ISSN (Print) 0473-453X

Discussion Paper No. 1029

ISSN (Online) 2435-0982

BLOOD TYPEAND
BLOOD DONATION BEHAVIOR

Shusaku Sasaki
Yoshifumi Funasaki
Hirofumi Kurokawa
Fumio Ohtake

Secondly Revised June 2020
Revised July 2018
May 2018

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Blood Type and Blood Donation Behavior

Shusaku Sasaki1, *, Yoshifumi Funasaki2, Hirofumi Kurokawa3, Fumio Ohtake4
1, *Corresponding author: Faculty of Economics, Tohoku Gakuin University, Associate

Professor, ssasaki.econ@mail.tohoku-gakuin.ac.jp
2

Graduate School of Economics, Osaka University

3

School of Economics and Management, University of Hyogo

4

Graduate School of Economics, Osaka University

1

Abstract
We empirically investigated how voluntary helping behavior is influenced by the number of its
potential recipients by using a nationwide survey in Japan (N = 1,333) and examining therelationship between blood type and blood donation behavior. It is generally known in Japanthat type O blood can be medically transfused to individuals of all blood groups; therefore, thepotential transfusion recipients’ number of the type O blood is the largest among the four bloodgroups. Our empirical analysis revealed that people with type O blood were more likely to havedonated blood in the past than those with the other blood types. This association was stronger
in a subsample of individuals who had knowledge relating to the above-mentioned widespreadutility of type O blood. In addition, our empirical analysis arrested the concern that potential
blood-type differences in altruistic attitudes might explain their differences in blood donation
behavior, by confirming that type O blood was not significantly related to other altruistic
behaviors (i.e., registration for bone-marrow donation, intention to donate organs, and making
monetary donations) or attitudes (i.e., general trust, altruism, reciprocity, and agreeableness).
After conducting further analyses, we concluded that the vast number of potential recipients oftype O blood causes different patterns of Japanese people’s blood donation behavior across thefour blood groups. This study added to the literature the real-world evidence concerning how
having many potential recipients affect people’s behavior of providing common goods.

Keywords: Inborn quality, ABO blood group, blood donation, common pool resource
JEL Classification Codes: I10, D64, C30

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1. Introduction
Historically, humans have provided goods that benefit other members of the same groupthrough collective action projects, including food procurement, irrigation development, andcollective protection. We currently do this through monetary donations, volunteer activities,
and blood donations. The characteristics and mechanisms of these behaviors have been ofinterest in many research areas (Bekkers & Wiepking, 2011).
Imagine that the goods provided by you have many potential recipients; in other words,
many members in the same group are waiting to consume the goods, which only one member
can consume at a time. How would this influence your providing behavior? The current study
answered this question, by examining the relationship between Japanese people’s blood typeand blood donation behavior.
When the number of potential recipients differs among suppliers, it means that an
expected gain of profit from their contribution behavior is heterogeneous. To illustrate, imagine
that two members—A and B—belong to the same group. Member A is good at acquiring “GoodA” because of their natural ability, and “Good A” is needed by many other members of thesame group. On the contrary, Member B is good at acquiring “Good B” because of their naturalability; but “Good B” is needed by a smaller number of other members than “Good A.” Howwill the A’s and B’s acquisition behavior differ? It is more likely that “Good A” will be
consumed by the other group members without disposal, and Member A will profit from having
contributed to their group. Therefore, it is possible that Member A will make more efforts toacquire “Good A” and provide it to their group members. In contrast, as we will explain indetail later, it is also possible that, since member A can more quickly and efficiently profit fromproviding “Good A” and contributing to their group, there will be more time and resources forthem to acquire and consume other goods that benefit themselves.
3Previous behavioral science studies, including evolutionary psychology, social
psychology, and behavioral economics, have implicitly assumed that an individual’s profit gainfrom contribution behavior is homogeneous (see the reviews of Chaudhuri, 2011; Engel, 2011;
Ledyard, 1995). Under this assumption, researchers have analyzed how social, environmental,
institutional, and personal factors influence individuals’ contribution behavior. However, inpractice, the profit gain from contribution behavior is often heterogeneous among them. For
example, in primitive societies, the types of food individuals can procure and how easily they
can procure them vary with their physical ability. In modern enterprises, the profit gain fromcontributing to the organization also varies depending on the ability, position, and status of thestaff. In fact, staff will consider heterogeneity and decide how they contribute to theorganization (Ployhart et al., 2014).
Recent behavioral science studies have started to investigate contribution behavior
under the assumption that an individual’s profit gain from contribution behavior isheterogeneous (Barclay & Reeve, 2012; Brock et al., 2013; Exley, 2016; Kölle, 2015;
Krawczyk & Le Lec, 2010). For example, in evolutionary psychology, Barclay and Reeve(2012) constructed a model that allows for differences in the size of the profit gain fromcontribution behavior, the cost of gaining the profit, and the opportunity cost of selecting thecontribution behavior; then, Barclay theoretically analyzed the effects of the differences in eachelement on individuals’ behavior. Some economics studies posited a model that allows forprobabilistic differences in the way individuals profit from their contribution behavior (Brocket al., 2013; Exley, 2016; Krawczyk & Le Lec, 2010). In this setting, individuals’ profit gainfrom their contribution behavior is heterogeneous among them after calculating the expectedvalue. These prior studies conducted laboratory experiments and examined participants’contribution behavior. However, to our knowledge, few studies have conducted such4

investigations outside laboratories, with one exception of Wildman and Hollingsworth’s study(2009) detailed in Section 2.
This study adds to the literature real-world evidence concerning how having manypotential recipients influence people’s behavior of providing common goods. Specifically, we
examined a nationwide sample in Japan to determine the relationship between blood type andblood donation behavior. We focused on this relationship for two reasons. First, the range ofblood transfusion recipients differs widely across blood type. As shown in Figure 1, type O
blood can be medically transfused to all individuals, regardless of blood type (Ministry of
Health, Labor and Welfare in Japan, 2017). In contrast, individuals with blood type A or B canprovide transfusions for those with the same blood type or those with blood type AB. Moreover,individuals with blood type AB can provide transfusions only for those with the same bloodtype.
In other words, the number of potential blood transfusion recipients of type O blood is
exceptionally the largest among the four blood types. As an example, with a sample of 100
Japanese people, the number of the potential recipients of blood types A, O, B, and AB are 50,
100, 30, and 10, respectively because the distribution of blood groups across Japan isapproximately A: O: B: AB = 4: 3: 2: 1 (Japanese Red Cross Tokyo Metropolitan Blood Center,2020).
[Insert Figure 1 here]
Second, people cannot select their blood type; therefore, blood type is exogenous.
Consequently, we can determine blood type as the starting point and identify its effect on blood
donation behavior. Here, we do not need to worry about the concern of reverse causality—that
donating blood might change a donor’s blood type to a certain blood type—or that, after5

knowing the wide range of transfusion recipients of type O blood, people might change theirblood type to type O.
Considering these two features, we examined the effect of the number of potential
recipients by observing how the blood donation behaviors of Japanese people differ across thefour blood groups. We employed a Japanese sample because almost all Japanese people (99.7%
of our sample), including both blood donors and non-blood donors,1 know their own blood
type. As explained further below, this seems not the case in other countries, and it has beenreported that approximately 30% of the American and 50% of the British populations know
their own blood type (Ciepiela et al., 2017). In addition, many Japanese people (73.9% of oursample) knew that type O blood could be transfused to individuals with the other blood types.
The first characteristic strengthens the validity of our study setting, where blood type isexogenously assigned to people. We did not need to worry about whether some people wouldfind out their own blood type only after donating blood, in addition to the two preceding
concerns. The second characteristic allowed us to analyze the relationship between blood typeand blood donation behavior, based more firmly on the premise that many of the sample knew
about the difference in the range of blood transfusion recipients across the four blood groups.
Does having type O blood, which has the largest number of potential blood transfusion
recipients, promote or inhibit the individual’s blood donation behavior? The literature inbehavioral science indicates both possibilities. Therefore, which possibility emerges in the realworld depends on empirical analysis.
The first possibility is that people with type O blood are more likely to donate theirblood than those with the other blood types. Recent economic studies introduced different
probabilities with which individuals profit from their contribution behavior, and experimentally

1This study calls people who have not donated their blood in the past “non-blood donors.”

