## _wp09227 ## Source details **Canonical URL:** [_wp09227](https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2009/_wp09227.pdf) ## Other formats - [Markdown version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2009/_wp09227.pdf.md) - [Structured JSON version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2009/_wp09227.pdf.json) --- ### I. Introduction — objective, options, model - Objective: - Assess economic impact of different options for creating and using fiscal space on growth, poverty, and education and health–related MDGs, and analyze transmission mechanisms at macro and meso (sectoral) levels. - Options for creating fiscal space analyzed: - Reducing spending in low-priority areas (prioritizing expenditures). - Increasing aid inflows (grants). - Increasing domestic revenue. - Uses of fiscal space simulated: - Increasing health and education expenditures (human development). - Increasing infrastructure expenditures. - A combination of human development and infrastructure increases. - Model and calibration: - MAMS (Maquette for MDG Simulation) calibrated for Burkina Faso. - MAMS: multisectoral recursive-dynamic real CGE model with an MDG module endogenizing health and education outcomes. - Key methodological notes: - MAMS embeds profit maximization for producers and utility maximization for households. - Labor market allows for reservation wages and unemployment; tracks richer set of sectors, households, and labor types than standard macro models. - Presentation approach: - Sections cover definition of fiscal space, MAMS features and baseline calibration, options for creating fiscal space, uses of fiscal space, and policy lessons. - In simulations, whenever fiscal space is created it is used; creation and use scenarios presented separately. ### II. Defining fiscal space — Burkina Faso context - Definition used: - Heller (2005a): fiscal space is “the availability of budgetary room that allows a government to provide resources for a desired purpose without any prejudice to the sustainability of a government’s financial position.” - General ways to create fiscal space: - Mobilize domestic revenue; borrow; secure external grants; prioritize expenditures differently; make spending more efficient. - Burkina Faso–specific facts: - Poverty-reducing expenditures: about 5.5 percent of GDP in 2007. - Total government expenditures and net lending: almost 26 percent of GDP in 2007. - Average share of poverty-reducing expenditures in other HIPC countries in 2007: about 8.8 percent of GDP. - Aid inflows in Burkina Faso: about 9.5 percent of GDP in 2007 (equivalent to about US$ 45 per capita); Gleneagles commitment: approximately US$85 per capita. - Revenue effort: approximately 13.5 percent of GDP in 2005; authorities committed to increase this to 17 percent. - Modeled uses of fiscal space: - Current and capital expenditures split into health, education, public infrastructure, and “other” government activities. - “Human development” = health + education expenditures. ### III. MAMS model and Burkina Faso calibration — structure and baseline - Model features: - Recursive-dynamic CGE extending IFPRI static CGE with MDG/education modules. - Circular flow: production activities, factors, institutions, demands, supplies; producers maximize profits; households maximize utility. - Labor: four education levels — (i) uneducated (primary or less), (ii) educated (secondary I), (iii) highly-educated (secondary II), (iv) tertiary. - Households: six types — (i) formal-sector wage-earners, (ii) informal–sector wage earners, (iii) cotton producers, (iv) food crop producers, (v) livestock keepers, (vi) “other” residual household. - Commodities: 14 outside government/MDG sectors (agriculture, processed goods, utilities, petroleum, services). - Production: nested technology (CES for value-added, Leontief for intermediates, Leontief top level). - Labor markets: two regimes — (i) full employment with market-clearing wage; (ii) unemployment with reservation wage negatively related to unemployment rate. - External sector: price-taker; infinitely elastic export demand and import supply at exogenous prices. - Prices: instant adjustment in real terms; CPI fixed as numéraire. Monetary policy and inflation not modeled. - Baseline calibration and data: - SAM mapping flows; data from sector studies, national statistics, government budgets, MDG costing database, 2003 household survey. - SAM for 2007 broadly matches national accounts for 2007. - Baseline keeps most macro variables stable in GDP terms. - Comparison with IMF staff macroeconomic framework: - IMF assumes gradual increase in revenue-to-GDP ratio until WAEMU target of 17 percent in 2018 and declining aid inflows in GDP terms thereafter. - MAMS baseline does not include the IMF revenue increase or IMF aid decline; these are treated as alternative scenarios. ### IV. Baseline projections and structural change (selected quantitative points) - Net loan disbursements trajectory (IMF framework / presented comparisons): - Peak of almost 4.5 percent in 2010. - Decline to about 2.5 percent in 2020. - Education and labor force composition (2007–2030, MAMS baseline): - Net primary school enrollment: 36 percent in 2000; 47 percent in 2007. - Share of labor with some education beyond primary: 5 percent in 2007; about 20 percent by 2030. - Factor-employment per 1000 value-added units (All sectors, selected figures from Table 1): - 2007: uneducated labor 22.0; educated labor 0.7; highly-educated (secondary) 0.3; highly-educated (tertiary) 0.2; private capital 1,149. - 2030: uneducated labor 8.8; educated labor 1.6; highly-educated (secondary) 0.4; highly-educated (tertiary) 0.3; private capital 934. - Change in percent, All sectors: - Uneducated: -60.0 - Educated: 116.3 - Highly-educated (secondary): 34.1 - Highly-educated (tertiary): 24.2 - Private capital: -18.7 ### V. Labor markets, wages, and absorptive capacity - Wage and factor-substitution patterns: - Increasing supply of educated labor tends to depress wages of abundant factors and raise wages of scarce factors (land, uneducated labor, private capital). - Educated labor shows limited wage decline because productivity of educated labor is assumed to increase strongly and reservation wages form a floor. - Unemployment dynamics: - 2007: elevated unemployment only for educated labor; other labor types at exogenous minimum unemployment. - Larger cohorts of highly-educated labor enter around 2015 (secondary) and 2020 (tertiary), potentially raising unemployment unless productivity or sectoral absorption increases. - Barriers to wage–productivity equalization (labor market segmentation): - Examples: unions, cultural barriers to migration. - Segmentation can make education privately rational as rent seeking if high-paying jobs are limited and wages rigid. - MAMS captures exogenous wage distortions via calibration but not full structural impediments. - Sectoral absorption: - Agriculture absorbs much new educated labor through factor substitution as educated labor becomes relatively cheaper. - Industry and nongovernment services increase education intensity and lower capital intensity, absorbing highly-educated labor. - Two real-world productivity interpretations: - Moving up the value chain in agriculture (fruit, agroprocessing) requires entrepreneurship, finance, and markets. - Expanding industry and services requires private discovery of business opportunities; mechanistic wage declines in the model are a simplified representation. ### VI. Creating fiscal space — scenarios, calibration, and leakages - Commonalities: - In all scenarios fiscal space is used to increase both human development and public infrastructure spending. - A. Prioritizing expenditures: - Reduce growth rate of “other” government current spending from baseline annual 6 percent gradually to 1.5 percent for 2012–15, returning gradually thereafter. - Result: total other government spending declines by about 2.5 percent of GDP by 2015. - Immediate cutback in capital investment for other government activities yields short-term fiscal space. - Modeling caveat: “other” government expenditures not linked to factor productivity in calibration; reduction assumed in subsectors with no/negative productivity impact. - Practical: “other” government expenditures ≈ 50 percent of current and capital expenditures in 2007 budget, complicating prioritization. - B. Increasing aid inflows: - Aid per capita increases from about US$ 45 in 2007 to about US$ 85 in 2015 (Gleneagles commitment). - Aid inflows rise from 9.5 percent of GDP in 2007 to about 14 percent in 2015; after 2015 held at US$ 85 per capita implying gradual decline in aid-to-GDP share as real GDP per capita rises. - Economic implication: percent increase in GDP terms smaller than per capita increase because real GDP per capita expands strongly. - C. Raising domestic revenue: - Revenue ratio raised to WAEMU target of 17 percent of GDP in 2015 from about 12.5 percent in 2007; maintained over long term so gain is permanent. - Implementation requires revenue administration and tax policy reforms (e.g., reduce administrative costs; broaden tax base by eliminating exemptions among over 200 taxes/fees). - Modeling caveat: MAMS does not capture potential efficiency gains from tax-system reform; realizing revenue increases without adverse effects requires design choices. - D. Comparing gains and leakages: - Permanence: prioritizing expenditures and raising revenue create permanent fiscal space; increased aid is temporary in GDP terms. - Cumulative fiscal space similar across scenarios until 2014, diverging thereafter. - Leakages: - Difference between created fiscal space and nominal increases in targeted expenditures (small for prioritized and aid; larger for revenue). - Larger leakage between nominal and real increases in targeted expenditures driven by factor price increases when education and health expenditures ramp up. - Scenario-specific leakages: - Prioritized: positive GDP growth reduces aid-to-GDP ratio (aid held at baseline US$), slightly offsetting gains. - Aid: short-term factor price increases raise nominal costs of producing other government activities, reducing fiscal space for targeted expenditures; reversed in longer term. - Revenue: negative effect on real GDP leads other government activities to grow faster in GDP terms than GDP, reducing room for targeted expenditures. ### VII. Macroeconomic impacts across creation scenarios - Growth impacts up to 2014: - Aid scenario: GDP impact positive and large. - Prioritization scenario: GDP impact slightly positive. - Revenue scenario: GDP impact slightly negative. - Mechanisms: - Revenue scenario: higher taxation crowds out private sector, reduces private investment and capital accumulation; dominates infrastructure positive effects. - Aid scenario: external financing enables reallocation from export production (cotton) to construction, health, education; improves overall labor productivity by reallocating factors to higher-productivity activities. - Prioritized scenario: smaller productivity gains because resources freed reallocate to sectors lacking the large productivity differentials (e.g., cotton to nonproductivity-enhancing uses). - Balance of payments: - Underlying real GDP growth assumption: 6 percent annually. - Aid scenario substantially widens the trade deficit; domestic scenarios show small changes. - Absorption channels: - Aid: increased domestic absorption driven by higher government absorption without crowding out private sector. - Prioritized: government absorption increases at expense of other government activities; overall government and private absorption practically unchanged. - Revenue: government absorbs more by crowding out private absorption. - Policy implication — spend-and-absorb: - Scaled-up aid effective only if fully spent and absorbed: fiscal policy should spend additional resources and monetary policy should allow real appreciation needed to increase absorption via widening trade deficit. ### VIII. Sectoral reallocation in the aid scenario and Dutch disease concerns - Two channels freeing production factors when aid or shocks occur: - Exports can decrease, releasing factors from export sectors. - Private-sector imports can increase, freeing factors from domestic import-competing and nontradable sectors via expenditure-switching. - Burkina Faso results in simulation: - Trade deficit widens mostly due to reduction in exports relative to baseline. - Exports stay flat for years then converge back; imports slightly exceed baseline. - Aid replaces foreign exchange earned by cotton, enabling factor reallocation from cotton to construction, health, education. - Cotton production remains flat for several years rather than growing; cotton shrinks relative to baseline. - Uneducated labor displaced from cotton migrates to other agricultural sectors, increasing noncotton agriculture beyond baseline; agriculture as whole expands with noncotton agriculture increases and overall GDP expansion. - Real exchange rate dynamics: - Real appreciation (via higher consumer prices with fixed nominal rate) reduces producer prices for exporters in domestic currency, lowering exporters’ marginal revenue product and profitability, causing scale-down and factor release. - Dutch disease considerations: - Real appreciation and export decline raise concerns about shrinking tradable sector and potential long-run growth damage if tradables are source of special productivity growth. - MAMS links productivity growth to export/import volumes; despite tradable-sector shrinkage, simulated overall GDP effect remains positive because productivity gains from human development and infrastructure outweigh export productivity loss. - Factor intensity adjustments: - Highly educated labor and private capital wages rise in expanding sectors; uneducated labor wages decline and get absorbed into agriculture. - Cotton/fiber sector reduces factor intensity for highly educated labor substantially and for capital moderately. - Magnitude of required real appreciation depends on ease of factor reallocation; large changes in relative factor prices required given sectoral factor-composition differences. ### IX. Distributional impacts, poverty, and MDG indicators - Household group impacts (six groups): - Formal-sector wage-earners; informal-sector wage earners; cotton producers; food crop producers; livestock keepers; “other” residual household. - Income distribution: - Education and health spending particularly benefit wage-earning households; formal wage-earners benefit notably in prioritized and aid scenarios. - Revenue scenario: formal wage-earners are largest payers of direct income taxes; this equalizes distributional impact relative to informal wage-earners who largely escape higher taxation. - Cotton agricultural households may be worse off under higher income taxation because they are relatively well organized and reap few benefits from higher education and health spending. - Poverty methodology and baseline: - Start from 2004 household survey (~8,500 individuals). - 2004 national poverty rate: about 45 percent. - MAMS simulations: poverty increases slightly to about 48 percent by 2007 due to adverse terms of trade shocks (higher oil prices, lower cotton prices). - Baseline: poverty declines gradually with annual real GDP growth of 6 percent; by 2030 baseline poverty ≈ 34 percent. - Comparative poverty outcomes: - Until 2014 aid scenario most effective at reducing poverty owing to large per capita consumption increase. - Prioritization and revenue scenarios have similar (small) poverty effects up to 2014. - Revenue scenario: lower per capita consumption offset by declining food prices, lowering the poverty line and offsetting falling consumption. - By 2030 poverty rates in aid and prioritization scenarios approach baseline as aid inflows recede in GDP terms and prioritization runs its course. - Heterogeneity across household groups: - Informal wage-earners: largest decline in poverty up to 2014. - Agricultural households: much higher poverty rates; reduction accelerates from about 5 percentage points in first period to 10.5 percentage points in second (source excerpt: “10.5 percentage points” cited). - Wage-earning households benefit strongly from expansion of education due to higher graduation rates and wage income. - MDG education and health indicators: - All three creation scenarios (prioritized, revenue, aid) improve education and health MDG indicators. - School indicator responds more strongly and gains are more cumulative; health indicator depends more on current real services per capita and requires repeated spending (e.g., malaria bed net distribution). - Human development spending yields better education and health outcomes than infrastructure in medium term for MDG indicators dependent on direct service delivery. ### X. Using fiscal space — human development vs infrastructure spending (aid-financed comparison) - Scenario design: - Compare use of fiscal space from increased aid inflows when (i) spending prioritized on human development (education and health), (ii) spending prioritized on infrastructure (public infrastructure), and (iii) combined use. - Key comparative findings: - Size of fiscal space: - Infrastructure scenario: spending increase relative to baseline somewhat larger because larger output effect creates additional fiscal space. - Human development scenario: nominal spending increase partly absorbed by rising nominal costs of delivering other government services (higher wages), reducing fiscal space for targeted expenditures. - Sectoral responses: - Human development increases nominal GDP share of education and health (mostly government). - Infrastructure increases private-sector inputs, especially construction. - Both scenarios result in relative decline in cotton sector. - Real vs nominal expansion: - Real expansion in education and health lags nominal expansion by many years due to shortage of highly educated labor (teachers, nurses); construction shows practically no gap between nominal and real. - Consequently, real increase in human development spending over medium term much smaller than nominal increase; no such large gap for infrastructure. - Bottlenecks: - Shortage of highly educated labor delays expansion of real services; initial spending mainly raises wages for current workforce with limited employment increases. - Construction expansion constrained mainly by private capital, less by skilled labor. - Longer-term dynamics: - Human development scenario induces major increase in supply of highly educated labor (students remain in school), expanding delivery of services in real terms and eventually reducing wages for highly educated labor. - Infrastructure scenario yields greater accumulation of private capital and substantially higher GDP growth, increasing savings and investment. - Growth comparison: - Infrastructure spending has a much larger impact on real GDP than human development spending over the projection period. - Cumulative infrastructure investment in real terms builds up faster than human capital because education cycles and training take multiyear periods. - Balance of payments and exchange rate: - Trade balance and absorption impacts similar across scenarios. - Infrastructure scenario: smaller negative impact on exports, smaller real appreciation, allowing larger import expansion for same trade deficit. - Human development scenario: larger transfer of private capital out of export sectors and greater export shrinkage causes larger price signals and higher appreciation. - After 2015, as aid inflows in GDP terms decline, appreciation reverses and depreciation required to rebuild export sectors occurs. - Distributional effects: - Human development spending strongly benefits wage-earning households (large rise in educated labor wages). - Infrastructure spending leaves income distribution relatively unchanged. - MDG effects: - Human development spending better for MDG education indicator; infrastructure also improves education via infrastructure lowering costs to access schools and income effects. - For MDG health indicator, infrastructure also yields good results through facilitation of service delivery and indirect income improvements. - Trade-offs and policy lessons: - By 2015 poverty likely reduced most by aid-financed infrastructure spending. - Focusing aid on human development yields better education and health MDG outcomes. - Trade-offs between sources of fiscal space: - Aid: increases domestic absorption and avoids political resistance but may be less reliable and increases donor dependence. - Domestic revenue: more permanent but faces political resistance. - Implementation caveats: - Expanding education and health services in real terms takes time due to capacity constraints (teachers, nurses). - Need to align pace of expenditure increases with training/program delivery capacity. - Monitor wage pressures to avoid wage-bill crowding out other expenditures. - Infrastructure spending promotes growth and also contributes to MDGs directly and indirectly. ### XI. Appendix I — Modeling MDG sectors in MAMS (mechanics and calibration highlights) - Purpose: - MAMS integrates MDG service delivery (health, education, water, sanitation) with economy-wide constraints to analyze MDG strategies, growth, and aid interactions. - MDG production block (Block 1): - Leontief fixed input-output coefficients for MDG-related services requiring labor (by education type), capital, and intermediate inputs. - Aggregate labor for MDG services nests three education categories with CES substitution (elasticity ω); government minimizes unit cost. - Capital accumulation: K_{t+1} = (1 - δ) K_t + I_t. - Intermediates split domestic vs imported with CES elasticity σ. - MDG outcomes block (Block 2): - Logistic functions for MDG indicators: MDG_k = ext_k / (1 + e^{-(β_k Z_k + γ_k)})^{η_k} capturing changing returns to scale and responsiveness. - Intermediate variable Z_k: Cobb-Douglas function of service levels and determinants (per-capita consumption, public infrastructure, other MDGs). - Calibration requires base-year values, 2015 targets, extreme values, elasticities, and placement of initial situation relative to logistic inflection point. - Education modeling (MDG 2): - Full cycle accounting: entry, pass, repeat, drop out dynamics by grade and cycle. - Net primary completion rate is product of entry and sequential graduation/passing rates over cycle