Corresponding author: Muttur R. Narayana (
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The paper offers a new explanation and prediction of empirical relationship between income and consumption inequalities and demographic dividend. The framework for the analysis is a modified National Transfer Accounts (NTA)-based modelling of the first demographic dividend with inequality-adjusted or inequality-discounted economic support ratio (ESR). The model is tested for India by calculating the inequality-adjusted demographic dividend (or the growth rate of ESR) for the period 2005-2050. The results show that income inequality is not higher than consumption one for all ages and these age-specific economic inequalities have remarkable effects on (i) lowering the observed age-specific distribution of labour income for select ages and consumption for all ages and (ii) reducing the size and duration of demographic dividend due to lower growth rate of ESR. In addition, income inequality effects are found to be stronger than consumption inequality effects in terms of reducing the size of demographic dividend. These results imply that (a) growth effects of the first demographic dividend are upward-biased if unadjusted for the economic inequalities; (b) attainment of goals and targets of the reduction in inequalities under UN-SDGs 2030 by redistributive economic policies are contributory to the maximization of economic growth through the first demographic dividend; and (c) economic inequalities do impact the size and duration of demographic dividend. Subject to the availability of data, the modified approach to the first demographic dividend calculation in this paper is of relevance for comparative studies between India and other countries to draw lessons from mutual experiences and to establish the generality of results.
India’s age structure transition from present to 2100 shows the highest share of working-age population (aged 19-60). If educated, healthy, skillful, gainfully and fully employable, an increasing share of working age population shall result in generation of productive income and its resultant consumption, savings and investment in the economy. This is contributory to higher economic growth in terms of higher growth rate of national income. This process of demographically-induced economic growth, driven by age structure transition, is called potential demographic dividend. However, in real economies, full economic conditions for the realization or reaping potential demographic dividend may not be in place. Consequently, potential demographic dividend may remain a policy objective or target to be attained.
Economic inequalities in the distribution of income and consumption are ubiquitous in real economies, irrespective of their levels of economic growth and development. The impact of inequalities on welfare, poverty and growth is well studied (Handbook of Income…, 2000, 2015;
NTA is a unique methodology that incorporates demographic variables into macroeconomic and income distribution analyses. Essentially, NTA provides with an aggregate framework for introduction of age into National Income and Product Accounts (NIPA). This framework treats an individual as a fundamental unit of analysis and gives quantitative estimates of resource inflows (e.g., labour and non-labour incomes) and outflows (e.g., consumption and savings) by age. This approach recognizes that production and consumption of goods and services differ by age or age groups. Further, the inflows and outflows are extended to the public (or general government) and private (i.e., households and corporate) sectors and allocation of resources is accounted for transfers and asset-based reallocations. Thus, NTA provides an aggregate accounting framework of all inter-age flows of resources that is consistent with NIPA in an accounting year.
An elaborate international study on the demographic dividend in the NTA framework is
Economic inequalities by socio-economic status (SES) have been studied in NTA framework for different countries. For instance,
A latest study on NTA-based economic inequalities by the socio-economic status is
Inequalities have been studied in India with a focus on income and consumption inequalities by various socio-economic dimensions. The latest comprehensive review of income, consumption and wealth inequalities in India is
This paper draws lessons from the above NTA literature to offer a new explanation and prediction of empirical relationship between the income and consumption inequalities by age (in brief, income inequality and consumption inequality) and demographic dividend with specific reference to India. Unlike the mentioned above approaches to inequality by the socio-economic status, this paper approaches to use the overall inequality by age where distribution of an NTA-variable is calculated across all individuals at each age. Overall inequality approach is used in
Are there unique patterns of age specific economic inequalities?
How does inequality relate to and impact the demographic dividend?
Will a higher inequality result in shorter and smaller demographic dividend? If yes, will income inequality have a stronger effect than consumption inequality on demographic dividend?
Will combined effects of income and consumption inequalities be stronger than individual inequality effect?
What do these analyses imply for growth effects of inequality through the demographic dividend channel?
To answer these questions, a modified NTA-based First Demographic Dividend Model is developed with inequality-adjusted or inequality-discounted Economic Support Ratio (ESR). The model is tested for India by calculating the overall inequality-adjusted demographic dividend (or growth rate of ESR) for 2005-2050. This approach incorporates both growth and distributional considerations in the study of demographic dividend and, hence, the results have wider implications for design and implementation of broader economic development policies. Subject to the comparability of labour income and consumption structures, nature and degree of inequality, demographic transition, the approach of this paper can be replicated in other countries. Both replicative and comparative studies are useful to establish the generality of results obtained for India in this paper.
Rest of this paper is organized as follows. Section 2 describes the past, present and future age structure transition of India over the period 1950-2100. The inequality-adjusted NTA-based First Demographic Dividend Model is presented in section 3. Variables and data descriptions are given in section 4. Empirical results are analyzed in section 5. Major conclusion and implications are included in section 6. All tables and figures are sequentially given in the Appendix.
Data on India’s population by single year age is available from the decennial population census reports. The latest Census was conducted in 2011 (Government of India 2011).
