econometrics project - what are the determinants of giving in uganda

2015

Dan Drummond

Econometrics project

01/04/2015

What are the determinants of giving in Uganda?

Dan Drummond H00124667

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What are the determinants of giving in Uganda?

I. Introduction and motivation

This paper aim to assess the determinants of giving within the Central African nation of

Uganda. To achieve this I have used data acquired by Catherine Porter, the University of

Oxford and the Centre for the Study of African Economies (CSAE) in 2014. The data contains

sector, education, nationality, gender, work responsibilities, altruistic preferences and

charitable preferences. This is of interest as there has been very little research on dictator

games within Africa, especially with a design that caters for further giving and uses charities

as the recipient.

Giving has been well researched in the ‘West’ on student populations, however there is very

little data on African nations. The participant was given 20,000 UGX (Ugandan shillings) and

asked to divide it between themselves and a popular charity. This aimed to highlight the

characteristics of altruism in Uganda. This is a relatively large ‘windfall’ as the GNI per capita

of Uganda is $510 (World Bank, 2014). The denominations were in 1,000 UGX bills and they

had a choice of 23 charities. I have used the data to form dummy variables, I then regressed

the dummies on total given to reveal how sector employed, nationality, age or education affect

charitable donations. However total given includes more than just the 20,000 UGX as the

individuals also had the option to give their own money.

Bekkers (2007) found people who are educated are usually more prosocial and trusting. I test

this finding by looking at how their level of education changes the total donation. Sector may

also effect giving as wages and pro-social behaviour will change between sectors. Civil servants

may give less as they feel they have contributed through their underpaid work (Buurman et al,

2009).

To reach a satisfactory conclusion I assess the current literature surrounding the topic of

Dictator Games and Generosity in Section 2, this shall then be discussed further by delving

into the theory surrounding the topic in Section 3. Section 4 shall then combine these two

sections to explain my variables and give weight to the econometric model. Section 5 shall be


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a discussion of the and the source attained, leading to Section 6 which is a discussion of the

estimation procedures, mainly the Gauss-Markov assumptions. Section 7 then details my

estimated results and their relevance. With Section 8 then pointing out the limitations of this

study and Section 9 concluding my findings. Leaving Section 10, the appendix, and Section 11,

the bibliography.

Certain characteristics determine giving within dictator games more than others. For each

year a Ugandan ages their contribution increases by 170 UGX and if they have a graduate

degree they will give 10,202 shillings more. While if they are employed in the public sector

they give 8,425 UGX less and if they are a student they give 3,654 less. While men were found

to give less, however this was insignificant. The participant’s nationality is inconclusive, with

their donation in line with economic theory but the result is insignificant at a p –value of 0.195.


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II. Literature review

A dictator game is whereby an individual is given a ‘windfall’ and asked to divvy it

between themselves and a recipient, in this case one of 23 charities. The game is not strategy

intense since the recipient cannot veto the donation therefore the dictator’s decision is final.

Economists expect individuals to be rational and take the full endowment, this is referred to

as income maximisation, however this is rarely the case and many split their endowment.

Dictator games have been researched in great depth with over a hundred dictator games being

published (Engel, 2011). The Dictator Games and human behaviour originate from Daniel

Kahneman’s ultimatum game 30 years ago (Guth et al. 1982). This game assessed how people

behave when they are given money and have to decide on how to divide it between them. From

here stemmed the dictator game, the difference between this and the ultimatum game is that

the recipient has no say in accepting or rejecting the terms of the offer. The amount given

changes depending on certain factors, Engel found that dictator games had been adapted to

either test Situational or Demographic effects on giving. Engel’s meta study covered 131 papers

and revealed that the mean donation across 616 treatments was 28.35%. The paper also

highlighted the most significant factors across all 131 papers. He found that 63.89% violate the

income maximisation hypothesis and those who give donate an average of 42.64%.

Furthermore it highlighted that if the individual is not anonymous they feel “urged” to donate.

The majority of economic testing is done on students, an easily accessible sample, however

Carpenter et al (2007) found that students are not representative of the entire population.

