Scientific Modelling and Research

Volume 2, Number 1 (2017) pp 19-36 doi 10.20448/808. | Research Articles


Population Growth and Life Expectancy: Predicting the Relationship

Sanni Eneji Ademoh 1
1 Maths and Statistics Department Rufus Giwa Polytechnic, Owo, Ondo State, Nigeria


The debate on the relationship between life expectancy and population growth rate has been undergoing and varies across countries. This study provided a non parametric inference of the relationship between life expectancy and population growth rate on historical data for about 194 countries of the world reported in 2013. The first theory stated that population growth rate does not stimulate life expectancy. The second theory viewed population growth rate as a factor that adversely affects the life expectancy. The study employed the Statistical Package for Social Sciences (SPSS 19) to establish and identify the countries of the world that fall below the world 70.01 years standard. Hence, summary, conclusion and recommendations were given to the government and the entire public based on the findings towards for further study.

Keywords: Life expectancy, Population, Growth, Rate, Relationship, SPSS19.

DOI: 10.20448/808.

Citation | Sanni Eneji Ademoh (2017). Population Growth and Life Expectancy: Predicting the Relationship. International Journal of Scientific Research in Statistics, 2(1): 19-36.

Copyright: This work is licensed under a Creative Commons Attribution 3.0 License

Funding : This study received no specific financial support.

Competing Interests: The author declares that there are no conflicts of interests regarding the publication of this paper.

History : Received: 24 March 2017/ Revised: 28 April 2017/ Accepted: 3 May 2017/Published: 5 June 2017

Publisher: Online Science Publishing


Life Expectancy is a statistical measure of how long individuals or organisms may live, based on the year of their birth, their current age and other demographic factors including gender. At a given age, life expectancy is the average number of year that is likely to be lived by group of individuals (of age x) exposed to the same mortality conditions until they die. The most commonly used measure of life expectancy is life expectancy at age zero, that is, Live Expectancy at Birth (LEB), which can be defined in two ways: Cohort Life Expectancy at Birth and Period Life Expectancy at Birth. Cohort LEB is the mean length of life of an actual birth cohort (all individual born in a given year) and can be computed only for cohorts that were born many decades ago, so that all their members died. However, Period LEB is the mean length of life of hypothetical cohort assumed to be exposed since birth until death of all their members to the mortality rate observed at a given year.

Bhargava (2003) uses a parametric panel data specification and found that the dynamics of demography indicators such as lagged life expectancy variable is a significant predictor of economic growth. Charkraborty and Idrani (2010) develops a theoretical model and checked its empirical consistency using a parametric cross-country regression. The author found that life expectancy has a strong and positive effect on capital accumulation.

The rate of growth of the African population since the middle of the century, compared to the rest of World is both alarming and distressing; especially when taken in the context of the deteriorating quality of life expectancy of ordinary people. It was observed, for instance that in 1953, Nigeria’s population as one of the African countries was put at 31 million, and ten years later, the officially accepted estimated figure was 56 million. In 1985, the estimated figure was 98 million. Nigerian population drew from 91 million in 1991 to 160 million in 2006 and it is estimated to be 173 million in 2012. The increase is 90 percent for the period 1991 to 2012. Presently, the population estimated figure is also put around 179 million. It means that within 21 years, Nigeria population increased by 79% (CIA World Fact Books, 2011).

Although, several factors have been identified on the propelling variables, the needed condition for such excessive population growth must be looked for in several perspectives. While, some school of thoughts have considered the relationship between population growth and economic development among other social environment and political indicators, there are no known literature that has expressly anchored the relationship between life expectancy and population growth rate, which is the major issue of investigation in this study.

1.1. Statement of the Problem

There is continued divergence of opinions regarding the consequences of life expectancy and population growth. The debate between positive impact and negative impact of population growth rate on the life expectancy is still ongoing. On the positive side, population growth induces technological advancements and innovations. This is because population growth encourages competition in business activities and, as the country’s population grows, the size of its potential market expands as well. The expansion of the market, in its turns, encourages entrepreneurs to set up new businesses (Simon, 1992).

A large population growth on the other side is not only associated with food problem but also imposes constraints on the development of savings, foreign exchange and human resources. The increase in demand for food leads to a decrease in natural resources, which are needed for a nation to survive. Other negative effects of population growth include poverty caused by low income per capita, famine, and disease since rapid population growth complicates the task of providing and maintaining the infrastructure, education and health care needed in modern economies, which reduce the life expectancy (Barro, 1991); (Mankiw et al., 1992). Thus, this study intends to make a significant contribution to the study of relationship between life expectancy and population growth rate on a general note.

1.2. Aim

The aim of this study is to predict the relationship between life expectancy and population growth rate. Hence, the specific objectives are:

  1. to determine the relationship between life expectancy and population growth rate;
  2. to predict the relationship between life expectancy and population growth rate across countries based on certain classification;
  3. to postulate a law relating the life expectancy and population growth rate; and
  4. to identify policy implications from the study.

1.3. Literature Review

1.3.1. Introduction

The literature review focuses on both general and empirical studies carried out to examine the relationship between life expectancy and population growth rate.