To start with, changes in India’s total population size in the period1950-2100 is shown in Figure
Considering the long term age structure transition over 150 years (see Fig.
In addition, India’s age structure transition (see Fig.
It is plausible to translate the above age structure transition in terms of dependency transition in 1950-2100 (see Fig.
In the presence of child labour and positive work-participation rate for elderly, all children and elderly may not be strictly considered as dependents. However, using NTA methodology, this can be corrected by calculation of the age profile of labour income and its impact on demographic dividend through the economic support ratio. These advantages of NTA methodology are elaborated in the following sections.
To start with, from the production side, per capita gross domestic product (GDP) can be defined as a product of labour productivity (or GDP per employee) and ratio of working population to total population (or number of employees per capita as a measure of labour force participation ratio).
Y(t)/N(t) = {Y(t)/L(t)}{L(t)/N(t)} (1)
To express (1) in growth rate terms, we take logarithms of both sides and differentiate with respect to time (t). The resultant equation in terms of growth rate (g) is as follows.
g[Y(t)/N(t)] = g[Y(t)/L(t)] + g[L(t)/N(t)] (2)
What distinguishes the NTA methodology from the general approach to the measurements of variables in (2) is related L(t) and N(t). That is, L(t) = ∑γ(a)P(a,t) is effective number of producers at age a and time t; and N(t) = ∑φ(a)P(a,t) is effective number of consumers at age a and time t, where γ(a,t) is productivity age profile at age a and time t and φ(a,t) is consumption age profile at age a and time t, and P(a,t) is total population at age a and time t.
As per NTA methodology (United Nations 2013), [L(t)/N(t)] is called Economic Support Ratio (ESR) or ratio of effective number of producers to effective number of consumers of goods and services. Effective number of workers refers to number of workers, adjusted for age differences in labour income, to the total population. This measure broadly captures the age variations in labour force participation, hours worked, unemployment, and productivity or wages. Effective number of consumers refers to number of consumers, adjusted for age differences in consumption levels, to the total population. Age structure transition leads to large shifts in the ESR and interacts with labour productivity to determine the economic growth (or growth rate of GDP per effective consumer). A positive growth rate of ESR means that the number of effective workers rises per unit of effective number of consumers. Thus, ESR is essentially different from the standard demographic dependency ratios because the age profile of labour productivity, calculated for measurement of effective number of workers, does capture the labour force participation of both children and elderly population.
Two types of demographic dividend can be distinguished in (2) depending on how dividends operate through (Mason et al. 2017).
First Demographic Dividend (FDD) operates through ESR. That is, given growth rate of labour productivity, the period during which growth of support ratio leads to increase economic growth (or growth of GDP per effective consumer).
Second Demographic Dividend operates through the growth rate of labour productivity.
However, the focus of this paper is on FDD.
Following United Nations (2013: 53), we note that NTA provide the aggregate and per capita flows for each age or age group but no distributional information within age groups. However, inequality is relevant in the FDD model if inequality exists in the age-specific distribution of per capita labour income [γ(a,t)] and per capita consumption [φ(a,t)]. Introduction of inequality into FDD model calls for a framework to integrate inequality through per capita labour income and consumption. For this purpose, we adjust the labour income and consumption profiles for overall inequality by age by multiplying the age profile of per capita labour income by (1-Gyat) and age profile of per capita consumption by (1-Gcat), where Gyat is Gini coefficient of per capita labour income and Gcat is Gini coefficient of per capita consumption at age a and time t.
First, γ(a,t) is adjustable for income inequality by age [γ(a,t)*].
γ(a,t)*= γ(a,t) (1-Gyat), (3)
where Gyat is a measure of inequality (e.g., Gini coefficient) in labour income distribution at age a and time t. In the same way, inequality-adjusted per capita consumption [φ(a,t)*] results in
φ(a,t)*= φ(a,t)(1-Gcat), (4)
where Gcat is a measure of inequality (e.g., Gini coefficient) in distribution of per capita consumption at age a and time t.
Using γ(a,t)* in (3) and φ(a,t)* in (4), the inequality-adjusted effective number of producers and consumers can be calculated as follows.
L(t)* = ∑γ(a,t)*P(a,t) (5)
N(t)* = ∑φ(a,t)*P(a,t) (6)
where L(t)* is inequality-adjusted effective number of producers, N(t)* is inequality-adjusted effective number of consumers.
Thus, growth effect of inequality-adjusted FDD is measured as follows.
g[Y(t)/N(t)]* = g[Y(t)/L(t)]t=0 + g[L(t)*/N(t)*] (7)
where g[Y(t)/L(t)]t=0 is growth rate of labour productivity evaluated at t=0. This implies that growth rate of labour productivity is constant over time.
Equation (7) is an empirical basis for calculation of the impact of economic inequalities on FDD for India. It can be calculated by the following sequential steps.
Age profiles of per capita labour income and per capita consumption are calculated.
Age specific Gini coefficients are calculated for labour income and consumption.
