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Being a member of a cooperative bank: ethical or financial decision?

Damien Egarius and Patrick Roger

Abstract

The definition of co-operative banks given by the International Cooperative Banking Association (ICBA) states that co-operative members, who buy and hold member shares, believe in the ethical values of honesty, openness, social responsibility and altruism. Using a unique database of 246,120 clients/members at a cooperative bank, we show that the trading volume of member shares is far from insignificant. The analysis of clients’ portfolios over a 13-month period shows that clients use member shares as standard savings and investment vehicles. These results show that the reasons given by the ICBA to become a member of a cooperative bank are unimportant in the decision process.

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We thank the associate editor (Jean-François Gajewski) and two anonymous referees who largely contributed to improve former versions of the paper. Patrick Roger acknowledges the financial support of the Chair in Behavioral Finance at EM Strasbourg Business School.

1. Introduction

1An important consequence of the 2008 crisis is the strengthening of capital requirements and solvency ratios in the banking sector (Basel II and III). Understanding how cooperative banks manage these prudential requirements is of particular interest because these banks could be committed to make decisions that contradict some of the non-financial goals they aim to achieve.

2The capital of cooperative banks is comprised of 1) member shares held by customers and 2) reserves made up of past profits. Most of these banks are not listed on an exchange, and cannot easily raise funds from the market. As a consequence, customers’ money is an important source of capital. However, as clients are potential share buyers, this situation creates a strong incentive for banks to sell shares to clients. Such an incentive could contradict one of the essential principles of cooperative banks, that of “voluntary and open membership,” as stated by the International Cooperative Alliance Statement.

3The first question addressed in this paper is to determine whether capital requirements and philosophical principles generate such a contradiction.

4The second related question is to know whether the statements of the European Association of Cooperative Banks (EACB) and the ICBA are true. The ICBA emphasizes that cooperative banks pursue social and moral goals. Co-operative members, who buy and hold member shares, are supposed to believe in the ethical values of honesty, openness, social responsibility and altruism.

  • 1 See « Promoting a European framework for Corporate Social Responsability », Doc 01/9, Commission of (...)

5These motivations originate from corporate social responsibility (CSR) through the values defended by the owner-members of the Bank. For example, the European Commission defines CRS as a concept which includes social, environmental, economic concerns in firms’ activities and interactions with the stakeholders.1

6Additionally, the European Association of Cooperative Banks (EACB) puts forward that clients become members for non-financial motives (EACB, 2007). These cooperative banks are controlled by their members and follow the “one member-one vote” rule. The members elect representatives among themselves, and these representatives have a say in the decisions made by the bank (Fonteyne, 2007; and Cuevas and Fischer, 2006).

7According to Ayadi et al. (2010), the density of members’ networks developed over long periods, often several decades, and sometimes centuries, makes this type of governance exist. Consequently, the cooperative banks are located locally and generally have a netting in the region (or the country) where they are.

8In this paper, we analyze the motivation of clients to buy (and sell) member shares. It is important to keep in mind that the price of member shares is constant over time and that these shares deliver a steady return. In other words, member shares are far less risky than ordinary stocks. Equally as relevant as the low return volatility is the fact that the voting rights of a member do not depend on the number of shares she holds. Consequently, trading social shares is not motivated by realizing capital gains.

9In this paper, we aim to answer the following question. Are member shares of cooperative banks used in the way advocated by the ICBA?

10To answer this question, our primary data source is a large sample of clients of one of the largest French cooperative banks. We had the opportunity to obtain the complete monthly data for 640,000 clients over a 13-month period between August 2011 and August 2012. These clients held 7.8 billion euros in different types of accounts and assets (savings accounts, life insurance policies, deposit accounts, stocks, bonds, etc.). Approximately 20% of these customers are members, i.e. they hold at least a member share. During the period under consideration, 48,442 (5065) buying (selling) trades of member shares were executed.

11Our main results are as follows. First, most trading activity is motivated by financial and investment reasons. In particular, we observe a very strong relationship between the number of shares hold by a client and her wealth. Moreover, this positive relationship holds between transactions of member shares and wealth. In fact, we show that wealthy members are more likely to buy and sell member shares than less wealthy members. The trading volume of a given member in a given month is strongly linked to her portfolio value at the end of the previous month. We observe that clients’ behavior is consistent with the financial theory, i.e., the clients buy member shares in order to diversify their risk. Moreover, approximately 25% of the customers buy member shares at the same time as they buy other securities. Over the 13-month period we analyze, the number of member shares increased by 10.24%. When examining a longer period of time (2007-2010), we observe a 135 % increase in the total number of member shares. This number jumps from four million in 2007 to 9.4 million in 2010.

12Finally, the gap between the number of members in cooperative banks and the number of participants in yearly general meetings is also a signal about the lack of interest of members in the governance of the bank. The EACB estimates participation rates between 5% and 8% (EACB, 2007), while in the UK, Spear (2004) finds participation rates between 1% and 5% for cooperatives of consumers. The participation rate decreases with the size and the age of the institution. For example, McKillop et al. (2002) find a 2% participation rate in the Irish credit unions. For French cooperative banks, Caire and Nivoix (2012) obtain rates between 1% and 7%, with an average of 3.68%. These figures seem to contradict the first principle that clients purchase member shares to participate at a democratic system. In fact, it is difficult to imagine that a client who buys five shares because she feels involved in the activities of the bank never participates in or attends general meetings. However, as buying five shares costs only 100€, such a low amount also excludes pure financial motives. Then it remains the possibility that these purchases are advised by the financial advisor of the bank, according to her own incentives, linked to increased capital requirements from the regulator.

13To summarize, the question we address in this paper is to determine whether the link between cooperative banks and their members is still in the spirit of F.G. Raiffeisen, who created cooperatives to favor the access to credit and payment instruments for the poor and rural populations. The alternative assumption is that cooperative banks are not different from other banks, thus they essentially develop commercial relationships with their customers.

14This paper is structured as follows. Section 2 defines cooperative banks and member shares and develops our research hypotheses. Section 3 presents the database and provides some descriptive statistics, especially about trading volumes of member shares. Section 4 presents our methodology and results. A short conclusion appears in section 5.

2. Cooperative banks and member shares

2.1. What is a cooperative bank?

  • 2 http://ica.coop/

15Strictly speaking, a cooperative bank is a bank owned by its members. Members are clients who own at least one member share of the bank. However, another definition is given by the ICBA: “Cooperative banks are autonomous associations of persons united voluntarily to meet their common economic, social and cultural needs and aspirations through a jointly owned and democratically controlled enterprise. Cooperatives are based on the values of self-help, self-responsibility, democracy, equality, equity and solidarity. In the tradition of their founders, cooperative members believe in the ethical values of honesty, openness, social responsibility and caring for others2.

16This definition exhibits three main features.

