Annual Natural Population Increase - An Empirical Study

8/8/2019 Annual Natural Population Increase - An Empirical Study

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Annual Natural Population Increase An Empirical Study

1. Introduction

2. Empirical Natural Increase Model

3. Data information

4. Empirical results: Model estimations and Hypotheses testing

5. Summary and Conclusions

6. References

Empirische Arbeit von

Eigner Franz, a0301345

Sagerschnig Sophie, a9951023

Tirnitz Benjamin, a

bei Frau Prof. Kaufmann,

PR Empirische Wirtschaftsforschung,

SS05: Juni 2005


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Annual Natural Population Increase An Empirical Study Eigner, Sagerschnig, Tirnitz SS05

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1.Introduction

In this paper we are interested in creating an empirical model, which is able to explain natural populationincrease for 105 countries worldwide and in identifying the various responsible factors and discoveringtheir relative impact. Besides we want to check, whether it is meaningful to create separate models for rich

and poor countries to get better results and to find out, if the same variables are important in explainingnat. population increase, or whether there are different significant variables.We do not use a formulated model based on a theoretical framework but we create a regression model byourselves based on considerations after dealing with literature about this problem.We generate our regression model with Eviews using the ordinary least squares (OLS) procedure whichproduces the most efficient estimations.

Population Growth Facts:12The world has experienced a rapid population growth since Industrial Revolution. The annual growth rateincreased in the last half of the 19

thcentury from almost zero to 0.5 percent. It reached the 2.0 percent

mark in the 1960s, and declined to about 1.4 percent by 2000.

Nowadays almost all of the world population growth takes place in developing countries. In contrast tomore developed countries, developing countries which we find mainly in Asia, Africa and Latin Americahad no distinctive mortality decline before World War II. The mortality revolution in developing countriesafterwards was not based on economic growth, but was a consequence of international foreign aid. In thesecond stage, death rates decreased rapidly, whereas birth rates kept high or even increased on account ofbetter health conditions. In the 1960s, these countries had a mean population growth of 2.5 percent.Around 1970, also birth rates began to fall and population growth reached the 1.9 percent mark in 2000.In the future world population is projected to increase to 7.8 billion by 2025 and to reach 8.9 billion by2050.

We can classify the countries after their population growth in 4 groups (Table 1)

Table 1: Tendency in population growth3Low fertility -Population decreases

Decreasing fertility Small population growth

Increasing mortality (Aids) -Decreasing population growth

Fast growing population

Developing countries (except forUSA, GBR, FRA), some developingcountries (Cuba)

among others: China,other countries in East-Asia, USA

Among others: Zimbabwe, Botswanaand South Africa, and other countries ofSub-Saharan Africa

Israel, various developingcountries like Palestine,Ethiopian

Basic information:Population change is composed of 3 important variables: births, deaths and migration. If we subtractdeaths from births, we get the natural increase of a population, which generally accounts for the greatestamount of population growth. Population growth itself arises from the natural increase rate added to the

net migration rate.

1Pay attention: for some introducing parts of this paper we are dealing with Population growth, the model itself works with Natural growth.2http://www.prb.org/Content/NavigationMenu/PRB/Educators/Human_Population/Population_Growth/Population_Growth.htm3

See Globale Trends 2002. Fakten Analysen Prognosen. Chapter: Weltbevlkerung und Verstdterung


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Population growth is based on a range of direct and indirect factors.4

The biggest direct factor is the young population structure.Approximately half of todays world population is younger than 25 years. which is more than ever before.This generation will be the parents of the future. As a consequence, world population will continue togrow, even if birth rates would commence to decline. Experts claim, that this factor is responsible for half

of the population growth. The second biggest direct factor is the high number of unwanted births .

About a quarter of population growth can be attributed to this factor. Abortion and prevention can lower thisnumber. Furthermore it is one of the main factors responsible for high maternal mortality in developingcountries.

Culture, religion, deficiency in education and health and discrimination of women are indirectfactors.Influences of such indirect factors are hardly to quantify.

The impact of HIV/Aidson world population growth is still small. Only 3 million people died in 2000on account of Aids. But it will gradually rise because approximately 6 million people are newly infectedeach year - with upward tendency. In the next 2 decades it could lead in many African countries to anenormous decrease of population growth, an increase of child mortality of more than 100 % and a fall of the

life expectancy rate to less than 50 years.

For our model, we will pick out some of these factors.