Community members were found to give 17% more on average, 32% of them gave their full

endowment, and 3 times more likely to give away their full endowment. They also found that

education has a positive correlation with giving, and a positive correlation between age and

donations as well as male students being less generous.

Eckel and Grossman (1996) employed a double anonymous dictator game and tested if the

motivation for altruism increases when the recipient was replaced with a renowned charity,

American Red Cross. They found that human behaviour can be motivated by altruism and


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further by the “deservingness” of the recipient. They concluded that “fairness and altruism

require context”.

Buurman et al (2009) tested risk averse and altruistic behaviour in public sector employees.

Their findings showed private sector workers to be more altruistic than public sector workers.

They concluded that this was because civil servants believe they’re underpaid and their job is

a substitute for charity.

Bekkers (2007) found that “generosity increased with age, education, income, trust, and

prosocial value orientation.” His research also covered current students and graduated, and

found that the effect of education on giving was only apparent post completion.

In his review article Andreoni (2006) found that higher educated individuals gave more, more

frequently and a higher fraction. He also found that age is positively correlated with generosity

and gender also effects generosity.

In their review article Bekkers and Wiepking (2007) concluded that members of the economics

faculty are more “self interested” and give less.

Tonin and Vlassopoulos (2014) found that “prosocial motivation” decreases over the length of

public sector employee’s career until it ceases to be prevalent. However they found that

occupation with in the civil service does not make effect prosocial behaviour.

Wang. M. (2013) found that the “upper socioeconomic class” were more altruistic in

comparison to the “lower socioeconomic class.” However this was from a “puzzle-liked dictator

game”, and if the individual does a task to earn their endowment it can lead to decreased

generosity. The lower socioeconomic class may feel their efforts required the reward more as

they are less income elastic.

My work shall contribute to the existing literature by applying the current dictator game and

generosity theory to a developing African nation. Checking for the significance of the theory

on the nation and then arguing for the relevance of the results or theory. I then apply my data

to the current gender and generosity debate, concluding that my data shows that gender has

an insignificant effect on giving. Followed up by recommending what could be done to further

this and current research surrounding the topic of generosity and dictator games in Africa.


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III. Economic model

The neoclassical economic theory believes that individual’s base decisions on utility

maximisation and will therefore take the full endowment for themselves. However Engel

found that the mean split across 131 papers was 28%. This may be explained using Andreoni’s

4 reasons for altruism found in his ‘Handbook of the Economics of Giving, Altruism and

Reciprocity’:

1. Contributes to a public good they consume

2. “Enlightened self-interest”

3. Altruism due to belief in the cause

4. “Warm-glow” – people enjoy giving

The higher the individual’s disposable income the more likely they are to donate to charity.

Data for income has not been collected but income and disposable income are correlated with

age, gender, education and sector employed.

I have used the sector worked in and the individual’s education level as a proxy for wage both

have a high positive correlation with wage; average age for workers without formal education

is 546 UGX compared to university educated employees whose average earnings are 2383

UGX (WageIndicator Survey, 2012).

Civil servants earn a fair wage in Uganda with 92% earning above the poverty line

(WageIndicator Survey), compared to 24.5% of the entire country being below the poverty line

(WorldBank). Meaning they should be more altruistic than the general public as on average

they earn more. However studies have found that public servants give less as they feel their

work is contribution enough (Buurman et al).

Students have been shown to be non-altruistic, one argument for this is less life experience the

other being income (Carpenter et al,). The older the participant the more generous, this may

possibly be because of more life experience and therefore more altruistic or higher disposable

income from lifetime savings (Carpenter et al; Engel). Gender has also been shown to effect

giving, women are found to give more (Eckel and Grossman; Engel), whereas ‘Generosity and


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Philanthropy: A Literature Review’ (Bekkers and Wiepking) had mixed findings, stating that

there was no reliable difference between genders in terms of generosity.

