The factors affecting monthly expenditure of ftu’s student

TABLE OF CONTENTS Page I. INTRODUCTION 1 II. METHODOLOGY 2 1. DEFINITION 2 1.1. Income 2 1.2. Expenditure 3 2. THEORIES OF CONSUMERS’ BEHAVIOR 3 3. THE KEYNESIAN CONSUMPTION FUNCTION 5 III. ECONOMETRIC MODEL 7 1. MODEL CONSTRUCTION 7 2. COEFFICIENTS PREDICTION 8 IV. DATA DESCRIPTION 9 V. EMPERICAL RESULTS 13 1. USING THE ABOVE DATA TO ESTIMATE THE REGRESSION MODEL BY OLS METHOD 13 2. MEANING OF THE REGRESSION COEFFICIENTS 14 3. TESTING THE SIGNIFICANCE OF THE REGRESSION COEFFICIENTS AND THE RELEVANCE OF THE REGRESSION FUNCTION 14 4. FIRST CURE: FOR THE REGRESSION MODEL 17 5. TESTING THE CONFORMITY WITH THE ASSUMPTIONS OF OLS METHOD 21 6. SECOND CURE: FOR THE HETEROSKEDASTICITY 23 7. FINAL REGRESSION MODEL 28 VI. CONCLUSION 29 VII. REFERENCES 30 I. INTRODUCTION Vietnam in recent years, along with nearly 200 countries around the world, has been integrating into the trend of globalization and exercising national campaigns towards the overall development in economic, political, social and cultural aspects. In this context, human capital is considered one of the key factors for Vietnam’s long-term revolution, and it is university students that make up an indispensable part in the domestic labor force in the future. Regarded as one of the most privileged universities in Vietnam, Hanoi Foreign Trade University has long attracted thousands of students from North to South every year. Each student, as a matter of fact, has his own family background, distinctive personalities as well as certain level of knowledge and experience. Such factors, certainly, have significant impacts on students’ daily life, in which students’ expenditure should be mentioned first of all. Therefore, after taking everything into consideration, we decided to choose and study the project: “THE FACTORS AFFECTING MONTHLY EXPENDITURE OF FTU’S STUDENT”. Although the government has tried to implement financial aid programs for university learners, we, especially those coming from provincial areas, have still met many difficulties in managing our spending every day. It is really not easy to allocate our limited source of money into a range of activities in the most effective way. Thus through our project, we would like to provide you with more in-depth understanding about some main factors dominating daily spending of FTU’s students. We hope that arguments and statistics in this project will be helpful for you in drawing a reasonable plan of expenditure for the time being. II. METHODOLOGY In this project, we consider three factors that may affect students’ monthly spending: income, students’ homeland and students’ characteristics. Homeland and characteristics are two qualitative variables. In general they have certain impacts on the ways students plan their expenditure. For instance, a student coming from rural area may consume less than one coming from a big city. Similarly, the amount of spending depends on whether the student is generous or thrifty, shopping-lover or shopping-averse. Income, by contrast, is a quantitative variable. It can be said that income and expenditure are two critical elements of the market economy, as everyone has to consider how to spend their disposable income in the most reasonable way. There also exists a close-knit relationship between those two factors, thus we will use microeconomic and macroeconomic theories and models to interpret it. 1. DEFINITIONS 1.1. Income There are two main types of income, which can be listed as personal income and disposable income. 1.1.1. Personal income (PI) Personal income is the income earned by households and non-corporate businesses. Unlike national income, it excludes retained earnings, which is the amount of revenue corporations have earned but have not paid out to stockholders as dividend. It also subtracts corporate income taxes and contributions for social insurance (mostly Social Security taxes). In addition, personal income includes interest income, the amount households receive from their holdings of government debt, and transfer payment, the amount they get form government transfer program such as welfare and social security. 