Malthus (1998) believes that the world's population tends to increase at a faster rate than its food supply whereas, population grows at a geometric rate, and production capacity only grows arithmetically. Therefore, in the absence of consistent checks on life expectancy and population growth, Malthus made the prediction that in a short period of time, scarce resources will have to be shared among an increasing number of individuals. However, such checks that ease the pressure of population explosion do exist, and Malthus distinguished between two categories: the preventive check and the positive one. The preventive check consists of voluntary limitations of life expectancy and population growth. Individuals before getting married and building a family, make rational decisions based on the income they expect to earn and the quality of life they anticipate to maintain in the future for themselves and their families. The positive check to population is a direct consequence of the lack of a preventive check. When society does not limit population growth voluntarily; diseases, famines and wars reduce population size and establish the necessary balance with resources. In traditional African society, the wealth of an individual was accessed by the share size of his household. The household may include several wives, numerous children, many relatives as well as a significant number of labourers. Moreover, these activities of man therefore tend to reduce life expectancy.

However, this household together contributed his pool of labour for farming and other productive purposes. Another index of a man’s wealth and status is the size of his herds of cattle, sheep and goats. Essentially then, the household is, in the past, the pivotal basis for assessing a man’s social relevance and importance in the society.

Simplicity of this setting was further accentuated because the traditional African society either little or no financial cost of the now basic concerns of social existence such as education, housing, food, transport, health and similar infrastructural necessities which form the nexus of modern developmental activities. However, the population density was low, the life style of people was simple and the individual and society were equilibrium with each other.

Overtime, and especially with colonialism, the situation changed and Nigeria and indeed most African countries entered a new period where the emphasis of social existence became anchored on the modernization process and a modern science. This led to a sharp reduction infant, and a significant rise in life expectancy.

Traditional social arrangements, however, continued to favour polygamy on the basis of family formation and to indicate both tacit and explicit preference for large family sizes. In fact, a large family was seen as a form of social security and a safety value against the deleterious effect of high infant and material mortality and short life expectancy.

In the same manner, the barrenness of women was more often than not linked to heinous or diabolic influence within the household or society. This was in essence a high degree of social obsession with issues of fertility and the survival of the lineage.

There was in addition, a preference for and pre-occupation to have, male children. The number of children, especially male children that a woman had, in fact, came to determine, to some extent, her standing and importance within the extended family.

In recent times, the situation has been further aggravated by certain religious and social beliefs that frown at or discourage modern contraceptives and abortion. This added to the effects of universality of conjugal relations, high illiteracy, social inequality suffered by women and the subsistence mode of production which defined and allotted social, economic and political roles to different individuals in the society.

However, a large population cannot be said to be entirely bad or undesirable. There is the widely persuasive preposition of the pro-population school that high population density is pre-requisite for technological advancement and economic development (World Health Organisation, 2004). Besides, in conventional economic terms, it has been argued that a large population meant a bigger market, a greater volume of production, higher productivity, smaller transport distance and a greater diversity of ideas for societal growth and development. The conflict between the pro-population and the anti-population schools highlighted the complication of the conflicts arising from the difficulties of establishing any correlation between population growth and economic development in African countries especially on the basis of such parameter as per capital national income and other economic indicators. The first consequence has to do with the deteriorating effects on the general development of the state. The growth in population tends to encourage migration to urban centres. Given the low level of our urbanization process, such massive migrations, as are now being witnessed in the continent, put a severe strain on the limited urban infrastructure and facilities through over-utilization, thereby giving rise to great inadequacy and frequent breakdowns.

These are also compounded by over-crowding, environmental population and degradation and increased anti-social behaviours, all of which lead to the deterioration of the standard of living and quality of life that frequently defy official solution, which however reduce life expectancy in the part of the community.

The proliferation of informal economic activities to help migrants find some gainful employment aggravates the level of environmental population. The nations or continent capacity to cope effectively with these problems become important. African population is comparatively young and non-working. Those within 0 – 15 years age bracket constitute about half or more precisely 47% of these population, while those aged sixty four years above account for about 02%. The consequence is that every productive Africa is unwillingly saddled with the responsibility of feeding, housing, clothing and educating a child. This is in comparison to the situation in some developed countries where on the average two or three economically productive person provide for only one non – productive citizen. The irony of the situation is deemed obvious given the low level of incomes and miserably low level of investment in developing countries. However, this can also reduce life expectancy. It is also observed that in Nigeria as is the case with most developing countries, the practice of having large families was more prevalent among the poor than among the rich. This practice certainty constitutes a strong strain on resource and poses a real threat to the security which the extended family system offers.

Rapid population growth are multifarious and multi dimensional. The implication for two productive, for example, Nigeria would have to double the existing two supplies and significantly explain is infrastructure, utilities and service within the next twenty years just to maintain the present per capital standard and quality of life because of the increased demand generated by the burgeoning population. For instance, the United Nations Fund for Population Activities (UNFPA) population card, Nigeria’s population today is projected to increase by about 11 persons per minute. This means an additional 660 hungry mouths to be fed every hour. Given the present estimated growth rate of 3 – 3.4% a year, the population of Nigeria is expected to double by the year 2020 to about 250 million. This is in spite of the unacceptable high infant mortality rate of 144 per thousand per year, a high maternal mortality rate of about 20 per thousand and a life expectancy of about 50 years.

