## 1cmrea2022002

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---

### Sectoral distribution of SOEs: background, scale, and institutional issues
- Key objectives:
  - SDN 2030 aims to raise average annual growth from 4½ percent to 8 percent over 2020-2030, increase employment, reduce poverty, and improve governance.
  - Fiscal objectives: reduce inefficient spending, increase non-oil revenues, and sustain public debt below 50 percent of GDP while providing resources to raise human and physical capital and improve social safety nets.
  - SOEs identified as large economic actors generating direct fiscal costs and indirect growth distortions via differential taxation, administered prices, and preferential sectoral policies.
- Scale and fiscal exposure:
  - SOE assets: CFAF 4,895 billion (20 percent of GDP).
  - SOE employment: around 40 thousand people; about half employed in the agricultural sector.
  - Direct subsidies to SOEs: CFAF 40 billion (0.2 percent of GDP) (not including indirect subsidization such as fiscal arrears).
  - Net results 2020: majority-government SOEs lost CFAF 33 billion in 2020.
  - SOE total debt: 12.6 percent of GDP in 2020.
  - SONARA’s debt: 67 percent of total SOE's public and publicly guaranteed debt.
  - Tax arrears by SOEs: amounting to 1.5 percent of GDP in 2021.
  - Administered prices without compensating public subsidies negatively affect performance of some SOEs (examples: CDC, SODECOTON, SEMRY and CAMPOST).
- Institutional and data challenges:
  - Progress in data collection but consistency and reliability remain limited.
  - Oversight fragmented with overlapping mandates; two centralized agencies exercise ownership functions while ministries perform technical oversight, creating potential conflicts of interest.
  - Reforms launched: 2016 SOE platform; 2017 legal framework with two laws for SOEs and EPs; 2018 comprehensive reform strategy; new decree on remuneration and ministerial circular on financial/operational information; diagnostic studies launched but not finalized.
  - CEMAC Directive 06/11 on Transparency and Governance issued, but implementation weak.

### Macroeconomic implications of SOE distortions
- Distortions and factor pricing:
  - Model calibration indicates in all main sectors (primary, secondary, tertiary) the labor cost faced by SOEs is higher than in non-SOEs, whereas the return to capital is lower — interpreted as above-market wages and below-market returns on capital in SOEs.
- Direct and indirect fiscal costs:
  - Direct fiscal costs include subsidies, recapitalizations, guarantees, and on-lending.
  - Indirect costs include lost tax revenues due to lower GDP and misallocation, and higher input costs for firms due to unreliable SOE provision.

### Simulated policy impact (headline and scenarios)
- Reform definition:
  - Reform scenario: gradual elimination, over a 5-year horizon, of about ½ of direct SOEs subsidization and ¼ of sectoral distortions.
- Simulation headline (15-year horizon):
  - GDP increase: about 25 percent relative to baseline.
  - Fiscal impact: improvement in the fiscal primary balance of about 6 percentage points of baseline GDP.
  - Sectoral rebalancing: substantial expansion of tertiary output, and to a lesser extent secondary output; primary sector output changes little relative to baseline.
  - Note: increase in GDP leads to a significant rise in the real wage; simulation does not incorporate this wage feedback, though higher market wage would likely increase formal employment.

### Model structure (high-level technical description)
- Economic structure:
  - Three sectors: primary, secondary, tertiary (indices i = 1,2,3).
  - Two firm types within each sector: SOEs (j = s) and non-SOEs (j = n).
- Production and aggregation:
  - Firm production uses Cobb-Douglas technology with labor share a_i; firm-level TFP A_ij.
  - Sectoral output y_ij aggregated into Y_i using technology with constant elasticity of substitution 1/φ.
  - GDP aggregates sectoral outputs with constant elasticity of substitution 1/σ and sector weights ω_i.
- Distortions (wedges):
  - Within-sector: SOE factor costs equal τ_i^w w (labor) and τ_i^r r (capital).
  - Cross-sector: sectoral outputs Y_i sold at price P_i but aggregated into GDP at price τ_i P_i.
  - Aggregate output: Y = f(K, L, tau-bar), where tau-bar collects distortions.
- Fiscal sector and dynamics:
  - Government revenue: tax on GDP at rate χ.
  - Transfers to SOEs per worker and per unit of capital: (τ_i^w − 1) w and (1 − τ_i^r) r.
  - Capital accumulation: K_{t+1} = (1 − δ) K_t + X_t with depreciation δ ≥ 0.
  - Representative household: allocates (1 − χ) Y_t to consumption C_t and investment X_t; supplies inelastic labor L_t = 1; intertemporal elasticity 1/γ; discount factor β < 1.

