tiểu luận kinh tế lượng tài chính FACTORS AFFECTING THE CRUDE BIRTH RATE IN DEVELOPING COUNTRIES IN 2017

Economic theories...5 3.1 The effect of GDP per capita on crude birth rate...5 3.2 The effect of female labor force participation rate on the crude birth rate6 3.3 The effect of infant m

FOREIGN TRADE UNIVERSITY FALCUTY OF BANKING AND FINACE

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ECONOMETRICS REPORT FACTORS AFFECTING THE CRUDE BIRTH RATE IN

DEVELOPING COUNTRIES IN 2017

CLASS ID: KTEE310 (1-1920).1_LT Members:

1 Nguyen Ngoc Diep – 1813340013

2 Dang Hong Nhung – 1813340048

3 Dang Thu Phuong – 1813340050

4 Pham Anh Thu – 1813340063

Lecturer: Dr Nguyen Thuy Quynh

Hanoi, December 2019

TABLE OF CONTENT

ABSTRACT 1

INTRODUCTION 2

SECTION 1 OVERVIEW OF THE TOPIC 4

1 The definition of Crude birth rate 4

2 The crude birth rate in developing countries 4

2.1 Developing countries 4

2.2 The crude birth rate in developing countries 4

3 Economic theories 5

3.1 The effect of GDP per capita on crude birth rate 5

3.2 The effect of female labor force participation rate on the crude birth rate6 3.3 The effect of infant mortality rate on crude birth rate 6

4 Related published researches 7

SECTION 2: MODEL SPECIFICATION 8

2.1 Methodology in the study 8

2.1.1 Method to derive the model 8

2.1.2 Method to collect and analyze data 8

2.2 Theoretical model specification 8

2.2.1 Specification of the model 8

2.2.2 Explanation of the variables 10

2.2.3 Description of the data 10

SECTION 3 ESTIMATED MODEL AND STATISTICAL 12

INFERENCE 12

3.1 Estimated Model 12

3.1.1 Estimation result 12

3.1.2 The sample regression model 12

3.1.3 The coefficient of determination 12

3.1.4 Meanings of estimated coefficients 13

3.1.5 Other results analysis 13

3.2 Hypothesis Testing 14

3.2.1 Testing the significance of an individual regression coefficient βjj 14

3.2.2 Testing the significance of the model 16

3.2.3 Test the assumptions of the classical model 17

3.3 Recommendation 19

CONCLUSION 21

REFERENCES 22

APPENDIX 23

1 The dataset of 132 developing countries in 2017 23

2 Command list used when analyzing dataset by Stata ver.12 29

3 The Stata estimation outputs 29

INDIVIDUAL ASSESSMENT 33

Crude birth rate is an important determinant of population growth (ordecline) - a crucial factor in the process of social and economic development.Developing countries face an environment that is less favorable for economicgrowth than did the developed countries of the past and overpopulation is themain cause of it

After discussing our group would love to take the topic “Factors affectingthe crude birth rate in developing countries” to gain a deeper insight in thatimportant socioeconomic factors Our report researched about the determinants ofcrude birth rate from 132 currently developing countries using the methods ofcross-sectional data analysis In which you will know now what has been leavingbig impact to the rate throughout the decades Due to the limited of dataresources, we can only pick up a few prominent factors from those countriesincluding GDP per capita, mortality rate (infant - per 1,000 live births), laborforce participation rate (female - % of female population ages 15+)

As a result of this study, if GDP per capita get higher and higher in thedeveloping countries, the number of children born per woman get lower Infantmortality rate may decrease due to poverty reduction as related with high income

In addition, crude birth may decrease due to increase in female labor forceparticipation rate

In the end, we can figure the solution for this matter to minimize the rate

in order to help developing countries

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First of all, we want to express our sincere thanks to our lecturer – Dr.Nguyen Thuy Quynh to have been doing her best to teach us Econometrics andproviding us great guide in order to finish this report This has been such anamazing yet challenging journey for us to finish this research of Econometricswhich is contain truthful data with proof of information source as well as doingthe calculation by ourselves

