Gates Open Res Gates Open Research 2572-4754 F1000 Research Limited London, UK 10.12688/gatesopenres.12804.1 Research Article Articles Mortality, fertility, and economic development: An analysis of 201 countries from 1960 to 2015

[version 1; peer review: 2 approved, 1 approved with reservations]

 Li Qingfeng Conceptualization Data Curation Formal Analysis Investigation Methodology Visualization Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-6390-6921 a 1 Tsui Amy O. Conceptualization Funding Acquisition Investigation Methodology Project Administration Supervision Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0001-9158-0503 1 Liu Li Formal Analysis Investigation Methodology Writing – Review & Editing 1 Ahmed Saifuddin Conceptualization Formal Analysis Methodology Writing – Review & Editing 1 Bill & Melinda Gates Institute for Population and Reproductive Health Department of Population, Family and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA qli28@jhu.edu

No competing interests were disclosed.

 1 3 2018 2018 2 14 20 2 2018 Copyright: © 2018 Li Q et al. 2018 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 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.

 Mortality fertility decline demographic dividend Gates Foundation OPP117_01 Gates Foundation [OPP117_01]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 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 development 1 . 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 literature 2 . 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 2050 3 . Women empowered to adapt voluntary measures to reduce fertility will benefit themselves, their children, and the local and global economy and environment 4 .

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 fixed 5 . Solow subsequently proposed that even reproducible factors would be swamped by rapid population growth 6 . 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 factor 7, 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 economy 9 .

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 growth 10, 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 transitions 1 . 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.

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 studies 12 . 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 R 2 = 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.

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 25 th 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 Secretariat 13 . 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

P t + 5 = M t , t + 5 ∗ P t ( 1 )

where P t is a column vector whose elements are the age-specific population at calendar time t; M t,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 development 14 .

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.

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.

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,

y i t = Y i t P i t = Y i t W i t W i t P i t = z i t s i t ( 2 )

where Y it is the gross domestic product (GDP) in country i in year t. y it is GDP per capita, P it is the total population, and W it is the number of workers. z it is the product per worker and w it 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 factors 15, 16 . The approach has been used in previous studies 17 . 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,

y i t h a t = y i t o b s 1 + D R i t o b s / 100 1 + D R i t h a t / 100 ( 3 )

where y i t h a t denotes the GDP per capita in country i in year t had there been no fertility decline; y i t o b s denotes the observed GDP per capita, i.e. from the World Bank database; D R i t h a t and D R i t o b s 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’ economies 1, 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 projection 18 . Those estimates vary, mainly because they cover different periods in time.

The contribution of fertility and mortality decline to the change in dependency ratio (DR) and GDP per capita in 10.
Region UN 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) 36 47 90 77 792 575 49,969 37,579 12,390
Australia/New Zealand (2) 29 51 54 54 1,826 1,265 1,203 1,064 139
EUROPE (39) 24 50 41 39 2,193 1,812 23,073 21,367 1,706
LATIN AMERICA AND THE CARIBBEAN (29) 38 50 92 83 826 681 8,364 6,379 1,985
Melanesia, Micronesia, and Polynesia (7) 56 64 97 91 600 503 12 10 2
NORTHERN AMERICA (2) 28 51 53 52 1,691 1,279 18,434 16,446 1,989
Northern Africa (5) 52 61 102 86 658 613 2,035 1,551 484
Sub-Saharan Africa (46) 80 85 96 83 644 575 3,333 3,012 321
World (178) 40 52 85 73 937 759 106,422 87,406 19,016

