## tnm1706

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---

### I. Purpose and scope of the Expenditure Assessment Tool (EAT)
- Reforming public expenditures is central to policy agendas across country groups:
  - AEs: containing or consolidating overall spending while responding to aging-related pressures.
  - EMs and LIDCs: improve spending efficiency to create fiscal space to expand priority spending on education, health and infrastructure.
- EAT functions and limits:
  - User-friendly, Excel-based benchmarking tool using economic and functional classification data.
  - Provides benchmarks of spending—levels and composition—against comparators (region, income group, and optional OECD).
  - Assesses spending relative to outcomes (the manual uses "outcomes" and "outputs" indistinctively).
  - Not a substitute for in-depth spending reviews; does not address detailed distributional impact, legal restrictions, or fully control for country-specific factors.
  - Efficiency assessment should be supplemented by econometric approaches and within-country input-outcome variation analysis.
- Manual roadmap:
  - Section II: tool overview.
  - Section III: benchmarking by economic classification.
  - Section IV: benchmarking by functional classification.
  - Section V: conclusion.
  - Application to Argentina as illustration.

### II. Coverage, data sources, and update protocol
- Coverage and definitions:
  - Information for all IMF member countries.
  - Fiscal data refer to general government (central plus subnational), consistent with WEO and FM definitions.
- Data sources included in the tool:
  - WEO, ASPIRE (World Bank), WDI (World Bank), WEF, WHO.
  - Specific indicators (public capital stock, government wage bill and employment, public sector wage premium, energy subsidies, pensions) compiled and maintained by Expenditure Policy Division (EPD) of FAD at the IMF.
- Temporal coverage and update frequency:
  - Illustration uses October 2016 WEO and other values as of October 2016.
  - Database updated twice a year to reflect latest available information and ensure consistency with publicly released WEO.
  - Users can select years from 2000 to 2015.

### III. Benchmarking and selection behavior
- Comparator options:
  - By income group (AEs, EMs, LIDCs), by region, and optional OECD comparator.
- Outputs behavior:
  - Charts display data for the chosen country and simple averages for comparator groups; some charts show individual-country data for regional comparators.

### IV. Government spending by economic classification — key diagnostics and Argentina case findings
- Tool tabs of relevance: “Total_spending”, “Wage_bill”, “Investment”, and “Energy Subsidies”.
- Total government spending diagnostics:
  - Trends for revenues and expenditures in percent of GDP; composition of spending changes (current vs capital); current budget structure by economic classification.
  - Use cases: identify sources of spending pressures, assess quality of adjustment/expansion, identify sizable budget items relative to comparators.
- Argentina — headline fiscal and spending statistics:
  - Government spending increased by 17.6 percentage points of GDP while revenues grew more slowly, resulting in a gradual decline in the fiscal overall balance.
  - The fiscal balance registered a deficit estimated at 6.6 percent of GDP in 2015.
  - Total government spending: 40.6 percent of GDP.
  - This is 10.5 percentage points of GDP more than the LAC average.
  - Composition:
    - Current spending: 37 percent of GDP (90 percent of total expenditures).
    - LAC average: current spending represents 83 percent of total expenditures.
    - Spending on social benefits: represents about 26½ percent of total expenditures in Argentina; LAC average: about 18 percent of total expenditures.
    - Compensation of employees: slightly above the LAC average.
  - Conclusion: budget structure heavily tilted toward current spending; spending growth primarily driven by current spending.
- Wage bill and employment diagnostics:
  - Typical context: wage bill usually represents about a quarter of the budget on average.
  - Comparators for public-private wage premia:
    - AEs average premium: around 5½ percent.
    - EMs and LIDCs average premium: around 12¼ percent.
  - Argentina-specific wage bill and employment findings:
    - Wage bill increased by nearly 6 percentage points since 2004 (after a decline in 2000–04).
    - Wage bill level in 2015: about 12½ percent of GDP.
    - Public sector average compensation is about 13 percent higher than in the private sector (public wage premium).
    - Public employment level: exceeds the EMs average of around 8 percent of the labor force and appears to be the main driver of the high wage bill spending.
  - Policy implications:
    - If high wage bill reflects large public employment share, employment measures such as attrition can provide short-term relief.
    - If high wage bill reflects generous government wages relative to private sector, containment of compensation can enhance spending efficiency.
- Investment (public and private) diagnostics:
  - Metrics: public and private investment trends, public capital stock, infrastructure quality indicators (roads, ports, railroads, air transport).
  - Data and methodology: gross fixed capital formation measures, public capital stock via perpetual inventory method, WEF infrastructure rankings (1 = best of 144).
  - Argentina findings:
    - Public capital spending as a share of GDP has been on an increasing path since 2002.
    - Private investment has been declining since 2007 and currently stands at about 3½ percent of GDP.
    - Outcome: relatively low public capital spending levels, a low level of capital stock, and poor infrastructure quality—particularly in air and road transportation—compared with peers.
    - Note: efficiency could also explain relatively low investment outcomes.
- Energy subsidies diagnostics and Argentina findings:
  - Rationale: subsidies cause environmental damage, large fiscal costs, discourage renewable investment, and enlarge income inequality.
  - Data: IMF Energy Subsidy Estimates Dataset; breakdowns by product and by component (pre-tax, foregone consumption tax, externality).
  - Definitions:
    - Pre-tax subsidy: difference between cost of supplying energy and price paid by consumers.
    - Post-tax consumer subsidy: pre-tax subsidy plus an appropriate “Pigouvian” tax for environmental damage and an additional consumption tax equivalent to prevailing VAT/GST on the energy supply cost plus externality cost.
  - Argentina energy subsidy statistics:
    - Post-tax energy subsidies in 2014: around 5 percent of GDP (earlier line), and elsewhere in the document a specific Argentina post-tax energy subsidy estimate: 1.1 percent of GDP (text presents both contexts; detailed breakdown uses the 1.1 percent of GDP figure for Argentina).
    - Composition of Argentina’s post-tax energy subsidy (totality of energy products):
      - Pre-tax subsidies: 2.4 percent of GDP.
      - Externalities: 1.5 percent of GDP.
      - Foregone consumption tax revenue: 1.2 percent of GDP.
    - Petroleum-specific: Argentina’s petroleum post-tax subsidy of 1.1 percent of GDP reflects only the externality and the foregone revenue components (i.e., excludes a pre-tax subsidy component for petroleum).
    - Comparator medians for the externality component (percent of GDP):
      - LAC median: 2.2 percent of GDP.
      - EM median: 3.9 percent of GDP.
      - OECD median: 1.3 percent of GDP.
    - Comparative observations:
      - Argentina’s externalities from energy subsidies are relatively small compared with other LAC countries.
      - Argentina has an above average pre-tax subsidy level (3 times the LAC median), indicating room for further work on subsidy elimination.
  - Policy implication:
    - Reforming generalized energy subsidies while mitigating impacts on low-income households (e.g., targeted income or in-kind transfers) can be cost-effective and equity-enhancing.

