## Box 1. Behavioral Responses to Demographic Changes

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---

### Analytical framework and purpose
- Compares two demographic scenarios:
  - (i) hypothetical scenario with size and age-gender composition unchanged from 2015 levels during the entire projection horizon;
  - (ii) baseline scenario using UN World Population Prospects (the 2015 Revision) medium-fertility projections.
- Impact of aging on revenue and expenditures is isolated by holding all other factors constant.
- Framework provides insights into potential impacts of demographic scenarios on governments’ future fiscal positions despite uncertainty in fertility, mortality, and migration realizations.

### Growth impact: production-function approach
- Aggregate output specification:
  - Yt = TFPt × Kt^(1−β) × Lt^β (equation (1))
- Log-linearized decomposition with age-gender cohorts:
  - Log(Yt) = Log(TFPt) + (1 − β) × Log(Kt) + β × Log(Σ_j N_j^t × LFP_j^t × E_j^t × w_j^t) (equation (2))
    - j = age-gender cohort; N = cohort size; LFP = cohort-specific labor force participation; E = employment rate; w = weight factor (adjusts employees to effective units of labor)
- Capital accumulation (perpetual inventory method):
  - Kt = (1 − δ) × Kt−1 + It (equation (3))
- Balanced-growth capital accumulation assumption:
  - 1 + gK_t = (1 + g_TFP_t)^(1/β) × (1 + gL_t) (equation (4))
- Projection assumptions:
  - Age- and gender-specific labor force participation and employment rates follow the average of trend rates for the past five years.
  - TFP grows at its historical average trend rate (TFP measured as a residual).
  - Capital accumulation follows the balanced-growth condition (equation (4)).
- Notes and caveats:
  - Adjustments for average hours worked by cohort are used where data limit more refined weighting.
  - Demographics could also affect productivity and investment; these effects are abstracted in the baseline assumptions.

### Revenue impact
- Government revenue assumed to remain at its historical level as a share of GDP (i.e., revenue grows with potential output), absent tax policy or administration changes.
- Possibilities not modeled in the simple approach:
  - Aging could change tax-composition (e.g., higher share of capital/property taxes).
  - Older cohorts may continue paying income and consumption taxes without contributing to GDP via earnings.
- Alternative data-intensive approach:
  - Estimate revenues from age-specific tax liability profiles (income, consumption, property taxes, social security contributions) and project them under population scenarios.

### Estimating age-related spending (pensions and health)
- Pension spending identity (percent of GDP):
  - PE = RR × CR × ODR × (1 / LP) (equation (5))
    - PE = Pension expenditure
    - RR = replacement rate = PE/pensioners ÷ (GDP/workers)
    - CR = coverage ratio = Pensioners ÷ (Population 65+)
    - ODR = old-age dependency ratio = Population 65+ ÷ Population 15–64
    - LP = share of workers in the total working-age population (Workers ÷ Population 15–64)
- Health expenditure identity:
  - HE = GHP × (1 / LP) × (1 + a × DR) (equation (6))
    - HE = Health expenditure (0–64 and 65+ components defined)
    - GHP = relative generosity of health package for younger population = HE_0–64 / (GDP / Population 0–64)
    - a = ratio of per capita health cost for older population to younger population (exogenously determined)
    - DR = dependency ratio = Population 65+ ÷ Population 0–64
- Projection approach for age-related spending:
  - Use long-term pension and health spending projections from Clements and others (2015), which are based on UN medium-fertility population projections and account for already implemented or intended policy reform measures.
  - To isolate demographic effects, keep the size of the average pension, generosity of health package, coverage ratio, and the a coefficient constant across scenarios (i.e., no new policy measures).

