OLG-CGE Models and Demographic Research

Robert E. Wright

Professor of Economics

University of Strathclyde

Presentation, “CGE Modelling for Policy Analysis-International Cases”, University of Helsinki Ruralia

Institute, Seinajoki, Finland, March 16-17, 2015

Financial support from the Economic and Social Research Council under the grant: “Developing an OLG-CGE Model for Scotland” is gratefully acknowledged.

Additional financial support was provided by the University of Ottawa and the University of Strathclyde.

COLLABORATORS

OLG-CGE Modelling of Demographic Change

• Katya Lisenkova, National Institute of Economic and Social Research, London

• Marcel Merette, Department of Economics, University of Ottawa

• Jokke Kinnunen, Statistics and Research Åland

CGE Modelling of Demographic Change

• Peter McGregor, University of Strathclyde

• Kim Swales, University of Strathclyde

• Nikos Pappas, Government of Greece

• Karen Turner, University of Strathclyde

• Patrizio Lecca, University of Strathclyde

• Kristinn Hermannsson, University of Glasgow

Overview

1. Motivation

2. Scottish Background

3. OLG-CGE Models

4. Three Sets of Results

5. Concluding Comments

1. Motivation

• Population ageing is the shift in the age-structure away from the younger to older age groups

• It is occurring in almost all countries

• Most “rich” countries are ageing rapidly

• Increase in the number/share of population aged 65 and older

• Decrease in the number/share of population aged 20 and younger

• Low, no or slow growth in the number/share of population aged 20 to 64 years

• Population ageing is NOT population decline

Figure 1

Age Distribution, Scotland, 1911

15 10 5 0 5 10 15

0-4

5-9

10-14

15-19

20-24

25-29

30-34

35-39

40-44

45-49

50-54

55-59

60-64

65-69

70-74

75-79

80-84

85+

Age g

roup

Percentage of total population

Men

Women

Figure 4

Age Distribution, Scotland, 2041

15 10 5 0 5 10 15

0-4

5-9

10-14

15-19

20-24

25-29

30-34

35-39

40-44

45-49

50-54

55-59

60-64

65-69

70-74

75-79

80-84

85-89

90+

Ag

e g

rou

p

Percentage of total population

Men

Women

• “Accommodating” population ageing will be (is) expensive in

welfare states

• Increase in the “demand” for state-supplied pensions and other age-

related benefits targeting at older people

• Big expected increases in government expenditure

• Big expected increases in taxes to pay for it

• Need “high” economic growth if ∆T > ∆G

• If not…

• Dominant view is that population ageing “suppresses” economic

growth

• Why? One main reason is slow/no/low labour force growth

• Welfare state: Those “in work” pay for those “not in work”

• Difficult “political economy” in democratic countries with “one man,

one vote” systems

• “Greying of the electorate coupled” with a steep upwards sloping

relationship between age and political participation

• Significant standard of living reductions likely

Policy options?

1. Increase immigration

2. Introduce/expand “family friendly” policies

3. Raise the “age of retirement”

4. Increase spending on education and human capital investment

5. “Older age” employment subsidies

6. Subsidise/expand research and development activities

7. “Focus” on capital-intensive industries

8. Improve the cost-effectivness of government

9. Do nothing

10. Do nothing and “hope” for a technological leap

2. Scottish Background

1,00

1,50

2,00

2,50

3,00

3,50

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

Figure 1Total Fertility Rate, Scotland and UK, 1951-2062

Scotland UK

60,0

65,0

70,0

75,0

80,0

85,0

90,0

95,0

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

Figure 2Life Expectancy at Birth, Scotland and UK, 1951-2062

Scotland Men Scotland Women UK Men UK Women

-50 000

-40 000

-30 000

-20 000

-10 000

0

10 000

20 000

30 000

40 000

195

1

195

3

195

5

195

7

195

9

196

1

196

3

196

5

196

7

196

9

197

1

197

3

197

5

197

7

197

9

198

1

198

3

198

5

198

7

198

9

199

1

199

3

199

5

199

7

199

9

200

1

200

3

200

5

200

7

200

9

201

1

201

3

201

5

201

7

201

9

202

1

202

3

202

5

202

7

202

9

203

1

203

3

203

5

203

7

203

9

204

1

204

3

204

5

204

7

204

9

205

1

205

3

205

5

205

7

205

9

206

1

Figure 3Net-migration, Scotland, 1951-2062

-100

-80

-60

-40

-20

0

20

40

60

80

100

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

Figure 4Net-migration rate (per 10,000 population), Scotland and UK, 1951-2062

UK Scotland

100

105

110

115

120

125

130

2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065

Figure 5Projected Population, Principal Projection, Scotland and UK, 2012-2062 (2012=100)

