMF). 16

17 Two datasets are used in this research, the CRA and KLD dataset. The CRA dataset is based on the sample period 2001 to 2013, with a total of 280 banks and 609 observations. The following criteria were used to include a bank in this dataset: The bank is a large bank in the CRA database, with at least in one sample year an asset size of at least one billion. The bank can be found in the Bankscope database, so financial data for the independent and control variables are available. The KLD dataset s sample period is from 2002 to 2011, with a total of 129 banks and 832 observations. The following criteria were used to include a bank in this dataset: The bank is on the list of banks from the Federal Deposit Insurance Corporation (FDIC), which can be downloaded from the website of the FDIC. This bank must have at least an asset size of two billion. The bank can be found in the KLD database, so that it is clear that the CSP variable is available. The bank can be found in the Bankscope database, so financial data for the independent and control variables are available. Initially, only the CRA dataset was used in this paper, but there are two reasons why the KLD dataset is also included. The first and main reason is that the amount of observations with a negative rating for the CRA dataset is low, only 35 of the 609 observations have a negative rating. Therefore, it could be that the results for samples based on the CRA dataset are biased due to the low amount of negative ratings. The dataset based on the KLD index does not have this problem. This dataset consists of 832 observations and 365 observations do have a negative rating. This is one reason why also the KLD dataset is used, because the possible problem for samples based on the CRA dataset do not exist for the samples based on the KLD dataset. The second reason to use the KLD dataset is because in 2002 the database of the KLD index was expanded from 650 companies to 1100 companies and in 2003 the database was even further expanded to a total of 3000 companies. Therefore, the problem found by Simpson and Kohers (2002) that the KLD index is not big enough to provide a large sample for one specific industry, is gone. 17

18 3.2 Variables When looking at the regression models from the papers mentioned in the literature review it is clear that there are many different interpretations about the right regression model. Therefore, the variables used in the regression model of this paper will be given and will be substantiate with some papers. The structure of this paragraph and some variables are based on the paper from Ongore and Kusa (2013). In the first part of this paragraph the financial performance indicator used in this paper will be given. In the second part of this paragraph information about the variable of research, the CSP variable will be provided. In the third part of this paragraph the control variables of this regression model, which are known determinants of bank performance, will be given. Also, a table with an overview of the variable used with their definitions and their sources will be given in this paragraph (table 3.1) Financial performance indicator A commonly used measure to indicate the performance of a corporation is the ROA. ROA can be calculated by dividing the net income by the total assets (Berk and DeMarzo, 2011). Also, in many reviewed papers ROA is used as a financial performance indicator. Therefore, this measure of financial performance will be used in the regression model Social performance indicator For both datasets a dummy variable will be created to measure the corporate social performances. In this paper a dummy variable is used with a minus one and a one, instead of the commonly used dummy variable with a zero and a one. The reason why this adjusted dummy variable is used in this paper can be found in the paper from Roman, Hayibor and Agle (1999). In their paper they reconstruct a table which was obtained from a paper written by Griffin and Mahon (1997). Griffin and Mahon presented in their paper a table with the possible relationships between CSP and CFP based on prior research. Roman et al. reconstructed this table, because some results of the table in the paper of Griffin and Mahon were not based on current standards. Some results were reclassified or removed and some results were added. A reason why some results were reclassified was because Roman et al. treated a negative rating 18

19 causing a negative result as a positive relationship. This is the reason why minus one is used. So, if there is a positive relationship and a negative rating, the minus one will cause a decline in the ROA. However, if there is a negative relationship and a negative rating, the minus one will cause an increase in the ROA. The CSP dummy variable for the CRA is minus one when the CRA rating is 3 (needs to improve) and 4 (substantial noncompliance). The CSP dummy variable is one when the CRA rating is 1 (outstanding). Banks receiving a CRA rating 2 (satisfactory) were omitted. This was also done by Simpson and Kohers (2002). The satisfactory ratings were omitted to provide a clear separation between banks with high social performance and low social performance. The dummy variable for the KLD index is minus one when the social rating is negative and one if the social rating is positive. The ratings from the KLD index are positive when the sum of the strengths minus the sum of the concerns is positive and a negative when the sum of the strengths minus the sum of the concerns is negative. Observations with a rating of zero were omitted Control variables In the paper written by Ongore and Kusa (2013) the determinants of bank performances were divided in two groups. The first group was based on bank-specific variables and the second group was based on macro-economic variables. Those two groups will be used to divide the control variables Bank-specific variables. The first few bank-specific variables are based on the CAMEL framework. Dang (2011) said the following about the CAMEL framework: It has proven to be an effective internal supervisory tool for evaluating the soundness of a financial firm, on the basis of identifying those institutions requiring special attention or concern. CAMEL stands for Capital adequacy, Asset quality, Management efficiency, Earning ability and Liquidity. Earning ability is not used as bank-specific control variable, because earning ability ratios is a synonym for financial performance indicators. Thus, in this regression model earning ability is used as dependent variable. The other four items are used as bank-specific control variables. 19