1. Members are volunteers. They can buy a member share and become a member or they can choose to be only clients. No pressure should be exerted on either side.

2. Members constitute a community, and as such, they pursue common social goals.

3. A cooperative bank is democratically controlled. Members elect their representatives who are chosen among members.

17These representatives have a say in decisions made by the bank (Fonteyne, 2007; Cuevas and Fischer, 2006; Caire and Nivoix, 2012). According to some authors, such as Ayadi et al. (2010), such a governance is possible because of the wide networks of members that have been established for a long time (decades and sometime centuries). Therefore, cooperative banks are well established in local areas.

2.2. What is a member share?

18In France, member shares of cooperative banks are defined by a law voted after World War II in 1947. Member shares exhibit strong particularities, compared to ordinary stocks.

  • 3 Note that some special situations allow to sell at other points in time (if members die, have finan (...)

19First, member shares are not anonymous. The bank knows any holder of member shares at any point in time. A client who wants to buy shares contacts her financial advisor who transmits the order to the board of representatives. The board then decides to accept or reject the request. The same procedure applies to sales. When a sale order is approved, it is executed after the yearly general meeting3 in a completely formalized process.

20The second important feature that distinguishes stocks from member shares is the price that remains constant for a member share. Thus, trading member shares cannot be motivated by the hope of capital gains. Moreover, a member share does not give a direct right to the profits of the bank. In fact, member shares of French cooperative banks earn a yearly interest rate that must be lower than the French corporate bond rate (called the TMO). The payoff generated by a member share is then “half-dividend, half-coupon”.

  • 4 The detailed rule may vary from one cooperative bank to another.

21Finally, member shares are also different from stocks in case of bankruptcy of the cooperative bank. Member shares do not give rights to the residual assets of the bank. Moreover, members are committed to pay the debts of the bank (according to the importance of their membership), and they remain liable for these amounts for a number of years.4

2.3. Hypotheses development

22The EACB suggests five reasons (listed in decreasing order of importance) as to why people choose to become members:

1) the participation in a democratic system;

2) the contribution to the development of a local community;

3) the opportunity to buy an attractive asset (member shares are presented as low-risk assets);

4) the benefit from loyalty programs (access to fair priced services);

5) other reasons.

23The EACB further emphasizes that non-consumerist motives dominate when clients buy member shares. However, Caire and Nivoix (2010) argue that the lack of individual data prevents drawing such conclusions and answering questions related to the motivations behind the purchasing of member shares. Our paper is an attempt to fill this gap. We then test the hypothesis that customers of cooperative banks trade member shares as they trade other financial securities. Not rejecting this hypothesis would be an indicator that customers become members essentially for financial motives. This would show that the reasons put forward by the ICBA are not the relevant ones to explain membership in cooperative banks.

3. Data and descriptive statistics

3.1. Customers’ database

24Our primary data source is a large database of 640,000 accounts of all clients from a cooperative bank in a given French region (Alsace). Our data are probably not representative of clients of all cooperative banks over the country but, in our case, this is an advantage.

  • 5 http://www.bundesbank.de

25In fact, the first French cooperative bank was launched in Alsace, a region that has still today strong relationships with Germany where there are more than 1,100 cooperative banks5. Therefore, the “cooperative spirit” is more pronounced in Alsace, compared to other French regions. Not being able to reject our hypothesis would be a strong indicator that clients (and members) of cooperative banks are similar to clients of commercial banks.

26In this paper, client’s portfolio means the complete set of financial products possessed by a given household or individual. A portfolio may contain savings accounts, bonds, stocks, mutual funds, life insurance contract, etc. Thus, the portfolio value of a given household is the aggregate value of all the products in its portfolio. The average portfolio value is 12,086€ over the complete sample, with a standard deviation of 41,218€. As usual for wealth distributions, the median is considerably lower than the mean. Here, the median is only 370€. There are also a few wealthy clients that move the average upward, thus resulting in a 99th percentile equal to 170,000€.Our sample also contains a large number of “sleeping accounts” that are endowed with only a few euros.

  • 6 It is common for cooperative banks to open a saving account to newborn children when parents have a (...)
  • 7 The age of majority in France.
  • 8 The database provides a dummy variable equal to one if the client lives in the mentioned location a (...)

27Many clients also hold nothing else than a saving plan within the bank6. This situation generally means that the main banking activity of the client is located in another bank. We then decided to delete these clients from the sample not to underestimate the portfolio value. In addition the accounts of customers who are less than 18 years old7 are deleted because we do not know who decides for them. Finally, we exclude clients for which exact home addresses are unknown to the bank, indicating that the client has moved without informing the bank8. We obtain a complete sample composed by 246 120 clients with 6.9 billion deposits.

28Table 1 provides descriptive statistics about monthly portfolio values. Panel A is related to the complete sample, and Panel B is restricted to members (clients holding at least one member share). The first column indicates the month at the end of which calculations are performed, N is the number of clients (members) entering the statistics provided in columns 3 to 6. Column 3 gives the average portfolio value and the three other columns give the quartiles for the cross-sectional distribution of portfolio values.

Table 1: Clients’ portfolio Values

Panel A: Global sample (members and not members)

This table gives monthly portfolio values (in euros) per client. Date indicates the month and the year of observation, N represents the number of clients observed at a given date. Mean is the average portfolio value of clients. Q1, Me, Q3 are respectively the first quartile, the median, the third quartile of the distribution of portfolio values per client.

Date

N

Mean

Q1

Me

Q3

08/11

246 120

27 601.39

1 222.64

8 135.06

28 729.83

09/11

246 120

27 599.67

1 206.54

8 105.48

28 756.59

10/11

246 120

27 626.53

1 186.58

8 085.76

28 843.41

11/11

246 120

27 588.80

1 190.86

8 090.73

28 714.05

12/11

246 120

27 574.59

1 147.83

8 107.80

28 664.92

01/12

246 120

28 256.47

1 207.13

8 367.11

29 521.85

02/12

246 120

28 367.57

1 198.00

8 346.12

29 605.41

03/12

246 120

28 267.16

1 085.55

8 224.42

29 438.90

04/12

246 120

28 532.54

1 195.22

8 451.62

29 815.88

05/12

246 120

28 379.80

1 157.18

8 341.56

29 643.31

06/12

246 120

28 348.68

1 078.79

8 270.18

29 586.96

07/12

246 120

28 710.68

1 212.62

8 468.19

30 021.19

08/12

246 120

28 790.95

1 208.72

8 416.18

30 008.10

29

Panel B: Members’ Deposits

This table gives monthly portfolio values (in euros) per member. Date indicates the month and the year of observation, N represents the number of members observed at a given date. Mean is the average portfolio value of clients. Q1, Me, Q3 are respectively the first quartile, the median, the third quartile of the distribution of portfolio values per member.