### Aggregation into fiscal impact and stock measures
- Net effects on fiscal balances grouped into:
  - Revenue, non-age-related expenditures, and age-related expenditures (pensions and health).
- Long-term elasticities for revenue and non-age-related expenditure relative to output:
  - For simplicity, set to one (i.e., they grow in line with economic growth).
  - Alternative formulation: R/R* = (Y/Y*)^ε_R, E/E* = (Y/Y*)^ε_E.
- Fiscal impact measure:
  - Difference in fiscal balances between demographic scenarios; reported both as percent of respective GDP and relative to the constant-demographics scenario GDP.
- Prospective cumulative liabilities from age-related expenditure:
  - Calculated as the net present value of PEt + HEt from 2015 onward, using discounting across the projection horizon (equation (7)).

### Data sources and scenarios
- Population scenarios:
  - UN World Population Prospects (the 2015 Revision) — historical cohort headcounts 1950–2015 and projections 2016–2100; eight fertility/mortality/migration variants (medium-fertility is the UN central projection).
- Labor market data:
  - OECD (age-gender participation and employment rates) and average hours worked by cohort.
- National accounts:
  - OECD and IMF sources.
- Age-related spending data:
  - IMF and OECD; pension and health benchmark projections from Clements and others (2015); health spending by age from De la Maisonneuve and Martins (2013).

### Country case studies: illustrative findings and parameters
- General finding (Korea and Poland under UN medium-fertility scenario):
  - Populations shrink and age significantly, slowing output growth and raising age-related spending.
- Fiscal cost example:
  - Korea: fiscal costs of demographic changes (difference in fiscal balances between medium-fertility scenario and constant-demographics scenario) estimated at 8½ percent of GDP.
  - Poland: example illustrates that an increase in projected pension replacement rate and a decrease in retirement age would significantly raise pension expenditure.
- Default/template parameters used for Korea case analysis:
  - Capital stock depreciation rate = 5.9 percent per year (in line with Nadiri and Prucha (1993)).
  - Long-term revenue and non-age-related expenditure elasticities = one.
  - Long-term interest rates = 2.6 percent (in line with Giglio and others (2015)); alternative real discount rates in the range between 3 and 7 percent noted.
  - Default per capita health spending ratio (65+ relative to <65) = 3.5 (average for a group of OECD countries from De la Maisonneuve and Martins (2013)); country-specific estimates may be used.

---

### Box 3. Assumptions Underpinning Estimates

### Estimating potential output
- Country-specific adaptations of assumptions used in equation (2) include modifying:
  - capital depreciation rate;
  - projected TFP growth rate;
  - capital accumulation (by relaxing the balanced-growth condition);
  - discount rates;
  - projecting cohort-specific labor force participation and employment rates based on models or using available projections from the ILO.
- When projecting labor force participation rates, consider hysteresis effects that could show up in permanently low labor force participation rates for certain cohorts.

### Estimating age-related spending
- Alternative to imputing key policy terms from equations (5) and (6):
  - Project age-related spending by targeting specific levels for policy-driven terms (e.g., explicit paths for replacement rates and coverage ratios).
- Example approach:
  - After deriving actual pension replacement and coverage ratios, determine specific paths for these two variables for the entire projection period.

### Practical modeling flexibility and data inputs
- Approaches presented are not the only way to assess fiscal impact of demographic changes; aspects can be further refined or modified (Appendix 2).
- Users can tailor scenario choices and parameter values in the template (worksheet “Basic info” and demographic worksheets) and overwrite default labor market projections to experiment with different policy settings.

