In Figure 11, I show how much each channel contributes to the gain in income per capita at each time period in my model. To arrive at these estimates, I compare the level of income per capital in each year in the base-case, to the level of income per capita in the same year in the alternate case i.e. when each individual channel is suppressed. These individual effects are then added up to obtain an estimate of the total effect that ignores interactions between channels. I then divide each individual effect by the total estimated effect to produce an estimate of the share of total income gain attributable to each individual effect at each time point.
Based on the results in Figure 11, the dependency effect is clearly the dominant one in the short-term. Fertility operates most strongly through the age-structure of the population to produce economic benefit for the first 55 years from 2025 through 2080. It explains at least 60 percent of the income gain from fertility for the first 10 years, and does not dip below 40 percent of the total for at least 35 years. As of the midpoint of this analysis, year 2060, the four dominant effects are dependency (41%), capital shallowing (21%), schooling (16%), and the land
0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30 1.35 1.40 In co me per ca pi ta (lo w - r el at iv e to med iu m -v ar ian t)
With age-specific
LFPRs
No age-specific
LFPRs
83
congestion effect (10%). In the long-tern, as of year 2095, these 4 factors are still the most dominant, however capital shallowing (34%) and land congestion (22%) overtake dependency (21%) as the most dominant channels. The schooling effect remains a dominant factor at 17%.
Figure 11: Contribution of Each Channel to Gain in Income per Capita in Malawi
-0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 Fr act io n o f ga in in in co m e pe r ca pi ta
84
3.5 ConclusionIn this chapter, I focus on the question of the effect of fertility change on economic development in the context of Malawi with a view to shedding light on the potential for a dividend given the country’s present demographic status i.e. the recent accelerated pace of fertility decline. In my introductory section, I also motivate my analysis by a desire to move beyond an examination of the causal association between fertility and economic growth to an examination of fertility as a parameter that produces its own individual, independent effect that is not colored by simultaneous variation in other growth factors. In this way, I examine what happens to economic growth in Malawi when fertility declines and other key growth determinants are held constant.
Following well-established methodology prescribed by similar exercises conducted with similar motivations, I examine effects not just in aggregate but taking into account the presence and absence of key parameters which are all specified on the basis of a solid microeconomic foundation. Malawi presents a useful case for this analysis as it has recently experienced a steep decline in fertility after decades of a sluggish fertility transition; one that has caused researchers and policy-makers alike to dismiss the possibility of a demographic dividend in Malawi as inconsequential. The simulation methodology I employ here, allows me to explore the potential for a dividend in Malawi in a new light; accounting for the unexpected decline in fertility while isolating and highlighting how much future economic growth in the region can be attributed to the change in age-structure that will inevitably result from this accelerated fertility transition.
While previous similar studies have considered a reduction in fertility as the difference between the medium and low fertility variants of the UN’s 2010 deterministic population scenarios, I innovate on this method by producing my own probabilistic projection population estimates accounting for the recent accelerated pace of decline and producing a series of probability intervals. I then compare projected economics outcomes under the previously released 2015 UN WPP (medium/baseline) scenario which is also deterministic and does not account for the recent accelerated fertility declines, with projected outcomes under my
85
probabilistic population scenario which does account for the recent accelerated pace of decline (low variant scenario). Taking the ratio of the low-variant scenario and the baseline (medium variant) scenario, I find that a reduction in fertility would cause an increase of 10 percent in income per capita at a 20-year time horizon and an increase of 26 percent at a longer time- horizon of 50 years.
Considering each channel individually, I find that the age-structure effect of Malawi’s fertility decline has the strongest effect of future income per capita and this effect will likely remain dominant for the 50 years between 2025 and 2075. This highlights not only the idea that there is a demographic dividend to be had in Malawi, but that given the recent change in the fertility status of the country, the window for the dividend is not very far in the future. Closely related to the dependency effect are the childcare and schooling effects that emerge as two other important channels for income growth in Malawi; both of which are tied to increased education and productive activity due to fertility decline. Capital shallowing and fixed factor congestion also emerge as important channels, and overtake the dependency effect in the long-term as having the strongest effects on income per capita growth.