2.Empirical Natural Increase Model

The following empirical model of natural population increase is postulated:

RNI = const + 1*GDP + 2*GROWTH + 3*EDU + 4*URB +5*POP15 + 6*POP65 +7*TFR + 8*HIV +9*LIFE + 10*GEM

Observations: 105, especially larger countries, worldwideDependent variable:RNI = Rate of Annual Natural Population Increase 2003 (Birth rate minus

Death rate, expressed in %)Explanatory Variables5:Economy: GDP = GDP per capita (PPP US$, 2002)

GROWTH = GDP per capita annual growth rate 1975-2002Education: EDU = Special index from the UNDP for education, 2002Population: URB = Percentage of people living in urban areas, 2002

POP15 = Population younger than 15 (in % of total population, 2002)POP65 = Population older than 65 (in % of total population, 2002)TFR = Total fertility rate (births per woman, 2002)

Health: HIV = HIV/AIDS among adults, Ages 15-49, 2003/2004 (%)

4See Globale Trends 2002. Fakten Analysen Prognosen. Chapter: Weltbevlkerung und Verstdterung5

selected definitions from PRB:

Total Fertility Rate (TFR)The average number of children a woman would have assuming that current age-specific birth rates remain constant throughout her childbearing years (usuallyconsidered to be ages 15 to 49)

Life Expectancy at Birth (LIFE)The average number of years a newborn infant can expect to live under currentmortality levels.

Percent Urban (URB)Percentage of the total population living in areas termed urban by that country. The population living in towns of 2,000 or more or in national and provincialcapitals is classified urban.

Rate of Natural Increase (RNI)The birth rate minus the death rate, implying the annual rate of population growth without regard for migration. Expressed as a percentage.


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LIFE = Life expectancy (2002/2003)Women: GEM = Emancipation rate of women (2002, UNDP)

This empirical model focuses on explaining the natural increase rate for 105 countries worldwide bycross-section analyses with 11 different explanatory variables. 2 variables, these are POP15 and TFR aredirect factors for RNI. All the others can be classified into the indirect factors.

Economic developments are assumed to be captured by the average growth rate of real GDP per capitabetween 1975 2002 and by the real GDP for the year 2002. Higher economic production leads to anincrease in the GDP and to higher economic development standards.

The level of Education of the population is measured by a special education index of the UNDP, whichmeasures a country's relative achievement in both adult literacy and combined primary, secondary andtertiary gross enrolment6, which in fact, delivers very similar results as the alphabet rate.

Population structure flows into this model by the percentage of population younger than 15, which can alsobe seen as an indicator for the amount of natural increase in the last 15 years, by the percentage ofpopulation older than 65, by the urban percentage and by the TFR.

Health conditions can be explained by the life expectancy and the amount of people who suffer from HIV. Women: Furthermore, the UNDP created the Gender Empowerment Measure index to describe the degree

of emancipation of women especially in rich countries (even today it is not calculable for many poorcountries)

All these factors are assumed to be responsible for annual natural increase. But we have to be aware of thefact, that we used 2002 data for these factors, whereas the dependent variable consists of data from period2003 on account of failing to find data for 2002. So we have to assume that the explanatory variables for2002 are not significant different from the ones for 2003. Nearly all of our variables contain values, whichdo not change much within one year (e.g. GDP or literacy rate). As a result of missing exact data for theHIV variable, we have partly estimates. The time period does not correspond exactly to the dependentvariable, and data are changing pretty fast, so we should be careful when we interpret the influence of thisvariable on the natural increase.

1. Our first principle hypothesis is that the existence of a high economic standard leads to a smallernatural increase.Especially in rural areas of poor countries children do offer a variety of material benefits for their parents.In rich countries women represent a larger part of the labour force and have more influence on political,social and economical aspects in rich countries. Therefore they have less time and interest in childcare andgiving birth.

2. Our second principle hypothesis is that better social indicators, like health and education shouldbe indicators for smaller natural increase.In particular for women it is enormously important, to have the opportunity to access education.The number of children that a couple will have is determined by many factors, including health, religion,culture, economic status and the ability to decide on the number of children many of them are related tothe status of women.7 This status of women can be measured by the GEM of the UNDP. Countries withhigh social security will have smaller families, because people are less in the need of getting many childrenfor economic benefit. Life expectancy in many poor countries is currently decreasing on account of a highmortality rate, caused by HIV. These hypotheses correspond to the first one of course, for the reason thathigher living standards generally correlate with higher economic standards.