Age profile of per capita labour income is adjusted for age-specific Gini coefficient of labour income to calculate the inequality-adjusted per capita labour income.
Age profile of per capita consumption is adjusted for age-specific Gini coefficient of consumption to calculate the inequality-adjusted per capita consumption.
Inequality-adjusted per capita labour income and per capita consumption are used to calculate the effective number of producers and consumers and Economic Support Ratio.
In the absence of time series data for calculation of the age profiles of labour productivity, consumption, and inequalities, they may be assumed as time-invariant or constant over time from the benchmark year. That is, γ(a,t) = γ(a), φ(a,t) = φ(a), Gcat = Gca, Gyat = Gya, for all t in equation (2) through equation (7). Under these assumptions, the equations for calculation of FDD are as follows.
g[Y(t)/N(t)] = g[Y(t)/L(t)]t=0 + g[L(t)/N(t)] (8)
g[Y(t)/N(t)]** = g[Y(t)/L(t)]t=0 + g[L(t)**/N(t)**] (9)
where
γ(a)**= γ(a)(1-Gya);
φ(a)**= φ(a)(1-Gca);
L(t)** = ∑γ(a)**P(a,t) is inequality-adjusted effective number of producers calculated with time invariant γ(a) and Gya in γ(a)**;
N(t)** = ∑φ(a)**P(a,t) is inequality-adjusted effective number of consumers calculated with time invariant φ(a) and Gca in φ(a)**;
and all other notations are the same as before.
Equation (9) explicitly shows that the inequalities affect growth but not vice versa. This simple formulation assumes away the reverse effects of growth on inequality. Further, growth effects of FDD are captured without inequalities in equation (8) and with inequalities in equation (9). The difference in results based on equation (8) and (9) for a given year is accountable for the growth effects of inequalities through FDD. However, the empirical results of this paper must be qualified by these assumptions in the formulation of (8) and (9).
Using equations (8) and (9), FDD is calculated up to 2050 from the benchmark year 2004-05. Next, equations (8) and (9) are recalculated from the new benchmark year 2011-12. The new benchmark year 2011-12 rescales the age profiles of labour income and consumption in 2011-12 using the age shapes of 2004-05. Thus, the difference in results of FDD from 2005 to 2050 and 2012-2050, based on equations (8) and (9), shows the impact of benchmark estimates on the size and duration of FDD for the comparable years.
To implement the operational model in section 3.3 above, data are required for measurement of variables and parameters relating to (a) age profiles of per capita labour income and consumption, (b) age-specific income and consumption inequalities, (c) growth rate of labour productivity and (d) population by single year age from 2004-05 to 2050. Description of variables and data sources and data limitations for these calculations are explained below.
Chapter 3 in NTA Manual (United Nations 2013) gives a detailed description of the methodology for: a) calculation of macro controls or control totals which are aggregate measures of economic flows as measured by gross disposable income in the System of National Accounts (SNA); b) steps in calculation of aggregate and per capita age profiles of variables using micro level and nationally representative surveys; and c) adjustments for macro controls to ensure consistency with survey-based estimates of age profiles. Macro controls are used to scale NTA age profiles so that the NTA macro controls match the estimates from the SNA. We follow this NTA methodology for the calculation of per capita age profiles of labour income and consumption. We do not repeat these methodological details here but focus on describing India’s databases for the calculations of age profiles of labour income and consumption. Further, we develop our methodology for calculations of: a) age profiles of inequalities in labour income and consumption; and b) growth rate of aggregate labour productivity. For all measurements, population data is taken from the latest United Nations population projections by single year age and medium-variant (United Nations 2019).
Macro control for labour income is sum of: a) compensation of employees; b) 2/3 of mixed income; and c) net compensation of employees from rest-of-world. Data for calculation of macro control of labour income in 2004-05 is National Accounts Statistics (Central Statistical Office 2015). Aggregate age profile of labour income is calculated based on individual income from wages and salaries and household income from self-employment (i.e., farm income and non-farm business income) in 2004-05 using the unit level data from the India Human Development Survey 2005 (
Aggregate age profiles of public and private consumption are separately calculated by education, health and other consumption. Next, aggregate public and private consumptions are summed and age profile of per capita consumption is obtained. Macro control for calculation of private consumption is Private Final Consumption Expenditure on education, health and others. Households account in India’s National Accounts Statistics (in the framework of SNA) includes Non-profit Institutions Serving Households (NPISHs). Thus, macro controls for private consumption includes consumption of both households and NPISHs. Macro control for calculation of public consumption is Government Final Consumption Expenditure. This refers to sum of individual (education and health) consumption and collective consumption (or public consumption). Source of data for these macro controls in 2004-05 is the National Accounts Statistics (Central Statistical Office 2018). Databases used for calculation of age profiles include India Human Development Survey 2005 (
Given macro adjustment, aggregate age profile of consumption is divided by age-specific population to calculate the per capita age profile in 2004-05. Age profile in 2011-12 is calculated by up-scaling the age profile of aggregate consumption in 2004-05 to macro control of consumption in 2011-12 (Central Statistical Office 2018). Per capita age profile of consumption in 2011-12 is calculated by dividing aggregate consumption profile by age specific population in 2011-12.