20 Capital adequacy is equal to equity divided by total assets. Thus, capital adequacy shows which part of the capital of the corporation is financed with own capital. A higher capital adequacy should have a positive effect on the financial performances of the corporation, because it means less interest costs, but also it leads to less direct and indirect bankruptcy costs, less agency costs and a higher financing flexibility (Damodaran, 2010). The indicator for asset quality is based on the indicator of The World Bank. Asset quality is equal to non-performing loans divided by gross loans. This indicator shows how many of the gross loans are non-performing loans. A higher asset quality ratio should lead in theory to lower financial performances, because there are more nonperforming loans or less gross loans. Management efficiency is calculated by dividing the operating expenses against the total assets. The control variable for management efficiency should have a negative coefficient, because higher operating expenses leads to lower financial performances (ceteris paribus). Liquidity, also known as the ability to convert assets to cash, is for the banking industry calculated as total loans divided by deposits and short-term funding. Total loans can be easily converted to cash and can therefore be used to pay deposits and short-term funding back if needed. A higher liquidity will have a positive effect on the financial performance, because a corporation with higher liquidity is more likely to be able to pay back the debt. Besides the four bank-specific control variables based on the CAMEL framework three other bank-specific control variables will be used. The other bank-specific control variables are coverage, overhead expenses and firm size, which are also used in the paper from Wu and Shen (2013). 20

21 Table 3.1: Variable definitions Definitions Source Dependent variable ROA (Net income/total assets)*100% Bankscope Independent variable CSP dummy Dummy based on CRA rating or/and KLD rating FFIEC/KLD index Control variable Capital adequacy (Equity/Total assets)*100% Bankscope Asset quality (Non-performing loans/gross Bankscope loans)*100% Management (Operating expenses/total assets) *100% Bankscope efficiency Liquidity (Total loan/deposits and S-T Bankscope funding)*100% Coverage (Loan loss reserves/non-performing Bankscope loans)*100% Overhead expenses (Overhead expenses/operating Bankscope income)*100% Firm size LN(Total assets) Bankscope Annual GDP growth The annual GDP growth rate IMF Annual rate of inflation The annual rate of inflation IMF Coverage is loan loss reserves divided by non-performing loans. The coverage ratio shows which part of the real losses were estimated. This ratio should have a positive impact on the ROA, because a higher coverage means that a firm is able to estimate the non-performing loans correct and could therefore get the right amount of loan loss reserves. 21

22 The bank-specific control variable overhead expenses is calculated by dividing overhead expenses against the operating income. Thus, this indicator shows how much of the operating income goes to the overhead expenses. Simpson and Kohers (2002) controlled also for overhead expenses, because the R&D expenses that might exist would probably be included in overhead expenses. Higher overhead expenses should have a negative effect on the financial performance. The last bank-specific control variable is firm size. Firm size is calculated by taking the natural logarithm of the total assets. Firm size should have a positive impact on the financial performance (e.g. Cordeiro and Sarkis, 1997; Wu and Shen, 2013) Macro-economic variables Two macro-economic control variables are used in this paper, which are the annual GDP growth and the annual rate of inflation. The annual GDP growth should have a positive effect on financial performance and the annual rate of inflation should have a negative impact on financial performance. 3.3 Regression model One regression model will be used in this research. The regression model has the following form: Y = β 0 + β 1 X 1 + β 2 X 2 + β k X k + ε The dependent variable Y will be ROA to measure the corporate performance. The independent variable β 1 is the CSP dummy variable. The other independent variables are the control variables. The error term in this regression model is ε. This leads to the following regression model: ROA = β 0 + β 1 *CSP dummy + β 2 *Capital adequacy + β 3 *Asset quality + β 4 *Management efficiency + β 5 *Liquidity + β 6 *Coverage + β 7 *Overhead expenses + β 8 *Firm size + β 9 *Annual GDP growth + β 10 *Annual rate of inflation + β k X k + ε 22

