Choosing the Balance between Human Development and Infrastructure Spending in Malawi Carolina Diaz-Bonilla DECPG, World Bank Malawi, June 20, 2007 Joint work with: Hans Lofgren, Pavel Lukyantsau, and Antonio Nucifora

Introduction The Malawi Growth and Development Strategy (MGDS) marks a policy shift towards economic growth and infrastructure development, and away from investments in human development (HD): –Belief that resource allocations in previous strategies were tilted towards general administration and social services at the expense of infrastructure services; –MDGS thus emphasizes the need to adjust these allocations. Simulation analysis will: –Create a base scenario in line with the GoM’s macro program –Explore the consequences of changing the balance of expenditures between infrastructure and HD sectors.

Introduction (cont) Use MAMS: Maquette for MDG Simulations. –Economywide simulation model to analyze development strategies. –For Malawi: not targeting MDGs, but monitor them. Rationale for economywide approach: –Economic effects at different levels: macro, sectors, labor market. –Sector-by-sector approach (partial equilibrium) analysis is not sufficient on its own Results provide an indication of the broad direction of changes in the economy and the likely trade-offs.

Outline Model Structure Database Simulations: Assumptions Simulations: Results

Model Structure Standard dynamic recursive computable general equilibrium (CGE) model AND Additional module that links specific MDG or poverty-related interventions to poverty and other MDG achievements. Requires relatively detailed treatment of government activities to make the link possible: –Malawi: 9 government activities.

Model Structure (cont) Some features of open-economy, dynamic- recursive CGE models: –Optimizing producers and consumers. –Supply-demand balance in factor and commodity markets (with flexible prices clearing most markets) –Expenditures = receipts for the three macro balances: government, savings-investment, rest of world –Imperfect transformation/substitutability in trade. –Updating of factor and population stocks and TFP; endogenous/exogenous mix.

Table: Model Disaggregation

Model Structure (cont) MAMS treatment of government spending: –Government purchases public services, disaggregated by function. –Government services produced using labor, intermediate inputs, and capital. –Provision of education, health, and water-sanitation services contribute directly to MDGs and influence factor productivity. –HD (education) influences size and composition of labor force. –Sources of government income: taxes, domestic borrowing, foreign borrowing, and foreign grants

Model Structure (cont) Education: –Disaggregated by cycle. –Endogenous student behavior: Shares of relevant totals that enter first grade; In a grade: shares that pass, continue, repeat, or drop out within or between cycles. –Within each cycle and between cycles, student behavior determined by logistic-CE structure (for arguments, see Table) –Enrollment in each cycle = old students that continue/repeat + graduates from earlier cycle + new entrants to school system.

Model Structure (cont) Labor (by level of education) defined as the sum of: –Remaining stocks from last year –Graduates and dropouts who enter the labor force –Net entrants from outside the school system

Table: Determinants of MDG achievements

Database Social Accounting Matrix (SAM): fiscal year 2004 Macro SAM: –National Accounts data averaged over calendar years 2003 and 2004 (SIMA, World Bank) –Balance of Payment information for last two quarters of 2003 and first two quarters of 2004 (IMF data), and –Government Budget for fiscal year Micro SAM: created from macro SAM above and 1998 Malawi micro SAM from IFPRI Government sectors: –Disaggregated using recurrent and development expenditure information from several Malawi Ministries.

Figure. SAM Structure

Also need more detailed data related to different MDGs in the labor market; ex: –Levels of service delivery to meet MDGs. –Stocks of students at different educational levels. –Stocks of labor by educational level. –Student behavioral patterns (ex: graduation rates) Elasticities in production, trade, consumption, and in the different MDG functions. Database (cont)

GDP Growth in MAMS GDP growth is determined by: –growth in factor employment –growth in productivity or efficiency of factor use (TFP)

GDP Growth in MAMS Factor employment –Labor: Stock growth depends on functioning of education sector Un/underemployment responds to wage pressures. –Capital: Government capital: grows in parallel with increases in government services Private capital: investment (  stock growth) driven by funding = [private savings] + [FDI] – [gov. borrowing] –Land: Stock growth is exogenous.

GDP Growth in MAMS (cont.) 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, ….)