Age profile of labour income inequality is calculated by age specific Gini coefficient. Using the age distribution of individual worker’s total labour income from wages and salaries from all types of employment, age-specific Gini coefficient is calculated for 2004-05 and 2011-12. Databases for these calculations are NSS 61st Round in 2004-05 (comprising 602,833 enumerated persons) and NSS 68th Round in 2011-12 (comprising 456,999 enumerated persons) on Employment and Unemployment Situation in India.
Age profile of consumption inequality refers to age-specific Gini coefficient. It is calculated in three steps. First, monthly per capita consumption expenditure (MPCE) at i-th age is calculated by dividing total household consumption expenditure on the Mixed Recall Period basis by household size and assigning this per capita household consumption expenditure equally to all household members regardless of their age.
Labour productivity or output per worker is measured by Gross Value Added (GVA) at constant prices. Growth of labour productivity in 2004-05 is calculated by Compound Annual Growth Rate (%) of GVA (at 1999-00 prices) between 1999-00 and 2004-05. Data for this is sourced from Planning Commission (2008). The calculated value of growth of labour productivity per year is 3.01 percent (in 2004-05). In the same way, growth of labour productivity in 2011-12 is calculated by Compound Annual Growth Rate (%) of GVA (at 2004-05 prices) between 2004-05 and 2011-12. Data for this calculation is taken from three sources: a) GVA from Central Statistical Office (2018); b) workforce data for 2004-05 from Planning Commission (2008); c) workforce data for 2011-12 from State of Working India (2018). The calculated value of growth of labour productivity per year is 6.90 percent (in 2011-12).
Two sets of empirical results are presented and analyzed: 1) basic results by age profiles of labour income, consumption and inequalities for 2004-05 and 2011-12; 2) analytical results on the FDD with inequalities over the period 2005 to 2050.
Figure
The results in Figure
Age profile of income inequality, measured by inequality in distribution of labour income by single year age, is shown in Figure
Figure
Interestingly, the calculated value of Gini coefficient within the elderly (≥ 60 years) is 0.421 in 2011-12. This is lower than the Gini coefficient for all age (0.514). This result is in contrast with results in other international studies. For instance, OECD (2019) reported the income inequality for two age groups: the elderly (aged over 65 years) and total population (all ages) for 36 OECD countries and BRICS countries in G20 countries. Our result of Gini coefficient for the elderly is higher than all OECD countries except Mexico (0.500) and our Gini coefficient for all ages is higher than all OECD countries. As compared to other BRICS countries, except China, India’s income inequality is higher. However, these comparisons should be treated as merely qualitative because of the differences in definition and measurement of income and inequality. For instance, our definition of income is limited to earnings and self-employed income and OECD (2019) includes income from work, private occupational transfers, and capital income.
Age profiles of consumption inequality by single year age in 2004-05 and 2011-12 are given in Figure
Consumption inequalities are higher in 2011-12 than in 2004-05 up to age 16 years. From age 17 years, inequalities in 2011-12 are higher or lower by specific ages. For instance, consumption inequalities are lower in 2011-12 for following ages: 17-19 years, 27-29 years, 38-42 years and from 81-90 years except for age 82 and 85.
A higher income inequality than consumption inequality is a general finding in many studies on inequalities in India including in the recent studies by
Our calculations of age-specific inequalities show that, unlike the labour income inequality which is zero up to age 7 (see Fig.
Figure
Figure
Analytical results on the FDD are calculated in the presence of both labour income and consumption inequalities and either labour income or consumption inequality.
Using the equations (8) and (9), and age profiles in 2004-05, the results of FDD from 2005 to 2050 are given in Table
However, if adjusted for labour income inequality as well as consumption inequality, the values of ESR and growth rate of ESR are smaller but duration of demographic dividend is longer than when unadjusted for the inequalities. This implies that economic inequalities do matter in terms of the size and duration of India’s FDD in the period 2005-2050. Moreover, income inequality has a stronger effect on reducing the size and duration of FDD than consumption inequality.
Table
Using the data presented in Table
This paper offers a new explanation and prediction of empirical relationship between income and consumption inequalities and NTA-based FDD model for India. The results show that India’s first demographic dividend (FDD) size and duration over the period 2005-2050 have six important determinants: 1) growth rate of aggregate labour productivity; 2) age profile of labour productivity; 3) age profile of consumption; 4) labour income inequality by age; 5) consumption inequality by age; 6) age structure transition. Overall results indicate that the inequalities have remarkable effects on (i) lowering the age-specific distribution of labour income for select ages and consumption for all ages, and (ii) reducing the size of demographic dividend due to lesser growth rate of economic support ratio (ESR). Income inequality effects are found to be stronger than consumption inequality effects in terms of reducing demographic dividend. These results imply that the growth effects of FDD are upward-biased if unadjusted for the economic inequalities. Thus, economic inequality does matter for India’s first demographic dividend realization.