Econometrics is the quantitative application of statistical andmathematical models using data to develop theories or test existing hypothesis ineconomics and to forecast future trends from historical data It subjects real-worlddata to statistical trials and then compares and contrasts the results against thetheory or theories being tested

Overpopulation is a hardship that every developing countries has to face,specially developing will struggle more to overcome it Keeping the crude birthrate at an acceptable rate is important so in the first step, we have to know whichthe determinants are Which is the reason why our group decide to choose thistopic, not only to understand more about the crude birth rate but also to reachsome effective solutions to minimize the rate, to help the developing countriesstrengthen their economy

In the report, we will use the econometric model to find out therelationship between GDP per capita, mortality rate (infant - per 1,000 livebirths), labor force participation rate (female - % of female population ages 15+)

by using collected data from World Bank, statistical website from governmentand others sources, whether they have positive or negative relationship, howsignificant are the impact And looking through the process, we may have somesolutions to the high crude birth rate in developing countries

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The report contains the following contents:

 Abstract

 Introduction

 Section I: Overview of the topic (Review of economic theories andstatement of research hypotheses)

 Section II: Model Specification

 Section III: Estimated model and statistical

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SECTION 1 OVERVIEW OF THE TOPIC

1 The definition of Crude birth rate

The crude birth rate is the number of live births occurring among the

population of a given geographical area during a given year, per 1,000 mid-year total population of the given geographical area during the same year

The crude birth rate is called "crude" because it does not take into accountage or sex differences among the population In our hypothetical country, the rate

is 15 births for every 1,000 people, but the likelihood is that around 500 of those 1,000 people are men, and of the 500 who are women, only a certain percentage are capable of giving birth in a given year

(Number of resident live births / Number of total population) x 1,000 Total Resident Live Births X 1,000 = Total Population

2.2 The crude birth rate in developing countries

In general, birth rates in countries with low or medium levels ofdevelopment are quite high due to many reasons from both social and economicaspects The birth rates in LEDCs are high whilst most MEDCs have a low birthrate due to their economic development The W.F.S estimate that only

1 O'Sullivan A, Sheffrin SM (2003) Economics: Principles in Action Upper Saddle River, New Jersey 07458: Pearson Prentice Hall.

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270,000,000 of the 900,000,000 couples in the world are using moderncontraception and so it is understandable why there is such a rapid growth in theworld’s population.

3 Economic theories

We would like to make some analysis in order to have a betterunderstanding about which determinants affect the crude birth rate of adeveloping countries According to some other related researches, some factorsthat have significant effect on the crude birth rate was GDP per capita, Femalelabor force participation rate and Infant mortality rate

3.1 The effect of GDP per capita on crude birth rate

GDP per capita shows how much economic production value can beattributed to each individual citizen Alternatively, this translates to a measure ofnational wealth since GDP market value per person also readily serves as aprosperity measure1 Therefore, higher GDP per brought in its wake higherstandards of living, better food, adequate clothing and shelter, as also protectionfrom the natural disasters of drought and famine Income should be a remarkablevalue of health is more reasonable in less developed countries than in rich ones Ifmany people do not have enough money to buy sufficient food, especiallychildren seldom suffer from a poor diet, and parents do not provide to feed theirchildren, there is a dramatic decrease in crude birth rate (Deaton, 2003)

In fact, the last two centuries have witnessed a fall in the death rate andthe consequent growth of population in today’s economically advanced countries.The Crude birth rate also fell According to search conducted in South Africa, thiswork finds evidence of grand effect of income on health outcomes which directlyaffect the Crude birth rate due to lower risk in giving birth (Case, 2004: 295)

1 According to Invetopedia.com

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While people in less developed countries do not benefit from adequatehealth cares, people in developed countries take suitable health cares Thisdifferences leads to a fact that children in LECD are seen as a part of labor forcewhich then contribute to high rate of birth.