1.1.2. Disposable income (DI) Disposable personal income is the net income that households and non-corporate businesses earn after fulfilling all their obligations to the government. It equals personal income minus personal taxes and certain non-tax payments (such as traffic tickets). DI = PI – personal taxes In the scope of our project, however, our studied subjects are FTU’s students who have no obligation to pay income tax. Thus they have entire disposal of what they earn, which means that their personal income also equals their disposable income. Besides, students’ earnings generally come from two main sources: family financial support and income from part-time jobs. Family financial support is the monthly amount supported by students’ families so that they can fulfill their daily life. Income from part-time jobs is what students earn when participating in the labor market, which is tax-free. 1.2. Expenditure Expenditure is the sum of money each individual uses for the purchase of goods and services to satisfy their needs. For instance, each month students have to pay for some urgent needs such as food, clothing, traveling fees, housing expenses (if students have to rent a house), and so on. Those all aim at responding to personal needs of students. 2. THEORIES OF CONSUMERS’ BEHAVIOR We assume that university students always try to maximize their own utility by using a number of certain resources. This means that although there are many ways of planning expenditure, students will only follow the choice that is most likely to optimize their satisfaction. Moreover, as there always exists a limit to students’ income, they have to consider how to allocate that restricted source for a variety of daily activities. In short, this part of our project has two main objectives. The first one is to study how students use their income to bring about maximum benefit for themselves. And the second one is to explain how income affects expenditure theoretically and realistically. The theories of consumers’ behavior, in microeconomics, begin with three basic assumptions about consumers’ preference. Firstly, preferences are complete. This means that consumers can rank their baskets of goods based on personal preferences or different levels of utility they may provide. Prices of goods have no effects on consumers’ choice in this case. Secondly, preferences are transitive. If a person prefers good A to good B, and good B to good C, certainly he will prefer good A to good C. Thirdly, in case of normal goods, consumers always prefer more to less. This is an obvious argument, because everyone feels more satisfied when consuming more goods and services. Generally our project still relies on those basic assumptions, but instead of goods, we aim to study different ways of planning expenditure of FTU’s students. Thus in the scope of this project, we will adjust the three assumptions as follows. Firstly, students can compare and rank different choices of spending based on their satisfaction. Secondly, of a student prefers choice A to choice B, and choice B to choice C, this means that he prefers choice A to choice C. Thirdly, students will choose the choice of expenditure that benefits them most. 3. THE KEYNESIAN CONSUMPTION FUNCTION In general, the basic form of consumption function is as follows: C = f(Yd) with Yd representing disposable income. But as afore-mentioned, since there is no personal income tax levied on university students, their disposable income also equals their personal income. In this case, the consumption function can be rewritten as : C = f(Y) This reflects the relationship between planned expenditure and disposable income. Generally students’ spending increases when income increases, but it is assumed to rise less quickly than income. The reason is that students tend to divide their earnings into two parts: consumption and savings. This means that they do not spend all their money on the purchase of goods and services but tend to save a small amount to deal with unexpected incidents in the future, such as illnesses, burglaries, house-moving, etc. This is a popular psychological phenomenon of almost every student in Vietnam, especially those coming from provincial areas to big cities to further their study. If consumption rises at a lower speed than income does, the ratio consumption/income will decrease as income increases. We use a linear function in the form of y = a + bx to build the consumption function. In particular, we have the standard Keynesian consumption function as follows:
where C = Students’ expenditure
= Autonomous consumption. This is the level of consumption that will take place even if income is zero. If an individual's income falls to zero, some of his existing spending can be sustained by using savings. This is known as dis-saving spending. MPC = Marginal propensity to consume. This is the change in consumption divided by the change in income, or in other words, it determines the slope of the consumption function. The MPC reflects the effect of an additional VND of disposable income on consumption.