### Key default and alternative quantitative assumptions (Annex 2)
- Population projections by age and gender
  - Default: UN medium-fertility variant scenario
  - Alternative: Seven other scenarios from UN population projections; EC aging working group projections (for EU countries); National authorities
- Total factor productivity projections
  - Default: Historical average
  - Alternative: Available country-specific projections
- Labor force participation projections
  - Default: Average of the past five years
  - Alternative: ILO projections
- Employment rate projections
  - Default: Average of the past five years
  - Alternative: ILO projections
- Long-term elasticity of revenue to GDP
  - Default: One
  - Alternative: Different from one depending on country-specific circumstance
- Long-term elasticity of non-age-related expenditure to GDP
  - Default: One
  - Alternative: Different from one depending on country-specific circumstance
- Capital depreciation rate
  - Default: 5.9 percent (following Nadiri and Prucha, 1993)
  - Alternative: Any other country-specific estimate
- Discount rate (long-term interest rate)
  - Default: 2.6 percent (following Giglio and others, 2015)
  - Alternative: Real discount rates of 3–7 percent suggested by the U.S. Office of Budget and Management (2003)
- Per capita health spending of individuals older than 65 years relative to those younger than 65 years
  - Default: 3.5 (average for a group of OECD countries, following De la Maisonneuve and Martins, 2013)
  - Alternative: Country-specific estimates, if available

*Source: Box 1. Behavioral Responses to Demographic Changes; Box 3. Assumptions Underpinning Estimates, How to assess Fiscal Implications of Demographic Shifts: a granular approach (IMF, September 2016).*

### Box 1. Behavioral Responses to Demographic Changes

### Box 1. Behavioral Responses to Demographic Changes

### Analytical framework and purpose
- Framework traces effects of changes in the size and age-gender composition of the population on key fiscal variables by comparing two demographic scenarios: (i) hypothetical scenario with size and age-gender composition unchanged from 2015 levels during the entire projection horizon; and (ii) baseline scenario using UN World Population Prospects (the 2015 Revision) medium-fertility projections.
- Impact of aging on revenue and expenditures is isolated by holding all other factors constant.
- The framework is intended to provide insights into potential impacts of demographic scenarios on governments’ future fiscal positions despite uncertainty in fertility, mortality, and migration realizations.

### Growth impact: production-function approach
- Aggregate output uses a Cobb-Douglas specification with constant returns to scale:
  - Yt = TFPt × Kt^(1−β) × Lt^β (equation (1))
- Log-linearized form decomposes labor into age-gender cohorts:
  - Log(Yt) = Log(TFPt) + (1 − β) × Log(Kt) + β × Log(Σ_j N_j^t × LFP_j^t × E_j^t × w_j^t) (equation (2))
  - j = age-gender cohort; N = cohort size; LFP = cohort-specific labor force participation; E = employment rate; w = weight factor (adjusts employees to effective units of labor)
- Capital stock estimated by perpetual inventory method:
  - Kt = (1 − δ) × Kt−1 + It (equation (3))
- Assumptions for projections:
  - Age- and gender-specific labor force participation and employment rates follow the average of trend rates for the past five years.
  - TFP grows at its historical average trend rate (TFP measured as a residual).
  - Capital accumulation follows balanced-growth condition:
    - 1 + gK_t = (1 + g_TFP_t)^(1/β) × (1 + gL_t) (equation (4))
- Notes and caveats:
  - Adjustments for average hours worked by cohort are used where data limit more refined weighting.
  - Demographics could also affect productivity and investment; these effects are abstracted in the baseline assumptions.

### Revenue impact
- Government revenue assumed to remain at its historical level as a share of GDP (i.e., revenue grows with potential output), absent tax policy or administration changes.
- Acknowledged possibilities not modeled in the simple approach:
  - Aging could change tax-composition (e.g., higher share of capital/property taxes).
  - Older cohorts may continue paying income and consumption taxes without contributing to GDP via earnings.
- Alternative data-intensive approach: estimate revenues from age-specific tax liability profiles (income, consumption, property taxes, social security contributions) and project them under population scenarios.