6

showed that those with a high probability were likely to display contribution behavior (Brocket al., 2013; Exley, 2016; Krawczyk & Le Lec, 2010). When the number of potential recipients
is vast, the donors expect that their blood will be used for transfusions. Since blood has a short
expiration date, donated blood may be discarded rather than used for transfusions (Japanese
Red Cross Tokai Hokuriku Blood Center, 2020). In other words, having many potential bloodtransfusion recipients means there is a lower probability that the donated blood will bediscarded as compared to having fewer potential recipients. Consequently, people with type O
blood are more likely to gain some altruistic or reputational benefits of their blood being used
for transfusions, the benefits overweight the costs of donating their blood, and then people with
type O blood are more likely to donate their blood than their counterparts.
The second possibility is that people with type O blood are less likely to donate theirblood than those with the other blood types. In general, the benefit gain from the same behavior
diminishes with frequency. Thus, people would prefer combining blood donation behavior andother behaviors that benefit themselves as opposed to repeatedly donating their blood. Since
people with type O blood more quickly and efficiently gain the benefits from their blood being
used for transfusions, they would have more time and resources to display other behaviors.
Consequently, people with type O blood might donate their blood less often as compared topeople with other blood types. This phenomenon is called “crowding out” in economics(Andreoni, 1989, 1990, 1993).
Psychological studies revealed another aspect of why people with type O blood might
be less motivated to donate their blood as compared to their counterparts. Studies have shownthat people display helping behaviors when they see a small number of specific recipientscompared to when they see a large number of unspecific recipients (Kogut & Ritov, 2005a, b,

7

2007; Slovic, 2007). Consequently, one might predict that people with type O blood will beless likely to donate their blood as compared to their counterparts.2
This study empirically investigated which of these two possibilities will emerge by
employing a Japanese sample. We hypothesized that people with type O blood will donate theirblood differently than people with the other blood types. In addition, if we identified a different
pattern of blood donation behavior between individuals with different blood types, weinvestigated whether the cause of such behavior is because type O blood can be medicallytransfused to all individuals, regardless of blood type.
Before entering the next section, we should note that if we find that people with type O
blood are more likely to donate blood than those with the other blood types, it might mean thatthe blood donation behavior of those with type O blood is promoted because of the firstpossibility or that the behavior of those with the other blood types is inhibited because of the
opposite action of the first one. In our empirical analysis, we compared the blood donation
behaviors of people with type O blood with those of their counterparts while bearing in mind
that each side has its own action.

2

Another approach can predict both positive and negative possibilities. We assume that people perceive others

to whom their blood can be transfused as members who belong to the same group. Since people tend to identify
others who share something in common, even if it is trivial, as in-group members (Chen & Li, 2009), thisassumption is plausible. Under this assumption, people with blood type O will feel that the size of the group they
belong to is large, because people with blood types A, B, and AB are also their group members. First, the large
size of their group means many group members will need a blood transfusion. If people with blood type O lookat this feature, it is predicted that they will be more likely to donate their blood because of its expected highdemand (Andreoni, 2007). Second, the large size of their group also means many other group members can donate
their blood. If people with blood type O look at this feature, it is predicted that they will be less likely to donate
their blood and yield that responsibility to the other group members (Panchanathan et al., 2013).

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2. Data and Methodology
2.1 Data Overview
To conduct our investigation, we used a dataset from a nationwide survey conducted in Japan,
the Preference Parameters Study of Osaka University (PPSOU; Osaka University, 2017). To
our knowledge, this is the only survey that included information concerning both Japanese
respondents’ blood type and blood donation behavior. We received permission for research useof this dataset from the Institute of Social and Economic Research at Osaka University. The
PPSOU data are available for research use upon reasonable request (http://www.iser.osakau.ac.jp/survey_data/eng_application.html).
The PPSOU survey is a panel survey that has been conducted annually since 2003. In
the first wave in 2003, a nationally representative sample of individuals aged 20 to 69 years
was obtained by using two-stage stratified random sampling by a self-administered placement
method. The PPSOU survey is based on the concepts of economic psychology and behavioral
economics. Thus, it collects respondents’ unique information, including economic preferences,personalities, and behavioral and psychological attributes, in addition to their basic socioeconomic characteristics. The PPSOU data have been used for vast empirical research inpsychology and behavioral economics (Hanaoka et al., 2018; Ikeda et al., 2010, etc.).
The current study used the cross-sectional dataset in the 2017 wave, because only thiswave included questions to identify respondents’ blood type, blood donation behavior, and theknowledge of the range of transfusion recipients of type O blood. The 2017 wave collected
1,517 valid responses for the current study (the valid response rate was 71.8%) and captured
their characteristics, attributes, and circumstances in 2016. We excluded from the dataset 180
respondents who were not 69 years old or younger in 2016, because 69 years old is the oldest

9age at which people in Japan can donate blood. We also excluded 4 respondents who answered
they did not know their own blood type. Our final sample size was 1,333.
Although the PPSOU survey has sometimes added a new sample to keep it nationally
representative, the 2017 sample somewhat deviates from that. In fact, the youngest person in
our sample is 26-years-old in 2016, and thus our sample does not include any members of thegeneral population aged 16–25 years, who can legally donate blood. Consequently, our sample
includes a relatively large proportion of the respondents in their forties, fifties, and sixties.
However, this feature would not impede our analysis of Japanese people’s blood donation
behaviors, because the rate of blood donation among younger generations is showing adeclining trend and, at present, people in their forties are the primary blood donors in Japan. In
addition, the rate of blood donation among elderly generations (the fifties and sixties) is
showing an increasing trend (Japanese Red Cross, 2017).

2.2 Data Features
Using a Japanese sample for this analysis provides some essential advantages. First, ourJapanese sample generally knew their own blood type (99.7%); this is not the case in othercountries. It has been reported that, in general, approximately 30% of the American and 50%of the British populations know their own blood type (Ciepiela et al., 2017). We also found that
only 33.2% of the American pregnant women knew it in one study (Shah et al., 2011), and 56%of the Canadian participants knew it in the other study (Blider-Candield & Cotterchio, 2014).
Second, 73.9% of our sample knew that type O blood could be transfused to individuals
with the other blood types. We learned from private interviews with medical practitioners thatthe transfusions of type O blood to people in the other blood groups is limited to emergency
situations, and the actual frequency of such transfusions is low. That is, the knowledge is shared10

by ordinary (non-clinical) Japanese people. This feature helps elucidate the pure impact ofpossessing type O blood and the knowledge relating to this blood type on the suppliers’
voluntary blood donation behavior. If type O blood was frequently transfused to individuals
with the other blood types in real medical situations, type O blood would have been more in
demand, and blood donor centers would have more frequently made requests that people with
type O blood donate blood. Consequently, such frequent requests might have promoted theblood donation behaviors of people with this blood type, even if they did not have theknowledge relating to blood type and blood transfusions. However, in the current study, we canignore this concern.3
Third, the distribution of Japanese people’s blood groups is well-dispersed. If almost
all members of the sample had a single particular blood type, we could not have investigated
our research question. Again, according to the Japanese Red Cross Tokyo Metropolitan Blood
Center (2020), the distribution of blood groups across Japan (approximate value) is A: O: B:
AB = 4: 3: 2: 1, and the distribution of blood groups in our sample was as follows: 38.9% for
A, 30.2% for O, 21.2% for B, and 9.7% for AB. Thus, the distribution of blood groups in our
sample is consistent with that seen across Japan.
Fourth, we can ignore any possible effects caused by people with Rh-negative blood,
because the proportion of people with Rh-negative blood is quite small, approximately 0.5%,
in Japan (Ibaraki Red Cross Blood Center, 2020). The proportion rises to 19% in Australia, as
one example (Australian Red Cross, 2020). This Japan’s feature allowed us to focus on a simple
relationship diagram, where individuals with type O blood Rh-positive can provide

