Do low compulsory education levels in Uganda inhibit charitable donations? Dictator game study based...

Do low compulsory education levels in Uganda

inhibit charitable donations?

Dictator game study based on Ugandan

questionnaires

Greig Donaldson

H00117898

Econometrics Project

Catherine Porter

Introduction

With overall donations to charity decreasing by 20% (Charities Aid

Foundation,2013) there is an interest for investigating the

underlying behaviour that motivates charitable donations. Many

factors are argued to explain this drop, including legislation and

the poor state of the economy. Demographics are a contributing

factor; this is the focal point of this study on charitable

donations within Uganda. Uganda’s World Giving Index ranking 48

(Charities Aid Foundation,2013) and economically worse off Malawi’s

ranking of 43 raises the question does Uganda’s poorer compulsory

education levels contribute to its lower charitable donations.

(WorldBankData,2011). The compulsory 7 years of education within

Uganda, with only a 27.6% enrolment rate in secondary education

compared to 34.2% in Malawi allows the extension of Pharoah and

Turner’s (1997) positive relationship between higher levels of

education and levels of charitable donations to our model. This

allows the justification of resources by charitable foundations to

enable a higher enrolment rate knowing their actions will be

rewarded socially but also financially thus allowing the

continuation of their actions. The study will be done through the

use of a dictator game with the dictator allocating a portion of the

20000 Ugandan Shillings they are given by the research team.

Literature Review

There has been much research investigating the determinants of

altruistic behaviour first coined by Comte (1852), with findings of

a combination of social, economic and demographic factors being

significant. Kitchin (1992) and Bakija and Heim (2011) found that

income levels influence the amount donated, with individuals with

2

higher incomes giving higher levels of donations. Bakija and Heim’s

(2011) argue falling donations are due to changes in taxes regarding

charitable donations Auten et al (2002) and Kitchin (1992) view of

lower after tax cost of donating encourages a higher level of

donations support this. Demographics is seen to be a contributing

factor with Carpenter et al (2008) finding more highly educated

students donate more. This can be extended outside the student

environment with Gittell and Tebaldi’s (2006) findings that

individuals with graduate degrees donate at a higher level. Further

demographic studies such as Eckel and Grossman (1998) conclude that

gender plays a role with females giving more than their male

counterparts this is supported by Carpenter et al (2008). While age

has shown to be significant with increases in donation levels until

plateauing at middle age and decreasing in old age (Carpenter at al,

2008). Henrich and Boyd (2005) extend the demographic argument

finding that less developed and more indigenous countries tend to

give more than western counterparts.

Daniel Kahneman was the first to use dictator games to describe

behaviour, finding that normal economic behaviour of maximising ones

income (utility) did not hold up under his investigation. This

school was furthered by Leider et al (2009) who found that social

distance has explanatory power, the closer the dictator is to the

recipient the more likely the dictator will give more. Carpenter et

al (2008) added to this, finding that the change from a standard

anonymous recipient to that of a ‘deserving recipient’ led to higher

amounts of donations (Epps and Singleton, 1986 and Goerg and Kaiser,

2009). This research will further this question taking into account

Carpenter’s (2008) deserving recipient argument whilst using Engel

(2011) multiple regression findings to test the extent to which

Carpenter’s (2008) and Gittel and Tebaldi’s (2006) findings of

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higher education levels lead to higher level of charitable donations

are true within the context of Uganda.

