## MODELING MONETARY POLICY IN RESOURCE-RICH ECONOMIES: THE CASE OF ANGOLA

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---

### A. Introduction
- Angola: small open economy with high reliance on the oil sector and vulnerability to frequent shocks (oil price and production fluctuations, global interest rate swings, investor risk appetite changes, and domestic supply shocks including climate change).
- Policy transition: move toward greater exchange rate flexibility and an inflation-targeting framework after the BNA Law (2021) which:
  - established a price stability mandate;
  - increased operational autonomy of the BNA;
  - limited monetary financing;
  - improved central bank governance.
- Purpose of the paper:
  - Use an extended semi-structural New Keynesian model to (i) model shock impacts on an Angola-calibrated economy and (ii) inform monetary policy through analysis of policy trade-offs.

### B. The Role of Oil for Angola’s Economy
- Key factual concentrations (2022):
  - oil accounted for more than 50 percent of fiscal revenues;
  - 95 percent of exports;
  - and 35 percent of GDP.
- Consequences of oil dependence:
  - Oil income fluctuations are the main source of macroeconomic volatility since 2002.
  - Non-resource sectors (construction, trade, transportation, services, finance) are closely linked to oil, mainly through the fiscal channel.
  - Oil price and production shocks cause disruptive fluctuations in fiscal and external balances, leading to weaker and more volatile growth.
- Recent historical dynamics:
  - Sharp and persistent decline of oil prices in 2014–16 led to reduced investment insufficient to offset natural decline of oil fields.
  - Covid-pandemic-related lower oil-sector investment further weakened production capacity, contributing to challenges in 2023.
  - Between November 2022–June 2023, extended oil maintenance operations caused production to drop below potential, contributing to a cyclical downturn; maintenance operations are transitory and not expected to change the trend.
  - The nominal exchange rate depreciated significantly in mid-2023 in response to lower foreign exchange earnings from reduced oil production.
- Policy context:
  - Exchange rate flexibility helps absorb shocks (especially for the fiscal sector) but complicates monetary policy due to high pass-through to inflation.
  - Short-term interest rates are not yet firmly established as an operating target; BNA communicates via a reference policy rate but interbank rates are partly misaligned due to excess liquidity and monetary formulation still relies partly on money aggregates.

### C. The Model of the Angolan Economy
- Model overview:
  - Semi-structural New Keynesian model with four blocks: aggregate demand, aggregate supply, external, and monetary.
  - Explicit oil sector to capture Angola’s terms-of-trade channel; aggregate demand split into oil and non-oil sectors.
  - Core behavioral relationships: IS curve, Phillips curve, Taylor-type forward-looking monetary reaction function, and uncovered interest parity (UIP), supplemented with external-account equations.
  - Variables in behavioral equations are expressed as "gaps" (percentage deviations from trend), with trends estimated within the model; disturbance terms ε represent shocks.
  - Quarterly data used.
- Representative equations and interpretations (as presented):
  - Non-oil output gap depends on lagged persistence, expected outlook, real interest rate gap, REER gap, oil output gap, and oil price gap.
  - Oil output gap dynamics driven by persistence parameter and an exogenous shock; trend growth evolves with shocks that average to zero over time.
  - Phillips Curve: headline CPI inflation influenced by non-oil output gap, REER gap, imported inflation, cost-push shock, and inflation expectations combining forward- and backward-looking elements.
  - UIP and exchange rate: nominal exchange rate depends on expected value, interest rate differential, country risk premium, and UIP shock.
  - International reserves deviation modeled as a function of the current account (exports function of oil gap and oil price gap) and imports (function of non-oil output gap and a structural term Z̄).
  - Currency risk premium deviation inversely related to deviation of reserves from desired levels, linking reserves to exchange rate pressure.
- Parameterization and estimation:
  - Most parameters calibrated drawing from QPM literature to reflect country-specific characteristics and address limited long time series.
  - Four key coefficients estimated using Bayesian methods: non-oil output gap sensitivity to oil gap; REER impact on output; inflation sensitivity to output gap; weight on inflation deviation from target in the policy reaction function. Posterior modes are generally close to priors.

### D. Assessing Cyclical Stance and Estimating Potential Output
- Method:
  - Kalman filter estimates unobservable time series for oil and non-oil output (gaps and trends) to assess cyclical position.
- Findings for 2023:
  - Non-oil and oil output gaps are estimated to be negative in 2023.
  - Cyclical weakness of non-oil sector largely attributed to negative spillovers from the oil sector, and lower oil revenues caused fiscal and financial sector effects (government domestic arrears to suppliers and foreign exchange backlogs) that adversely impacted non-oil activity.
- Oil sector decomposition:
  - Oil sector exhibits both structural (trend) and cyclical weakness; cyclical weakness mainly due to maintenance operations and expected to be temporary.
  - The declining trend—largely from underinvestment to offset declining oil fields—has broadly stabilized and begun to stabilize since 2022, supported by authorities’ robust investment projections in the sector.
  - Real oil prices (Angola’s oil prices adjusted for US inflation) appear aligned with trend.
- Note:
  - Figure 2 decomposes potential output of oil and non-oil sectors; the direct oil contribution is presented while total oil sector contribution is higher when including indirect effects via non-oil sector.

