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

FOREIGN TRADE UNIVERSITY FALCUTY OF BANKING AND FINACE ====000==== ECONOMETRICS REPORT FACTORS AFFECTING THE CRUDE BIRTH RATE IN DEVELOPING COUNTRIES IN 2017 CLASS ID: KTEE310 (1-1920).1_LT Members: Nguyen Ngoc Diep – 1813340013 Dang Hong Nhung – 1813340048 Dang Thu Phuong – 1813340050 Pham Anh Thu – 1813340063 Lecturer: Dr Nguyen Thuy Quynh Hanoi, December 2019 TABLE OF CONTENT ABSTRACT INTRODUCTION .2 SECTION OVERVIEW OF THE TOPIC The definition of Crude birth rate The crude birth rate in developing countries .4 2.1 Developing countries 2.2 The crude birth rate in developing countries Economic theories 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 Related published researches .7 SECTION 2: MODEL SPECIFICATION 2.1 Methodology in the study 2.1.1 Method to derive the model 2.1.2 Method to collect and analyze data .8 2.2 Theoretical model specification 2.2.1 Specification of the model 2.2.2 Explanation of the variables .10 2.2.3 Description of the data .10 SECTION 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 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 The dataset of 132 developing countries in 2017 .23 Command list used when analyzing dataset by Stata ver.12 .29 The Stata estimation outputs 29 INDIVIDUAL ASSESSMENT 33 ABSTRACT Crude birth rate is an important determinant of population growth (or decline) - a crucial factor in the process of social and economic development Developing countries face an environment that is less favorable for economic growth than did the developed countries of the past and overpopulation is the main cause of it After discussing our group would love to take the topic “Factors affecting the crude birth rate in developing countries” to gain a deeper insight in that important socioeconomic factors Our report researched about the determinants of crude birth rate from 132 currently developing countries using the methods of cross-sectional data analysis In which you will know now what has been leaving big impact to the rate throughout the decades Due to the limited of data resources, we can only pick up a few prominent factors from those countries including GDP per capita, mortality rate (infant - per 1,000 live births), labor force participation rate (female - % of female population ages 15+) As a result of this study, if GDP per capita get higher and higher in the developing countries, the number of children born per woman get lower Infant mortality rate may decrease due to poverty reduction as related with high income In addition, crude birth may decrease due to increase in female labor force participation rate In the end, we can figure the solution for this matter to minimize the rate in order to help developing countries INTRODUCTION 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 and providing us great guide in order to finish this report This has been such an amazing yet challenging journey for us to finish this research of Econometrics which is contain truthful data with proof of information source as well as doing the calculation by ourselves Econometrics is the quantitative application of statistical and mathematical models using data to develop theories or test existing hypothesis in economics and to forecast future trends from historical data It subjects real-world data to statistical trials and then compares and contrasts the results against the theory 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 birth rate at an acceptable rate is important so in the first step, we have to know which the determinants are Which is the reason why our group decide to choose this topic, not only to understand more about the crude birth rate but also to reach some effective solutions to