The empirical results also imply that the attainment of reduction in inequalities by redistributive economic policies and investments in human capital for increasing the effective number of consumers are contributory to maximization of economic growth through FDD channel. For instance, in the framework of UN-SDGs 2030, redistributive and human capital investment policies for attainment of targets under the following goals, among other, are contributory to reduction in inequality and increase economic growth: Goal 1 (No Poverty), Goal 2 (Zero Hunger), Goal 3 (Good Health and Well-being), Goal 4 (Quality Education), Goal 5 (Gender Equality), Goal 8 (Decent Work and Economic Growth) and Goal 10 (Reduced Inequalities). However, a detailed study is needed to link between the attainments of targets under these goals, inequalities and FDD for India. This analysis may also have important implications on explaining and predicting the economic and demographic factors which influence the growth rate of labour productivity, age profile of labour productivity, age profile of consumption, labour income inequality by age, consumption inequality by age, and age structure transition.
If distribution of income and consumption change in the process of economic growth and demographic transition, the nature and degree of inequalities by age may also change. These dynamic implications can be captured in this paper if a time series of age profiles of labour income and consumption and inequalities in their distribution can be calculated. Subject to the availability of data in future, these time series calculations of the age profiles can be attempted. This shall be useful to offer either supporting or confronting evidence for India’s growth effects of inequality through FDD tested in this paper.
Economic inequalities in this paper are calculated without controlling for any socio-economic status of individuals. Given the socio-economic diversity and disparities, and if controlled for education or other socio-economic status, a future study of inequalities by age may offer new insights into income, consumption and other NTA variables.
Subject to the comparability of labour income and consumption structures, nature and degree of inequality, and demographic transition, the approach of this paper can be replicated for comparative studies in developing countries. These include the BRICS countries who are members of NTA Global Research Network and have constructed NTA profiles. Such replicative and comparative studies will be useful in terms of establishing the generality of results obtained for India in this paper.
Early versions of this paper were presented at the technical sessions at (a) the Africa NTA Conference on Population and Development held by the Regional Consortium for Research in Generational Economy (CREG) (28-30 October 2019; Somone, Senegal); (b) the NTA Global Meeting on Population and the Generational Economy (3-7 August 2020; Honolulu, Hawaii, USA); (c) the Asia-Pacific Regional Virtual Meeting “Using the National Transfer Accounts (NTA) for Intergenerational Policy Advancement” (5-6 October 2021). The author is grateful to Professors Andrew Mason, Sang-Hyop Lee, Latif Damani, Bazlul Khondker and Maria Rivera, Doctors Tim Miller and Wassana Im-Em, and other distinguished participants in the above-mentioned conferences for constructive suggestions; and to Fiscal Policy Institute (Bengaluru, India) for support and encouragements. In addition, an earlier version of this paper has benefited from the perspicacious and technical comments by Professor Ronald Lee, two anonymous reviewers and the editor of this Journal. However, usual disclaimers apply.