### E. The Impulse Response Analysis
- Approach:
  - Impulse response analysis simulates shocks from steady state with variables expressed as percent deviations from steady states; shocks simulated include (i) a hike in monetary policy rate; (ii) an aggregate demand shock with lagged monetary response; (iii) an international oil price shock.
- Simulation results (summarized):
  - Monetary policy shock:
    - A 1 percentage points unanticipated increase in the BNA’s key monetary policy rate is passed to interbank and real rates.
    - Tighter policy leads to nominal and real exchange rate appreciation.
    - Cumulative nominal exchange rate appreciation of 0.2 percent over the medium-term.
    - Impact on inflation estimated at 0.2 percent over a year, with full transmission materializing.
    - Transmission assumes interbank rates align with policy rates.
  - Delayed monetary policy response:
    - Following a 1 percent of GDP demand shock: a two-quarter delay in monetary policy response leads to roughly 0.3 percentage points higher inflation and 0.5 percentage points depreciation of the nominal exchange rate in a year.
    - Depreciation pressures reduce reserves and increase the currency risk premium relative to the counterfactual.
    - Results align with Clarida and others (1999): passive/delayed monetary policy amplifies fluctuations; timely interest-rate adjustments help stabilize the economy and return variables to steady states.
  - Oil price shock:
    - A 10 percent decrease in the real oil price (Angola’s oil prices adjusted for US inflation) causes a contraction in output (negative output gap) while raising inflation.
    - Inflation rises due to currency depreciation from lower external flows.
    - Monetary policy, in the simulation, foregoes output and raises interest rates to prevent second-round inflationary effects—reflecting a negative supply-side/cost-push shock and highlighting policy trade-offs.
    - The extent of optimal interest rate response depends crucially on monetary policy credibility.

### F. Monetary Policy Credibility: Simulation Findings
- Two credibility scenarios compared for an oil price shock: baseline monetary policy credibility and relatively higher credibility (anchored expectations).
- Mechanism:
  - Vary credibility by adjusting the weight on the forward-looking component in inflation expectations; higher credibility increases forward-looking behavior and anchors expectations.
- Quantified outcomes from the higher credibility scenario:
  - The need for interest rate hikes in response to an adverse supply shock is lower by almost one-third at its peak than in the baseline scenario.
  - The higher credibility scenario yields a more contained inflationary outturn and lower cumulative output loss over the medium-term.
- Note:
  - A higher demand shock and longer delay in monetary policy response leads to higher economic volatility and inflation.

### G. Policy Implications (high-level and specific)
- High-level trade-offs highlighted:
  - Stabilizing output versus mitigating second-round inflationary effects.
  - Maintaining a stable exchange rate versus preserving international reserves.
- Costs of delay:
  - Delaying monetary policy reaction to a demand shock materially increases inflation, nominal depreciation, and reserve losses.
- Institutional and operational preconditions to harness credibility benefits:
  - Demonstrated strong track record of decisive action when warranted.
  - Effective transmission mechanism — including aligning interbank rates with the announced policy rate.
  - Robust communication framework.
- Operational recommendations and recent measures:
  - BNA’s progress on Forecasting and Policy Analysis System (FPAS) supports forward-looking policy.
  - Improving interbank liquidity management is a key priority; recent BNA measures include increasing mandatory reserve requirements and removing the custody fee on commercial banks' excess balances at the central bank to reduce downward pressure on interbank rates.
  - Further steps to align interbank markets with the policy rate:
    - Greater cooperation between fiscal and monetary authorities to coordinate money market operations.
    - Separate the maturity lines of their instruments.
    - Develop money markets.
- Managing reserves:
  - Reducing reserve deviations helps limit currency risk premium increases and exchange rate pressure.

### H. Calibrated Parameters and Model Steady States (selected)
- Selected calibrated parameters (Behavioral Equations) as presented:
  - Headline inflation credibility parameters:
    - 훽ଵ 0.55
    - a1 0.65
    - 훿 0.5
    - 훽ଶ 0.1
    - a2 0.1
    - 훽ଷ 0.15
    - a3 0.1
    - Oil GDP weight 훽ਸ 0.2
    - a4 0.05
    - 휔 0.35
    - a5 0.05
  - Exchange rate / interest rate / reserves parameters:
    - 휌௦ 0.75
    - c1 0.5
    - 휃 0.6
    - c2 2
    - 훿 0.9
    - c3 0.3
    - 휌௣௥௘௠ 0.8
    - 휓௠ 0.5
- Model steady states (Policy Variables Domestic / Foreign):
  - Inflation target: 7 / 2
  - Neutral real interest rate: 2.5 / 0.5
  - Neutral nominal interest rate: 9.5 / 2.5
  - Potential growth: non-oil sector 3.5
  - Potential growth: oil sector 2
  - Potential growth: total 3 / 2
  - Exchange rates:
    - Real exchange rate depreciation -2
    - Nominal exchange rate depreciation 3
  - Country risk premium 4
  - Terms of trade improvement 0
  - Informal market exchange rate spread 5