Underlying Theory

Early neoclassical economic theory highlighted the concept of

rational behaviour, that an individual given a set of choices will

choose the set which maximises their utility or welfare. In the

context of this study the dictator should maximise their income by

donating nothing to charity and keeping the 20000 UGX. The concept

of game theory must be highlighted before we can carry out our

study, building on how rational individuals interact with another

agent and how scarce economic resources are allocated. (Montet and

Serra, 2003) Building on Aumann (1989) ‘sidea that enforcement can

be built into non-cooperative games where all situations relating to

the game must be modelled into the game. Within the context of this

study we look at economic games that have imperfect information as

the dictator does not know exactly what happened last time they made

a move in the game. We build on the neoclassical idea of rational

behaviour studying players who behave in a consistent manner, with

cognitive rationality showing consistency between the individual’s

actions and beliefs supplementing with instrumental rationality

where an individual shows consistency between opportunities and

fixed preferences (Montet and Serra, 2003). We build upon Schelling

(1960) (cited in Montet and Serra, 2003) idea of a ‘focal-point’

where decisions within games are made upon not just on consequences

but also culture of the dictator moving away from the traditional

idea of consequential equilibrium that rational choice theory

stipulates. Thus we extend our study to include not only

traditional economic theories but also the emerging fields or

behavioural and experimental economics. The study will look to use 4

Kahneman’s thoughts of ‘how consumers arrive at decisions that

appear to be irrational’ in donating money to charity rather than

maximising their payoff as game theory and utility maximisation

advocates they should do.

MAYBE SOMETHING AS TO EACH VARIABLE IE INCOME MAXIMISATION

UTLITY MAXIMISATION GRAPHS ETC

Unemployment as economic variable

The Data

The underlying sample contains 149 observations obtained from

research questionnaires within Kampala, Uganda. It was obtained at

random from a population at three separate locations to capture a

true representation of that population including a range of

occupations, qualifications and personal beliefs. We would expect

questionnaires to contain some element of measurement error through

the presence of non-response bias and poor design (Source).

The data was cleaned using an excel document, by transforming any

blank observations to N/A, this allowing it to be used within Eviews

to interpret the outputs of the data.

The central tendencies we see in Figure 1 (see Appendix) show a wide

range in the value of charitable donations by individuals. Totgive

exhibits a is a wide range of variation with a minimum of 0 USX

given to charity up to 40000 USX, double the 20000USX distributed by

the research team, given away by an individual. The mean of 9778 USX

and the standard deviation of 9359 USX shows the broad range when it

comes to values of charitable donations made by individuals.

A variance in the levels of qualification achieved by individuals

within the sample is seen. The mean of 5.109 showing on average

individuals achieve an undergraduate degree, whilst our minimum of

5

2(see attached questionnaire) relates to completion of primary

school education which is compulsory within Uganda and a maximum of

10.

Further exploration of the data shows that through the use of dummy

variables to measure level of qualifications a distinct variation

can be seen within the sample. The average of 33.56% of participants

completed secondary school/diploma while 65.75% of individuals

completed an undergraduate or higher degree, the remaining 0.69%

completing only the compulsory basic education. (Figure 1)

We can see little variation within the age of the participants. With

a mean of 32.14, minimum of 20.00 and maximum of 63.00 we can see

that there is a narrow range within our data. We have a random

sample with a mix of males (0.5342) and females (0.4658) within our

study. (Figure 1)

From this data we can see there is a wide variation in the levels of

qualifications gained which we would expect from a random sample,

the variation in levels of qualifications and totgive allows us t

investigate reasons behind this.

Econometric Model

Consistent with the underlying economic theory the relationship

between an individual’s level of education relations and the level

of charitable donations within Uganda can be set out as the

following theoretical equation.

TOTGIVE = β₀ + β₁(SECONDARYEDUC) + β₂( ADVANCEDEDUC)

(1)

+ β₃(STUDENT) + β₄( CIVILSERVANT)

(2)

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+ β₅(MALE) + β₆(UGANDAN) + β₇(DP)

(3)

+ β₈(SECONDARYDP) + β₉( ADVANCEDEDUCDP) + β₁₀(CIVILSERVANTDP) + β₁₁(

STUDENTDP) (4)

See table 1 (appendix) for variable description.