I should note here some important limitations of this chapter. As stated in section 3.1, there is some skepticism surrounding the new MDHS TFR figures for Malawi i.e. is the sudden sharp decline in TFR a real one or simply due to spurious data. While I have not been able to identify any inconsistencies in the micro-data I investigated for this study, I will say here that while these results may apply in the aggregate, an important extension of this study will be a micro- level analysis of these effects as a way of determining is they hold up when assessed with household data from a variety of sources other than the MDHS.
In the second chapter of this dissertation, I conduct an exercise in which I use National Transfer Accounts data to estimate the dependency ratio, i.e. I estimate the dependent versus productive population as a function of the production and consumption levels at each age rather than as a function of fixed age-limits. I believe that there might be some interesting variations in
86
the income per capita outcome if the present exercise is conducted using this alternative estimation of the dependency ratio. In fact, it is very possible that the dependency effect here might very well be greater if age-specific economic behavior is explicitly accounted for. This will form the basis of a very interesting follow-up study. Furthermore, literature on this subject does indeed point to some additional channels that can potentially be incorporated in these simulations which could also demonstrate greater effects of fertility decline than those reported here. These include the effect of fertility on savings, the effect of fertility on health, the effect of market imperfections and a model that incorporates more than one sector—preferably one that is intrinsically inefficient such as agriculture which is a very dominant sector in the Malawian economy.
Therefore, while the recent acceleration of fertility decline in Malawi will likely result in economic benefit, the magnitude of this benefit both at the micro and macro-levels remains somewhat open to debate, but given the results demonstrated here, it is safe to say that the answer to the question posed at the beginning of this chapter, all things being equal, given the recent acceleration of fertility decline, as a whole Malawi’s demographic dividend future has indeed improved for the better.
87
APPENDIX
Section 1
Life Cycle Production and Consumption
I use National Transfer Accounts (NTA) data to construct age profiles of production and consumption. The NTA framework focuses on what Lee and Mason (2011) describe as the generational economy. This generational economy is defined as; “(1) the social institutions and economic mechanisms used by each generation or age group to produce, consume, share, and save resources; (2) the economic flows across generations or age groups that characterize the generational economy; (3) explicit and implicit contracts that govern intergenerational flows; (4) the intergenerational distribution of income or consumption that results from the foregoing’’ (Lee and Mason, 2011).
Following from this definition, there are four main activities that bring about this generational economy; working, consuming, sharing and savings (Mason and Lee 2011). They need not all occur simultaneously. Consumption occurs throughout the entire lifecycle at a level that varies by age. Working occurs only at certain ages and generally excludes the very young and very old. Due to their inability to work, the very young and very old primarily survive by way of sharing and savings.
NTA simply quantifies the value of these various activities at each individual age. The relationship between each activity is captured by the following equation;
𝑌𝑌
𝐼𝐼+ 𝑌𝑌
𝐴𝐴+ 𝜏𝜏
𝑔𝑔+
+ 𝜏𝜏
𝑓𝑓+=𝐶𝐶+𝛽𝛽+ 𝜏𝜏
𝑔𝑔−+ 𝜏𝜏
𝑓𝑓− (1)Where
𝑌𝑌
𝐼𝐼 is labor income,𝑌𝑌
𝐴𝐴 is asset income,𝜏𝜏
𝑔𝑔+,𝜏𝜏
𝑔𝑔−,𝜏𝜏
𝑓𝑓+ and𝜏𝜏
𝑓𝑓− are transfers to (-) and from (+) the government (g) or family (f),𝐶𝐶
is consumption and𝛽𝛽
is savings.88
𝐶𝐶 − 𝑌𝑌
𝐼𝐼=𝑌𝑌
𝐴𝐴− 𝛽𝛽+ 𝜏𝜏
𝑔𝑔+
−
𝜏𝜏
𝑔𝑔−+𝜏𝜏
𝑓𝑓+−
𝜏𝜏
𝑓𝑓− (2)In essence, the left-hand side of Eq. 2, also called the Life Cycle Deficit is the excess of resources consumed by each group. It is financed by asset-based reallocations and net public and private transfers, and the relationship holds true at the individual, age-cohort and aggregate levels. Using this information to estimate support ratios abandons pre-defined age cut-offs and incorporates age-specific differences in specific economic behavior such as labor market participation and productivity. It also captures consumption behavior at different ages.
