MAMS: A Tool for Public Finance and Development Strategy Analysis

1. MAMS: A Tool for Public Finance and Development Strategy Analysis Hans Lofgren Carolina Diaz-Bonilla Hans Timmer DECPG Presentation for the Public Finance Analysis and Management Core Course, PREM Learning Week, April 27, 2007

2. Introduction • MAMS (Maquette for MDG Simulations) originally designed for MDG strategy analysis. • Given the broad nature of MDGs and the important role of the government in MDG strategies, MAMS is also a framework for analysis of medium-to-long-run economywide public-finance issues.

3. Introduction • MAMS is being applied in numerous countries: • 19 in Latin America and the Caribbean (in collaboration with the UNDP and UNDESA) • 7 in Sub-Saharan Africa • In Ethiopia (the pilot country), MAMS has been extensively used by the World Bank and the government in the analysis of MDG and Poverty Reduction Strategies, as well as independent studies on demography, labor market, and aid/budget policy.

4. Introduction Outline of presentation • Issues in MDG strategy analysis – what an analytical framework should consider • The Structure of MAMS • Data for MAMS • MAMS simulations

5. 1. Issues in MDG strategy analysis • A framework for analysis of MDG strategies should consider the following factors: • Synergies between different MDGs • Role of non-government service providers • Demand-side conditions (incentives, infrastructure, incomes) • Role of economic growth • Macro consequences of increased government spending under different financing scenarios • Diminishing marginal returns (in terms of MDG indicators) to services and other determinants. • Unit service costs depend on efficiency and input prices (e.g. wages)

6. 1. Issues in MDG strategy analysis • A simple first approach establishes feasible strategies and evaluate costs in an fixed-coefficient fixed-price framework (UNMP) • Such a framework does not consider important factors influencing the design of MDG strategies  it is limited and possibly misleading

7. 2. Model Structure • MAMS may be described as an extended, dynamic-recursive computable general equilibrium (CGE) model designed for MDG analysis. • MAMS is complementary to and draws extensively on sector and econometric research on MDGs. • Motivation behind the design of MAMS: • An economywide, flexible-price model is required. • Standard CGE models provide a good starting point • But Standard CGE approach must be complemented by a satisfactory representation of 'social sectors'.

8. General Features • Many features are familiar from other open-economy, CGE models: • Computable  solvable numerically • General  economy-wide • Equilibrium  • optimizing agents have found their best solutions subject to their budget constraints • quantities demanded = quantities supplied in factor and commodity markets • macroeconomic balance • Dynamic-recursive  the solution in any time period depend on current and past periods, not the future.

9. MDGs • Extensions capture the generation of MDG outcomes. • MAMS covers MDGs 1 (poverty), 2 (primary school completion), 4 (under-five mortality rate), 5 (maternal mortality rate), 7a (water access), and 7b (sanitation access). • The main originality of MAMS compared to standard CGE models is the inclusion of (MDG-related) social services and their impact on the rest of the economy. • Social services may be produced by the government and the private sector.

10. Government • Government services are produced using labor, intermediate inputs, and capital (fixed coefficients for capital, intermediate inputs, and aggregate labor; flexible coefficients for disaggregated labor). • Government consumption is classified by function: social services (education, health, water-sanitation), infrastructure and “other government”. • Government spending is split into • Recurrent: consumption, transfers, interest • Capital • Government spending is financed by taxes, domestic borrowing, “money printing”, foreign borrowing, and foreign grants. • Model tracks government domestic and foreign debt stocks (including foreign debt relief) and related interest payments.

11. MDG “production” • Together with other determinants, government social services determine the "production" of MDGs. • MDGs are modeled as being “produced” by a combination of factors or determinants (table following) using a (reduced) functional form that permits: • Imposition of limits (maximum or minimum) given by logic or country experiences • Replication of base-year values and elasticities • Calibration of a reference time path for achieving MDGs • Diminishing marginal returns to the inputs • Two-level function: • Constant-elasticity function at the bottom: Z = f(X) • Logistic function at the top: M(DG) = g(Z)

12. Determinants of MDG outcomes

13. Logistic function in MAMS

14. Modeling education in MAMS • Service measured per student in each teaching cycle (primary, secondary, tertiary). • Model tracks evolution of enrollment in each cycle • Educational outcomes as functions of a set of determinants: for each cycle, rates of entry, pass, repeat, and drop out; between cycles, share that continues • MDG 2 (net primary completion rate) computed as product of 1st grade entry rate and primary cycle pass rates for the relevant series of years.