### V. Government spending by functional classification — health, education, and social protection
- Functional focus and approach:
  - Benchmarks for each function include levels, modalities of provision, coverage, incidence, and outcomes; comparisons to regional and income-group comparators.
- Health diagnostics and Argentina findings:
  - AEs public health expenditure averages about 6¾ percentage points of GDP; EMs and LIDCs much lower.
  - Efficiency frontier uses HALE as output and total health expenditure per capita as input; DEA technique estimates distance to frontier.
  - Argentina findings:
    - Health outputs (infant deaths, HALE, life expectancy at birth, number of physicians) are more favorable in Argentina than the LAC and EM average.
    - Since 2009, total health expenditure (percent of GDP) has declined, mainly reflecting a reduction in public health expenditure.
    - Total health expenditure per capita (PPP$ adjusted) is slightly above average LAC and EMs.
    - Argentina shows relatively good efficiency of total (public and private) health spending: loss in HALE due to spending inefficiency is small and below the EM average.
    - Same HALE could be attained by spending less—detailed analysis needed to identify options for savings without compromising outcomes.
  - Recommended next steps: granular analysis controlling for per capita income, education, sanitation, natural endowments, historical life expectancy, and behavioral factors.
- Education diagnostics and Argentina findings:
  - Metrics: government education spending (percent of GDP and of government expenditure), spending per student by level, teacher-student ratios; inputs and outputs for frontier analysis include teacher-student ratios and spending per student as inputs; net school enrolment and secondary PISA scores as outputs.
  - Argentina findings:
    - Public education spending is high relative to other EM and LAC countries; spending as percent of GDP slightly above peer average.
    - Spending per student is well above peers, particularly for secondary education.
    - High student-teacher ratios—mainly for secondary but also for primary—appear responsible for the spending gap.
    - Education performance is good, but frontier analysis reveals large spending inefficiencies in secondary education: Argentina lies far from the efficient frontier despite above-average test scores and net enrolment.
  - Policy implication:
    - Tackling education inefficiencies in secondary education has significant potential for generating fiscal savings without jeopardizing outcomes; reforms should be preceded by thorough analysis of the education system and determinants.
- Social protection diagnostics and Argentina findings:
  - Indicators: social assistance spending (percent of GDP), coverage (share of poorest 20 percent receiving transfers), benefit incidence (share of total transfers received by poorest 20 percent), income distribution (income share top 10 percent, bottom 20 percent, Gini), pension indicators (retirement age, pension expenditures, old age dependency ratio, eligibility ratio), and pension spending projections.
  - Argentina findings:
    - Income distribution (Gini and income share of top 10 percent) appears more favorable than the rest of LAC, partly due to higher social protection spending.
    - Social assistance spending and pension eligibility are higher than the average LAC.
    - Despite favorable distribution metrics, room to improve social impact:
      - Share of income held by the bottom 20 percent is similar to the average LAC country.
      - Benefit incidence for social assistance programs is good, but coverage of these programs is among the lowest in LAC—indicating potential to enhance coverage and targeting.

### VI. Conclusions and suggested enhancements
- EAT role:
  - Provides an accessible tool to depict and assess government expenditures for any specific country; offers information on expenditure levels, composition and outcomes, and benchmarking against regional and income comparators.
  - Helps identify areas to enhance efficiency or streamline spending but is not a substitute for in-depth analysis.
- Suggested future work to enrich the tool:
  - Estimation of efficiency scores for health and education expenditures.
  - Detailed analysis exploiting cross-classification by functional and economic classification once such data becomes widely available.

