Skip to content
ALL Metrics
-
Views
-
Downloads
Get PDF
Get XML
Cite
Export
Track
Research Article

Mortality, fertility, and economic development: An analysis of 201 countries from 1960 to 2015

[version 1; peer review: 2 approved, 1 approved with reservations]
PUBLISHED 01 Mar 2018
Author details Author details

Abstract

Background: The efficient utilization of the economic opportunities effected by rapid reductions in fertility and mortality is known as the demographic dividend. In this paper, our objectives are to (1) estimate the contribution of fertility and mortality decline during the period 1960-2015 to demographic dividend due to change in age structure, and (2) assess the economic consequences of population age structure change.
Methods: Employing the cohort component method, we performed population projections with different scenarios of changes in mortality and fertility between 1960 and 2015 in 201 countries. We specifically focused on low- and middle-income countries in Asia, Latin America and the Caribbean (LAC), Northern Africa, and sub-Sahara Africa (SSA)
Results: The child dependency ratio, defined as the number of children (0-14 years) per 100 working age population (15-64 years), would be 54 higher than the observed level in 2015 in both Asia and LAC, had fertility not declined. That means that every 100 working age population would need to support an additional 54 children. Due to the less substantial fertility decline, child dependency ratio would only be 16 higher if there were no fertility decline in SSA. Global GDP (constant 2011 international $) would be $19,016 billion less than the actual level in 2015 had the fertility decline during 1960-2015 not occurred, while the respective regional decreases are $12,390 billion in Asia, $1,985 billion in LAC, $484 billion in Northern Africa, and $321 billion in SSA.
Conclusions: SSA countries may accelerate the catch-up process in reducing fertility by investing more in family planning programs. This will lead to a more favorable dependency ratio and consequently facilitate a demographic dividend opportunity in SSA, which, if properly utilized, will spur economic development for the coming decades.

Keywords

Mortality, fertility decline, demographic dividend

Introduction

Rapid reductions in fertility and mortality during the last half-century have resulted in dramatic changes to the population age structures of many countries, which economists have argued have been demonstrably conducive to economic development1. Countries with a high proportion of their population in working ages are better able to use their resources for economic development due to reduced expenditures related to caring for child and elderly dependents. The efficient utilization of the economic opportunities that result in part from a favorable demographic transition is termed “the demographic dividend”.

The association between fertility and mortality declines and the demographic transition has been extensively studied and well documented, but their relationship with economic development has not been systematically investigated in the health literature2. A comprehensive demonstration of its health and economic benefits strengthens the advocacy for fertility decline through programs that directly influence fertility levels, such as meeting women’s need for family planning.

Such an investigation links several sustainable development goals (SDGs). Fertility decline results in a smaller total population, which alleviates the burden on earth’s life-support system imposed by a global population set to rise to 9 billion by 20503. Women empowered to adapt voluntary measures to reduce fertility will benefit themselves, their children, and the local and global economy and environment4.

Over two centuries ago, Malthus argued that unconstrained population growth would lead to catastrophic consequences because the amount of many production factors, such as land, is fixed5. Solow subsequently proposed that even reproducible factors would be swamped by rapid population growth6. The variation in population growth rates is an important factor in explaining differences in long-term economic performance across countries. The implications of the theories proposed by Malthus and Solow, as well as others, are pessimistic for countries with sustained high fertility rates. According to this framework, fertility decline results in a smaller total population, which in turn increases the ratio of fixed and reproducible factors to labor.

Additionally, lower fertility levels are also associated with higher investments in human capital, another important production factor7,8. Moreover, lower fertility means that women’s time spent on bearing and caring for children declines and may translate into a higher female labor participation rate, which independently contributes to the economy9.

At the aggregate level, fertility also significantly impacts the population age structure. Lower fertility implies fewer children and a lower child dependency ratio, defined as the ratio of children (i.e. aged 0–14 years) to the working age population (i.e. aged 15–64 years). Holding other factors constant, such as the labor participation rate, a larger proportion of working age population can lead to greater output per capita.

