MDG-consistency in the Macroeconomic Models

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

• Project completed in 2012

• Housed at Ministry of Finance

• For use by:

– Ministry of Finance– Planning Commission– Provincial Departments of Planning

Pakistan MDG Costing based on Revised Macroeconomic Framework

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

Key Steps

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

Key Steps-II

• 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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Past Modeling Efforts

• 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 morning-

after 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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Macro Model for Government’s Use

• 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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Model Development

MDG Costing in Macro Model

Sectoral Production Investment-Savings Balance of PaymentsAgriculture GDP-current prices Current AccountIndustry Net Foreign Income ExportsPrivate Services Foreign Savings ImportsPublic Services Total Resources Incomes (net)GDP(fc) real prices Consumption Current Transfers

Private Investment Capital AccountFiscal Balance Public investment Financing GapDirect Taxes National SavingsIndirect TaxesCustoms DutyNon-Tax RevenueCurrent Expenditure Global GDPDevelopment Expenditure GDP DeflatorTransfers to Provinces NFC Award Formulae

InputsFertilizer, Water, AreaLabour, Capital, Capacity

Pro-poor current & Development spending

MDGs

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 9

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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Needs Assessment – Model Usages

Forecast for 2014-15 (called baseline) on a business as usual (no policy change) scenario

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2014 2015GDP Growth (%) 4.6 5.0Total Investment (% of GDP) 15.8 17.1Private Investment (% of GDP) 9.4 10.6National Savings (% of GDP) 14.0 15.5Labour Demand (%) 2.1 2.3Tax Revenues (Rs. Billion) 3094 3548Fiscal Deficit (% of GDP) -5.0 -4.4Tax Revenues (% of GDP) 10.6 10.3Exports ($ Million) 29397 31937Import ($ Million) 45155 48328Current Account Balance ($ Million) -5761 -5949Financing Gap ($ Million) -5601 -5789

Demonstration - BAU

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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Demonstration - Simulations

Demonstration – Scenarios 2014-15

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BAU

Credit to Private Sector

Improves by 10%

Oil import bill

increases by 10%

GDP Growth (%) 5.0 5.2 4.5Total Investment (% of GDP) 17.0 17.6 15.5Private Investment (% of GDP) 10.4 11.1 8.9National Savings (% of GDP) 15.4 16.0 13.5Labour Demand (%) 2.2 2.3 2.0Tax Revenues (Rs. Billion) 3540 3537 3556Fiscal Deficit (% of GDP) -4.5 -4.4 -4.6Tax Revenues (% of GDP) 10.3 10.3 10.4Exports ($ Million) 31935 32233 31934Import ($ Million) 48195 48302 49771Current Account Balance ($ Million) -5798 -5919 -7341Financing Gap ($ Million) -5638 -5759 -7181

• MDGs selected for costing• Costing methodology• Integrating MDG Costing in Macro Model

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Opening up MDG Costing

• 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

DD MonthYear © 2012 Oxford Policy Management Ltd 15

Choice of MDGs

• 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


Costing Methodology

• 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.)


Costing Methodology - II

• Inspired from “MAMS Model” (World Bank)• Each MDG produced by a combination of

determinants permits:– 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

MDG Production Function

– 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


Integration Issues

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

• Public health spending • The adult literacy rate • Girls’ primary enrolment rate • Access to clean water • Access to sanitation


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


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


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


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


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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MDGParametersα,β γ

Elasticities MDG base

MDG additionalpoint

MDG target

Inputs base

Inputs additionalpoint

Cost of target

Components in generating MDG costs

• Disaggregating local-level sectoral and public finance data

• Localizing MDGs

• Improving specifications yet not complicating computational processes


Way Forward

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Thank You

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MDG-consistency in the Macroeconomic Models