Date

N

Mean

Q1

Me

Q3

08/11

101 073

37 525.38

2 061.12

12 123.33

41 743.34

09/11

101 777

37 258.85

2 016.69

11 972.84

41 378.79

10/11

102 665

37 101.99

1 958.10

11 830.66

41 155.44

11/11

103 042

36 798.71

1 955.62

11 681.89

40 734.02

12/11

103 724

36 661.15

1 923.80

11 605.15

40 398.01

01/12

105 133

37 369.41

1 952.94

11 833.75

41 278.26

02/12

106 411

37 321.78

1 903.01

11 735.93

41 043.20

03/12

107 733

37 025.61

1 752.95

11 451.58

40 620.46

04/12

108 797

37 251.81

1 906.08

11 673.43

40 850.21

05/12

108 797

37 029.74

1 856.85

11 537.09

40 578.14

06/12

110 465

36 904.44

1 759.81

11 447.05

40 383.02

07/12

109 700

37 428.10

1 939.98

11 717.52

40 901.26

08/12

110 082

37 513.74

1 918.53

11 574.62

40 888.52

30In the global sample, the average portfolio value is 28,126€, with a standard deviation of 63,020€ and a median of 8262€. Panel B shows that members account for approximately 40% of the complete sample, but these clients hold 57% of the cumulated portfolio value. Members hold portfolios that are worth 37,168€ on average. Therefore, the average value of members’ portfolios is significantly higher than the value of non-members’ portfolios. Moreover, the number of members increases by 9% during the 13-month period under study. This increase partly explains the structure of purchases during the period.

31Table 2 provides information on the demographics of members. 52.50% of members are female and 47% are either single, divorced or widowed. Half of the members own their houses. Finally, it is interesting to note that approximately 50% of members are 50 years old and more.

Table 2: Demographics

This table gives demographics for the 110 082 members at the end of the period. N indicates the frequency of members and Percent is the frequency expressed as a percentage of all members.

Variable

N

Percent (%)

GENDER

Male

52 294

47.50

Female

57 788

52.50

MARITAL STATUS

Single

33 942

30.83

Divorced

8 651

7.86

Widowed

9 091

8.26

Partner

58 398

53.05

PRIMARY RESIDENCE

Non Owner

54 472

49.48

Owner

55 610

50.52

AGE

]17, 30]

17 768

16.14

]30, 40]

17 364

15.77

]40, 50]

19 994

18.16

]50, 60]

18 381

16.70

]60, 70]

15 823

14.37

> 70

20 752

18.85

  • 9 As mentioned in Campbell (2006), this is a common difficulty encountered in such studies.

32We are aware that households in our sample may hold other accounts in other banks9. Nevertheless, we think that the sample is sufficiently large and the data sufficiently detailed to perform a careful analysis of the reasons why clients trade member shares.

3.2. Categories of trades

33Let us recall that (1) the unit price of a member share is always equal to 20€, (2) membership is not necessary to benefit from all of the services provided by the bank, (3) a single share gives the same rights as one million shares when it comes to vote at the general meeting, (4) the interest rate paid by member shares is decided every year in May; it applies immediately for one year, and is paid in July of each year. Consequently, according to point (1), in our analysis we refer to numbers of shares traded. According to point (3), a member’s power of vote does not depend on her investment. When a customer buys shares on a given date, the amount of interest he will eventually receive depends on the number of complete holding months until the following May. Concerning taxation, payoffs of member shares are considered as dividends on stocks. But contrary to stocks, trading member shares do not generate transaction costs.

34There are 110,082 members in our sample at the end of August 2012. If membership is not related to financial reasons, members should only hold one share per member. However, table 3 reveals that members hold approximately 10.98 million shares, an average of 100 shares per member. It is also worth noting that the median holding is five shares and the third quartile is about 50 shares.

Table 3: Evolution of Shares

35This table gives times series of the number of member shares held per member. Date indicates the month and the year of observation, N represents the number of members present at a given date. Sum and Mean give respectively, the sum and the average number of shares held by members. St-D, Q1, Me, Q3 and P99 gives the standard deviation, the first quartile, the median, the third quartile and the 99th percentile of the monthly distribution of the total amount of shares held per member.

Date

N

Sum

Mean

St-D

Q1

Me

Q3

P99

08/11

101 073

9 965 187

98.59

269.53

1

5

60

1232

09/11

101 777

9 989 110

98.15

268.72

1

5

57

1231

10/11

102 665

10 025 502

97.65

267.51

1

5

55

1226

11/11

103 042

10 037 162

97.41

268.35

1

5

52

1227

12/11

103 724

10 131 617

97.68

270.38

1

5

52

1239

01/12

105 133

10 267 977

97.67

273.42

1

5

51

1250

02/12

106 411

10 380 703

97.55

274.60

1

5

51

1250

03/12

107 733

10 487 978

97.35

274.70

1

5

51

1250

04/12

108 797

10 842 627

99.66

282.73

1

5

51

1260

05/12

108 797

10 844 669

99.68

282.76

1

5

51

1260

06/12

110 465

11 136 144

100.81

285.93

1

5

52

1274

07/12

109 700

10 892 439

99.29

283.17

1

5

50

1267

08/12

110 082

10 985 618

99.79

284.69

1

5

51

1278

36Figure 1 shows the time-series of the aggregate number of shares held by members. Apart from the strong growth rate already mentioned, the curve is almost flat between April and May. The reason is simple: trades are frozen just prior to the yearly general assembly. For a more precise understanding of the long-term evolution of membership, we obtained figures of membership for the preceding years. The results show that there were four million outstanding shares in 2007, 4.6 million in 2008, and 9.4 million in 2010. These numbers demonstrate that the main part of the increase occurred after the 2008 financial crisis, a strong argument in favor of the use of member shares by the bank to respond to increasing capital requirements.

Figure 1: Total number of shares over the period

37This graph gives the monthly time series of the aggregate number of member shares held by members over the period 08/2011-08/2012. Number of Shares (in millions) indicates the number of member shares held by members.

Image 1000000000000294000001BCAA99E18F.jpg

38To understand more deeply member shares trades, figure 2 depicts the different types of trades. Purchases may be realized by clients or non-clients. Among clients, some are already members while others become members by buying member shares.

39On the selling side, only members can sell shares, but they either sell a part of their shares, or they sell all their shares and surrender their membership. For the latter, we distinguish those who stay clients of the bank from those who leave the bank. The above categories of share trades may have diverse motivations. For this reason, they will be distinguished in the econometric analysis of the next section.

3.3. The dynamics of trades

40In this subsection, we partition trades according to the typology described in subsection 3.2.

41Let us denote St the aggregate number of member shares at the end of month t, St varies as follows:

Image 100000000000015F00000021F94BB318.jpg

42where BEMt (SEMt) denotes the purchases (sales) of existing members within month t and NMt (LMt) denotes trades (purchases or sales) realized by new members (leaving members) within month t. All the numbers on the right hand side are positive.