This literature has led me to the following hypotheses’:

Null hypothesis: H0 Alternative: H1

Age Age does not affect total given Change in age causes a change in total given

Gender Females do not give more than men

Females give differently to men

Education A post graduate degree does not affect total given

A post graduate degree changes total given

Sector Civil servants will not have different giving habits

Civil servants giving will be different

Students Students giving will not differ from that of the general public

Students will give differently to the general public

Nationality Ugandans won’t give differently in comparison to other nationalities

Ugandans will give differently in comparison to other nationalities


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IV. Econometric model

𝑡𝑜𝑡𝑔𝑖𝑣𝑒 = 𝛽0 + 𝛽1𝐴𝑔𝑒 + 𝛽2𝑀𝑎𝑙𝑒 + 𝛽3𝑃𝑜𝑠𝑡𝐵 + 𝛽4𝐶𝑖𝑣𝑆𝑒𝑟𝑣 + 𝛽5𝑆𝑡𝑢𝑑𝑒𝑛𝑡

+ 𝛽6𝑈𝑔𝑎𝑛𝑑𝑎𝑛 + 𝑢

Age

Age is the age of the questionnaire respondent in years. The range for age was 20 to 63 with

only 2 individuals not answering. The mean age was 32.14 and the median age was 30. 77/148

individuals were in the age group 20-30. This variable is included as both Engel and Carpenter

et al found that age was significant in altruism.

Male

Male is the dummy variable for the gender of the individual, with 1 being male and 0 being

female. With 80/148 participants are male therefore making the mean 0.544, and baseline is

female. This variable is included as there’s contradictory findings surrounding gender, with

Eckel and Grossman and Engel finding women give more and Bekkers and Wiepking finding

gender insignificant.

Education

Symbol Variable Post_B Education beyond a Bachelor’s degree (e.g

Masters or PhD)

This dummy variable has been created from the questionnaire, the question stated 9 different

levels of education from 1 being none to 9 being a PhD, I used this to create 3 Dummy variables.

High School or lower, with only one individual not attending high school, 30 finishing high

school, and the rest were higher education. Bachelor’s consisting of 88 individuals who had

done a Bachelor’s degree. Post_B was 29 participants who had a graduate degree, everything

below Post_B is the baseline. Bekkers, Bekkers and Wiepking and Andereoni all find a positive

relationship between education and philanthropy.

Sector


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Symbol Variable Civ_Serv Civil Servant Student Academic Student

The two sectors are dummy variables attained from the results of the questionnaire. The

results are from 1 to 10, with 1 being private firm, 2 to 4 being a form of Civil Servant, 5 an

NGO worker, 6 Self-employed, 7 Volunteer, 8 not employed, 9 Other and 10 being a Student.

I then obtained 4 dummies consisting of 2 sets of group data. I did not use the NGO and

Volunteers, nor 1, 6, 8 or 9, thus making them my base line. 55/148 of the respondents were

Civil Servants and 40/148 was students. Students and civil servants have been included as

they’ve been found to be less altruistic (Carpenter et al; Buurman et al).

Ugandan

This is a dummy variable based on the individual’s nationality. If the individual is Ugandan it

is 1, if not it is 0. The mean was 0.716, the other nationalities are the bench mark. This is

included in the model as Engel’s Meta Study found that non-western nations gave more.

I have avoided the dummy trap as I have baselines for all my dummies. The model is also a

level/level model and all figures are in UGX.


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V. Data

This data was collected by Catherine Porter, the experiment measured how much of

the 20,000 UGX endowment the 148 recipients gave to charity. The data was collected from 3

separate locations, an up market coffee shop, Ministry of Finance and Economics students at

the Makerere University. It consisted of a dictator game whereby the subject was given two

envelopes, one that read “take with you” and one that read “leave with research team”. The

respondent received 20,000 UGX (approximately £4.51 [XE.com, 2015]). The individual filled

out a questionnaire consisting of 12 questions reflecting demographic, social, economic and

behavioural characteristics. Then the individual chose if they wanted to donate to charity and

if so which of the 23 charities they wanted to donate to. Of the 23 charities, 8 were Ugandan,

11 were children orientated, 15 were international charities and 2 were religious. The data was

inserted into excel, where I created the necessary dummy variables, then exported into EViews

as to enable regression.

