21 differences in pro-sociality between STAN and UI subjects as arising from selection effects rather than socialization.<sup>17</sup> Table 4 presents a description of the sample in the two institutions.

The two samples differ on a number of dimensions, even though the students join their respective institutions straight out of high school. STAN subjects are less than a year older and far less likely to be women.<sup>18</sup> The samples differ on their attitudes and preferences as well, with the public sector STAN students exhibiting slightly _greater_ tolerance for risk (contrary to expectations), according to both a behavioral measure, “Risk Gambles” (Eckel and Grossman, 2008), and a stated preference measure “Prepared to take Risks” (a question taken from the World Values Survey). STAN subjects are also more religious, more trusting, and have stronger fairness preferences. Finally, STAN subjects report being in better financial health personally. However, the financial background of the subjects is not particularly different: we asked a large subsample about their parents’ monthly income and results were statistically identical across the two groups.

We estimate the following OLS regression to compare the pro-social motivation of individuals who have selected into the public sector, but not yet worked there, STAN students, with UI students, who have not selected into the public sector and are much more likely to go to the private sector:

# DONATEi = α+ βOFFICIALi + δCONTROLSi + εi

Our focus is the coefficient β, where OFFICIALi is a dummy variable equal to 1 if subject _i_ is from STAN. The variable DONATEi is, as before, subject i's donation to the charity and ε is the error term.

Unobserved characteristics of the different subject pools could give rise to a spurious association between their location (at STAN or UI) and their donations to the Indonesian Red Cross. For example, public sector subjects give significantly more to charity than UI students if they were significantly richer.<sup>19</sup> Differences in group composition might also matter. It is well-known, for example, that some types of individuals are more pro-social than others (e.g., older versus younger subjects, or women versus men). If gender or age influence government employment for reasons unrelated to pro-social motivations, it becomes more difficult to infer that pro-social individuals join the government because they are pro-social. We therefore control for objective individual characteristics that are known to influence pro-social behavior, age and gender.<sup>20</sup> We also control, as before, for the extent to which subject _i_ has confidence that the charity will be paid, to 17 There is the issue of socialization within STAN itself, prior to joining the public sector. While we cannot completely rule out the fact that subjects may be socialized at the institution, we note that there are no age effects, so subjects are not becoming more pro-social over time. However, whether subjects are socialized into becoming more pro-social during their first year is a question our data cannot address.

18 When we asked administrators about this gender gap, they stated that while STAN admits an equal number of males and females, females are reluctant to accept because public sector employment entails an obligation to relocate frequently, away from their families.

19 In the 2013 sample, we asked students to state their family income levels. STAN students reported parental income as slightly higher than UI students (but not significant: p>0.8).

20 The Indonesian public service does not have any employment regulations that systematically privilege applications from demographic groups that might be more pro-social (i.e., affirmative action programs that favor groups that happen to have higher pro-social motivation). On the contrary, one group known to be more pro-social, women, is significantly under-represented among the (more pro-social) STAN subjects.