Empirical studies have identified a strong correlation between a favorable population age structure and rapid economic growth10,11. It has been estimated that as much as one-third of the economic growth in the “East Asia Miracles” economies of Hong Kong, Singapore, South Korea, and Taiwan, from the early 1960s to 1990s, was derived from their rapid fertility transitions1. Figure 1 compares the population pyramids of Nigeria and South Korea. In 1960 their population age structures were similar, with the dependency ratio (population aged 0–14 and 65+ years divided by the population aged 15–64 years) being 80 and 87 in Nigeria and South Korea, respectively. According the World Bank, the GDP per capita (constant 2011 US$) was 50% higher in Nigeria than South Korea in 1960. Fifty-five years later, the dependency ratio had decreased to 37 in South Korea while it increased to 88 in Nigeria. During the same period, the GDP per capita rose to $24,871 in South Korea, a 26-fold increase. The increase was less than 2-fold in Nigeria in constant dollars.

ee61fe3f-8a5a-4791-a8d1-e16f63e10fda_figure1.gif

Figure 1. Population pyramids of Nigeria and South Korea in 1960 and 2015.

Figure 2 illustrates the relationship between economic growth and the ratio of children to working age population in 120 low- and middle-income countries (LMICs) in Asia, Latin America and the Caribbean (LAC), Northern Africa, and sub-Sahara Africa (SSA). The vertical axis is the change in GDP per capita during the period 1990 to 2015. GDP is based on purchasing power parity (PPP) and is measured in constant 2011 international dollars. The horizontal axis is the child dependency ratio in 2015. We use 1990 as the starting year, instead of 1960 that is used in subsequent sections, since PPP-converted GDP data only first became available in the World Bank database in 1990. High-income countries are excluded since most of them had completed their demographic transitions long before 1990. The inverse relationship between economic growth and the ratio of youth to working age population during the past two decades is consistent with findings from previous studies12. This helps justify our use of the child dependency ratio as the indicator with which to investigate the relationship between mortality, fertility, and economic development. During the 25-year period considered, the increase in GDP per capita was greater in those countries that had achieved a lower child dependency ratio by 2015. A linear regression analysis of the change in GDP per capita against the 2015 child dependency ratio shows a satisfactory goodness of fit with R2 = 0.44. The slope of the linear fitted line is -109 (95% CI: -134,-85), In other words, each one- unit change in 2015 child dependency ratio is associated with 109 fewer international dollars in 2015 GDP per capita.

ee61fe3f-8a5a-4791-a8d1-e16f63e10fda_figure2.gif

Figure 2. Change in GDP per capita from 1960 to 2015 and the ratio of children to working age population in 2015 in 120 low- and middle-income countries in Asia, LAC, Northern Africa, and SSA.

Methods

Data sources

We obtained data on the fertility, mortality, and population age structure for 201 countries during the decades 1960 to 2015 from World Population Prospects (WPP), the 2017 Revision. This is the 25th round of official United Nations (UN) population estimates, published in June 2017 by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat13. The economic data were obtained from the International Comparison Program Database of the World Bank.

Dependency ratios are used as indicators of the population age structure. Similar to the child dependency ratio defined above, the aged dependency ratio is the ratio of the number of elders (65 years and above) to the working age population. The total dependency ratio equals the sum of child and aged dependency ratios. As a commonly-used fertility measure, total fertility rate (TFR) is the number of children a woman would have over her lifetime if she were to experience the observed period age-specific fertility rates.

Cohort component method (CCM)

CCM is a demographic projection method used by the UN to generate WPP estimates. It employs a transition matrix to predict population by age from one period to the next. Following WPP 2017, our projections were made for five-year intervals. The basic equation for the CCM is

Pt+5=Mt,t+5Pt(1)

where Pt is a column vector whose elements are the age-specific population at calendar time t; Mt,t+5 is a transition matrix constructed from the age-specific fertility and mortality.

All 201 countries included in the WPP 2017 database are used in this study. Among them, 187 countries have data on GDP per capita (PPP, constant 2011 international $) in the World Bank’s International Comparison Program Database. The majority of the figures and tables which follow below are based on countries in 4 regions (Asia, LAC, Northern Africa, and SSA) that are relevant to this study. Population projections for the following three scenarios were made using Stata 14: what would the 2015 dependency ratio be if, during the period from 1960 to 2015, there had been (1) neither a fertility nor mortality reduction; (2) no fertility reduction; (3) no mortality reduction? Based on these estimates we further assessed what the GDP per capita would be in 2015 under these three scenarios.