### Calibration of technological, preference, fiscal, and distortion parameters
- Technology and preferences:
  - Baseline calibration set to φ = σ = 0.5.
  - Sectoral weights vector ω = (0.15, 0.80, 0.25) chosen to match pre-reforms employment distribution.
  - Sectoral labor shares vector a = (0.30, 0.47, 0.65).
  - Capital depreciation rate δ = 0.1.
  - Discount factor β = 0.95.
  - Risk-aversion/utility curvature γ = 2.
- Fiscal and sectoral distortions:
  - Tax rate χ = 0.1 (matches tax revenue ratio in Cameroon).
  - Sectoral distortions vector τ = (1,5,2) calibrated using ratio of sectoral product taxes to sectoral value added.
- Within-sector SOE distortions and TFP derivation:
  - Labor distortions τ^l (SOE labor-price distortions) derived from ratio of labor productivity (nominal value added / employment) of SOEs to non-SOEs.
  - Capital distortions τ^k derived from ratios of firms’ nominal value added to capital stocks.
  - Derived values:
    - τ^l = (1.7, 3.4, 11.6).
    - τ^k = (0.06, 0.23, 0.01).
  - TFPs computed as Solow residuals; firm output prices inferred from equilibrium conditions.
- Empirical inputs and calibration notes:
  - Calibration based on 21 SOEs with partial time series of employment and nominal investment over 2009-2019.
  - Employment uses firm-year values for 2016 (or closest year).
  - Capital stocks calculated from investment data using permanent inventory model with sectoral real capital formation time series back to 1993.
  - SONARA excluded from calibration to be conservative.

### Policy reform scenarios (detailed)
- Efficient benchmark: distortions optimal value τ* = 1 at firm and sectoral levels.
- Scenario 1 — Partial removal of SOE subsidization:
  - Remove 1/2 of SOEs subsidization schemes gradually over a 5-year horizon.
  - Each sector i: initial (τ^l, τ^k) changes linearly to reach 1/2(τ^l + 1, τ^k + 1) in year 5.
- Scenario 2 — SOE subsidy removal plus partial sectoral policy reform:
  - In addition to Scenario 1, remove 1/4 of sectoral distortions.
  - Sectoral distortions in year 5 equal 3/4 τ + 1/4 τ*.

### Simulation results and quantitative outcomes
- Aggregate dynamics:
  - GDP increases by about 15 percent over the first five years while reforms are gradually carried out.
  - One-third of the initial five-year GDP increase is caused by SOEs reform alone; the remainder mostly from reduction of sectoral distortions.
  - By year 15 (10 years after completion of reforms), GDP has grown by about 25 percent.
  - At reform announcement (year 0), consumption jumps and investment falls temporarily; by year 2 investment grows beyond pre-reform level and continues to increase.
- Fiscal effects:
  - Fiscal balance improves by about 6 percentage points (of baseline GDP).
  - Two-thirds of the fiscal improvement attributed to the SOEs subsidy reform alone.
- Sectoral and factor reallocations:
  - Strong rebalancing toward tertiary sector and, to a lesser extent, secondary sector.
  - Primary sector value-added and capital stocks largely unaffected after 15 years.
  - Primary sector employment falls by as much as 20 percent, offset by increase in sectoral TFP from SOEs subsidy reform.
- Headline summary:
  - Gradual elimination of 1/2 of estimated subsidies to SOEs and removal of 1/4 of cross-sectoral distortions would raise GDP by about 25 percent over a 15-year horizon and improve the fiscal deficit by about 6 percentage points of baseline GDP.
- Fiscal accounting caveat:
  - Calculation conservative because reducing SOE distortions toward 1 lowers per-worker wage premium costs but higher market wages following reforms can raise government spending on residual premium rates still in place.

### Main conclusions and policy recommendations on SOEs and competitiveness
- Expected macro effects:
  - Reforms of SOEs subsidization and reductions of sectoral distortions can substantially increase GDP and investment, rebalance the economy toward secondary and tertiary sectors, and improve the fiscal balance without additional fiscal cost by reducing inefficient subsidization and improving sectoral policies.
- Identified distortions:
  - SOEs pay higher wages than non-SOE firms and earn lower returns to capital, implying labor and capital subsidies that distort within-sector allocation and impose direct fiscal costs.
  - Cross-sector distortions skew the economy toward the primary sector, lowering aggregate productivity and discouraging investment.
- Priority measures:
  - Improve governance and transparency of SOEs and public enterprises.
  - Collect complete information on financial relations between public budget and SOEs to assess direct and indirect subsidization.
  - Strengthen and consolidate institutional framework for SOE governance and oversight; adopt performance contracts and improve disclosure of contingent liabilities.
  - Over the medium term, open capital of commercially-oriented SOEs to private sector participation to reduce fiscal risks and refocus public action on goods and services with clear public utility rationale.
- Data and analysis needs:
  - Better SOE gross capital formation data and more disaggregated time series to improve calibration and assess productivity impacts from managerial reforms.