6http://www.undplao.org/HD%20measurement.htm7http://www.prb.org/Content/NavigationMenu/PRB/Educators/Human_Population/Women/The_Status_of_Women1.htm


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3. Our third principle hypothesis is concerning the population structure. It is clear that countries with a high percentage of young people, tend to have a higher birth rate and as aresult should have a higher natural increase. So POP15 should be pos. correlated with RNI, as opposed toPOP65, which indicates the living standard and should have a negative effect on RNI. The TFR is also animportant criterion, which depends partially on social habits. Women who tend to get married early in life(mainly in rural areas), will on average bear more children. TFR should have a pos. effect on RNI. An

interesting factor is urban population, which adds up to 76 percent in developed countries and only to 40percent in poor ones, although urbanization in poor countries took on a new quality in the last decades. SoURB is assumed to have a neg. effect on RNI.

Education, urbanization and labour force participation [] have a strong correlation with levels offertility.8 But we have to be careful not to make the mistake, confusing correlation with causation.

3.Data Information

Some parts of the data9 used are obtained from UNDP Statistics, a database which offers many social andeconomic statistics and from the UNDP Human Development Report 2004. Another reference is the

PRB 2004 World Population Data Sheet, and the datafinder of the PRB, which are both available on thewebsite of the PRB. For some data, which were missing, MS Encarta Professional Edition 2005 was used.The data set, which is indexed after the GDP, consists of 105 countries, which are selected by availabilityof the data and by the size of the countrys total population.At first view, the ranges of the different values of the explanatory variables are very large, which isapparent from Table 2.

Table 2: General information about the dataLowest Highest Mean

LIT in %10 Burkina Faso: 17,1 Several countries up to: 99 83,85

LIFE in years Japan: 81,6 Zambia: 32,4 67,27

POP15 in % Italy: 14,1 Uganda: 50,1 30

RNI in % Ukraine: -0,8 Niger: 3,5 1,24

HIV in % Botswana: 37,3 Several states: 0,1 2,38TFR in % Niger: 8 Hong Kong: 1 2,92

GDP in $ per capita Luxembourg: 61190 Niger: 520 10953

Pay attention: different population sizes are not considered in the calculated mean

We become aware of the idea, that the varieties of these countries may perhaps make the estimation of asingle model more difficult. So it could be useful to compare rich countries with poor ones and to separatethem in later calculations. Nonetheless we have to keep in mind that this could be an inadequatearrangement too. (Table 1)

Table 3: Comparison: rich poorPoorest 30 Richest 30

LIT in % 63,9 96,9

LIFE in years 52,4 78.1POP15 in % 41,4 18.1

RNI in % 2,21 0,35

HIV in % 5,21 0,35

TFR in % 4,7 1,7

GDP in $ per capita 1541 26951

8See 49UNDP Human Development Report 2004 / UNDP Statistics: GDP, GROWTH, LIT, EDU, URB, POP15, POP65, TFR, LIFE, GEM

PRB 2004 World Population Data Sheet / Datafinder of the PRB:RNI, HIV10Adult literacy rate(>15 years, 2002 UNDP)


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-1

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0 10 20 30 40

HIV

RNI

Austria itself is a typical rich country with a high life expectancy of 78.5 years, a Natural Increase of 0 %,a TFR of 1.3 % and a GDP of 29220 USD. The population under the age of 15 amounts to 16.2 %.The most important outliers are the United Arab Emirates, which have a high GDP of 22420 USD, butonly 77% of the inhabitants are able to read and the natural increase and the TFR are unusually high.Botswana has a RNI of 0.1 %, population growth itself (with migration) amounts to -0.8 %, but has aLIFE value of only 39.7 years and belongs to the poorest developing countries in the world.

As you can see in Table 4 RNI correlates strongly with POP15, POP65 and TFR, but very low withGROWTH and moderately with all other variables.The explanatory variables are also correlated among each other. Relatively high correlations can naturallybe seen between the variables that can be integrated in one umbrella term (cp. Empirical natural growthmodel). For example LIFE and HIV show a high negative correlation (r=-0.75) as well as POP15, POP65and TFR (r= - 0.81, r= 0.87, -0.74). However we have to keep in mind that a high correlation between twoindependent variables doesnt have to be desirable, because it might cause Multicollinearity.