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India’s first demographic dividend: calculated values of economic support ratio and its growth rates, India, 2005-2050
Year | Economic Support Ratio (ESR) | Growth rate of ESR (%) | ||||||
---|---|---|---|---|---|---|---|---|
Unadjusted for inequality | Adjusted for both inequalities | Adjusted for income inequality | Adjusted for consumption inequality | Unadjusted for inequality | Adjusted for both inequalities | Adjusted for income inequality | Adjusted for consumption inequality | |
2005 | 0.918 | 0.651 | 0.423 | 1.411 | ||||
2006 | 0.923 | 0.654 | 0.425 | 1.420 | 0.572 | 0.559 | 0.524 | 0.608 |
2007 | 0.928 | 0.658 | 0.428 | 1.429 | 0.606 | 0.582 | 0.546 | 0.641 |
2008 | 0.934 | 0.662 | 0.430 | 1.438 | 0.622 | 0.594 | 0.558 | 0.658 |
2009 | 0.940 | 0.666 | 0.432 | 1.448 | 0.630 | 0.609 | 0.571 | 0.668 |
2010 | 0.946 | 0.670 | 0.435 | 1.458 | 0.630 | 0.614 | 0.574 | 0.670 |
2011 | 0.951 | 0.674 | 0.437 | 1.466 | 0.531 | 0.526 | 0.488 | 0.568 |
2012 | 0.956 | 0.678 | 0.439 | 1.475 | 0.562 | 0.566 | 0.525 | 0.603 |
2013 | 0.962 | 0.682 | 0.442 | 1.484 | 0.583 | 0.590 | 0.547 | 0.626 |
2014 | 0.967 | 0.686 | 0.444 | 1.493 | 0.584 | 0.590 | 0.546 | 0.628 |
2015 | 0.973 | 0.689 | 0.446 | 1.503 | 0.572 | 0.577 | 0.533 | 0.616 |
2016 | 0.978 | 0.693 | 0.449 | 1.512 | 0.552 | 0.559 | 0.516 | 0.595 |
2017 | 0.984 | 0.697 | 0.451 | 1.520 | 0.533 | 0.529 | 0.490 | 0.573 |
2018 | 0.989 | 0.701 | 0.453 | 1.529 | 0.528 | 0.522 | 0.482 | 0.568 |
2019 | 0.994 | 0.704 | 0.455 | 1.538 | 0.542 | 0.537 | 0.495 | 0.585 |
2020 | 1.000 | 0.708 | 0.458 | 1.547 | 0.559 | 0.555 | 0.509 | 0.605 |
2021 | 1.004 | 0.712 | 0.460 | 1.555 | 0.475 | 0.469 | 0.427 | 0.516 |
2022 | 1.010 | 0.715 | 0.462 | 1.564 | 0.512 | 0.508 | 0.462 | 0.558 |
2023 | 1.015 | 0.719 | 0.464 | 1.573 | 0.530 | 0.523 | 0.475 | 0.578 |
2024 | 1.020 | 0.723 | 0.466 | 1.582 | 0.523 | 0.512 | 0.462 | 0.572 |
2025 | 1.025 | 0.726 | 0.468 | 1.590 | 0.500 | 0.489 | 0.438 | 0.551 |
2026 | 1.030 | 0.729 | 0.470 | 1.598 | 0.454 | 0.443 | 0.393 | 0.504 |
2027 | 1.035 | 0.733 | 0.472 | 1.606 | 0.450 | 0.434 | 0.384 | 0.499 |
2028 | 1.039 | 0.736 | 0.474 | 1.614 | 0.436 | 0.416 | 0.368 | 0.484 |
2029 | 1.044 | 0.739 | 0.475 | 1.622 | 0.419 | 0.398 | 0.352 | 0.465 |
2030 | 1.048 | 0.741 | 0.477 | 1.629 | 0.395 | 0.372 | 0.329 | 0.439 |
2031 | 1.051 | 0.744 | 0.478 | 1.635 | 0.337 | 0.311 | 0.274 | 0.374 |
2032 | 1.055 | 0.746 | 0.479 | 1.641 | 0.325 | 0.299 | 0.262 | 0.362 |
2033 | 1.058 | 0.748 | 0.481 | 1.647 | 0.305 | 0.282 | 0.245 | 0.343 |
2034 | 1.061 | 0.750 | 0.482 | 1.652 | 0.277 | 0.257 | 0.218 | 0.316 |
2035 | 1.063 | 0.752 | 0.482 | 1.656 | 0.240 | 0.225 | 0.186 | 0.280 |
2036 | 1.065 | 0.753 | 0.483 | 1.660 | 0.176 | 0.164 | 0.128 | 0.212 |
2037 | 1.067 | 0.754 | 0.484 | 1.663 | 0.157 | 0.148 | 0.114 | 0.191 |
2038 | 1.068 | 0.755 | 0.484 | 1.666 | 0.131 | 0.128 | 0.095 | 0.163 |
2039 | 1.069 | 0.756 | 0.484 | 1.668 | 0.096 | 0.106 | 0.074 | 0.128 |
2040 | 1.070 | 0.756 | 0.485 | 1.669 | 0.052 | 0.074 | 0.041 | 0.085 |
2041 | 1.070 | 0.756 | 0.485 | 1.670 | -0.006 | 0.022 | -0.008 | 0.024 |
2042 | 1.069 | 0.756 | 0.485 | 1.670 | -0.025 | 0.011 | -0.022 | 0.008 |
2043 | 1.069 | 0.756 | 0.484 | 1.670 | -0.045 | -0.002 | -0.036 | -0.011 |
2044 | 1.068 | 0.756 | 0.484 | 1.669 | -0.064 | -0.019 | -0.054 | -0.029 |
2045 | 1.067 | 0.756 | 0.484 | 1.668 | -0.090 | -0.040 | -0.077 | -0.053 |
2046 | 1.066 | 0.755 | 0.483 | 1.667 | -0.129 | -0.078 | -0.112 | -0.095 |
2047 | 1.065 | 0.755 | 0.483 | 1.665 | -0.127 | -0.073 | -0.106 | -0.094 |