Results

Demographic implications of fertility and mortality declines

Globally, child and total dependency ratios declined significantly from 1960 to 2015, with large country-level and regional variations. The declines in SSA are the smallest among the five regions - the median change in both child and total dependency ratios is close to zero. On the other hand, Asia and LAC have experienced dramatic changes in both fertility levels and dependency ratios, with median changes in the range of 30 to 40 units.

The decompositions of the contributions from fertility and mortality declines to the change in dependency ratios were conducted at both regional and country levels. Table 1 and Table 2 show the results from country-level and regional-decomposition, respectively. Mortality decline was considered in the estimations since it is an important determinant of population age structure. However, our discussion is mainly on fertility for two reasons. First, as illustrated in Table 1 and Table 2, the effect of mortality decline is smaller than that of fertility change. Second, no government, including those facing a tremendous challenge of population aging, have ever proposed slowing mortality decline to make the population age structure more conducive to economic development14.

The contribution of fertility decline to the change in the dependency ratio was smallest in SSA than in any other region. The 2015 child dependency ratio was 80 in SSA and would have been 96 had there been no fertility decline from 1960 to 2015. In other words, every 100 working age population in SSA would have to support 16 more children without the fertility declines that transpired in SSA countries. In Asia, the observed 2015 child dependency ratio was 36 and would have been 90 had there been no fertility decline. The fertility transition in the LAC region similarly reduced the child dependency ratio to 38, which would have been 92 had there been no fertility decline. The results are 52 vs. 102 in Northern Africa, meaning the fertility almost halved the burden of children on working age population.

These results are illustrated in Figure 3. We simulated how much higher the dependency ratios would be if mortality and/or fertility had been constant in the decades from 1960 to 2015. The percentage change in the child dependency ratio was positive and large in the constant fertility scenario in Asia and the LAC regions. The results underscore how notably fertility declines have reduced the child dependency ratio during the period 1960–2015. On the other hand, the percentage change in the child dependency ratio is negative for the constant mortality scenario, but the size of the change is marginal. The combined impact of mortality and fertility changes is positive for all of these five regions, but their size is substantially greater in Asia and LAC than in SSA.

ee61fe3f-8a5a-4791-a8d1-e16f63e10fda_figure3.gif

Figure 3. The percentage change in child dependency ratio from 1960 to 2015 in three fertility and mortality scenarios compared with UN data by regions.

S1 = scenario 1, constant mortality and fertility; S2 = scenario 2, constant fertility; S3 = scenario 3, constant mortality.

Figure 4 shows a clear positive relationship between fertility levels in 1960 and the contribution of 1960–2015 fertility declines to 2015 total dependency ratios. This implies that previously high-fertility countries have catching up and they have a large potential to alter dependency ratio through fertility decline.

ee61fe3f-8a5a-4791-a8d1-e16f63e10fda_figure4.gif

Figure 4. The relationship between total fertility rate in 1960 and the contribution of fertility decline to the change in total dependency ratio from 1960 to 2015.

From the analyses conducted here, with the exception of most SSA and several Asian countries, a higher TFR in 1960 is associated with a larger contribution by fertility decline to the change in the child dependency ratio. The majority of high-fertility Asian countries have significantly reduced their TFR, resulting in smaller dependency ratios.

Economic implications of fertility and mortality declines

There are several direct and indirect economic implications resulting from the changes in population age structure and consequences for the dependency ratio. By definition, GDP per capita can be broken down into GDP per worker and the proportion of working age population in the total population. By definition GDP per capita can be expressed as,

yit=YitPit=YitWitWitPit=zitsit(2)

where Yit is the gross domestic product (GDP) in country i in year t. yit is GDP per capita, Pit is the total population, and Wit is the number of workers. zit is the product per worker and wit the share of workers in the country at time t. In the present study, the number and proportion of workers are proxied by the working age population. Consequently, an increase in GDP per capita may be attributable to the change in either productivity per worker or the proportion of workers in the population.