Figure 2: Categories of Clients

43This graph gives information about members’ origin and destination from date t to date t + 1. Clients on the left of the table are bank customers who can be members or non-members. Not Clients, on the right of the table are people outside the bank’s network, but they can become clients and/or members. The set of members contains three subcategories: Existing Members, Leaving Members and New Members. The first subcategory is the subset of clients who are already members when they buy or stay members when they sell a part of their shares. The second subcategory is composed by members selling their entire portfolio of member shares. These people can stay as clients of the bank or can leave the bank (they become Not Clients).The third subcategory is composed by New Members who purchase member shares while they are non-members before.

Image 100000000000023F0000017F2FB10D90.jpg

44Table 4 summarizes the monthly trades. Panel A gives the monthly values of NMt and LMt and Panel B provides the values for BEMt and SEMt Panel A shows that while the number of members increases over time, the median value of sales is greater than the median value of purchases. Overall, over the 12-month period the trades realized by these two categories are almost equal; the number of sold shares exceeds the number of purchased shares by a few hundred. This finding means that the evolution of the aggregate number of shares is explained by the trades of existing members (see panel B).

45According to the features defining member shares, Panel B exhibits surprising figures. For example, consider the sale of 3,900 shares in a single trade in October 2011 (column Max, Panel B of table 4). Such a trade means that the client sells a part of her shares, giving up the interest accumulated since the end of May, that is, four months of interest. More generally, selling shares months before the general assembly is costly in terms of interest lost (recall the unit price is always 20€). Such a trade is then irrational except for sales due to liquidity shocks. However, it could be rational to sell shares during the year if another, more profitable, investment is proposed by the bank.

Table 4: Evolution of trades of the different sets of Members

Panel A: Leaving Members and New Members’ trades

This table gives the number of member shares bought and sold by Leaving Members and New Members in the bank. Date indicates the month and the year of observation, N represents the number of New Members or Leaving Members observed at a given date. Sum, Mean and Me denote the sum, the mean and the median of member shares bought or sold by New Members and Leaving Members at a given date. St-D and Max are respectively the standard deviation and the maximum of the monthly distribution of member shares bought or sold per member. Note that in May there are very few transactions because it is not possible to buy member shares if you are not already a member.

Date

Leaving Members

New Members

N

Sum

Mean

Me

St-D

Max

N

Sum

Mean

Me

St-D

Max

09/11

73

9984

136.77

5

389.37

3000

777

12125

15.6

1

81.9

1500

10/11

82

9803

119.55

6

223.56

1000

970

19652

20.26

1

96.86

1150

11/11

358

32951

92.04

100

107.71

1580

735

12772

17.38

1

71.19

1000

12/11

56

2873

51.3

2

137.78

755

738

32538

44.09

1

200.82

3000

01/12

67

3131

46.73

1

122.93

650

1476

55035

37.29

1

189.89

5000

02/12

43

1994

46.37

2

154.07

887

1321

36736

27.81

1

121.94

1500

03/12

35

3275

93.57

5

282.64

1376

1357

39338

28.99

1

120.48

2000

04/12

42

1025

24.4

1

75.42

421

1106

102307

92.5

5

313.83

5000

05/12

-

-

-

-

-

-

-

-

-

-

-

-

06/12

70

4985

71.21

1

165.34

1001

1738

118004

67.9

2

277.55

4250

07/12

1451

223873

154.29

11

324.07

3747

686

44687

65.14

2

253.38

3500

08/12

117

19676

168.17

5

481.84

2500

499

34005

68.15

2

260.96

2725

Panel B: Existing Members’ trades

This table gives the number of member shares bought and sold by Existing Members. Date indicates the month and the year of observation, N represents the number of Existing Members who buy and sell at a given date. Sum, Mean and Me denote the sum, the mean and the median of member shares bought or sold by New Members and Leaving Members at a given date. St-D and Max are respectively the standard deviation and the maximum of the monthly distribution of member shares bought or sold per member.

Existing Members

Date

SELL

BUY

N

Sum

Mean

Me

St-D

Max

N

Sum

Mean

Me

St-D

Max

09/11

54

17603

325.98

67.5

707.75

4250

2328

39385

16.92

3

118.45

5000

10/11

39

14945

383.21

170

675.54

3900

1745

41488

23.78

2

107.24

1950

11/11

123

16882

137.25

100

260.34

1995

1666

48721

29.24

2

151.41

3003

12/11

42

6738

160.43

47

257.82

1150

1899

71528

37.67

2

172.57

5000

01/12

26

3083

118.58

34.5

217.28

999

1886

87539

46.42

3

272.05

10000

02/12

26

3736

143.69

23.5

317.16

1376

2019

81720

40.48

3

179.63

4000

03/12

24

1750

72.92

31

86.56

304

1991

72962

36.65

3

133.12

2650

04/12

14

2154

153.86

87

188.17

635

2396

255521

106.64

4

296.41

5000

05/12

-

-

-

-

-

-

17

2042

120.12

3

257.77

850

06/12

36

15687

435.75

172.5

635.53

2500

2811

194143

69.07

6

177.3

3000

07/12

762

208090

273.08

133.5

592.85

11500

7614

143571

18.86

5

96.83

3747

08/12

37

6427

173.70

37

434.81

2512

2949

85277

28.92

3

137.6

4350

4. Methodology and results

4.1. Classification of members

46Table 4 (part B) shows that buyers and sellers realize 44,301 trades, that is, 40,724 purchases and 3577 sales which correspond to 23,774 (3516) members who buy (sell) shares, as shown in table 5.

47Table 5 also provides a synthetic distribution of the number of trades. More than 80% of the members realize only one trade, but almost 8% of the members realize more than three trades and more than 4% of the members trade more than 10 times during the year of our study. For these categories, the trading activity is due to “member share saving plans” subscription. The bank proposes to clients to purchase automatically (without meeting with her financial advisor after subscription) member shares on a monthly basis.

48Another interesting and maybe surprising figure in table 5 is the 502 members buying and selling during this period, some exhibiting intense trading activity. According to the features of these member shares, it is difficult to determine the rationale for this significant trading activity.

Table 5: Number of Trades over the period

49This table gives the number of members (26 788) classified according to their number of trades within the period (08/2011-08/2012) and the nature of their position (Buy, Sell or Buy and Sell). N is the number of members in a given category of trades.

Number of Trades

Buy

Sell

Buy and Sell

N

1

19 564

2 977

-

22 541

2

1 930

36

262

2 228

Between 3 and 10

791

1

147

939

11

987

0

93

1 080

Total

23 272

3 014

502

26 788

50In the following analyses, we divide the members of our sample into different categories using standard dummy variables. First, we distinguish between men and women; second, we define an investor dummy. A client is called an investor (in member shares) if she buys either more than the third quartile of the distribution of member shares (50 shares), or if she is already a member. Remember that purchasing one more shares does not increase voting rights and the power within the bank. This variable allows to identify trades which cannot be realized for non-financial motives such as those exhibited by EACB or ICBA.