minimize the rate, to help the developing countries strengthen their economy In the report, we will use the econometric model to find out the relationship between GDP per capita, mortality rate (infant - per 1,000 live births), labor force participation rate (female - % of female population ages 15+) by using collected data from World Bank, statistical website from government and others sources, whether they have positive or negative relationship, how significant are the impact And looking through the process, we may have some solutions to the high crude birth rate in developing countries The report contains the following contents:  Abstract  Introduction  Section I: Overview of the topic (Review of economic theories and      statement of research hypotheses) Section II: Model Specification Section III: Estimated model and statistical Inferences Conclusion References Once again, we sincerely thank our instructor – Dr Nguyen Thuy Quynh for supporting us to implement this report During the progress of the report, despite all of the efforts, we certainly cannot avoid the errors, we look forward to your comments so that our team can improve this report SECTION OVERVIEW OF THE TOPIC 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 account age 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 Total Population X 1,000 = Total Population The crude birth rate in developing countries 2.1 Developing countries A developing country (or a low and middle income country (LMIC), less developed country, less economically developed country (LEDC), or underdeveloped country) is a country with a less developed industrial base and a low Human Development Index (HDI) relative to other countries.1 2.2 The crude birth rate in developing countries In general, birth rates in countries with low or medium levels of development are quite high due to many reasons from both social and economic O'Sullivan A, Sheffrin SM (2003) Economics: Principles in Action Upper Saddle River, New Jersey 07458: Pearson Prentice Hall aspects The birth rates in LEDCs are high whilst most MEDCs have a low birth rate due to their economic development The W.F.S estimate that only 270,000,000 of the 900,000,000 couples in the world are using modern contraception and so it is understandable why there is such a rapid growth in the world’s population Economic theories We would like to make some analysis in order to have a better understanding about which determinants affect the crude birth rate of a developing countries According to some other related researches, some factors that have significant effect on the crude birth rate was GDP per capita, Female labor 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 be attributed to each individual citizen Alternatively, this translates to a measure of national wealth since GDP market value per person also readily serves as a prosperity measure1 Therefore, higher GDP per brought in its wake higher standards of living, better food, adequate clothing and shelter, as also protection from the natural disasters of drought and famine Income should be a remarkable value of health is more reasonable in less developed countries than in rich ones If many people not have enough money to buy sufficient food, especially children seldom suffer from a poor diet, and parents not provide to feed their children, 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 and the consequent growth of population in today’s economically advanced countries The Crude birth rate also fell According to search conducted in South Africa, this According to Invetopedia.com work finds evidence of grand effect of income on health outcomes which directly affect the Crude birth rate due to lower risk in giving birth (Case, 2004: 295) While