regions from 1960 to 2015

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

 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
Country UN WPP If no fertility decline If 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
Afghanistan 89 84 5 21 22 4 -2 -1 -19 1,748 59 54 5
Armenia 44 29 16 71 136 -46 64 124 -46 8,180 24 20 4
Azerbaijan 40 32 8 123 160 -23 105 137 -20 16,699 161 119 42
Bahrain 30 27 3 262 282 79 221 241 44 44,456 61 38 23
Bangladesh 53 45 8 106 127 -19 71 90 -44 3,133 505 370 135
Bhutan 47 40 7 121 143 -9 78 97 -37 7,736 6 4 2
Brunei Darussalam 38 33 6 176 209 -12 165 199 -33 74,600 31 21 10
Cambodia 56 49 6 85 98 -16 68 82 -38 3,291 51 39 12
China 38 24 13 159 272 -49 120 226 -74 13,570 18,958 13,214 5,744
China, Hong Kong SAR 36 15 21 252 729 -98 129 398 -68 53,490 388 233 155
China, Macao SAR 27 16 11 188 304 20 166 295 -19 100,518 60 43 17
China, Taiwan Province of China 35 19 17 156 340 -51 141 323 -64 n/a n/a n/a n/a
Cyprus 42 24 18 64 121 -12 54 115 -25 30,383 35 30 6
Dem. People's Republic of Korea 45 31 14 55 86 -12 41 76 -35 n/a n/a n/a n/a
Georgia 50 28 22 20 57 -26 11 51 -39 9,025 36 33 2
India 52 44 9 75 94 -22 53 72 -43 5,754 7,532 5,988 1,544
Indonesia 49 42 8 90 110 -18 62 78 -23 10,368 2,677 2,062 614
Iran (Islamic Republic of) 40 33 7 171 212 -22 122 157 -44 16,010 1,271 853 418
Iraq 78 72 5 35 38 -8 20 22 -18 14,929 539 467 72
Israel 64 46 18 17 33 -23 9 30 -43 31,971 258 242 16
Japan 64 21 43 8 45 -11 -15 44 -44 37,818 4,840 4,698 142
Jordan 66 60 6 78 89 -26 64 75 -39 8,491 78 59 18
Kazakhstan 50 40 10 50 68 -23 39 54 -19 23,522 418 358 60
Kuwait 30 27 3 287 310 49 260 283 35 69,329 273 165 108
Kyrgyzstan 55 48 7 58 67 -10 45 52 -10 3,238 19 16 3
Lao People's Democratic Republic 60 54 6 52 59 -2 35 40 -15 5,434 36 30 6
Lebanon 47 35 12 95 141 -40 86 133 -55 13,087 77 59 18
Malaysia 45 36 8 127 165 -33 114 152 -46 24,989 768 551 217
Maldives 38 32 6 204 241 -3 129 156 -25 11,994 5 3 2
Mongolia 49 43 6 115 133 -23 86 102 -28 11,409 34 25 9
Myanmar 50 42 8 87 109 -31 64 84 -40 5,071 266 206 59
Nepal 61 53 9 52 65 -27 23 34 -44 2,301 66 55 11
Oman 32 29 3 230 243 103 173 184 65 40,139 169 108 61
Pakistan 65 58 7 48 56 -12 33 39 -13 4,695 889 746 143
Philippines 58 51 7 77 93 -35 67 82 -37 6,875 699 545 154
Qatar 18 16 1 541 558 325 493 511 268 119,749 297 164 133
Republic of Korea 37 19 18 147 332 -52 123 301 -68 34,178 1,729 1,239 490
Saudi Arabia 41 37 4 165 181 29 123 136 15 50,724 1,601 1,081 519
Singapore 37 21 16 130 260 -42 118 257 -67 80,892 448 331 117
Sri Lanka 51 37 14 72 115 -41 59 102 -54 11,062 229 184 45
State of Palestine 76 71 5 56 61 -13 41 46 -29 2,654 12 10 2
Syrian Arab Republic 73 66 7 61 71 -34 44 54 -46 n/a n/a n/a n/a
Tajikistan 62 57 5 63 69 -2 45 50 1 2,641 23 18 4
Thailand 40 25 15 139 249 -49 117 220 -57 15,237 1,046 749 297
Timor-Leste 90 84 7 5 6 -1 -13 -12 -25 2,151 3 3 0