Table 4: Complete Correlation Matrix - General ModelEDU GDP GEM GROWTH HIV LIFE POP15 POP65 RNI TFR URB

EDU 1.000000 0.622135 0.738999 -0.036310 -0.646040 0.868367 -0.785985 0.703723 -0.672268 -0.778563 0.593525GDP 0.622135 1.000000 0.832173 -0.188010 -0.284417 0.644472 -0.667809 0.716462 -0.531158 -0.491397 0.596159

GEM 0.738999 0.832173 1.000000 -0.059106 -0.343677 0.696804 -0.602292 0.643468 -0.505868 -0.500015 0.582351

GROWTH -0.036310 -0.188010 -0.059106 1.000000 0.101357 -0.065051 -0.009974 0.038999 -0.057323 -0.021065 -0.047489

HIV -0.646040 -0.284417 -0.343677 0.101357 1.000000 -0.754571 0.398881 -0.323546 0.330239 0.508010 -0.266503

LIFE 0.868367 0.644472 0.696804 -0.065051 -0.754571 1.000000 -0.631165 0.565291 -0.477688 -0.664783 0.600966

POP15 -0.785985 -0.667809 -0.602292 -0.009974 0.398881 -0.631165 1.000000 -0.813390 0.872977 0.872291 -0.472072

POP65 0.703723 0.716462 0.643468 0.038999 -0.323546 0.565291 -0.813390 1.000000 -0.857838 -0.741279 0.457561

RNI -0.672268 -0.531158 -0.505868 -0.057323 0.330239 -0.477688 0.872977 -0.857838 1.000000 0.914094 -0.367022

TFR -0.778563 -0.491397 -0.500015 -0.021065 0.508010 -0.664783 0.872291 -0.741279 0.914094 1.000000 -0.415457

URB 0.593525 0.596159 0.582351 -0.047489 -0.266503 0.600966 -0.472072 0.457561 -0.367022 -0.415457 1.000000

Scatterplots11: EDU, GDP, GEM, HIV, LIFE, POP15, POP65, TFR, URB

11without Cuba, Maldives, Nigeria

-1

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10 20 30 40 50 60

POP15

RNI

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LIFE

RNI

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GDP

RNI

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-0.8

-0.4

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1.2

1.6

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50 60 70 80 90 100

URB

RNI

4.Empirical Results: Model estimations and Hypotheses testing

4.1 Basic Model

Table 5 shows us the results of Equation 1 for the basic model which takes all the variables for allcountries.

Table 5: Equation 1 Basic Model (all variables)

The very low number of included observations in Equation 1, which is even lower than the excluded ones,is mainly caused by the variable GEM, which is not available for many countries. Besides this, we notice,that primarily direct and population factors are significant in this model. Adjusted R-squared is extremelyhigh, but we have to take into account, that this unrestricted model leads to a lost of grades of freedom andso to a reduction of the efficiency of the estimations. As we saw in Table 3, growth does not correlate withany of the variables. This is represented in the equation. GROWTH seems not to be of significantrelevance for natural increase. Also URB andGDP are clearly not significant.

Dependent Variable: RNIMethod: Least SquaresDate: 07/31/05 Time: 16:24Sample(adjusted): 2 99Included observations: 48Excluded observations: 50 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob.

EDU 0.519146 0.751422 0.690884 0.4940GDP 3.50E-06 7.56E-06 0.463114 0.6460

GEM -0.614249 0.379371 -1.619127 0.1139GROWTH -0.002468 0.013595 -0.181542 0.8569HIV 0.053487 0.039657 1.348760 0.1856LIFE 0.048083 0.012108 3.971039 0.0003

POP15 0.011743 0.010061 1.167177 0.2506POP65 -0.072498 0.012399 -5.847327 0.0000

TFR 0.804613 0.088842 9.056647 0.0000URB -0.001397 0.002112 -0.661241 0.5126

C -4.032997 0.817835 -4.931311 0.0000

R-squared 0.959983 Mean dependent var 0.795833Adjusted R-squared 0.949167 S.D. dependent var 0.892214S.E. of regression 0.201160 Akaike info criterion -0.171386Sum squared resid 1.497213 Schwarz criterion 0.257431Log likelihood 15.11325 F-statistic 88.76010Durbin-Watson stat 2.007713 Prob(F-statistic) 0.000000

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TFR

RNI

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4.2 General Model

After removing redundant variables (see Table 6), these are URB, EDU, GEM, GDP and GROWTH, weuse Eviews for looking at the residuals from the regression in tabular and graphical form (Actual, Fitted,Residual Table), in order to find outliers, which appear not explainable by our model and which could

lead to Heteroskedasticity. After removing these countries (Cuba, Maldives, Nigeria) from our data set weobtain Equation 2.