years. - Determinants include services per student, household consumption, infrastructure, health performance, wage incentives. - General equilibrium integration: - Competition for scarce resources between MDG services and other sectors; loanable funds market and financing choices (grants/loans vs domestic taxation) matter. - Foreign financing can reduce negative domestic impacts but may generate Dutch disease depending on import share of spending. - Recursive-dynamic with option for forward-looking government decision rules. ### XII. Appendix II — Burkina Faso 2007 budget composition (selected aggregates and shares) - Total 2007 budget (Table II.1): - Total: 555,455,882,000 (Sections Dotations) and 563,730,097,473 (Montants Engagés). - Of which education, health, and infrastructure: 250,562,519,471; 260,016,802,524 — Percent: 45% and 46%. - Of which other government: 304,893,362,529; 303,713,294,949 — Percent: 55% and 54%. - 2007 capital budget (Table II.2): - Total: 474,892,877,000 (Dotation); 338,129,342,474 (Total Annuel Décais.). - Of which education, health, and infrastructure: 228,203,856,786; 168,717,459,199 — Percent: 48% and 50%. - Of which other government: 246,689,020,214; 169,411,883,275 — Percent: 52% and 50%. - Selected ministry allocations (examples from Tables II.1 and II.2; values preserved as in source): - Ministère de la Santé: 56,000,958,000; 57,367,880,907 (Table II.1 current/total); Capital: 31,584,488,000; 29,154,690,768 (Table II.2). - Min. Enseign.de Base & Alphabétisation: 69,733,407,000; 78,710,057,951 (Table II.1); Capital: 39,842,844,000; 28,381,258,344 (Table II.2). - Min. des Infrastruct., Transport et Habitat: 34,729,577,000; 29,926,046,272 (Table II.1); Capital: 118,733,521,000; 86,188,705,480 (Table II.2). - Dépenses Communes Interministérielles: 132,214,769,000; 121,622,396,738 (Table II.1); Capital: 27,824,311,000; 22,150,310,814 (Table II.2). *Source: _wp09227 (Authors' calculations and calibration for Burkina Faso).* ### References.............................................................................................................. ### References ### I. Introduction - Objective: assess economic impact of different options for creating and using fiscal space on growth, poverty, and education and health-related Millennium Development Goals (MDG), and analyze transmission mechanisms at macro and meso (sectoral) levels. - Options for creating fiscal space analyzed: - Reducing spending in low-priority areas - Increasing aid inflows (grants) - Increasing domestic revenue - Uses of fiscal space simulated: - Increasing health and education expenditures - Increasing infrastructure expenditures - A combination of human development and infrastructure increases - Model and calibration: - MAMS (Maquette for MDG Simulation) calibrated for Burkina Faso used for analysis. - MAMS is a multisectoral real CGE model expanded to incorporate MDGs (health, education, water-sanitation). - Key methodological notes: - MAMS embeds profit maximization for producers and utility maximization for households. - Labor market allows for reservation wages and unemployment; useful for analyzing effects of increased education spending. - MAMS tracks a richer set of sectors, households, and labor types than standard macro models. - Presentation approach: - Sections cover definition of fiscal space (Section II), MAMS features and baseline calibration (Section III), options for creating fiscal space (Section IV), uses of fiscal space (Section V), and policy lessons (Section VI). - In simulations, whenever fiscal space is created it is used, and vice versa; alternative scenarios for creation and use are presented separately for clarity. ### II. Defining fiscal space - Definition cited: Heller (2005a) — fiscal space is “the availability of budgetary room that allows a government to provide resources for a desired purpose without any prejudice to the sustainability of a government’s financial position.” - General ways to create fiscal space: - Mobilize domestic revenue - Borrow from domestic and external sources - Secure external grants - Prioritize expenditures differently - Make spending more efficient - For Burkina Faso, three sources particularly relevant: - Prioritizing expenditures: poverty-reducing expenditures are relatively small compared to central government size. - Poverty-reducing expenditures accounted for about 5.5 percent of GDP in 2007 whereas total government expenditures and net lending reached almost 26 percent of GDP. - Average share of poverty-reducing expenditures in other HIPC countries in 2007 was about 8.8 percent of GDP (IMF (2008a) — note: bibliographic detail in source). - Scaled-up aid: Burkina Faso receives substantial aid but per-capita aid is far below the Gleneagles’ target. - Aid inflows in Burkina Faso totaled about 9.5 percent of GDP in 2007. This is equivalent to about US$ 45 per capita; Gleneagles commitment was approximately US$85 per capita. - Increasing revenue: Burkina Faso’s revenue effort is low—approximately 13.5 percent of GDP in 2005—and the authorities have committed to increase this to 17 percent, which would create substantial fiscal space. - Modeled uses of fiscal space in the paper: - Current and capital expenditures split into health, education, public infrastructure, and “other” government activities. - Human development refers to health and education expenditures. ### III. The MAMS model and Burkina Faso calibration A. Features of MAMS - Model type and purpose: - MAMS is a recursive-dynamic CGE model designed for medium- to long-run development strategy analysis. - Starting point: static standard CGE model developed at IFPRI (Lofgren et al., 2002), extended by (i) recursive dynamics and (ii) an MDG module endogenizing MDG and education outcomes. - Core structure: - Comprehensive circular flow: factors of production, production activities, institutions (households, government, rest of world), demands (intermediate and final: consumption, investment, exports), and supplies (domestic producers and imports). - Producers maximize profits; households make utility-maximizing consumption decisions; both take prices as given. - Covers generation of MDG and education outcomes and roles of government functions (Appendix I referenced in source). - Disaggregation for Burkina Faso: - Households: six types - (i) formal-sector wage-earners - (ii) informal–sector wage earners - (iii) cotton producers - (iv) food crop producers - (v) livestock keepers - (vi) one “other” residual household (pensioners, independent businesses, others) - Notes: households are distinguished by composition of earnings and expenditures; household survey (2004) shows wage-earning households least poor with average (unweighted) poverty rate of about 10 percent; agricultural households most poor with national (weighted average) poverty rate of about 55 percent. - Commodities: 14 types outside government and MDG sectors, covering agricultural products (including cereals and cotton); processed goods (cotton fiber for export and manufactured goods for domestic use); utilities (including water and sanitation); petroleum (imported); and services (including construction). - Production factors: - Labor differentiated by four education levels: - (i) uneducated labor with completed primary education or less - (ii) educated labor with completed secondary I education - (iii) highly-educated labor with secondary II education - (iv) tertiary education - Private capital and land included - Public infrastructure enhances total factor productivity (TFP) - Behavioral and market assumptions: - Producers: profit maximization in perfectly competitive setting; factor inputs adjusted subject to CES production function constraints. - Nested two-level production technology: bottom level CES aggregates primary factors into value-added; Leontief aggregates intermediate inputs; top level Leontief aggregates value-added and intermediate inputs into final output. - Demand side: - Domestic commodities can be exported or sold domestically; imperfect transformability between exports and domestic sales. - Household demand determined via a linear expenditure system; depends on household income (net of direct taxes and savings) via fixed marginal income share, own price negatively, and subsistence minimum demand. - Labor markets: - Two regimes per labor market: (i) full employment (unemployment at exogenous minimum) with market-clearing wage; (ii) unemployment (above exogenous minimum) with reservation wage negatively related to unemployment rate. - Nonlabor factors (land and private capital) assumed market-clearing. - Exogenous sector-specific wage differentials allowed; in Burkina Faso calibration agricultural labor wages set below other sectors. - Government: - Collects direct income taxes, indirect sales taxes (largest revenue source), and import duties; spends on calibrated expenditure categories. - Capital stock required to support current government activities is endogenous; capital investment in public infrastructure is exogenous while maintenance and operation spending is endogenized. - External sector: - Burkina Faso assumed price-taker with infinitely elastic export demands and import supplies at exogenous prices. - Price and time considerations: - Prices adjust instantly in MAMS; model depicts medium-term outcome after price adjustments. - MAMS keeps the consumer price index (CPI) fixed and uses it as numéraire; all prices are “real” prices deflated by CPI. - Monetary policy and inflation are not modeled; savings defined as largely fixed share of post-tax household income; labor supply depends on exogenous population growth. B. Baseline Calibration of MAMS - Calibration core: social accounting matrix (SAM) mapping flows between production activities, factors, institutions, and commodities. - Data sources used: sector studies (especially education), national statistics office data (input/output, national accounts), government budgets, MDG costing database, and 2003 household survey. - Macro alignment: - SAM for 2007 broadly matches Burkina Faso’s national accounts for 2007. - MAMS simulation keeps most macroeconomic variables stable in GDP terms (Figure 1 referenced in source). - Comparison with IMF staff macroeconomic framework: - For 2007 IMF framework and MAMS are similar; IMF projection differs thereafter in two aspects: - (i) IMF assumes gradual increase in revenue-to-GDP ratio until it reaches the WAEMU target of 17 percent in 2018 (Figure 1, Panel 1). - (ii) Aid inflows decline in GDP terms over the medium term (Figure 1, Panel 2). - Interpretation: - IMF framework projects replacement of aid with revenue: decline in aid results from lower grant inflows as donor support subsides and external borrowing declines due to debt sustainability concerns. - Increase in revenue effort aims to compensate for lower aid and maintain expenditure levels. - Effects in IMF framework (Figure 1 panels referenced): - Government expenditures in GDP terms stay broadly stable. - Overall consumption declines (Panel 3) as resource transfer from private to public sector crowds out private consumption. - Investment in GDP terms is stable (Panel 4). - Trade deficit narrows (Panel 5) as lower private consumption reduces import demand (Panel 6). - Additional numerical and contextual details: - IMF projections shown correspond to macroeconomic framework underlying the second review of the PRGF-supported program (IMF (2008b) — bibliographic detail in source). - WDI statistics: Burkina Faso’s average aid-to-GDP ratio for 2001–06 was about 13 percent; this is more than twice the ratio for sub-Saharan Africa (5 percent) or low-income countries (about 6 percent). - Fiscal aid-to-GDP ratio averaged about 10 percent for 2001–06 and is projected to decline (additional details referenced in source). *Source: _wp09227 - References (PDF chapter/section) — content supplied in the prompt.