CGE and IO models to model job effects
Maximiliano Mendez-Parra
Dirk Willem te Velde
17 September 2015
Introduction
• There are a range of analytical tools to assess job impacts, e.g.:
– Ex-ante: CGE and IO models and production function approaches.
– Ex-post: tracer studies, econometric studies, value chain studies.
• Using the non-direct effects in IO and CGE models can be used to ensure
allocation of scarce resources is not biased towards projects that lead to
direct job effects only.
• Pros and cons
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A DFI Project Funnel
(What information is collected? What incentives exist?
What procedures are in place to source projects?
How do additionality / job impact feature?)
Investment Committee
Other rules/procedures
?????
Potential projects on the
radar screen of Investment
Officers
Ex-ante impact indicators
Projects making
it to CIP stage
FPFPFP
projects
Where do we need better ex-ante tools?
Direct job effects
Indirect job
effects
Other effects (e.g.
second order
productivity
effects)
Manufacturing
Very important
Potentially important
Less important
Financial services
Less important
Less important
Very important
Medium important
Very important
Less important
Infrastructure
Less important
Temporary
Very important
Agriculture
Very important
Less important
Less important
Sector of DFIs’
investment
Tourism
Input-output analysis (I-O)
•
•
•
•
•
•
•
•
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I-O Analysis allows to identify the effect of a change in an exogenous variable (e.g.
exports) on output and employment.
Multiplier: How much output/employment of each sector is needed to produce one
unit in each sector?
It considers the direct effect (on the own sector) and the indirect effects in the rest of
the sectors of the economy.
It also takes into account the 2nd order effects induced by the demand of the other
sectors in the production of inputs.
It can be used to assess the effect of production on environment and on the use of
natural resources. It is also used to evaluate the effects on imports of an increase in
final demand.
It is an ex-ante analysis.
It is a relatively low-tech method. Standard matrix knowledge required. It can be
implemented using Excel.
Data availability good. WIOT, OECD, Eora-MRIO, IFPRI SAMs, National sources.
Input-output analysis (I-O)
•
•
•
•
•
•
•
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Final demand (e.g. exports) is exogenous. Increases in output or employment do not have
additional effects on the economy.
Prices are constant before and after the experiment. All the effects are assumed to be quantity
effects.
Fixed proportions between inputs (Leontief model). No substitution possible between factors of
production. Technology fixed.
Although is possible to introduce constraints, it is assumed that all sectors can deliver what
each other sector is demanding. I.e. No friction between exports of barley and beer…
I-Os are calculated/estimated.
Data may be outdated: I-Os do not reflect the actual internal transactions and technology and
they tend to be updated by discrete jumps. Need to make assumptions about the economic
structure.
Sectoral disaggregation limited.
– Compatible international disaggregation: 57 GTAP sectors, 35 WIOT, 25 Eora-MRIO
– National sources vary: 124 Argentina, 58 Tanzania, 106 UK, 98 Scotland
Effect on output and labour demand of one unit increase
in demand in each sector in Tanzania
– Less than primary education
0.14
Sisal
Labour with less than primary education
0.12
Fisheries
0.10
Cashews
0.08
Sorghum
Pulses
Oilseeds
Coconuts Millet
Other crops
0.06
0.04
Tobacco
Coffee
Vegetables
Fruits
Cotton
Cassava
Cattle
Rice
Sugarcane
Retail and wholesale trade
Rice milling
0.02
Other milling
Textiles and clothing
Non-metals
Metals
0.00
0.00
0.50
1.00
1.50
Output multiplier
2.00
2.50
3.00
Effect on output and labour demand of one unit increase
in demand in each sector in Tanzania
– Primary education completed
Multiplier of labour with primary education completed
0.60
0.50
Fisheries
Cashews
Sugarcane
0.40
Pulses
Oilseeds Plantains
Vegetables Cattle
Coconuts
Other root crops
Rice
Leaf tea
Cotton
Sisal
0.30
Maize
Meat, fish and dairy
Sorghum
Maize milling
Millet Rice milling
Cassava
Hotels and catering
Other private services
Tobacco curing and
processing
Electricity
Beverages
Transport
and
storage
Other milling
Textiles and clothing
Non-metals
Other cereals
Wood products Rubber products
Machinery and vehicles
Tobacco
0.20
0.10
0.00
0.00
0.50
1.00
1.50
Output multiplier
2.00
2.50
3.00
Computable General Equilibrium Models
What is useful:
•
Could include feedback and second order effects of DFI affected projects, e.g.:
–
–
–
–
–
Labour market effects of investment: demand effects can increase employment which can then raise wages
(and decrease employment), or just wages if labour supply is fixed/segmented.
Dutch disease effects of investment: large investments (e.g. in natural resources) can lead to exchange rate
effects which can effect competitiveness
Inclusion of policy feedback rules: interest and exchange rates
Co-ordinated investment approaches
International linkages
What is problematic:
•
Depends on accuracy of individual equations (could be estimated econometrically depending on data)
•
Aggregated level (often link to a SAM)
•
Fixed costs of understanding and designing complex models
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Impact Dutch aid on Dutch exports: (IO & CGE approaches give
similar results for exports but different results for jobs)
export €bn
3.5
employment
16,000
14,000
3
12,000
2.5
10,000
2
8,000
1.5
6,000
1
4,000
0.5
2,000
0
Econometric / IO
(Klasen et al)
CGE Short run (Fic and CGE Long run (Fix and
Te Velde)
te Velde)
0
Econometric / IO
(Klasen et al)
CGE Short run (Fic and CGE Long run (Fix and
Te Velde)
te Velde)
employment
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Conclusions
• IO models are useful ex-ante tools (e.g. three fold differences in terms of
impact between sectors) but miss out some important productivity effects
(constant technology)
• CGE models can incorporate wider national impacts and feedback loops
(difference between 15,000 jobs created, or less), but also lack structural
change components
• There is often a lack of appropriate IO/CGE models relevant for the
specific investment opportunity; we don’t know how wrong we are
ignoring them. Can we use rules of thumb derived from IO and CGE
models for wider application?
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