Table 6: Redundant Variables TestRedundant Variables: URB EDU GDP GEM GROWTH

F-statistic 0.744980 Probability 0.594904Log likelihood ratio 4.604244 Probability 0.466058

RNI = const + 1*POP15 + 2*POP65+ 3*TFR + 4*LIFE + 5*HIV

Table 7: Equation 2 General ModelDependent Variable: RNIMethod: Least Squares

Date: 06/28/05 Time: 22:57Sample: 1 59 62 80 82 105Included observations: 97Excluded observations: 5


POP15 0.037825 0.009490 3.985786 0.0001POP65 -0.081097 0.012195 -6.650099 0.0000

TFR 0.406058 0.052331 7.759470 0.0000LIFE 0.039396 0.005904 6.672763 0.0000HIV -0.018547 0.008037 -2.307725 0.0233C -3.030037 0.574349 -5.275604 0.0000

R-squared 0.938562 Mean dependent var 1.153608Adjusted R-squared 0.935187 S.D. dependent var 1.053843S.E. of regression 0.268292 Akaike info criterion 0.266381Sum squared resid 6.550254 Schwarz criterion 0.425642

Log likelihood -6.919492 F-statistic 278.0351Durbin-Watson stat 2.014705 Prob(F-statistic) 0.000000

Testing the model:

Before we carry out regression analyses we check our data for Homoskedasticity, no autocorrelation andnormal distribution because these are assumptions which provide efficient estimations when testingvariables using the OLS procedure.We use the White Heteroskedasticity Test in order to control for Homoskedasticity, the Jarque-BeraTest in order to check for normal distribution and the Durbin Watson Test, which measures the serialcorrelation in the residuals in order to check for no autocorrelation.

Homoskedasticity

As can be seen in Table 8 our data unfortunately feature Heteroskedasticity, which is shown by theprobability value of 0.000002. This value is smaller than 0.05, which marks the critical value forHomoskedasticity. Heteroskedasticity means the variance of the residuals in the linear regression model isdifferent across the sample. Referring to our sample it could mean that either the poor or the rich countriesdiffer more strongly in the Rate of Annual Natural Population Increase.As we use the OLS procedure to estimate the parameters, the estimators and forecasts are still unbiasedand consistent but on the other hand inefficient and no longer BLUE. Tests of hypotheses andinterpretation of the results must be handled with care.


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0

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-0.75 -0.50 -0.25 0.00 0.25 0.50 0.75

Series: ResidualsSample 1 105Observations 97

Mean -2.31E-15

Median -0.009486

Maximum 0.715757

Minimum -0.864046Std. Dev. 0.261212

Skewness -0.201898Kurtosis 3.999708

Jarque-Bera 4.698306

Probabil ity 0.095450

Table 8White Heteroskedasticity Test: with cross terms

F-statistic 7.122745 Probability 0.000000Obs*R-squared 63.25390 Probability 0.000002

Autocorrelation and normal distribution

Other assumptions of a regression analyses - no autocorrelation and normal distribution are given as youcan see in Table 7and Table 9. The Test for normal distribution shows a probability value of 0.09, whichis higher than the critical value of 0.05 and means that normal distribution is given. The test forautocorrelation shows a value of 2.015, which is very close to value 2 and means that no autocorrelationexists.

Table 9: Test for normality of the error terms with Jarque-Bera

Interpretation of the results:

Adjusted R^2 for Equation 2 amounts to 0.93, which means that 93% of the variance of the independentvariable RNI can be explained by this model. Compared to the explanatory power of Equation 1, valuesare a slightly worse. Of course we have to realize that these values can hardly be compared with eachother, because in Equation 2 more explanatory variables and much more observations are used than in

Equation 1. Furthermore R^2 and even adjusted R^2 always increase when adding explanatory variables.Though for such cases