### I. Gender Gaps and Potential Growth — Stylized Facts and Simulations
- Key gender statistics:
  - Expected years of schooling: women 7 years; men 9.2 years.
  - Informal employment: women 89.8 percent; men 71.2 percent.
  - Female labor force participation gap relatively reduced: about 4 percentage points.
  - Female Labor Force Participation (FLFP) in Angola is more than 70 percent (2022), leading to an FPLP gender gap of -4 percentage points in 2022 versus regional and global averages: sub-Saharan Africa -12 pp, upper middle-income countries -17 pp, world -25 pp.
  - Almost 90 percent of women employed in the informal sector (88 percent in 2022).
  - Angola’s overall income gap between men and women stood at 22 percent (women earn 78 cents for every kwanza earned by men).
  - Adolescent birth rate 138.4 births per 1,000 women ages 15–19.
  - Child marriage rate: 30 percent of women aged 20 to 24 married or in union before age 18.
  - Angola’s Global Gender Gap Index (2022) position: 125th out of 146 countries; Education Attainment score 0.69.
- Education and STEM trends:
  - Between 2015 and 2018 the weight of STEM professions in total professional graduations fell from 23 percent to 11 percent.
  - The proportion of females graduating in STEM professions declined by 28 percentage points between 2015 and 2018 (from about 33 percent to less than 5 percent); for males the decline was 25 percentage points (from about 37 percent to 12 percent).
- Simulated macroeconomic gains from closing education gaps:
  - Simulations use a Cobb-Douglas production function with learning-adjusted labor inputs and a five-year lag for education-to-workforce entry.
  - Baseline projections:
    - Global LAYS increases at a rate of 1.4 percent per year.
    - Male LAYS reaches 7.3 years and female LAYS reaches 5.3 years by 2050.
    - Potential output estimated to grow at an average rate of 4.1 percent per year.
  - Scenario 1 (close LAYS gender gap by 2050):
    - Female LAYS grows at an average annual rate of 2 percent; male LAYS maintains baseline growth.
    - Male and female LAYS reach 7.3 years by 2050.
    - Potential output increases to 4.3 percent on average—an increase of 0.17 percentage points compared to the baseline.
  - Scenario 2 (close LAYS gap by 2032, ten-year closure):
    - Female LAYS reaches 5.7 years by 2032; both sexes attain 7.3 LAYS by 2050.
    - Annual potential output growth of 4.36 percent.
  - Aggregate impact statements:
    - Improvement of real GDP between 4.6 and 5.7 percent by 2050 when eliminating gender gaps in education (simulation range reported).
- Qualitative findings:
  - Reducing the gender education gap can lead to higher potential output.
  - More educated women are more likely to enter the formal sector and obtain better-paying, more productive jobs.

### J. Production Function Framework and Labor Specification (methodology for gender simulations)
- Cobb-Douglas production function:
  - Y_t = A_t K_t^(α) L_t^(1−α)
- Capital stock:
  - Estimated using the perpetual inventory method with a depreciation rate of 0.06.
- Labor input composition:
  - L_t = L_t^f + L_t^m
  - Learning-adjusted labor inputs follow Filmer et al. (2020) formulation with LAYS, returns to education, hours worked, and employed population as components.
- Data construction:
  - Where LAYS data limited, expected years of schooling (EYS) from HDI used and scaled by average LAYS/EYS ratios for available years (2017, 2018, 2020).

### K. Policies to Close Gender Gaps in Angola — Targeted Interventions and Financing
- Main barriers identified:
  - Transaction and opportunity costs despite public education being free.
  - Early pregnancy: adolescent fertility rate of 138 births per 1000 women; adolescent mothers face stigma and economic difficulties.
- Authorities' targeted interventions (guided by NDP and supported projects):
  - Improvement in reproductive health services (SRH), including contraceptives and prevention of early marriage, targeting about 300 thousand adolescent girls and boys.
  - Expansion of accelerated learning programs to allow youth and adults with age/class mismatch to complete primary and secondary education.
  - Financial incentives for adolescent girls:
    - Scholarships for children attending cycle 1 of secondary school.
    - Extra bonus of approximately US$38 (AOA 25,000) for girls registering for the first time in cycle 1 of secondary school.
    - Scholarship duration: three years; payments via the Kwenda mechanism.
  - Expand education supply and support high-quality teaching: construct new schools or rehabilitate/expand existing schools and strengthen instruments to attract best candidates.
- Project financing notes:
  - World Bank support: US$ 250 million project “Girls Empowerment and Learning for All Project”.
  - World Bank allocated US$ 10 million under the project to increase accelerated learning program attendance by 250 thousand people (to a total of 1 million by end-2025).
  - World Bank allocated US$ 38 million under the Girls Empowerment and Learning for All Project.