As you can see from the graph above, we always have: 0 < MPC < 1. If MPC equals to 1, this means that students’ spending always equals students’ income, which is irrational in reality. Actually when a student’s income reaches a certain level, he will not spend all the money but keep a certain amount as savings. Certainly, savings will increase as income increases, thus MPC can never equal to 1. In conclusion, there is a positive relationship between disposable income (Yd) and students’ spending (C). The gradient of the consumption curve gives the marginal propensity to consume. The intercept gives the autonomous consumption, which exists even if students have no current disposable income. III. ECONOMETRIC MODEL 1. MODEL CONSTRUCTION a) Variables: - Dependent variable: EXP: Student’s monthly expenditure (unit: thousand dong) - Independent variables: + CHA (dummy): Student’s character Generous = 1 Economical = 0 + HOM (dummy): Student’s homeland Urban area = 1 Rural area = 0 + FFS: Family financial support (unit: thousand dong) + INC: Student’s monthly income (from tuition, part-time jobs, etc) (unit: thousand dong) b) Regression model: - Population regression function: (PRF):
(Ui: disturbance term) - Sample regression function: (SRF):
(ei: residual) 2. COEFFICIENTS PREDICTION -
: positive – A generous student (CHA = 1) tends to spend more than an economical one (CHA = 0) -
: positive – A student who comes from an urban area (HOM = 1) tends to spend more than one who comes from a rural area (HOM = 0) -
: positive – If monthly family financial support increases, student’s monthly expenditure increases too. -
: positive – If a student’s monthly income increases, his/her expenditure increases too. IV. DATA DESCRIPTION The primary data is collected from a survey which has been conducted among 83 FTU students in April 22, 2011. The dataset is interpreted as cross-sectional. The results of the survey has been obtained as follows: No  CHA  HOM  FFS  INC  EXP   1  1  0  2000  0  2000   2  1  1  2000  0  2000   3  1  0  1500  0  1500   4  0  1  2000  0  2000   5  1  1  1000  0  1000   6  1  1  1500  0  1500   7  0  1  400  0  400   8  1  1  500  0  500   9  1  1  600  0  600   10  1  0  2500  500  3000   11  1  1  1500  500  2000   12  1  1  0  2000  1500   13  1  1  2000  0  2000   14  1  0  500  1500  3000   15  1  1  2000  0  1500   16  1  0  3000  900  3700   17  1  1  300  1000  1300   18  0  1  1000  0  900   19  0  1  500  0  500   20  1  0  1500  0  1500   21  0  1  500  0  500   22  0  0  600  0  500   23  0  1  500  400  600   24  1  1  0  1500  1500   25  1  1  2000  1000  3000   26  1  0  500  500  1000   27  1  1  3000  0  2500   28  0  1  500  1000  1200   29  1  1  2000  0  1500   30  1  0  2000  1000  3000   31  1  1  500  1000  1500   32  1  0  2000  1000  3000   33  0  0  1000  0  700   34  0  1  2000  0  1500   35  0  1  0  1200  800   36  1  1  400  0  400   37  1  1  500  900  1200   38  0  1  1000  1000  1000   39  1  1  2000  0  1500   40  0  1  400  4000  4000   41  1  1  1000  1000  2000   42  1  1  400  400  700   43  1  1  1000  1200  2000   44  1  1  1000  1500  2500   45  0  1  1000  0  1000   46  0  1  1000  0  700   47  1  0  2000  1000  2000   48  1  0  2000  0  2000   49  1  1  2000  600  2500   50  0  0  2000  500  2000   51  0  0  700  0  600   52  0  0  2000  0  2000   53  1  1  3000  1000  3500   54  1  1  2000  500  2300   55  1  1  1000  1000  2000   56  1  1  0  2000  1500   57  0  1  3000  0  3000   58  1  0  2000  1000  3000   59  0  0  1000  0  800   60  1  1  2500  1000  3000   61  1  0  1500  0  1200   62  1  0  3000  0  2000   63  1  1  2000  500  2500   64  1  1  3000  0  3000   65  1  0  1500  1300  2500   66  1  0  2000  1600  2000   67  1  0  2000  0  2000   68  0  1  0  2000  1500   69  1  0  1000  1800  2800   70  1  1  1800  1200  3000   71  1  0  2000  1000  2000   72  1  1  600  1000  1500   73  1  1  3500  0  3500   74  1  0  2000  0  2000   75  0  1  500  1500  1500   76  1  1  1000  2000  2500   77  0  0  400  500  800   78  1  0  2000  0  2000   79  0  1  200  1000  1200   80  0  1  700  2500  2500   81  1  0  1500  1200  2000   82  1  1  1500  0  1500   83  0  1  2000  0  1800   V. EMPERICAL RESULTS 1. USING THE ABOVE DATA TO ESTIMATE THE REGRESSION MODEL BY OLS METHOD Model 1: OLS, using observations 1-83 