Key issues in incorporating MDG-consistency in the Macroeconomic Models

1. Key issues in incorporating MDG-consistency in
the Macroeconomic Models
Expert Group Meeting on MDG-consistent Macroeconomic Modeling for Planning in
South Asia
1-2 October 2013, Nepal

2. Pakistan MDG Costing based on Revised
Macroeconomic Framework
• Project completed in 2012
• Housed at Ministry of Finance
• For use by:
– Ministry of Finance
– Planning Commission
– Provincial Departments of Planning

3. Key Steps
A. Macro Model
– Understanding on model specifications and parameters
– Interviews were conducted to assess capacity in economic
Ministries towards model building and management
– A complete literature review of existing models
– We relied on official published data from various sources
– Model equations and identities were specified in line with
macroeconomic theory
– Model output was validated using internal (sensitivity
analysis) and external (other comparable model results)
methods

4. Key Steps-II
B. MDG Costing
–
–
–
–
Identified the MDGs to use
Identified indicators
Literature review to identify drivers for indicators
Collected / calculated appropriate data for identified
indicators
– Calculated stock of development spending
– Review of MDG elasticities used in literature
– Integrated MDG relationships into Macro model

5. Past Modeling Efforts
• Public Sector
• Macro-consistency Framework: Planning Commission of Pakistan
• Financial Programming Framework: Ministry of Finance
• Financial Programming Framework: State Bank of Pakistan
• Non-Governmental Sector
• Integrated Financial Programming-CGE-microsimulation model:
Sustainable Development Policy Institute
• Integrated Social Sector Planning Model: Social Policy Development
Centre
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6. Macro Model for Government’s Use
• By 2012 the old models were dormant
• Due to frequent transfers of civil servants, capacity could not
be retained
• The result is that informed policy decisions are hampered
• Executive branch has no support tool to evaluate morningafter impact of public policy decisions
• In the face of external shocks there is no early warning alarm
system to preempt Government’s response – hence
difficulty in risk management
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7. Model Development
• The underlying principles for development of the model were:
–
–
–
–
Ease of data gathering (use of available data)
Tractability
Parsimony in updating of model
Ease of training
• The model developed is based on 34 behavioral equations
apart from fundamental accounting identities
Current State of Macroeconomic Data:
• Real Sector Accounts: No recent supply and use or input
output table– model works with published data
• Fiscal Sector Statistics:
– For development spending reliance on pro-poor data
– Service delivery based data not available
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8. MDG Costing in Macro Model
Sectoral Production
Agriculture
Industry
Private Services
Public Services
GDP(fc) real prices
Fiscal Balance
Direct Taxes
Indirect Taxes
Customs Duty
Non-Tax Revenue
Current Expenditure
Development Expenditure
Transfers to Provinces
Inputs
Fertilizer, Water, Area
Labour, Capital, Capacity
Investment-Savings
GDP-current prices
Net Foreign Income
Foreign Savings
Total Resources
Consumption
Private Investment
Public investment
National Savings
Balance of Payments
Current Account
Exports
Imports
Incomes (net)
Current Transfers
Capital Account
Financing Gap
Global GDP
GDP Deflator
NFC Award Formulae
Pro-poor current
& Development
spending
MDGs

9. Needs Assessment – Model Usages
Examples of kind of analysis we can do with this model:
• Result on GDP, if:
–
–
–
–
–
–
–
Productivity of Agriculture sector improves / reduces
Employment changes
Credit to private sector increases / decreases
Inflation increases / decreases
Industrial capacity utilisation (energy availability) changes
Population growth rate
Public sector investment changes
• Result on Current Account Balance, if:
–
–
–
–
GDP of trading partners changes
Diaspora patterns change
Domestic GDP changes
Rate of Taxation changes
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10. Needs Assessment – Model Usages
Examples of kind of analysis we can do with this model:
• Government Tax Revenue, if:
– National GDP changes
– Imports increase / decrease
– LSM improves / reduces
• MDG attainment, if:
– Finances are available / not available (gap analysis)
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11. Demonstration - BAU
Forecast for 2014-15 (called baseline) on a business as
usual (no policy change) scenario
GDP Growth (%)
Total Investment (% of GDP)
Private Investment (% of GDP)
National Savings (% of GDP)
Labour Demand (%)
Tax Revenues (Rs. Billion)
Fiscal Deficit (% of GDP)
Tax Revenues (% of GDP)
Exports ($ Million)
Import ($ Million)
Current Account Balance ($ Million)
Financing Gap ($ Million)
2014
4.6
15.8
9.4
14.0
2.1
3094
-5.0
10.6
29397
45155
-5761
-5601
2015
5.0
17.1
10.6
15.5
2.3
3548
-4.4
10.3
31937
48328
-5949
-5789
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12. Demonstration - Simulations
Example Scenarios:
• Impact on key macroeconomic indicators, if:
• Credit to Private Sector Improves by 10%
• Oil import bill increases by 10%
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13. Demonstration – Scenarios
2014-15
GDP Growth (%)
Total Investment (% of GDP)
Private Investment (% of GDP)
National Savings (% of GDP)
Labour Demand (%)
Tax Revenues (Rs. Billion)
Fiscal Deficit (% of GDP)
Tax Revenues (% of GDP)
Exports ($ Million)
Import ($ Million)
Current Account Balance ($ Million)
Financing Gap ($ Million)
Credit to
Private
Oil import
Sector
bill
Improves by increases by
BAU
10%
10%
5.0
5.2
4.5
17.0
17.6
15.5
10.4
11.1
8.9
15.4
16.0
13.5
2.2
2.3
2.0
3540
3537
3556
-4.5
-4.4
-4.6
10.3
10.3
10.4
31935
32233
31934
48195
48302
49771
-5798
-5919
-7341
13
-5638
-5759
-7181