15. Intertemporal behavior Dynamics: • Updating of stocks of factors (different types of labor and capital, other factors) and debt (domestic and foreign) • TFP • Endogenous part depends on economic openness and growth in government infrastructure stocks. • Exogenous part captures what is not explained in model (institutions, new technologies, ….) • GDP growth is determined by: • growth in economywide TFP (influenced by labor-force composition) • growth in factor employment (mostly endogenous)

16. Flexible modeling framework • MAMS has evolved from an Ethiopia-specific pilot version to one that is more widely applicable, and may include: • multiple sectors • multiple households • wide range of taxes • NGO + private MDG/HD services • special-case sectors (resource-based export sectors, regulated utilities) • MAMS can also be used as an simple two-sector (government – private) framework for dynamic macro analysis. • MAMS works with standard approaches to poverty and inequality analysis: • aggregate poverty elasticity • representative household • microsimulation (integrated, top-down)

17. Typical Simulations and Indicators • MAMS scenarios relevant to public-finance analysis may differ in terms of: • level and composition of government spending; • financing of government spending (different types of taxes, domestic borrowing, money printing) • government efficiency • Outcome indicators of interest include the evolution of: • Private and government consumption and investment, exports, imports, value-added, taxes; all indicators may be national totals are disaggregated • Domestic and foreign debt stocks • MDG indicators (poverty, non-poverty MDGs)

18. 3. Data • Basic data needs are similar to other CGE models: • Social Accounting Matrix (SAM); factor and population stocks; shares and elasticities in trade, production, and consumption • Data (and model) disaggregation highly flexible outside the government and the labor market • Data requirements specific to MAMS: • In SAM: government consumption and investment disaggregated by MDG-related functions; labor disaggregated by educational achievement; • Education parameters: stocks of students by educational cycle; student behavioral patterns (ex: rates of passing, repetition, dropout); population data with some disaggregation by age; • MDG data: base-year indicators; elasticities; service expansion required to reach MDGs (MDG scenarios) • Other worksheets • Ex: debt, foreign debt relief, growth rates

19. 3. Data • The data demands define a research agenda for empirical research. • Likely key sources: • Standard data publications (macro aggregates, government budget, balance of payments) • World Development Indicators (WDI) (Labor stocks; Value-added in Ag/Ind/Srv; Population) • Public Expenditure Reviews and Country Economic Memoranda • Sector-focused MDG studies (health, education, water-sanitation, public infrastructure) • Existing SAMs

20. MDG Values for Ethiopia

21. 4. Simulations • BASE (business-as-usual scenario) • No MDG targeting • Government demand and GDP growth close to recent trends • Scenario calibrated around current resource availability • MDG-BASE (core MDG scenario): • Government service growth is sufficient to achieve all HD MDGs (2, 4, 5, 7a, 7b) by 2015 • Foreign grants are unconstrained; adjust to meet the government financing gap

22. Evolution over Time for MDG 2Net Primary School Completion Rate (%)(By Simulation) Note: 2015 target for MDG 2 = 100%

23. Evolution over Time for WagesWorkers with Secondary-School Education(By Simulation) Note: Wages are shown in Ethiopian Birr

24. Foreign Aid Per Capita (US$)By Simulation

25. Real Exchange Rate Note: Indexed at 100 in 2005

26. Illustrative Simulations • MDG-INFCUT (cut in infrastructure) • Government receives 85 percent of the aid as under MDG-Base. • Government focus on human development: • Maintains its spending on MDG human development targets (defined here to include primary education, health, and water-sanitation), while cutting spending on infrastructure. • MDG-HDCUT (cut in human development) • Government receives 85 percent of the aid as under MDG-Base. • Government focus on infrastructure: • Maintains its spending on infrastructure, while cutting spending on MDG human development targets.

27. Evolution over Time for MDG 2Net Primary School Completion Rate (%)(By Simulation) Note: 2015 target for MDG 2 = 100%

28. MAMS Ethiopia findings • Key results: • Foreign aid per capita increases five-fold to US$79 in 2015 as compared to 2005. • Heavy reliance on foreign aid appreciates the real exchange rate appreciation and skews production toward non-tradables. • In the educated part of the labor market, wage increases are initially rapid but will later slow down when labor supplies increase and the scaling-up period is concluded. • Relative to an emphasis on infrastructure, a human development focus puts the economy on a slower growth track

29. Trade-offs between Human Development (HD) and Poverty

30. Illustrative Simulations • MDG-MIX: • MDG scenario with smaller increase in foreign aid • Grant aid relative to the base scenario is only half as large; in per-capita terms, foreign aid reaches US$51 in 2015. • Direct tax collection adjusts to assure that government receipts are sufficient to cover government spending • MDG-GPRD: • more rapid government productivity growth but otherwise is identical to MDG-Base • MDG scenario with increased government productivity • To explore the potential for government productivity in facilitating progress toward the MDGs • Productivity of government labor and intermediate input use improves by an additional 1.5 percent per year whereas government investment efficiency grows at the same annual rate.