Section 1 contains two dummy variables measuring the relationship

between total donation and an individual’s level of qualification

and as proxy education. We expect to see a positive coefficient to

indicate as education levels increase total donations increase. As

a base we use basic education level. The creation of the dummy terms

allows for a more precise measurement of effect of changes of

qualification levels than using a scale as different levels may not

yield the same increase.

Section 2 contains two dummy variables measuring the relationship

between total donation and an individual’s sector of work and by

proxy their income. As a base we use working in the private sector.

We expect to see a negative coefficient here as it is expected those

out with the private sector earn less and students earn less than

civil servants. The creation of the dummy terms allows for an

identification and comparison of different working sectors.

Section 3 contains three dummy variables measures the relationship

between total donation and the demographics of that individual. In

line with current literature we expect to see a negative coefficient

regarding males, a positive coefficient for Ugandan. We also expect

a positive coefficient for when an individual believe the poor are

deserving (see table 1 for explanation). The creation of dummies

allows for a comparison at a simple level involving these factors.

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The final section of the model contains four dummy interaction terms

measuring the relationship between total donations and qualification

levels of those whom believe the poor are deserving. We would expect

a positive coefficient as qualification levels increase those whom

identify with the poor should donate more. We also measure the

relationship between sectors of work whom believe the poor are

deserving and total donations. We look to examine whether

identification with the poor will outweigh which sector of work and

thus give a positive coefficient

Within our model we do not control for age, as discussed earlier age

can be significant to levels of charitable donations (Carpenter et

al 2008), however within this data we have little variance in age

(figure 1), with almost all of our participants being in the highest

giving section of ‘middle age’ or students which has been controlled

for through the use of a dummy variable therefore we have omitted

age from our model.

Estimation methods and Estimated Results

Ordinary Least squares is used to study the relationship between

education beyond the compulsary level and levels of charitable

donation allowing our model to be fitted to the observed data set.

Before running our regression and testing for biasedness/consistency

and efficiency, it is important to point out that models used in

econometric analysis are based on economic intuition and not

statistical significance. Although a variable may be statistically

insignificant, it may stay within the model as it intuitively makes

sense.

The first regression run;

TOTGIVE c QUAL EMPLOYED UGANDAN MALE DP FOREIGNAID

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We use the level of qualifications (QUAL) as a proxy for education

levels, with a higher level of qualification being comparative to

higher education levels. We expect that higher qualification levels

should result in higher levels donated.

H0: Qual is statistically significant postive

H1: H0 is not true

(Figure 2) for for OLS outputs

We observe that with all other variables taking the form of zero an

individual will donate 5131 USX to charity within this game. We fail

to reject H0 that Qual is statistically significant postive as it

showns significance at 5% level (p-value .026). As expected qual

has a positive impact as qualifiation levels increase we see an

increase in amount donated ceteris paribus an increase of 1090.09

USX for every extra level of qualifications this seems economically

significant as we would expect higher qualification levels to lead

to individuals with higher incomes . Being employed, by proxy having

an income, has a negative impact on charitable donations, this is

counter intuitive economically as we expect those employed to have a

higher income and therefore demonstrate higher levels of donations.

Being more indigenous seems to have a negative impact this goes

against (source) view of indigenous givers giving more. Our low

adjusted r-squared (0.09) and therefore a low amount of explanation

of the variation in the model, the economically counter intuitive

negative co-efficent of being employed and the simplistic assumption

that for every 1 unit increase in qualification level there is the

same ceteris paribus increase in amount donated leads us to conclude

our model is incorrectly specified. Our use of proxies for education

levels (qual) and income (employed) that our proxies are correlated

with our error term in some way, or that our proxy is not a ‘good’

9

therefore the effect of qual is still correlated with being

employed.

We therefore look to create dummies to allow a better explanation of

qualification levels and employment sectors. The creation of

secondaryeduc relates to acquiring secondary school

qualification/diploma and advancededuc shows those acheieving

qualifications of a undergraduate or higher level with a benchmark

of basiceduc (completeing the compulsary eduaction). The student and

civilservant dummies allow the idetification of sector of work thus

expanding the previous explanatory power of employed comapred to a

benchmark of working for a private firm.

