household income.<sup>24</sup> We use the method suggested by Grosh and Baker and Ahmed and Bouis to predict parental household expenditure. In our questionnaire we included questions on asset ownership drawn from the Household Income Expenditure and Welfare Monitoring Survey (HICE/WMS) 2000, a nationally representative survey, conducted by the Central Statistical Authority (CSA), which collects detailed data on household consumption and expenditures. By regressing household consumption on household assets, we obtained coefficients to predict household consumption for the parental _p_ household of our subjects. We run the weighted regression _a a ˆ a EXPi_ = ∑α _Di_ + _ui_ on the HICE/WMS 2000 data to obtain estimates of α , the coefficient for each asset, which we then use to predict _a_ = 1 _EXP_<sup>�</sup> _i_ for our own sample.<sup>25</sup> The regression has an R<sup>2</sup> of 0.17. The average student comes from a household with predicted annual expenditures of 6,606 Birr (777 USD), which corresponds to monthly expenditures of 550 Birr (65 USD) or daily expenditures of 18 Birr (2 USD). The mean estimated household expenditure for our sample is slightly higher than the mean for the entire population, which is 5,403 Birr (635 USD) per year.

To get an insight whether poorer qualified health students are more likely to serve in a rural area, we conducted a medical knowledge test. The variable _TSCORE_ is the score on this test, expressed as a percentage.<sup>26</sup> _ADDIS_ and _DIST_ capture the individual’s familiarity with rural areas. Students who grew up or whose parents are still living in a rural area may be more likely to go back.

As set out in section 2, the variable willingness to help the poor is our proxy for intrinsic motivation. It is an indicator variable for ranking ‘opportunity to help the poor’ as the most important characteristic. Using this definition, we find that 26 percent of the health workers has high intrinsic motivation (see Table 1)

> 24 We also considered a variable for own current expenditures, constructed as the first component from principal component analysis on the students’ ownership of assets like watch, radio, tv, bicycle, refrigerator, etc, but this variable had no statistically significant effect in any of the estimations.

> 25 Where _Di_ represents a dummy variable that the asset is present in the household. The regression uses population weights as calculated by the Central Statistical Authority. Since the estimates of the coefficients are quite sensitive to outliers, we exclude the richest 10% of households in our prediction and only consider household with expenditures equal or below 10,000 Birr (1,176 USD) per year.

> 26 We would like to thank Agnes Soucat, Gebreselassie Okubaghzi and the team of health professionals from Addis Ababa University who designed the test. The tests are different for the nursing and medical students – with  some overlap – and contained 52 multiple choice questions to be answered in 30 minutes. The test contains two parts: (i) knowledge of evidence based medicine; and (ii) technical knowledge on how to deliver medical interventions. In the first part they are asked to identify the effectiveness of specific interventions on a) child mortality, b) maternal mortality, c) malaria, and d) illness due to HIV and tuberculosis in Ethiopia. In the second part they are asked knowledge questions about medical facts and interventions. Each correct answer was given the same weight, and the final score is the sum of the correct replies, expressed as a percentage.

11