The average donated was 9845 UGX with a minimum of 0 and a maximum of 40,000. Only

16% (23/148) of the subjects were pure gamesman (did not give any of their

endowment/income maximisers). 7 individuals gave above 20,000 with 1 giving 30,000 and

4 giving 40,000. This pulled the mean substantially above the median, 8,000.

The distribution (Figure 1) is partially in line with Engel’s description of a two peaked

distribution, with one peak representing pure gamesman and the other the equalitarian

option. The first peak for ‘give nothing’ can be seen, however the second peak is at the full

endowment with a very small peak at the equal split. The difference may be due to our

participants having the option to give more than the full endowment. The distribution does

show that the data is slightly positively skewed at 1.04, skewness is a measure of asymmetry

of the variable’s distribution around its mean.

The standard deviation measures the asymmetry of data around the mean. The standard

deviation is 9356; showing that the data is highly asymmetrical due to the large spread.


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Non Binary:

Variable Definition Mean Minimum Maximum Std. Dev Totgive Total Given 9,844.595 0 40,000 9,355.934 Age Age in years 32.143 20 63 10.119

Binary Variables:

Variable Definition Mean Fraction Std. Dev

Male Of male gender 0.544 80/147 0.5

Post_B Has a Graduate degree 0.196 29/148 0.4

Civ_serv Civil Servant 0.372 55/148 0.485

Student Currently a student 0.27 40/148 0.446

Ugandan Of Ugandan nationality 0.716 106/148 0.452


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VI. Estimation procedures

The model is a level-level model and was estimated using OLS in EViews. OLS is an

appropriate method of estimation as it minimises the difference between the responses and

the predicted responses by linear approximation.

For the specification of my model I chose to follow economic tuition (based on my literature

review) over statistical significance (Ramsey RESET test). As I believe statistical significance

and specification can lead to data mining.

The Best Linear Unbiased Estimators (BLUE) has the following 5 conditions:

1. Linear in parameters

2. Random sampling

3. No Perfect collinearity

4. Zero conditional mean

5. Homoskedasticity

Linearity: 𝑦 = 𝛽0 + 𝛽1𝑥 + 𝑢

The model is linear as it is a linear combination of observed values for the dependent variable

(totgive).

Random Sampling: 𝑛{(𝑥𝑖1 … 𝑥𝑖𝑘 , 𝑦𝑖): 𝑖 = 1,2 … 𝑛}

The population is random in sample because individuals were picked randomly, however the

locations were not. Also the variation in variables does not equal zero and are therefore

random.

No Perfect Collinearity: ∑ (𝑋𝑖 − �̅�𝑛𝑖=1 )2 ≠ 0

The Spearman Rank (Figure 2) has one value above 0.5, with student and age having a

collinearity of -0.618. Therefore I do not have perfect collinearity as I can regress the equation.

However I have high collinearity, this is expected as 35 of the 54 individual aged 20-25 are

students (64.8%).


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Zero Conditional Mean: 𝐸(𝑢𝑖|𝑥𝑖) = 0

𝐻0: 𝜎1 = 𝜎2 = 0

𝐻1: 𝜎1 ≠ 𝜎2 ≠ 0

If 𝐻𝑜 is correct I have misspecification

If 𝐻1 reject 𝐻𝑜

Ramsey Reset Test F-statistic 2.43506 Prob. F(2,136) 0.0914 Log likelihood ratio 5.101599 Prob. Chi-Square(2) 0.078

Misspecification is when you fail to include a relevant independent variable or use an incorrect

functional form. I fail to reject the null hypothesis at the 0.1 p-value (10% significance),

therefore my model is misspecified. The Ramsey RESET tests for statistical specification

whereas I am specifying according to economic intuition. Therefore I am going to assume that

my model is correctly specified as I do not have theory to support adding any additional

variables that I possess. My model is also in correct form or would not regress.

MLR1-4 hold therefore we have unbiased estimators.