When transfusions of type O blood to people in the other blood groups are limited to emergency situations, it
implies the objective probability that type O blood being used for transfusions will only be slightly higher thanthat for the other blood groups. However, according to prospect theory in behavioral economics, people tend tooverestimate small probabilities (Tversky & Kahneman, 1992). Thus, even if people know the above fact, they
could be sensitive to the slight difference in the probability of the blood being used for transfusion.
3

11

transfusions for those with Rh-positive in all blood groups. Rh-negative blood is another kindof universal blood, which can be transfused to individuals with Rh-positive blood. If the
proportion of people with Rh-negative blood was larger in Japan, the relationship diagram
relating people who received a blood transfusion would have been more complicated, and thepredicted effect of type O blood would also have been more complicated.
Additionally, using the PPSOU dataset enabled us to directly investigate (and reject)
the possibility that other mechanisms could explain our results. One major concern is that
economic preferences, personalities, and behavioral and psychological attributes may differ
across blood groups, which would cause different patterns of blood donation behaviors acrossthe groups. For example, people with type O blood likely donate their blood, possibly because
they have more altruistic attitudes when compared to people with other blood types. Several
psychological studies have already rejected the validity of this concern for samples fromseveral countries, including Japan (Cramer & Imaike, 2002; Nawata, 2014; Rogers & Glendon,
2003; Wu et al., 2005); however, some people in Japan might still believe that blood typedetermines economic preferences, personalities, and behavioral and psychological attributes,
and holding such a belief might unconsciously characterize their economic preferences,personalities, and behavioral and psychological attributes.
By using the PPSOU dataset, we addressed the above concern directly. The PPSOU
survey collects information relating to respondents’ economic preferences, personalities, andbehavioral and psychological attributes, including altruistic attitudes. In our analysis, weinvestigated the effect of blood type on blood donation behavior after controlling for suchcharacteristics; concurrently, we also examined whether these characteristics differ acrossblood groups.

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Furthermore, we sought to reconfirm the advantages of using the PPSOU dataset bycomparing it with a dataset used in a related study. To our knowledge, only one empirical study
by Wildman and Hollingsworth (2009) focused on different ranges of blood transfusion
recipients across blood type and analyzed the relationship between blood type and blood
donation behavior. Using the blood donors’ dataset of the Australian Red Cross, they reported
quite complex results. Australian female blood donors with type O blood Rh-negative (the most
universal blood) had a lower frequency of blood donations as compared to those with type O
blood Rh-positive, who had a higher frequency of blood donations as compared to those with
type A blood Rh-positive and with type AB blood Rh-positive. Also, Australian male blood
donors with type O blood Rh-positive had a lower frequency of blood donations as comparedto those with type B blood Rh-positive.
We recognize that their study is quite novel in being the first to examine the relationshipbetween blood type and blood donation behavior. Nevertheless, it is worthwhile to readdress
this question with our PPSOU data. First, their sample did not contain people who have nevergiven their blood in the past. That is, their study did not investigate how having type O blood
influences the first-time blood donation behavior of such the people.
Second, the proportion of the Australian people with Rh-negative blood (17.3% of theirsample) is higher than that of the Japanese people. Therefore, the relationship diagram relating
people who received a blood transfusion could be more complicated in Australia than in Japan.
Actually, their reported results were complex.
Third, their dataset did not include information concerning the sample’s knowledge ofthe ranges of blood transfusion recipients across blood type, preferences, personality traits, andbehavioral and psychological attributes; therefore, it is difficult to empirically explore thebackground mechanism for their results.
13

As reported above, the Australian female blood donors with type O blood Rh-negative
donated their blood less frequently than those with type O blood Rh-positive, which might beconsistent with the second prediction we noted in our Introduction; that is, since they more
quickly and efficiently benefited from their blood being used for transfusions, there might bemore time and resources for them to take other behaviors. The authors themselves presented
another interpretation that the demand for type O blood Rh-negative creates an expectation thatthose with this blood type should donate, and such an expectation could decrease thereputational benefit they gain from their blood donation; however, it is difficult to empiricallyconfirm which interpretation is plausible, because their dataset did not include information
concerning the sample’s preferences, personality traits, and behavioral and psychologicalattributes.

2.3 Variables and Descriptive Statistics
2.3.1 Blood donation behavior
The PPSOU survey asked respondents whether they had donated blood at least once in the past
few years and, furthermore, whether they had donated blood once or more within the past year.
We set up two binary dependent variables that correspond to answers for those questions. The
respondents who answered ‘yes’ to the latter question were coded as necessarily answering‘yes’ to the former question as well.
In addition, the survey ascertained whether the respondents, who had not donated bloodin the past, had behaved in that way for some health reasons. We used their responses in ourfurther analysis.

142.3.2 Blood type
The PPSOU survey asked respondents whether their blood type is A, B, O, or AB. Although
this survey allowed them to answer that they did not know their blood type, only four of oursample did not. In addition, although the respondents’ blood type was self-reported in thissurvey, medical studies have already confirmed that such self-reported ABO blood type ishighly accurate (Alkebsi et al., 2019; Bider-Canfield & Cotterchio, 2014; Ito et al., 2001). We
set up four binary independent variables that identify whether the respondents’ blood type is A,
B, O, or AB, respectively.

2.3.3 Knowledge of the range of transfusion recipients of type O blood
The PPSOU survey asked respondents whether they thought whether the statement, which is“type O blood can be transfused to individuals with the other blood types,” is correct orincorrect. We identified those who responded that this statement is correct as having theknowledge of the range of transfusion recipients of type O blood.

2.3.4 Covariates
Our analysis included covariates to control for the effects of respondents’ socio-economic
attributes, health status, economic preferences, personalities, and behavioral and psychologicalattributes. As some examples, the PPSOU survey included the questions of Ten Item
Personality Inventory (TIPI), which was developed by Gosling et al. (2003), and was translated
into Japanese by Oshio et al. (2012). Our analysis controlled for the effects of the respondents’big five personality traits (extroversion, agreeableness, conscientiousness, neuroticism, andopenness to experiences; their Cronbach alpha coefficients were 0.65, 0.36, 0.46, 0.40, and15

0.48, respectively).4 Our analysis also used their responses to some statements (e.g. “I feel
happy when I do a good deed that I think benefits others”) in order to consider potential
differences in behavioral and psychological attributes across blood groups.
Table 1 in the next subsection showed the details, and Online Appendix A in the
supplementary information introduced questions and variables of health status, economic
preferences, personalities, and behavioral and psychological attributes.

2.3.5 Descriptive statistics
Table 1 shows that 11.6% of our sample have donated blood at least once within the past few
years and that 5.5% have done so once or more within the past year. According to the websiteof the Japanese Red Cross (2017), 5.6% of the Japanese population donated blood in 2016,
which is a similar percentage to that shown in our sample. Additionally, as mentioned earlier,the distribution of blood type across Japan is consistent with that in our sample, and 73.9% of
our sample knew that type O blood could be transfused to individuals with the other blood
types.
[Insert Table 1 here]

2.4 Analytic Strategy
Since our two dependent variables are binary, we used a logistic regression for the estimation.
We also applied to the estimation robust standard errors clustered at the respondents’ prefecturelevel.