* ### 5.5 percent by 2020. Net loan disbursements would decline from a peak of almost 4.5 percent in 2010 to about ### _wp09227 - 5.5 percent by 2020. Net loan disbursements would decline from a peak of almost 4.5 percent in 2010 to about ### MAMS baseline calibration and IMF forecasts - Net loan disbursements trajectory: - Peak of almost 4.5 percent in 2010 - Decline to about 2.5 percent in 2020 - Fiscal space scenarios (modeled separately from the baseline): - Raise the revenue-to-GDP ratio to the WAEMU target of 17 percent (in line with the baseline assumption of the IMF framework) - Increase aid inflows to the Gleneagles target of US$85 per capita (this differs from the IMF framework where aid inflows decline) - MAMS baseline does not include the increase in revenue or the decline in aid inflows; these are treated as alternative scenarios in the section on creating fiscal space - Figure 1 (panels summarized in source) compares MAMS simulation and IMF projection for: - Revenue, Expenditures, and Overall Balance (Percent of GDP) - Aid inflows (Percent of GDP) — total and grant inflows, MAMS vs IMF - Consumption (Percent of GDP) - Investment (Percent of GDP) — total and government investment, IMF (thick line) & MAMS (thin line) - Trade balance and Exports/Imports of Goods and Services (Percent of GDP) ### Structural change in the MAMS simulation (2007–2030) - Education and labor force composition: - Net primary school enrollment rate increased from 36 percent in 2000 to 47 percent in 2007 - MAMS baseline assumes continuation: gross enrollment rates increase for all school types throughout 2007–2030 (Figure 2, panel 1) - Share of labor with some education beyond primary school: - 5 percent in 2007 - about 20 percent by 2030 (panel 2) - Supply increases particularly for: - Educated labor (lower secondary school degree) - Highly educated labor (full secondary or tertiary education) (panel 3) - Key macro adjustments and transmission channels for absorbing educated labor: - Wage adjustment - Factor substitution - Unemployment - Sector composition - External adjustment ### Wage adjustment and factor substitution (findings) - Wage dynamics: - Factors becoming more abundant (educated labor) tend to see decreasing wage rates; scarce factors (land, uneducated labor, private capital) see wage increases - Educated labor shows only minimal wage restraint relative to uneducated labor despite increased supply because: - Productivity of educated labor is assumed to increase strongly throughout the simulation period - Unemployment of educated labor dampens downward wage adjustment due to reservation wages forming a wage floor - Panel 4 (Wage Rates, 2007–2030) conforms to these patterns except for educated labor - Factor substitution outcomes (Table 1 highlights): - Production becomes more intensive in relatively cheaper factors (education-intensive production rises) - Agriculture, initially intensive in uneducated labor, declines in that intensity as educated labor becomes relatively cheaper and substitutes for uneducated labor; agriculture absorbs most new supply of educated labor (Panel 5) - Industry and nongovernment services increase education intensity and lower capital intensity—substituting highly educated labor for capital—absorbing most new entrants of highly educated labor (Panel 6) - Quantitative factor-intensity changes (selected figures from Table 1): - All sectors, factor employment per 1000 value-added units: - 2007: uneducated labor 22.0; educated labor 0.7; highly-educated labor (secondary) 0.3; highly-educated labor (tertiary) 0.2; private capital 1,149 - 2030: uneducated labor 8.8; educated labor 1.6; highly-educated labor (secondary) 0.4; highly-educated labor (tertiary) 0.3; private capital 934 - Change in percent, All sectors: - Uneducated: -60.0 - Educated: 116.3 - Highly-educated (secondary): 34.1 - Highly-educated (tertiary): 24.2 - Private capital: -18.7 ### Unemployment dynamics and labor market fragmentation - Reservation wages and unemployment: - MAMS calibration allows for unemployment via reservation wages that form a wage floor; this can prevent wages falling enough to clear labor markets - 2007: elevated unemployment evident only for educated labor (Panel 7); unemployment rates for other labor types are at exogenous minimum levels (search unemployment) - As larger cohorts of highly-educated labor enter (secondary around 2015; tertiary around 2020), reservation wages for highly-educated labor become binding and unemployment rises - Long-run forces that could reduce unemployment for educated labor: - Reservation wage modeled as a negative function of unemployment (reservation wage would adjust downward) - Decline in wages would reduce the number of students seeking high levels of education - Such adjustment processes require a long period to take effect - Absorptive capacity and sectoral constraints: - Ability to create suitable jobs for educated entrants depends on switching employment from uneducated to educated labor, governed by the producer’s first order condition (marginal cost equals marginal revenue product) - Switch depends on wage rates (marginal cost) and productivity (revenue product) - CES production technology implies productivity is a function of employment levels, elasticity of substitution, and factor-specific productivity term (Panel 8) - Assumption of strong productivity growth for educated labor is critical for smooth absorption; without it, baseline would show sharply rising unemployment and falling wages for educated labor ### Two real-world interpretations of productivity growth for educated labor - Moving up the value chain in agriculture: - Simply replacing an uneducated worker with an educated one yields only marginal productivity gains - Realizing productivity gains requires shifting to high-value areas (fruit production, agroprocessing) where skills are indispensable - Requires entrepreneurship, effective financial sector, and exploitation of higher-value agricultural opportunities - Expanding industry and nongovernment services: - Industrial and service sectors must provide jobs for highly educated entrants in the medium term - In MAMS this occurs mechanistically via wage declines; in reality, businesses must find opportunities to establish or expand lines of business—a discovery process that is inherently uncertain ### Productivity, government, and sectoral implications - Productivity gains for highly educated labor particularly benefit government because government health and education services are intensive users of this labor; productivity gains increase the efficiency of government services and reduce factor input required for a given output (Panel 8 and supporting notes) - Sectoral and rural/urban fragmentation: - Agriculture typically pays significantly lower wages than industry or services; this sectoral and likely rural/urban divide implies current factor allocation is inefficient - If labor could migrate from low wage/low productivity agricultural jobs to higher wage/high productivity industrial or service jobs, overall productivity and income would improve - Abundance of uneducated labor could provide comparative advantage for light manufacturing (assembly, textile), but large part of this labor is "locked up" in agriculture, limiting effective use of this advantage *Source: Authors' calculations.* ### 2. The barriers that prevent the equalization of wages and productivity over sectors— ### 2. The barriers that prevent the equalization of wages and productivity over sectors— ### Barriers to wage–productivity equalization and labor market segmentation - Examples of barriers: - Unions that raise wages in formal sectors above market-clearing levels. - Cultural barriers that prevent migration from rural to urban areas. - Mechanism and consequences: - If sectors that could generate skilled employment are segmented (wages kept high, employment limited), increasing the supply of educated labor may mostly yield higher unemployment because wage rigidity prevents employment generation. - For individual workers, education can still be privately rational as a means to access segmented, high-paying jobs; education then functions largely as rent seeking. - Modeling note: - Labor market segmentation is captured in the Burkina Faso calibration through exogenous wage distortion parameters, but these do not fully capture the structural impediments that cause the distortions or their impact on structural change. ### Sector composition effects of changing relative wages - Key transmission channels: - Agriculture is relatively intensive in uneducated labor and land, the two factor categories showing the strongest wage rate increase (Panel 4). - Agriculture passes part of higher factor costs through to consumers (Panel 10), lowering demand and production. - As a result, the share of agricultural production in real terms declines. - Two service sectors are relatively intensive in highly educated labor; declines in their wage rates allow them to lower producer prices, increasing demand and production. - Factor substitution: - Part of higher factor costs is absorbed through factor substitution, i.e., by replacing uneducated labor with relatively cheaper (after adjusting for productivity differences) educated labor. ### External adjustment and real exchange rate dynamics - Sectoral shift: - Resources shift from the tradable (agriculture) to the nontradable (services) sector, tending to create an external imbalance. - Real exchange rate adjustment: - A real exchange rate depreciation keeps the balance of payments in equilibrium (Panel 11). - Within WAEMU membership, real exchange rate depreciation is achieved by lower inflation than in other countries rather than nominal depreciation. - The real exchange rate deflated by producer prices depreciates by even more, enhancing producer competitiveness. - Producer prices in the export sector are exogenously given and do not decline, which raises relative producer prices and profitability in the export sector. - Trade shares: - The real depreciation keeps the share of exports and imports in terms of real GDP relatively constant (Panel 12). - In nominal terms, export and import shares increase because the depreciation increases their nominal value (Figure 1, Panel 6). ### IV. CREATING FISCAL SPACE — scenarios and calibration - Common use of fiscal space across scenarios: - Fiscal space in all scenarios is used to increase both human development and public infrastructure spending. A. Prioritizing Expenditures - Implementation: - Create fiscal space by shifting resources