Scotland UK

80

85

90

95

100

105

110

115

120

2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065

Figure 7Projected Population Aged 20-64, Principal Projection, Scotland and UK, 2012-2062

(2012=100)

Scotland UK

40,0

45,0

50,0

55,0

60,0

65,0

70,0

75,0

80,0

85,0

2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065

Figure 14Unadjusted and Adjusted Total Dependency Ratio, Principal Projection,

Scotland and UK, 2012-2062

Total UK Scotland

Adjusted UK Adjusted Scotland

Demographic “shock”

3. OLG-CGE Models

OLG = “over-lapping generations”

CGE = “computable general equilibrium”

What are CGE Models?

• A computer general equilibrium (CGE) model is a description of aneconomy using a system of simultaneous equations

• The “general equilibrium” idea implies that all the markets, sectorsand industries are modelled together with corresponding inter-linkages

• This is opposed to “partial equilibrium” that only takes intoaccount a part of the system (e.g. labour market), which neglectspotential feed-backs

• This mathematical representation, coupled with a solver algorithm,ensures the “computable” nature of the model i.e. empirical resultsare generated

• Models can be used for “what if” simulations thereby allowing one

to obtain numerical results for endogenous variables based on

assumptions about exogenous variables, functional forms, and

parameter values

• Most CGE models rest on neo-classical economic assumptions

• Consumers are assumed to maximise their utility subject to a budget

constraint (demand-side)

• Producers are assumed to maximise profit, given the prices of goods

and factor of production costs (supply side)

• For each good and factor of production, equilibrium price is

calculated where that demand equals supply

• The first CGE model was constructed by Johansen in 1960.

• Have been largely used to analyse the macro-economic effects of a

large number of economic policies such as liberalising foreign trade,

decreasing government expenditure and increasing taxation.

• Main strength of CGE models lies with their flexibility, in the sense

that their “general” nature means that they can be adapted to a variety

of problems depending on the creativity and ingenuity of the

researcher.

• CGE modelling is an “art”

CGE models are not in a strict sense a forecasting models.

However, given a set of precise assumptions, it can be used to

generate expected future time paths of key macroeconomic

variables:

Output, employment, unemployment, labour force participation,

investment, wage rates, inflation and competitiveness.

By varying these assumptions alternative time paths are

produced, which can be compared in order to evaluate the likely

consequences of alternative policies.

Some Useful Features:

Substitution in production or consumption

Non-passive supply side i.e. both demand and supply considered so

“supply matters” (no the case in I-o)

Relative prices become endogenous (e.g. the real wage and regional

competitiveness are determined within the model)

Producers and consumers allowed to respond to relative price changes

through substitution in production or demand

More flexible technology and demands structures (non-linearities)

• More flexible technology and demands structures (non-

linearities)

• Over lapping generations (OLG) structure makes them more

“demographically friendly”

• Can be used to “price” shocks, changes and policies e.g. In £s

or per cent output loss

• Consistency with microeconomic “circular flow”

underpinnings.

Figure 1. Micro-economic “Circular Flow”

Firms

Households

Factor

Mrkts

Goods

Mrkts

Computable

Calibration

Accuracy is evaluated by back “forecasting” (backcasting)

• Not surprising that more recently such model have been applied to a

series demographic problems largely (but not exclusively) around the

impacts of population ageing

• The type of CGE model that is best suited to demographic research

builds in the notion of “over lapping generations” (OLG-CGE)

• This approach more explicitly allows the interaction of age effects,

which in most demographic problems is of central importance.