With the exception of neuroticism’s coefficient, the coefficients in our sample are almost consistent with thosereported in the original paper of Gosling et al. (2003), which were 0.68 (extroversion), 0.40 (agreeableness), 0.50
(conscientiousness), 0.73 (neuroticism), and 0.45 (openness to experiences).
416

In the basic analysis, we conducted the regression with a full sample, clarifying therelationship between blood type and blood donation behavior. When a different pattern of blood
donation behavior was found for people with type O blood, we next investigated whether sucha result was generated because type O blood has a wider population of potential recipients thanthe other blood types. For the latter analysis, we conducted the regression with the subsamples.
We divided the full sample into two groups: those who answered that the statement “type O
blood can be transfused to individuals with the other blood types” is correct, and those who
answered that it is incorrect. We then investigated whether the estimated parameter of type O
blood was significantly different from zero in the former group but not the latter. If so, it would
have directly supported that people with type O blood donate their blood differently, becauseof their large number of potential recipients.
In the further analysis, we examined (and rejected) the other possible explanations forour results. In so doing, we further confirmed our interpretations of the results. All the statisticalanalyses were conducted with Stata version 15 (LightStone Corp, Tokyo, Japan).

3. Results
3.1 Basic Results
The proportions of respondents who had donated blood at least once within the past few years
were 9.6% (in our blood type A group), 14.9% (blood type O), 9.6% (blood type B), and 14.0%(blood type AB) (chi-square (3) = 9.013, p = 0.046).
Table 2 shows the results of logistic regressions with the full sample. In columns 1 to
3, the estimated parameter of the type O blood variable was positive and significant (all p values<.01 except for column 1, p’s<.05), which indicates that people with type O blood were more
likely to have donated blood at least once within the past few years than were people with the17other blood types. In particular, column 3 shows that they were especially more likely to havedone so than people with blood type A, whose blood donation behaviors were similar to thoseof people with blood type B. These results remained stable also when using another dependent
variable—respondents who donated blood once or more within the past year. The findings here
are consistent with our first prediction.
[Insert Table 2 here]
Intriguingly, column 6 shows that people with type AB blood were more likely to havedonated blood once or more within the past year than were people with blood type A (p’s<.05).
Although this finding is confusing at first glance, it might be consistent with our second
prediction; i.e., respondents recognized a small number of specific recipients, and thus they
were promoted to donate their blood. However, the type AB blood’s effect does not seem to berobust, because it became weakened in column 3 (p’s<.10).
In sum, we found a different pattern of blood donation behavior for people with type O
blood, as we expected. Following our analytic strategy, the next step was to investigate whether
this result was generated because type O blood has a wider population of potential recipients
than the other blood types.
Table 3 shows the results of logistic regressions with the subsamples. The results
revealed that the type O blood variable had a significant positive impact on blood donation
behaviors but only for the subsample of respondents who answered that the statement “type O
blood can be transfused to individuals with the other blood types” is correct (columns 1 to 4;
all p’s<.01 except for column 3, p’s<.05). In contrast, columns 5 to 8 showed dissimilar results
when we used the other subsample—those who answered that the statement is incorrect.
When using the smaller size sample in the former case than in the full sample analysis
case, the coefficient size of the type O blood variable and its significance were indifferent from18

those in the full sample analysis case (of course, we cannot completely deny the possibility thatthe latter results were influenced by their further small sample size). The findings more directly
support that people with type O blood were likely to have donated their blood because of thelarge number of potential recipients.
[Insert Table 3 here]
We discuss here the validity of our subsample analysis in the step 2, which supposedthat having type O blood is correlated with possessing the knowledge relating to this blood
type and that the covariates’ coefficients differ between the two subsample groups. In fact, there
was a weak positive correlation between the type O blood variable and the knowledge variable.
This means that people with type O blood were more likely to have the knowledge relating totheir blood type as compared to their counterparts. This intuitively understandable finding does
not impede but rather supports our interpretation that people with type O blood were likely tohave donated blood because of the knowledge of the large number of potential recipients.
Figure 2 presents the marginal effects of the type O blood variable in the selected model
specifications. The likelihood to have donated blood within the past few years was 4.7–6.4%
higher in people with type O blood than in people with the other blood types, when using thefull sample and the subsample of respondents who answered that the statement “type O bloodcan be transfused to individuals with the other blood types” is correct. However, when using
the other subsample, we cannot reject the null hypothesis that the marginal effect of the type O
blood variable was indifferent from zero.
[Insert Figure 2 here]
Finally, we checked that estimations using the probit regression model and the linear
probability model produced similar results. We also found similar tendencies from estimations
using sampling weights: however, the significance for the type O blood’s effect became a little
19

weakened. Considering that the PPSOU survey is a panel survey and its sample consequently
includes a relatively large proportion of elderly respondents, the result indicates the possibility
that the relationship between having type O blood and blood donation behavior is observed
mainly among them. We then confirmed this possibility, by dividing the sample into those whoare relatively young and those who are elderly and conducting the estimation (please see Online
Appendix B in the supplementary information).

3.2 Further Results
3.2.1 The first concern: altruistic attitudes
This section examined (and rejected) the other possible explanations for our results. First, wearrested the concern that people with type O blood (vs. the other types) were more likely tohave donated their blood, possibly because they had more altruistic attitudes. We wish to restatethat our model specification included covariates related to economic preferences, personalities,
and behavioral and psychological attributes, including some altruistic attitudes, to consider
their potential differences across blood groups; however, it is still possible that these covariates
failed to sufficiently control for the differences. If unobserved altruistic factors remained after
controlling for these covariates and if the type O blood variable was a proxy one for the factors,
the type O blood variable should have had a significantly positive effect not only on blood
donation behaviors but also on the other altruistic behaviors. Thus, we checked the effects ofthe type O blood on the other altruistic behaviors, as a negative control (Lipsitch et al., 2010).
Table 4 presents results that negate the first concern. Rows 1 to 8 show that the type O
blood variable had no effect on the other altruistic behaviors, including registering for bonemarrow donation, organ-donation intention, and making monetary donations. In addition, rows
9 to 12 show that, after controlling for socio-economic and health status attributes, economic
20

preferences, and the other personalities, and behavioral and psychological attributes aside fromthe dependent variable, we did not find any significant relationship between type O blood andany altruistic attitudes. These findings deny the explanation that, since they naturally had morealtruistic attitudes, people with type O blood were more likely to have donated blood than theircounterparts.
[Insert Table 4 here]

3.2.2 The second concern: health status
We addressed the concern that people with type O blood were more likely to donate blood,
possibly because they were healthier than people with other blood types. In fact, medical
studies have reported that the risks of contracting diseases indeed differ between blood groups(He et al., 2012; Wolpin et al., 2009, etc.). We again wish to state that our model specifications
included and controlled for variables that captured respondents’ current health conditions;however, these variables might have failed to represent differences in terms of congenital or
chronic health conditions.
To address this concern, we conducted the regression after excluding respondents whoreported, “I have donated blood; but I have not donated within the past few years because of
my health condition” or “I want to donate my blood; but I cannot because of my healthcondition.” Since the results shown in Table 5 were robustly similar to those previously
obtained, we propose that this second concern was not a significant factor.
[Insert Table 5 here]213.2.3 The third concern: demand
We addressed the concern that people with type O blood were more likely to donate blood,
possibly because type O blood was more in demand than the other types of blood. For example,
blood donor centers might frequently make requests that people with type O blood donate blood.
To consider the possible difference in demand, we controlled for information relating to theinventory ratio of stocks for each blood type in a prefecture, where each respondent lives. If
the inventory ratio of stocks of a particular blood type is low in a prefecture, it would be likelythat the blood donor centers in the area would request that people with that blood type donate
blood. The Ministry of Health, Labor and Welfare in Japan (2012) published weekly reports
concerning the inventory ratios of stocks of blood types for all prefectures in 2012. Using thisinformation, we created variables relating to the annual averages and standard deviations ofthese inventory ratios of stocks in a prefecture, in which each respondent lives.5 Table 6 showsthat type O blood’s parameter remained positive and significant even after controlling for the
information concerning the inventory ratio of stocks for blood types. Therefore, this thirdconcern is not crucial.
[Insert Table 6 here]