*Source: tnm1706 - References (PDF).*

### References ....................................................19

### tnm1706 - References ....................................................19

### ACRONYMS
- AEs: Advanced Economies
- ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity
- DEA: Data Envelopment Analysis
- EAT: Expenditure Assessment Tool
- EMs: Emerging Markets
- EPD: Expenditure Policy Division
- FAD: Fiscal Affairs Department
- FM: Fiscal Monitor
- GFS: Government Finance Statistics
- HALE: Healthy Life Expectancy
- IMF: International Monetary Fund
- LAC: Latin America and Caribbean
- LIDCs: Low-Income Developing Countries
- OECD: Organization for Economic Co-operation and Development
- PISA: Program for International Student Assessment
- WB: World Bank
- WDI: World Development Indicators
- WEF: World Economic Forum
- WEO: World Economic Outlook
- WHO: World Health Organization

### I. INTRODUCTION — Purpose and Scope of the Expenditure Assessment Tool (EAT)
- Reforming public expenditures is central to policy agendas across country groups:
  - AEs face containing or consolidating overall spending while responding to aging-related pressures (Clements et al., 2015a; and IMF, 2014a).
  - EMs and LIDCs aim to improve spending efficiency to create fiscal space to expand priority spending on education, health and infrastructure (IMF, 2014a).
- EAT provides information to assess public expenditures using economic and functional classification data, offering benchmarks of spending—levels and composition—against comparators.
- Comparator options:
  - Region and income group (AEs, EMs, LIDCs).
  - Option to add OECD as a comparator.
- EAT assesses spending relative to outcomes (the manual uses "outcomes" and "outputs" indistinctively).
- Role and limits of EAT:
  - User-friendly, Excel-based benchmarking tool that is a starting point for deeper sectoral analysis.
  - Not a substitute for in-depth spending reviews; does not address detailed distributional impact, legal restrictions, or fully control for country-specific factors.
  - Efficiency assessment should be supplemented by econometric approaches and within-country input-outcome variation analysis.
- Manual structure:
  - Section II: overview of the tool.
  - Section III: outputs benchmarking spending by economic classification.
  - Section IV: outputs from benchmarking by functional classification.
  - Section V: conclusion.
  - Application to Argentina as an illustration.

### II. OVERVIEW — Coverage, Data Sources, and Updates
- Coverage:
  - Information for all IMF member countries.
  - Fiscal data refer to general government (central plus subnational), consistent with WEO and FM definitions.
- Data sources shown in the "Index" tab include:
  - WEO, ASPIRE (World Bank), WDI (World Bank), WEF, WHO.
  - Specific indicators (public capital stock, government wage bill and employment, public sector wage premium, energy subsidies, pensions) compiled and maintained by Expenditure Policy Division (EPD) of FAD at the IMF.
- Temporal coverage and updates:
  - Illustration uses October 2016 WEO and values from other sources as of October 2016.
  - Database in the tool will be updated twice a year to reflect latest available information and ensure consistency with publicly released WEO.
  - Users can select years from a lookup field ranging from 2000 to 2015.

### A. Country of Choice and Comparators — Selection and Benchmarking Behavior
- Running EAT requires specifying country of interest and benchmarks in the “selection” tab.
- Benchmarking options:
  - By income group (AEs, EMs, LIDCs) and by region.
  - Possibility of adding OECD as a third benchmark.
- Outputs:
  - Charts display data for the chosen country and simple averages for comparator groups; some charts show individual-country data for regional comparators.

### TABLE 1 — Options for Regional Breakdown (selected entries)
- Regional groupings and examples:
  - AEs: Advanced Economies
  - EU: European Union (28 countries included)
  - Eurozone: Euro Area (19 countries)
  - SSA: Sub-Saharan Africa
  - CEMAC: Economic and Monetary Community of Central African States: Cameroon, Central African Republic, Chad, Equatorial Guinea, Gabon, and Republic of Congo
  - COMESA: Common Market of Eastern and Southern Africa: Burundi, Comoros, Democratic Republic of Congo, Eritrea, Ethiopia, Kenya, Madagascar, Malawi, Mauritius, Rwanda, Seychelles, Swaziland, Uganda, Zambia, and Zimbabwe
  - SADC: Southern African Development Community: Angola, Botswana, Democratic Republic of Congo, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia, and Zimbabwe
  - GCC: Gulf Cooperation Council countries: Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates
  - MENAP: Middle East, North Africa, Afghanistan, and Pakistan
  - MENAP Oil Importers: Afghanistan, Djibouti, Egypt, Jordan, Lebanon, Mauritania, Morocco, Pakistan, Sudan, Syria, and Tunisia
  - MENAP Oil Exporters: Algeria, Bahrain, Iran, Iraq, Kuwait, Libya, Oman, Qatar, Saudi Arabia, United Arab Emirates, and Yemen
  - CCAC: Caucasus and Central Asia: Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan
  - CIS: Commonwealth of Independent States: Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyz Republic, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan
  - EM Asia: Emerging and Developing Asia: Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, Fiji, India, Indonesia, Kiribati, Lao P.D.R., Malaysia, Maldives, Marshall Islands, Micronesia, Mongolia, Myanmar, Nepal, Palau, Papua New Guinea, Philippines, Samoa, Solomon Islands, Sri Lanka, Thailand, Democratic Republic of Timor-Leste, Tonga, Tuvalu, and Vanuatu
  - Pacific Islands and Small States: Bhutan, Fiji, Kiribati, Maldives, Marshall Islands, Micronesia, Mongolia, Palau, Papua New Guinea, Samoa, Solomon Islands, Democratic Republic of Timor-Leste, Tonga, Tuvalu, and Vanuatu
  - ASEAN: Singapore, Brunei Darussalam, Cambodia, Indonesia, Lao P.D.R., Malaysia, Myanmar, Philippines, Thailand, and Vietnam
  - LA6: Brazil, Chile, Colombia, Mexico, Peru, and Uruguay
  - Fragile States: 37 Fragile States (IMF, 2015b)

### III. GOVERNMENT SPENDING BY ECONOMIC CLASSIFICATION — Tabs and Focus
- Relevant EAT tabs:
  - “Total_spending”, “Wage_bill”, “Investment”, and “Energy Subsidies”.
- Purpose:
  - Benchmarking spending by economic classification to identify trends, composition, and potential pressures.