### Estimating age-related spending (pensions and health)
- Pension spending identity (percent of GDP) decomposed into four elements (following Clements and others, 2014):
  - PE = RR × CR × ODR × (1 / LP) (equation (5))
    - PE = Pension expenditure
    - RR = replacement rate = PE/pensioners ÷ (GDP/workers)
    - CR = coverage ratio = Pensioners ÷ (Population 65+)
    - ODR = old-age dependency ratio = Population 65+ ÷ Population 15–64
    - LP = share of workers in the total working-age population (Workers ÷ Population 15–64)
- Health expenditure identity (following Clements and others, 2015):
  - HE = GHP × (1 / LP) × (1 + a × DR) (equation (6))
    - HE = Health expenditure (0–64 and 65+ components defined)
    - GHP = relative generosity of health package for younger population = HE_0–64 / (GDP / Population 0–64)
    - a = ratio of per capita health cost for older population to younger population (exogenously determined)
    - DR = dependency ratio = Population 65+ ÷ Population 0–64
- Projections for age-related spending:
  - Use long-term pension and health spending projections from Clements and others (2015), which are based on UN medium-fertility population projections and account for already implemented or intended policy reform measures.
  - To isolate demographic effects, the size of the average pension, generosity of health package, coverage ratio, and the a coefficient are kept constant across scenarios (i.e., no new policy measures).

### Aggregation into fiscal impact and stock measures
- Net effects on fiscal balances grouped into:
  - Revenue, non-age-related expenditures, and age-related expenditures (pensions and health).
- For simplicity, long-term elasticities for both revenue and non-age-related expenditure relative to output are set to one (i.e., they grow in line with economic growth). Alternative elasticities could be used: R/R* = (Y/Y*)^ε_R, E/E* = (Y/Y*)^ε_E.
- Fiscal impact of demographic changes is measured as the difference in fiscal balances between demographic scenarios; reported both as percent of respective GDP and relative to the constant-demographics scenario GDP.
- Prospective cumulative liabilities from age-related expenditure calculated as the net present value of PEt + HEt from 2015 onward, using discounting across the projection horizon (equation (7)).

### Data sources and scenarios
- Population scenarios: UN World Population Prospects (the 2015 Revision) — historical cohort headcounts 1950–2015 and projections 2016–2100; eight fertility/mortality/migration variants (medium-fertility is the UN central projection).
- Labor market data: OECD (age-gender participation and employment rates) and average hours worked by cohort.
- National accounts: OECD and IMF sources.
- Age-related spending data: IMF and OECD; pension and health benchmark projections from Clements and others (2015); health spending by age from De la Maisonneuve and Martins (2013).

### Country case studies: illustrative findings and parameters
- General finding for Korea and Poland under UN medium-fertility scenario: populations shrink and age significantly, slowing output growth and raising age-related spending.
- Fiscal cost example:
  - Korea: fiscal costs of demographic changes (difference in fiscal balances between medium-fertility scenario and constant-demographics scenario) estimated at 8½ percent of GDP.
  - Poland: example illustrates that an increase in projected pension replacement rate and a decrease in retirement age would significantly raise pension expenditure.
- Default/template parameters used for Korea case analysis:
  - Capital stock depreciation rate = 5.9 percent per year (in line with Nadiri and Prucha (1993)).
  - Long-term revenue and non-age-related expenditure elasticities = one.
  - Long-term interest rates = 2.6 percent (in line with Giglio and others (2015)); alternative real discount rates in the range between 3 and 7 percent noted.
  - Default per capita health spending ratio (65+ relative to <65) = 3.5 (average for a group of OECD countries from De la Maisonneuve and Martins (2013)); country-specific estimates may be used.

*Source: Box 1. Behavioral Responses to Demographic Changes, How-to notes, Fiscal Affairs Department, International Monetary Fund | September 2016.*

### Box 3. Assumptions Underpinning Estimates

### Box 3. Assumptions Underpinning Estimates

### Estimating potential output
- Assumptions used to estimate potential output (equation 2) can be adapted to country-specific circumstances by modifying:
  - capital depreciation rate;
  - projected TFP growth rate;
  - capital accumulation (by relaxing the balanced-growth condition);
  - discount rates;
  - projecting cohort-specific labor force participation and employment rates based on models or using available projections from the ILO.
- When projecting labor force participation rates, the analysis could consider the presence of hysteresis effects, which could show up in permanently low labor force participation rates for certain cohorts.