This approach is a simplification of the actual change in GDP per capita, which can be affected by a variety of socio-economic, geographic, institutional and international factors15,16. The approach has been used in previous studies17. An increased total dependency ratio means a reduced proportion of working age population, which, assuming a fixed worker productivity, indicates a lower GDP per capita.

We simulated the GDP per capita that would have occurred had one factor not changed assuming that worker productivity did not change from 1960 to 2015. The gap between the actual and hypothetical values can be interpreted as the impact of the change in population age structure on GDP per capita. It is easy to show that,

yithat=yitobs1+DRitobs/1001+DRithat/100(3)

where yithat denotes the GDP per capita in country i in year t had there been no fertility decline; yitobs denotes the observed GDP per capita, i.e. from the World Bank database; DRithat and DRitobs denote the total dependency ratio under those two scenarios.

As seen in the last three columns in Table 1 and Table 2, GDP would be much lower in most of the countries if the fertility decline between 1960 and 2015 had not occurred. Global GDP would decrease from the actual $106,422 billion to $87,406 billion without the fertility decline. The regional reductions are $12,390 billion in Asia, $1,706 billion in LAC, $484 billion in Northern Africa, and $321 billion in SSA. The estimated contributions are comparable to those of other studies. Bloom and Williamson (1998) and Bloom and Finlay (2009) suggested that the demographic transition accounted for between one fourth and two fifths of the “economic miracle” in East Asian Tigers’ economies1,17. A study projected that the demographic dividend could increase GDP per capita by about 11–32% in selected SSA countries over 2010–2040 under the UN’s low-fertility projection18. Those estimates vary, mainly because they cover different periods in time.

Table 1. The contribution of fertility and mortality decline to the change in dependency ratio (DR) and GDP per capita in 10.

RegionUN WPP
2017
Had there
been no
fertility
change
Had there
been no
fertility and
mortality
change
GDP, PPP (constant 2011
international $)
Child
DR
Total
DR
Child
DR
Total
DR
Child
DR
Total
DR
World
Bank
No
fertility
decline
Benefit
of fertility
decline
Asia (48)3647907779257549,96937,57912,390
Australia/New Zealand (2)295154541,8261,2651,2031,064139
EUROPE (39)245041392,1931,81223,07321,3671,706
LATIN AMERICA AND
THE CARIBBEAN (29)
385092838266818,3646,3791,985
Melanesia, Micronesia,
and Polynesia (7)
5664979160050312102
NORTHERN AMERICA (2)285153521,6911,27918,43416,4461,989
Northern Africa (5)5261102866586132,0351,551484
Sub-Saharan Africa (46)808596836445753,3333,012321
World (178)40528573937759106,42287,40619,016

regions from 1960 to 2015

Note: the number of countries in each region is in the parenthesis.

Table 2. The contribution of fertility and mortality decline to the change in dependency ratio (DR) and GDP per capita in 201 countries from 1960 to 2015