### L. Gender Budgeting — Status and Recommendations
- Background:
  - Gender budgeting (GB) uses fiscal policies and PFM tools to promote gender equality; Gender Equality Marker (GEM) methodology introduced in the 2022 budget.
- Recent allocations and trends:
  - Programs with limited consideration of gender equality/women’s empowerment (G1): allocated 14 percent of current expenditures in 2022 and 25 percent in 2023.
  - Programs with significant impact (G2) or principal objective (G3) gender equality/women’s empowerment: increased from 7.8 percent in 2022 to 12 percent of current expenditures in 2023.
  - More than 80 percent of funds targeted to programs with strong impact in reducing gender inequalities: poverty, education, and maternal health.
- Implementation status:
  - Use of gender markers paused in the 2024 budget to recalibrate and align with gender filters in the NDP spanning 2024–27.
- Immediate next steps recommended:
  - (i) Prompt resumption in the use of markers in the 2025 budget and expansion to include all line ministries.
  - (ii) Improve the quality of assessments involved in the use of gender markers.
  - (iii) Increase allocation of budgetary resources to programs with gender impact (in line with NDP priorities).
  - (iv) Strengthen accountability through preparation of a first gender budget statement as a report with the budget proposal on gender-related allocations, expected outcomes in terms of equality, and explanation of last year execution.
  - Assess the impact of fiscal systems on gender equality and address direct or indirect discrimination against women; implement tax policies and incentives to reduce barriers to women’s economic participation.

*Source: Introduction — 1agoea2024002.*

### Introduction____________________________________________________________________________ 3

### MODELING MONETARY POLICY IN RESOURCE-RICH ECONOMIES: THE CASE OF ANGOLA

### A. Introduction
- Angola is a small open economy with high reliance on the oil sector and vulnerability to frequent shocks: oil price and production fluctuations, global interest rate swings, investor risk appetite changes, and domestic supply shocks (including climate change).
- Authorities are transitioning toward greater exchange rate flexibility and an inflation-targeting framework following the BNA Law passed in 2021, which established a price stability mandate, increased operational autonomy of the BNA, limited monetary financing, and improved central bank governance.
- The paper uses an extended semi-structural New Keynesian model to (i) model shock impacts on an Angola-calibrated economy and (ii) inform monetary policy through analysis of policy trade-offs.

### B. The Role of Oil for Angola’s Economy
- Key factual concentrations (2022):
  - oil accounted for more than 50 percent of fiscal revenues;
  - 95 percent of exports;
  - and 35 percent of GDP.
- Consequences of oil dependence:
  - Oil income fluctuations are the main source of macroeconomic volatility since 2002.
  - Non-resource sectors (construction, trade, transportation, services, finance) are closely linked to oil, mainly through the fiscal channel.
  - Oil price and production shocks cause disruptive fluctuations in fiscal and external balances, leading to weaker and more volatile growth.
- Recent historical dynamics:
  - The sharp and persistent decline of oil prices in 2014–16 led to reduced investment insufficient to offset natural decline of oil fields.
  - Covid-pandemic-related lower oil-sector investment further weakened production capacity, contributing to challenges in 2023.
  - Between November 2022–June 2023, extended oil maintenance operations caused production to drop below potential, contributing to a cyclical downturn; maintenance operations are transitory and not expected to change the trend.
  - The nominal exchange rate depreciated significantly in mid-2023 in response to lower foreign exchange earnings from reduced oil production.
- Policy context:
  - Exchange rate flexibility helps absorb shocks (especially for the fiscal sector) but complicates monetary policy due to high pass-through to inflation.
  - Short-term interest rates are not yet firmly established as an operating target; BNA communicates via a reference policy rate but interbank rates are partly misaligned due to excess liquidity and monetary formulation still relies partly on money aggregates.