In developed countries in Europe and Asia, the life expectancy across those nations is higher than African continent because of some likely factor like health diet, clean water supply, low rate of violence, less poverty, high medical care, good exercise, careful planning, among others contributed to their lengthy life span. Countries in Asia and Europe hold many of the top rank in the list of the world 15 healthiest countries with an average life span of between 80-84 years. Australia (81.9), Hong Kong (82.12), Andorra (82.5), Singapore (83.75), San Marino (83.07), Japan (83.91), Italy (81.86); (WFB, 2011). Porter (1996) employed a Solow-Swan economic growth model with exogenous saving rate to determine the relationship between population growth and economic growth. The model assumed that both the saving rate and the consumption rate are given. Assuming, a household owns the input and manages the technology. The production technology is assumed to take the form

Y = f (K, L),                                                                         (1)

Where Y is total output,
K is total physical capital,
And L is the size of the labour input

The production function exhibits positive and diminishing marginal products with respect to each input and also exhibits constant returns to scale. The economy is assumed to be a one-sector economy, where output can be either consumed or invested and capital depreciates at a constant positive rate (δ). The growth rate of population is exogenous. The model further assumes that this growth rate is a constant (n) and that labour supply per person is given. Normalizing the population size at time zero and the work intensity to one yield the following is the labour input

L = en                                                                                                             (2)

The net increase in per capita capital is:

k= sf (k) − (n + δ) k                                                                             (3)

The first term on the right-hand side (RHS) is saving per capita out of output per capita and the second term is the effective depreciation per capita. Defining a steady state as a situation in which the quantities, such as capital, population, and output, grow at constant rates. In the Solow-Swan model, a steady state exists if the net increase in per capita capital is equal to zero. Denoting steady state values with an asterisk the steady state values are given by:

sf (k*) = (n + δ)k*, y* = f (k*) and c* = (1 − s)f(k*).                                                   (4)

Since the per capita values are constant in steady state, the levels of total output, total consumption, and total capital must grow at the same rate, which is the same as that of population growth (n). An increase in the rate of population growth in steady state does not affect the growth rate of the per capita variables, since these rates are equal to zero in steady state. However, an increase in fertility does lead to a decrease in the level of capital per capita and therefore to a decrease in output and consumption per capita. This is the capital dilution effect. An increase in the population growth rate leads to a decline in the growth rate of the per capita variables. For model with exogenous saving rates, higher population growth leads to lower standard of living per capita measured either as consumption or in growth of consumption.

Becker and Hoover (1998) develops altruistic models of intergenerational transfers where the behaviour of individuals is guided by a utility function that is increasing in own consumption and the utility achieved by one’s offspring. The utility of the offspring depends, in turn, on their own consumption and the utility of their offspring. Through this inter-linking chain, the current generation consumes and transfers resources to its children influenced by its concern not only for its own children but for all future generations. An important implication of this model is that familial transfers will neutralize fiscal policy. When a government exercises expansionary fiscal policy, it stimulates the economy by increasing current spending financed by issuing debt. From the perspective of intergenerational transfers, the policy is an effort to stimulate spending by transferring resources to current generations from future generations. According to this model however, the public policy is undone by altruistic households. They compensate future generations by increasing their saving and accumulating wealth, exactly offsetting the increase in public debt. This model implies that public intergenerational transfers and private intergenerational transfers are perfect substitutes. A change in public transfers is matched dollar for dollar by a compensating change in private transfers.


3.1. Introduction

This describes theoretical model, empirical model and the research design. The research design reveals the type of data and method of data collection.

3.2. Theoretical Framework

In this work, the following were postulated.

  1. That x and y are directly proportional, where y is the life expectancy and x is the population growth rate,   y α x
  2. That, they are related by the function, y = αxβ. ε                                       (5)

Where y = ln y, β is the parameter and ε is the random error.

This gave rise to the non-linear model which can be made intrinsically linear using the log-log transformation. Following the log-log transformation, there was a form regression model which can be estimated using the ordinary least square (OLS). 

3.3. Regression Analysis

3.3.1. Introduction

3.32. Error and Hypothesis Testing

For model in (7) above, given the parameter in (12) and (13), we have the sum squared error

3.4. Correlation Analysis

3.5. Data Type and Source

This study makes use of published data of the United Nations, Department of Economic and Social Affairs, Population Division in 2013.


4.1. Introduction

The analysis of this work was done by the use of Statistical Package for Social Sciences (SPSS 19). Firstly, Global Life Expectancy at Birth of 70.01 years was considered in order to identify countries that fall below the world standard of 70.01. Out of 194 countries of the world considered, only 74 countries (about 38.1%) met the world standard of 70.01. African countries were the country that mostly fell below the world standard.Similarly, the study classified the population growth rate in terms of log (Growth) and with log growth < 1 and log growth ≥ 1 were segregated. Moreover, the study showed that about 123 countries (63.4%) had growth rate.