Turkey 50 38 12 105 148 -37 66 100 -47 23,382 1,830 1,355 475
Turkmenistan 53 46 6 83 97 -21 69 82 -26 14,992 83 65 19
United Arab Emirates 17 16 1 538 552 337 464 478 265 65,975 604 336 268
Uzbekistan 48 41 6 97 114 -14 84 99 -13 5,700 177 134 42
Viet Nam 43 33 10 117 158 -25 102 144 -43 5,667 530 393 137
Yemen 77 72 5 40 43 7 9 9 2 2,641 71 60 11
Australia/New Zealand
Australia 51 28 23 38 82 -17 26 82 -44 43,832 1,043 925 119
New Zealand 53 31 22 43 102 -37 35 101 -54 34,646 160 139 21
EUROPE
Albania 44 26 18 119 242 -58 102 211 -56 11,025 32 24 9
Austria 49 21 28 35 105 -17 20 101 -41 44,075 383 343 40
Belarus 44 23 21 26 72 -25 24 63 -20 17,230 163 151 12
Belgium 54 26 28 20 55 -14 5 53 -40 41,723 471 441 30
Bosnia and Herzegovina 43 21 23 62 174 -41 44 152 -55 10,902 39 32 6
Bulgaria 52 21 30 11 60 -24 7 53 -25 17,000 122 118 4
Channel Islands 47 22 25 37 78 2 24 77 -22 n/a n/a n/a n/a
Croatia 51 22 28 14 50 -14 -2 46 -39 20,636 87 83 4
Czechia 50 23 27 19 49 -7 8 47 -25 30,381 322 304 19
Denmark 56 26 30 15 54 -20 6 52 -34 45,484 259 246 13
Estonia 54 25 29 7 17 -2 1 13 -10 27,329 36 35 1
Finland 58 26 32 13 60 -26 -1 60 -51 38,994 214 204 10
France 59 29 30 14 53 -23 1 51 -46 37,766 2,434 2,313 121
Germany 52 20 32 23 89 -19 7 85 -42 43,784 3,578 3,321 256
Greece 53 22 30 22 55 -1 3 51 -32 24,095 270 251 19
Hungary 47 21 26 15 26 6 7 18 -3 24,831 243 232 11
Iceland 52 31 21 48 102 -34 42 101 -46 42,674 14 12 2
Ireland 54 33 20 40 82 -28 32 80 -47 60,944 286 251 36
Italy 56 21 35 18 76 -18 1 72 -42 34,245 2,038 1,915 123
Latvia 52 23 29 6 22 -6 6 17 -2 23,057 46 45 1
Lithuania 50 22 28 18 71 -24 16 62 -20 26,971 79 75 4
Luxembourg 44 24 20 45 54 35 24 52 -9 95,311 54 47 7
Malta 49 21 27 35 126 -36 23 125 -57 34,380 15 13 2
Montenegro 48 27 21 40 93 -31 30 82 -39 15,291 10 9 1
Netherlands 53 26 27 29 90 -28 21 89 -42 46,354 785 714 72
Norway 52 27 25 29 65 -11 19 63 -29 63,670 331 301 30
Poland 44 21 22 37 99 -21 28 90 -32 25,299 968 869 99
Portugal 53 22 32 29 126 -36 13 107 -51 26,548 277 251 26
Republic of Moldova 35 21 13 71 128 -19 62 119 -28 4,747 19 16 3
Romania 48 23 25 15 38 -7 7 30 -13 20,538 408 390 18
Russian Federation 44 24 19 29 67 -18 27 57 -10 24,124 3,471 3,187 284
Serbia 49 25 24 18 55 -21 8 43 -28 13,278 118 111 7
Slovakia 42 22 20 49 114 -22 43 109 -28 28,254 154 134 19
Slovenia 49 22 27 26 63 -5 11 65 -33 29,097 60 56 5
Spain 51 23 29 31 87 -13 15 81 -38 32,216 1,495 1,352 143
Sweden 58 27 31 13 29 -1 1 28 -24 45,488 444 423 21
Switzerland 49 22 27 38 80 3 19 78 -29 56,511 470 418 52
TFYR Macedonia 42 24 18 66 138 -30 52 121 -40 12,760 27 22 4
Ukraine 45 22 23 19 51 -11 18 43 -6 7,465 333 315 18
United Kingdom 56 27 28 21 63 -20 9 62 -43 38,509 2,518 2,342 176
LATIN AMERICA AND THE CARIBBEAN
Antigua and Barbuda 45 36 10 78 98 2 66 90 -21 20,114 2 2 0
Argentina 57 39 17 15 21 2 5 16 -19 19,101 829 786 43
Aruba 45 27 18 79 160 -46 73 155 -54 n/a n/a n/a n/a