3.2.4 The fourth concern: Rh-negative
We addressed the concern that people with type O blood Rh-negative were more likely todonate blood than those with the other blood types and this behavior consequently shaped our
results. Type O blood Rh-negative is quite rare in Japan; therefore, people with this blood typemight think that providing donations of this blood type is also rare. This rarity could make them

The Ministry of Health, Labor and Welfare in Japan published the report every week only in 2012. Thus, ouranalysis assumed that the annual averages and standard deviations of the inventory ratios of stocks of blood types
are almost indifferent from 2012 to 2016.
522

more likely to donate their blood to others within the same group; consequently, this has thepotential to negate our hypothesis.
Our survey did not capture whether a respondent had Rh-positive or Rh-negative blood,
and we could not directly control its effect in our estimations. According to the Ibaraki Red
Cross Blood Center (2020), only 0.15% of the Japanese population has O Rh-negative blood(a proportion of 1:670 people). Therefore, our type O blood sample (N = 403) included very
few people with type O blood Rh-negative. Even if there were such individuals in the sample,
their proportion is likely to have been extremely small (i.e., one or two people), meaning theywould not have greatly influenced our estimation results. As an illustration, we shall imagine
that there existed two people with type O blood Rh-negative, and both donated their blood.
Even after excluding the two observations, the ratio of blood donors among people with type
O blood would be 14.5%, which is almost indifferent from that in the full sample (14.9%).
Therefore, this fourth concern was not a significant factor.

4. Discussion, Limitation, and Conclusion
This study examined the relationship between blood type and blood donation behavior toinvestigate the effect of differences in the number of potential recipients on the behavior ofproviding common goods. We focused on this relationship because type O blood can bemedically transfused to all individuals; thus, the number of potential recipients of type O bloodis exceptionally the largest among the four types. We analyzed responses from a nationwide
questionnaire survey in Japan. We did so because almost all Japanese people know their own
blood type, and many know that type O blood can be medically transfused to all individuals.
We found from our basic analysis that people with type O blood were more likely tohave donated their blood in the past than those with the other blood types. We further found a23strong positive association between having type O blood and blood donation behavior in a
subsample of individuals who knew that type O blood could be medically transfused toindividuals of all blood groups. Based on these results, we interpreted that the more frequent
blood donation behavior of those with type O blood was characterized by the large number of
their potential blood transfusion recipients.
Furthermore, we strengthened this interpretation by denying the concerns that the otherfactors completely explained the above association between blood type and blood donation
behavior. As one example, as previous psychological studies had done, we examined whether
economic preferences, personalities, and behavioral and psychological attributes, including
altruistic attitudes, varied by blood type. We confirmed that their differences did not explain
the blood type differences in blood donation behavior.
When the number of potential recipients differs among the suppliers, it means that a
supplier’s profit gain from contribution behavior is heterogeneous among them. Previous
studies in evolutionary psychology, social psychology, and behavioral economics analyzed
their helping behavior under the assumption that the profit gain from contribution behavior ishomogeneous (Chaudhuri, 2011; Engel, 2011; Ledyard, 1995). However, recent behavioral
science studies have started to theorize the effect of the heterogeneous gain and used laboratory
experiments to empirically test their theoretical predictions (Barclay, 2012; Brock et al., 2013;
Exley, 2016; Kölle, 2015; Krawczyk & Le Lec, 2010). This study added evidence outside thelaboratory to the literature.
Theoretically, people with type O blood—those with the largest number of potential
blood transfusion recipients—may be more likely to donate their blood as compared to theircounterparts. In contrast, they also may be less likely to do so; for example, because there are24many unspecified potential recipients. Our results show that the former was supported in a realworld setting of Japanese people’s blood donation behaviors.
Next, we discuss why people with type AB blood were as likely to donate their bloodas those with type O blood in the past. The number of potential blood transfusion recipients issmallest for those with type AB blood among the four blood groups. They might be promoted
to donate by the specified small number of potential recipients; that is, their blood donation
behavior might support the latter prediction.
However, this tendency was unstable. When we changed the dependent variable fromblood donation experience within the past year to that within the past few years, the type AB
blood’s effect became weakened (p’s<.10). Furthermore, we did not find their significant effectfrom some specifications in our further analysis. In our sample, the proportion of the people
with type AB blood was 9.7%, and the proportion of the people who donated blood within thepast year was 5.5%. Thus, we cannot deny the concern that these small proportions accidentally
generated the above tendency.
We did not find any differences in blood donation behavior between people with blood
type A and B, probably because the difference in the number of the potential blood transfusion
recipients between them is relatively small (see the Introduction). Thus, we examined how
blood donation behaviors differ between the following two cases: one case where the numberof potential blood transfusion recipients is large (people with type O blood), and the other casewhere its number is small (those with blood type A and B). We expect future research toinvestigate how helping behaviors differ as the number of potential recipients changes linearly.
Here, we consider reciprocal altruism theories in evolutionary psychology (Barclay &Willer, 2007; Nowak & Sigmund, 1998a, b; Takahashi & Mashima, 2006; Trivers, 1971),
discussing the mechanism of why people with type O blood frequently donated their blood in25this study. The theories states that people behave altruistically toward those with whom they
have no direct relationship, because they can enhance their social reputation through theiraltruistic behavior. Thus, the frequent blood donation behavior of people with type O blood
might be caused by said reputational benefit. However, they might donate their blood inanonymous settings. In such a case, their blood donation behavior is not observed by others,
and it is difficult for them to gain the reputational benefit from their behaviors being directly
observed. Some studies in evolutionary psychology have noted that people display helping
behavior also in anonymous settings, because their helping behavior in non-anonymous
settings spill-overs in anonymous settings (Delton et al., 2011; Kiyonari et al, 2000).
However, it is difficult to completely determine whether our study setting is anonymousor non-anonymous. When people enter a blood donation center, passersby outside might
witness their actions. The other blood donors inside might also witness their blood donation
behavior. Furthermore, their blood donation behavior is observed and recorded by the Japanese
Red Cross. We expect to future research to overcome this limitation and, after experimentallydevising anonymous or non-anonymous settings, examine the differences in helping behavior
per the number of potential recipients.
One related experimental study in evolutionary psychology showed that people are lesslikely to cooperate in a public goods game with a threshold, or a situation in which any amountof public goods provided above the threshold is not returned to the group (Van Vugt & Hardy,2009). The study also showed that, in a non-anonymous setting, they consider the reciprocal
concern and continue to provide the public good even after the threshold is exceeded. This
study is similar to our study, because both focused on a situation in which helping behavior
may be wasted. In contrast, there are differences between the two. For example, in the earlier
study, all the provided public goods were wasted after the threshold was exceeded; while, in26

our study, donated blood was only possibly wasted. In addition, the earlier study focused onmonetary support and our study focused on physical support. Considering these differences,
we still expect future research to explore how different the helping behaviors we focused are
in anonymous and non-anonymous settings.
Our findings can practically contribute to blood collection activities. Existing studies
have examined which monetary or non-monetary incentive promotes blood donation behavior(see the review of Chell et al., 2017). The current study revealed that people’s likelihood todonate their blood depends on whether (they think that) their blood is more likely to be used
for transfusions. Concretely, the probability of having donated their blood in the past varied byaround 5%, depending on whether their blood type was O, and this variation is not small. To
encourage blood donation behaviors, it could be vital to inform people with type O blood that,in an emergency, their donated blood could be used for all transfusions, regardless of blood
type. In contrast, people with the other blood types (in particular, blood types A and B) mightlose their will to donate their blood if they know this feature of type O blood. Therefore, it
could be also critical not to discourage people with blood types A and B from donating theirblood by informing them that types A and B blood are also needed as much as type O blood.
Finally, we should note that our study does not guarantee that the similar findings will
be observed outside of Japan. Also, although there exist two types of blood donation, including
whole blood donation and component blood donation, our dataset does not distinguish between
the two. Therefore, our study did not reveal which type of blood donation behavior is promoted
by having type O blood. However, despite some limitations, our study added a new insight tothe literature, by providing the real-world evidence concerning how having many potential
recipients influence people’s behavior of providing common goods and then discussing how
the evidence contributes in practice.
27