23 3.4 Hypotheses Based on the literature review it can be said that the overall finding is that there is a positive relationship between CSP and CFP. Also, the two reviewed papers with results for the banking industry concluded that there is a positive relationship between CSP and CFP. Therefore, the first hypothesis of this research is: Hypothesis 1: There is a positive relationship between CSP and CFP for the banking industry in the US. The second hypothesis is based on findings of the YouGov-Cambridge survey and the Edelman Trust Barometer. Both surveys concluded that the trust in and the reputation of the financial sector was very low in The breaking point of the trust in the banking industry can be found in the Edelman Trust Barometer 2008 and The Edelman Trust Barometer 2008 used data obtained in 2007 and the Edelman Trust Barometer 2009 used data based on surveys in The Edelman Trust Barometer 2008 and 2009 show that in the US the trust in the banking industry declined from 69% in 2007 to 36% in The decline in trust in the banking industry of 33% was the greatest decline among all the industries. If the first hypothesis is correct, so that there is a positive relationship between CSP and CFP for the banking industry in the US, it means that banking corporations with high CSP perform better than banking corporations with low CSP. But, it could be that this result is affected by the lower trust of people in the banking corporations. Thus, whether banking corporations with high CSP are valued higher by the people after the subprime mortgage crisis than banks with high CSP before the subprime mortgage crisis. If this is true it will be translated into a higher positive relationship between CSP and CFP after the crisis than before the crisis. Based on these assumptions the second hypothesis is: Hypothesis 2: The positive coefficient of the CSP dummy will be higher after the subprime mortgage crisis than before this crisis. 23

24 3.5 Samples The two datasets mentioned in paragraph one are used to create nine different samples. The first three samples are only made to measure whether there is a positive relationship between CSP and CFP for the banking industry for the whole sample period. The remaining samples are created to determine whether there is a positive relationship between CSP and CFP for the banking industry and whether the coefficient for the dummy variable is higher after the crisis than before the crisis. The first sample is based on the CRA dataset. This sample has a sample period from 2001 to 2013 with 609 observations from 280 banks. Only 35 observations received a negative rating. The next sample is based on the KLD dataset. This sample has a sample period from 2002 to 2011 with 832 observations from 129 banks. 365 from the 832 observations got a negative rating. The third sample used to measure whether there is a positive relationship between CSP and CFP is based on both the CRA and the KLD dataset. Some observations were found in both datasets and therefore 28 duplicated observations were deleted. Sometimes these duplicated observations did have different ratings and in this case the rating from the KLD dataset was used. The CRA rating is based on the ability of banking corporations to meet the credit needs of all segments. The KLD rating is based on many more social aspects. Therefore, the KLD dataset was used instead of the CRA dataset when there were duplicated observations with different ratings. This sample has a sample period from 2001 to 2013 and is based on 1414 observations from 394 banking corporations. A negative rating was given to 399 observations. The three samples above are used only to measure if there is a positive relationship between CSP and CFP for the whole sample period. The next six samples are also used to determine whether the coefficient for the dummy variable is higher after the crisis than before the crisis. Therefore, the CRA dataset, the KLD dataset and the merged sample will be divided in two parts. 24

25 First, the CRA dataset will be divided in two parts. The sample of the first part has a sample period from 2001 to 2007 with 401 observations from 232 banks. Only 15 observations got a negative rating in this part, so the results could be biased. The sample based on the second part has a sample period from 2008 to 2013 with 208 observations from 208 banking corporations and with only 20 negative ratings. Second, the KLD dataset will be divided in two parts. One part contains the years 2002 to 2007 and has 485 observations from 127 banks. A negative rating was given to 180 observations. The second part was based on the years 2008 to 2011 with 347 observations from 117 banking corporations. 185 observations got a negative rating. Third, the two parts from the merged sample will be regressed. The first part is based on the years 2001 to 2007 with 869 observations from 347 banks and 399 bad ratings. The second part from this merged sample has a sample period from 2008 to 2013 with 545 observations from 256 banks and 205 observations with a negative rating. These nine samples will be regressed and with the regression results conclusions can be made about the hypotheses. The empirical results from these regressions can be found in the next chapter. 25