Bahamas 41 29 12 101 147 -15 88 137 -34 21,670 8 6 2
Barbados 50 29 21 54 132 -53 45 122 -60 15,390 4 4 1
Belize 57 51 6 92 106 -24 79 91 -24 8,061 3 2 1
Bolivia (Plurinational State of) 64 53 11 54 71 -32 31 47 -53 6,532 70 58 12
Brazil 44 32 11 117 172 -36 95 145 -48 14,666 3,021 2,224 796
Chile 45 30 15 89 150 -34 68 128 -51 22,537 400 314 87
Colombia 46 35 10 132 182 -40 112 160 -52 12,985 626 443 184
Costa Rica 45 32 13 127 194 -42 107 171 -55 14,914 72 51 20
Cuba 43 23 20 99 223 -46 85 206 -57 n/a n/a n/a n/a
Curaçao 52 29 24 55 146 -56 48 141 -65 n/a n/a n/a n/a
Dominican Republic 58 47 10 99 131 -50 73 102 -58 13,372 141 103 37
Ecuador 56 45 10 88 115 -32 66 91 -45 10,777 174 132 41
El Salvador 57 44 12 87 125 -48 60 94 -59 7,845 50 38 12
French Guiana 63 55 8 36 39 12 26 32 -20 n/a n/a n/a n/a
Grenada 51 40 11 108 152 -55 94 136 -60 12,735 1 1 0
Guadeloupe 56 30 26 65 175 -66 55 168 -78 n/a n/a n/a n/a
Guatemala 69 61 8 57 66 -20 35 44 -36 7,293 119 96 22
Guyana 54 46 8 88 110 -42 83 104 -41 7,063 5 4 1
Haiti 62 55 8 46 54 -9 27 34 -20 1,651 18 15 3
Honduras 60 53 7 90 105 -19 65 79 -40 4,311 39 29 10
Jamaica 49 35 14 101 160 -46 90 146 -53 8,105 23 17 6
Martinique 57 29 28 58 178 -66 48 171 -80 n/a n/a n/a n/a
Mexico 51 42 10 106 139 -36 89 121 -45 16,668 2,098 1,543 555
Nicaragua 54 46 8 108 131 -30 81 103 -48 4,961 30 22 8
Panama 55 43 12 80 110 -31 67 98 -47 20,674 82 64 18
Paraguay 57 47 9 76 97 -29 68 90 -40 8,639 57 45 12
Peru 53 43 10 101 134 -37 71 101 -51 11,768 369 274 96
Puerto Rico 50 28 22 65 154 -49 58 146 -54 n/a n/a n/a n/a
Saint Lucia 41 28 13 168 276 -59 146 246 -62 10,677 2 1 1
Saint Vincent and the Grenadines 47 36 11 148 209 -57 123 176 -55 10,463 1 1 0
Suriname 51 41 10 118 161 -56 108 150 -61 14,767 8 6 2
Trinidad and Tobago 43 30 13 103 173 -50 97 166 -57 31,283 43 32 10
United States Virgin Islands 61 33 28 63 181 -75 58 175 -81 n/a n/a n/a n/a
Uruguay 56 33 23 15 34 -13 6 30 -29 19,831 68 65 3
Venezuela (Bolivarian Republic of) 53 43 10 105 137 -41 91 122 -51 n/a n/a n/a n/a
Melanesia, Micronesia, and Polynesia
Fiji 53 44 9 87 113 -43 75 100 -52 8,756 8 6 2
New Caledonia 48 34 14 76 124 -39 63 112 -53 n/a n/a n/a n/a
Papua New Guinea 67 61 6 39 45 -16 29 36 -39 n/a n/a n/a n/a
Solomon Islands 75 69 6 25 27 3 14 16 -12 2,053 1 1 0
Vanuatu 69 62 7 58 68 -29 40 49 -40 2,807 1 1 0
Guam 52 39 14 91 140 -49 80 130 -63 n/a n/a n/a n/a
Kiribati 63 57 6 58 65 -16 46 53 -25 1,874 0 0 0
Micronesia (Fed. States of) 62 55 7 63 76 -33 56 68 -39 3,285 0 0 0
French Polynesia 45 35 11 108 153 -40 95 141 -58 n/a n/a n/a n/a
Samoa 74 65 9 51 66 -54 43 60 -72 5,559 1 1 0
Tonga 74 64 10 38 52 -50 35 50 -62 5,189 1 0 0
NORTHERN AMERICA
Canada 47 24 24 55 147 -36 45 144 -52 42,983 1,545 1,313 232
United States of America 51 29 22 34 80 -25 25 78 -44 52,790 16,889 15,132 1,757
Northern Africa
Algeria 53 44 9 116 148 -38 84 113 -53 13,724 547 391 157