### C. The Model of the Angolan Economy
- Model overview:
  - Semi-structural New Keynesian model with four blocks: aggregate demand, aggregate supply, external, and monetary.
  - Explicit oil sector to capture Angola’s terms-of-trade channel; aggregate demand split into oil and non-oil sectors.
  - Core behavioral relationships: IS curve, Phillips curve, Taylor-type forward-looking monetary reaction function, and uncovered interest parity (UIP), supplemented with external-account equations.
  - Variables in behavioral equations are expressed as "gaps" (percentage deviations from trend), with trends estimated within the model; disturbance terms ε represent shocks.
  - Quarterly data used.
- Representative equations and interpretations (variables presented as in source):
  - Non-oil output gap equation: non-oil output gap (푦ො
௧
௡௢௜௟
) depends on lagged persistence, expected outlook, real interest rate gap (푟푟ෞ
௧ିଵ
), REER gap (푧̂
௧
), oil output gap (푦ො
௧
௢௜௟
), and oil price gap (푟푒푙푝푂퐼퐿
෣
).
  - Oil output gap dynamics: oil production gap (푦ො
௧
௢௜௟
) driven by persistence parameter 휌
௬
ො
 and an exogenous shock (휀
௬
ො
); trend growth 훥푦ത
௧
௢௜௟
 evolves with shocks (휀
௧
௬
ത
) that average to zero over time.
  - Phillips Curve: headline CPI inflation (휋
௧
) influenced by non-oil output gap, REER gap, imported inflation (휋
௧
௠
), cost-push shock (휀
௧
గ
), and inflation expectations (휋
௧
௘
) combining forward- and backward-looking elements.
  - UIP and exchange rate: nominal exchange rate (푆
௧
) depends on expected value (푆
௧
௘
), interest rate differential (푖
௧
∗
 and 푖
௧
), country risk premium (푝푟푒푚
௧
), and UIP shock (휀
௧
௦
).
  - International reserves deviation (푟푒푠ෞ
௧
) modeled as a function of current account approximated by exports (function of oil gap and oil price gap) and imports (function of non-oil output gap and a structural term Z̄).
  - Currency risk premium deviation (푝푟푒푚ෟ
௧
) inversely related to deviation of reserves from desired levels, linking reserves to exchange rate pressure.
- Parameterization and estimation:
  - Most parameters calibrated drawing from QPM literature (Benes (2015), Andrle (2013a), Alichi (2008)) to reflect country-specific characteristics and address limited long time series.
  - Four key coefficients estimated using Bayesian methods: non-oil output gap sensitivity to oil gap; REER impact on output; inflation sensitivity to output gap; weight on inflation deviation from target in the policy reaction function. Posterior modes are generally close to priors (Figure 8 referenced).

### D. Assessing Cyclical Stance and Estimating Potential Output
- Method:
  - A Kalman filter estimates unobservable time series for oil and non-oil output (gaps and trends) to assess cyclical position.
- Findings for 2023:
  - Non-oil and oil output gaps are estimated to be negative in 2023 (Figure 2).
  - Cyclical weakness of non-oil sector largely attributed to negative spillovers from the oil sector, and lower oil revenues caused fiscal and financial sector effects (government domestic arrears to suppliers and foreign exchange backlogs) that adversely impacted non-oil activity.
- Oil sector decomposition:
  - Oil sector exhibits both structural (trend) and cyclical weakness; the cyclical weakness is mainly due to maintenance operations and is expected to be temporary.
  - The declining trend—largely from underinvestment to offset declining oil fields—has broadly stabilized and begun to stabilize since 2022, supported by authorities’ robust investment projections in the sector.
  - Real oil prices (Angola’s oil prices adjusted for US inflation) appear aligned with trend, indicating no major misalignment in current pricing of Angola’s oil.
- Note: Figure 2 decomposes potential output of oil and non-oil sectors; the direct oil contribution is presented while total oil sector contribution is higher when including indirect effects via non-oil sector.

### E. The Impulse Response Analysis
- Approach:
  - Impulse response analysis simulates shocks from steady state with variables expressed as percent deviations from steady states; shocks simulated include (i) a hike in monetary policy rate; (ii) an aggregate demand shock with lagged monetary response; (iii) an international oil price shock.
- Simulation results (summarized):
  - Monetary policy shock (Figure 4):
    - A 1 percentage points unanticipated increase in the BNA’s key monetary policy rate is passed to interbank and real rates.
    - Tighter policy leads to nominal and real exchange rate appreciation.
    - Cumulative nominal exchange rate appreciation of 0.2 percent over the medium-term.
    - Impact on inflation estimated at 0.2 percent over a year, with full transmission materializing.
    - Transmission assumes interbank rates align with policy rates.
  - Delayed monetary policy response (Figure 5):
    - Comparison following a 1 percent of GDP demand shock: a two-quarter delay in monetary policy response leads to roughly 0.3 percentage points higher inflation and 0.5 percentage points depreciation of the nominal exchange rate in a year.
    - Depreciation pressures reduce reserves and increase the currency risk premium relative to the counterfactual.
    - Results align with Clarida and others (1999): passive/delayed monetary policy amplifies fluctuations; timely interest-rate adjustments help stabilize economy and return variables to steady states.
  - Oil price shock (Figure 6):
    - A 10 percent decrease in the real oil price (Angola’s oil prices adjusted for US inflation) causes a contraction in output (negative output gap) while raising inflation.
    - Inflation rises due to currency depreciation from lower external flows.
    - Monetary policy, in the simulation, foregoes output and raises interest rates to prevent second-round inflationary effects—reflecting a negative supply-side/cost-push shock and highlighting policy trade-offs.
    - The extent of optimal interest rate response depends crucially on monetary policy credibility.