3.2 The effect of female labor force participation rate on the crude birth rate

There is a tight relationship between female labor force participation rate

on the crude birth rate The higher rate of female in labor force shows that theyspend more time for career, which may reduce time for family They also awaremore about the higher standard living when they have just 1 or 2 children It’sbeen proving by the fact now happening in developed countries such as Westerncountries Furthermore, lower rates of fertility can, in principle, free up asignificant amount of women’s time, hence allowing them to enter the labor forcemore easily And this is of course independent of health complications – havingchildren is very time consuming even when enjoying perfect health

3.3 The effect of infant mortality rate on crude birth rate

Infant mortality is the death of young children under the age of 1 Thisdeath toll is measured by the infant mortality rate (IMR), which is the number ofdeaths of children under one year of age per 1000 live births The under-fivemortality rate, which is referred to as the child mortality rate, is also an importantstatistic, considering the infant mortality rate focuses only on children under oneyear of age1 Owing to the weak healthcare facilities in those countries and loweducation attainment in some area, infant deaths are quite high Since mortalityrates are usually high, parents make up for this by increasing the number ofchildren they have

1 "Under-Five Mortality" UNICEF Retrieved 2017-03-07

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4 Related published researches

There is strong evidence of a causal link between fertility (havingchildren) and labor market outcomes (participation, employment, wages, etc.) In

a recent study Lundborg, Plug and Rasmussen (2017) show that women who aresuccessfully treated by IVF (in vitro fertilization) in Denmark earn persistentlyless because of having children They explain the decline in annual earnings bywomen working less when children are young and getting paid less when childrenare older

Goldin and Katz (2002) shows that there is evidence that women’s controlover their own fertility is linked to career investments and subsequent changes inlabor market outcomes There are many other studies that find similar effects onfemale labor supply when there are exogenous shocks to fertility

In 2015, Zareena Ali, Talib Hussain, Fawad Azam’s study “EmpiricalRelationship of Crude birth rate, Female Literacy Rate and GDP Per Capita withChild Mortality Rate in Pakistan” shows that decrease in Crude birth rate meanschild mortality rate will be also low It is clear that Crude birth rate is high it isdifficult for parent and government to provide efficient health facilities children

as compare to developed nations and vice versa

Ghazi M Farooq, in his book ‘Fertility in Developing Countries (1985)’,takes evidence from the works of eminent economists like Frederiksen, (1969)and Zachariah(1973) to conclude the following, “a reduction in mortality isconsidered a necessary, although insufficient condition for reduction in Fertility”

He also mentions that, according to WHO report of 1974, countries with highrates of mortality have high Crude birth rates as well Empirical analysis on therelationship between mortality and Crude birth rates has gained momentum inrecent years

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SECTION 2: MODEL SPECIFICATION

2.1 Methodology in the study

2.1.1 Method to derive the model

We use ordinary least squares method (OLS) for multiple regressionanalysis The method of ordinary least squares is attributed to Carl FriedrichGauss, a German mathematician Under certain assumptions, the method of leastsquares has some very attractive statistical properties that have made it one of themost powerful and popular methods of regression analysis, for it has best linearunbiased estimator

2.1.2 Method to collect and analyze data

2.1.2.1 Collect the data

The dataset is cross-sectional data of Crude birth rate, GDP Per Capita,Female labor force participation rate and Infant mortality rate for the year 2017 in

132 developing countries1 (listed in the appendix) The data is secondary andcollected from World Data Bank – a high precision data source

2.1.2.2 Analyze the data

The analysis is carried out using Stata version 12 to analyze the datasetand interpret the correlation matrix between variables

2.2 Theoretical model specification

2.2.1 Specification of the model

According to previous published researches, our group has established afunction to analyze the relationships between 3 independent variables and theCrude birth rate as well as the effects of those variables toward the dependentvariable:

1 According to https://icqi.org/developing-countries-list/

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CBR = f (GDP, LBF, IMR)