### Estimating age-related spending
- Rather than using available long-term pension and health care spending projections to impute key policy terms from equations (5) and (6), age-related spending could be projected by targeting specific levels for these policy-driven terms.
- Example approach: after deriving actual pension replacement and the coverage ratios, determine the specific paths for these two variables for the entire projection period.

### Practical modeling flexibility and data inputs
- The approaches presented are not the only way to assess the fiscal impact of demographic changes; certain aspects could be further refined or modified (Appendix 2).
- Users can tailor scenario choices and parameter values in the template (worksheet “Basic info” and demographic worksheets) and overwrite default labor market projections to experiment with different policy settings.

### Key default and alternative quantitative assumptions (Annex 2)
- Population projections by age and gender
  - Default: UN medium-fertility variant scenario
  - Alternative: Seven other scenarios from UN population projections; EC aging working group projections (for EU countries); National authorities
- Total factor productivity projections
  - Default: Historical average
  - Alternative: Available country-specific projections
- Labor force participation projections
  - Default: Average of the past five years
  - Alternative: ILO projections
- Employment rate projections
  - Default: Average of the past five years
  - Alternative: ILO projections
- Long-term elasticity of revenue to GDP
  - Default: One
  - Alternative: Different from one depending on country-specific circumstance
- Long-term elasticity of non-age-related expenditure to GDP
  - Default: One
  - Alternative: Different from one depending on country-specific circumstance
- Capital depreciation rate
  - Default: 5.9 percent (following Nadiri and Prucha, 1993)
  - Alternative: Any other country-specific estimate
- Discount rate (long-term interest rate)
  - Default: 2.6 percent (following Giglio and others, 2015)
  - Alternative: Real discount rates of 3–7 percent suggested by the U.S. Office of Budget and Management (2003) as part of the guidance to federal agencies for regulatory analysis
- Per capita health spending of individuals older than 65 years relative to those younger than 65 years
  - Default: 3.5 (average for a group of OECD countries, following De la Maisonneuve and Martins, 2013)
  - Alternative: Country-specific estimates, if available

*Source: Box 3. Assumptions Underpinning Estimates, How to assess Fiscal Implications of Demographic Shifts: a granular approach (IMF, September 2016).*

### References

### _howtonote1602 - References

### Cited works on demographics and economic growth
- Auerbach, Alan, Laurence Kotlikoff, and Willi Leibfritz. 1998. “Generational Accounting around the World.” IMES Discussion Paper 98-E-2, Institute for Monetary and Economic Studies, Bank of Japan, Tokyo.
- Bloom, David E., David Canning, and Günther Fink. 2010. “Implications of Population Ageing for Economic Growth.” Oxford Review of Economic Policy 26(4): 583–612.
- Bloom, David E., David Canning, Günther Fink, and Jocelyn E. Finlay. 2007. “Fertility, Female Labor Participation, and Demographic Dividend.” NBER Working Paper 13583, National Bureau of Economic Research, Cambridge, MA.
- Bloom, David E., David Canning, and Jaypee Sevilla. 2003. “The Demographic Dividend: A New Perspective on the Economic Consequences of Population Change.” RAND, Santa Monica, CA.
- Bloom, David E., Somnath Chatterji, Paul Kowal, Peter Lloyd-Sherlock, Martin McKee, Bernd Rechel, Larry Rosenberg, and James P Smith, 2015, “Macroeconomic Implications of Population Ageing and Selected Policy Responses.” Lancet 385(9968): 649–57.
- Futagami Koichi, and Tetsuya Nakajima. 2001. “Population Aging and Economic Growth.” Journal of Macroeconomics 23(1): 31–44.
- Higgins, Matthew. 1998. “Demography, National Savings, and International Capital Flows.” International Economic Review 39(2): 343–69.
- Lee, Ronald, and Andrew Mason. 2010. “Fertility, Human Capital, and Economic Growth over the Demographic Transition.” European Journal of Population 26(2): 159–82.
- Lee, Ronald, Andrew Mason, and Daniel Cotlear. 2010. “Some Economic Consequences of Global Aging.” Health, Nutrition, and Population Discussion Paper, World Bank, Washington, DC, December.
- Prettner, Klaus. 2009. “Population Ageing and Endogenous Economic Growth.” Working Paper 8/2009, Vienna Institute of Demography, Vienna.
- Prettner, Klaus, and Alexia Prskawet. 2010. “Demographic Change in Models of Endogenous Economic Growth. A Survey.” Vienna Institute of Demography Working Paper 8/2010, Vienna.
- Thurow, Lester. 1969. “The Optimum Lifetime Distribution of Consumption Expenditures.” American Economic Review 59(3): 324–30.
- United Nations. 2015. “World Population Prospects: The 2015 Revision.” Department of Economic and Social Affairs, United Nations, New York. http://esa.un.org/wpp/.