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33

Acknowledgements
We thank Elke Weber, Eric J. Johnson, Yoichi Hizen, Yoko Ibuka, Takanori Ida, Yuta
Kawamura, Haruko Noguchi, Masao Ogaki, Erika Seki, and the participants at the ABEF’s
11th Annual Conference 2017, the Applied Economics Workshop 2018, the Health Economics
Workshop 2018, the Japan Economic Association 2018 Spring Conference, the 17th Panel
Survey Conference, and the 2018 SABE/IAREP conference for the discussions, comments, andencouragement. We used the data from the Preference Parameters Study of Osaka University,
which had been conducted as part of the 21st Century Center of Excellence Program“Behavioral Macro-dynamics Based on Surveys and Experiments,” the Global Center of
Excellence Program “Human Behavior and Socioeconomic Dynamics,” the Grants-in-Aid for
Scientific Research (S) Humanities and Social Sciences (Social Sciences) (15H05728), and theGrants-in-Aid for Scientific Research (A) Humanities and Social Sciences (Social Sciences)(26245041). In addition, in preparing this paper, the authors were supported by the Japan
Society for the Promotion of Science (17J07242) and the International Joint Research
Promotion Program of Osaka University. The funders and supporters had no role in study
design, data collection and analysis, decision to publish or preparation of the manuscript.

Conflict of interest
We declare no conflicts of interest.

34

Figure 1. Difference in the number of transfusion recipients for each blood group35

Figure 2. Marginal effect of having type O blood (with 95% CI)

36

Table 1. Descriptive statistics
Number of observations = 1,333
Mean

SD

Min

Max

Have donated blood at least once within the past few years (yes=1; no=0)

0.116

0.321

0

1

Have donated blood once or more within the past year (yes=1; no=0)

0.055

0.228

0

1

Blood type A (yes=1; no=0)

0.389

0.488

0

1

Blood type O (yes=1; no=0)

0.302

0.459

0

1

Blood type B (yes=1; no=0)

0.212

0.409

0

1

Blood type AB (yes=1; no=0)

0.097

0.296

0

1

0.739

0.439

0

1

Age (years)

53.08410.13026

69

Gender (female=1; male=0)

0.535

0.499

0

1

Married (yes=1; no=0)

0.806

0.396

0

1

Household income levels (10,000 yen)642.236400.07250

2,100

Educational years

13.288

2.035

9

21

Body mass index

22.8483.379

13.33344.983

Subjective health status

3.327

0.918

1

5

Mental illness

2.615

0.839

1

5

Variables name
1. Blood-donation behavior

2. Blood type

3. Knowledge of the range of transfusion recipients of type O blood
Type O blood can be transfused to individuals with the other blood types (correct=1; incorrect=0)4.1. Covariates: Socio-economic attributes (in 2016)

4.2. Covariates: Health status

4.3. Covariates: Economic preferences
Time discounting factor

0.882

0.224

-1.837

1.126

Absolute risk aversion

0.000023

0.000057-0.000400

0.000133

Extraversion

8.157

2.485

2

14

Agreeableness10.086

1.774

3

14

Conscientiousness

8.114

2.087

2

14

Neuroticism

7.828

2.042

2

14

Openness to experience

7.790

2.109

2

14

General trust "In general, most people are trustworthy."

3.209

0.720

1

5

Altruism "I feel happy when I do a good deed that I think benefits others."

3.791

0.658

1

5

3.735

0.529

1

5

Conformity "Behaving similarly to people around me makes me feel comfortable."

2.905

0.873

1

5

Social norm "I never cut into a line of people."

4.342

0.949

1

5

Religious attitude "I am deeply religious."

1.662

1.033

1

5

Belief in fortune-telling based on blood-types "A person’s blood type indicates their character."

2.794

0.967

1

5

4.4. Covariates: Personality traits

4.5. Covariates: Behavioral and psychological attributes

Reciprocity "If someone does me a favor, I am prepared to return it."37

Note: The supplementary information introduces questions and variables of health status, economic preferences, personalities, and behavioral and psychological attributes.

Table 2. Full-sample analysis
Estimation method:
Logistic regression (coefficient)

Blood type O

Dependent variable:
I have donated blood at least once
within the past few years

I have donated blood once or more
within the past year

1

2

3

4

5

6

0.421**

0.496***

0.535***

0.481**

0.565**

0.674***(0.176)(0.184)(0.192)(0.237)(0.229)(0.248)

Blood type B
Blood type AB-0.081

0.026(0.227)(0.359)0.397*

0.600**(0.234)(0.305)

Covariates:
Socio-economic status

×

×

×

Health status×

Economic preferences

×

×

×

×

×

×

×

×

×

×

Personalities

×

×

×

×

Behavioral and psychological attributes

×

×

×

×

Number of observations

1,333

1,333

1,333

1,333

1,333

1,333

Log pseudo-likelihood-452.510-433.801-432.785-268.129-254.745-253.779

Notes: This table shows the logistic regressions of blood type on blood donation behavior. Standard errors are reported in brackets. Significantly different from zero at the *10% level, **5% level, and***1% level. The baseline in columns 1, 2, 4, and 5 is blood type A, B, or AB. The baseline in columns 3 and 6 is blood type A.

38

Table 3. Subsample analysis
Estimation method:
Logistic regression (coefficient)

Blood type O

Dependent variable:
I have donated bloodat least once
within the past few years

I have donated bloodat least once
within the past few years

I have donated bloodonce or more
within the past year

1

2

3

4

5

6

7

8

0.681***

0.704***

0.682**

0.787***

0.084

0.043

0.077

0.312

(0.213)(0.225)(0.277)(0.304)(0.416)(0.435)(0.430)(0.459)Blood type B
Blood type AB

Subsample:

I have donated bloodonce or more
within the past year

-0.1270.069-0.1200.221(0.270)(0.351)(0.382)(0.602)

0.458

0.572

-0.0260.744(0.293)(0.389)(0.453)(0.847)

Subsample 1 who answered that the statement, which is“type O blood can be transfused to individuals with theother blood types,” is correct.

Subsample 2 who answered that it is incorrect.

Number of observations

985

985

985

985

348

348

348

348

Log pseudo-likelihood-305.795-304.924-188.174-187.642-112.277-112.248-56.383-56.035

Notes: This table shows the logistic regressions of blood type on blood donation behavior. Standard errors are reported in brackets. Significantly different from zero at the *10% level, **5% level, and ***1% level. The
baseline in columns 1, 3, 5, and 7 is blood type A, B, or AB. The baseline in columns 2, 4, 6, and 8 is blood type A. All the model specifications include covariates of socio-economic status, health status, economic preferences,personalities, and behavioral and psychological attributes.