Firm-side

One sector. One good. Cobb-Douglas production function

Factor prices equal to their marginal product

Labour demand disaggregated by types of labour

Three types of assets

Household assets

Government assets

Foreign assets

One region (RUK only to close the model)

Household-side • 21 generations – from 0-4 to 100+

– First 4 generations only affect public consumption

– Starting from 20-24 generations optimise their consumption/savings

• Four types of households depending on level of qualification– Three working qualifications (high, medium, low)

– One non-working

• Two types of nationalities– British born

– Foreign born

• Age/qualification-specific productivity (age-earnings profiles)

• Age-specific labour force participation rates

Overlapping generations structure

A1 A2 A3 A4 A5

T1 G1

T2 G2 G1

T3 G3 G2 G1

T4 G4 G3 G2 G1

T5 G5 G4 G3 G2 G1

T6 G6 G5

T7 G7 G5

T8 G8 G5

T9 G9 G5

T10 G10 G9 G8 G7 G6

T11 G11 G10

T12 G12 G10

T13 G13 G10

T14 G14 G10

Age

Tim

e

Demography: Standard approach

• Generations are “born” when they enter labour force

• They live for certain number of years

• At specified age they die with certainty

• Fixed (certain) life expectancy

• One variable parameter – “fertility rate”

Problems with standard approach

Population structure is a result of three demographic

processes

– Fertility (people appear at the beginning)

– Migration (people appear/disappear in the middle)

– Mortality (people disappear in the middle and at the

end)

Why use standard approach?

Certainty is required to use standard household optimisation problem

Household Utility Function

Household Budget Constraint

Euler Equation

a

at

a

cU

1)1(

1 1

1

Aa+1,t+1 = Aa,t (1+ rt+1)+Wa,t -Ca,t

Ca+1,t+1

Ca,t,

=1+ rt+1

1+ r

æ

èç

ö

ø÷

1/q

Demography: Our Approach

Variable “mortality” rate (includes both mortality and migration)

Life expectancy uncertainty on individual level but not on generation level, as mortality schedule is known today and at every point in time in the future

Perfect annuity market (accidental bequests distributed implicitly)

Alternative –bequests distributed between children (e.g., Hans Fehr, Sabine Jokisch, Laurence J. Kotlikoff, 2003, 2004, 2005)

New household problem

• Household Utility Function

• Household Budget Constraint

a

ta

taa

a

csU

1)1(

11

,

,1

Aa+1,t+1 =1

sa,t

Aa,t (1+ rt+1)+Wa,t -Ca,t( )

sa,t -- probability of survival for age a at time tsa,t=popa+1,t+1/popa,t

New household problem

• Household Utility Function

• Household Budget Constraint

• Euler Equation

a

ta

taa

a

csU

1)1(

11

,

,1

Aa+1,t+1 =1

sa,t

Aa,t (1+ rt+1)+Wa,t -Ca,t( )

Ca+1,t+1

Ca,t,

=1+ rt+1

1+ r

æ

èç

ö

ø÷

1/q

sa,t -- probability of survival for age a at time tsa,t=popa+1,t+1/popa,t

Government revenues

• Two taxes– Wage tax

– Consumption tax

• Separate pension contributions

• Return on government assets

• UK fiscal transfer

Government expenditures

• Three types of public consumption

1. Age-independent (fixed level per capita)

2. Health expenditures (mostly for old age)

3. Education expenditures (mostly for young age)

• Transfers (qualification-specific)

• Pensions (for 65+ year old)

Age-specific public expenditures• At the moment calibrated on the National Transfer

Accounts (NTA) for Canada

• NTA measure economic flows across age groups in a manner consistent with National Accounts. This is a very new approach to measuring intergenerational transfers

• UK NTAs are in the process of preparation– David McCarthy and James Sefton at Imperial college

represent the UK in the NTA project

– First estimates of UK National Transfer Accounts (April 2011)

Age-related components of the model

• Age-specific labour force participation rates

• Age-qualification-specific productivity profile

• Age-specific government spending on health and education

• Age-specific consumption profile (from calibration stage)

ASIDE: Ageing and Consumption

A key part of the ageing impacts puzzle is missing:

(1) Consumption of almost any good or service differs by age

(2) Age-consumption profile is inverted U- or J-shaped

• With population ageing there will be increases in demand for some goods/services and decreases in demand for other goods and services

• “Average” consumption increases and then decreases with age

• Clear supply-side effects.