We run the following regression:

TOTGIVE c SECONDARYEDUC ADVANCEDEDUC STUDENT CIVILSERVANT MALE

UGANDAN DP

H0: Secondaryeduc, Advancededuc are statistically significant

postive

H1: H0 is not true

Figure 3 for OLS outputs

The changes in our model have allowed higher explanatory power with

an increased adjusted r-quared of 0.29 thus a higher variation in

the model is explained. All other variables taking the form of zero

an individual will donate 10129 USX to charity within this game. We

failto reject H0 that Secondaryeduc, Advancededuc are statistically

significant postive but do note that qualifications and therefore

education levels past the compulsary level only become significant

once they reach a more advanced stage. However we see that

economially secondaryeduc is still significant as obtaining a

secondary level of qualification leads to an increase in donation

10

levels (p-values 0.2050, 0.0001). At advanced levels of

qualifications the effcet becomes statistically significant (p-

value 0.0001) this fall in line with economic reasoning that those

with higher levels of education, have more knowledge of the world

and have higher incomes, therefore should demonstrate higher

donation levels. This can be seen through the existence of

disposable income those with higher qualification levels and by

proxy eduaction levels, will exhibit higher levels of disposable

income therefore the disutility of donating money will be less than

those on lower levels of qualifications and by proxy income. We

observe that the use of dummy variables section of employment has

now became statistically significant Student (p-value, 0.0003) and

civil servant (0.000) at a 5% level. Ceteris paribus a reducation of

8047.61 of USX donated if the individual is a student, this is line

with Carpenter’s (2008) analysis and can be seen economically as

students exhibit lower levels of disposable income, therefore the

disutilty of donating is higher compared to those working within a

private firm(our benchmark). However the ceteris paribus decrease of

9917.20 if an individual is a civil servant is not in line with any

literature and requires more analysis as the results show that

ceteris paribus civil servants donate less than students. The

disposable income argument here is broken as students shuld have a

lower disposable income than civil servants and the disutility they

experience through donating and not maximising their income should

be higher than civil servants experience.

With higher qualification levels only becoming significant once more

advanced and our model satisfying MLR 1-5 we take into consideration

Montet and Serra’s (2003) view that modelling level of charitable

donations must incorporate ones customs and beliefs. We do this by

including a dummy variable for whether people feel the poor are poor

11

through no fault of their own, therefore they are deserving poor.

(DP) We then interact this variable with our dummies for level of

qualification and sector of work to allow the identifiaction of

personal beliefs showing dominance over eduaction levels therefore

by proxy income levels.

We run the regression:

TOTGIVE c SECONDARYEDUC ADVANCEDEDUC SECONDARYDP ADVANCEDEDUCDP

STUDENT CIVILSERVANT MALE UGANDAN DP STUDENTDP CIVILSERVANTDP

H0: Secondarydp, Advanceddp are statistically significant postive

H1: H0 is not true

(Figure 4 for OLS outputs)

The changes in our mode allow higher explanatory power with an

increased adjusted r-squared of 0.32 thus a higher variation in the

model is explained. All other variables taking the form of zero an

individual will donate 15047 USX to charity within this game. We

fail to reject H0 that Secondarydp, Advanceddp are statistically

significantly postive as the interaction of secondary level

qualifications and deserving poor (secondarydp) is now significant

at a 1% level which is valid within our small sample. This

interaction term allows us to distinguish those whom have secondary

levels of qualifications whilst also feeling the poor are deserving.

This identification allows us to examine that personal believes

affect our level of donations and study what effect it has at

different levels of qualifications. Ceteris paribus obtaining

qualifications at a seconary level and feeling the poor are

deserving causes a 7265.062 increase in USX donated suggesting that

maximising their income is not their main goal, but that they aim to

12

maximise their utility and the utility they gain from donating money

is more than the utility they lose by not maximising their income.