Table-1. The population growth rate and life expectancy of countries across seven continent, 2013.
Country Population growth life expectancy Log of Log of Life Class Global    
mid-2013 rate Growth Rate Expectancy Standard
Afghanistan 30,551,674 2.39 60.75 0.3784 1.7835 FALSE TRUE FALSE TRUE
Albania 3,173,271 0.3 77.29 -0.5229 1.8881 TRUE FALSE TRUE FALSE
Algeria 39,208,194 1.84 70.93 0.2648 1.8508 FALSE FALSE FALSE FALSE
Angola 21,471,618 3.09 51.68 0.49 1.7133 FALSE TRUE FALSE TRUE
Antigua and Barbuda 89,985 1.03 75.87 0.0128 1.8801 FALSE FALSE FALSE FALSE
Argentina 41,446,246 0.86 76.21 -0.0655 1.882 TRUE FALSE TRUE FALSE
Armenia 2,976,566 0.18 74.47 -0.7447 1.872 TRUE FALSE TRUE FALSE
Aruba 102,911 0.45 75.39 -0.3468 1.8773 TRUE FALSE TRUE FALSE
Australia 23,342,553 1.31 82.4 0.1173 1.9159 FALSE FALSE FALSE FALSE
Austria 8,495,145 0.37 81.05 -0.4318 1.9088 TRUE FALSE TRUE FALSE
Azerbaijan 9,413,420 1.11 70.64 0.0453 1.8491 FALSE FALSE FALSE FALSE
Bahamas 377,374 1.45 75.15 0.1614 1.8759 FALSE FALSE FALSE FALSE
Bahrain 1,332,171 1.66 76.53 0.2201 1.8838 FALSE FALSE FALSE FALSE
Bangladesh 156,594,962 1.19 70.46 0.0755 1.8479 FALSE FALSE FALSE FALSE
Barbados 284,644 0.5 75.29 -0.301 1.8767 TRUE FALSE TRUE FALSE
Belgium 11,104,476 0.44 80.45 -0.3565 1.9055 TRUE FALSE TRUE FALSE
Belize 331,900 2.38 73.78 0.3766 1.8679 FALSE FALSE FALSE FALSE
Benin 10,323,474 2.69 59.2 0.4298 1.7723 FALSE TRUE FALSE TRUE
Bhutan 753,947 1.6 68.04 0.2041 1.8328 FALSE TRUE FALSE TRUE
Bolivia (Plurinational State of) 10,671,200 1.64 67.11 0.2148 1.8268 FALSE TRUE FALSE TRUE
Botswana 2,021,144 0.87 47.41 -0.0605 1.6759 TRUE TRUE TRUE TRUE
Brazil 200,361,925 0.85 73.8 -0.0706 1.8681 TRUE FALSE TRUE FALSE
Brunei Darussalam 417,784 1.35 78.45 0.1303 1.8946 FALSE FALSE FALSE FALSE
Burkina Faso 16,934,839 2.84 56.14 0.4533 1.7493 FALSE TRUE FALSE TRUE
Burundi 10,162,532 3.16 53.9 0.4997 1.7316 FALSE TRUE FALSE TRUE
Cambodia 15,135,169 1.75 71.63 0.243 1.8551 FALSE FALSE FALSE FALSE
Cameroon 22,253,959 2.52 54.88 0.4014 1.7394 FALSE TRUE FALSE TRUE
Canada 35,181,704 1 81.41 0 1.9107 FALSE FALSE FALSE FALSE
Cape Verde 498,897 0.83 74.92 -0.0809 1.8746 TRUE FALSE TRUE FALSE
Central African Republic 4,616,417 1.98 49.93 0.2967 1.6984 FALSE TRUE FALSE TRUE
Chad 12,825,314 2.98 50.98 0.4742 1.7074 FALSE TRUE FALSE TRUE
Channel Islands 162,018 0.5 80.23 -0.301 1.9043 TRUE FALSE TRUE FALSE
Chile 17,619,708 0.88 79.85 -0.0555 1.9023 TRUE FALSE TRUE FALSE
China 1,385,566,537 0.61 75.25 -0.2147 1.8765 TRUE FALSE TRUE FALSE
China, Hong Kong SAR 7,203,836 0.74 83.28 -0.1308 1.9205 TRUE FALSE TRUE FALSE
China, Macao SAR 566,375 1.78 80.29 0.2504 1.9047 FALSE FALSE FALSE FALSE
Colombia 48,321,405 1.29 73.93 0.1106 1.8688 FALSE FALSE FALSE FALSE
Comoros 734,917 2.4 60.77 0.3802 1.7837 FALSE TRUE FALSE TRUE
Congo 4,447,632 2.55 58.63 0.4065 1.7681 FALSE TRUE FALSE TRUE
Congo, Democratic Republic of the 67,513,677 2.72 49.84 0.4346 1.6976 FALSE TRUE FALSE TRUE
Costa Rica 4,872,166 1.37 79.83 0.1367 1.9022 FALSE FALSE FALSE FALSE
Curaçao 158,760 2.17 77.04 0.3365 1.8867 FALSE FALSE FALSE FALSE
Cyprus 1,141,166 1.08 79.76 0.0334 1.9018 FALSE FALSE FALSE FALSE