Where:

 CBR: Crude birth rate, crude (per 1,000 people) (%)

 GDP: GDP per capita (current US$)

 LBF: Labor force participation rate, female (% of female population ages

15+) (%)

 IMR: Mortality rate, infant (per 1,000 live births) (%)

According to other researches and due to norms of the society, our groupdecide to choose the regression analysis model which can best explain the impact

of those above independent variables on the crude Crude birth rate

2.2.1.1 Population regression model

PRF: lnCBR = β 1 + β 2 lnGDP i + β 3 lnLBF i + β 4 lnIMR i + u i

Where:

 β1: the intercept term of the model

 β2: the regression coefficient of “GDP per capita” GDP

 β3: the regression coefficient of “ Infant mortality rate” IMR

 β4: the regression coefficient of “ Female labor force participation rate”LBF

 ui: the disturbance term of the model, represents other factors that affectCBR but not mentioned in the model

2.2.1.2 Sample regression model

SRF: lnCBR = β̂ 1 + β̂ 2 lnGDP i + β̂ 3 lnLBF i + β̂ 4 lnIMR i + û i

Where:

 β̂1: the estimator of β1

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 β̂2: the estimator of β2

 β̂3: the estimator of β3

 β̂4: the estimator of β4

 ûi: the estimator of − the residuals term ui

2.2.2 Explanation of the variables

No Variables Meaning Unit regression analysis Expected sign of

-3. lnLBF Female labor forceparticipation rate %

 The dependent variable is lnCBR

 The explanatory variables are lnGDP, lnLBF and lnIMR

2.2.3 Description of the data

2.2.3.1 Data sources

The dataset was collected from the official website of World Bank,includes 132 observations of 132 developing countries in 2017

2.2.3.2 Statistical description of the variables

Running the command “sum CBR GDP LBF IMR” to detail about all thevariables, the result obtained including the number of observations (Obs), theaverage value (Mean), the standard deviation (SD) as well as the minimum (Min)and maximum (Max) values of each variable as the table below:

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4 7 1 9

4 LBF 132 40.76862 9.302781 13.07577 55.87072

2.2.3.3 Correlation matrix between variables

Running the command “corr CBR GDP IMR LBF” to analyze thecorrelation between the variables, we have the result is the table of correlationmatrix between variables:

According to the Correlation matrix between variables:

• The correlation coefficient between GDP per capita and CBR is -0.7954, which

is negative and quite high Therefore, GDP per capita has a negative effect onCBR, an increase in GDP per capita results in a major decrease in CBR

• The correlation coefficient between IMR and CBR is 0.8827 which is positiveand extremely high Therefore, IMR has a positive effect on CBR, an increase inIMR results in a huge increase in CBR

• The correlation coefficient between LBF and CBR is -0.0262, which is negativeand pretty low Therefore, LBF has a positive effect on CBR, an increase in LBFresults in an slight decrease in CBR

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SECTION 3 ESTIMATED MODEL AND STATISTICAL

INFERENCE

3.1 Estimated Model

3.1.1 Estimation result

Run the command reg lncbr lngdp lnlbf lnimr to compute the estimation

result, the result obtained is a table:

Variables Coefficient ^βj T P-value Confident interval (95%) Constant 3.519069 8.52 0.000 [2.701712; 4.336425]

lngdp 0.1006663 3.69 0.000 [0.1545756; 0.046757]

lnlbf 0.1795842 2.87 0.005 [0.3035; 0.0556684]

3.1.2 The sample regression model

We have the Sample Regression Model:

Lncbr = ^βj1 + ^βj2lngdp + ^βj3lnlbf + ^βj4lnimr + u^i

According to the estimated result from Stata using the Ordinary Least

Squares (OLS) method, we obtained the Sample Regression Function (SRF) as

below:

Lncbr = 3.519069  0.1006663 lngdp  0.1795842 lnlbf + 0.342345 lnimr + u^i

3.1.3 The coefficient of determination

The coeffiecient of determination R2 (R-squared) = 0.8075 means 80.75%

of the total variation in the dependent variable, which is Crude birth rate, is

explained by the explanatory variables, which are Female labor force rate, GDP

per capita and Infant mortality rate; the remains are due to other factors.