Our hypothesis that higher qualification levels whilst believing the

poor are deserving is supported as the effect of obtaining advanced

qualifications and feeling poor are deserving ceteris paribus causes

a 13874.77 increase in USX donated at a 5% significance level (p-

value 0.0032). This is in line with economic theory that those with

higher qualifications, have a higher income, higher disposable

income and the disutility they experience from donating is less,

however it shows that the utility they achieve by donating (due to

their personal belief) causes them to increase the overall utility

they experience. We observe sector of work is statistically

significant factor at 5% level; student (p-value, 0.0313) or civil

servant (p-value, 0.0096) reducing the amount one donates to

charity. Economically we see that there is still a contradiction

with civil servants donating 1588.99 USX less than students. We see

that the sector one works in and feeling the poor are deserving has

a positive effect on monies donated however this effect is not

statistically significant at any level (p-valles, 0.8174, 0.8002).

Given the high-pvalues of SECONDARYEDUC, ADVANCEDEDUC,

CIVILSERVANTDP, STUDENTDP conduct an F-test to verify that these

variables are jointly significant and have no explanatory power and

can be omitted. Given our F-statistic (Figure 5 and 6) and high p-values we fail to reject H0 that B1,B2,B10,B11 are jointly insignificant and hence exclude

these variables from our model.

We re-run our regression with the remaining variables:

TOTGIVE c SECONDARYDP ADVANCEDEDUCDP STUDENT CIVILSERVANT MALE UGANDAN DP

H0: SECONARYDP ADVANCEDEDUCDP is statistically significantly positive

H1: H0 is not true

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(Figure 7 for OLS outputs)

MLR 1-3 are given as all our parameters are linear in nature,

although our sample size is small, we assume that it was correctly

drawn from the population as a random sample and therefore can be

used as a representation of the population for our estimation. By

looking at our independent variables, we conclude that none of the

parameters are perfectly linear correlated because none of the

variables are a constant multiple of another. We also note that our

model is correctly specified shown by a Ramsay Rest Test (Figure 8)

(p-value 0.8696 >0.05) and that we demonstate heteroskedasticity

shown by a White test. (0.067>0.05)(Figure 9)

We note that all other variables taking the form of zero an

individual will donate 12064.51 USX to charity within this game.

Falling in line with the economic theory that not all individuals

within a dictator game will keep their full endowment. We fail to

reject our H0 as obtaining qualifications to a secodary level (past

complusary) and believing the poor are deserving (secondarydp)

increases the amount donated by 4003.592 USX within the game at a 5%

signifiance level (p-value, 0.0453). Our h0 is supported by

acquiring advanced levels of qualifications and believing the poor

are deserving (advancededucdp) increases amount donated by 10642.40

USX significant at a 5% significance level (p-value 0.000). These

conclusions support the economic theory of gaining utility from the

act of donating due to ones believes can outweigh the disutility

experienced from non income maximising actions. Being a student

decreases the amount donated by 8065.378 at a 5% significance level

(p-value, 0.002) and that working as a civil servant decreases the

amount donated by 10106.32 USX at a 5% significance level (p-value,

0.000). Being Ugandan and therefore more indigenous to the charity

is signifacant at a 10% level (p-value, 0.0746) ceteris paribus

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causing an increase of 3551.350 USX in amount donated this is line

with Henrich and Boyd’s (2005) findings. We see that in our model

males donate ceteris paribus 5.028 USX more than females going

against the existng literature. However due to the high p-value

(0.9973) we feel this is not a significant factor within our model.

This may be down to the small sample size problems or evidence that

most males in Uganada control cash flow. Further we must note that

believing the poor are desrving (DP) has a negative effect on amount

donated, this may be expained as those whom have a basic

(compulsary) education may agree with this view but due to their loq

qualifiactions donate less as other factors such as sector of work

and by proxy income levels have more of an effect.