away from other government activities toward human development and infrastructure. - In the baseline, current spending on other government activities grows at an annual rate of 6 percent; in the prioritized scenario, the growth rate is gradually reduced until it reaches 1.5 percent for 2012–15, with the growth path returning gradually to the baseline afterward (Figure 3, Panel 1). - Quantitative outcome: - Total other government spending declines gradually relative to GDP, with a total reduction of about 2.5 percent of GDP by 2015 (Panel 2). - Sources of fiscal space: - Decline in current other government spending (gradual). - Immediate cutback in capital investment for other government activities because reduced current spending lowers required capital stock (short-term sizable fiscal space). - Modeling caveat: - In MAMS calibration for Burkina Faso, other government expenditures are not linked to factor productivity, so the reduction is assumed to occur only in subsectors with no or negative productivity impact (e.g., administrative expenditures, defense, nonproductive culture/sport). - Practical challenge: - “Other” government expenditures account for approximately 50 percent of both current and capital expenditures in Burkina Faso’s 2007 budget; many such expenditures are desirable and potentially productive, complicating prioritization decisions. B. Increasing Aid Inflows - Calibration: - Aid inflows increase from about US$45 per capita in 2007 to about US$85 per capita in 2015, in line with the Gleneagles commitment (Figure 4, Panel 1). - This corresponds to an increase in aid inflows from 9.5 percent of GDP in 2007 to about 14 percent in 2015 (Panel 2). - After 2015, aid inflows are held at US$85 per capita, implying a gradual decline in the aid-to-GDP share as real GDP per capita rises. - Economic implication: - The increase in GDP terms is much smaller in percent than in per capita terms because real GDP per capita expands strongly in this period. C. Raising Domestic Revenue - Calibration: - Revenue effort raised to the WAEMU target of 17 percent of GDP in 2015, starting from about 12.5 percent in 2007 (Figure 5). - The revenue ratio is maintained over the long term, making the gain in fiscal space permanent. - Implementation requirements: - Reforms of both revenue administration and tax policy. - Practical reforms could include lowering administrative costs and broadening the tax base by eliminating exemptions. Current context: Burkina Faso’s taxes and fees number over 200. - Modeling caveat: - Achieving revenue increases without adverse effects requires design choices; MAMS does not capture potential efficiency gains from tax-system reforms. D. Comparing gains in fiscal space across scenarios - Permanence: - Prioritizing expenditures and raising revenue are programmed to create permanent fiscal space; increased aid is temporary in GDP terms. - Trajectory: - Cumulative fiscal space is relatively similar across scenarios until 2014, after which they diverge (Figure 6, Panel 2). - Leakages between created fiscal space and increase in targeted expenditures: - Leakage types and magnitudes: - Difference between created fiscal space and nominal increases in targeted expenditures (small for prioritized and aid scenarios; somewhat larger for the revenue scenario). - Larger leakage between nominal and real increases in targeted expenditures (thin dotted line), driven by large increases in factor prices when education and health expenditures are ramped up. - Sources of scenario-specific leakages: - Prioritized scenario: - Positive GDP growth response reduces the aid-to-GDP ratio (aid held at baseline in US$ terms), slightly offsetting GDP-term gains in fiscal space. - Aid scenario: - Increase in prices of factors needed for government services raises nominal costs of producing other government activities, reducing fiscal space for targeted expenditures in the short term; reversed in the longer term as factor costs fall below baseline. - Revenue scenario: - MAMS shows a negative effect on real GDP; other government activities are programmed to grow at baseline rates, now higher than GDP growth in this scenario, increasing their GDP share and leaving less room for targeted expenditures. E. Macroeconomic impact Growth impact - General: - Increased human development and public infrastructure spending raises TFP and real GDP in all scenarios. - Comparative impacts up to 2014: - Aid scenario: GDP impact is positive and large. - Prioritization scenario: GDP impact is slightly positive. - Revenue scenario: GDP impact is slightly negative (Figure 8). - Mechanisms: - Revenue scenario negative impact arises from higher taxation crowding out the private sector, reducing private investment and capital accumulation; this effect dominates positive infrastructure effects in the model. - Aid scenario large positive impact arises from external financing enabling a reduction in export production (mostly cotton) and reallocation of factors to construction and health and education services, improving overall labor productivity given sectoral productivity differences. - Prioritized scenario has smaller productivity gains because resources freed from other government activities reallocate to sectors that do not generate the large productivity differentials seen when factors shift out of cotton. - Modeling caveats and reform implications: - Growth effects of revenue-raising depend on tax reform design; potential efficiency gains from broadening the tax base and simplifying taxes are not captured by MAMS. Balance of payments impact - Underlying growth assumption: - Model uses an underlying annual real GDP growth rate of 6 percent. - Trade balance: - Aid scenario substantially widens the trade deficit; domestic scenarios show comparatively small changes (Figure 10, Panel 1). - Absorption: - Aid scenario: increased domestic absorption (Panel 2) driven by higher government absorption without crowding out the private sector (Panel 3). - Prioritized scenario: increased government absorption comes at the expense of other government activities, leaving overall government and private absorption practically unchanged (Panel 4). - Revenue scenario: government absorbs more by crowding out private absorption (Panel 5). - Policy implication — spend-and-absorb: - Effective use of scaled-up aid requires fully spending and absorbing additional aid: fiscal policy should spend additional resources and monetary policy should allow the real appreciation needed to increase absorption via a widening trade deficit. - The aid scenario’s increase in overall absorption requires consistent fiscal and monetary policies (spend-and-absorb). Reallocation of factors in the aid scenario - Requirement for service delivery expansion: - Scaling up spending on human development and infrastructure requires a supply response to ramp up production in sectors providing these services (health, education, construction). - This generally implies increasing production factors in those sectors and shrinking other sectors to release factors. - Exception: - If aid is spent exclusively on imports (e.g., imported medicines), service delivery can improve without changes in domestic production. - Adjustment channels: - When government spending has sizable nontradable components, the aid scenario frees factors via reallocation between sectors (details continue beyond this excerpt). *Source: Authors' calculations from the IMF working paper chapter 2 (Burkina Faso calibration and fiscal-space scenarios).* ### 1. Exports can decrease, which releases production factors from export sectors; or ### _wp09227 - 1. Exports can decrease, which releases production factors from export sectors; or ### Trade balance and factor reallocation channels - Two channels through which aid or shocks free production factors: - Exports can decrease, which releases production factors from export sectors. - Private-sector imports can increase, which frees factors from domestic import-competing and nontradable sectors through expenditure-switching effects. - In the Burkina Faso simulation the trade deficit widens mostly as a result of a reduction in exports relative to the baseline (Figure 10, Panel 6). - In absolute levels: - Exports stay flat for a number of years and ultimately converge back to baseline levels (Figure 11, Panel 1). - Imports slightly exceed baseline levels (Figure 11, Panel 2). - Aid inflows replace foreign exchange earned by cotton (Burkina Faso’s main export sector), enabling factor reallocation from cotton to construction, health, and education (Figure 11, Panel 3). - Real exchange rate appreciation is central to this transmission mechanism. ### Burkina Faso simulation: sectoral outcomes and labor reallocation - Cotton sector dynamics: - Cotton bears the adjustment burden—its production remains flat for several years instead of growing as in the baseline (Figure 11, Panel 4). - Cotton shrinks relative to the baseline but not necessarily drastically in absolute terms. - Agricultural response: - Uneducated labor from cotton that is not demanded in construction or human development services migrates to other agricultural sectors, increasing noncotton agriculture production beyond baseline levels (Figure 11, Panel 5). - Agriculture as a whole expands with increases in noncotton agriculture and overall GDP expansion from scaling up aid (Figure 11, Panel 6). - Import increase is relatively mild because: - The direct import share of government expenditures is small. - Private demand does not easily switch to imports in the Burkinabè MAMS specification. - Substitutability and sectoral structure: - The substitution elasticity between domestically produced tradable and imported goods is small in the Burkinabè specification—these are not close substitutes. - Limited substitutability reflects underdeveloped manufacturing; many imported products (e.g., cars, machinery) have little domestic competition, and domestic manufacturing (e.g., local food processing) also has little import competition. - Consequently, it is easier to shift resources from the export sector (cotton and fiber production) to other sectors in demand than to shift demand from domestically produced goods to imports. ### Real exchange rate, prices, wages, and factor intensity adjustments - Real appreciation: - A real appreciation of the currency is a central part of the aid scenario transmission mechanism to shift factors from export to high-demand sectors (Figure 12, Panel 1). - In Burkina Faso the nominal exchange rate is fixed; aid spending raises consumer prices, leading to an appreciation of the real exchange rate deflated by the CPI (Figure 12, Panel 2). - Price and producer-price dynamics: - A general increase in consumer prices produces higher producer prices except for exporters (exporters’ prices are fixed in international markets), resulting in a relative decline in producer prices for exporters (Figure 12, Panel 3). - Producer prices for education, construction, and health rise strongly in the aid scenario (Figure 12, Panel 3). - Steps in the reallocation/adjustment process: 1. Nominal or real appreciation reduces producer prices for export production in domestic currency, lowering marginal revenue product for exporters. 