12it could be better to use the information criterions Akaike and Schwarz, which

furnish slightly better values for the new model. What is more the F-Statistic adds up to 278 and indicatesthat our model as a whole is very significant. The remaining, significant variables are capturing populationcharacteristics, which could be easily explained by the high correlations with RNI, and also Healthcharacteristics. From the five significant variables, TFR, LIFE and POP65 have a strong influence onRNI, whereas POP15 and HIV seem to be less important. POP15, TFR and LIFE correlate positively andPOP65 and HIV negatively with RNI.More exactly: Provided that values of all the other variables keep the same, the following holds: WhenTFR increases by one percent, RNI increases by 0.4 percent, when LIFE increases by one year, RNIincreases by 0.04 percent and so on. The results concerning TFR, POP15, POP65 and HIV are in harmony

with our correlation matrix and verify our assumptions; only the trend of the correlation of the variableLIFE with RNI is surprising. We expected that RNI is less in countries where life expectancy is higherand the correlation Matrix (Table 3) confirms this assumption but in this model the trend of the correlationof the variable LIFE with RNI is in the opposite direction. This may be caused by East-Europeancountries, which disturb our correlation.

Important insignificant variables: EDU, GDP, GROWTH, URB, GEM

Do we have a closer look at the variables, which are not significant. As we have seen before, GROWTH

12With more explanatory variables, but not with more observations. In this case also the values of Akaike and Schwarz are not mmeaningful fir comparison.


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corresponds very little with RNI, and t-statistics verify its unimportance for our model. Surprisingly, EDUand GDP are also not significant, although for both variables in the scatter plots as well as in the corr.matrix (-0.67, -0.53) a negative correlation is discernible. It seems that either these correlations are notplain enough (outlier) or they are not linear and so not valid for our model. The corr. matrix shows forURB and RNI correlation with a value of only 0.36, and the scatter plot proves that its influence seems tobe of no importance. GEM is not available for many countries and probably for the same reasons as forEDU and GDP its not significant in our model.

Comparison Equation 1 with Equation 2:

The Equation 2 delivers better results for our model, because all the variables are clearly significant andfurthermore it has a higher value of degrees of freedom, which indicates a higher flexibility for the model.Artificial high explanatory power as in Equation 1, on account of too many expl. var. in the samplecould lead to a poor adjustment outside our sample (for forecasts). The variance of model 2 will be higherthan that without the irrelevant variables and hence the coefficients will be inefficient.

4.3 Model for rich countries

Now we desire to check, if separate models for the 30 richest / 35 poorest countries of our data give us abetter insight into some correlations and causalities. It is intelligible, that (alleged) more homogenouscountries can be compared with each other with less external effects, which distortion our results.

After sorting out the non significant explanatory variables for the 30 richest countries, which are HIV,GDP, GROWTH, LIT, EDU and POP15, the model in Table 10 remains.

RNI = const + 1*LIFE + 2*GEM + 3*POP65+ 4*URB + 5*TFR

Table 10: Equation 3 - Rich Countries (top 30)13Dependent Variable: RNIMethod: Least SquaresDate: 06/16/05 Time: 00:19Sample(adjusted): 2 30Included observations: 25Excluded observations: 4 after adjusting endpoints


LIFE 0.088754 0.010191 8.709370 0.0000GEM -0.661161 0.166811 -3.963526 0.0008

POP65 -0.078053 0.007989 -9.769816 0.0000URB -0.003980 0.001536 -2.590957 0.0179TFR 0.729414 0.061881 11.78737 0.0000

C -5.915063 0.717432 -8.244773 0.0000

R-squared 0.971274 Mean dependent var 0.268000Adjusted R-squared 0.963714 S.D. dependent var 0.423743

S.E. of regression 0.080718 Akaike info criterion -1.990150Sum squared resid 0.123792 Schwarz criterion -1.697620Log likelihood 30.87688 F-statistic 128.4840Durbin-Watson stat 1.808970 Prob(F-statistic) 0.000000

Testing the model:

Both Homoskedasticity (p=0.46) and normal distribution (p=0.19, Table 11) are given and the Durbin-Watson Test shows that there is no autocorrelation. Therefore there is no problem in using the linearregression analyses.