Egypt 62 54 8 73 89 -32 39 48 -21 10,096 947 741 206
Libya 49 43 6 128 151 -25 93 113 -38 n/a n/a n/a n/a
Morocco 52 42 10 110 146 -46 83 115 -56 7,286 254 184 69
Sudan 82 75 6 26 29 -11 15 18 -19 4,290 166 149 17
Tunisia 46 34 11 130 186 -43 91 140 -59 10,750 121 86 35
Western Sahara 45 41 4 125 131 56 88 93 22 n/a n/a n/a n/a
Sub-Saharan Africa
Angola 98 93 5 16 17 1 -5 -4 -20 6,231 174 161 13
Benin 86 80 6 8 7 19 -9 -8 -12 1,987 21 20 1
Botswana 55 49 6 89 101 -16 68 79 -23 15,356 34 26 8
Burkina Faso 92 88 5 5 4 30 -13 -14 6 1,551 28 27 1
Burundi 90 85 5 7 6 21 -3 -4 6 749 8 7 0
Cabo Verde 55 48 7 85 102 -33 65 81 -46 5,919 3 2 1
Cameroon 86 80 6 8 7 15 -6 -7 4 2,991 68 66 2
Central African Republic 90 83 7 2 2 -9 -14 -14 -17 626 3 3 0
Chad 100 95 5 -5 -7 19 -17 -18 3 2,048 29 29 (1)
Comoros 76 70 5 30 32 -3 15 17 -16 1,413 1 1 0
Congo 84 78 6 12 12 4 2 2 -3 5,543 28 26 1
Côte d'Ivoire 84 78 5 37 41 -31 14 16 -21 3,251 75 64 11
Democratic Republic of the Congo 97 92 6 -5 -7 12 -16 -17 -1 750 57 59 (2)
Djibouti 57 50 6 65 72 14 49 55 2 3,139 3 2 1
Equatorial Guinea 68 63 5 31 29 50 10 9 22 27,238 32 28 4
Eritrea 85 78 7 18 20 -16 0 2 -18 n/a n/a n/a n/a
Ethiopia 82 76 6 23 24 2 5 6 -16 1,533 153 139 14
Gabon 67 60 8 21 20 33 -1 -2 8 16,837 32 30 3
Gambia 92 88 4 0 -2 38 -21 -23 6 1,588 3 3 0
Ghana 73 67 6 39 43 -12 26 29 -18 3,930 108 93 15
Guinea 84 79 6 11 12 7 -8 -8 -16 1,184 14 14 1
Guinea-Bissau 80 75 5 9 9 20 -4 -4 5 1,424 3 2 0
Kenya 78 74 5 57 61 -16 38 42 -20 2,836 134 107 27
Lesotho 67 60 7 38 45 -15 21 27 -19 2,777 6 5 1
Liberia 83 78 6 21 23 2 -1 0 -23 785 4 3 0
Madagascar 80 75 5 37 40 -7 17 19 -21 1,376 33 29 5
Malawi 91 85 6 18 19 9 -7 -7 -5 1,089 19 18 2
Mali 102 97 5 -1 -2 16 -23 -23 -21 1,919 34 34 (0)
Mauritania 76 71 5 29 31 -1 17 19 -15 3,602 15 13 2
Mauritius 42 27 14 144 249 -60 130 231 -68 18,864 24 17 7
Mayotte 83 76 7 51 58 -34 40 48 -56 n/a n/a n/a n/a
Mozambique 94 87 6 7 8 1 -11 -10 -18 1,118 31 30 1
Namibia 68 62 6 43 47 0 25 28 -11 9,913 24 20 4
Niger 112 106 5 -4 -4 2 -18 -17 -27 897 18 18 (0)
Nigeria 88 83 5 6 6 3 -8 -8 -4 5,671 1,027 999 28
Rwanda 77 72 5 48 51 -1 26 29 -16 1,716 20 16 3
Réunion 53 37 16 93 158 -58 78 142 -71 n/a n/a n/a n/a
Sao Tome and Principe 87 81 6 13 14 -1 4 5 -14 2,942 1 1 0
Senegal 85 80 6 26 29 -8 5 8 -27 2,297 34 31 4
Seychelles 43 31 12 124 190 -50 108 169 -53 25,525 2 2 1
Sierra Leone 83 78 5 12 12 10 -11 -13 15 1,316 10 9 1
Somalia 97 92 5 -1 -2 12 -14 -14 -4 n/a n/a n/a n/a
South Africa 53 45 8 74 92 -30 59 74 -29 12,425 687 547 140
South Sudan 84 77 6 19 21 2 -3 -1 -21 1,808 21 20 2
Swaziland 69 64 5 50 54 -3 30 34 -12 8,054 11 9 2
Togo 81 76 5 23 25 0 7 8 -7 1,351 10 9 1
Uganda 102 97 4 7 7 5 -6 -7 6 1,693 68 66 2
United Republic of Tanzania 93 87 6 13 15 -5 -3 -2 -15 2,491 134 126 8
Zambia 92 87 5 20 21 -5 5 5 -1 3,627 58 53 5
Zimbabwe 80 74 5 44 49 -19 28 31 -14 1,891 30 25 5