### F. Policy Implications (high-level points implied by analysis)
- The presence of large oil-induced external shocks and high exchange rate pass-through implies:
  - The transition to an inflation-targeting framework and increased central bank autonomy (BNA Law 2021) are critical steps, but operational challenges remain (e.g., aligning interbank rates with policy rates, managing excess liquidity).
  - Timely monetary policy responses matter: delayed reaction to demand shocks materially increases inflation, nominal depreciation, and reserve losses.
  - In negative oil price shocks, central bank faces a trade-off between stabilizing inflation (via higher interest rates) and supporting output; the optimal stance depends on monetary policy credibility.
  - Managing reserves and reducing reserve deviations helps limit currency risk premium increases and exchange rate pressure.

*Source: Introduction — 1agoea2024002.*

### 23.      This section examines the role of monetary policy credibility for the optimal response

### This section examines the role of monetary policy credibility for the optimal response to an oil price shock

### Monetary policy credibility: simulation findings
- Simulation compares two scenarios in the context of an oil price shock: baseline monetary policy credibility and relatively higher credibility (Figure 3).
- To evaluate varying credibility levels, the weight on the forward-looking component in inflation expectations is adjusted; as credibility increases, agents become more forward looking and inflation expectations become more anchored.
- Key quantified outcomes from the higher credibility scenario:
  - The need for interest rate hikes in response to an adverse supply shock is lower by almost one-third at its peak than in the baseline scenario.
  - The higher credibility scenario yields a more contained inflationary outturn and lower cumulative output loss over the medium-term.
- Note: A higher demand shock and longer delay in monetary policy response leads to higher economic volatility and inflation.

### Policy implications (paras 24–27)
- The analytical framework enables simulation of key external and domestic shocks in Angola’s oil intensive economy and highlights heightened trade-offs for the central bank during adverse external shocks such as an oil price shock:
  - Stabilizing output versus mitigating second-round inflationary effects.
  - Maintaining a stable exchange rate versus preserving international reserves.
- Costs of delaying monetary policy reaction to a demand shock are quantified in terms of economic volatility; monetary policy decisions affect the economy with a time lag, so a forward-looking analysis is valuable for designing optimal policies.
- The BNA’s continued progress and commitment to developing the Forecasting and Policy Analysis System (FPAS) is a significant step toward supporting forward-looking monetary policy.
- Improvements in the monetary policy framework and credibility lower output costs and economic volatility during shock periods by reducing the actual need for interest rate hikes when expectations are credibly anchored.
- Preconditions for a central bank to harness credibility benefits:
  - Demonstrated strong track record of decisive action when warranted.
  - Effective transmission mechanism—including aligning interbank rates with the announced policy rate.
  - Robust communication framework.
- Improving interbank liquidity management is a key priority; recent BNA measures include increasing mandatory reserve requirements and removing the custody fee on commercial banks' excess balances at the central bank to reduce downward pressure on interbank rates.
- Further steps recommended to align interbank markets with the policy rate:
  - Greater cooperation between fiscal and monetary authorities to coordinate money market operations.
  - Separate the maturity lines of their instruments.
  - Develop money markets.
- Reference note: See 2023 Angola Article IV Consultations Staff Report, Annex V: Improving BNA’s Communication Channels.

### Extended results and calibrated parameters (figures and steady states)
- Figures referenced: Figure 4 (Monetary Policy Response Shock), Figure 5 (Delayed Monetary Policy Response to Demand Shock), Figure 6 (An Oil Price Shock), Figure 7 (Calibrated Parameters (Behavioral Equations)), Figure 8 (Bayesian Estimation of Key Parameters), Figure 9 (Model Steady States).
- Selected calibrated parameters (Behavioral Equations) as presented:
  - Headline inflation credibility parameters:
    - 훽ଵ 0.55
    - a1 0.65
    - 훿 0.5
    - 훽ଶ 0.1
    - a2 0.1
    - 훽ଷ 0.15
    - a3 0.1
    - Oil GDP weight 훽ସ 0.2
    - a4 0.05
    - 휔 0.35
    - a5 0.05
  - Exchange rate / interest rate / reserves parameters:
    - 휌௦ 0.75
    - c1 0.5
    - 휃 0.6
    - c2 2
    - 훿 0.9
    - c3 0.3
    - 휌௣௥௘௠ 0.8
    - 휓௠ 0.5
- Model steady states (Policy Variables Domestic / Foreign):
  - Inflation target: 7 / 2
  - Neutral real interest rate: 2.5 / 0.5
  - Neutral nominal interest rate: 9.5 / 2.5
  - Potential growth: non-oil sector 3.5
  - Potential growth: oil sector 2
  - Potential growth: total 3 / 2
  - Exchange rates:
    - Real exchange rate depreciation -2
    - Nominal exchange rate depreciation 3
  - Country risk premium 4
  - Terms of trade improvement 0
  - Informal market exchange rate spread 5