4.2. Empirical study

51The first intuitive idea to explain purchases and sales is purely financial. People buy shares because their wealth increases and they look for diversification; they sell shares because of consumption needs or other liquidity shocks. If trades are motivated by such reasons, the evolution of wealth of buyers and sellers should be different. If these reasons are consistent with the financial theory, we expect that buyers exhibit an increase of their portfolio value and sellers exhibit a decline of their portfolio value during the period under scrutiny.

52Figure 3 illustrates this premise, giving the time series of the average portfolio values (in euro) for buyers and sellers. We construct these curves by computing, for each of the 23,774 buyers and each of the 3516 sellers, their monthly portfolio values from August 2011 to August 2012, regardless of the date the trades were realized. We calculate the following:

Image 100000000000015C00000030457C62AE.jpg

53with xijtthe amount held in asset j by member i at date t. Each point on the curve is obtained by aggregating clients’ portfolios on date t and dividing by the portfolio value at month 08/2011 as follows:

Image 10000000000001710000004E7D2AC903.jpg

54The average portfolio value of buyers appears on the increasing curve, and the corresponding curve for sellers is the decreasing one. The portfolio value of buyers increases by 9.31% while it decreases by 12.52% for sellers. For other clients a slight increase of 3.63% of their portfolio value is observed. The result is consistent with the idea that members purchase member shares for investment motives. The natural interpretation of the decreasing curve for sellers is that customers decide to sell shares because they need money for economic reasons (liquidity shocks, buy customer goods etc.). To explain the evolution of portfolio values we determine the growth of individual portfolios after one purchase or sale of a member shares. The growth rate of a portfolio value is evaluated by:

Image 1000000000000162000000350FDE0425.jpg

55where Pit denotes customer i’s portfolio value at the end of month t. The trade is assumed to be realized during month t. The results are reported in table 6. After a purchase, the portfolio value increases by 1% on average, but after a sale, the portfolio value decreases by a striking 10%.

56The monthly distribution of the growth index shows that customers take the proceeds of sales away from the bank (to reimburse debts in the case of liquidity shocks, to buy consumer goods or invest in real estate). Conversely, when clients buy member shares, their portfolio values increase significantly, indicating that financial motives are far from negligible.

57To analyze more deeply the evolution of portfolio values displayed by Figure 3, we check whether this evolution may be due to trades of other financial securities. Table 7 identifies the number of customers who simultaneously trade member shares and financial securities. To better differentiate between investment motives and other motives we classify customers into categories as defined in section 4.1.

Figure 3: Evolution of total deposits: Sellers vs. Buyers

Image 100000000000024E000001F5AA88468A.jpg

58First, within our time frame, transactions are mainly initiated by investor members. Approximately 51% of clients purchase member shares for investment motives. More surprisingly, within the category of other members, 25% of them invest in at least one other financial product when they purchase member shares.

59On the sell side, the decrease in portfolio values is mainly due to the sales of member shares. Approximately 9.39% of the sellers simultaneously sell member shares and other securities. Finally, the reinvestment in another product following a sale is almost nonexistent, involving less than 2.87% of the sellers.

Table 6: Portfolio variation after purchases and sales

Image 100000000000024C00000064E73830EC.jpg

Purchases

Sales

Date

N

P10

ME

P90

N

P10

ME

P90

09/11

3105

0.84

1.00

1.46

127

0.10

0.90

1.04

10/11

2715

0.80

1.01

2.01

121

0.11

0.89

1.08

11/11

2401

0.83

1.01

1.65

481

0.69

0.95

1.03

12/11

2637

0.77

1.00

1.52

98

0.07

0.88

1.11

01/12

3362

0.81

1.03

2.02

93

0.06

0.92

1.07

02/12

3340

0.81

1.01

1.87

69

0.12

0.96

1.43

03/12

3348

0.78

1.00

1.60

59

0.11

0.83

1.03

04/12

3502

0.90

1.01

1.64

56

0.03

0.88

1.11

05/12

17

0.95

1.01

1.37

06/12

4549

0.82

1.00

1.51

106

0.07

0.87

1.06

07/12

8300

0.97

1.00

1.12

2213

0.60

1.00

1.22

08/12

3448

0.89

1.00

1.22

154

0.01

0.92

1.02

Table 7: Member shares transactions bounded to other securities

60This table shows the proportion of clients who purchase or sale member shares (defined by category) at the same time of other securities. N gives the number of customers within each of the four categories (Other members, Investor members, sellers). n indicates the number of customers who purchase or sell at least one security when they buy or sell at least one member share within the period. Freq. denotes the percentage of customers who purchase or sale securities in a given category.

Image 100000000000024300000028C3D16724.jpg

Purchases

Other Members (N: 9 593)

Investor Members (N: 14 181)

n

Freq.

Number

n

Freq.

Number

Purchase securities

2 432

25.35%

3 176

1 825

12.87%

2 585

Sale securities

324

3.38%

375

1 450

10.22%

1 973

Sales

Sellers ( N: 3516)

n

Freq.

Number

Purchase securities

101

2.87%

113

Sale securities

330

9.39%

571

4.3. The influence of portfolio value on trading activity

61To explore the impact of portfolio value on members’ share trades, we implement a two-step analysis. First, we capture the heterogeneity between groups of clients by tests of differences in means over a set of variables. In a second step, we perform a multivariate analysis to test whether some variables influence the number of shares traded by clients.

4.3.1. Sellers and buyers peculiarities

62Table 8 provides the means of several variables in the different subgroups. Number of securities denotes the average number of financial securities held by clients the month before a transaction occurs (date t -1). To avoid overestimations of the number of financial securities held by some categories of customers, member shares do not count in this variable. Portfolio value gives the total portfolio value (in euros) of client i in month t -1. Transaction is the number of shares purchased or sold by customer i in month t and Age is the client’s age at the time of the trade.

Table 8: Sellers and Buyers: mean comparisons

63This table provides comparisons between subcategories of buyers and sellers (Male vs. Female, Investor Members vs. other Members, and Low sellers vs. Large sellers). In each table, Number, Portfolio value, Transaction and Age are respectively the average number of financial securities (different from member shares) held at date t -1 the average portfolio value at date t -1, the average number of shares purchased or sold at date t and the average age of buyers or sellers. N is the number of clients. Diff. gives the difference between averages. Standard errors are displayed between parentheses. ***, **, * indicates a significant difference at 1%, 5% and 10%.