Limitations

The results we have found must be taken into consideration within

the limitations of the study. Although the small sample size (149

observations) has allowed quick results there are doubts about the

reliability and precision of our estimates due to the nature that

this sample may not be representative of the distribution of the

population. A higher standard error is found within small samples

compared to larger samples, leading to much wider confidence

intervals and less precise estimates. Small sample size studies can

produce false-positive results, over-estimating the magnitude of an

association, such as may be the case between deserving poor and

level of education within this study. We therefore find it difficult

to extend and generalize conclusions found here to the larger

external population and must instead allow the conclusions found

15

here to be a framework for which larger sample size studies can be

designed around.

Further limitations with our results arise from the data collection

method used within our study. We make several assumptions when

believing that data gathered from a sample of students, coffee

drinkers and civil servants will be a true indicator of behaviour in

other populations limiting our assumption of a random sample.

Further to this the use of questionnaires relates to the presence of

measurement error as “questionnaire quality is a keystone since it

affects quality of data” (). The non- response to some questions

with almost 7% of participants failing to answer certain questions

on the back of the questionnaire give thoughts to non-response bias

which raises fears of measurement errors within the collected sample

thus questioning whether we break MLR 4 that we have a zero

conditional mean. Questionnaire design gives rise to the problems

with measurement error; the use of subjective questions causes

problems in that respondents may not agree on the scale given and

this may explain the high non-response within these categories. In

addition to this Hoffman’s experimenter effect has to be addressed

although this data was collected in anonymous manner there is a risk

that the presence of three white educated women may have led to

distorted levels of donations due to Ugandan’s feeling pressure to

donate. In future studies there should be the exploration of

ensuring non-response rates are lower by using better designed

questionnaires and reduce the risk of experimenter effect by using

individuals whom are representative of the population being

examined.

The most prevalent limitation of this study is the absence of a

variable explaining reported income, the use of dummies for student,

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private firm and civil servant as a proxy goes some way towards

tackling this issue. These dummies do not account for the variance

of income within each sector and results show the most significant

effect on donations seems to be sector of work which is highly

correlated with levels of income. At highly statistically

significant advanced academic levels there is a strong correlation

between higher qualifications and higher income. The use of proxy

variables must be viewed with knowledge if the bias it may have

created. Strong relationship between income and charitable donations

has already been proven by Kitchin (1992) and Bakija and Heim(2011)

which this study agrees with. To truly analyse the effect of

education levels out with the compulsory in Uganda there is a need

to incorporate reported income in to the model, allowing a much

better representation of the problem.

Summary Findings

Our analysis shows increased qualification levels and as proxy

education levels beyond the compulsory level within Uganda alone do

not significantly explain the variation within levels of charitable

donations in a dictator game. It is concluded in a correctly

specified and unbiased model that for qualification level

differences to be significant there is the need of the presence of

some personal belief that the poor are deserving. Once these two

conditions are met we view a progressive increase in amount donated

shown as qualification levels increase. This is particularly true

for those with advanced qualifications and by proxy education with a

statistically significant effect at a 0.01 level. This suggests

that an understanding of the benefits of charitable donations can

only be understood once at a more advanced level and may in some way

be explained by those with higher levels of qualification more

17

likely to have a higher income. However it must be noted that

education levels may not be the main driving factor behind donation

levels, shown by the high statistical and economic significance of

what sector a donor works within. We see that being a civil servant

will cause you to donate less than if one is a student, refuting

Carpernter’s (2008) theory. The fact that higher qualification

levels can be correlated with a higher paying job and therefore

higher income must be noted. There is the need for further studies

in this area incorporating income levels instead of using proxies to

allow a better understanding of the problem.