Heteroskedasticity: 𝑉𝑎𝑟(𝜀|𝑋𝑖) = 𝜎𝜀2∀𝑖

𝐻0 ∶ 𝛾1 = 𝛾2 = … = 𝛾𝑘 = 0

𝐻1 ∶ 𝐻0 𝑖𝑠 𝑖𝑛𝑐𝑜𝑟𝑟𝑒𝑐𝑡

If 𝐻𝑜 is correct you have homoscedasticity

If 𝐻1 reject 𝐻𝑜

White test (Figure 3): F Stat: 2.43 and p value: 0.0014

BP Test (Figure 4): F Stat: 3.14 and p value: 0.0065

Therefore both tests show heteroskedasticity, as I fail to reject the null hypothesis at a p-value

of 0.1. So I’ve created a Heteroskedasticity robust output (Figure 5), therefore MLR.5 holds

and thus my estimators are BLUE.


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VII. Estimated results

Heteroskedasticity robust results:

Variable Coeffecient t-Stat Age 170.42** 2.110081 Male -672.09 -0.55289 Post_B 10201.71* 4.116463 Civ_Serv -8425.004* -3.52358 Student -3654.291*** -1.74938 Ugandan 2971.743 1.3037

*Significant at 1% **Significant at 5% ***Significant at 10%

Ugandan and Male have been kept in because of economic theory. All other variables

are significant within the recommended 10% level.

Age has a positive effect, if all other variables are held constant (ceteris paribus) then for each

additional year the individual donates 170.42 UGX more, significant at the 5% level. Engel

found that the middle aged very rarely give nothing and the elderly never give nothing. The

life expectancy in Uganda in 2012 was 58.7 (Unicef, 2012), therefore I define Middle aged as

40 and elderly as 55 and older. None of the individuals older than 37 gave nothing, and only

one individual above 35 gave nothing (35 and above accounts for 44/146). This is in line with

economic theory. Figure 7 displays a scatter of Age and Totgive; the trend line shows the

progressive increase of donations across age.

Male has negative effect as under ceteris paribus if the individual is male they give 672.09 UGX

less, insignificant at the 10% level. Engel found that women were more generous, our data

displays a similar result. However this variable is not significant, and is therefore in line with

Bekkers and Wiepking’s findings.

Post_B has a positive effect as under ceteris paribus if the individual has graduate degree they

give 10,201.71 UGX more, significant at the 1% level. Bekkers found that graduates had

increased generosity in comparison to those of lower educational status, theorising that this

be due to greater income, prosocial value orientations and trust. This was supported by


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Andereoni who found that higher levels of education gave more, more frequently and a greater

fraction.

Civ_Serv has a negative effect as under ceteris paribus if the individual is a Civil Servant they

give 8425.004 UGX less, significant at the 1% level. Buurman et al found that civil servants are

less inclined to donate than private sector employees, my results support this with civil

servants give less than the normal citizens, and students.

Student has a negative effect as under ceteris paribus if the individual is a student they give

3654.291 UGX less, significant at the 10% level. Carpenter et al found that students are 32%

less likely to give the full endowment. Out of those who gave everything 6/44 were students,

lower than Carpenter’s. Engel found similar; students more likely to give nothing, less likely

half the endowment or to give the full endowement. Of those who were pure gamesman and

gave nothing 9/23 were students, and 4 were civil servants.

Ugandan has a positive effect as under ceteris paribus if the individual is Ugandan they give

2971.743 UGX more, insignificant at the 10% level but has economic theory. Engel found a

correlation between the level of development and altruistic behaviour, with indigenous

communities being the most generous. Kampala is the capital, and cannot be categorised as

indigenous but it can be categorised as developing.


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VIII. Limitations of the study

Khaneman and Tversky (1971) proposed a law of small numbers, this law required

there to be 150 or more subjects for the sample to be representative of the population. Our

sample only has 148, therefore the law does not hold and this cannot be seen as representative

sample.

The sample is of higher income individuals and not representative of all the population. The

sample was held in three locations leading to some level of sample bias. The locations were an

Economics department in a university, the ministry of finance and a high end coffee shop.