13Listed according to the size of the GDP


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The explanatory power ofEquation 3 is a bit higher than the explanatory power of the general model.TFR, LIFE, POP65 have a big influence on RNI, whereas the influence of GEM and URB is muchsmaller. Again, with the exception of the trend of the correlation of the variable LIFE with RNI, allvariables are correlated with RNI as assumed.In contrast to the general model, the variables GEM and URB get significant in this model, whereasPOP15 and HIV are no longer significant. That POP15 and HIV are no longer significant can possibly beexplained by the idea, that the rich countries, which are analysed in this model, do not differ muchamong each other in these variables. Or this may be because they are closely associated with POP65 abbr.LIFE and are captured by these factors. GEM and URB are variables, in which mainly the rich countriesdiffer, so they are probably more meaningful here.

4.4 Model for poor countries

Now we analyse the 35 poor countries in our sample separately. After removing the non significantexplanatory variables, which are TFR, URB, POP65, EDU, GROWTH and GDP, the model in Table 12remains.

RNI = const + 1*HIV + 2*LIFE+ 3*POP15

Table 12: Equation 4 - Poor Countries (last 35)Dependent Variable: RNIMethod: Least SquaresDate: 06/16/05 Time: 00:22Sample: 71 105Included observations: 34Excluded observations: 1


HIV -0.042547 0.010648 -3.995726 0.0004

LIFE 0.019116 0.010338 1.849079 0.0743POP15 0.118213 0.015861 7.453131 0.0000

C -3.483876 1.173389 -2.969072 0.0058

R-squared 0.810285 Mean dependent var 2.088235Adjusted R-squared 0.791314 S.D. dependent var 0.742136S.E. of regression 0.339024 Akaike info criterion 0.784641Sum squared resid 3.448123 Schwarz criterion 0.964213Log likelihood -9.338892 F-statistic 42.71069Durbin-Watson stat 1.919063 Prob(F-statistic) 0.000000

Testing the model:

We check for normal distribution (p=0.19) and Homoskedasticity (p=0.96), which are both given. Andafter ruling out autocorrelation (Durbin Watson =1.91), we carry out linear regression analyses.


In contrast to the general model and the model for rich countries, there is more unexplained variance left.Akaike and Schwarz are also inferior to the other models. This may indicate that other importantvariables, which are not included in theEmpirical natural growth model (ct. S. 2) are important in order topredict RNI for these countries.In this model the variables HIV and POP15 are significant at an alpha level of 5% and the variable LIFE issignificant at an alpha level of 10 %. The coefficients of LIFE, POP15 and HIV are in agreement with ourassumptions.In contrast to the general model, POP65 and TFR are not significant anymore, which is surprising,


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because in the scatter plot14

a tight correlation with RNI can be noticed. Also in this case it might be thatPOP65 and also TFR are captured by POP15.

Comparison Equation 3 and 4

When we take a closer look at the 2 smaller models for poor and rich countries, we see that differentvariables show a significant contribution in explaining the population growth. We also recognize thatthere is a large gap between the explanatory power of the 2 smaller models. (rich: 0.96, poor: 0.79). Thiscan be due to the fact

15, that differences between the group of developing countries are bigger than

between the whole developed and developing regions. Even within a country, like in India, fertility rateand population growth rates are more varying within the country than between the totality of developedand developing countries. Developed countries have similar governments (democracy, social marketeconomy), similar culture and economic data are quite the same within one country, so population growthis much easier to rate here. This would also explain the small number of variables, which are significantfor the poor countries in our model. As a consequence it is thoroughly meaningful, calculating thesemodels, in order to confirm these statements and to get more insight into the differences between rich andpoor countries, concerning natural increase.

5.Summary and Conclusions

In our models, explaining Natural Population Increase, we could not find an influence of the economicstandard on RNI, which is surprising because in literature often a positive correlation between economicstandard or wealth and RNI is assumed.

16So our first hypothesis that the existence of a high economic

standard leads to a smaller natural increase cannot be fully affirmed by our outcomes. Nevertheless thecorrelation matrix and its scatter plot indicate a positive correlation, which could imply that a high GDPleads to a smaller RNI, but for other reasons GDP is not important in our model.We also couldnt find an influence of Education on RNI in any of the models, and of GEM in the general

model. So our assumptions that high education leads to a positive effect in our model cannot be affirmed,either. However we see in the scatter plots, that a high education and a high GEM often correspond with asmaller natural increase. The same as for GDP applies to GEM and EDU.Regarding the health variables we could find an influence of HIV and LIFE. As expected, HIVcorrelates negatively with RNI in the model for poor countries and in the general model. The influenceof life expectancy on RNI is in the opposite direction as expected. As life expectancy is often low incountries with high population growth, and high in countries with little population growth, we thought thathigh life expectancy would indicate little population growth.17 This can be due largely to some countriesin East-Europe, which distort our correlation. Our hypothesis regarding health can be affirmed partially,namely the assumption that HIV influences the population increase in our model negatively.Our hypothesis concerning population structure could be confirmed for all variables. A high amount of

young people and a high fertility rate lead to a high population increase, whereas a high amount of oldpeople and urbanisation is correlated with a population decrease.