Quality of Expenditures “Quality” is implicitly assumed constant: i.e. efficiency in the use of expenditures does not change The degree of efficiency is determined by the assumptions in the model (elasticities) Simulations of the impact of improvements in efficiency can be carried out by: –Changing elasticities –Decreasing the share of “Other Government Services”, which is assumed as ‘non-productive’ expenditure in MAMS

Simulations BASE : projection of economic program pursued by current government, which is underpinned by the IMF PRGF. Trade-off simulations: –SIM-INFRA : reallocation of public expenditure toward infrastructure, agricultural, and irrigation services (less HD) –SIM-SOCIAL : reallocation of public expenditure toward human development (education, health, and water-sanitation) services (less infra). SIM-HISTORICAL Robustness simulation: Simulations under lower GDP growth assumption.

BASE Simulation Based on the implementation of the economic program pursued by the current government. Projects into the future the consequences of continuing current policies and growth rates Note: Assumption that adequate reforms adopted and Malawi is not hit by exogenous shocks.

BASE Simulation (cont) All sources of financing for government are exogenous and follow projections of GoM economic program.

BASE Simulation (cont) Endogenous government expenditure; clears fiscal account. No changes in the current composition of expenditures; shares across sectors are maintained constant (as in 2003/04 fiscal year). Special functioning of education sector: –government expenditure set to grow at a rate such that spending per enrolled student (“educational quality”) remains constant between 2004 and 2015.

BASE Simulation (cont) Government infrastructure capital stock (roads) has an effect on the productivity of other sectors in the economy: –Elasticity of TFP generated such that, ceteris paribus, the sum of the GDP changes across all activities linked to the public infrastructure capital stock per additional Kwacha spent on investments in this capital stock is equal to 0.2 (an implicit rate of return on public capital) Agriculture activity also affected in all simulations by the productivity in both the government agriculture and irrigation sectors. –Assumption linked to GoM strategy assumption that Malawi would increase its efficiency to help with macroeconomic stability. Elasticity of factor productivity for labor with respect to health: –Linked to under-5 and maternal mortality rates relative to base year.

BASE: Some Results (annual growth rates)

Trade-Off Simulations Public expenditures in infrastructure vs. HD sectors. SIM-INFRA simulation: –Growth in spending on infrastructure sectors is exogenously set higher by 1.5 times the growth rate in BASE. –Implies that expenditure growth rates for the HD sectors are endogenously scaled down to stay within fiscal space limits. –Fiscal space defined by foreign inflows and domestic revenue rules that are unchanged across the simulations. SIM-SOCIAL simulation: –Growth in spending on infrastructure sectors is exogenously set lower by 1.5 times the growth rate in BASE. –Implies that expenditure growth rates for the HD sectors are endogenously scaled up to stay within fiscal space limits. In both, public expenditure on the remaining sector (“other gov services”) grows at the same rate as in BASE.

Infra vs HD: Some Results (annual growth rates)

MDG 1: Headcount Poverty Rate

MDG 2: Net Primary School Completion Rate (%)

Real GDP at Factor Cost (2003/04 bn Kwacha)

Trade-Off Simulations (cont) Focus on infrastructure results in an increase in the growth rate of real GDP at factor cost from 6% to 6.3% per year. Faster growth rate, exports, investments and (monetary) poverty reduction, but slower progress in other human development indicators. Focus on social sectors leads to a slower economic growth rate and slower poverty reduction, but more rapid progress on human development indicators. GDP decreases to 5.6% per year

SIM-HISTORICAL Simulation Analyzes the expected economywide outcomes of continuing with pre-2004 trends and unchanged policies. Results: scenario is unsustainable –High amount of government expenditure (holding constant the trends in net foreign borrowing, foreign grants) results in high levels of domestic debt and interest payments that are unsustainable. –Household per capita consumption plummets, and therefore poverty rises rapidly.

Robustness Simulation Lower Growth Repeat BASE, SIM-INFRA, and SIM-SOCIAL simulations under lower growth assumptions. –BASE 4% rather than 6% Lower growth in all macro aggregates. Smaller budget for government => lower government expenditure per sector. Improvements in poverty and all other HD indices are more modest.

Summary Sound macroeconomic policies are critical for both growth and human development indicators. Higher infrastructure spending leads to faster GDP growth (and reductions in monetary poverty), but at the expense of slower improvements in HD indicators Higher HD sector spending leads to faster growth in health, education, and other HD indicators, but at the cost of a slower growth rate and poverty reduction. Note: we assume that “quality” of expenditures and investments does not change.

Thank You All analysis has limitations. If we limited presentations to analysis without limitations, then we would live in a world without presentations.