2048 | 1.063 | 0.754 | 0.482 | 1.663 | -0.131 | -0.077 | -0.108 | -0.100 |
2049 | 1.062 | 0.754 | 0.482 | 1.662 | -0.143 | -0.083 | -0.114 | -0.112 |
2050 | 1.060 | 0.753 | 0.481 | 1.659 | -0.163 | -0.098 | -0.128 | -0.133 |
. India’s first demographic dividend: calculated values of economic support ratio and its growth rates, India, 2011-2050
Year | Economic Support Ratio (ESR) | Growth rate of ESR (%) | ||||||
---|---|---|---|---|---|---|---|---|
Unadjusted for inequality | Adjusted for both inequalities | Adjusted for income inequality | Adjusted for consumption inequality | Unadjusted for inequality | Adjusted for both inequalities | Adjusted for income inequality | Adjusted for consumption inequality | |
2011 | 0.966 | 0.736 | 0.477 | 1.492 | ||||
2012 | 0.972 | 0.741 | 0.479 | 1.502 | 0.632 | 0.641 | 0.608 | 0.665 |
2013 | 0.978 | 0.746 | 0.482 | 1.512 | 0.655 | 0.667 | 0.630 | 0.692 |
2014 | 0.984 | 0.751 | 0.485 | 1.522 | 0.653 | 0.659 | 0.623 | 0.690 |
2015 | 0.991 | 0.756 | 0.488 | 1.533 | 0.637 | 0.635 | 0.598 | 0.673 |
2016 | 0.997 | 0.760 | 0.491 | 1.543 | 0.621 | 0.617 | 0.581 | 0.657 |
2017 | 1.003 | 0.765 | 0.494 | 1.552 | 0.593 | 0.582 | 0.549 | 0.626 |
2018 | 1.009 | 0.769 | 0.497 | 1.562 | 0.583 | 0.567 | 0.535 | 0.616 |
2019 | 1.015 | 0.773 | 0.499 | 1.572 | 0.600 | 0.584 | 0.549 | 0.635 |
2020 | 1.021 | 0.778 | 0.502 | 1.582 | 0.620 | 0.613 | 0.573 | 0.660 |
2021 | 1.026 | 0.782 | 0.505 | 1.591 | 0.536 | 0.528 | 0.493 | 0.571 |
2022 | 1.032 | 0.787 | 0.507 | 1.601 | 0.577 | 0.569 | 0.532 | 0.614 |
2023 | 1.038 | 0.791 | 0.510 | 1.611 | 0.596 | 0.589 | 0.550 | 0.635 |
2024 | 1.045 | 0.796 | 0.513 | 1.621 | 0.587 | 0.577 | 0.539 | 0.625 |
2025 | 1.050 | 0.800 | 0.515 | 1.631 | 0.561 | 0.547 | 0.509 | 0.600 |
2026 | 1.056 | 0.804 | 0.518 | 1.640 | 0.516 | 0.496 | 0.459 | 0.553 |
2027 | 1.061 | 0.808 | 0.520 | 1.649 | 0.508 | 0.482 | 0.444 | 0.546 |
2028 | 1.066 | 0.812 | 0.522 | 1.658 | 0.490 | 0.459 | 0.422 | 0.527 |
2029 | 1.071 | 0.815 | 0.524 | 1.666 | 0.471 | 0.435 | 0.399 | 0.507 |
2030 | 1.076 | 0.819 | 0.526 | 1.674 | 0.446 | 0.406 | 0.373 | 0.479 |
2031 | 1.080 | 0.822 | 0.528 | 1.681 | 0.389 | 0.344 | 0.317 | 0.415 |
2032 | 1.084 | 0.824 | 0.530 | 1.688 | 0.372 | 0.327 | 0.301 | 0.398 |
2033 | 1.088 | 0.827 | 0.531 | 1.694 | 0.348 | 0.307 | 0.281 | 0.375 |
2034 | 1.092 | 0.829 | 0.532 | 1.700 | 0.315 | 0.283 | 0.255 | 0.344 |
2035 | 1.095 | 0.831 | 0.534 | 1.705 | 0.275 | 0.252 | 0.221 | 0.306 |
2036 | 1.097 | 0.833 | 0.534 | 1.709 | 0.211 | 0.192 | 0.163 | 0.239 |
2037 | 1.099 | 0.834 | 0.535 | 1.713 | 0.189 | 0.180 | 0.150 | 0.219 |
2038 | 1.101 | 0.836 | 0.536 | 1.716 | 0.162 | 0.163 | 0.132 | 0.193 |
2039 | 1.102 | 0.837 | 0.537 | 1.719 | 0.128 | 0.138 | 0.106 | 0.159 |
2040 | 1.103 | 0.838 | 0.537 | 1.721 | 0.086 | 0.106 | 0.074 | 0.117 |
2041 | 1.103 | 0.838 | 0.537 | 1.722 | 0.032 | 0.054 | 0.026 | 0.060 |
2042 | 1.104 | 0.838 | 0.537 | 1.723 | 0.012 | 0.039 | 0.010 | 0.041 |
2043 | 1.104 | 0.839 | 0.537 | 1.723 | -0.009 | 0.023 | -0.008 | 0.022 |
2044 | 1.103 | 0.839 | 0.537 | 1.723 | -0.028 | 0.012 | -0.020 | 0.005 |
2045 | 1.103 | 0.839 | 0.537 | 1.723 | -0.052 | -0.006 | -0.040 | -0.018 |
2046 | 1.102 | 0.838 | 0.536 | 1.722 | -0.087 | -0.047 | -0.077 | -0.057 |
2047 | 1.101 | 0.838 | 0.536 | 1.721 | -0.088 | -0.045 | -0.074 | -0.058 |
2048 | 1.100 | 0.838 | 0.536 | 1.720 | -0.093 | -0.047 | -0.076 | -0.064 |
2049 | 1.099 | 0.837 | 0.535 | 1.719 | -0.105 | -0.059 | -0.089 | -0.075 |
2050 | 1.097 | 0.836 | 0.535 | 1.717 | -0.125 | -0.076 | -0.108 | -0.094 |
Total population of India, 1950-2100.