#### A. Total Government Spending — Interpretation and Use
- Charts and diagnostics:
  - Trends for revenues and expenditures, in percent of GDP, for the country of choice.
  - Composition of changes in total spending (contributions of current and capital spending).
  - Current budget structure by economic classification (goods and services, compensation of employees, interests, other current spending—which includes social benefits, subsidies to public corporations and private enterprises, and grants—and consumption of fixed capital).
- Use cases:
  - Identify sources of recent spending pressures or containment challenges.
  - Assess quality of adjustment or expansion (e.g., whether capital spending declined while current spending increased).
  - Identify budget items sizable relative to comparator averages.
- Data source:
  - Revenues, expenditures, and breakdown by economic classification are drawn from the World Economic Outlook.

#### Data and Case Study Note
- Data lookback selection:
  - Users can choose years between 2000 to 2015.
- Case study (Argentina):
  - Over the past decade, spending in Argentina has outpaced GDP growth as well as the spending increase exhibited by most comparators.
  - During 2004-15, expenditures grew by [text ends in source].

*Source: IMF FAD Expenditure Assessment Tool (EAT).*

### 17.6 percentage points of GDP while revenues grew more slowly, resulting in a gradual decline

### 17.6 percentage points of GDP while revenues grew more slowly, resulting in a gradual decline

### Fiscal balance and overall spending
- Government spending increased by 17.6 percentage points of GDP while revenues grew more slowly, resulting in a gradual decline in the fiscal overall balance.
- The fiscal balance registered a deficit estimated at 6.6 percent of GDP in 2015.
- Spending growth in comparator groups:
  - Latin American and Caribbean (LAC) average increase: 4.9 percentage points of GDP.
  - Emerging Markets (EMs) average increase: about 5.3 percentage points of GDP.
- Argentina’s government expenditure:
  - Total government spending: 40.6 percent of GDP.
  - This is 10.5 percentage points of GDP more than the LAC average.
  - Composition:
    - Current spending: 37 percent of GDP (90 percent of total expenditures).
    - LAC average: current spending represents 83 percent of total expenditures.
  - Spending on social benefits:
    - Represents about 26½ percent of total expenditures in Argentina.
    - LAC average: about 18 percent of total expenditures.
  - Compensation of employees: slightly above the LAC average.
- The budget structure is heavily tilted towards current spending, reflecting spending growth having been primarily driven by current spending.

### Compensation and employment
- Context and diagnostic approach:
  - The wage bill usually represents about a quarter of the budget on average.
  - Charts and comparisons assess wage bill size (relative to GDP and total spending), public employment as share of working age population, and public-private wage differentials.
  - Policy implications:
    - If a high wage bill reflects a large share of public employees, employment measures such as attrition can provide short-term relief.
    - If a high wage bill reflects generous government wages relative to the private sector, containment of compensation can enhance spending efficiency.
  - Typical public-private wage premiums (from referenced studies):
    - Advanced Economies (AEs) average premium: around 5½ percent.
    - EMs and LIDCs average premium: around 12¼ percent.
- Data sources and caveats:
  - Government wage bill and employment data: IMF FAD Government Compensation and Employment Dataset (IMF, 2016c).
  - Coverage varies: general government, non-financial public sector, central or budgetary government.
  - Comparability issues exist (e.g., recording of bonuses or in-kind benefits).
  - Wage premia data: IMF FAD Public-Private Wage Premium Dataset (IMF, 2016d).
  - Employment comparisons may reflect national choices about government roles and private sector service provision; measures to downsize the wage bill must be evaluated in context.
- Case study (Argentina):
  - Over the last decade, government spending on the wage bill has been increasing and is currently above the mean for comparator groups.
  - After a gradual decline in 2000-04, the wage bill increased by nearly 6 percentage points since 2004.
  - Wage bill level in 2015: about 12½ percent of GDP.
  - Public sector average compensation is about 13 percent higher than in the private sector (public wage premium).
  - Public employment level: exceeds the EMs average of around 8 percent of the labor force, and appears to be the main driver of the high wage bill spending.

### Investment (public and private)
- Diagnostic focus:
  - Public and private investment trends, public capital stock, and qualitative infrastructure indicators (roads, ports, railroads, air transport).
  - Relationship between public capital stock and infrastructure quality indicates investment efficiency.
- Data sources and methodology:
  - Investment and public capital stock series: WDI, WEO, and IMF Investment and Capital Stock Dataset (IMF, 2016c).
  - Public and private investment measured using gross fixed capital formation of general government and private sector respectively.
  - Public capital stock constructed using the perpetual inventory method.
  - Quality of infrastructure ranking: World Economic Forum’s Global Competitiveness Report (WEF, 2015); ranking of 1 = best performer among 144 reporting countries.
- Case study (Argentina):
  - Public capital spending as a share of GDP has been on an increasing path since 2002.
  - Private investment has been declining since 2007 and currently stands at about 3½ percent of GDP.
  - Outcome: relatively low public capital spending levels, a low level of capital stock, and poor infrastructure quality outcomes—particularly in air and road transportation—when compared to peer countries.
  - Note: efficiency could also be responsible for relatively low investment outcomes.

