1

Applications of National Transfer Accounts (NTA) in Research and

Policy Making

Sang-Hyop Lee,

East-West Center & University of Hawaii

Presented to the Workshop on“Shaping Social Protection in Africa: the NTA Approach”

May 13-27, 2009

Mombasa, Kenya

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An Economic Lifecycle

0

100

200

300

400

500

600

0 20 40 60 80

Age

Per

Cap

ita C

on

su

mp

tio

n a

nd

Lab

or

Inco

me Consumption

Labor Income

Large deficits at young and old ages.

Reallocations from surplus to deficitages required.

Population Age Distribution, 2005

0

0.01

0.02

0.03

0.04

0 10 20 30 40 50 60 70 80 90 100

Pro

po

rtio

n

China

Kenya

Germany

3 Population Age Distributions

Aggregate Lifecycle

Germany, Aggregate C and Yl

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

0.02

0 10 20 30 40 50 60 70 80 90 100

Labor Income

Consumption

Kenya, Aggregate C and Yl

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0 10 20 30 40 50 60 70 80 90 100

Labor Income

Consumption

• Based on per capita profile for developing countries weighted by UN estimates of 2005 age structure.

• Two features are of interest

• Overall dependency: Total difference between labor income and consumption.

• Direction of IG flows: Do flows to children or the elderly dominate?

Research Questions about the Economic Lifecycle Will change in age structure lead to demographic

dividend? Are the dividends sustainable? What policies are needed?

Will fertility decline lead to a decline in spending on children and, in particular, their human capital? Quantity-quality tradeoff: Becker Political economy arguments: Preston

Can the finance of health care and long term care be improved?

Can policies raise the labor production by the elderly? Age at retirement: Gruber and Wise Productivity of older workers:

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Other Sources of Funding Consumption (Reallocation System) Familial Transfers Asset-based Reallocations

Interest, dividends, rent from personal assets Home and other consumer durables Dis-saving

Public Transfers Social Security System

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Research Questions about the Reallocation Systems How do reallocation systems vary across

countries and over time? What is the impact of policies that expand or

contract public transfers to the elderly? Crowd out private transfers? If so, does this effect

fertility? Crowd out saving and thereby reduce economic

growth? Can we “stress test” reallocation systems?

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The NTA Projects…

Develop a system of economic accounts that can be used to study the macroeconomic implications of change in age structure.

Estimate the accounts with historical depth for economies with different cultures, levels of development, economic systems and policies.

Analyze and explain variation in the economic lifecycle and the reallocation

systems, macroeconomic effects of population aging, economic implications of pension, health care, education,

child subsidies, and other policy. Led by Ron Lee and Andrew Mason.

Currently 24 country teams are participating.

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Important Features of the NTA Comprehensive approach:

All mechanisms for shifting resources from one age group to another are incorporated into the accounts.

Both public and private institutions are incorporated. The role of the family is emphasized.

NTA is consistent with and complementary to National Income and Product Accounts.

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Evidence to present Consumption by children

Lee and Mason: HK tradeoff curve. Ogawa et al.: Spending on children in Taiwan

and Japan Consumption profiles for the elderly Labor income of elderly

Participation Productivity

Reallocation system (simulation)

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TW

J P

SIHU

ATKR

SE

FI

FR

TH

US BR

MX

CL

CR UY

ID

PH

IN

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

ln(TFR)

ln(H

um

an c

apit

al p

er c

hild

)

Tradeoff between HK and TFR: International Cross-section

Source: Lee and Mason, forthcoming, European Journal of Population.

Estimated elasticity of HK/W per child wrt

TFR is -1.05.

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0.000

0.200

0.400

0.600

0.800

1.000

1.200

0 10 20 30 40 50 60 70 80 90+

C(a

)/Y

l(30

-49)

Developing countries

US

Japan

More on education

More on elderly (health care)

Consumption: Industrialized vs. Developing Countries.