• Requires multi-sector modelling

5. Results 1: Size versus Age-specific Effects

• Hypothesis: Relative importance of “size” versus “age-specific effects"

• Demographic shock: “ONS 2010-based principal population projection”

• Run model with age-specific effects “turned off”

• Run model with age-specific effects “turned on”

• Macro-economic variable of interest: Output per person

5. Results 2: Immigration and Economic Growth

• “Grow” the labour force

• Hypothesis: Relative importance of different levels of net migration

• Demographic shock: “2010-based variant population projection with

different level of net-migration”

• Run model with different levels of net-migration

• Macro-economic variable of interest: Output per-person

Additional Assumptions

Levels of net-migration:

1. Zero

2. 10,000

3. 20,000

4. 30,000

5. 40,000

6. 50,000

Sex ratio: 50% female, 50% male

Age structure of immigration: < age 45 distributed equally

5. Results 3: Do Nothing and “Hope” for Technological

Change

How big of a leap is needed?

What rate of technological change is needed

“Solow Residual” estimate of TFP growth

• Rise in output with constant capital and labour

• Rate of growth in total factor productivity

• Not an “explanation”

• Growth accounting exercise or growth decomposition

• Question: What causes the residual?

Estimate of TFP growth

Solow (1957): USA, 1909-1949, 1-2% per year

Maddison (1987): 1950-1973, France 4.0%

Germany 4.3%

Japan, 5.8%

UK, 3.1%

USA, 1.9%

Kohli and Werner (1997): Korea, 1971-1991, 3.5%

1971, 4.2%

1991, 2.4%

Technological “shocks”

Positive shocks “steepens” the production function

Examples of “big” shocks:

1. Information technology/computing “revolution”

Krueger (1993): USA, 1984-1989: Wages 10-15% higher for jobs that

involve using PCs

2. Reduction in shipping costs: Wind-to coal-powered freight ships

3. Large reductions in mortality: Labour productivity increase “caused”

by improved health. “Healthy workers are more productive workers”,

increased participation, increased labour supply

• Baseline scenario: circa 15% welfare loss

• How much technological change is needed to counteract this welfare

loss?

• Arithmetic suggests total factor productivity growth of 0.14% year is

needed to keep output per-person constant

• Over the past two decades in the UK total productivity growth over

the past two decades has averaged around 2% per year

• So about 7% of the recent past historical growth is needed.

• Technological change needed to counteract population ageing is

considerable.

• What is going to be the source of this change?

• Complications:

“Age-bias” in technological change

“Skill-bias” in technological change

6. Concluding Comments?

Special issue of Journal of Economic Modelling

Volume 35, 2013

“Demographic Applications of Over-lapping Generations

Computable General Equilibrium (OLG-CGE) Models”

Giorgio Garau, Patrizio Lecca and Giovanni Mandras, “The Impact of Population

Ageing on Energy Use: Evidence from Italy”

Katerina Lisenkova, Marcel Mérette and Robert Wright, “Population Ageing and the

Labour Market: Modelling Size and Age-specific Effects”

Patrick Georges, Katerina Lisenkova and Marcel Mérette, “Can the Ageing North

Benefit from Expanding Trade with the South?”

Axel Börsch-Supan and Alexander Ludwig, “Modelling the Effects of Structural

Reforms and Reform Backlashes: The Cases of Pension and Labour Market Reforms”

CGE-OLG Demographic Modelling Teams/Research

Groups

Department of Economics, University of Ottawa: Marcel Mérette

National Institute for Economic and Societl Research: Katya Lisenkova

Max Planck Institute for Social Law and Social Policy: Axel Börsch-Supan

Vienna Institute of Demography: Alexia Fürnkranz-Prskawetz

INGENUE Team, France: CEPII, CEPREMAP, MINI-University of Paris X and OFCE:

Vincent Touze

Department of Human and Social Sciences, University of Calgliari: Giorgio Garau

Ministry of Economy, Trade and Industry: Kazuhiko Oyamada

Regional Economic Applications Laboratory, University of Illinois at Urbana-Champaign:

Geoff Hewings

Department of Economics, Loyola University, Seville, Alejandro Cardenete