### Energy subsidies
- Rationale for assessment:
  - Energy subsidies can cause environmental damage, impose large fiscal costs, discourage renewable investment, and enlarge income inequality as benefits are often captured disproportionately by upper-income groups.
  - Reforming generalized energy subsidies while mitigating impacts on low-income households (e.g., targeted income or in-kind transfers) can be cost-effective and equity-enhancing.
- Data and breakdowns:
  - Energy subsidy estimates: IMF Energy Subsidy Estimates Dataset (IMF, 2016b) accompanying “How Large Are Global Energy Subsidies?” (Coady et al., 2015).
  - Presented breakdowns include subsidies by energy product (petroleum, coal, natural gas, electricity) and by component (pre-tax, foregone consumption tax, externality) to capture impacts on expenditures, revenues, and the environment.
  - Definitions:
    - Pre-tax subsidy: difference between cost of supplying energy and price paid by consumers.
    - Post-tax consumer subsidy: pre-tax subsidy plus an appropriate “Pigouvian” tax for environmental damage and an additional consumption tax equivalent to prevailing VAT/GST on the energy supply cost plus externality cost.
- Case study (Argentina):
  - Post-tax energy subsidies in 2014: around 5 percent of GDP.
  - Comparators:
    - EM median: 5.3 percent of GDP.
    - OECD median: 1.6 percent of GDP.
    - LAC median: 4 percent of GDP.
  - Breakdown by product in Argentina:
    - Natural gas: estimated at 2 percent of GDP.
    - Electricity: estimated at 1.8 percent of GDP.
    - Petroleum subsidies: account for only (text truncated in source).

*Technical Notes and Manuals 17/06 | 2017*

### 1.1 percent of GDP. This subsidy structure contrasts with both LAC and EM medians, that show

### tnm1706 - 1.1 percent of GDP. This subsidy structure contrasts with both LAC and EM medians, that show

### Energy subsidies and composition
- Argentina’s post-tax energy subsidy estimate: 1.1 percent of GDP.
- Composition of Argentina’s post-tax energy subsidy (totality of energy products):
  - Pre-tax subsidies: 2.4 percent of GDP.
  - Externalities: 1.5 percent of GDP.
  - Foregone consumption tax revenue: 1.2 percent of GDP.
- Petroleum-specific structure:
  - Argentina’s petroleum post-tax subsidy of 1.1 percent of GDP reflects only the externality and the foregone revenue components (i.e., excludes a pre-tax subsidy component for petroleum).
- Comparator medians for the externality component (as percent of GDP):
  - LAC median: 2.2 percent of GDP.
  - EM median: 3.9 percent of GDP.
  - OECD median: 1.3 percent of GDP.
- Comparative observations:
  - Argentina’s externalities derived from energy subsidies are relatively small compared with other LAC countries.
  - In contrast to Argentina, the LAC and EM medians show larger shares of petroleum subsidies within the post-tax total.
  - The OECD median country has higher shares of petroleum and coal subsidies than Argentina.
- Policy implication noted:
  - Argentina’s above average pre-tax subsidy level (3 times the LAC median) leaves room for further work on subsidy elimination.

### Government spending by functional classification — overview
- Functional classification focuses on three functions: health, education and social protection.
- The assessment presents benchmarking charts for each function, including levels, modalities of provision, coverage, incidence, and outcomes, with comparisons to regional and income-group comparators.

### Health
- Context and challenge:
  - Public health expenditure averages about 6¾ percentage points of GDP in AEs.
  - EMs and LIDCs have much lower public health expenditure.
  - Aging populations in AEs put pressure to stabilize the ratio of public health spending to GDP without adversely affecting health outcomes.
- Efficiency and outcomes:
  - The health efficiency frontier uses HALE (Healthy Life Expectancy) as output and total health expenditure per capita as input; distance to the DEA efficiency frontier indicates loss in HALE due to inefficiencies and potential savings.
  - HALE data source: WHO; other health data: WB.
  - DEA technique used to build the efficiency frontier.
- Case study: Argentina
  - Health outputs (infant deaths, HALE, life expectancy at birth, number of physicians) are more favorable in Argentina than the LAC and EM average.
  - Since 2009, total health expenditure (percent of GDP) has declined, mainly reflecting a reduction in public health expenditure.
  - Total health expenditure in Argentina is slightly above the average LAC and EMs when expressed in per capita, PPP$ adjusted terms.
  - Argentina shows relatively good efficiency of total (public and private) health spending: the loss in HALE due to spending inefficiency is small and is below the EM average.
  - The same HALE could be attained by spending less—detailed analysis needed to identify options for savings without compromising outcomes.
- Data caveat:
  - Analysis of total health spending refers to the effectiveness of total (public and private) health spending; extrapolation to public health sector assumes private and public sectors are equally efficient.
- Recommended analytic next steps:
  - More granular analysis to control for other determinants of health (per capita income, educational attainment, access to sanitation and clean water, natural endowments, historical life expectancy, habits such as tobacco and alcohol use).