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Public consumption

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Age

Nor

mal

ized

(30

-49

labo

r in

com

e)

Japan 2004

US 2003

Uruguay 94

Thailand 04

Philippine 99

Mexico 04

Indonesia 02

Hungary 05

Finland 04

Costa Rica 04

Chile 97

Taiwan 03

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Labor Income: Industrialized vs. Developing Countries

Mexico (2005)

0.0

0.4

0.8

1.2

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90+

Ratio

to th

e ave

rage Y

L of 3

0-49

Germany (2003)

0.0

0.4

0.8

1.2

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90+

Ratio

to th

e ave

rage Y

L of 3

0-49

More on elderly

More on children

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Kenya: High Participation, Low Productivity for Children and Elderly Kenya (1994)

0

0.4

0.8

1.2

1.6

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70

LFPR Kenya LFPR Avg

Productivity Kenya

Productivity Avg

Kenya (1994)

-0.2

0.2

0.6

1.0

1.4

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90+

Ratio

to th

e ave

rage

YL

of 30

-49

Implication: Due to high participation, delaying retirement has little effect on elderly labor income.

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Labor Income as a Source of Funding Consumption for 65+ (Above Average)

38.9

35.5

34.2

32.5

28.1

24.2

23.8

22.9

21.3

20.6

17.4

16.9

0.0 10.0 20.0 30.0 40.0 50.0

Philippines (1999)

China (2002)

Kenya (1994)

India (2004)

Mexico (2004)

Costa Rica (2004)

S.Korea (2000)

Indonesia (2005)

Chile (1997)

Brazil (1996)

Thailand (2004)

Average

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Labor Income as a Source of Funding Consumption for 65+ (Below Average)

16.9

15.4

14.7

11.7

10.9

5.8

4.7

4.1

3.9

3.5

3.2

1.8

0.0 5.0 10.0 15.0 20.0

Average

US (2003)

Taiw an (1998)

Japan (2004)

Hungary (2005)

Spain (2000)

Sw eden (2003)

Finland (2004)

France (2001)

Slovenia (2004)

Germany (2003)

Austria (2000)

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Research Questions about the Reallocation Systems How do reallocation systems vary across

countries and over time? What is the impact of policies that expand or

contract public transfers to the elderly? Crowd out private transfers? If so, does this effect

fertility? Crowd out saving and thereby reduce economic

growth? Can we “stress test” reallocation systems?

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The First Demographic Dividend

( ) ( ) ( )

( ) ( ) ( )

Y t L t Y tgr gr gr

N t N t L t

First Demographic Dividend

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Economic Support Ratio Kenya 1950-2050

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

1950

1960

1970

1980

1990

2000

2010

2020

2030

2040

2050

Low (1.9)

Medium (2.4)

High (2.9)

Noted: Scenarios based on most recent UN Projections; In 2050 TFR for low scenario is 1.9, for medium scenario is 2.4, and for high scenario is 2.9.

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The Second Demographic Dividend Population aging can lead to an accumulation of

wealth to meet pension needs for retirement.

If workers save more (relying on asset-based reallocations) in anticipation of aging, higher income is possible even after the first dividend period has come to an end.

Alternatively, workers can rely on transfer wealth (PAYGO pension programs, familial transfer), which has little effect on growth (in our model, we assume that the % of transfer wealth is fixed).

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Dividends: Medium scenario, tau = 0.35

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

2000 2015 2030 2045 2060 2075 2090

1st dividend

2nd dividend

2nd dividend weak in simulation because of low consumption

among elderly.

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Some Remarks

The gains from relying heavily on asset-based reallocations are realized in the form of higher assets with small gains in consumption.

Later in the simulation (not shown), gains in consumption are substantially higher with smaller reliance on transfer programs to support the elderly. For example, using the medium scenario per capita

consumption is higher in 2100 by 14% for tau=0.35 than tau=0.6.

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Policy Implications

Good policies that are consistent with poverty reduction goals that do NOT undermine work and saving incentives, and

promote growth and that are financially sustainable.

One set of policy implications are economic policy that can best accommodate population

policy Influencing population change and age structure, per se.

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Conclusions Population matters

Population size and age structure Policy matters

Implication for growth; but underdeveloped financial markets may limit investment opportunities.

Early policy response is essential to realize the demographic dividend.

The NTA provides a research tool. Economic lifecycle Reallocation system

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The National Transfer Accounts project is a collaborative effort of East-West Center, Honolulu

and Center for the Economics and

Demography of Aging, University of California - Berkeley

Lee, Ronald (ronlee), Co-Director

Mason, Andrew (amason), Co-Director

Auerbach, Alan (auerbach)

Miller, Tim (tmiller)

Lee, Sang-Hyop (leesang)

Donehower, Gretchen (gstockma)

Ebenstein, Avi (ebenstei)

Wongkaren, Turro (turro)

Takayesu, Ann (takayesa)

Boe, Carl (cboe)

Comelatto, Pablo (pabloc)

Sumida, Comfort (comfort)

Schiff, Eric (eric)

Stojanovic, Diana (diana)

Langer, Ellen (erlanger)

Chawla, Amonthep (beet)

Pajaron, Marjorie Cinco (pajaron)

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Taiwan Key Institution: The Institute of Economics,

Academia Sinica, Taipei, Taiwan. Tung, An-Chi(actung), Country Leader

Lai, Mun Sim (Nicole)(munsim) Liu, Paul K.C.(kliu)

Andrew Mason Japan

Key Institutions: Nihon University Population Research Institute and the Statistics Bureau of Japan, Tokyo, Japan.