14. Opening up MDG Costing
• MDGs selected for costing
• Costing methodology
• Integrating MDG Costing in Macro Model
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15. Choice of MDGs
• We have 7 MDGs and 14 indicators in this study
• We have chosen these MDGs because:
– These MDGs can be affected through government budgetary
allocations,
– Survey data is available for these MDGs at both national and
provincial level
– Targets have been developed for these MDGs for Pakistan
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© 2012 Oxford Policy Management Ltd
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16. Costing Methodology
• A top-down costing methodology as opposed to ‘needs based’
analysis
• ‘Needs based’ analysis are not sufficient because they only
provides an estimate of the cost of reaching a target MDG value
– so we are not able to estimate the costs of reaching another
MDG value or the MDG outcome from the inputs which we
actually have or expect to have in the future. Therefore, a
‘needs based’ analysis cannot be integrated into a macro model
• ‘Needs based’ analysis have proved extremely complicated to
implement
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© 2012 Oxford Policy Management Ltd
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17. Costing Methodology - II
• Once we have MDG functions we can determine the cost
required to achieve a given MDG target
• We are fitting a relationship between inputs and the MDGs
• Each MDG “produced” by a combination of determinants
– MDG value is dependent on a composite input variable
– Composite input variable is dependent on values of inputs (government
spending, private consumption, etc.)
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© 2012 Oxford Policy Management Ltd
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18. MDG Production Function
• Inspired from “MAMS Model” (World Bank)
• Each MDG produced by a combination of
determinants permits:
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–
–
–
Imposition of a limit for the mdg variable
Replication of base-year values and elasticities
Calibration to an “additional point”
Diminishing marginal returns to the inputs
• Two-level function:
1. Logistic function at the top: MDGVALUE = LOGIT(Z)
2. Constant-elasticity function at the bottom: Z = Constant
Elasticity of Substitution Function of govt spending ,
private consumption, public infrastructure, and other
mdgs

19. Integration Issues
– Only limited pro-poor spending categories to work with
so one spending line drives many MDGs making it
impossible to “cost” them independently
– Pro-poor spending for mother and child health is
infeasibly low so need to combine it with health facilities
spending
– Elasticities information very sparse in literature so need
to test robustness
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© 2012 Oxford Policy Management Ltd
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20. MDG Drivers-Poverty and Hunger
• Poverty
– GDP growth and the share of share of the lowest quintile in total
consumption
• Hunger
– the proportion of children U5 who are underweight is affected by
•
•
•
•
•
DD MonthYear
Public health spending
The adult literacy rate
Girls’ primary enrolment rate
Access to clean water
Access to sanitation
© 2012 Oxford Policy Management Ltd
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21. MDG Drivers- Education (1)
• Net Primary enrolment rates are affected by
– Public education spending
– The share of the lowest quintile in consumption
– The % children U5 under weight
– The adult literacy rate
• Girls primary enrolment is affected by
– Public education spending
– The share of the lowest quintile in consumption
– % children U5 under weight
– Net primary enrolment rate
– The adult literacy rate
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© 2012 Oxford Policy Management Ltd
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22. MDG Drivers- Education (2)
• The adult literacy rate is affected by
– Public education spending
– The share of the lowest quintile in consumption
– % children U5 under weight
– Net primary enrolment rate
– Girls primary enrolment
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© 2012 Oxford Policy Management Ltd
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23. MDG Drivers-Health (1)
• Infant/child mortality is affected by
– Public health spending
– Share of the Lowest quartile in consumption
– Adult literacy rate
– Infant vaccination rates
– % deliveries attended by qualified personnel
– % treatment of malaria
– Incidence of TB
– % access to safe water
– % access to sanitation
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© 2012 Oxford Policy Management Ltd
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24. MDG Drivers- Health (2)
• the % deliveries attended by qualified health personnel are affected by
– Public health spending
• the contraceptive prevalence rate is affected by
– Public health spending
– Adult literacy
• The Incidence of Malaria (% under treatment) is affected by
– Public health spending
– % access to safe water
• The Incidence of TB is affected by
– Public health spending
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© 2012 Oxford Policy Management Ltd
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25. MDG Drivers- Environmental sustainability
• The % households able to access to safe water is affected by
– Infrastructure spending
– Adult literacy rate
• The % of households with access to sanitation is affected by
– Infrastructure spending
– Adult literacy rate
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© 2012 Oxford Policy Management Ltd
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26. Components in generating MDG costs
MDG
Parameters
α,β γ
Elasticities
MDG
base
MDG Cost of
Inputs MDG
Inputs
base additional additional target target
point
point
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27. Way Forward
• Disaggregating local-level sectoral and public finance data
• Localizing MDGs
• Improving specifications yet not complicating
computational processes
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© 2012 Oxford Policy Management Ltd
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28. Thank You
www.sdpi.org, www.sdpi.tv
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