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Besides the coefficient of determination, we also take the adjusted R2(´R2) under consideration, because adding more variables into the model cansometimes make the R2 less significant The ´R2 of the model is also pretty high:about 0.8030, not so different from the R2, which mean the model can still explainfor approximately 80% of the fluctuations and the variables added arereasonable.

3.1.4 Meanings of estimated coefficients

 The constant term is estimated to be ^βj1 = 3.519069: When

every explanatory variable equals to 0, the expected value of Crude birth rate

will be 3.519069%

 The regression coefficient of lngdp is estimated to be ^βj2 =

0.1006663 Holding other explanatory variables unchanged, if GDP per capita

(lngdp) increases by 1%, the expected value of Crude birth rate will decrease

by 0.1006663%

 The regression coefficient of lnlbf is estimated to be ^βj3 =

0.1795842:

Holding other explanatory variables unchanged, if the Female labor force

rate (lnlbf) increases by 1%, the expected value of Crude birth rate will

decrease by 0.1795842%

 The regression coefficient of lnimr is estimated to be ^βj4 = 0.342345:

Holding other explanatory variables unchanged, if the Infant mortality

rate (lnimr) increases by 1%, the expected value of Crude birth rate will

increase by 0.342345%

3.1.5 Other results analysis

 Number of observations: Obs = 132

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 The Explained Sum of Squares represents the variation of theestimated lncbr values about their sample mean, or explained by the regressionmodel: ESS = 22.4561898, which has the degree of freedom of n − k = 128.

 The Residual Sum of Squares represents the unexplained variation

of the dependent variable lncbr about the regression line: RSS = 5.35368885,which has the degree of freedom of k − 1 = 3

 The Total Sum of Squares represent the total variation of theactual values of the dependent variable lncbr about their sample mean: TSS =27.8098787, which has the degree of freedom of n − 1 = 131

3.2 Hypothesis Testing

3.2.1 Testing the significance of an individual regression coefficient ^βj j

State the Hypotheses:{H0: βj j=0

H1: βj j ≠0

3.2.1.1 The confidence interval approach

According to the results from Stata using the Ordinary Least Squaresregression analysis method, we obtained the confidence interval for theregression coefficients of each variable at a significance level of 5% asbelow:

of 0 doesn’t belong to the confidence interval of each variable Therefore, theregression coefficients of these variables are statistically significant at 5% level

of significance

3.2.1.2 The T-distribution approach

Specify the critical t-value: t α/ 2 n−k=t0.025128 =1.979, where:

 n: the number of observations or sample size, n = 132

 k: the number of variables, k = 4

 α: the significance level, α = 0.05, for the two-tailed test, α⁄2 =0.025

According to the test statistic t s=

^

βj j−0

SE(^B j) of each variable at thesignificance level of 5% obtained from the results, we have:

For the variable lngdp, its absolute value is |ts| = 3.69 > 1.979, we can

reject H0 Therefore, the regression coefficient of lngdp is statistically

significant at 5% level of significance

For the variable lnlbf, its absolute value is |ts| = 2.87 > 1.979, we can

reject H0 Therefore, the regression coefficient of lnlbf is statistically

significant at 5% level of significance

For the variable lnimr, its absolute value is |ts| = 10.05 > 1.979, we can

reject H0 Therefore, the regression coefficient of lnimr is statistically

significant at 5% level of significance

For the variable constant, its absolute value is |ts| = 8.52 > 1.979, we

can reject H0 Therefore, the regression coefficient of constant is statistically

significant at 5% level of significance

3.2.1.3 The P-value approach

The P-value is the lowest significance level at which the Null Hypothesis

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