### Cited works on fiscal sustainability, public finance, and pensions
- Clements, Benedict, David Coady, and Sanjeev Gupta, eds. 2012. The Economics of Public Health Care Reform in Advanced and Emerging Economies. Washington, DC: International Monetary Fund.
- Clements, Benedict, Kamil Dybczak, Vitor Gaspar, Sanjeev Gupta, and Mauricio Soto. 2015. “The Fiscal Consequence of Shrinking Populations.” IMF Staff Discussion Note SDN/15/21, International Monetary Fund, Washington, DC.
- Clements, Benedict, Frank Eich, and Sanjeev Gupta, eds. 2014. Equitable and Sustainable Pensions: Challenges and Experience. Washington, DC: International Monetary Fund.
- De la Maisonneuve, Christine, and Joaquim Oliveira Martins. 2013. “Public Spending on Health and Long-Term Care: A New Set of Projections.” OECD Economic Policy Paper 06, Organisation for Economic Co-operation and Development, Paris.
- Dolls, Mathias, Karina Doorley, Alari Paulus, Hilmar Schneider, Sebastian Siegloch, and Eric Sommer. 2015. “Fiscal Sustainability and Demographic Change: A Micro Approach for 27 EU Countries.” IZA Departmental Paper No. 9618, Institute for the Study of Labor, Bonn.
- European Commission. 2015. The 2015 Ageing Report—Underlying Assumptions and Projection Methodologies. Brussels: Economic and Financial Affairs, European Commission. http://ec.europa.eu/economy_finance/publications/.
- European Commission. 2012. Fiscal Sustainability Report 2012. European Economy 8|2012. Brussels: Economic and Financial Affairs, European Commission. http://ec.europa.eu/economy_finance/publications/european_economy/2012/pdf/ee-2012-8_en.pdf.
- Krogulski, Krzysztof, Robert Sierhej, Francisco Vazquez, and Csaba Feher. 2014. “The Polish Pension System: Fiscal Impact of the 2014 Changes and Remaining Policy Challenges.” Selected Issues Paper, IMF Country Report 14/174, International Monetary Fund, Washington, DC.
- Lee, Ronald, and Ryan Edwards. 2002. “The Fiscal Effects of Population Aging in the U.S.: Assessing the Uncertainties.” In Tax Policy and the Economy, Vol. 16, edited by James Poterba, 141–80. Cambridge, MA: National Bureau of Economic Research. http://www.nber.org/chapters/c10865.
- McMorrow, K., and W. Roeger. 1999. “The Economic Consequences of Ageing Populations (A Comparison of the EU, US and Japan).” European Economy, Economic Papers 138. Economic and Financial Affairs, European Commission, Brussels. http://ec.europa.eu/economy_finance/publications/publication11151_en.pdf.
- Office for Budget Responsibility. 2015. “Fiscal Sustainability Report.” London. https://www.gov.uk/government/publications.
- Velculescu, Delia. 2010. “Some Uncomfortable Arithmetic Regarding Europe’s Public Finances.” IMF Working Paper 10/177, International Monetary Fund, Washington, DC.
- International Monetary Fund. 2015. “Uneven Growth: Short- and Long-Term Factors.” Chapter 3 in World Economic Outlook. Washington, DC, April.