Panel A: Purchases vs. Sales

Purchases

(1)

(N: 23 774)

(

(N

Sales

(2)

(N: 3 516)

Diff. (1)-(2)

Number

5.80

(5.21)

5.02

(5.11)

0.78***

Portfolio value

48 254.20

(92 750.50)

33 251.10

(74 845.60)

15 003.10***

Transaction

40.05

(178.70)

170.70

(401.00)

-130.65***

Age

51.48

(18.37)

48.03

(17.14)

3.45***

Panel B: Gender

Purchases

Sales

Male

(1)

(N:11 291)

Female

(2)

(N:12 483)

Diff. (1)-(2)

Male

(1)

(N: 1 801)

Female

(2)

(N: 1 715)

Diff. (1)-(2)

Number

5.94

(5.56)

5.67

(4.87)

0.27***

5.42

(5.86)

4.60

(4.14)

0.82***

Portfolio value

47 484.30

(99 846.20)

48 960.10

(85 725.80)

-1475.80

35 844.70

(82 243.20)

30 515.90

(66 067.90)

5 328.80**

Transaction

38.79

(185.50)

41.21

(172.30)

-2.43

178.00

(426.40)

163.00

(372.30)

15.00

Age

49.18

(17.95)

53.58

(18.50)

-4.40***

47.43

(16.58)

48.65

(17.70)

-1.22**

Panel C: Category of members

PURCHASES

Investor Members (1)

(N : 14 181)

Other Members (2)

(N : 9 593)

Diff. (1)-(2)

Number

6.52

(5.67)

3.46

(2.01)

3.06***

Portfolio value

58 362.90

(101 595.00)

15 454.00

(40 228.80)

42908.90***

Transaction

51.23

(202.90)

3.52

(5.66)

47.71***

Age

53.57

(18.01)

44.68

(17.88)

8.89***

64Panel A confirms that the average sale exceeds the average purchase by 130 member shares and that the portfolio value of buyers greatly exceeds the portfolio value of sellers (the difference between the two is 15,003€). Sellers are also younger than buyers and hold fewer financial products.

65Panel B shows no significant differences in member shares traded between men and women. Male sellers appear to be wealthier than female sellers, while the reverse is true for buyers (though the difference is not significant). Finally, males, on average, are younger. This is not surprising given that the expected lifetime of a women is longer and our population includes a large number of elderly clients.

66Panel C shows that investor members are prominent (14,181investor members) and are also wealthier than other members with a difference between investor members and non-investor members of approximately 42,000€. This difference is an indication of the key role financial motives play in the purchase of member shares. Investor members also hold more financial products and are older than other members. Again, this result is not surprising as we expect new members to be younger than investor members.

67Finally, table 8 shows that financial motives are at the heart of membership in cooperative banks. To examine more closely the relationship between these variables and the propensity to purchase or sell, we turn to a multivariate regression analysis.

4.3.2. Regression analysis on trades

68To study whether portfolio value is an important variable to explain members’ share trades, we run the following regression where the dependent variable may be the amount of sales or purchases by categories:

Image 1000000000000229000000450EAD70C5.jpg

69The dependent variable is the logarithm of the total amount of member shares (in euros) bought or sold by the client i in month t. Port_value it-1 is the logarithm of the portfolio value of client i at the end of the month before the transaction occurs. Number of securitiesit-1 is the number of financial securities (different from member shares) held by the client i at the end of the month before the transaction occurs. is a dummy variable equal to one if the client i is overdrawn on his deposit account at the end of month t-1, and zero otherwise. Agei is the age of client i. Partneri is a dummy variable equal to one if the client lives with a partner (married, civil union or cohabitation), and zero otherwise. This variable takes into account the fact that married people are less risk averse (Love, 2010). As a consequence, we expect they invest less in member shares.

70Femalei is a dummy variable equal to one if the client is a female, and 0 otherwise. We introduce this variable because a number of studies suggest that men trade more aggressively than women because men are more overconfident than women (Barber and Odean, 2001). Men seem also to be less risk averse than women (Jianakoplos and Bernasek, 1998; Sundén and Surette, 1998). Finally, αj and εit denote, respectively, the regression coefficients and the error term.

71We estimate these regressions for all subcategories defined in section 4.1 using a pooled OLS regression method with client-level clusters because some clients purchase and sell more than once in a given month.

72Table 9 displays the results of three regressions - sales, purchases and all transactions.

Table 9: Estimation results of the regression analysis.

73This table gives the estimation results (pooled OLS with client-level clusters) of the following regression:

Image 100000000000023B0000002DD7F52B3D.jpg

74The dependent variable is the logarithm of the total amount of shares bought or sold by the client i at date t. Intercept, Portfolio, Number of securities, Overdraft , Age, Partner, Female are respectively the intercept of the regression, the logarithm of portfolio value of client i by the end of the month before the transaction occurs, the number of financial securities (different from member shares) held by the client i the month before the transaction occurs, a dummy variable equal to 1 if the client i is overdrawn on his deposit account at the end of the month before the transaction occurs and 0 otherwise, client’s i age, a dummy variable equal to 1 if the client lives with a partner (married, civil union or cohabitation) and 0 otherwise, a dummy variable equal to 1 if the client is a female and 0 otherwise. ***,**,* indicates a significant coefficient at 1%, 5% and 10% level. Standard errors are displayed between parentheses. N, Obs., R2 are the number of customers, the number of transactions and the coefficient of determination of the regression.

Purchases

Sales

All

Intercept

1.5945***

(0.0506)

0.7695***

(0.0889)

1.7538***

(0.0485)

Portfolio Value

0.2548***

(0.0058)

0.6795***

(0.0123)

0.2675***

(0.0055)

Number of securities

0.0343***

(0.0027)

-0.0574***

(0.0096)

0.0279***

(0.0026)

Overdraft

-0.3294***

(0.0304)

0.1861***

(0.0600)

-0.3503***

(0.0316)

Age

0.0091***

(0.0007)

0.0033**

(0.0016)

0.0079***

(0.0007)

Partner

-0.0960***

(0.0250)

-0.1110**

(0.0545)

-0.0829***

(0.0256)

Female

-0.0178

(0.0234)

0.0418

(0.0513)

-0.0371

(0.0242)

N

23 774

3516

26788

Obs.

40 724

3 577

44 301

R2

0.2320

0.5461

0.2067

Table 10: Estimation results of the regression analysis: by subcategories

75This table gives the estimation results (pooled OLS with client-level clusters) of the following regression

Image 100000000000023E00000040DCD66BB3.jpg

76The dependent variable is the logarithm of the total amount of shares bought or sold by the client i at month t Intercept, Port_value Number of securities, Overdraft, Age, Partner, Female, Married are respectively the intercept of the regression, the logarithm of portfolio value for client i the end of the month before the transaction occurs, the number of financial securities (different than member shares) held by the client i the month before the transaction occurs, a dummy variable equal to 1 if the client i is overdrawn on his deposit account at the end of the month before the transaction occurs and 0 otherwise, client’s i age, a dummy variable equal to 1 if the client lives with a partner (married, civil union and cohabitation) and 0 otherwise, a dummy variable equal to 1 if the client is a female and 0 otherwise. ***, **,* indicates a significant coefficient at 1%, 5% and 10% level. Standard errors are displayed between parentheses. N, Obs., R2 gives the number of customers, the number of transactions and the coefficient of determination.