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 rate 9 . Declining fertility also encourages greater savings within the working age population for retirement 14 . 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 nutrition 19 .

 Data availability

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

The 2017 Revision of World Population Prospects: https://esa.un.org/unpd/wpp/

International Comparison Program Database of the World Bank: http://www.worldbank.org/en/programs/icp

Bloom DE Williamson JG : Demographic Transitions and Economic Miracles in Emerging Asia. The World Bank Economic Review. 1998;12(3):419–55. 10.1093/wber/12.3.419 Caldwell JC : Toward A Restatement of Demographic Transition Theory. Popul Dev Rev 1976;2(3/4):321–66. 10.2307/1971615 Griggs D Stafford-Smith M Gaffney O : Policy: Sustainable development goals for people and planet. Nature. 2013;495(7441):305–7. 23518546 10.1038/495305a Ahmed S Li Q Liu L : Maternal deaths averted by contraceptive use: an analysis of 172 countries. Lancet. 380(9837):111–25. 22784531 10.1016/S0140-6736(12)60478-4 Malthus TR : An essay on the principle of population; or, A view of its past and present effects on human happiness. Reeves & Turner.1888. Reference Source Solow RM : A Contribution to the Theory of Economic Growth. Q J Econ. 1956;70(1):65–94. 10.2307/1884513 Becker GS Glaeser EL Murphy KM : Population and Economic Growth. Am Econ Rev. 1999;89(2):145–9. 10.1257/aer.89.2.145 Galor O Weil DN : Population, Technology, and Growth: From Malthusian Stagnation to the Demographic Transition and beyond. Am Econ Rev. 2000;90(4):806–28. 10.1257/aer.90.4.806 Bloom DE Canning D Fink G : Fertility, Female Labor Force Participation, and the Demographic Dividend. J Econ Growth. 2009;14(2):79–101. 10.1007/s10887-009-9039-9 Bloom DE Canning D Sevilla J : The Demographic Dividend A New Perspective on the Economic Consequences of Population Change. 1 ed : RAND Corporation.2003. 10.7249/MR1274 Brander JA Dowrick S : The role of fertility and population in economic growth: empirical research from aggregate cross-national data. J Popul Econ. 1994;7(1):1–25. 12287546 10.1007/BF00160435 Crespo Cuaresma J Lutz W Sanderson W : Is the demographic dividend an education dividend? Demography. 2014;51(1):299–315. 24302530 10.1007/s13524-013-0245-x UN: World population prospects: the 2017 revision. Population division of the department of economic and social affairs of the United Nations Secretariat, New York.2017. Reference Source Lee R Mason A Miller T : Life Cycle Saving and the Demographic Transition: The Case of Taiwan. Popul Dev Rev. 2000;26:194–219. Reference Source Maddison A : A Comparison of Levels of GDP Per Capita in Developed and Developing Countries, 1700–1980. J Econ Hist. 1983;43(1):27–41. 10.1017/S0022050700028965 Acemoglu D Johnson S Robinson JA : The Colonial Origins of Comparative Development: An Empirical Investigation. Am Econ Rev. 2001;91(5):1369–401. 10.1257/aer.91.5.1369 Bloom DE Finlay JE : Demographic Change and Economic Growth in Asia. Asian Econ Policy R. 2009;4(1):45–64. 10.1111/j.1748-3131.2009.01106.x Mason A Lee R : Labor and Consumption across the Lifecycle. J Econ Ageing. 2013;1–2:16–27. 24363989 10.1016/j.jeoa.2013.06.002 3866966 Fabic MS Choi Y Bongaarts J : Meeting demand for family planning within a generation: the post-2015 agenda. Lancet. 2015;385(9981):1928–31. 24993915 10.1016/S0140-6736(14)61055-2 4393371 10.21956/gatesopenres.13868.r26289 Reviewer response for version 1 Adioetomo Sri Moertiningsih 1 Referee Quarina Qisha 1 Co-referee Faculty of Economics and Business, University of Indonesia, Depok, Indonesia