### Education
- Context and metrics:
  - Education spending typically a high share of public spending; challenges differ across income groups.
  - Figure 8 metrics include government education spending (percent of GDP and of government expenditure), education spending per student by level, and teacher-student ratios.
  - Inputs and outputs for frontier analysis: teacher-student ratios and education spending per student (PPP$) as inputs; net school enrolment and secondary-education overall PISA scores as outputs.
  - PISA note: last version available is for 2012; the 2015 version expected to be published by end-2016.
- Case study: Argentina
  - Public education spending in Argentina is high relative to other EM and LAC countries.
  - Argentina’s public spending on education as percent of GDP is slightly above the average for peer economies.
  - Spending per student stands well above peers, particularly for secondary education.
  - High student-teacher ratios—mainly for secondary but also for primary—seem responsible for the spending gap.
  - Education performance is good, but frontier analysis reveals large spending inefficiencies in secondary education: Argentina lies far from the efficient frontier despite above-average test scores and net enrolment.
- Policy implication:
  - Tackling education inefficiencies in secondary education has great potential for generating fiscal savings without jeopardizing outcomes; reforms should be preceded by a thorough analysis of the education system and determinants.

### Social protection
- Definitions and indicators:
  - Social assistance programs: non-contributory transfers in cash or in-kind.
  - Charts show social assistance spending (percent of GDP), coverage (share of poorest 20 percent receiving transfers), and benefit incidence (share of total transfers received by poorest 20 percent).
  - Income distribution indicators presented: income share held by highest 10 percent, income share held by bottom 20 percent, and Gini coefficient.
  - Pension indicators: retirement age by gender, public pension expenditures as percent of GDP, old age dependency ratio (people aged 60 and older as a share of the 15-59 population), eligibility ratio (pensioners as a share of the elderly), and projected pension spending over the next fifteen years.
  - Data sources: ASPIRE for social assistance; EPD pension database for pension indicators and projections.
- Case study: Argentina
  - Income distribution (Gini coefficient and income share of the top 10 percent) appears more favorable than the rest of LAC, partly due to higher social protection spending.
  - Argentina’s social assistance spending and pension eligibility are higher than the average LAC.
  - Despite favorable distribution metrics, there is room to improve social impact:
    - Share of income held by the bottom 20 percent is similar to the average LAC country.
    - Benefit incidence for social assistance programs is good, but coverage of these programs is among the lowest in LAC—indicating potential to enhance coverage and targeting.

### Conclusion and future work
- Purpose of EAT:
  - EAT is an easy and user-friendly tool for depicting and assessing government expenditures for any specific country; it provides information on expenditures—levels, composition and outcomes—and benchmarking against regional and income comparators.
  - EAT can help identify areas to enhance efficiency or streamline spending but is not a substitute for in-depth analysis.
- Suggested future areas of work to enrich the tool:
  - Estimation of efficiency scores for health and education expenditures.
  - Detailed analysis of expenditures exploiting cross-classification by functional and economic classification once such data becomes widely available.

*Technical Notes and Manuals 17/06 | 2017*

### References

### References

### Key references cited
- Celasun, Oya, Francesco Grigoli, Keiko Honjo, Javier Kapsoli, Alexander D. Klemm, Bogdan Lissovolik, Jan Luksic, Marialuz Moreno-Badia, Joana Pereira, Marcos Poplawski-Ribeiro, Baoping Shang, and Yulia Ustyugova, 2015, “Fiscal Policy in Latin America: Lessons and Legacies of the Global Financial Crisis,” IMF Staff Discussion Note 15/06 (Washington: International Monetary Fund).
- Chailloux, Alexandre B., Nir Klein, and Christopher Wilson, 2016, “Public Expenditure Efficiency in Ireland,” IMF Selected Issues Paper 16/257 (Washington: International Monetary Fund).
- Charnes, Abraham, William W. Cooper, and Eduardo Rhodes, 1978, “Measuring the Efficiency of Decision Making Units,” European Journal of Operational Research, Vol. 2, Issue 6, pp. 429–444.
- Clements, Benedict, David Coady, Stefania Fabrizio, Sanjeev Gupta, Trevor Alleyne, and Carlo Sdralevich, 2013, “Energy Subsidy Reform: Lessons and Implications,” (Washington: International Monetary Fund).
- Clements, Benedict, Kamil Dybczak, Vitor Gaspar, Sanjeev Gupta, and Mauricio Soto, “The Fiscal Consequences of Shrinking Populations,” 2015a, IMF Staff Discussion Note No. 15/21 (Washington: International Monetary Fund).
- Clements, Benedict, Ruud de Mooij, Sanjeev Gupta, and Michael Keen, 2015b, “Inequality and Fiscal Policy,” (Washington: International Monetary Fund).
- Coady, David, Maura Francese, and Baoping Shang, 2014, “The Efficiency Imperative,” Finance & Development. International Monetary Fund, Vol. 51, Issue 4, pp. 30–32.
- Coady, David, Robert Gillingham, and Rolando Ossowski, John Piotrowski, Shamsuddin Tareq, and Justin Tyson, “Petroleum Product Subsidies: Costly, Inequitable, and Rising,” 2010, IMF Staff Position Note No: 10/05 (Washington: International Monetary Fund).
- Coady, David, and Nan Geng, 2015, “From Expenditure Consolidation to Expenditure Efficiency: Addressing Public Expenditure Pressures in Lithuania,” IMF Working Paper No. 15/278, (Washington: International Monetary Fund).
- Coady, David, Ian Parry, Louis Sears, and Baoping Shang, 2015, “How Large Are Global Energy Subsidies?” IMF Working Paper 15/105, (Washington: International Monetary Fund).
- Eurogroup, 2016, “Thematic Discussions on Growth and Jobs: Common Principles for Improving Expenditure Allocation,” Eurogroup Statement and Remarks Press Release (Brussels), http://www.consilium.europa.eu/press-releases-pdf/2016/9/47244647137_en.pdf.
- Gaertner, Matthew, and Maximilien Queyranne, 2015, “Status of Fiscal Adjustment and Challenges Ahead”, Portugal IMF Selected Issues Paper No. 15/127 (Washington: International Monetary Fund).
- Grigoli, Francesco, and Javier Kapsoli, 2013, “Waste Not, Want Not: The Efficiency of Health Expenditure in Emerging and Developing Economies,” IMF Working Paper No. 13/187 (Washington: International Monetary Fund).
- Hallaert, Jean-Jacques, 2016, “Belgium: Making Public Expenditure More Efficient,” IMF Selected Issues Paper No. 16/78 (Washington: International Monetary Fund).
- Hallaert, Jean-Jacques, and Maximilien Queyranne, 2016, “From Containment to Rationalization: Increasing Public Expenditure Efficiency in France,” IMF Working Paper No. 16/7 (Washington: International Monetary Fund).
- Seiford, Lawrence M., and Robert M. Thrall, 1990, “Recent Development in DEA: The Mathematical Programming Approach to Frontier Analysis,” Journal of Econometrics, Vol. 46, Issue 1–2, pp. 7–38.
- World Economic Forum (WEF), 2015, The Global Competitiveness Index Historic Dataset, in “Global Competitiveness Report” 2005–2015, (Geneva).