Czech Republic 10,702,197 0.42 77.59 -0.3768 1.8898 TRUE FALSE TRUE FALSE
Côte d'Ivoire 20,316,086 2.31 50.51 0.3636 1.7034 FALSE TRUE FALSE TRUE
Denmark 5,619,096 0.4 79.29 -0.3979 1.8992 TRUE FALSE TRUE FALSE
Djibouti 872,932 1.52 61.62 0.1818 1.7897 FALSE TRUE FALSE TRUE
Dominican Republic 10,403,761 1.23 73.29 0.0899 1.865 FALSE FALSE FALSE FALSE
Ecuador 15,737,878 1.57 76.36 0.1959 1.8829 FALSE FALSE FALSE FALSE
Egypt 82,056,378 1.63 71.06 0.2122 1.8516 FALSE FALSE FALSE FALSE
El Salvador 6,340,454 0.66 72.49 -0.1805 1.8603 TRUE FALSE TRUE FALSE
Equatorial Guinea 757,014 2.77 52.88 0.4425 1.7233 FALSE TRUE FALSE TRUE
Eritrea 6,333,135 3.2 62.59 0.5051 1.7965 FALSE TRUE FALSE TRUE
Ethiopia 94,100,756 2.55 63.32 0.4065 1.8015 FALSE TRUE FALSE TRUE
Fiji 881,065 0.73 69.72 -0.1367 1.8434 TRUE TRUE TRUE TRUE
Finland 5,426,323 0.34 80.45 -0.4685 1.9055 TRUE FALSE TRUE FALSE
France 64,291,280 0.55 81.71 -0.2596 1.9123 TRUE FALSE TRUE FALSE
French Guiana 249,227 2.48 77.02 0.3945 1.8866 FALSE FALSE FALSE FALSE
French Polynesia 276,831 1.07 76.12 0.0294 1.8815 FALSE FALSE FALSE FALSE
Gabon 1,671,711 2.36 63.31 0.3729 1.8015 FALSE TRUE FALSE TRUE
Gambia 1,849,285 3.18 58.7 0.5024 1.7686 FALSE TRUE FALSE TRUE
Ghana 25,904,598 2.13 60.99 0.3284 1.7853 FALSE TRUE FALSE TRUE
Greece 11,127,990 0.03 80.69 -1.5229 1.9068 TRUE FALSE TRUE FALSE
Grenada 105,897 0.38 72.69 -0.4202 1.8615 TRUE FALSE TRUE FALSE
Guadeloupe 465,800 0.5 80.84 -0.301 1.9076 TRUE FALSE TRUE FALSE
Guam 165,124 1.27 78.71 0.1038 1.896 FALSE FALSE FALSE FALSE
Guatemala 15,468,203 2.51 71.96 0.3997 1.8571 FALSE FALSE FALSE FALSE
Guinea 11,745,189 2.54 55.92 0.4048 1.7476 FALSE TRUE FALSE TRUE
Guinea-Bissau 1,704,255 2.39 54.17 0.3784 1.7338 FALSE TRUE FALSE TRUE
Guyana 799,613 0.54 66.2 -0.2676 1.8209 TRUE TRUE TRUE TRUE
Haiti 10,317,461 1.38 62.96 0.1399 1.7991 FALSE TRUE FALSE TRUE
Honduras 8,097,688 2 73.7 0.301 1.8675 FALSE FALSE FALSE FALSE
Iceland 329,535 1.14 82.01 0.0569 1.9139 FALSE FALSE FALSE FALSE
India 1,252,139,596 1.24 66.28 0.0934 1.8214 FALSE TRUE FALSE TRUE
Indonesia 249,865,631 1.21 70.72 0.0828 1.8495 FALSE FALSE FALSE FALSE
Iran (Islamic Republic of) 77,447,168 1.3 73.9 0.1139 1.8686 FALSE FALSE FALSE FALSE
Iraq 33,765,232 2.89 69.43 0.4609 1.8415 FALSE TRUE FALSE TRUE
Ireland 4,627,173 1.13 80.58 0.0531 1.9062 FALSE FALSE FALSE FALSE
Israel 7,733,144 1.3 81.72 0.1139 1.9123 FALSE FALSE FALSE FALSE
Italy 60,990,277 0.21 82.29 -0.6778 1.9153 TRUE FALSE TRUE FALSE
Jamaica 2,783,888 0.52 73.45 -0.284 1.866 TRUE FALSE TRUE FALSE
Jordan 7,273,799 3.5 73.78 0.5441 1.8679 FALSE FALSE FALSE FALSE
Kazakhstan 16,440,586 1.04 66.44 0.017 1.8224 FALSE TRUE FALSE TRUE
Kenya 44,353,691 2.67 61.56 0.4265 1.7893 FALSE TRUE FALSE TRUE
Kiribati 102,351 1.54 68.75 0.1875 1.8373 FALSE TRUE FALSE TRUE
Korea, Dem. People's Republic of 24,895,480 0.53 69.9 -0.2757 1.8445 TRUE TRUE TRUE TRUE
Korea, Republic of 49,262,698 0.53 81.37 -0.2757 1.9105 TRUE FALSE TRUE FALSE
Kuwait 3,368,572 3.61 74.24 0.5575 1.8706 FALSE FALSE FALSE FALSE
Kyrgyzstan 5,547,548 1.35 67.48 0.1303 1.8292 FALSE TRUE FALSE TRUE
Lao People's Democratic Republic 6,769,727 1.86 68.08 0.2695 1.833 FALSE TRUE FALSE TRUE
Lebanon 4,821,971 3.04 79.81 0.4829 1.9021 FALSE FALSE FALSE FALSE