26 4. Empirical results In the first paragraph of this chapter information about the correlation between ROA and the other variables can be found for the nine samples. The regression results from the samples one to three can be found in the second paragraph. In the third paragraph the results of the regressions from the remaining six samples can be found. One sample has a significant CSP dummy in the multivariate regression. Hence, it could be that this result is obtained because there is reverse causality. In the fourth paragraph this sample is regressed to see whether there is reverse causality or not. In the fifth paragraph an overall conclusion regarding the hypotheses found in chapter 3 will be given. This conclusion is based on the empirical results. Also, the results of the control variables will be summarized in this paragraph. 4.1 Correlation The correlation matrices for all nine samples can be found in the appendix. Only the correlation between ROA and the other variables will be covered in this paragraph. The explanation of the correlation between ROA and the other variables will not be done by sample, but by variable. The CSP dummy is in the nine correlation matrices only significant in two correlation matrices. The CSP dummy in the correlation matrix from the CRA sample shows a positive and significant correlation with ROA at a 90% confidence level. The CSP dummy in the KLD sample with a sample period from 2008 to 2011 has a negative correlation with ROA at a 95% confidence level. Even though there seems to be a correlation for both dummy variables, the correlation is very close to zero, which means that there is no linear relationship between these two CSP dummies and ROA. Therefore, based on the correlation matrices, it can be said that there is no linear relationship between the CSP dummy and ROA. In all correlation matrices the control variable capital adequacy has a weak positive linear relationship with ROA. All results are significant at a 99% confidence level. 26

27 Most correlations between asset quality and ROA have a weak or moderate negative linear relationship at a 99% confidence level. However, this correlation is for two samples insignificant, the CRA sample with a sample period from 2001 to 2007 and the merged sample with a sample period from 2001 to The correlation between management efficiency and ROA is for seven correlation matrices weak to moderate negative. The samples with a sample period till 2007 do have different results. The correlation is insignificant for the KLD sample, the correlation for the CRA sample is weak positive at a confidence level of 99% and the correlation for the merged sample is significant at a 0.01 level of significance, but it shows no linear relationship. Annual GDP growth has a weak positive correlation with ROA, with a confidence level of 99% for six samples. The correlation between annual GDP growth and ROA is for the three samples with a sample period till 2007 insignificant. The control variables liquidity, coverage, firm size and annual rate of inflation all have an insignificant or a significant, but no linear, relationship with ROA. 4.2 Regression analyses (samples one to three) In this paragraph the results for the CRA sample, the KLD sample and the merged sample can be found. These results are only needed for hypothesis one. Both the univariate and multivariate regressions will be given Univariate regressions Table 4.1: Regression results from the regression of the CRA sample with the sample period 2001 to CSP dummy is the independent variable and ROA is the dependent variable Coefficient Significance level T-value CSP dummy N = 609 R 2 = 0.63% F =

28 In contrast with the findings in paragraph one the regression results without control variables shows a positive and significant coefficient for the CSP dummy in table 4.1. At a 90% confidence level it can be concluded that a change in the CSP dummy from minus one to one leads to an increase of the financial performance indicator ROA of %. Because this variable is significant, it is justified to include this variable in the multivariate regression. Table 4.2: Regression results from the regression of the KLD sample with the sample period 2002 to CSP dummy is the independent variable and ROA is the dependent variable Coefficient Significance level T-value CSP dummy N = 832 R 2 = 0.00% F = 0.01 The results in table 4.2 are the reverse of the results in table 4.1. The CSP dummy has a negative, but insignificant coefficient, which is in line with the results of the correlation analysis in paragraph one. Because of the insignificancy it can be concluded that the coefficient of this CSP dummy is achieved by chance. The probability that this insignificant dummy will become significant in a multivariate regression is very low. Nevertheless, any dummy that is insignificant in a univariate regression will be included in a multivariate regression. Table 4.3: Regression results from the regression of the merged sample with the sample period 2001 to CSP dummy is the independent variable and ROA is the dependent variable Coefficient Significance level T-value CSP dummy N = 1414 R 2 = 0.01% F = 1.68 Table 4.3 shows that the CSP dummy for the merged sample in this univariate regression is positive, but insignificant. 28