Ogawa, Naohiro(ogawa), Country LeaderMatsukura, Rikiya(matukura)

Fukui, Takehiro(jstat) Kondo, Makoto(kondo)

Akasaka, Katsuya(akasaka) Nemoto, Kazuro(nemoto) Makabe, Naomi(makabe)

Sato, Ryoko(rsato) Ogawa, Maki(mogawa) Murai, Minako(murai)

Obayashi, Senichi(obayashi) Suzuki, Kosuke(Suzuki)

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AustraliaKey Institution: Australia National University

Jeromey Temple, Country LeaderBrazil

Turra, Cassio(cturra), Country Leader Lanza Queiroz, Bernardo(lanza)

Renteria, Elisenda Perez(elisenda) Chile

Key Institution: United Nations Economic Commission for Latin America and the Carribean, Santiago, Chile

Bravo, Jorge(jbravo2), Country LeaderChina

Key Institution: China Center for Economic Research, Beijing, China.

Ling, Li(Lingli), Country Leader Chen, Quilin(Chen)

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France Wolff, Francois-Charles(wolff), Country Leader

Bommier, Antoine(bommier) Thailand

Key Institution: Economics Department, Thammasat University. Phananiramai, Mathana(Mathana), Country Leader

Chawla, Amonthep (Beet)(amonthep) Inthornon, Suntichai(Suntichai)

India Key Institution: Institute for Social and Economic Change, Bangalore

Narayana, M.R.(narayana), Country Leader Nanak Kakwani(kakwani) Ladusingh, L.(ladusingh)

MexicoKey Institution: Consejo Nacional de Población

Partida, Virgilio (virgilio), Country Leader Mejía-Guevara, Iván(ivan)

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Indonesia Key Institution: Lembaga Demografi, University of Indonesia, Jakarta, Indonesia.

Maliki(maliki), Country Leader Wiyono, Nur Hadi(nhwiyono)

Nazara, Suahasil(nazara) Chotib(chotib) Philippines

Key Institution: Philippine Institute for Development Studies. Racelis, Rachel H.(Rachel), Country Leader

Salas, John Michael Ian S.(Salas) Sweden

Key Institution: Institute for Future Studies, Stockholm, Sweden. Lindh, Thomas(lindh), Country Leader

Johansson, Mats(Mats) Forsell, Charlotte (charlotte)

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Uruguay Bucheli, Marisa(marisa), Country Leader

Furtado, Magdalena(furtado) South Korea

An, Chong-Bum (cban)Lee, Sang-Hyop (leesang)Chun, Young-Jun (yjchun)

Gim, Eul-Sik (kuspia)

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Austria Key Institution: Vienna Institute of Demography

Fuernkranz-Prskawetz, Alexia (alexia), Country LeaderSambt, Joze(joze)

Costa Rica Key Institution: CCP, Universidad de Costa Rica

Rosero-Bixby, Luis(lrosero), Country Leader Slovenia

Sambt, Joze(joze), Country Leader

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United States Key Institution: Center for the Economics and Demography of Aging

Lee, Ronald(ronlee), Country LeaderMiller, Tim(tmiller)

Ebenstein, Avi(ebenstei) Boe, Carl(cboe)

Comelatto, Pablo(pabloc) Donehower, Gretchen(gstockma)

Schiff, Eric(eric) Langer, Ellen(erlanger)

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INTRODUCING African Country Teams

COUNTRYCOUNTRY TEAMTEAM

Kenya Germano MwabuMoses Muriithi Reuben Mutegi

Mozambique Gilberto NorteRamos Muanamoha

Nigeria Adedoyin SoyiboAkanni LawansonOlanrewaju Olaniyan

Senegal Latif DramaniFahd NdiayeOuarme Alaya

South Africa Haroon BhoratMorne OosthuizenToughedah Jacobs

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The End