Gender

Purchases

Female purchases

Male purchases

Female sales

Male sales

Other Members

Investors Members

Intercept

1.4710***

(0.0703)

1.6995***

(0.0706)

0.6843***

(0.1158)

0.8937***

(0.1293)

2.8547***

(0.0361)

0.3627***

(0.1164)

Portfolio Value

0.2577***

(0.0085)

0.2507***

(0.0080)

0.6841***

(0.0152)

0.6771***

(0.0178)

0.0785***

(0.0041)

0.4181***

(0.0130)

Number of Securities

0.0377***

(0.0038)

0.0317***

(0.0036)

-0.0673***

(0.0119)

-0.0529***

(0.0120)

0.0465***

(0.0063)

0.0019

(0.0031)

Overdraft

-0.2677***

(0.0431)

-0.3918***

(0.0429)

0.2746 ***

(0.0788)

0.1059

(0.0908)

-0.0261

(0.0240)

-0.3780***

(0.0423)

Age

0.0096***

(0.0010)

0.0086***

(0.0012)

0.0054**

(0.0022)

0.0011

(0.0025)

0.0002

(0.0006)

0.0077***

(0.0010)

Partner

-0.0647*

(0.0343)

-0.1233***

(0.0367)

-0.0776

(0.0731)

-0.1287

(0.0841)

0.0923***

(0.0200)

-0.1099***

(0.0326)

Female

-

-

-

-

-0.0518***

(0.0187)

-0.0145

(0.0309)

N

12 483

11 291

1 715

1 801

9 593

14 181

Obs.

21 244

19 480

1 741

1 836

9 594

31 130

R-Square

0.2278

0.2371

0.5632

0.5309

0.0888

0.2311

77Whatever the dependent variable, a strong and positive relation appears between the portfolio value and the amount of member shares traded. This result confirms that clients decide to invest a part of their savings in member shares and increase this amount when the value of their portfolio increases over time.

78Moreover, elderly people have a tendency to purchase more member shares than other customers. However, age is less significant when sales are considered. The holding of financial securities is positively and significantly linked to the amount of shares purchased. This relationship confirms that a diversification purpose can be a partial explanation for purchases. Indeed, as customers already hold several products, financial advisors could propose they invest in member shares by arguing for the diversification and the quasi-absence of a risk-free asset in the economy. Such advice may increase the capital of the bank.

79We also find a negative and highly significant relation between the financial difficulties of clients (Overdraft) and the amount of member shares purchased. This result is quite intuitive and indicates that clients with cash problems do not buy, in general, member shares. A positive and significant relation is found when considering sales and indicates that an overdrawn client the month before the sale occurs trades an amount higher than other clients. This result is not surprising and confirms sales in the case of liquidity shocks.

80Finally, we found that Partner is negatively related to the number of member shares purchased or sold. However, Partner is less significant on the selling side. Controlling for gender, we find a negative relation between the purchases and Female and a positive relation between sales and Female. However, this result is not statistically significant.

81Regression results on subcategories of clients are presented in table 10. First, there are no significant differences in transaction size on the buy side across genders. Males and females have the same motives that drive the number of member shares purchased. Furthermore, portfolio value is positively and significantly linked to the number of member shares purchased and sold, and the number of securities in portfolios is positively (negatively) linked to the number of member shares purchased (sold). Finally, there is a gender effect concerning banking overdraft, especially for sales, and the variable Age is significant for purchases and in a lesser extent for female sales.

82Comparing investor members versus other members, we find that for both portfolio value is significant for explaining the number of shares purchased. Coefficient is particularly high when we consider investor members. Thus, economic motives probably dominate when considering share purchases. Age is positively linked with investor members and indicates that elderly members purchase more than younger members. Partner is positively and significantly related with the amount purchased for other members. These results suggest that Partner could impact members’ share trades.

4.3.3. What impacts the propensity to buy and sell?

83We now use logistic regressions to understand the determinants of the probability to buy and sell. Most clients trade once. Only 502 members buy and sell over the period. Therefore, we consider net positions in the regressions, and we estimate the following cross-sectional logit regression:

Image 10000000000002410000001FD19D7A28.jpgBi is a dummy variable equal to one if client i is a net buyer (seller) of shares, and 0 otherwise. Port_value is the logarithm of the monthly portfolio value held by client i .We introduce this variable to determine whether wealthier people are more likely to purchase member shares. SILFAi is the proportion of direct holding of listed financial products (bonds, stocks and mutual funds shares) to total wealth held by client i. We use this variable to determine whether people who are more inclined to buy these categories of products might also purchase member shares. MFMi is a dummy variable equal to one if one or more members of the family of client i (parents, children, wife or husband) also holds shares, and zero otherwise. Partneri is a dummy equal to one if client i is Partner, and zero otherwise. Agei and Femalei are defined as in equation (5) .

84The results of the estimations appear in table 11. Purchases are explained by the portfolio value of customers and confirm that clients of cooperative banks buy member shares for investment purposes. This relationship between portfolio value and the probability of buying is also a sign of the willingness of cooperative banks to increase their capital and to sell shares to wealthy people who have the capacity to invest a part of their savings in shares.

85The variable Age does not significantly impacts the probability of buying shares. Clients with large portfolios of stocks and bonds also hold more member shares compared to clients not invested in stocks and bonds. This finding could be justified either by diversification motives and/or attitude toward risk or simply because these clients are wealthy and hold large and diversified portfolios. Clients with a member of their family (wife, husband or child) who is also a member of the cooperative bank have a higher probability of becoming a member. Thus, the intergenerational relationship could be an explanation of such results. As people have a tendency to stay in the same banks as their parents, the financial advisor can then easily sell member shares to the children. This is consistent with the relationship dilemma previously referenced and can explain why 7% of the members are less than 18 years of age.

86The variable Partner reduces the purchasing probability and is consistent with the results obtained in studies focused on household finance, especially the results on risk-taking behavior. For instance, Love (2010) finds that married investors are less risk averse than single investors, a result that stands for both men and women. Other studies emphasize that living with a partner increases stock market participation (Bertaut 1998, Agnew et al. 2003, and Christiansen et al. 2010). Because shares of cooperative banks are less risky than ordinary stocks, married investors may be less prone to buy member shares and may prefer assets with higher risk/return tradeoff, such as life insurance contracts, stocks and bonds.