39

Table 4. The first concern: altruistic attitudes
Number of observations = 1,333

Independent variable:
Blood type O

1. Dependent variable: Other altruistic behaviors
1.1. Bone-marrow donation(1)-0.332

I have registered as a bone-marrow donor.

(0.450)(2)(3)

I have registered as a bone-marrow donor, or

0.061

I want to register as a bone-marrow donor, but I have not yet.

(0.177)

I have registered as a bone-marrow donor,-0.118

I want to register as a bone-marrow donor, but I have not yet, or(0.127)

I want to register as a bone-marrow donor, but I cannot register because of my age or health1.2. Organ donation-0.270(4)

I have signed an organ-donation consent form.

(5)

I have signed an organ-donation consent form, or-0.012

I have a will, but I have not signed it yet.

(0.151)(0.203)1.3. Monetary donation(6)-4,366.546

Entire monetary donations for the past year

(4,821.701)(7)-82.231

Monetary donations to disaster aid(388.926)(8)-9,147.630

Monetary donations to religious groups(7,699.593)
2. Dependent variable: Altruistic attitudes

0.003

(9)

General trust "In general, most people are trustworthy."(10)

Altruism "I feel happy when I do a good deed that I think benefits others."(0.038)-0.040(0.030)(11)

Reciprocity "If someone does me a favor, I am prepared to return it."

0.045
(0.031)(12)-0.055

Agreeableness (a Big 5 personality trait)(0.103)
Notes: Significantly different from zero at the *10% level, **5% level, and ***1% level. Since the dependent variables in rows 1 to 5 are binary, we use logistic
regression for the estimations. For the experience of monetary donations in rows 6 to 8, the original question items are as follows: "not making a donation," "1 yen~ 5,000 yen," ..., "500,000 yen ~ 1,000,000 yen," "1,000,000 yen or more." Therefore, when the dependent variable is experience of monetary donation, we useinterval regression for the estimations. The altruistic attitudes in rows 9 to 12 are ordinal variables whose values are 5, 4, 3, 2, and 1. Therefore, we regard them as
continuous variables and use OLS regression for the estimations. The baseline in all the model specifications blood type A, B, or AB. All the model specifications
also include covariates of socio-economic status, health status, economic preferences, personalities, and behavioral and psychological attributes.

40

Table 5. The second concern: health status
Estimation method:
Logistic regression (coefficient)

Blood type O

Dependent variable:
I have donated bloodat least once
within the past few years

I have donated bloodat least once
within the past few years

I have donated bloodonce or more
within the past year

1

2

3

4

5

6

7

8

0.512***

0.544***

0.557**

0.647**

0.475**

0.521***

0.514**

0.617**(0.184)(0.195)(0.232)(0.254)(0.190)(0.202)(0.234)(0.256)

Blood type B
Blood type AB

Excluded respondents:

I have donated bloodonce or more
within the past year

-0.104-0.029-0.0600.017(0.228)(0.363)(0.225)(0.348)0.415

0.597*

0.412*

0.587*(0.254)(0.314)(0.247)(0.313)

We excluded the respondents who answered:"I have donated blood; but I have not donated within thepast few years because of my health condition."

Number of observations

1,097

1,097

1,097

1,097

Log pseudo-likelihood-403.713-402.595-242.268-241.261

We excluded the respondents who answered:"I have donated blood; but I have not donated within thepast few years because of my health condition," or"I want to donate my blood; but I cannot because of myhealth condition."
946
946
946
946
-386.327-385.366-235.012-234.100

Notes: This table shows the logistic regressions of blood type on blood donation behavior. Standard errors are reported in brackets. Significantly different from zero at the *10% level, **5% level, and ***1% level. The
baseline in columns 1, 3, 5, and 7 is blood type A, B, or AB. The baseline in columns 2, 4, 6, and 8 is blood type A. All the model specifications include covariates of socio-economic status, health status, economic preferences,personalities, and behavioral and psychological attributes.

41

Table 6. The third concern: demand
Estimation method:
Logistic regression (coefficient)

Blood type O

Dependent variable:
I have donated blood at least once
within the past few years

I have donated blood once or more
within the past year

1

2

3

4

5

6

0.489***

0.509***

0.527***

0.574**

0.562**

0.690**(0.188)(0.186)(0.199)(0.243)(0.251)(0.271)

Blood type B
Blood type AB-0.1120.088(0.221)(0.366)0.330

0.563*(0.235)(0.303)

Additional covariates:
Annual average of the inventory ratio of stocks

×

×

×

×

×

1,333
-428.872

1,333-428.070

×

×

×

×

×

1,333
-251.321

1,333-250.537of blood groups for each respondent's prefecture
Annual standard deviation of the inventory ratio of stocksof blood groups in each respondent's prefecture
Number of observations
Log pseudo-likelihood

1,333-432.901

1,333-252.776

Notes: This table shows the logistic regressions of blood type on blood donation behavior. Standard errors are reported in brackets. Significantly different from zero at the *10% level, **5% level, and ***1% level. The
baseline in columns 1, 2, 4, and 5 is blood type A, B, or AB. The baseline in columns 3 and 6 is blood type A. All the model specifications include covariates of socio-economic status, health status, economic preferences,personalities, and behavioral and psychological attributes.

42Online Appendix A. Questions and variables
The following are survey questions for capturing respondent’s health status, economic
preferences, personalities, and behavioral and psychological attributes. We added
explanations for how we created the variables from the answers (if necessary).

1. Health statusa) BMI: What is your height and weight?
Height:centimeters, Weight:

kilograms

Note: We calculated out the indicator of BMI, using the following equation:
BMI = Weight in kilograms ÷ (Height in meters)2b) Subjective health status: How would you describe your current health status: Is it
excellent, very good, good, fair, or poor?
1.

Excellent2.

Very good

3.

Good

4.

Fair

5.

Poor
1

Note: We reconstructed the answers on the opposite scale from 1 to 5, where “1” means“poor” and “5” means “excellent.”c) Mental illness: How true for you is each of the following statements? Answer for eachon a scale from 1 to 5, where “1” means “it is particularly true for you” and “5” means“it doesn't hold true at all for you.”⚫

I have been feeling stressed lately⚫

I have been feeling depressed lately⚫

I haven’t been sleeping well lately⚫

I have been feeling lonely lately

Notes: First, we reconstructed the answers on the opposite scale from 1 to 5, where“1” means “it doesn't hold true at all for you” and “5” means “it is particularly true
for you.” Second, we calculated out the indicator of mental illness, by summing up
the answers for the fours statements and dividing the value by 4.

2. Economic preferencesa) Time discounting factor: Suppose that you are to receive money from someone. You
can either choose to receive the money today, or 7 days from today, but the amounts
2

will be different. Compare the amounts and dates below in Option “A” and Option“B,” and indicate which option you prefer for each of the nine choices.

Option A
Receive today
JPY 3,005
JPY 3,003
JPY 3,008
JPY 3,000
JPY 3,005
JPY 3,009
JPY 3,001
JPY 3,002
JPY 3,008

orOption B
Receive 7 days
from today

Which ONE do you prefer?

JPY 3,014
JPY 3,297
JPY 3,037
JPY 3,000
JPY 5,951
JPY 3,068
JPY 3,119
JPY 2,996
JPY 3,011

Option A

Option B

A
A
A
A
A
A
A
A
A

B
B
B
B
B
B
B
B
B

Notes: As similarly in the literature (Harrison et al., 2002), we asked the respondents
to choose between two options, “A” and “B.” For example, we asked them to choose
between “A”—receiving today JPY 3,005, and “B”—receiving in 7days JPY 5,951.
From each situation, we obtained response data, which revealed the switching point,where each respondent switched his or her choice from option “A” to “B.” At theswitching point, the today’s option is equivalent to the delayed option.
We calculate out time discounting factor using the following way. We first takethe average of the two monetary amounts for Option A at the point of switching from3

Option B to Option A. Second, we divide the numerator by the average of the twomonetary amounts for Option B at the switching point.
In general, the respondents with a low time discounting factor are predicted totake impatient behavior.

b) Absolute risk aversion: Suppose that there is a “speed lottery” with a 50% chance of
winning JPY 100,000 (USD 1,000). If you win, you receive a prize right away. If you
lose, you receive nothing. How much would you spend to buy a ticket for this lottery?
Choose Option “A” if you would buy the ticket at that price, or choose Option “B” if
you would not.