33. Government Mix: MDG-MIX • The PV of total foreign aid 2006-2015 falls from US$30.9 billion to US$20.2 • As a result of less foreign aid, the appreciation of the real exchange rate is less pronounced whereas export growth increases and import growth slows down. • Huge increase in direct taxes • From 6.3% of GDP in 2005 to close to 25% in 2015. • Strong dampening impact on growth in household factor incomes, consumption, savings and investments • Results in slower growth in the private capital stock and private GDP • GDP falls from 5.2% under MDG-Base to 4.3% in this scenario.

34. Government Mix: MDG-MIX (cont) • Although the scenario MDG-Mix has a more realistic outcome for foreign aid, it has the drawbacks of reducing private and over-all GDP growth, achieving only a subset of the MDGs (MDG 1 is far from being reached) and generating an even larger government share in GDP.

35. Government Productivity: MDG-GPRD • Compared to MDG-Base, results show: • Declines in foreign aid needs (to $26.6 billion; $60 per capita in 2015) • Declines in the GDP share for the government (to 51.7 percent) • Whereas the deterioration in terms of poverty reduction, private consumption growth, and GDP growth is minor. • However, it should be noted that such efficiency gains may be particularly difficult to bring about in the context of rapid government expansion.

38. Foreign Aid vs Tax Increase FGEXP: double the increase in foreign aid in each year (2004-2020); entirely in grant form; the resources are used for HD and infrastructure spending. TAXEXP: impose same increases in real government service and capital stock growth (as in FGEXP) but finance this expansion with an increase in direct taxes.

39. Foreign Aid vs Tax Increase (cont) • Addition to foreign aid under FGEXP has a positive impact on all components of domestic final demand (absorption). • Increase in foreign aid increases the trade deficit that Uganda can accommodate, leading to exchange rate appreciation that brings about slightly slower export growth and more rapid import growth (both rates move by 1%). • Consumption and investment growth both expand by an additional 0.5-0.7% compared to the base scenario. • GDP growth increases by around 0.5%, boosted by more rapid growth in private investment and more rapid expansion for the educated labor force. • The scenario has positive effects on all MDG indicators.

40. Foreign Aid vs Tax Increase (cont) • Under TAXEXP, direct tax increase imposed on upper two quartiles in rural and urban areas. • Direct taxes increase: 3% of GDP in 2003 to 11% in 2020. • Given unchanged trade deficit and little change in GDP growth, the expansion in government demand comes at the expense of private consumption and investment • Growth rates of which decline by 0.3-0.4%. • Distributional consequences driven by the tax policy • Bottom two quartiles: marginal increase in per-capita consumption; • Upper two quartiles: consumption declines by 0.4% per year. • Compared to base: generates higher poverty rate but slight improvements in the other MDG indicators; compared to FGEXP: all MDG indicators perform less well.

41. Foreign Aid vs Tax Increase (cont)

42. Foreign Aid vs Tax Increase (cont) • Scenarios show that attempts to let the government grow more rapidly in the absence of a parallel increase in foreign aid brings difficult trade-offs to the fore: human development services and the stocks of public infrastructure increase more rapidly while leaving households and the private sector with less resources for consumption and investment, developments that puts a break on progress in the human development area.

43. More Examples • Dominican Republic: • Achievement of the MDGs under different financing mechanisms: foreign borrowing, domestic borrowing, taxation. • Foreign grants not an option. • Private sector an important player. • Malawi: • Trade-offs between spending on infrastructure versus human development. • Large debt and interest payments. • Through Poverty Reduction Growth Facility strategy pursuing fiscal discipline and macro stability. • If can lower debt burden, and therefore the interest payments, can then re-focus these resources into infrastructure or human development sectors.

44. 5. Conclusions • MAMS is a flexible framework for dynamic development strategy analysis. • MAMS considers the links between MDG indicators, growth, alternative compositions and levels of government spending and alternative government financing policies. • What has been done so far points to the need for a better understanding of several issues, most importantly the determinants of MDG and education outcomes (single- and cross-country econometric work).

45. References • Bourguignon, François, Hans Lofgren, and Carolina Diaz-Bonilla. 2006. Aid, service delivery and the MDGs in an economy-wide framework. Mimeo. World Bank. • Lofgren, Hans and Carolina Diaz-Bonilla. 2006. “Economywide Simulations of Ethiopian MDG Strategies.” Paper presented at the Ninth Annual Conference on Global Economic Analysis, Addis Ababa. June. • Lofgren, Hans and Carolina Diaz-Bonilla. 2006. “MAMS: An Economywide Model for Analysis of MDG Country Strategies: Technical Documentation.” Paper presented at the Ninth Annual Conference on Global Economic Analysis, Addis Ababa. June.