Economics students and professors have been found to be self-interested.

A small sample means that the standard deviation is larger leading to inaccurate results. As

the standard deviation is the difference each sample is away from the mean, since the lower

the sample the larger the difference between means. This leads to a wider confidence interval

and hence significance level of 10%.

Kampala is the capital of Uganda, therefore the average demographics are higher than the rest

of the country. The sample is therefore not representative of the population of Kampala as the

sample has higher income, more education and better employment. The sample is also not

representative of Uganda as it is within Kampala where the demographics are higher than that

of the rest of the country.

The use of a questionnaire creates measurement error through non response bias and bias

from the design of the questionnaire and questions, such as the wording or weighting of the

questions. This could of lead to possible measurement error, such as the confusion with

question 12, whereby people were asked if they would like to give their own money whereby

36 said yes but only 9 actually gave their own money. If the data input into the model is bias

then the results of the model will also be bias. The questionnaire had errors, with 2.7% of

people not turning the page over and 10.8% of people not answering question 8. The


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questionnaire may also induce bias as the envelope may decrease the amount of pure

gamesman as lack of anonymity changes their donations (Eckel and Grossman).

The model would be more accurate with the addition of income. With Q7 being a potential

proxy for income, it asks for the level of responsibility within the individuals role. This is not

a great proxy for income as different sectors will pay different amounts and students will not

answer the question, however they still have an income. Also the pay scale for civil servants is

publicly available and could have been featured in the questionnaire as 37% of the sample are

civil servants. I have included sector within my model, this will account for some of the

variation in income as the private sector wage will differ from that of the public, however I

have not used it as a proxy for income as it is too ambiguous.


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IX. Conclusion

Variable Null hypothesis: H0

Alternative: H1

Coefficient t-Stat Result to H0

Interpretation

Age Age does not affect total given

Change in age causes a change in total given

170.42** 2.110 Reject

Age positively affects the amount given

Gender (Male)

Females do not give more than men

Females give differently to men

-672.09 -0.553 Fail to reject

Gender does not affect total given

Education (Post_B)

A post graduate degree does not affect total given

A post graduate degree changes total given

10201.71* 4.116 Reject

Having a graduate degree has a strong positive effects on giving

Sector (Civ_Serv)

Civil servants will not have different giving habits

Civil servants giving will be different

-8425.004* -3.524 Reject

Being a civil servant has strong negative effects on giving

Students (Student)

Students giving will not differ from that of the general public

Students will give differently to the general public

-3654.291*** -1.749 Reject

Being a student has a negative effect on giving

Nationality (Ugandan)

Ugandans won’t give differently in comparison to other nationalities

Ugandans will give differently in comparison to other nationalities

2971.743 1.304 Fail to reject

Being Ugandan does not affect giving

*Significant at 1% **Significant at 5% ***Significant at 10%

I have found that Age, graduate degree, sector employed and whether or not a student

effect the amount individuals in Kampala, Uganda give by varying amounts. As age increases

the participant gives significantly more and if the individual has a graduate degree they give

substantially more. While if they are employed in the public sector they give 8,425 UGX less


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and if they are a student they give 3,654 less. I thought that civil servants would give less than

the rest of the public but more than students. This may be because civil servants believe their

job gives enough to society.

Gender was insignificant, there was contradictory findings for this and my findings support

Bekkers and Wiepking’s findings. Nationality was insignificant but the findings were in line

with Engel’s theory of developing nations and donations. The sample was not representative

of Uganda or Kampala, this limits the further use of the study.

Further research should aim to be more representative of geographic regions or certain

demographics and include income. This may allow for a significant result for nationality which

may support or contradict Engel’s theory. Income would also give us a more representative

data on altruism in Africa, as we would have amount given relative to income.


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X. References Andreoni, J. (2006) ‘Philanthropy’, in Kolm, S.-C. and Ythier, jean M. (eds) Handbook of

the Economics of Giving, Altruismand Reciprocity, Volume 2.