Age structure transition, India, 1950-2100.
Dependency transition, India, 1950-2100.
Age profiles of per capita labour income and consumption, India, 2004-05 and 2011-12.
Age-specific Gini coefficient for per capita labour income, India, 2004-05 and 2011-12.
Age-specific Gini coefficients for per capita consumption, India, 2004-05 and 2011-12.
Inequality-adjusted per capita age profiles of labour income and consumption, India, 2004-05 and 2011-12.
Growth effects of inequality through FDD, India, 2006-2050.
Growth effects of inequality through FDD, India, 2012-2050.
Muttur R. Narayana, PhD in Economics, Academic and Research Consultant, Fiscal Policy Institute, Government of Karnataka, Bengaluru, India, 560060. E-mail:
For instance, India’s National Education Policy 2020 (Government of India 2020a) reflects the needs of human capital by emphasizing on investments in education development from the early childhood to higher education. If implemented successfully, this policy shall be contributory for reaping India’s future demographic dividend.
A new web-based global resources on Demographic Dividend: Investing in Human Capital, jointly hosted by John Hopkins Bloomberg School of Public Health and Bill and Melinda Gates Institute for Population and Reproductive Health, is available at: https://demographicdividend.org/
Census of India 2021, 16th census – scheduled to be held in 2021 – has been postponed to 2022 due to COVID-19 pandemic. The processing of Census 2021 results is expected by 2024.
This definition of a child coincides with different laws in India, such as, Juvenile Justice Act, 2015 and Protection of Children against Sexual Offences Act, 2012. In addition, Indian Contract Act, 1872 prohibits persons below 18 years to enter into a contract and Mines (Amendment) Act, 1952 prohibits them to work in mines and the Building and Other Construction Workers’ (Regulation of Employment and Conditions of Service) Act, 1996 prohibits them from working in notified building and other construction works.
FDD can be modelled from the consumption side as well. This is given in United Nations (2013: 27). In this case, equation (1) is modified as follows: C(t)/N(t) = {(1-s)Y(t)/L(t)}{L(t)/N(t)}, where s is savings rate.
This formulation of inequality adjustment by multiplicative factor (1-G) is traceable to Sen’s (Sen 1973) welfare function: W=Y(1-G), where Y is per capita income and G is a measure of relative inequality. Or, W is a measure of inequality-discounted per capita income or “that level of per capita income which, if shared by all, would produce the same welfare (W) as the value of W generated by actual distribution of income” (Sen, 1973: 42). Further, UNDP (1993) used this formulation of inequality adjustment to calculate the distribution-adjusted Human Development Index. Prados de la Escosura (2017) used this adjustment factor to trace the historical evolution of real per capita GDP and Sen’s welfare function from 1850 to 2015 for Spanish economy.
Mixed Recall Period refers to the household consumption expenditure over 365 days recall period on five infrequently purchased non-food items (clothing, footwear, education, medical care (institutional), and durable goods) and 30 days recall period on the rest of items.
The reference years for calculation of growth of labour productivity are the base years for the estimation of India’s national income. For instance, over the period 1999-00 to 2011-12, three official base years were used: 1999-00, 2004-05 and 2011-12. Thus, growth of labour productivity is calculated between 1999-00 and 2004-05 and between 2004-05 and 2011-12, using the base years’ prices in 1999-00 and 2004-05 respectively.
In a broader context, these implications may be useful to distinguish inequality of outcome (or consequences of unequally distributed income and wealth) and inequality of opportunity (or key dimensions necessary for fulfilling one’s potential). A recent study by ESCAP (2019) shows the inequality of opportunity in Asia and the Pacific for education sector.
For instance, the latest official population projections for India is from 2011 to 2036 (Government of India 2020c). These projections are limited to 5-year interval (2011, 2016, 2021, 2026, 2031and 2036) and 17 broad age-groups (0-4 years to 80+ years) and by singe-years age from 5 years to 23 years.
Demographic dividend is analysed in a non-NTA framework as well. See, for instance, Bloom (2012) and Bloom et al. (2003).
Working age population includes youth population especially student population who are enrolled in higher education. For instance, the latest All India Survey on Higher Education 2019-20 (Government of India 2020d) show that the gross enrolment ratio in higher education (or post-secondary education) is 27.1 percent for those aged 18-23.
As per NIPA, the net disposable income for an age group consists of the income earned as part of the productive process, net property income earned from holding financial assets and liabilities, net transfers. Net disposable income is equal to public and private consumption of the age groups plus saving. Thus, full NTA include savings account. Further, a complete set of NTA also includes three additional sub-accounts: an account that documents bequests and other wealth transfers; an account of holding gains that incorporates changes in assets prices and the value of transfer systems; and a balance sheet that reports both assets and transfer wealth. Boundaries of NTA constructions of the savings and other subaccounts are expanded by NTA Network researchers (United Nations 2013; 51-52 pp). India’s NTA constructions are not yet expanded to the savings and other subaccounts. These theoretical foundations of NTA are analysed by Mason and Lee (2011a).