Our study shows some limitations, which may diminish its explanatory power and could lead to poorresults for future data. It should be taken into consideration, that

14only with the data from the 35 poorest countries15see Globale Trends 2002. Fakten Analysen Prognosen. Chapter: Weltbevlkerung und Verstdterung16http://www.bmz.de/de/themen/armut/hintergrund/index.html#Die%20Ursachen%20von%20Armut17

http://www.weltpolitik.net/Sachgebiete/Weltwirtschaft%20und%Globalisierung/Grundlagen/Grundlagen/Akteure%20der%20Weltwirtschaft.html


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In specifying the model, we implicitly assumed that X causes Y. Although R^2 measures the goodness offit, it cannot be used to identify causality. In other words, the fact that X and Y are highly correlated doesnot indicate whether changes in X cause changes in Y or vice versa. The situation can also go both ways, asituation known as feedback. For instance: Inadequacies in social services lead to rapid populationgrowth. However in most poor countries we see that overpopulation can reduce food production gainsresulting from modernizing.

we havent included all countries of the world in our study we possibly should have used some other explanatory variables, for example the use of contraception18, we

could have used an econometrical model as basis for our estimations (macro/microeconomic19). it perhaps would have been better to use mean values of the explanatory variables (for instance 1975-2003)

instead of numbers from one year to obtain a more robust model. we should have weighted variables after population size to avoid distortions caused by smaller countries. it would have been meaningful to line up the models to 4 groups, as explained in Table 1 forgetting

superior results. Because the biggest problem was certainly the heterogeneity of and within the countries.By the proposed enhanced differentiation robustness and explanatory power of the model should raise.

6.References

Data and Statistics:

http://hdr.undp.org/statistics/ or http://cfapp2.undp.org/hdr/statistics/data/rc_select.cfm Zugriff: 04.06.2005UNDP - Human Development Report 2004. Statistics.

http://www.prb.org/ Zugriff: 04.06.2005Population Reference Bureau.

http://www.prb.org/datafind/datafinder5.htm Zugriff: 04.06.2005Datafinder of the PRB.

General Information:

Stiftung Entwicklung und Frieden: Globale Trends 2002. Fakten Analysen Prognosen. Hg. von Hauchler, Messner,Nuscheler. - Frankfurt a. M.: Fischer Taschenbuch Verlag 2001.

http://www.prb.org/Content/NavigationMenu/PRB/Educators/Human_Population/Human_Population__Fundamentals_of_Growth_and_Change.htm Erstellungsdatum: November 2000, Zugriff: 04.06.2005

http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/ Erstellungsdatum: 02.07.2005, Zugriff: 21.07.2005Kimball's Biology Pages: Checks on Population Growth, Human Population Growth.P > Populations > how regulated / human

http://www.ecopop.ch/A2BULLETINS/bulletin41-2004.htm Zugriff: 28.06.2005Eco Pop Union for environment and population. Bulletin ECOPOP. Nr. 41 February 2004

Macro- / Microeconomic Theories:

http://www.berlin-institut.org/pages/buehne/buehne_bevwiss_prskawetz_oekonomie.htmlErstellungsdatum: 4.06.2003, Zugriff: 21.07.2005Alexia Prskawetz: Bevlkerungskonomie. Max Planck Institute for Demographic Research. Rostock

http://www.payer.de/entwicklung/entw31.htm Erstellungsdatum: 16.02.2005, Zugriff: 21.07.2005Bettina Eckl und David Prm : Einfhrung in Entwicklungslnderstudien. published by Margarete Payer (Juni 00).especially: 1.3 konomisch-demographische Theorien

18 neg. effect on RNI shown inhttp://www.ecopop.ch/A2BULLETINS/bulletin41-2004.htm19http://www.berlin-institut.org/pages/buehne/buehne_bevwiss_prskawetz_oekonomie.html and http://www.payer.de/entwicklung/entw31.htm

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Annual Natural Population Increase - An Empirical Study

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