87Last, gender has few impacts on the purchase probability, though some studies, such as Sundén and Surette (1998), Jianakoplos and Bernasek (1998) Barber and Odean (2001), and Agnew et al. (2003), find evidence that gender has important implications in risk preferences and impacts portfolio choices.

88On the sell side, regression (2) suggests that there is a negative relation between the portfolio value and the propensity to sell. This result highlights that members decide to sell shares as doing so provides a source of wealth, and in this way, it confirms the notion that sales are motivated by liquidity shocks.

Table 11: Estimation results of the logistic regression

This table gives the results of the following logistic regression: Image 10000000000001E30000001FFE0D331D.jpg

89The dependent variable is a binary variable equal to 1 if the client i is a net buyer (net seller) of shares, 0 otherwise. Port_value, Age, SILFA, MFM, Partner, Female are respectively the logarithm of client i‘s average portfolio value, client i‘s age, the proportion of listed financial products (stocks and bonds) held by client i on the total portfolio value, a dummy variable equal to 1 if the client i has a member of his family who is also member, a dummy variable equal to 1 if the client is Partner and 0 otherwise, a dummy variable equal to 1 if the client is a female and 0 otherwise. ***, **,* indicates a significant coefficient at 1%, 5% and 10% level. Standard errors are displayed between parentheses. Obs., N, Intercept, Likelihood ratio denotes the number of observations, the number of buyers or sellers, the intercept and the likelihood ratio of the regression.

Purchases

(1)

Sales

(2)

Portfolio Value

0.2052***

(0.0039)

-0.0380***

(0.0084)

Age

0.0001

(0.0004)

-0.0089***

(0.0011)

SILFA

0.6558***

(0.0434)

0.2035*

(0.1225)

MFM

1.2938***

(0.0176)

-0.2351***

(0.0439)

Partner

-0.4422***

(0.0166)

0.1961***

(0.0415)

Female

-0.0317**

(0.0141)

-0.1103***

(0.0345)

Intercept

-4.1633***

(0.0360)

-2.6425***

(0.0739)

Obs.

246 120

112 440

N

23 774

3 516

Likelihood ratio

10396.7349

180.7461

90Client’s age is negatively related to the propensity to sell. This result is not surprising because middle-aged people (28 to 40 years of age) in our database are, in some cases, poorer than elderly customers. Indeed, middle-aged customers hold fewer securities, are less wealthy and have more overdrafts on their deposit accounts, which may explain why these members decide to sell shares. Sales of shares provide them with enough wealth to face liquidity needs. However, sales can also occur when the relationship between a client and her financial advisor is broken.

91Holding listed financial assets increases the propensity to sell, but the coefficient is not statistically significant. However, having a family member who is also a member of the bank strongly reduces the propensity to sell. This suggests a long relationship with the bank, in general, over several generations, reduces the propensity to sell or to surrender membership.

92In contrast to purchases, we find that living with a partner increases the probability of selling shares. We explain this result by the high proportion of members who live with a partner and sell shares. Moreover, being female decreases the propensity to sell because, in general, males are poorer than females and more frequently overdraft their accounts; thus, they can be more prone to sell shares.

5. Conclusion

93Our study confirms that customers use member shares as an investment and/or saving vehicle. More than 40,000(3,000) purchases (sales) are observed over our one-year period of observation. Purchases are highly driven by investment motives; a strong positive link exists between the portfolio value before the purchase and the number of shares purchased. This link is confirmed when examining the level of wealth and the propensity to purchase. Accordingly, as wealth is a determining factor of purchases, member shares can be considered similar to any other securities.

94We also find that most clients hold an irrational number of member shares (the median value of member shares held is five). Of non-investor members, 25% simultaneously purchase member shares and other financial securities. A few new clients purchase shares in subsequent months after entering the bank compared to existing clients.

95Our main results are difficult to reconcile with non-financial motives. Such evidence could be due to financial advisors’ incentives to sell member shares. Indeed, capital constraints (Basel II and III) have been reinforced after the crisis of 2008. To satisfy these commitments, cooperative banks need to propose member shares to their customers. Thus, cooperative banks have a tendency to behave as other banks in terms of financial assets, and member shares are more and more viewed as standard financial assets.

96This raises the question whether a real (non-financial) membership exists in cooperative banks. Several studies highlight a low participation rate in the general assembly, which is the most democratic moment of the year for cooperative banks (Caire and Nivoix, 2012). This fact suggests that the sense of belonging to a community is weak and that clients are ambivalent about the way they consider the specificities of cooperative banks.

97Cooperative banks need to clarify their “cultural” identity by highlighting the differences between themselves and commercial banks. They need to provide customers with incentives to participate in the democratic governance of the bank. Up to now, our results show that the principles stated by the ICBA and EACB are mainly theoretical. The reasons for becoming a member are essentially the following: 1) buying member shares as usual investment products 2) Being convinced by a financial advisor to purchase member shares, and 3) buying member shares together with other financial securities in a standard process of portfolio diversification.

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Notes

1 See « Promoting a European framework for Corporate Social Responsability », Doc 01/9, Commission of the European Communities, où la responsabilité sociale des entreprises est définie par: “Corporate social responsibility is essentially a concept whereby companies decide voluntarily to contribute to a better society and a cleaner environment. At a time when the European Union endeavours to identify its common values by adopting a Charter of Fundamental Rights, an increasing number of European companies recognise their social responsibility more and more clearly and consider it as part of their identity. This responsibility is expressed towards employees and more generally towards all the stakeholders affected by business and which in turn can influence its success.”

2 http://ica.coop/

3 Note that some special situations allow to sell at other points in time (if members die, have financial difficulties or move to another location).

4 The detailed rule may vary from one cooperative bank to another.

5 http://www.bundesbank.de

6 It is common for cooperative banks to open a saving account to newborn children when parents have an account with the bank. A lot of these accounts are neither active nor closed.

7 The age of majority in France.

8 The database provides a dummy variable equal to one if the client lives in the mentioned location and zero if the client does not live at the mentioned address.

9 As mentioned in Campbell (2006), this is a common difficulty encountered in such studies.

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References

Electronic reference

Damien Egarius and Patrick Roger, « Being a member of a cooperative bank: ethical or financial decision? », Finance Contrôle Stratégie [Online], 19-4 | 2016, Online since 26 January 2017, connection on 19 October 2017. URL : http://fcs.revues.org/1882

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About the authors

Damien Egarius

LaRGE Research Center, EM Strasbourg Business School, Université de Strasbourg

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Patrick Roger

LaRGE Research Center, EM Strasbourg Business School, Université de Strasbourg.
Adresse : EM Strasbourg Business School, 61 avenue de la forêt noire, 67000 Strasbourg,
email : proger@unistra.fr, page web https://sites.google.com/site/strasproger/home

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