Construction of NTA profiles incorporates data from a wide variety of sources including household surveys, administrative data and national accounts data. Please see the United Nations National Transfer Accounts Manual for full details on the construction methodology (UN, 2013).
Estimating the Support Ratio Using NTA Data
The support ratio is in essence an indicator that captures how resources are allocated within the NTA’s generational economy. Also commonly called the “economic” support ratio, it specifies the relationship between those who support the economy and the consumption requirements of the entire population.
The demographic dividend depends on the rate at which the support ratio changes. Both the dividend and the support ratio are estimated by combining NTA age profiles of income and consumption with age-specific population projections. As notes in Section 2, NTA age profiles of labor income and consumption have only been estimated for a single year. Therefore projections of the support ratio and demographic dividend are based on the assumption that the forces that determine labor income and consumption behavior (such as labor force participation, productivity and unemployment rate) do not change over time.
89
The demographic dividend has been formalized in Mason and Lee (2006). If those who support the economy (also called effective producers) are denoted by L and the entire population of consumers (effective consumers) is denoted by N, then;
𝐿𝐿(𝛽𝛽) =∑ 𝑃𝑃
𝑝𝑝(𝛽𝛽,𝛽𝛽)∙ 𝛾𝛾(𝛽𝛽)
(3)𝑁𝑁(𝛽𝛽) =∑ 𝑃𝑃
𝑝𝑝(𝛽𝛽,𝛽𝛽)∙ 𝛼𝛼(𝛽𝛽)
(4)Where
𝑃𝑃(𝛽𝛽,𝛽𝛽)
is the population aged a at time t,𝛾𝛾(𝛽𝛽)
represents NTA age-specific variations in productivity, and𝛼𝛼(𝛽𝛽)
represents age-specific variations in consumption. The L and N estimated here are the essential components of the support ratio and demographic dividend estimations. Section 2 -0.020 -0.0100.000 0.010 0.020 0.030 0.040 0.050 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90+ % C ont ribut io n AgeFigure 9: Age-specific contributions to difference in effective producers between Nigeria and Mozambique
90
-0.010.00 0.01 0.02 0.03 0.04 0.05 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90+ % C ont ribut io n AgeFigure 10: Age-specific contributions to difference in effective consumers between Nigeria and Senegal
-0.01 0.00 0.01 0.02 0.03 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90+ % C ont ribut io n Age
Figure 11: Age-specific contributions to difference in effective consumers between Nigeria and Ghana
-0.20 0.00 0.20 0.40 0.60 0.80 1.00
Nigeria vs. S. AfricaNigeria vs. Ghana Nigeria vs. MozambiqueNigeria vs. Senegal Nigeria vs. Ethiopia
Figure 12 : Contribution of Age-Specific Production Rate vs. Age Structure to Number of Effective Producers.
91
-0.60 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00 Nigeria vs. S. Africa
Nigeria vs. Mozambique Nigeria vs. Senegal
Figure 13: Contribution of Age-Specific Consumption Rate vs. Age Structure to Number of Effective Consumers.
92
BIBLIOGRAPHY
Acemoglu, D. (2010). Theory, general equilibrium, and political economy in development economics. The Journal of Economic Perspectives, 24(3), 17-32.