Lesotho 2,074,465 1.08 49.5 0.0334 1.6946 FALSE TRUE FALSE TRUE
Liberia 4,294,077 2.58 60.25 0.4116 1.78 FALSE TRUE FALSE TRUE
Libya 6,201,521 0.9 75.21 -0.0458 1.8763 TRUE FALSE TRUE FALSE
Luxembourg 530,380 1.35 80.45 0.1303 1.9055 FALSE FALSE FALSE FALSE
Macedonia 2,107,158 0.07 75.13 -1.1549 1.8758 TRUE FALSE TRUE FALSE
Madagascar 22,924,851 2.79 64.51 0.4456 1.8096 FALSE TRUE FALSE TRUE
Malawi 16,362,567 2.85 55.1 0.4548 1.7412 FALSE TRUE FALSE TRUE
Malaysia 29,716,965 1.61 74.93 0.2068 1.8747 FALSE FALSE FALSE FALSE
Maldives 345,023 1.89 77.68 0.2765 1.8903 FALSE FALSE FALSE FALSE
Mali 15,301,650 3.01 54.82 0.4786 1.7389 FALSE TRUE FALSE TRUE
Malta 429,004 0.3 79.66 -0.5229 1.9012 TRUE FALSE TRUE FALSE
Martinique 403,682 0.24 81.3 -0.6198 1.9101 TRUE FALSE TRUE FALSE
Mauritania 3,889,880 2.45 61.48 0.3892 1.7887 FALSE TRUE FALSE TRUE
Mauritius 1,244,403 0.37 73.54 -0.4318 1.8665 TRUE FALSE TRUE FALSE
Mayotte 222,152 2.71 79.05 0.433 1.8979 FALSE FALSE FALSE FALSE
Mexico 122,332,399 1.21 77.38 0.0828 1.8886 FALSE FALSE FALSE FALSE
Micronesia (Fed. States of) 103,549 0.16 68.93 -0.7959 1.8384 TRUE TRUE TRUE TRUE
Mongolia 2,839,073 1.49 67.36 0.1732 1.8284 FALSE TRUE FALSE TRUE
Montenegro 621,383 0.05 74.76 -1.301 1.8737 TRUE FALSE TRUE FALSE
Morocco 33,008,150 1.41 70.84 0.1492 1.8503 FALSE FALSE FALSE FALSE
Mozambique 25,833,752 2.47 50.2 0.3927 1.7007 FALSE TRUE FALSE TRUE
Myanmar 53,259,018 0.84 65.08 -0.0757 1.8134 TRUE TRUE TRUE TRUE
Namibia 2,303,315 1.87 64.34 0.2718 1.8085 FALSE TRUE FALSE TRUE
Nepal 27,797,457 1.15 68.19 0.0607 1.8337 FALSE TRUE FALSE TRUE
Netherlands 16,759,229 0.27 80.94 -0.5686 1.9082 TRUE FALSE TRUE FALSE
New Caledonia 256,496 1.32 76.19 0.1206 1.8819 FALSE FALSE FALSE FALSE
New Zealand 4,505,761 1.02 81.04 0.0086 1.9087 FALSE FALSE FALSE FALSE
Nicaragua 6,080,478 1.44 74.67 0.1584 1.8731 FALSE FALSE FALSE FALSE
Niger 17,831,270 3.85 58.14 0.5855 1.7645 FALSE TRUE FALSE TRUE
Nigeria 173,615,345 2.78 52.29 0.444 1.7184 FALSE TRUE FALSE TRUE
Norway 5,042,671 1 81.42 0 1.9107 FALSE FALSE FALSE FALSE
Oman 3,632,444 7.89 76.43 0.8971 1.8833 FALSE FALSE FALSE FALSE
Pakistan 182,142,594 1.66 66.48 0.2201 1.8227 FALSE TRUE FALSE TRUE
Palestine, State of 4,326,295 2.51 73.12 0.3997 1.864 FALSE FALSE FALSE FALSE
Panama 3,864,170 1.62 77.46 0.2095 1.8891 FALSE FALSE FALSE FALSE
Papua New Guinea 7,321,262 2.14 62.31 0.3304 1.7946 FALSE TRUE FALSE TRUE
Paraguay 6,802,295 1.7 72.2 0.2304 1.8585 FALSE FALSE FALSE FALSE
Peru 30,375,603 1.26 74.68 0.1004 1.8732 FALSE FALSE FALSE FALSE
Philippines 98,393,574 1.71 68.63 0.233 1.8365 FALSE TRUE FALSE TRUE
Poland 38,216,635 0.01 76.32 -2 1.8826 TRUE FALSE TRUE FALSE
Portugal 10,608,156 0.04 79.83 -1.3979 1.9022 TRUE FALSE TRUE FALSE
Qatar 2,168,673 5.9 78.3 0.7709 1.8938 FALSE FALSE FALSE FALSE
Rwanda 11,776,522 2.74 63.62 0.4378 1.8036 FALSE TRUE FALSE TRUE
Reunion 875,375 1.16 79.52 0.0645 1.9005 FALSE FALSE FALSE FALSE
Saint Lucia 182,273 0.83 74.69 -0.0809 1.8733 TRUE FALSE TRUE FALSE
Saint Vincent and the Grenadines 109,373 0.01 72.41 -2 1.8598 TRUE FALSE TRUE FALSE
Samoa 190,372 0.76 73.01 -0.1192 1.8634 TRUE FALSE TRUE FALSE