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Appendix

Figure 1

TOTGIVE BASICEDSECONDARYE

DADVANCEDE

D AGE QUAL MALEFEMAL

E

Mean9842.466

0 0.0068 0.3356 0.657532.1438

5.1096

0.5342

0.4658

Median8000.000

0 0.0000 0.0000 1.000030.0000

5.0000

1.0000

0.0000

Maximum

40000.0000 1.0000 1.0000 1.0000

63.0000

10.0000

1.0000

1.0000

Minimum 0.0000 0.0000 0.0000 0.0000

20.0000

2.0000

0.0000

0.0000

Footnote

variables included but not reported for lack of space; ADVANCEDEDUCDP,CIVILSERVANTCIVILSERVANTDP SECONDARYDP STUDENT SECT EMPLOYED DP FOREIGNAID ADVANCEDEDUCDPSTUDENTDP UGANDAN

Table 1

TOTGIVE Total donation in Ugandan Shillings (USX)

Qual Qualification: measured on scale with 1 being no qualifications

EMPLOYED Dummy variable taking form of 1 if employed 0 if not

FOREIGNAID Dummy variable taking form of 1 if belive forgein aid is good forUganda 0 if not

MALE Dummy variable taking form of 1 if male 0 if not

UGANDAN Dummy variable taking form of 1 if Ugandan 0 if not

DP Dummy variable taking form of 1 if belive that people are poor through no fault of their own,

19

therefore there exists deserving poor 0 if not

SECONDARYEDUC Dummy variable taking form of 1 if have qualification of completed secondary school or diploma 0 if not

ADVANCEDEDUC Dummy variable taking form of 1 if have undergraduate degree or above 0 if not

STUDENT Dummy variable taking form of 1 if primary sector of work is student 0 if not

CIVILSERVANT Dummy variable taking form of 1 if primary sector of work is civil servant 0 if not

SECONDARYDP Interaction dummy term taking form of 1 if have qualifiaction levels of secondary school or diploma and believe poor are deserving

ADVANCEDEDUCDP Interaction dummy term taking form of 1 if havequalifiaction levels of undergrauate or above and believe poor are deserving

CIVILSERVANTDP Interaction dummy term taking form of 1 if primary sector of work is civil servant and believepoor are deserving

STUDENTDP Interaction dummy term taking form of 1 if 1 if primary sector of work is stuent and believe poor are deserving

Figure 2

20

Dependent Variable: TOTGIVEMethod: Least SquaresDate: 03/25/14 Time: 15:20Sample: 1 149Included observations: 129

VariableCoefficien

t Std. Errort-Statistic Prob.

QUAL 1090.092 484.1474 2.251570 0.0261EMPLOYED -2127.975 1865.981 -1.140405 0.2564UGANDAN -3134.195 1969.170 -1.591632 0.1141MALE 1296.043 1691.917 0.766020 0.4451DP 1803.983 1964.282 0.918393 0.3602

FOREIGNAID 1197.065 2252.093 0.531534 0.5960C 5131.300 4186.240 1.225754 0.2227

R-squared 0.141446 Mean dependent var 10356.59Adjusted R-squared 0.099222 S.D. dependent var 9692.780

S.E. of regression 9199.350 Akaike info criterion 21.14439

Sum squared resid 1.03E+10 Schwarz criterion 21.29957

Log likelihood -1356.813 Hannan-Quinn criter. 21.20744

F-statistic 3.349905 Durbin-Watson stat 1.701593Prob(F-statistic) 0.004327

Figure 3


VariableCoefficien


SECONDARYEDUC 2215.861 1739.018 1.274202 0.2050ADVANCEDEDUC 7736.450 1958.317 3.950561 0.0001

STUDENT -8047.610 2147.604 -3.747250 0.0003CIVILSERVANT -9917.204 2199.596 -4.508648 0.0000

MALE -17.65200 1527.318 -0.011558 0.9908UGANDAN 2939.962 2018.990 1.456155 0.1479

DP 1455.698 1744.455 0.834471 0.4056C 10129.35 2763.919 3.664852 0.0004





F-statistic 8.413695 Durbin-Watson stat 2.137997

21

Prob(F-statistic) 0.000000

Figure 4


VariableCoefficie

nt Std. Errort-Statistic Prob.