Price ofthe “speed lottery” ticket

JPY 10
JPY 2,000
JPY 4,000
JPY 8,000
JPY 15,000
JPY 25,000
JPY 35,000
JPY 45,000
JPY 50,000

Which ONE do you prefer?
Option A
Option B(buy the “speed lottery”(DO NOT buy the “speed
ticket)lottery” ticket)
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B

4

Notes: we use answers for a hypothetical question related to a speed lottery andmeasure a respondent’s risk tolerance from absolutely risk neutral to absolutely risk
averse. This approach to elicit risk aversion using a hypothetical lottery is also taken
by previous studies (Cramer et al., 2002; Guiso & Paiella, 2004; Hartog et al., 2002).
Specifically, this question asks respondents about their willingness to pay (𝜋1 )for a hypothetical lottery with a 50 percent chance of winning JPY 100,000 (USD
1,000) or nothing otherwise. Since the expected value of the lottery is JPY 50,000(USD 500), we interpret this to mean that a respondent whose 𝜋1 is lower than the
expected value is more risk averse. We calculate the indicator of absolute risk
aversion using the following equation:

Absolute Risk Aversion =50,000 − 𝜋120.5(0.5 × 100,000 − 2 × 0.5 × 100,000 × 𝜋1 + 𝜋12 )In general, the respondents with a low indicator of absolute risk aversion are
predicted to take risk-tolerant behavior.

53. Personality traits
Big 5 personality traits: Please circle ONE applicable number next to each statement
to indicate the extent to which you agree or disagree with that statement. You shouldrate the extent to which the pair of traits applies to you, even if one characteristic
applies more strongly than the other.

DisagreeStrongly

DisagreeModerately

Disagree
A Little

Neither
Agree NorDisagreeAgree
A Little

Agree
Moderately

Agree
Strongly

A: Extraverted, enthusiastic

1

2

3

4

5

6

7

B: Critical, quarrelsome

1

2

3

4

5

6

7

C: Dependable, self-disciplined

1

2

3

4

5

6

7

D: Anxious, easiliy upset

1

2

3

4

5

6

7

E: Open to new experiences, complex

1

2

3

4

5

6

7

F: Reserved, quiet

1

2

3

4

5

6

7

G: Sympathetic, warm

1

2

3

4

5

6

7

H: Disorganized, careless

1

2

3

4

5

6

7

I: Calm, emotionally stable

1

2

3

4

5

6

7

J: Conventional, uncreative

1

2

3

4

5

6

7

I see myself as;

Notes: The Big 5 personality traits are a unifying framework comprising five basic
characteristics: extroversion, agreeableness, conscientiousness, neuroticism, andopenness to experiences. The 2017 PPSOU survey included the questions of Ten Item
Personality Inventory (TIPI), which was developed by Gosling et al. (2003), and wastranslated into Japanese by Oshio et al. (2012).

6

4. Behavioral and psychological attributesa) Altruism, trust, and belief in fortune-telling based on blood-types: To what extent doyou agree with each of the following statements? Answer on a scale from 1 to 5, where“1” means “you agree completely” and “5” means “you disagree completely.” Of
course, you may choose any number in between.
⚫

Altruism: I feel happy when I do a good deed that I think benefits others (such as
picking up trash in a park)⚫

Trust: In general, most people are trustworthy⚫

Belief in fortune-telling based on blood-types: A person’s blood type indicates
their character

Note: For our analysis, we reconstructed the answers on the opposite scale from 1 to5, where “1” means “you disagree completely” and “5” means “you agree completely.”b) Reciprocity, conformity, social norm, and religious attitude: How true for you is eachof the following statements? Answer for each on a scale from 1 to 5, where “1” means“it is particularly true for you” and “5” means “it doesn't hold true at all for you.”⚫

Reciprocity 1: If someone does me a favor, I am prepared to return it

7⚫

Reciprocity 2: I go out of my way to help somebody who has been kind to me
before⚫

Reciprocity 3: I am ready to undergo personal costs to help somebody who helped
me before⚫

Conformity: Behaving similarly to people around me makes me feel comfortable⚫

Social norm: I never cut into a line of people⚫

Religious attitude: I am deeply religious

Notes: We reconstructed the answers on the opposite scale from 1 to 5, where “1”means “it doesn't hold true at all for you” and “5” means “it is particularly true foryou.” In addition, we respectively calculated out the indicator of negative reciprocity,by summing up the answers for the three statements and dividing the value by 3.

8

Online Appendix B. Age-group difference in the relationship between blood-type and blood donation behavior
Estimation method:
Logistic regression (coefficient)

Blood type O

Dependent variable:
I have donated bloodat least once
within the past few years

I have donated bloodonce or more
within the past year

I have donated bloodat least once
within the past few years

I have donated bloodonce or more
within the past year

1

2

3

4

5

6

7

8

0.379

0.319

0.504*

0.586*

0.773***

1.006***

0.922**

1.081***(0.262)(0.256)(0.294)(0.310)(0.250)(0.277)(0.376)(0.403)

Blood type B
Blood type AB-0.371-0.216

0.529

0.508

(0.257)(0.471)(0.405)(0.547)0.326

0.811**

0.507

0.022

(0.291)(0.344)(0.541)(0.774)

The respondents aged 26 to 54

Subsample:

The respondents aged 55 to 69

Number of observations

699

699

699

699

634

634

634

634

Log pseudo-likelihood-255.257-253.905-148.186-146.662-166.021-165.163-92.211-91.878

Notes: This table shows the logistic regressions of blood type on blood donation behavior. Standard errors are reported in brackets. Significantly different from zero at the *10% level, **5% level, and ***1% level.
The baseline in columns 1, 3, 5, and 7 is blood type A, B, or AB. The baseline in columns 2, 4, 6, and 8 is blood type A. All the model specifications include covariates of socio-economic status, health status,
economic preferences, personalities, and behavioral and psychological attributes.

9References for Supplementary Information
Cramer, J.S., Hartog, J., Jonker, N., & Van Praag, C.M. (2002). Low risk aversion
encourages the choice for entrepreneurship: An empirical test of a truism. Journal of
Economic Behavior & Organization, 48(1), 29–36.
Gosling, S. D., Rentfrow, P. J., & Swann, W. B., Jr. (2003). A Very Brief Measure of the
Big Five Personality Domains. Journal of Research in Personality, 37, 504–528.
Guiso, L., & Paiella, M. (2004). The role of risk aversion in predicting individual
behaviors.

CEPR

Discussion

Paper

No.

4591.

Available

at

SSRN:

https://ssrn.com/abstract=608262
Harrison, G.W., Lau, M.I., & Williams, M.B. (2002). Estimating individual discount rates
in Denmark: A field experiment. American Economic Review, 92(5), 1606–1617.
Hartog, J., Ferrer-i-Carbonell, A., & Jonker, N. (2002). Linking measured risk aversion
to individual characteristics. Kyklos, 55(1), 3–26.
Oshio, A., Abe, S., & Cutrone, P. (2012). Development, Reliability, and Validity of theJapanese Version of Ten Item Personality Inventory (TIPI-J). The Japanese Journalof Personality, 21(1), 40–52.

10