Bekkers, R. (2007) ‘Measuring Altruistic Behavior in Surveys: The All-or-Nothing Dictator

Game’, Survey Research Methods, 1.

Bekkers, R. and Wiepking, P. (2007) ‘Generosity and Philanthropy: A Literature Review’,

Science of Generosity. doi: 10.2139/ssrn.1015507.

Besamusca, J. and Tijdens, K. (2012) Wages in Uganda: WageIndicator survey 2012.

Buurman, M., Dur, R. and van den Bossche, S. (2009) ‘Public Sector Employees: Risk Averse

and Altruistic?’, SSRN Electronic Journal. doi: 10.2139/ssrn.1441844.

Carpenter, J., Connolly, C. and Myers, C. K. (2007) ‘Altruistic behavior in a representative

dictator experiment’, Experimental Economics. Springer, 11(3), pp. 282–298. doi:

10.1007/s10683-007-9193-x.

Eckel, C. C. and Grossman, P. J. (1996) ‘Altruism in Anonymous Dictator Games’, Games

and Economic Behavior, 16(2), pp. 181–191. doi: 10.1006/game.1996.0081.

Engel, C. (2011) ‘Dictator games: a meta study’, Experimental Economics. Springer, 14(4),

pp. 583–610. doi: 10.1007/s10683-011-9283-7.

Guth, W., Schmittberger, R. and Schwarze, B. (1982) ‘An experimental analysis of ultimatum

bargaining.’, Journal of Economic Behaviour and Organization, 3.

List, J. A. (2007) ‘On the Interpretation of Giving in Dictator Games’, Journal of Political

Economy, 115(3), pp. 482–493. doi: 10.1086/519249.

The World’s Trusted Currency Authority (no date). Available at: xe.com (Accessed: 31 March

2015).

Tonin, M. and Vlassopoulos, M. (2014) ‘Are Public Sector Workers Different? Cross-

European Evidence from Elderly Workers and Retirees’, Institute for the Study of Labour,

8238.

Tversky, A. and Kahneman, D. (1971) ‘Belief in the law of small numbers.’, Psychological

Bulletin, 76(2), pp. 105–110. doi: 10.1037/h0031322.


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Uganda (no date). Available at: http://data.worldbank.org/country/uganda (Accessed: 5

March 2015).

‘Uganda Statistics’ (2013). UNICEF. Available at:

http://www.unicef.org/infobycountry/uganda_statistics.html (Accessed: 10 March 2015).

Wang, M. (2013) ‘Does higher socioeconomic class predict increased altruistic behavior?

Evidence from a modified dictator game’, Erasmus University Rotterdam.


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XI. Appendix

Figure 1: Dot plot of totgive data

Figure 2: The distribution of the dependent variable totgive (total given)

0

10,000

20,000

30,000

40,000

50,000

25 50 75 100 125

TOTGIVE

0

10

20

30

40

50

60

0 10000 20000 30000 40000

Series: TOTGIVE

Sample 1 148

Observations 148

Mean 9844.595

Median 8000.000

Maximum 40000.00

Minimum 0.000000

Std. Dev. 9355.934

Skewness 1.037433

Kurtosis 3.823861

Jarque-Bera 30.73352

Probability 0.000000


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Figure 3: Spearman Rho rank displaying the correlation of my independent variables Heteroskedasticity Test: White

F-statistic 2.428093 Prob. F(21,123) 0.0014

Obs*R-squared 42.49408 Prob. Chi-Square(21) 0.0036

Scaled explained SS 85.68952 Prob. Chi-Square(21) 0.0000

Test Equation:

Dependent Variable: RESID^2

Method: Least Squares

Date: 03/17/15 Time: 20:32

Sample: 1 148

Included observations: 145

Collinear test regressors dropped from specification Variable Coefficient Std. Error t-Statistic Prob. C -8192379. 1.57E+08 -0.052055 0.9586