### Gender gaps and potential growth (selected findings)
- Gender gaps are particularly salient in education and informal employment:
  - Expected years of schooling: women 7 years; men 9.2 years.
  - Informal employment: women 89.8 percent; men 71.2 percent.
  - Female labor force participation gap relatively reduced: about 4 percentage points.
- Simulations indicate that policies to increase female education attainment and prevent early pregnancies could yield an additional annual GDP growth rate increase ranging from 0.17 to 0.21 percentage points.
- Stylized facts and inequalities:
  - Female Labor Force Participation (FLFP) in Angola is more than 70 percent (2022), leading to an FPLP gender gap of -4 percentage points in 2022 versus regional and global averages: sub-Saharan Africa -12 pp, upper middle-income countries -17 pp, world -25 pp.
  - Almost 90 percent of women employed in the informal sector (88 percent in 2022).
  - Angola’s overall income gap between men and women stood at 22 percent (women earn 78 cents for every kwanza earned by men).
  - Adolescent birth rate 138.4 births per 1,000 women ages 15–19.
  - Child marriage rate: 30 percent of women aged 20 to 24 married or in union before age 18.
- Education and STEM:
  - Between 2015 and 2018 the weight of STEM professions in total professional graduations fell from 23 percent to 11 percent.
  - The proportion of females graduating in STEM professions declined by 28 percentage points between 2015 and 2018 (from about 33 percent to less than 5 percent); for males the decline was 25 percentage points (from about 37 percent to 12 percent).
- Angola’s Global Gender Gap Index (2022) position: 125th out of 146 countries; Education Attainment score 0.69.
- Selected policy priorities noted: increase female education attainment, prevent early pregnancies, ramp up gender budgeting to ensure interventions are well-funded and effective.

*Source: 1agoea2024002 - 23.      This section examines the role of monetary policy credibility for the optimal response (IMF).*

### 9.      Model. To illustrate the impact of closing gender gaps on potential GDP growth, we employ

### 9.      Model. To illustrate the impact of closing gender gaps on potential GDP growth, we employ

### Production function framework and labor specification
- Cobb-Douglas production function:
  - Y_t = A_t K_t^(α) L_t^(1−α)  (equation as presented)
- Variables:
  - Y_t: GDP in real terms
  - A_t: total factor productivity
  - K_t: stock of capital
  - L_t: effective labor input
- Capital stock:
  - Estimated using the perpetual inventory method with a depreciation rate of 0.06.
- Labor input composition:
  - L_t = L_t^f + L_t^m (female and male effective labor summed)
  - Assumption: employment of women does not affect employment of men, and vice versa.
- Learning-adjusted measure of labor input (following Filmer et al., 2020):
  - L_t^f = e^(E_t^f)^R_t^f H_t^f Q_t^f  (as specified)
  - L_t^m = e^(E_t^m)^R_t^m H_t^m Q_t^m  (as specified)
  - Components:
    - E: average learning adjusted years of schooling (LAYS)
    - R: returns to education
    - H: hours worked per year
    - Q: number of employed females (or males)
  - LAYS combines expected years of schooling and learning outcomes (quality).
  - A five-year lag is employed to account for the time it takes for a child to finish education and enter the workforce.

### Scenarios (LAYS gender gap closure between 2023 and 2050)
- Baseline:
  - No policy intervention altering the human capital gap.
  - LAYS continues on current growth trajectory; LAYS ratio between males and females remains constant.
  - Average LAYS is 6.3 by 2050, with males obtaining 7.3 LAYS and females 5.3 LAYS.
- Scenario 1:
  - Policy focuses on increasing female human capital, closing the LAYS gender gap by 2050.
  - Male LAYS follows baseline trajectory.
  - LAYS for both males and females are 7.3 in 2050.
- Scenario 2:
  - More aggressive policy: education gaps closed in 10 years while male education continues baseline trajectory.
  - Female LAYS reaches 5.7 by 2032 (closing the education gap), and both sexes attain 7.3 LAYS by 2050.
- Data construction note:
  - Where LAYS data is limited for Angola, estimates use expected years of schooling (EYS) from HDI.
  - Average ratio of LAYS to EYS for years available (2017, 2018, 2020) applied to EYS data 1990–2021.