### IMF policy papers, datasets, and manuals cited
- International Monetary Fund (IMF), 2013a, “Case Studies on Energy Subsidy Reform: Lessons and Implications,” IMF Policy Paper (Washington), http://www.imf.org/external/np/pp/eng/2013/012813a.pdf.
- ——, 2013b, “Fiscal Policy and Income Inequality,” IMF Policy Paper (Washington), http://www.imf.org/external/np/pp/eng/2014/012314.pdf.
- ——, 2013c, “Rethinking the State—Selected Expenditure Reform Options,” Country Report No. 13/6 (Washington).
- ——, 2014a, Fiscal Monitor—Public Expenditure Reform: Making Difficult Choices (Washington), http://www.imf.org/external/pubs/ft/fm/2014/01/pdf/fm1401.pdf.
- ——, 2014b, Government Finance Statistical Manual 2014 (Washington), http://www.imf.org/external/Pubs/FT/GFS/Manual/2014/gfsfinal.pdf.
- ——, 2015a, “Making Public Investment More Efficient: Annex I,” IMF Policy Paper, (Washington), http://www.imf.org/external/np/fad/publicinvestment/data/info.pdf.
- ——, 2015b, “IMF Engagement with Countries in Post-Conflict and Fragile Situation—Stocktaking.” IMF Policy Paper (Washington), https://www.imf.org/external/np/pp/eng/2015/050715.pdf.
- ——, 2015c, “Making Public Investment More Efficient,” IMF Policy Paper (Washington), http://www.imf.org/external/np/pp/eng/2015/061115.pdf.
- ——, 2016a, “Case Studies on Managing Government Compensation and Employment—Institutions, Policies, and Reform Challenges,” IMF Policy Paper (Washington), http://www.imf.org/external/np/pp/eng/2016/040816ab.pdf.
- ——, 2016b, “Country-level Energy Subsidy Estimates Dataset,” (Washington), http://www.imf.org/external/np/fad/subsidies/data/codata.xlsx.
- ——, 2016c, “IMF FAD Government Compensation and Employment Dataset,” (Washington).
- ——, 2016d, “IMF FAD Public-Private Wage Premium Dataset,” (Washington).
- ——, 2016c, “IMF Investment and Capital Stock Dataset,” (Washington), http://www.imf.org/external/np/fad/publicinvestment/data/data.xlsx.
- ——, 2016f, “Managing Compensation and Employment—Institutions, Policies and Reform Challenges,” IMF Policy Paper (Washington), http://www.imf.org/external/np/pp/eng/2016/040816a.pdf.