### Cited works on labor markets, productivity, and aging workforce
- Borsch-Supan, Axel. 2002. “Labor Market Effects of Population Aging.” Labor 17(S1): 5–44.
- Feyrer, James. 2007. “Demographics and Productivity.” Review of Economics and Statistics 89(1): 100–9.
- Frosch, Katharina. 2009. “Do Only New Brooms Sweep Clean? A Review on Workforce Age and Innovation.” MPIDR Working Paper 2009-005, Max Planck Institute for Demographic Research, Rostock, Germany.
- Göbel, Christian, and Thomas Zwick. 2009. “Age and Productivity—Evidence from Linked Employer Employee Data.” ZEW Discussion Paper 09-020, Center for European Economic Research, Mannheim, Germany.
- Grob, Ueli, and Stefan C. Wolter. 2007. “Demographic Change and Public Education Spending: A Conflict between Young and Old?” Education Economics 15(3): 277–92.
- Kulish, Mariano, Kathryn Smith, and Christopher Kent. 2006. “Ageing, Retirement and Savings: A General Equilibrium Analysis.” Research Discussion Paper 2006-06, Reserve Bank of Australia, Sydney.
- Skirbekk, Vegard. 2003. “Age and Individual Productivity: A Literature Survey” MPIDR Working Paper 2003-28, Max Planck Institute for Demographic Research, Rostock, Germany.
- Tyers, Rod, and Qun Shi. 2007. “Demographic Change and Policy Responses: Implications for the Global Economy.” World Economy 30(4): 537–66.
- Felix, Alison, and Kate Watkins. 2013. “The Impact of an Aging U.S. Population on State Tax Revenues.” Federal Reserve Bank of Kansas City. http://www.KansasCityFed.org.
- Elborgh-Woytek, Katrin, Monique Newiak, Kalpana Kochhar, Stefania Fabrizio, Kangni Kpodar, Philippe Wingender, Benedict Clements, and Gerd Schwartz. 2013. “Women, Work and the Economy: Macroeconomic Gains from Gender Equity.” IMF Staff Discussion Note SDN/13/10, International Monetary Fund, Washington, DC.

### Methodological, data, and sector-specific references
- Giglio, Stefano, Matteo Maggiori, and Johannes Stroebel. 2015. “Very Long Run Discount Rates,” Quarterly Journal of Economics 130(1): 1–53.
- Nadiri, Ishaq, and Ingmar Prucha. 1993. “Estimation of the Depreciation Rate of Physical Capital and R&D Capital in the U.S. Total Manufacturing Sector.” NBER Working Paper 4591, National Bureau of Economic Research, Cambridge, MA.
- Sanderson, Warren, and Sergei Scherbov. 2008. “Rethinking Age and Aging.” Population Bulletin [Population Reference Bureau] 63(4).
- Peterson, Peter. 1999. “Gray Dawn: The Global Aging Crisis,” Foreign Affairs 42 (January/February).
- U.S. Office of Management and Budget. 2003. Circular A-4. Washington, DC. (https://www.whitehouse.gov/sites/default/files/omb/assets/regulatory_matters_pdf/a-4.pdf ).
- Howe, Neil, Richard Jackson, and Keisuke Nakashima. 2007. The Aging of Korea: Demographics and Retirement Policy in the Land of the Morning Calm. Global Aging Initiative, Center for Strategic and International Studies, Washington, DC.

*Source: _howtonote1602 - References (PDF).*

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