CountryUN WPPIf no fertility declineIf no fertility or
mortality decline
GDP ( constant 2011 international $)
Total
DR
Child
DR
Aged
DR
Total
DR
Child
DR
Aged
DR
Total
DR
Child
DR
Aged
DR
GDP
per
capita
World
Bank
No
fertility
decline
Benefit
of
fertility
decline
Asia
Afghanistan8984521224-2-1-191,74859545
Armenia44291671136-4664124-468,18024204
Azerbaijan40328123160-23105137-2016,69916111942
Bahrain30273262282792212414444,456613823
Bangladesh53458106127-197190-443,133505370135
Bhutan47407121143-97897-377,736642
Brunei Darussalam38336176209-12165199-3374,600312110
Cambodia564968598-166882-383,291513912
China382413159272-49120226-7413,57018,95813,2145,744
China, Hong Kong SAR361521252729-98129398-6853,490388233155
China, Macao SAR27161118830420166295-19100,518604317
China, Taiwan Province
of China
351917156340-51141323-64n/an/an/an/a
Cyprus42241864121-1254115-2530,38335306
Dem. People's
Republic of Korea
4531145586-124176-35n/an/an/an/a
Georgia5028222057-261151-399,02536332
India524497594-225372-435,7547,5325,9881,544
Indonesia4942890110-186278-2310,3682,6772,062614
Iran (Islamic Republic of)40337171212-22122157-4416,0101,271853418
Iraq787253538-82022-1814,92953946772
Israel6446181733-23930-4331,97125824216
Japan642143845-11-1544-4437,8184,8404,698142
Jordan666067889-266475-398,491785918
Kazakhstan5040105068-233954-1923,52241835860
Kuwait30273287310492602833569,329273165108
Kyrgyzstan554875867-104552-103,23819163
Lao People's
Democratic Republic
605465259-23540-155,43436306
Lebanon47351295141-4086133-5513,087775918
Malaysia45368127165-33114152-4624,989768551217
Maldives38326204241-3129156-2511,994532
Mongolia49436115133-2386102-2811,40934259
Myanmar5042887109-316484-405,07126620659
Nepal615395265-272334-442,301665511
Oman322932302431031731846540,13916910861
Pakistan655874856-123339-134,695889746143
Philippines585177793-356782-376,875699545154
Qatar18161541558325493511268119,749297164133
Republic of Korea371918147332-52123301-6834,1781,7291,239490
Saudi Arabia41374165181291231361550,7241,6011,081519
Singapore372116130260-42118257-6780,892448331117
Sri Lanka51371472115-4159102-5411,06222918445
State of Palestine767155661-134146-292,65412102
Syrian Arab Republic736676171-344454-46n/an/an/an/a
Tajikistan625756369-2455012,64123184
Thailand402515139249-49117220-5715,2371,046749297
Timor-Leste9084756-1-13-12-252,151330
Turkey503812105148-3766100-4723,3821,8301,355475
Turkmenistan534668397-216982-2614,992836519
United Arab Emirates1716153855233746447826565,975604336268
Uzbekistan4841697114-148499-135,70017713442
Viet Nam433310117158-25102144-435,667530393137
Yemen77725404379922,641716011
Australia/New Zealand
Australia5128233882-172682-4443,8321,043925119
New Zealand53312243102-3735101-5434,64616013921
EUROPE
Albania442618119242-58102211-5611,02532249
Austria49212835105-1720101-4144,07538334340
Belarus4423212672-252463-2017,23016315112
Belgium5426282055-14553-4041,72347144130
Bosnia and Herzegovina43212362174-4144152-5510,90239326
Bulgaria5221301160-24753-2517,0001221184
Channel Islands472225377822477-22n/an/an/an/a
Croatia5122281450-14-246-3920,63687834
Czechia5023271949-7847-2530,38132230419
Denmark5626301554-20652-3445,48425924613
Estonia542529717-2113-1027,32936351
Finland5826321360-26-160-5138,99421420410
France5929301453-23151-4637,7662,4342,313121
Germany5220322389-19785-4243,7843,5783,321256
Greece5322302255-1351-3224,09527025119
Hungary47212615266718-324,83124323211
Iceland52312148102-3442101-4642,67414122
Ireland5433204082-283280-4760,94428625136
Italy5621351876-18172-4234,2452,0381,915123
Latvia522329622-6617-223,05746451
Lithuania5022281871-241662-2026,97179754
Luxembourg4424204554352452-995,31154477
Malta49212735126-3623125-5734,38015132
Montenegro4827214093-313082-3915,2911091
Netherlands5326272990-282189-4246,35478571472
Norway5227252965-111963-2963,67033130130
Poland4421223799-212890-3225,29996886999
Portugal53223229126-3613107-5126,54827725126
Republic of Moldova35211371128-1962119-284,74719163
Romania4823251538-7730-1320,53840839018
Russian Federation4424192967-182757-1024,1243,4713,187284
Serbia4925241855-21843-2813,2781181117
Slovakia42222049114-2243109-2828,25415413419
Slovenia4922272663-51165-3329,09760565
Spain5123293187-131581-3832,2161,4951,352143
Sweden5827311329-1128-2445,48844442321
Switzerland492227388031978-2956,51147041852
TFYR Macedonia42241866138-3052121-4012,76027224
Ukraine4522231951-111843-67,46533331518
United Kingdom5627282163-20962-4338,5092,5182,342176
LATIN AMERICA AND
THE CARIBBEAN
Antigua and Barbuda453610789826690-2120,114220