Dependent variable: EXP  Coefficient  Std. Error  t-ratio  p-value    const  -23.7348  107.466  -0.2209  0.82578    CHA  158.541  80.3945  1.9720  0.05215  *   HOM  15.2599  74.9691  0.2035  0.83924    FFS  0.864879  0.0468649  18.4547  <0.00001  ***   INC  0.81998  0.0500468  16.3843  <0.00001  ***   Mean dependent var  1803.614   S.D. dependent var  870.3021   Sum squared resid  7810729   S.E. of regression  316.4452   R-squared  0.874241   Adjusted R-squared  0.867792   F(4, 78)  135.5590   P-value(F)  2.67e-34   Log-likelihood  -593.0369   Akaike criterion  1196.074   Schwarz criterion  1208.168   Hannan-Quinn  1200.933   Excluding the constant, p-value was highest for variable 2 (HOM)   From the above result, we obtain the following regression function: (SRF) EXPi = -23.7348 + 158.541 CHAi + 15.2599 HOMi + 0.864879 FFSi + 0.81998 INCi + ei (1) 2. MEANING OF THE REGRESSION COEFFICIENTS -
= -23.7348 means that if an economical student who comes from an rural area has no family financial support and no income, he/she will spend -23.7348 thousand dong on average every month. -
= 158.541 means that a generous student will spend 158.541 on average more than an economical one, provided that they come from the same homeland areas and have the same family financial support and income every month. -
= 15.2599 means that a student who comes from an urban area spend 15.2599 on average more than another student who comes from a rural area, provided that they have the same character, family financial support and income every month. -
= 0.864879 means that every month if the family financial support of one student increases (or decreases) by one thousand dong, he/she will spend 0.864879 dong more (or less) on average; provided that his/her character, homeland and monthly income remain unchanged. -
= 0.81998 means that every month if the income of one student increases (or decreases) by one thousand dong, he/she will spend 0.81998 dong more (or less) on average; provided that his/her character, homeland and monthly family financial support remain unchanged. 3. TESTING THE SIGNIFICANCE OF THE REGRESSION COEFFICIENTS AND THE RELEVANCE OF THE REGRESSION FUNCTION a) The significance of the regression coefficients: - Intercept
:
Formula:
If
, then
Since | t | = 0.2209 < t0.05(78) = 1.66, we accept H0. There is sufficient sample evidence to claim that
, that is, the intercept is not significant. - Slope
:
Formula:
If
, then
Since | t | = 1.972 > t0.05(78) = 1.66, we reject H0. There is insufficient sample evidence to claim that
, that is, the slope is significant. - Slope
:
Formula:
If
, then
Since | t | = 0.2035 < t0.05(78) = 1.66, we accept H0. There is sufficient sample evidence to claim that
, that is, the slope is not significant. - Slope
:
Formula:
If
, then
Since | t | = 18.45 > t0.05(78) = 1.66, we reject H0. There is insufficient sample evidence to claim that
, that is, the slope is significant. - Slope
:
Formula:
If
, then
Since | t | = 16.38 > t0.05(78) = 1.66, we reject H0. There is insufficient sample evidence to claim that
, that is, the slope is significant. b) The relevance of the regression function:
Formula:
If
, then
Since F = 63.2313 >
, we reject H0. There is insufficient sample evidence to claim that
, that is, the regression function is relevant. 4. FIRST CURE: FOR THE REGRESSION MODEL a) The coefficient
and the variable HOM: - From the above analysis, when conducting T-test with respect to
, we have sufficient evidence to conclude that
, that is, the slope is not significant. - If the variable HOM is omitted, we obtain the following result when running a regression model having three independent variables: CHA, FFS, INC. Model 1: OLS, using observations 1-83 Dependent variable: EXP  Coefficient  Std. Error  t-ratio  p-value    Const  -11.1501  87.3646  -0.1276  0.89877    CHA  157.774  79.8175  1.9767  0.05157  *   FFS  0.863175  0.0458309  18.8339  <0.00001  ***   INC  0.82031  0.049716  16.4999  <0.00001  ***   Mean dependent var  1803.614   S.D. dependent var  870.3021   Sum squared resid  7814878   S.E. of regression  314.5195   R-squared  0.874175   Adjusted R-squared  0.869396   F(3, 79)  182.9514   P-value(F)  1.85e-35   Log-likelihood  -593.0589   Akaike criterion  1194.118   Schwarz criterion  1203.793   Hannan-Quinn  1198.005     After the va