2. Export sector scales down production and releases factors as it becomes less profitable. 3. Factors are absorbed in sectors with higher demand (construction, health, education) where marginal revenue products exceed marginal factor costs. 4. Wage rates adjust: - Wage rates for highly educated labor and private capital increase (Panel 4) because demand in expanding sectors is high. - Uneducated and educated labor are in surplus and their wage rates decline to be absorbed elsewhere, often in agriculture (Panel 4). 5. New equilibrium is achieved via changes in factor prices and factor intensity so that marginal costs equal marginal revenue products across sectors. - Factor intensity in cotton/fiber sector: - Factor intensity for highly educated labor in the cotton/fiber sector declines substantially relative to baseline (Panel 5). - Factor intensity for capital in cotton/fiber declines moderately (Panel 5). - Magnitude of appreciation depends on ease of factor reallocation: - If sectors had similar factor composition, small appreciation could trigger large factor outflows from exports; in an extreme case a small appreciation could eventually shut down the export sector. - For Burkina Faso, sector factor compositions differ substantially; sizable changes in relative factor prices and factor intensity are required, so a large real appreciation is needed to induce reallocation. - This difference in factor composition is a form of real rigidity overcome only by a large change in the real exchange rate. ### Dutch disease considerations and productivity linkage - Combination of real appreciation and decline in export production is associated with concerns about Dutch disease, potentially shrinking the tradable sector and damaging long-run growth if tradables are a source of special productivity growth. - MAMS incorporates a channel linking productivity growth to export and import volumes. - Despite potential productivity losses from tradable-sector shrinkage, overall GDP effect in the simulation remains positive: - Productivity gains from a more educated and healthier workforce and better infrastructure outweigh productivity loss from export shrinkage. - Outside the model, reduction in cotton size is a concern because cotton: - Is an important income source for a large part of the population. - Has special attributes such as effective vertical integration that provides farmers with inputs. ### Impact on income distribution - Distributional impacts are measured by changes in household consumption by household group (Figure 13). - Six representative household groups in MAMS: two wage-earning households, three agricultural households, and one residual household (pensioners, independent businesses, and other groups). - Key distributional patterns: - Spending on education and health services particularly benefits wage-earning household groups (formal sector wage earners benefit notably in prioritized and aid scenarios; Panels 1 and 2). - Revenue scenario illustrates tax policy effects: - Formal sector wage-earning households benefit from higher education and health spending but are also the largest payers of direct income taxes, equalizing distributional impact (Panel 3). - Informal sector wage earners largely escape higher taxation and retain benefits from higher education and health spending. - Cotton agricultural households become part of the tax net (sector relatively well organized/part of formal economy) and are overall worse off under higher income taxation because they reap few benefits from higher education and health spending. ### Impact on MDGs and poverty - Poverty impact depends on: - Increase in per capita consumption. - Distribution of the consumption increase across households. - Simulation methodology: - Start from 2004 household survey (~8,500 individuals) for income and household classification. - Extrapolate per capita consumption paths using expenditure growth rates by household group from MAMS. - Compute poverty rate by comparing per capita consumption to the poverty line extrapolated from 2004 with price changes for the consumption bundle of the poorest household. - Calculations performed for 2014 (when all three scenarios are comparable) and 2030. - Baseline and simulated poverty rates: - 2004 national poverty rate: about 45 percent. - In MAMS simulations poverty increases slightly to about 48 percent by 2007 because of adverse terms of trade shocks (higher oil prices and lower cotton prices). - Baseline scenario: poverty decreases gradually over the simulation period due to an annual real GDP growth rate of 6 percent. - By 2030 baseline poverty declines to about 34 percent. - Heterogeneity across household groups (Figure 15): - Poverty reduction is small for wage earners in the formal sector because most are already above the poverty line. - Wage earners in the informal sector experience the largest decline in the poverty rate among household groups up to 2014 (Panel 2). *Source: Authors' calculations from the Burkina Faso MAMS simulation as presented in the supplied IMF chapter excerpt.* ### 10.5 percentage points, compared to an average of about 6 percentage points for all ### _wp09227 - 10.5 percentage points, compared to an average of about 6 percentage points for all ### Poverty outcomes by household group - Wage-earning households benefit strongly from the expansion of education because most graduates from secondary or tertiary education are members of the wage-earning household groups in the formal and informal sectors; with these two household groups becoming more educated, average wage income rises, which is key to their income growth. - For 2014–30 the poverty rate of wage-earners in the informal sector does not decline much further because by 2014 it has already declined to levels comparable to formal sector wage-earners. - Poverty rates are much higher for the agricultural household groups than for other households (Panels 3–5). The reduction in poverty accelerates from about 5 percentage points in the first period to 10 percentage points in the second. - One reason for the more sluggish performance in the first period is that income growth for these household groups lags behind that of other households: average annual per capita income growth rate for agricultural households for 2007–4 is 1.2 percent, compared to about 4 percent for the wage-earning households. - Another factor is that wage-earning households have significantly higher savings rate, and the return on these savings (i.e., private capital investment) also bolsters their income growth. - In the second period, per capita income growth accelerates for agricultural groups—reducing poverty more quickly—as rising wage rates for land and uneducated labor in the baseline benefits them. - The other household group is relatively well off, with a poverty rate just slightly higher than that of informal wage-earning groups (Panel 6). The poverty rate declines steadily for this group throughout the simulation period. ### Comparative scenario effects on poverty and consumption - Until 2014 the aid scenario has the most effect on reducing poverty (Figure 14, Panel 1), owing to the large increase in per capita consumption. - The poverty effect in the priority and revenue scenarios are similar (though small compared to the baseline), even though the increase in per capita consumption is larger in the former. - In the revenue scenario: - Lower per capita consumption is offset by declining food prices (Panel 2), which is a major component in the consumption basket of the poorest household; as a result, the poverty line declines, which offsets the effect of falling per capita consumption. - By 2030 poverty rates in the aid and prioritization scenarios approach that of the baseline, reflecting mostly a decline in consumption expenditure growth rates relative to the earlier period as aid inflows recede in GDP terms and expenditure prioritization has run its course. ### Education and health MDG indicators (responses and mechanisms) - All three scenarios (prioritized, revenue, aid) are effective in improving the education and health MDG indicators, which are primarily a function of education and health spending and per capita income (Figure 16). - Improvements display a stronger trend in the school indicator than in the health indicator. - In the Burkinabè specification: - The health indicator depends more on current real services per capita—a function of current spending—than the school indicator, where gains are more easily locked in (i.e., they tend to depend on cumulative spending). - Example: distribution of malaria bed nets is effective but requires repetitive spending each year; if spending stops and distribution ceases, malaria rates will go up again. - Health interventions usually require continued efforts like malaria bed net distribution; education gains are more cumulative. ### Using fiscal space — human development vs infrastructure spending (overview) - Fiscal space in the Burkina Faso MAMS model can be used to promote: - human development through higher education and health spending; - GDP growth through higher infrastructure spending; - both, via an expansion in both types of spending. - This section compares human development to infrastructure spending while considering only one source of fiscal space: increased aid inflows. ### Differences between human development and infrastructure spending scenarios - Size of fiscal space: - The spending increase relative to the baseline is somewhat larger for the infrastructure scenario (Figure 17, Panels 1 & 2) because this scenario has a bigger output effect, which creates additional fiscal space. - In the human development scenario the spending increase is not limited to targeted expenditures (health and education) but spending increases also on other government expenditures that do not contribute to human development goals. The fiscal space for targeted expenditures in the human development spending scenario is smaller than the overall expenditure increase. - This is caused by a large increase in wage rates that raises the cost of delivering other government services, somewhat crowding out human development spending. - Sector response: - Higher human development spending increases the nominal GDP share of providers of health and education services, which are mostly government sectors (Panel 3). - Public infrastructure requires a large private sector input, in particular from the construction sector (Panel 4). - The counterpart to the increase in these sectors is a decline in the cotton sector relative to the baseline. - Real spending increase: - In real terms the expansion in the education sector lags for many years behind the nominal expansion (Panel 5; similar for health services), whereas there is practically no gap between the nominal and real expansion in the construction sector (Panel 6). - Consequently, the real increase in human development spending over the medium term is much smaller than the nominal increase (Panel 7) whereas there is no such difference for infrastructure spending (Panel 8). - In the long run, the real increase in spending exceeds the nominal spending increase in both scenarios, reflecting the supply response of the economy. - Bottlenecks: - The delay in increasing health and education services in real terms reflects a bottleneck in the form of a shortage of highly educated labor, teachers and nurses, to deliver these services. - The increase in spending on these services leads initially to higher wages (Panel 9) until a sufficient number of new teachers and nurses has been trained, a lengthy process. - During this period the spending increase leads mainly to an increase in wages for the current workforce and only a very limited increase in employment and services. - No such bottlenecks hinder expansion of the construction sector—it requires mostly private capital—and wage rate increases are much more modest (Panel 10). ### Longer-term labor supply and accumulation effects - Human development scenario: - Increased education spending and high wages for highly educated labor make it attractive for students to remain in school and obtain secondary and tertiary education degrees, leading to a major increase in the supply of highly educated labor (Panel 11). - With this new supply, delivery of health and education services expands in real terms, and wage rates for highly educated labor plunge. - Infrastructure scenario: - Educated labor supply increases also, but by less than in the human development scenario (Panel 12). - Accumulation of private capital increases substantially because higher GDP growth raises saving and thereby investment. ### Macroeconomic impact — growth - Both higher education and health spending and increased infrastructure capital raise TFP in the MAMS model: - Higher education spending lifts educational attainments, raising labor productivity and TFP. - Higher health spending improves health indicators, positively impacting labor productivity and TFP. - An increase in the infrastructure capital stock raises TFP. - Comparing growth effects: - Infrastructure spending has a much larger impact on real GDP (Figure 18, Panel 1). - The output effect depends on the additional stock of infrastructure and human capital created. Capital stock accumulation can be proxied by cumulative infrastructure and human development spending in real terms (Panel 2). - Bottlenecks and delays in expanding health and education services imply that infrastructure capital is built up much faster than the human capital stock; the cumulative difference in real spending narrows gradually but at the end of the projection period the cumulative gap still exceeds 50 percent. - Even if education and health services can be expanded in real terms, building human capital takes time because of multiyear education cycles; this accounts for the large output difference in the short run. ### Balance of payments impact - Impact on the trade balance and absorption in the human development and infrastructure scenarios is similar (Figure 19, Panels 1 and 3). - Differences in composition and real exchange rate: - In the infrastructure scenario the negative impact on exports is smaller—which allows a larger expansion in imports for the same trade deficit—and so is the real exchange rate appreciation (Panels 2 and 4). - Reasons: - Infrastructure spending has a larger output effect, raising production in construction and reducing the need to shift resources into this sector. - The construction sector can be expanded relatively easily by shifting private capital from cotton to construction. - Increasing public education and health services requires shifting capital from export sectors to sectors intensive in highly educated labor, then replacing highly educated labor in these sectors with capital, and finally shifting highly educated labor to the public sector. This requires a larger transfer of private capital and a more pronounced shrinking of the export sector, resulting in larger price signals and higher appreciation in the human development spending scenario. - After 2015 aid inflows in GDP terms become smaller, leading to a reversal in appreciation in both scenarios and eventually the depreciation necessary to rebuild the export sectors. ### Impact on income distribution - Human development spending strongly benefits the relative income position of wage-earning households as a result of the large increase in wages for educated labor (Figure 20). - Since these households account for most of the educated labor, and educated-labor wages are an important source of their income, the increase in wage rates raises their income considerably. - In the longer term this effect recedes somewhat as wage rates for educated labor decline. - By comparison, the income distribution remains relatively unchanged when infrastructure spending is increased. ### Impact on MDGs (poverty, education, health) - Poverty: - Human development and infrastructure spending are equally effective in reducing poverty up to 2015, but infrastructure spending is more effective thereafter (Figure 21, Panel 1). - The income effect is larger throughout in the infrastructure spending scenario, but in the earlier period food prices decline more sharply in the human development spending scenario, which lowers the poverty line and offsets the lower income growth in this scenario. - The larger decline in food prices under human development spending results from a more pronounced decline in wages for uneducated labor in this scenario, reflecting fewer employment opportunities for uneducated labor released from the export sector compared to infrastructure spending, where overall economic growth is higher. - In the longer term, the relative income gains in the infrastructure-spending scenario become larger and dominate the poverty effect. - Regarding poverty impact by household group, the human development spending scenario has a particularly strong effect on wage-earning households due to the large increase in wage rates for highly educated workers, at least in the earlier period. - MDG education and health indicators: - For the MDG education indicator, human development spending achieves better outcomes due to the large increase in education spending, but infrastructure spending also brings substantial improvement. - Reasons: the decision to seek education depends on education quality, per capita household consumption, and the level of infrastructure (which lowers costs of getting to schools). Infrastructure spending affects the last two factors positively. - The real increase in infrastructure spending is larger than that in education and health spending. - For the MDG health indicator, infrastructure spending also yields good results through direct facilitation of service delivery and indirect income effects, though gains take some years to materialize. ### Lessons and trade-offs - None of the scenarios dominates the others; trade-offs must be considered: - Poverty is likely to be reduced most by 2015 through an aid-financed increase in infrastructure spending. - Focusing aid on human development spending yields a better outcome for the education and health MDG indicators. - Trade-offs also exist regarding sources of fiscal space: - Mobilizing additional aid will allow an increase in domestic absorption and avoids political resistance to domestic revenue measures, but is potentially less reliable and makes the government more dependent on donors. - Domestic sources of fiscal space may be more permanent but face political resistance (e.g., taxpayer lobbying). - These trade-offs need to be resolved at the country level, taking into account country-specific preferences. The model-based analysis of fiscal space can help identify and quantify likely trade-offs. - Implementation caveats: - Increasing education and health services in real terms takes time because capacity (trained teachers, nurses, highly educated labor) must be built up. - Expanding expenditure in these areas requires careful preparation to align the pace of expenditure increases with the ability of education and training programs to deliver suitably educated workers. - There may be a need to monitor wage pressures to avoid large increases in the wage bill that could crowd out other expenditures. - Infrastructure spending promotes growth and contributes to MDGs: - In MAMS infrastructure has a direct positive impact on education and health MDG indicators because it facilitates delivery of services and an indirect effect through higher growth and per capita income. - Simulations show infrastructure spending yields very substantial improvements in education and health MDG indicators through these channels, though they take some years to fully materialize. *Source: Authors' calculations (content excerpt from the provided PDF).* ### Appendix I. Modeling of MDG Sectors in MAMS ### Appendix I. Modeling of MDG Sectors in MAMS ### Overview of MAMS - MAMS (Maquette for MDG Simulations) is an economy-wide, dynamic general equilibrium framework designed to analyze interactions between delivery of human development (HD) services (health, education, water, and sanitation), the Millennium Development Goals (MDGs), growth, and foreign aid. - Purpose: complement sectoral studies by embedding MDG strategies in a comprehensive economy-wide framework to capture macro and labor-market constraints that affect MDG outcomes. - Focused MDGs: universal primary school completion (MDG 2; net primary completion rate), under-five and maternal mortality rates (MDGs 4 and 5), incidence of HIV/AIDS and other major diseases (part of MDG 6), and access to improved water sources and sanitation (parts of MDG 7). Poverty reduction (MDG 1) is monitored; public infrastructure is explicitly modeled. ### The “production” of the MDGs: key model structure and mechanisms - Three essential reasons the relationship between government spending and MDG outcomes is not fixed: - Returns to scale of government spending vary with service delivery: increasing returns at low levels and decreasing returns at high levels (network effects, learning effects, synergies, higher marginal costs at remote coverage). - Effectiveness of government spending depends on other variables (health conditions, public infrastructure, income levels, education premiums). - Costs of service delivery change with macroeconomic conditions (skill- and capital-intensity, scarcity of educated labor, tighter financial conditions, budgetary impacts across MDG and non-MDG spending). - Two-block modeling of a typical MDG (except MDG 2 which is treated in education): - Block 1: production of MDG-related services (fixed input-output coefficients / Leontief assumption across labor, capital, intermediate inputs). - Inputs required for level Q of service delivery: LQ, KQ, INTQ. - Aggregate labor L combines three education categories: N (less than completed secondary), S (completed secondary), T (completed tertiary). Constant elasticity of substitution, ω, across labor types; government minimizes unit cost. - Capital accumulation: K_{t+1} = (1 - δ) K_t + I_t where δ is the constant depreciation rate. - Intermediate inputs split between domestic (INT_d) and imported (INT_m) with constant elasticity of substitution σ (≥ 0). - Block 2: MDG outcomes as functions of service delivery and other determinants using logistic functions to capture changing returns to scale: - Logistic form: MDG_k = ext_k / (1 + e^{-(β_k Z_k + γ_k)})^{η_k} (text