people in less developed countries not benefit from adequate health cares, people in developed countries take suitable health cares This differences leads to a fact that children in LECD are seen as a part of labor force which 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 they spend more time for career, which may reduce time for family They also aware more about the higher standard living when they have just or children It’s been proving by the fact now happening in developed countries such as Western countries Furthermore, lower rates of fertility can, in principle, free up a significant amount of women’s time, hence allowing them to enter the labor force more easily And this is of course independent of health complications – having children 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 This death toll is measured by the infant mortality rate (IMR), which is the number of deaths of children under one year of age per 1000 live births The under-five mortality rate, which is referred to as the child mortality rate, is also an important statistic, considering the infant mortality rate focuses only on children under one year of age1 Owing to the weak healthcare facilities in those countries and low education attainment in some area, infant deaths are quite high Since mortality rates are usually high, parents make up for this by increasing the number of children they have "Under-Five Mortality" UNICEF Retrieved 2017-03-07 Related published researches There is strong evidence of a causal link between fertility (having children) and labor market outcomes (participation, employment, wages, etc.) In a recent study Lundborg, Plug and Rasmussen (2017) show that women who are successfully treated by IVF (in vitro fertilization) in Denmark earn persistently less because of having children They explain the decline in annual earnings by women working less when children are young and getting paid less when children are older Goldin and Katz (2002) shows that there is evidence that women’s control over their own fertility is linked to career investments and subsequent changes in labor market outcomes There are many other studies that find similar effects on female labor supply when there are exogenous shocks to fertility In 2015, Zareena Ali, Talib Hussain, Fawad Azam’s study “Empirical Relationship of Crude birth rate, Female Literacy Rate and GDP Per Capita with Child Mortality Rate in Pakistan” shows that decrease in Crude birth rate means child mortality rate will be also low It is clear that Crude birth rate is high it is difficult 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 is considered a necessary, although insufficient condition for reduction in Fertility” He also mentions that, according to WHO report of 1974, countries with high rates of mortality have high Crude birth rates as well Empirical analysis on the relationship between mortality and Crude birth rates has gained momentum in recent years raising children with work and also because they have higher hopes for their children’s future 21 CONCLUSION Our research examined the statistically dependent relationship of the crude birth rate on GDP rate, infant mortality rate, labor force participation rate The results obtained after this research are consistent with the economic theories and some previous published researches Specially:  When the GDP per capita increases, the Crude birth rate is expected to decrease, and vice versa, holding other variables remain unchanged  When the female labor force participation rate increases, the Crude birth rate is expected to decrease, and vice versa, holding other variables remain unchanged  When the mortality rate increases, the Crude birth rate is expected to increase, and vice