Acemoglu, D., & Johnson, S. (2007). Disease and development: the effect of life expectancy on economic growth. Journal of Political Economy, 115(6), 925-985.
Agyei-Mensah S. (2007). New Times, New Families: The Stall in Ghanaian Fertility. Paper presented at the African Population Conference, Arusha, December 2007.
Ahlburg, D. A. (1987). The impact of population growth on economic growth in developing nations: the evidence from macroeconomic-demographic models.
Alkema, L., Raftery, A. E., Gerland, P., Clark, S. J., & Pelletier, F. (2012). Estimating trends in the total fertility rate with uncertainty using imperfect data: Examples from West Africa.
Demographic Research, 26(15).
Ansu, Y., (2012). Financing Economic Transformation in Africa. Keynote address to the World Bank’s Third Debt Management Facility Stakeholders’ Forum. Accra. Ghana. June 15, 2012.
Ashraf, Q. H., Weil, D. N., & Wilde, J. (2013). The effect of fertility reduction on economic growth.
Population and Development Review, 39(1): 97-130.
Banerjee, A. V., & Duflo, E. (2005). Growth theory through the lens of development economics.
Handbook of Economic Growth, 1, 473-552.
Barro R.J. and Sala-i-Martin X. (1995). Technological diffusion, convergence and growth. Journal
Of Economic Growth 2(1): 1-26.
Becker, G. S. (1960). An economic analysis of fertility. In Demographic and economic change in developed countries (pp. 209-240). Columbia University Press.
Becker, G. S., & Lewis, H. G. (1973). On the Interaction between the Quantity and Quality of Children. Journal of political Economy, 81(2, Part 2), S279-S288.
Becker, G. S., Duesenberry, J. S., & Okun, B. (1960). Universities-National Bureau. An economic analysis of fertility. Demographic and economic change in developed countries, 209-240. Beegle, K., Christiaensen, L., Dabalen, A., and Gaddis, I., (2016). Poverty in a Rising Africa.
Washington D.C.: World Bank.
Benhabib, J., & Spiegel, M. (1994). The role of human capital in economic development.
Evidence from aggregate cross-country data. Journal of Monetary Economics, 34, 143– 173.
Benhabib, J., & Spiegel, M. (2005). Human capital and technology diffusion. In P. Aghion & S. Durlauf (Eds.), Handbook of economic growth (Vol. 1, pp. 935–966). Amsterdam, The Netherlands: Elsevier.
93
Birdsall, N. (1988). Economic approaches to population growth. Handbook of Development Economics 1: 477-542.
Bloom, D.E. & Williamson, J.G. (1998). "Demographic transitions and economic miracles in emerging Asia". The World Bank Economic Review. -- Vol. 12, no. 3 (September 1998), pp. 419-455.
Bloom D.E. & Sachs J.D., (1999)"Geography, Demography, and Economic Growth in Africa," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 29(2), pages 207-296.
Bloom, D. E., Canning, D., & Malaney, P. N. (2000). Population dynamics and economic growth in Asia. Population and Development Review, 26, 257-290.
Bloom, D.E., Canning, D. and Sevilla, J. (2003). The Demographic Dividend: A New Perspective on the Economic Consequences of Population Change’, Rand Corporation, Santa Monica, Arlington.
Bloom, D. E., & Canning, D. (2004). Global demographic change: Dimensions and economic significance (No. w10817). National Bureau of Economic Research.
Bloom, D. E., Canning, D., Fink, G., & Finlay, J. (2007). Realizing the demographic dividend: Is Africa any different. Program on the Global Demography of Aging, Harvard University, 1- 23.
Bloom, D. E., & Finlay, J. E. (2009). Demographic change and economic growth in Asia. Asian
Economic Policy Review, 4(1), 45-64.
Bloom, D. E., Canning, D., Fink, G., & Finlay, J. E. (2009). Fertility, female labor force
participation, and the demographic dividend. Journal of Economic Growth, 14(2), 79-101. Bloom, D. E., Canning, D., Fink, G., & Finlay, J. E. (2010). The cost of low fertility in Europe.