expresses the role of ext_k, β_k, γ_k, η_k in shaping responsiveness, returns, and sign conventions). - Intermediate variable Z_k defined by a Cobb-Douglas function: Z_k = ∏_{i=1}^{n} Q_{k}^{φ_k} D_{ik}^{φ_{ik}} (Equation 6 form). - Determinants (illustrated in Ethiopia application and Table I.1): service levels Q_k, per-capita household consumption, public infrastructure, and other MDGs (e.g., water and sanitation affecting health). - Parameter interpretation: ext_k (extreme value), β_k (responsiveness), γ_k (shape at starting point), η_k (replicates initial value and slope), φ_k and φ_{ik} (elasticities in Z_k). ### Data and calibration requirements - Block 1 (production): require government spending by function and by type (current vs. capital); current outlays disaggregated into wages by labor type and intermediate inputs; employment numbers; elasticities of factor substitution. These are integrated into the Social Accounting Matrix (SAM). - Block 2 (outcomes): require (a) base-year values and 2015 targets for MDG indicators; (b) extreme values for MDG indicators; (c) elasticities of MDG indicators with respect to determinants; (d) position of initial situation relative to logistic inflection point; (e) a scenario of 2015 values for arguments of Equation (6). - Calibration approach: - Use sectoral studies, econometric evidence, and expert judgment to set φ_k and φ_{ik}. - A simultaneous-equation solution can generate η_k, β_k, and γ_k to (i) replicate base-year MDG_k, (ii) place the inflection point at a specified distance from initial Z_k, and (iii) achieve the MDG for the Z_k defined by the specified scenario. - Alternative: endogenize φ_k and φ_{ik} if imposing full base-year elasticities, with implications for model structure and loss of exogenous specification of Z_k. ### Modeling education and MDG 2 (primary completion) - Complete education sector accounting across cycles: primary, secondary, tertiary. - Student dynamics: in each grade and cycle a student may pass, drop out, or repeat. Passing students either progress within cycle or graduate; graduates may continue or exit. - Logistic/Cobb-Douglas structure applied to: - Entry rates (to first grade of each cycle, out of qualified population). - Passing rates (from each grade within a cycle). - MDG 2 indicator: net (on-time) primary completion rate = product of relevant entry rate and the sequence of graduation/passing rates over the cycle years (e.g., for a four-year primary cycle, MDG 2 in year t = entry rate in t-3 × graduation rates in t-3, t-2, t-1, and t). - Education-specific determinants of Z variables (Ethiopia example): educational services per student enrolled, per-capita household consumption, public infrastructure, health performance proxied by MDG 4, and wage incentives (relative wage gain for moving one notch in the labor market). - Expansion implications: rising primary completion increases enrollment pressure in subsequent cycles; eventual increase in supply of educated labor with time lags. ### General equilibrium integration and dynamics - MAMS is an open-economy CGE with simultaneous determination of MDG achievements, private goods and services supply and demand, and factor market equilibrium. - Key interactions captured: - Competition for scarce resources (labor, intermediate inputs, investment funding) between MDG services and other sectors; potential wage hikes for educated labor. - Positive long-run growth effects as graduates enter the labor force and raise growth potential. - Loanable funds market: MDG investments compete with other investments; financing choices matter. - Foreign financing (grants/loans) can limit negative impacts on domestic consumption and investment but may generate Dutch disease effects; severity depends on import share of additional spending. - Real exchange rate appreciation reduces tradable production and increases imports, with trade deficit financed by aid inflows. - Domestic financing (taxes or borrowing) may be modeled with endogenous tax rate adjustments to meet government savings or foreign aid targets, or to stabilize fiscal solvency indicators (e.g., ratio of government debt to GDP). - Dynamic features and timing: - Recursive-dynamic with some non-recursive features (e.g., government investment driven by future service provision decisions). - Model can be solved simultaneously for full planning horizon; government can be assumed to have perfect foresight when selecting growth paths for government services subject to MDG achievement constraints by 2015. - Time lags matter especially for education’s impact on labor-skill structure and the economy. ### Model capabilities and analytical uses - Captures supply- and demand-side mechanisms through which service delivery and other determinants interact in MDG achievement. - Analyzes competition over scarce resources and the role of MDG services in augmenting economy-wide resources through labor-market effects and promoting long-run growth. - Simulates alternative foreign aid scenarios, including Dutch disease phenomena and aid’s role in expanding the pool of savings. - Solves forward-looking scenarios and analyzes sequencing and timing of large programs via a multi-year simultaneous solution. *Source: _wp09227 - Appendix I. Modeling of MDG Sectors in MAMS* ### Appendix II. Budget Composition in Burkina Faso ### Appendix II. Budget Composition in Burkina Faso ### Table II.1. Burkina Faso: 2007 Budget SectionsDotations Montants Engagés - Présidence du Faso: 3,878,766,000 10,416,436,771 - Secr. Gén. Gouvernement: 370,431,000 341,539,414 - Premier Ministère: 2,545,839,000 2,530,653,322 - Parlement: 7,207,498,000 7,207,498,000 - Conseil économique et social: 1,650,777,000 1,604,217,821 - Min. chargé des relations avec Parlement: 237,528,000 200,798,130 - Min. Administration Territoriale / Sécur.: 14,525,171,000 16,027,022,838 - Ministère de la Justice: 5,273,021,000 4,719,030,715 - Ministère de la Défense: 53,879,945,000 53,919,732,671 - Ministère des Affaires Etrangères et Coop. Rég.: 18,572,554,000 17,547,466,865 - Ministère de la sécurité: 16,122,549,000 17,056,840,859 - Ministère des Finances et du Budget: 14,830,245,000 20,667,798,167 - Ministère de la Culture, Arts et Tourisme: 2,662,434,000 2,717,213,350 - Min. Travail, Emploi& Jeunesse: 576,876,000 814,831,817 - Min. Fonct Publ et Réforme de l'Etat: 2,095,800,000 2,341,166,138 - Min. de l'Information: 3,439,123,000 3,517,279,934 - Ministère de la Promotion de la Femme: 647,592,000 627,059,744 - Ministère des sports et des Loisirs: 1,588,154,000 2,675,150,570 - Ministère de la Santé: 56,000,958,000 57,367,880,907 - Min.de l'Action Sociale et de la Solidarité Nle: 5,062,270,000 5,276,983,803 - Min. Enseign.de Base & Alphabétisation: 69,733,407,000 78,710,057,951 - Min. Enseign.Sec. Sup.& Rech Scient: 41,924,258,000 47,870,333,078 - Min. du Commerce, Promotion Entr. Et Artisanat: 2,590,981,000 1,772,952,093 - Min. des Mines, Carrières et Energie: 1,492,117,000 1,473,552,885 - Min. Agri. Hydrauliq.et Ress. Halieutiq: 21,929,880,000 20,415,969,992 - Min.des Ressources Animales: 4,248,647,000 3,647,745,837 - Min. de l'Environnement et Cadre de Vie: 3,332,940,000 3,851,414,450 - Min. des Infrastruct., Transport et Habitat: 34,729,577,000 29,926,046,272 - Min. des postes et télécommunications: 8,361,904,000 6,730,120,692 - Min. des transports: 2,688,050,000 2,045,253,408 - Min. de l'Economie et du Developpement: 4,682,307,000 3,607,331,075 - Ministère de la Promotion des Droits Humains: 291,346,000 284,031,163 - Min. de la Jeunesse et de l'emploi: 4,509,505,000 3,596,458,102 - Min; de l'Habitat et de l'Hurbanisme: 1,020,567,000 324,002,706 - Grande Chancellerie: 255,897,000 235,739,535 - Conseil Supérieur de la Communication: 838,173,000 835,464,922 - Inspection Générale d'Etat: 389,106,000 377,342,840 - Conseil Constitutionnel: 464,200,000 354,788,160 - Conseil d'Etat: 509,334,000 476,445,721 - Cour des Comptes: 397,039,000 374,459,162 - Cour de Cassation: 405,871,000 396,931,383 - CENI: 7,278,476,000 7,224,657,472 - Dépenses Communes Interministérielles: 132,214,769,000 121,622,396,738 - Total: 555,455,882,000 563,730,097,473 - Of which education, health, and infrastructure: 250,562,519,471 260,016,802,524 - Percent: 45% 46% - Of which other government: 304,893,362,529 303,713,294,949 - Percent: 55% 54% ### Table II.2. Burkina Faso: 2007 Capital Budget SectionsDotation Montants Total Annuel Décais. - Présidence du Faso: 11,948,053,000 7,735,489,820 - Secr. Gén. Gouvernement: - - - Premier Ministère: 2,306,500,000 1,391,542,755 - Parlement: 317,000,000 317,000,000 - Conseil économique et social: 1,297,127,000 1,237,895,850 - Min. chargé des relations avec Parlement: - - - Min. Administration Territoriale / Sécur.: 4,977,670,000 3,590,275,487 - Ministère de la Justice: 2,631,181,000 2,878,152,281 - Ministère de la Défense: 10,250,123,000 9,888,859,000 - Ministère des Affaires Etrangères et Coop. Rég.: 988,000,000 1,446,000,000 - Ministère de la sécurité: 3,798,006,000 3,742,033,266 - Ministère des Finances et du Budget: 5,423,424,000 25,746,284,209 - Ministère de la Culture, Arts et Tourisme: 905,836,000 628,178,812 - Min. Travail, Emploi& Jeunesse: 30,000,000 947,839,792 - Min. Fonct Publ et Réforme de l'Etat: 243,829,000 243,827,310 - Min. de l'Information: 985,752,000 952,335,455 - Ministère de la Promotion de la Femme: 301,756,000 280,500,793 - Ministère des sports et des Loisirs: 332,707,000 507,694,186 - Ministère de la Santé: 31,584,488,000 29,154,690,768 - Min.de l'Action Sociale et de la Solidarité Nle: 1,139,027,000 873,532,564 - Min. Enseign.de Base & Alphabétisation: 39,842,844,000 28,381,258,344 - Min. Enseign.Sec. Sup.& Rech Scient: 25,412,413,000 14,619,906,424 - Min. du Commerce, Promotion Entr. Et Artisanat: 10,011,120,000 2,592,188,457 - Min. des Mines, Carrières et Energie: 30,394,321,000 4,058,347,762 - Min. Agri. Hydrauliq.et Ress. Halieutiq: 104,915,093,000 69,928,228,949 - Min.des Ressources Animales: 11,014,359,000 2,945,588,674 - Min. de l'Environnement et Cadre de Vie: 6,214,940,000 2,732,942,902 - Min. des Infrastruct., Transport et Habitat: 118,733,521,000 86,188,705,480 - Min. des postes et télécommunications: 2,995,446,000 1,814,822,649 - Min. des transports: 3,429,750,000 1,797,173,369 - Min. de l'Economie et du Developpement: 9,838,176,000 7,235,953,885 - Ministère de la Promotion des Droits Humains: - - - Min. de la Jeunesse et de l'emploi: 3,767,744,000 1,510,003,936 - Min; de l'Habitat et de l'Hurbanisme: 489,589,000 122,856,091 - Grande Chancellerie: - - - Conseil Supérieur de la Communication: 303,963,000 303,962,500 - Inspection Générale d'Etat: 37,106,000 18,552,686 - Conseil Constitutionnel: 26,000,000 - - Conseil d'Etat: 148,000,000 148,000,000 - Cour des Comptes: - - - Cour de Cassation: 33,702,000 18,407,204 - CENI: - - Dépenses Communes Interministérielles: 27,824,311,000 22,150,310,814 - Total: 474,892,877,000 338,129,342,474 - Of which education, health, and infrastructure: 228,203,856,786 168,717,459,199 - Percent: 48% 50% - Of which other government: 246,689,020,214 169,411,883,275 - Percent: 52% 50% *Source: Appendix II. Budget Composition in Burkina Faso.* --- _Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2009/_wp09227.pdf_