versa, holding other variables remain unchanged The document has been conducted totally based on the team members’ knowledge which is constructed from learning and researching in the course of the Financial Econometrics course This is a practical possibility for us to better understand analysis and tests and apply the knowledge to reach useful conclusions about inter-influence among social matters This is the end of our report, we would like to thank our teacher Nguyen Thuy Quynh for your generous help and thorough support and guidance in Financial Econometrics of this semester, which is beneficial to our future career 22 REFERENCES Zareena Ali, Talib Hussain, Fawad Azam (2015), Empirical Relationship of Crude birth rate, Female Literacy Rate and GDP Per Capita with Child Mortality Rate in Pakistan, International Journal of Scientific & Engineering Research Damodar N Gujarati, and Dawn C Porter, Basic Econometrics, 5th Edition Mart – Nisan (2015), Determination of the relationship between poverty and health equality, Kırgız – Türk Sosyal Bilimler Enstitüsü, Celalabat – KIRGIZDSTAN "Birthrate – definition of birthrate by the Free Online Dictionary, Thesaurus and Encyclopedia" Thefreedictionary.com Retrieved 17 October 2011 Rai, Piyush Kant; Pareek, Sarla; Joshi, Hemlata (2013) Regression Analysis of Collinear Data using r-k Class Estimator: Socio-Economic and Demographic Factors Affecting the Total Fertility Rate (TFR) in India (PDF) Journal of Data Science Bloom, David; Canning, David; Fink, Günther; Finlay, Jocelyn (2009) Fertility, female labor force participation, and the demographic dividend Journal of Economic Growth Vandenbroucke, Guillaume (December 13, 2016) The Link between Fertility and Income Federal Reserve Bank of St Louis (USA) Internet references Birth rate, crude (per 1,000 people) | Data Data.worldbank.org Retrieved 11 March 2017 Fertility and Birth Rates Child Trends 24 March 2015 Retrieved 17 May 2016 https://www.childtrends.org/?indicators=fertility-and-birth-rates 10 Sandra Tzvetkova and Esteban Ortiz-Ospina, Working women: What determines female labor force participation? https://ourworldindata.org/women-in-the-labor-force-determinants#fertility 23 APPENDIX The dataset of 132 developing countries in 2017 Country Name Year CBR GDP IMR LBF Afghanistan 2017 33.211 556.3021 49.5 35.6757 Albania 2017 11.934 4532.889 41.68215 Algeria 2017 24.846 4048.285 20.6 17.87622 Angola 2017 41.281 4095.813 53.4 49.59951 Argentina 2017 17.205 14591.86 9.3 42.0955 Armenia 2017 14.298 3914.501 11.6 45.9757 Azerbaijan 2017 14.6 4147.09 20.4 48.72085 Bahrain 2017 14.365 23715.48 6.2 20.9464 Bangladesh 2017 18.501 1563.994 26.5 30.24604 Belarus 2017 10.8 5761.747 2.7 49.67278 Barbados 2017 10.683 16327.61 11.6 49.34992 Belize 2017 21.081 4956.808 11.8 39.95018 Benin 2017 36.621 827.4298 61.9 49.24158 Bhutan 2017 17.459 3390.714 25.6 40.20886 Bolivia 2017 22.076 3351.124 22.8 41.81027 Bosnia and Herzegovina 2017 8.293 5394.591 5.1 39.13215 Botswana 2017 25.396 7893.676 30.8 48.49141 Brazil 2017 14.125 9880.947 13.2 43.37548 Burkina Faso 2017 38.419 642.0404 50.4 44.75085 Bulgaria 2017 8228.012 6.2 46.41809 Cambodia 2017 22.889 1385.26 25.1 48.41209 Cameroon 2017 35.9 1421.587 52.2 47.03808 24 Central African Republic 2017 35.66 471.6032 86.5 45.82657 Cabo Verde 2017 19.919 3295.341 17.2 46.95409 Chad 2017 42.683 664.3033 73.1 45.70818 China 2017 12.43 8759.042 7.9 43.91837 Chile 2017 12.711 15037.35 6.3 41.76013 Colombia 2017 15.098 6375.932 12.6 43.14002 Comoros 2017 32.365 1312.366 52.8 42.23955 Congo, Dem Rep 2017 41.732 467.0742 70 48.39688 Congo, Rep 2017 33.378 1702.571 37.1 48.76684 Cote d'Ivoire 2017 35.993 1557.183 61 41.42751 Costa Rica 2017 14.246 11752.54 7.6 38.06687 Croatia 2017 8.9 13383.68 