Competing interests: No competing interests were disclosed.

 9 4 2018 Copyright: © 2018 Adioetomo SM and Quarina Q 2018 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve

This article adds to the ‘not many’ available analyses that tried to explain the relationship between population growth and economic development (filling the gap). The availability of long-term population projection by age and trends in economic growth among developing countries made the analysis feasible. Congratulations.

Demographic Dividend is an emerging issue that tries to provide a solution or explanation on the long debate whether population growth has a negative or positive effect on economic growth. The work by economist demographers such as David Bloom, David Canning and Jaypee Sevilla (2003), other economists cited in Birdsall, Kelley and Sinding 2001 strongly favor the finding that it is the changes in age structure, that is the declining proportion of children accompanied by the rapid growth of population at the working age that boost economic growth.

The increase in the size of population at the working age 15-64 years decreases dependency ratio which boost economic growth through several assumptions: (1) the large size of workers will produce larger size of output which increases the national GDP; (2) this will promote savings (capital accumulation); (3) the decreasing family size (due to family planning) eases women to participate in the labor force which in turn helps increase GDP growth; (4) but the most important thing is policy on human capital development (Bloom et.al. 2003).

There is also an issue about the impact of the very rapid fertility decline and the demographic transition in developing countries due to strong family planning program, such as in China, Thailand, and Indonesia. In the Western countries, the process of demographic transition occurred slowly and took more than one hundred years and accompanied by social and economic development. While in these developing countries such as Indonesia, demographic transition took only in three decades, when social and economic development in these countries have just begun. The population at the working age are not ready to utilize the favorable age structure for economic growth due to the low quality of workers.

Economist and policy makers (especially in Indonesia) argue that demographic dividend is not automatically being harnessed. To be able to boost economic growth appropriate policy measures, especially investment on human capital is the key to reap the demographic dividend.

Nevertheless this article is well appreciated, it adds to the existing literature which tried to provide the explanation on the link between population growth and economic growth. This article is powerful as advocacy tool for policy makers, that family planning program is not a cost, it is an investment.

But this analysis can be well improved through the inclusion of other variables measuring the quality of human capital of the related countries.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

Reviewer Expertise:

NA

We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

 10.21956/gatesopenres.13868.r26288 Reviewer response for version 1 Ram Usha 1 Referee Department of Public Health and Mortality Studies, International Institute for Population Sciences, Mumbai, India

Competing interests: No competing interests were disclosed.