### Results and key statistics
- Baseline scenario:
  - Global LAYS increases at a rate of 1.4 percent per year.
  - Male LAYS reaches 7.3 years and female LAYS reaches 5.3 years by end of projection.
  - Potential output estimated to grow at an average rate of 4.1 percent per year.
- Scenario 1 (close gap by 2050):
  - Female LAYS grows at an average annual rate of 2 percent; male LAYS maintains baseline growth.
  - Male and female LAYS reach 7.3 years by 2050.
  - Potential output increases to 4.3 percent on average—an increase of 0.17 percentage points compared to the baseline.
- Scenario 2 (close gap by 2032, ten-year closure):
  - Female LAYS reaches 5.7 years by 2032, catching up with males; both reach 7.3 years by 2050.
  - Annual potential output growth of 4.36 percent.
- Aggregate impact statements:
  - Earlier closure of the education gap yields sooner and larger gains to human capital and output.
  - Improvement of real GDP between 4.6 and 5.7 percent by 2050 when eliminating gender gaps in education (simulation range reported).
- Qualitative finding:
  - Reducing the gender education gap can lead to higher potential output, consistent with empirical evidence (Kochhar, Jain-Chandra and Newiak 2016).
  - More educated women are more likely to enter the formal sector and obtain better-paying, more productive jobs (Ouedraogo and Gomes 2023).

### Limitations and further analysis
- Need for further analytical study on:
  - Impact of reducing gender education gap on female informal employment.
  - Effects of the informal economy on GDP and how measures to increase informal economy productivity and formalization would change macroeconomic gains.
- Simulations likely underestimate the impact on reducing female employment in the informal sector due to measurement difficulties and data availability.

### Policies to close gender gaps in Angola (targeted interventions)
- Identified main barriers to female schooling:
  - Transaction and opportunity costs: additional costs (transportation, administrative fees, indirect fees) despite public education being free; girls in low-income households expected to contribute to income-generating activities.
  - Early pregnancy: Angola adolescent fertility rate of 138 births per 1000 women; adolescent mothers face stigma and economic difficulties, reducing likelihood of continuing formal education and increasing entry into unstable, low-quality jobs.
- Authorities' targeted interventions (guided by NDP and supported projects):
  - Improvement in reproductive health services (SRH): sexual reproductive health services, contraceptives, prevention of early marriage, targeting about 300 thousand adolescent girls and boys.
  - Expansion of accelerated learning programs: allow youth and adults with age/class mismatch to complete primary and secondary education via daytime and evening classes.
  - Financial incentives for adolescent girls:
    - Scholarships for children attending cycle 1 of secondary school.
    - Extra bonus of approximately US$38 (AOA 25,000) for girls registering for the first time in cycle 1 of secondary school.
    - Scholarship duration: three years; payments via the Kwenda mechanism.
  - Expand education supply and support high-quality teaching: construct new schools or rehabilitate/expand existing schools and strengthen instruments to attract best candidates.
- Project financing notes:
  - World Bank support: US$ 250 million project “Girls Empowerment and Learning for All Project”.
  - World Bank allocated US$ 10 million under the project to increase accelerated learning program attendance by 250 thousand people (to a total of 1 million by end-2025).
  - World Bank allocated US$ 38 million under the Girls Empowerment and Learning for All Project (note: allocation figures provided in source).

### Gender budgeting: status and recommendations
- Background:
  - Gender budgeting (GB) uses fiscal policies and PFM tools to promote gender equality (Stotsky, 2006).
  - Gender markers introduced in the 2022 budget using the Gender Equality Marker (GEM) methodology (UN System 2018).
- Recent allocations and trends:
  - Programs with limited consideration of gender equality/women’s empowerment (G1): allocated 14 percent of current expenditures in 2022 and 25 percent in 2023.
  - Programs with significant impact (G2) or principal objective (G3) gender equality/women’s empowerment: increased from 7.8 percent in 2022 to 12 percent of current expenditures in 2023.
  - More than 80 percent of funds targeted to programs with strong impact in reducing gender inequalities: poverty, education, and maternal health.
- Implementation status:
  - Use of gender markers paused in the 2024 budget to recalibrate and align with gender filters in the NDP spanning 2024-27.
- Immediate next steps recommended:
  - (i) Prompt resumption in the use of markers in the 2025 budget and expansion to include all line ministries.
  - (ii) Improve the quality of assessments involved in the use of gender markers.
  - (iii) Increase allocation of budgetary resources to programs with gender impact (in line with NDP priorities).
  - (iv) Strengthen accountability through preparation of a first gender budget statement as a report with the budget proposal on gender-related allocations, expected outcomes in terms of equality, and explanation of last year execution.
  - Assess the impact of fiscal systems on gender equality and address direct or indirect discrimination against women; implement tax policies and incentives to reduce barriers to women’s economic participation (Baer, Cotton and Gavin 2023; M. J. Stotsky 1996).

_Italic: Source: IMF staff estimates and analysis as presented in the chapter text._

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_Source: https://www.imf.org/-/media/files/publications/cr/2024/english/1agoea2024002.pdf_