46.30458 Cuba 2017 10.386 8541.211 3.8 37.87795 Czech Republic 2017 10.8 20379.9 2.6 44.52194 Djibouti 2017 21.944 1953.904 51.2 41.14536 Dominican Republic 2017 19.799 7222.554 24.8 39.93416 Ecuador 2017 19.968 6213.501 12.5 41.23987 Egypt, Arab Rep 2017 27.05 2440.51 18.7 23.65963 El Salvador 2017 18.429 3902.238 12.2 41.20707 Equatorial Guinea 2017 33.725 9738.434 64.5 36.9742 Estonia 2017 10.5 20200.38 2.2 48.36037 Ethiopia 2017 32.775 768.0104 40.6 46.55624 Fiji 2017 21.56 6006.361 21.4 32.78549 Gabon 2017 32.161 7212.536 33.7 40.4939 Gambia, The 2017 38.967 672.7805 40 44.34451 25 Georgia 2017 13.718 4045.417 8.9 45.67697 Ghana 2017 29.839 2025.886 36.1 46.78736 Guatemala 2017 24.912 4470.611 22.8 34.16211 Guinea 2017 36.765 821.6515 66.3 53.20849 Guinea-Bissau 2017 35.697 736.7256 55.7 48.23539 Guyana 2017 20.202 4586.055 25.9 35.85683 Haiti 2017 24.75 765.614 50.8 47.85012 Honduras 2017 21.859 2432.935 15.6 36.57516 Hungary 2017 9.7 14278.87 3.8 45.54184 India 2017 18.083 1981.499 31.5 22.05255 Iran, Islamic Rep 2017 19.011 5627.749 12.8 19.04 Iraq 2017 29.693 5143.661 23.2 14.39359 Jamaica 2017 16.286 5060.545 12.8 45.6349 Jordan 2017 22.622 4168.642 14.3 17.67443 Kenya 2017 29.296 1568.202 31.8 48.71668 Kazakhstan 2017 21.64 9030.319 9.2 48.51319 Korea, Rep 2017 29742.84 2.8 42.00722 Kyrgyz Republic 2017 24.8 1242.77 17.9 40.16333 Lao PDR 2017 23.955 2423.846 39 49.2006 Latvia 2017 10.7 15684.56 3.6 49.95253 Lebanon 2017 17.67 7838.343 6.6 24.94475 Lesotho 2017 27.17 1232.787 68.8 45.73422 Liberia 2017 33.426 698.7018 55 49.00624 Libya 2017 19.38 5792.065 10.6 24.4003 Lithuania 2017 10.1 16809.65 3.6 50.53573 Madagascar 2017 32.897 448.4008 39.1 48.86202 26 Malawi 2017 34.593 356.7176 36.8 48.61959 Maldives 2017 14.891 9801.625 7.7 22.85715 Mali 2017 42.078 828.6132 63.6 43.8154 Malta 2017 9.2 27241.09 6.1 39.61968 Mauritania 2017 34.132 1161.785 52.8 31.65363 Mauritius 2017 10.7 10484.91 12.9 39.22482 Mexico 2017 17.918 9281.101 11.6 37.08664 Moldova 2017 10.274 2724.493 13.8 48.84665 Mongolia 2017 24.833 3671.948 14.6 45.54147 Morocco 2017 19.399 3036.171 20.1 24.17301 Mozambique 2017 37.876 441.6178 55.5 52.20323 Myanmar 2017 17.703 1249.829 38 40.45817 Namibia 2017 29.105 5646.456 29.7 48.38159 Nepal 2017 20.196 900.5739 27.6 55.87072 Netherlands 2017 9.9 48554.99 3.3 46.1816 Nicaragua 2017 21.06 2168.191 15.9 39.11969 Niger 2017 46.54 375.8695 49.1 43.17082 Nigeria 2017 38.384 1968.56 76.9 45.41532 Oman 2017 19.786 15170.35 9.8 13.07577 Pakistan 2017 28.599 1466.843 58.8 21.73872 Panama 2017 19.263 15166.12 13.6 39.68002 Papua New Guinea 2017 27.364 2640.154 38.9 48.55794 Paraguay 2017 20.787 5680.581 17.8 39.50409 Peru 2017 18.134 6700.811 11.5 45.82752 Poland 2017 10.6 13861.05 3.9 44.75302 Philippines 2017 21.036 2981.934 22.9 38.25031 27 Romania 2017 9.7 10792.96 6.6 43.42876 Russian Federation 2017 12.9 10750.59 6.5 48.49227 Rwanda 2017 32.062 762.4992 28.2 51.66313 Samoa 2017 24.689 4307.805 14 36.46495 Saudi Arabia 2017 18.319 20803.74 6.4 16.7542 Senegal 2017 35.12 1367.219 32.8 39.95949 Sierra Leone 2017 33.939 499.3807 81.3 49.98005 Slovak Republic 2017 10.7 17579.26 4.8 45.64594 Slovenia 2017 9.8 23449.57 1.8 46.49464 Solomon Islands 2017 32.877 2077.126 17.6 43.40831 South Africa 2017 20.908 6127.462 29.6 45.03629 Somalia 2017 41.919 309.0661 78.8 20.78578 Sri Lanka 2017 16.106 4104.631 6.7 34.94235 St Lucia 2017 12.143 10003.26 15.2 45.44325 St Vincent and the 2017 Grenadines 14.403 7149.631 15.3 41.0689 Sudan 2017 32.542 3015.024 43.3 26.34891 Suriname 2017 18.791 5379.119 17.4 38.04352 Tanzania 2017 37.075 1004.841 38.8 48.28434 Tajikistan 2017 31.364 806.0416 31.3 32.06842 Thailand 2017 10.513 6578.189 8.2 45.65504 Togo 2017 33.55 619.0664 48.7 49.60434 Tonga 2017 