Argentina57391715212516-1919,10182978643
Aruba45271879160-4673155-54n/an/an/an/a
Bahamas412912101147-1588137-3421,670862
Barbados50292154132-5345122-6015,390441
Belize5751692106-247991-248,061321
Bolivia (Plurinational
State of)
6453115471-323147-536,532705812
Brazil443211117172-3695145-4814,6663,0212,224796
Chile45301589150-3468128-5122,53740031487
Colombia463510132182-40112160-5212,985626443184
Costa Rica453213127194-42107171-5514,914725120
Cuba43232099223-4685206-57n/an/an/an/a
Curaçao52292455146-5648141-65n/an/an/an/a
Dominican Republic58471099131-5073102-5813,37214110337
Ecuador56451088115-326691-4510,77717413241
El Salvador57441287125-486094-597,845503812
French Guiana635583639122632-20n/an/an/an/a
Grenada514011108152-5594136-6012,735110
Guadeloupe56302665175-6655168-78n/an/an/an/a
Guatemala696185766-203544-367,2931199622
Guyana5446888110-4283104-417,063541
Haiti625584654-92734-201,65118153
Honduras6053790105-196579-404,311392910
Jamaica493514101160-4690146-538,10523176
Martinique57292858178-6648171-80n/an/an/an/a
Mexico514210106139-3689121-4516,6682,0981,543555
Nicaragua54468108131-3081103-484,96130228
Panama55431280110-316798-4720,674826418
Paraguay574797697-296890-408,639574512
Peru534310101134-3771101-5111,76836927496
Puerto Rico50282265154-4958146-54n/an/an/an/a
Saint Lucia412813168276-59146246-6210,677211
Saint Vincent and the
Grenadines
473611148209-57123176-5510,463110
Suriname514110118161-56108150-6114,767862
Trinidad and Tobago433013103173-5097166-5731,283433210
United States Virgin
Islands
61332863181-7558175-81n/an/an/an/a
Uruguay5633231534-13630-2919,83168653
Venezuela (Bolivarian
Republic of)
534310105137-4191122-51n/an/an/an/a
Melanesia, Micronesia, and Polynesia
Fiji5344987113-4375100-528,756862
New Caledonia48341476124-3963112-53n/an/an/an/a
Papua New Guinea676163945-162936-39n/an/an/an/a
Solomon Islands75696252731416-122,053110
Vanuatu696275868-294049-402,807110
Guam52391491140-4980130-63n/an/an/an/a
Kiribati635765865-164653-251,874000
Micronesia (Fed.
States of)
625576376-335668-393,285000
French Polynesia453511108153-4095141-58n/an/an/an/a
Samoa746595166-544360-725,559110
Tonga7464103852-503550-625,189100
NORTHERN AMERICA
Canada47242455147-3645144-5242,9831,5451,313232
United States of
America
5129223480-252578-4452,79016,88915,1321,757
Northern Africa
Algeria53449116148-3884113-5313,724547391157
Egypt625487389-323948-2110,096947741206
Libya49436128151-2593113-38n/an/an/an/a
Morocco524210110146-4683115-567,28625418469
Sudan827562629-111518-194,29016614917
Tunisia463411130186-4391140-5910,7501218635
Western Sahara4541412513156889322n/an/an/an/a
Sub-Saharan Africa
Angola9893516171-5-4-206,23117416113
Benin868068719-9-8-121,98721201
Botswana5549689101-166879-2315,35634268
Burkina Faso928855430-13-1461,55128271
Burundi908557621-3-46749870
Cabo Verde5548785102-336581-465,919321
Cameroon868068715-6-742,99168662
Central African
Republic
9083722-9-14-14-17626330
Chad100955-5-719-17-1832,0482929(1)
Comoros767053032-31517-161,413110
Congo847861212422-35,54328261
Côte d'Ivoire847853741-311416-213,251756411
Democratic Republic
of the Congo
97926-5-712-16-17-17505759(2)
Djibouti57506657214495523,139321
Equatorial Guinea686353129501092227,23832284
Eritrea857871820-1602-18n/an/an/an/a
Ethiopia827662324256-161,53315313914
Gabon67608212033-1-2816,83732303
Gambia928840-238-21-2361,588330
Ghana736763943-122629-183,9301089315
Guinea8479611127-8-8-161,18414141
Guinea-Bissau807559920-4-451,424320
Kenya787455761-163842-202,83613410727
Lesotho676073845-152127-192,777651
Liberia8378621232-10-237854 30
Madagascar807553740-71719-211,37633295
Malawi9185618199-7-7-51,08919182
Mali102975-1-216-23-23-211,9193434(0)
Mauritania767152931-11719-153,60215132
Mauritius422714144249-60130231-6818,86424177
Mayotte837675158-344048-56n/an/an/an/a
Mozambique94876781-11-10-181,11831301
Namibia68626434702528-119,91324204
Niger1121065-4-42-18-17-278971818(0)
Nigeria88835663-8-8-45,6711,02799928
Rwanda777254851-12629-161,716 20163
Réunion53371693158-5878142-71n/an/an/an/a
Sao Tome and
Principe
878161314-145-142,9421 10
Senegal858062629-858-272,29734 314
Seychelles433112124190-50108169-5325,525221
Sierra Leone83785121210-11-13151,31610 91
Somalia97925-1-212-14-14-4n/an/an/an/a
South Africa534587492-305974-2912,425 687547140
South Sudan8477619212-3-1-211,80821202
Swaziland696455054-33034-128,054119 2
Togo817652325078-71,35110 91
Uganda102974775-6-761,693 68662
United Republic of
Tanzania
938761315-5-3-2-152,4911341268
Zambia928752021-555-13,62758535
Zimbabwe807454449-192831-141,89130255