European Journal of Population/Revue Européenne de Démographie, 26(2), 141-158.
Bongaarts, J. (2006). The causes of stalling fertility transitions. Studies in Family Planning, 37(1): 116.
Bongaarts, J. (2008). Fertility Transitions in Developing Countries: Progress or Stagnation?
Studies in Family Planning, 39(2), 105–110.
Bongaarts, J. (2010). The causes of educational differences in fertility in sub-Saharan Africa. Vienna Yearbook of Population Research, 8, 31–50.
Bongaarts, J. and Casterline, J. (2013). Fertility Transition: Is sub-Saharan Africa Different?
Population Development Review, 38 (Suppl 1): 153–168.
Bourmpoula, V., Kapsos, S. & Pasteels, J.M. (2015). ILO labor force estimates and projections: 1990-2050 (2015 edition). ILO, Geneva.
Brander, J. A., & Dowrick, S. (1994). The role of fertility and population in economic growth.
Journal of Population Economics, 7(1), 1-25.
Caldwell, J. C., Orubuloye, I. O., & Caldwell, P. (1992). Fertility decline in Africa: A new type of transition? Population and Development Review, 211-242.
94
Canning, D. E. and Schofield, H. (2007). The effect of fertility on female labor supply and household poverty in Indonesia. Boston: Harvard School of Public Health.
Canning, D., Raja, S., Yazbeck, A.S., (2015). Africa's Demographic Transition: Dividend or Disaster? Africa Development Forum series. Washington, DC: World Bank.
Chandrasekhar, C. P., Ghosh, J., and Roychowdhury, A. (2006). The 'Demographic Dividend' and Young India's Economic Future. Economic and Political Weekly 41(49): 5055-5064. Cleland, J., Onuoha, N., & Timaeus, I. (1994). Fertility change in sub-Saharan Africa: a review of
the evidence. The onset of fertility transition in Sub-Saharan Africa, 1-20.
Coale, A. J., & Hoover, E. M. (1958). Population growth and economic development in low- income countries: a case study of India’s prospects. Princeton, N.J.: Princeton University Press.
Cochrane, S. H., Khan, M. A., & Osheba, I. K. T. (1990). Education, income, and desired fertility in Egypt: A revised perspective. Economic Development and Cultural Change, 38, 313– 339.
Cuaresma, J. C., Lutz, W., & Sanderson, W. (2014). Is the demographic dividend an education dividend? Demography, 51(1), 299-315.
Das Gunpta (1991). "Decomposition of the Difference Between Two Rates and Its Consistency When More than Two Populations are Involved." Mathematical Population Studies 3(2): 105-1 25.
Doepke, M., Hazan, M., & Maoz, Y. (2007). The Baby Boom and World War II: A Macroeconomic Analysis (No. 13707). NBER Working Paper.
Duflo, E. (2001). Schooling and Labor Market Consequences of School Construction in
Indonesia: Evidence from an Unusual Policy Experiment. American Economic Review, 91(4), 795-813.
Eastwood, R., and Lipton, M. (2011) Demographic transition in sub-Saharan Africa: How big will the economic dividend be? Population Studies, 65(1): 9-35.
Ehrlich, P. R. (1968). The population bomb. New York: Ballantine.
Ehrlich, P. R., & Ehrlich, A. H. (1990). The population explosion. New York: Simon & Schuster. Eltigani, E. E. (2003). Stalled fertility decline in Egypt, why? Population and Environment, 25(1):
41-59.
Enke, S. (1971). Economic consequences of rapid population growth. The Economic Journal, 81(324), 800-811.
Ezeh, A. C., Mberu, B. U., and Emina, J. O. (2009). Stall in fertility decline in Eastern African countries: regional analysis of patterns, determinants and implications. Philosophical