C 15047.95 3913.972 3.844676 0.0002SECONDARYEDUC -3308.546 3469.735 -0.953544 0.3422SECONDARYDP 7265.062 4040.970 1.797851 0.0747ADVANCEDEDUC -3256.511 4073.960 -0.799348 0.4257ADVANCEDEDUCDP 13874.77 4604.600 3.013241 0.0032

STUDENT -8994.860 4126.777 -2.179633 0.0313CIVILSERVANT -10583.85 4004.277 -2.643136 0.0093

MALE -239.8190 1528.229 -0.156926 0.8756UGANDAN 3322.600 2017.107 1.647210 0.1022

DP -4968.532 3732.556 -1.331134 0.1857CIVILSERVANTDP 1116.606 4402.460 0.253632 0.8002

STUDENTDP 1050.937 4540.915 0.231437 0.8174






Figure 5

H0: B1 = B2 = B10 =B11 = 0

H1: At least 1 or both are different from zero.

Wald Test:Equation: Untitled

Test Statistic Value dfProbabilit

y

F-statistic 0.355820 (4, 119) 0.8395Chi-square 1.423279 4 0.8401

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As F-stat follows an F Distribution of F(4,119) our critical value for 5% significance level is 2.4472. Since 0.356< 2.4472, we therefore fail to reject our null hypothesis hence the B1, B2, B10, B11 do not have a jointly significant impact on total donations.

Figure 6

H0 = B1=B2=B10=B11=0

H1=H0 is not true

Redundant Variables: SECONDARYEDUC ADVANCEDEDUC CIVILSERVANTDP STUDENTDP

F-statistic 0.355820 Prob. F(4,119) 0.8395Log likelihood ratio 1.557507

Prob. Chi-Square(4) 0.8164

Figure 7


VariableCoefficien


C 12064.51 2518.407 4.790532 0.0000SECONDARYDP 4003.592 1979.779 2.022242 0.0453

ADVANCEDEDUCDP 10642.40 2116.883 5.027391 0.0000STUDENT -8065.378 2071.518 -3.893463 0.0002

CIVILSERVANT -10106.32 2079.540 -4.859881 0.0000MALE 5.028344 1491.369 0.003372 0.9973

UGANDAN 3551.350 1974.830 1.798307 0.0746DP -2065.672 1877.360 -1.100307 0.2733






Figure 8

H0: Model has no omitted variables and is correctly specified

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H1: Model is misspecifiedRamsey RESET Test:

F-statistic 0.027054 Prob. F(1,122) 0.8696Log likelihood ratio 0.029047


Given our high p-value (0.8696) >0.05 we fail to reject H0 that our model has no ommited variables is correctly specified at a 5% significance level.

Figure 9

H0: Homoskedasticity

H1: Heteroskedasticity

Heteroskedasticity Test: White

F-statistic 1.489560 Prob. F(34,96) 0.0678

Obs*R-squared 45.24189 Prob. Chi-Square(34) 0.0942

Scaled explained SS 63.95851 Prob. Chi-Square(34) 0.0014

P-value 0.067>0.05 so we fail to reject h0 of homoskedaticity

Due to our p-value (0.067) >0.05 we fail to reject H0 that we have homeskedasicity within our model. We confirm this by running a Breusch Pagan test:

Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 1.501527 Prob. F(11,119) 0.1396

Obs*R-squared 15.96629 Prob. Chi-Square(11) 0.1424

Scaled explained SS 22.57155


The high p-value (0.1396)>0.05 supports our assumption of homoskedisicity.

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