The estimation from this approach disregards the correlation between worker productivity and population age structure. Some studies have found that an increasing proportion of working age population is associated with improved worker productivity. Several mechanisms have been proposed to explain the association. As discussed above, an increased proportion of working age population, mostly brought about by rapid fertility decline, can be associated with an increased female labor participation rate9. Declining fertility also encourages greater savings within the working age population for retirement14. These behavioral changes promote the accumulation of financial and human capital, which will result in improved productivity per worker. Due to these associations between the proportions of working age population and worker productivity, the estimations of the impact of population age structure on GDP per capita presented here are conservative.

Limitations

Although this paper has used widely recognized population data from the UN and World Bank and applied well-established demographic projection methods, it nevertheless is subject to limitations. Particularly, we assessed the changes in fertility, mortality, and dependency ratios for the decades from 1960 to 2015, a somewhat extended period of time during which many countries may have experienced short-term demographic and socioeconomic fluctuations. Our analysis cannot account for the impact of short-term variations on dependency ratios. As discussed above, the assumed independence of population size and age structure and worker productivity may be an oversimplification.

Discussion

This study fills an important gap in the current literature on population welfare and reproductive and family health in LMICs. In PubMed we located about 500 articles published in English from Jan 1, 1990 to June 30, 2017 that included terms “fertility decline” or “mortality decline” in the titles or abstracts. But only 7 of them had “demographic dividend” in the titles or abstracts. Admittedly, many studies on the demographic dividend are published in the economic literature and thus may or may not be included in the PubMed database. However, our search results indicate that the demographic dividend perspective, which is appealing to policy makers, has not penetrated the health literature, and has not been fully utilized as evidence of the importance of improved reproductive, maternal and child health.

This study is the first to retrospectively assess the contribution of fertility and mortality declines to the change in national dependency ratios over the past five decades. It has also estimated the economic consequences of these demographic changes. Contrasting SSA to Asian and LAC countries sheds new light on the historical relationship between fertility, mortality and economic development. A favorable dependency ratio has enabled many Asian and LAC countries to realize the demographic dividend and to transform their predominantly rural agrarian economies into urban industrialized ones. During this period of development, many millions of people worldwide have been lifted out of poverty and their health substantially improved.