### Figures, data sources, and notes (as presented)
- Figure 3. Government Spending and Economic Classification
  - Source: IMF FAD Expenditure Assessment Tool (EAT).
  - Note: 1/ Coverage refers to general government as per World Economic Outlook. 2/ Dashlines are the average of LAC.
  - Panels and data points shown include: Argentina—Fiscal balance (in percent of GDP), 2004–2015; Argentina—Economic Classification (in percent of total), Latest Value Available (Goods and Services; Compensation of Employees; Interest Bill; Other Current Spending; Capital Spending); LAC—Economic Classification (in percent of total), Latest Value Available; Current and Capital Spending (in percent of GDP), 2015 2/; General Government Spending (in percent of GDP) 2015; Change in Total Spending (in percent of GDP), 2004–2015.
- Figure 4. Government Wage Bill
  - Source: IMF FAD Expenditure Assessment Tool (EAT). IMF FAD Government Wage Bill and Employment Dataset.
  - Notes: 1/ Dashlines are the average for countries in the regional benchmark group. 2/ Public-private wage differential (as a percent of private wage); based on review of regression-based studies that control for skill differentials.
  - Panels and metrics shown include: Wage Bill 1/ Latest Value Available (in percent of GDP); Wage Bill 1/ Latest Value Available (in percent of total spending); Wage Premia 2/ (in percent of private wage); Government Wage Bill to GDP; Government Employment to Working-age Population (in percent); Trends in Wage Bill and Employment for Argentina; Benchmarking for Wage Bill and Employment (in percent), 2015 or Most Recent Year.
- Figure 5. Investment Infrastructure
  - Source: IMF FAD Expenditure Assessment Tool (EAT), World Economic Outlook, World Development Indicators, IMF Investment and Capital Stock Dataset, and World Economic Forum.
  - Panels and metrics shown include: Capital Stock and Infrastructure Quality, 2015 (Ranking: 1 = best, 144 = worst; Public Capital Stock, percent of GDP; Quality of Overall Infrastructure; Quality of Air Transport; Quality of Roads; Quality of Ports; Quality of Railroads); Argentina—Evolution of Investment (in percent of GDP) with series for Private investment, Public investment, Total investment (2000–2015).
- Figure 6. Energy Subsidies
  - Source: IMF FAD Expenditure Assessment Tool (EAT), IMF Energy Subsidy Estimates.
  - Note: 1/ Dashlines are the median for countries in the region.
  - Panels shown: Energy Subsidies by Product, in percent of GDP, 2014 1/; Energy Subsidies by Component, in percent of GDP, 2014 1/.
- Figure 7. Health Expenditure
  - Source: IMF FAD Expenditure Assessment Tool (EAT), World Bank, World Health Organization.
  - Notes: 1/ Dashlines are the average of LAC. 2/ Healthy life expectancy (HALE) is a measure of health expectancy that applies disability weights to health states to compute the equivalent number of years of life expected to be lived in full health.
  - Panels and metrics shown include: Healthy Expectancy, 2015 2/ versus Total health expenditure per capita, PPP$; Health Efficiency Frontier, Latest Value Available 1/; Health Expenditure—Different Metrics, Latest Value Available (Total health expenditure (% of GDP); Health expenditure, public (% of GDP); Health expenditure, public, % of government expenditure; Health expenditure, public, % of total health expenditure; Out-of-pocket health expenditure, % of total health expenditure; Total health expenditure per capita, current US$ (rhs); Total health expenditure per capita, PPP$-adjusted (rhs)); Argentina—Health Expenditure Trend (various series, 2001–2013); Health Indicators and Health System Characteristics Indicators, Latest Version Available (Number of infant deaths per 1,000 people; Life expectancy at birth; Hospital beds per 1,000 people (rhs); Nurses and midwives per 1,000 people (rhs); Physicians per 1,000 people (rhs)).
- Figure 8. Government Education Expenditure
  - Source: IMF FAD Expenditure Assessment Tool (EAT), World Bank.
  - Note: 1/ Dashlines are the average of LAC.
  - Panels and metrics shown include: Education Indicators, Latest Value Available (Adult literacy rate; Net Enrollment, primary; Net Enrollment, secondary; PISA Total (rhs)); Government Education Spending and Outcome, Secondary, Latest Value Available 1/ (Education spending per student, PPP$, secondary; PISA score, overall); Teachers and Outcome, Secondary, Latest Value Available 1/ (Teacher-student ratio, per 100 students, secondary; PISA score, overall); Government Education Spending and Outcome, primary, Latest Value Available 1/; Government Education Expenditure, Latest Value Available (% government expenditure; % GDP Primary Secondary); Government Education Expenditure per Student, PPP$ adjusted, Latest Value Available (Primary Secondary Tertiary); Teachers and Outcome, Primary, Latest Value Available 1/.
- Figure 9. Social Protection
  - Source: IMF FAD Expenditure Assessment Tool (EAT), World Economic Outlook, ASPIRE and IMF Pension Indicators.
  - Notes: 1/ Dashlines are the average of LAC. 2/ Coverage is (number of individuals in the quintile who live in a household where at least one member receives the transfer)/(number of individuals in that quintile). Benefit incidence is equal to (sum of all transfers received by all individuals in the quintile)/(sum of all transfers).
  - Panels and metrics shown include: Pension Indicators, 2014; Population Indicators, Latest Value Available (Total Population, in millions; Population Density, per sq.km; Annual Rate of growth of Population, % (rhs, 10x); Unemployment Rate, % (rhs)); Income Distribution Indicators, Latest Value Available (Nominal GDP per capita, thousands US$; Income share of top 10%; Income share of bottom 20%; Gini Coefficient); Pension Spending panels (Pension spending (percent of GDP); Pension Spending Change, 2015–30 (percent of GDP); Retirement Age, Male (rhs); Retirement Age, Female (rhs); Old Age Dependency Ratio (rhs); Coverage, Pensioners to population 65 and older (rhs); Coverage, Contributors to working age population (rhs)); Social Assistance Spending (Social Assistance Spending, % of GDP); Social Assistance Coverage and Benefit Share of Poorest 20 percent (in percent), Latest Value Available 1/ 2/ (Social Assistance Coverage and Benefit incidence panels for ARG and regional benchmarks).

### Contact and imprint (as presented)
- TNM/17/06
- International Monetary Fund
- Fiscal Affairs Department
- 700 19th Street NW
- Washington, DC 20431
- USA
- Tel: 1-202-623-8554
- Fax: 1-202-623-6073

*Source: tnm1706 - References (PDF).*

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_Source: https://www.imf.org/-/media/files/publications/tnm/2017/tnm1706.pdf_