Sao Tome & Principe 192,993 2.58 66.24 0.4116 1.8211 FALSE TRUE FALSE TRUE
Saudi Arabia 28,828,870 1.85 75.37 0.2672 1.8772 FALSE FALSE FALSE FALSE
Senegal 14,133,280 2.9 63.28 0.4624 1.8013 FALSE TRUE FALSE TRUE
Seychelles 92,838 0.55 73.12 -0.2596 1.864 TRUE FALSE TRUE FALSE
Sierra Leone 6,092,075 1.88 45.34 0.2742 1.6565 FALSE TRUE FALSE TRUE
Singapore 5,411,737 2.02 82.2 0.3054 1.9149 FALSE FALSE FALSE FALSE
Slovakia 5,450,223 0.09 75.32 -1.0458 1.8769 TRUE FALSE TRUE FALSE
Slovenia 2,071,997 0.24 79.47 -0.6198 1.9002 TRUE FALSE TRUE FALSE
Solomon Islands 561,231 2.09 67.53 0.3201 1.8295 FALSE TRUE FALSE TRUE
Somalia 10,495,583 2.87 54.88 0.4579 1.7394 FALSE TRUE FALSE TRUE
South Africa 52,776,130 0.78 57.11 -0.1079 1.7567 TRUE TRUE TRUE TRUE
South Sudan 11,296,173 4.02 54.97 0.6042 1.7401 FALSE TRUE FALSE TRUE
Spain 46,926,963 0.44 82 -0.3565 1.9138 TRUE FALSE TRUE FALSE
Sri Lanka 21,273,228 0.81 74.23 -0.0915 1.8706 TRUE FALSE TRUE FALSE
Sudan 37,964,306 2.11 61.92 0.3243 1.7918 FALSE TRUE FALSE TRUE
Suriname 539,276 0.88 70.9 -0.0555 1.8506 TRUE FALSE TRUE FALSE
Swaziland 1,249,514 1.49 49.19 0.1732 1.6919 FALSE TRUE FALSE TRUE
Sweden 9,571,105 0.65 81.74 -0.1871 1.9124 TRUE FALSE TRUE FALSE
Switzerland 8,077,833 1.02 82.51 0.0086 1.9165 FALSE FALSE FALSE FALSE
Syrian Arab Republic 21,898,061 0.67 74.37 -0.1739 1.8714 TRUE FALSE TRUE FALSE
Taiwan 23,329,772 0.24 79.26 -0.6198 1.8991 TRUE FALSE TRUE FALSE
Tajikistan 8,207,834 2.43 67.14 0.3856 1.827 FALSE TRUE FALSE TRUE
Tanzania, United Republic of 49,253,126 3.02 61.36 0.48 1.7879 FALSE TRUE FALSE TRUE
Thailand 67,010,502 0.3 74.27 -0.5229 1.8708 TRUE FALSE TRUE FALSE
Timor-Leste 1,132,879 1.66 67.3 0.2201 1.828 FALSE TRUE FALSE TRUE
Togo 6,816,982 2.57 56.41 0.4099 1.7514 FALSE TRUE FALSE TRUE
Tonga 105,323 0.43 72.59 -0.3665 1.8609 TRUE FALSE TRUE FALSE
Trinidad and Tobago 1,341,151 0.28 69.81 -0.5528 1.8439 TRUE TRUE TRUE TRUE
Tunisia 10,996,515 1.1 75.77 0.0414 1.8795 FALSE FALSE FALSE FALSE
Turkey 74,932,641 1.22 75.09 0.0864 1.8756 FALSE FALSE FALSE FALSE
Turkmenistan 5,240,072 1.27 65.39 0.1038 1.8155 FALSE TRUE FALSE TRUE
Uganda 37,578,876 3.33 59.02 0.5224 1.771 FALSE TRUE FALSE TRUE
United Arab Emirates 9,346,129 2.52 76.75 0.4014 1.8851 FALSE FALSE FALSE FALSE
United Kingdom 63,136,265 0.57 80.45 -0.2441 1.9055 TRUE FALSE TRUE FALSE
United States of America 320,050,716 0.81 78.86 -0.0915 1.8969 TRUE FALSE TRUE FALSE
United States Virgin Islands 106,627 0.1 80.05 -1 1.9034 TRUE FALSE TRUE FALSE
Uruguay 3,407,062 0.34 77.14 -0.4685 1.8873 TRUE FALSE TRUE FALSE
Uzbekistan 28,934,102 1.35 68.19 0.1303 1.8337 FALSE TRUE FALSE TRUE
Vanuatu 252,763 2.21 71.48 0.3444 1.8542 FALSE FALSE FALSE FALSE
Venezuela (Bolivarian Republic of) 30,405,207 1.49 74.55 0.1732 1.8724 FALSE FALSE FALSE FALSE
Viet Nam 91,679,733 0.95 75.87 -0.0223 1.8801 TRUE FALSE TRUE FALSE
Western Sahara 567,315 3.21 67.61 0.5065 1.83 FALSE TRUE FALSE TRUE
Yemen 24,407,381 2.3 63.02 0.3617 1.7995 FALSE TRUE FALSE TRUE
Zambia 14,538,640 3.21 57.66 0.5065 1.7609 FALSE TRUE FALSE TRUE
Zimbabwe 14,149,648 2.81 59.84 0.4487 1.777 FALSE TRUE FALSE TRUE
-0.48319 -0.44939
Source: Department of Economic and Social Affairs, United Nations, 2013.