24.626 4217.477 13.8 38.83575 Trinidad and Tobago 2017 13.302 16076.08 16.9 42.23215 Tunisia 2017 18.01 3494.319 14.7 26.55365 Turkmenistan 2017 24.615 6587.09 40.6 41.70059 28 Turkey 2017 16.264 10499.75 9.7 32.76042 Uganda 2017 38.947 631.5227 35.2 49.08048 Uruguay 2017 13.963 16437.24 6.8 45.44741 Uzbekistan 2017 22.1 1826.567 20.2 41.42057 Vanuatu 2017 30.015 2976.107 22.9 43.62141 Vietnam 2017 16.979 2365.622 16.9 47.76406 West Bank and Gaza 2017 29.905 3254.486 17.7 20.82764 Zimbabwe 2017 31.732 1602.404 35.4 51.00696 29 30 Command list used when analyzing dataset by Stata ver.12 Sum Corr Gen lncbr = ln(cbr) Gen lngdp = ln(gdp) Gen lnimr = ln(imr) Gen lnlbf = ln(lbf) Reg lncbr lngdp lnimr lnlbf Vif Imtest, white 10.Ovtest The Stata estimation outputs The output of the command sum cbr gdp imr lbf Variable Obs Mean cbr gdp imr lbf 132 132 132 132 22.89062 6366.174 26.17424 40.76862 Std Dev 9.824675 7191.897 20.74638 9.302781 Min Max 309.0661 1.8 13.07577 46.54 48554.99 86.5 55.87072 Figure Data description 31 The output of the command corr CBR GDP IMR LBF corr (obs=132) cbr cbr gdp imr lbf lngdp lncbr lnimr lnlbf 1.0000 -0.6306 0.8443 0.0650 -0.8055 0.9767 0.8605 0.0408 gdp imr lbf lngdp lncbr lnimr lnlbf 1.0000 -0.5720 1.0000 -0.0537 0.1614 1.0000 0.8298 -0.7764 -0.1710 1.0000 -0.6826 0.8067 -0.0054 -0.7954 1.0000 -0.7299 0.9095 0.0797 -0.8191 0.8827 -0.0564 0.1433 0.9863 -0.1547 -0.0262 1.0000 0.0700 1.0000 Figure Correlation matrix between variables The output of the command reg lncbr lngdp lnimr lnlbf reg lncbr lngdp lnimr lnlbf Source SS df MS Model Residual 22.4561898 5.35368885 128 7.4853966 041825694 Total 27.8098787 131 21228915 lncbr Coef lngdp lnimr lnlbf _cons -.1006663 342345 -.1795842 3.519069 Std Err .0272452 0340508 0626258 4130837 t -3.69 10.05 -2.87 8.52 Number of obs F( 3, 128) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.000 0.000 0.005 0.000 = = = = = = 132 178.97 0.0000 0.8075 0.8030 20451 [95% Conf Interval] -.1545756 2749697 -.3035 2.701712 -.046757 4097203 -.0556684 4.336425 Figure Results of linear regression analysis in Stata using Ordinary Least Squares (OLS) method 32 The output of the command vif Variable VIF 1/VIF lngdp lnimr lnlbf 3.13 3.07 1.03 0.319623 0.325854 0.966304 Mean VIF 2.41 Figure Results of test for multicollinearity phenomenon in the model The output of the command imtest,white White's test for Ho: homoskedasticity against Ha: unrestricted heteroskedasticity chi2(9) Prob > chi2 = = 5.83 0.7571 Cameron & Trivedi's decomposition of IM-test Source chi2 df p Heteroskedasticity Skewness Kurtosis 5.83 7.84 0.00 0.7571 0.0495 0.9545 Total 13.67 13 0.3976 Figure Results of test for heteroskedasticity violation The output of the command ovtest 33 Ramsey RESET test using powers of the fitted values of lncbr Ho: model has no omitted variables F(3, 125) = 2.44 Prob > F = 0.0679 Figure Results of test for model misspecification 11 34 INDIVIDUAL ASSESSMENT Based on each member’s attitude towards the group work we will grade our team members Ngoc Diep Hong Nhung Thu Phuong Anh Thu Ngoc Diep - 9.5 10 9.5 Hong Nhung 10 - 10 9.5 Thu Phuong 10 9.5 - 9.5 Anh Thu 10 9.5 10 - Average score 10 9.5 10 9.5 Evaluator 35 ... INTRODUCTION .2 SECTION OVERVIEW OF THE TOPIC The definition of Crude birth rate The crude birth rate in developing countries .4 2.1 Developing countries. .. did the developed countries of the past and overpopulation is the main cause of it After discussing our group would love to take the topic Factors affecting the crude birth rate in developing countries ... less developed industrial base and a low Human Development Index (HDI) relative to other countries. 1 2.2 The crude birth rate in developing countries In general, birth rates in countries with