 13 3 2018 Copyright: © 2018 Ram U 2018 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve

My general observations on the paper are as below:

The paper very clearly demonstrates economic advantages through empowering women as a result of fertility reduction over a period of 55 years. In the past, many researchers have attempted to demonstrate the same by projecting population based on various assumption on future fertility regime. However, the present research is different in the sense it compares hypothetical, yet, simple assumptions of constant fertility and constant fertility and mortality and simulates 2015 situation with actual experiences for 2015 in 201 countries covering different regions.

The present work clearly advocates that if the countries and global communities had not invested in the family planning, the world and each country could have been unmanageably very different and possibly it would have been difficult to achieve the current wellbeing levels of the populations and especially the lives of the women who had been benefited the most from these investments. It might have sometimes difficult for some countries when they adopted certain path which were the violation of human right but corrective steps by mechanism of Global pressures and guidelines for changing the course of program focus. In the past few decades, there has been a feeling around the globe that the population problems have largely been solved with the exception of a few geographies.

The paper again demonstrates incomplete agenda of reproductive rights of women and advocates more economic returns than the investment required to be made in the family planning program. The value of research is enhanced in view of London Submit of 2012 that expressed its commitment to reach 120 million unreached women out there needing family planning services. Even country like India that has already reached TFR of 2.2 per women in 2015 has more than 30 million unmet need for contraception (if we express demand for modern method then this number would be close to 45 million).

This also means that our demographic goal may be replacement fertility but demand for contraception in any country would go beyond this and would depend on method-mix, access to basket of methods. In this context, I strongly believe that such research work provides program managers and policy makers of country to rethink about the invest strategies for empowering women. It will be observed then that this option would be easier and cheaper than any other option available to empower women. I also believe that such analysis would be very useful at state level for a country like India that has huge demographic diversity.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

NA

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

 10.21956/gatesopenres.13868.r26290 Reviewer response for version 1 May John 1 Referee Turbat Vincent 1 Co-referee Georgetown University, Washington, DC, USA

Competing interests: No competing interests were disclosed.

 12 3 2018 Copyright: © 2018 May J and Turbat V 2018 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

First, we would suggest that the paper focuses on the main topic, namely the relationship between mortality and fertility, on the one hand, and the demographic dependency ratio and economic development (the demographic dividend), on the other. Therefore, the discussion on Malthus (see Introduction) is not needed and only sidetracks the reader.

Second, the demographic dependency ratio as used in the paper is rather narrow with respect to young dependents (age group 0 to 14). We would suggest the authors use a broader age group for the young dependents, namely age group 0 to 19 completed (or age group under age 20 exact). So the entire paper might use this extended group of young dependents. In this respect, see John F. May and Vincent Turbat, “Demographic Dividend in Sub-Saharan Africa: Two Issues that Need More Attention”, Journal of Demographic Economics, vol. 83(1), March 2017, pp. 77-84. 1

Third, some definitions used in the paper need to be refined or expanded. The demographic dividend is a process leading to an economic surplus; it should not be presented as a result. Likewise, the definition of productivity used in the paper is weak. We believe the authors meant the average production per capita.

Fourth, we believe the authors used the 2017 UN Population Division projections; however, this is not clearly explained in the paper and sometimes the reader is under the impressions that the authors used their own population projections (see section on CCM) page 5). In addition, the economic model used in the paper needs more explanations and also double-checking. For instance, the explanation of equation (2) page 12 should cite s(it) and not w(it) (see 3 rd line below equation (2).

Fifth, we have some problems with Table 1, first line pertaining to Asia. The Child DR with no fertility change is 90, but the Total DR with no fertility change is 77; how can the latter figure be lower than the first? In addition, the Child DR and Total DR with no fertility and mortality change jump to 792 and 575, respectively. How is this possible, especially as the authors explain that fertility is a more important factor than fertility to explain changes in the DR? We were puzzled.

Finally, the style of the paper needs to be improved. It seems that the paper was written rather rapidly. In addition, Table 2 (which is very long) could be presented as an Annex.

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

Partly

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Partly

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

Reviewer Expertise:

Demography (JM) and economy (VT)

We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above.

References : THE DEMOGRAPHIC DIVIDEND IN SUB-SAHARAN AFRICA: TWO ISSUES THAT NEED MORE ATTENTION. Journal of Demographic Economics .2017;83(01) : 10.1017/dem.2016.21 77-84 10.1017/dem.2016.21