AGE 2303469. 8320087. 0.276856 0.7824

AGE^2 16331.11 115068.9 0.141925 0.8874

AGE*MALE -120890.1 2685683. -0.045013 0.9642

AGE*POST_B -9873001. 3362552. -2.936164 0.0040

AGE*CIV_SERV -3890594. 3111609. -1.250348 0.2135

AGE*STUDENT 9490386. 9760562. 0.972320 0.3328

AGE*UGANDAN -706674.3 3255570. -0.217066 0.8285

MALE -29794966 95988055 -0.310403 0.7568

MALE*POST_B 1.18E+08 57259238 2.053714 0.0421

MALE*CIV_SERV -81058268 73220551 -1.107043 0.2704

MALE*STUDENT -95456320 60642014 -1.574095 0.1180

MALE*UGANDAN 1.20E+08 66247017 1.817495 0.0716

POST_B 2.47E+08 1.28E+08 1.934782 0.0553

POST_B*CIV_SERV 1.29E+08 82709973 1.564090 0.1204

POST_B*STUDENT -1.29E+08 1.38E+08 -0.930111 0.3541

POST_B*UGANDAN 63488120 76884447 0.825760 0.4105

CIV_SERV 15452240 1.63E+08 0.094613 0.9248

CIV_SERV*UGANDAN 57310660 1.43E+08 0.399641 0.6901

STUDENT -1.38E+08 2.40E+08 -0.574752 0.5665

STUDENT*UGANDAN -76357162 67287360 -1.134792 0.2587

UGANDAN 9129231. 1.11E+08 0.082125 0.9347 R-squared 0.293063 Mean dependent var 55129806

Adjusted R-squared 0.172366 S.D. dependent var 1.17E+08

S.E. of regression 1.06E+08 Akaike info criterion 39.93839

Sum squared resid 1.39E+18 Schwarz criterion 40.39003

Log likelihood -2873.533 Hannan-Quinn criter. 40.12191

F-statistic 2.428093 Durbin-Watson stat 2.220192

Prob(F-statistic) 0.001354


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Figure 4: White test for heteroskedasticity within my model Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 3.135212 Prob. F(6,138) 0.0065

Obs*R-squared 17.39438 Prob. Chi-Square(6) 0.0079

Scaled explained SS 35.07585 Prob. Chi-Square(6) 0.0000

Test Equation:

Dependent Variable: RESID^2


Date: 03/05/15 Time: 18:56

Sample: 1 148

Included observations: 145 Variable Coefficient Std. Error t-Statistic Prob. C 52122176 43621273 1.194880 0.2342

AGE -605395.0 1135303. -0.533245 0.5947

MALE 25736757 19779629 1.301175 0.1954

POST_B 41373327 26357811 1.569680 0.1188

CIV_SERV -84348153 27517607 -3.065243 0.0026

STUDENT -58758945 27772251 -2.115743 0.0362

UGANDAN 67013658 25890775 2.588322 0.0107 R-squared 0.119961 Mean dependent var 55129806

Adjusted R-squared 0.081699 S.D. dependent var 1.17E+08

S.E. of regression 1.12E+08 Akaike info criterion 39.95051





Figure 5: Breusch-Pagan test for heteroskedaticity within my model

Dependent Variable: TOTGIVE


Date: 03/17/15 Time: 21:07

Sample: 1 148

Included observations: 145

White Heteroskedasticity-Consistent Standard Errors & Covariance Variable Coefficient Std. Error t-Statistic Prob. AGE 170.4154 80.76252 2.110081 0.0367

MALE -672.0931 1215.606 -0.552887 0.5812

POST_B 10201.71 2478.271 4.116463 0.0001

CIV_SERV -8425.004 2391.034 -3.523582 0.0006

STUDENT -3654.291 2088.909 -1.749378 0.0824

UGANDAN 2971.743 2279.468 1.303700 0.1945

C 4642.149 2798.224 1.658963 0.0994 R-squared 0.367037 Mean dependent var 9800.000

Adjusted R-squared 0.339516 S.D. dependent var 9364.976

S.E. of regression 7610.929 Akaike info criterion 20.75963



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Figure 6: White heteroskedasticity robust output for my model.

Figure 7: Scatterplot for age and total given with trendline

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econometrics project - what are the determinants of giving in uganda

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