4.2. Scatter Diagram

Scatter diagram for life expectancy against population growth rate and log of life expectancy against log of population growth rate were considered to see the behaviour of the countries under consideration. The study discovered that the behaviour of both diagrams were the same, but the negative slope in both diagrams implied that,  population growth increased ,then the life expectancy at birth decreased. See Fig. 1 below.

Fig-1. Scatter Diagram of Life Expectancy against Growth Rate
Source: Department of Economic and Social Affairs, United Nations, 2013.

4.3. Regression Analysis

The Estimated Regression model is of the form

Log growth rate = 6.20-3.34 log life expectancy

This Rate of growth given by β* = -3.3411 indicated a negative rate of change which also suggest that as log growth rate increases, log life expectancy decreases. Consequently, life expectancy decreases with population growth. This is further collaborated by the hypotheses

Ho: β1= 0
H1 :β1=0

Where Hois reported, the negative effect on life expectancy is very significant.

4.4. Regression Analysis: log of Population Growth Rate Versus Log of Life Expectancy

The regression equation is
loggrate = 6.20 - 3.34 lohlexp
Predictor     CoefSECoef      T      P    VIF
Constant    6.1997   0.9102   6.81  0.000
lohlexp    -3.3411   0.4937  -6.77  0.000  1.000
S = 0.409637   R-Sq = 20.2%   R-Sq(adj) = 19.8%
Analysis of Variance
Source           DF       SS      MS      F      P
Regression        1   7.6860  7.6860  45.80  0.000
Residual Error  181  30.3722  0.1678
Total           182  38.0582

4.5. Correlation Analysis

The correlation analysis of the study showed that when comparing the relationship between life expectancy and population growth rate, there is a negative relationship

(-0.4832) between the two variables satisfying the postulation that increase in growth rate decrease life expectancy.


5.1. Introduction

This aspect summarizes the study and makes conclusion based on the result. The policy implications from the findings are also presented.

5.2. Summary

The relationship between the life expectancy and the population growth rate has therefore been fundamental to the policy makers in different countries of the world. However, there has been no consensus whether population growth is beneficial or detrimental to the life expectancy since the relationship of the two varies among countries. But, the study can summarily established that while the population growth rate increases then the life expectancy tends to decrease and vice versa through the use regression and correlation approach.

5.3. Conclusion

Conclusively, the finding of the study supported the first stage of demographic transition called pre-Malthusian regime, which predicts the relationship between the population’s growth rate and life expectancy to remain parallel since the increase in one leads to decrease in the other.

5.4. Recommendations

In view of the findings that life expectancy will increase, if the population growth rate decreases and vice-versa. Therefore, life expectancy will definitely decrease in view of the fact that the continuous practice of raising large family will affect life expectancy on raising large family involve a lot of stress in providing necessary benefit for their up keeping, feeding, clothing, provision of better health facilities, education and other care, which bring along stress, agitation especially in paying bills for education, health, feeding, and clothing among others. Health wise, the stress and related cause will affect the life expectancy.

It is hereby recommended that:

  1. The citizen especially African should be encouraged desisting from raising large family.
  2. The introduction of a legislature improving sanction on whoever raises large family as it is practiced in China and Indian.
  3. Introduction of preventive measures during sexual relationship to curb unwanted pregnancies by the government.
  4. Enforcing and introducing abortion or other measure to curb raising large family, though the government has to have a political will as many religious bodies will definitely kick against the policy.
  5. Proper orientation should be given to would – be newly wedded couples, married and singles about the benefits and disadvantages of not raising large family.
  6. Hospital should be used as a measure to advise or even sanction any family that go against national figure of the family.
  7. Pregnant mother should be enlightened on the benefits of raising small family during ante– natal clinics.


Barro, J., 1991. Impacts of real exchange rate misalignment on trade creation and diversion within regional trading blocks: The case of comesa.. Shaker: Verlag, Germany.

Becker, A.J. and E.M. Hoover, 1998. Population growth and economic development in low- income countries. Princeton: Princeton University Press. pp: 610-619.

Bhargava, A., 2003. Population growth in a model of economic growth with human capital accumulation and horizontal R &D. Milan: University of Milan. pp: 510-517.

Charkraborty and Idrani, 2010. Capital structure in an emerging stock market. A case of India. Research in International Business and Finance, 24(3): 295-314. View at Google Scholar | View at Publisher

CIA World Fact Books, 2011. 12(1): 89-95.

Malthus, T.R., 1998. An essay on the principles of population. Cambridge: Cambridge University Press. pp: 121-131.

Mankiw, G., D. Roemer and P. Weil, 1992. A contribution to the empirics of economic growth. Quarterly Journal of Economics, 9(1): 313-320.

Porter, C.B., 1996. Health, wealth and population in the early days of the industrial revolution. London: George Routledge & Sons. pp: 111-121.

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About the Authors

Sanni Eneji Ademoh
Maths and Statistics Department Rufus Giwa Polytechnic, Owo, Ondo State, Nigeria

Corresponding Authors

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