Assessing the contribution of fertility decline to the change in population age structure and GDP per capita provides a strong argument for expanding reproductive, maternal and child health interventions. Our study estimated the contribution of fertility declines, by far the more dominant factor, to the change in dependency ratios in 201 countries over the past five decades. Lower dependency ratios for countries as a whole as well as for individual households offer the opportunity to reallocate scarce resources toward better education, health care and nutrition. Improved health benefits for youth also confer stronger physical and cognitive performance with social and economic consequences that can disrupt poverty cycles.

The past half-century has been characterized by rapid demographic transitions and historically unprecedented economic growth in most parts of the world, with the exception of the SSA region. The population age structures in Asia and LAC experienced dramatic changes during the period 1960–2015. At the same time, countries in these regions were transformed from mostly rural agrarian economies with high fertility and mortality to largely urban industrialized ones with low fertility and mortality. In contrast, most SSA countries have lagged in their demographic transitions and economic development.

Based on a decomposition analysis of 201 countries, we found that fertility decline from 1960 to 2015 played a large role in changing the population age structure and lowering dependency ratios. Over this period, fertility decline contributed greatly to the reduction of the child dependency ratio in Asia and LAC while in contrast, its contribution in SSA was minor. The main reason is that fertility declined in SSA countries only marginally. The TFR in SSA fell from 6.67 in 1960 to 5.10 in 2015. During the same period, the TFR in LAC decreased from 5.89 to 2.14, and in Asia the change was from 5.81 to 2.20. The difference in the demographic transitions among these regions is consistent with the variation in their economic development.

Countries with slow fertility declines will need to accelerate the transitions in order to achieve a dependency ratio favorable for realizing a demographic dividend. Satisfying unmet need for family planning and providing full and voluntary access to a range of contraceptive methods have proven to be effective measures to reduce fertility. The implication of our study for policymakers is that expanding and intensifying the provision of effective reproductive, maternal, and child health interventions, particularly contraceptive access and nutrition enrichment, can accelerate ongoing fertility and mortality declines that contribute to population health as well as economic productivity and poverty alleviation. The induced benefits cover all three layers of the new paradigm of sustainable development - earth's life-support system, society, and economy. To ensure reaching the demographic dividends, governments of SSA countries should also encourage investments in human capital and ensure adequate employment, along with increased gender equity and nutrition19.

Data availability

All data used in the study are freely available online (no registration needed). Below are links to access the datasets:

Comments on this article Comments (0)

Version 1
VERSION 1 PUBLISHED 01 Mar 2018
Comment
Author details Author details
Competing interests
Grant information
Copyright
Download
 
Export To
metrics
Views Downloads
Gates Open Research - -
PubMed Central
Data from PMC are received and updated monthly.
- -
Citations
CITE
how to cite this article
Li Q, Tsui AO, Liu L and Ahmed S. Mortality, fertility, and economic development: An analysis of 201 countries from 1960 to 2015 [version 1; peer review: 2 approved, 1 approved with reservations]. Gates Open Res 2018, 2:14 (https://doi.org/10.12688/gatesopenres.12804.1)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
track
receive updates on this article
Track an article to receive email alerts on any updates to this article.

Comments on this article Comments (0)

Version 1
VERSION 1 PUBLISHED 01 Mar 2018
Comment
Alongside their report, reviewers assign a status to the article:
Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

Are you a Gates-funded researcher?

If you are a previous or current Gates grant holder, sign up for information about developments, publishing and publications from Gates Open Research.

You must provide your first name
You must provide your last name
You must provide a valid email address
You must provide an institution.

Thank you!

We'll keep you updated on any major new updates to Gates Open Research

Sign In
If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password.

The email address should be the one you originally registered with F1000.

Email address not valid, please try again

You registered with F1000 via Google, so we cannot reset your password.

To sign in, please click here.

If you still need help with your Google account password, please click here.

You registered with F1000 via Facebook, so we cannot reset your password.

To sign in, please click here.

If you still need help with your Facebook account password, please click here.

Code not correct, please try again
Email us for further assistance.
Server error, please try again.