---

### Financial inclusion, digital finance, and microfinance interactions
- Role and benefits:
  - Financial development promotes economic growth, enhances productivity growth and capital accumulation; financial inclusion can reduce income inequality and poverty.
  - Financial inclusion is particularly important for household risk management and for easing financing constraints of MSMEs.
- Fintech and digital finance:
  - Key drivers: access to infrastructure, financial and digital literacy, and institutional quality.
  - Digital finance tends to increase financial inclusion but could present financial stability risks if regulation and supervision lag.
- Mobile money and usage in Cameroon:
  - Active mobile agent outlets per km2: increased from 4 on average in 2014 to 230 in 2020.
  - Active mobile agent outlets per km2: Sub-Saharan average is 503 in 2020.
  - Active mobile agent outlets per capita: grew from 35 agents in 2014 to 709 in 2020; Sub-Saharan Africa average 528 in 2020.
  - Total value of annual mobile money transactions grew from below ½ percentage points in 2014 to about 55 percent of GDP in 2020.
  - Number of transactions per year for 1,000 adults: about 700 in 2014 → about 70,000 in 2020.
  - Number of transactions per active account: about 5 per year in 2014 → about 126 on average in 2020.
  - Composite indicator of Digital Financial Inclusion suggests relatively low level despite good infrastructure and transaction volumes; low usage in areas like utility payments and wages.
- Traditional banking and microfinance access:
  - Commercial bank branches per 100,000 adults: Cameroon 2.15 (2019).
  - Commercial bank branches per 1,000 km2: Cameroon 0.68 (2019).
  - These are about ½ of Sub-Saharan Africa median: 4.02 (branches per 100,000 adults) and 1.28 (branches per 1,000 km2) at end-2019.
  - Microfinance performs better: all microfinance institution branches per 100,000 adults: Cameroon 3.58 (2014) → 4.61 (2019).
  - ATMs per 100,000 adults: Cameroon 0.97 (2014) → 1.45 (2019).
- Empirical finding on mobile money and microfinance:
  - Dynamic panel regressions (23 Sub-Saharan countries, 2011-2020) find mobile banking activity negatively associated with micro-finance institutions’ lending.
  - Magnitude: a 1 standard deviation increase in mobile banking activity is associated with lower credit by micro-finance institutions of 0.6 percentage of GDP (column (1)) and 0.8 percentage of GDP (column (3)).
  - Negative association becomes statistically insignificant when country fixed effects are omitted, indicating within-country correlations.
- Cointegration evidence for growth:
  - Quarterly data 2013-2020: variables are I(1); Johansen test finds at least one long-run relationship.
  - Long-run elasticity of GDP per capita with respect to credit to the private sector: 0.36 (confidence interval minimum 0.05, maximum 0.68).
  - Long-run elasticity of GDP per capita with respect to investment: 0.11.
  - Interpretation: 1 percent increase in ratio of credit to the private sector to GDP results in 0.36 percent increase in GDP per capita in the long run.
- Policy implications:
  - Despite rapid mobile money growth, access to financial services remains low; reforms to improve financial inclusion for individuals and firms should be accelerated.
  - A sound banking system is a prerequisite for progress in financial inclusion and financial deepening.
  - Mobile banking supports payments and transfers and could further support intermediation activities of microfinance institutions.

### Banking-sector reforms and financial inclusion policy actions
- Suggested actions under the National Strategy for Inclusive Finance 2021-2025:
  - Improve financial literacy and land registries.
  - Create credit bureaus and strengthen information-sharing institutions.
  - Strengthen mobile banking infrastructure and complementarities with the banking system for microconsumer loans.
  - Review and phase out new taxes inconsistent with inclusion: the income tax on non-profit micro-finance institutions and the tax on mobile transactions.
- Banking-sector reforms recommended:
  - (i) Restructure the two failed banks.
  - (ii) Continue implementation of the 2019 law allowing a clean-up of past NPLs; hire and train judges and clerks and make specialized commercial courts operational.
  - (iii) Review tax policies (including VAT on interest revenues, and taxation of provisions at write-off) in upcoming tax diagnostic to facilitate intermediation.
  - (iv) Further strengthen SRC resources and organizational structure consistent with mandates.

---

### Potential growth, public investment efficiency, and policy priorities
- Potential growth estimates:
  - Statistical filters (1993–2018): potential output growth between 3.9 to 4.4 percent; average 4.8 percent over last decade; maximum 5.5 percent in 2014 and 2015.
  - Production-function (1993–2019): potential output growth between 3.2 percent and 5.4 percent; average 4.5 percent over last decade; COVID-19 reduces potential growth to 2.2 percent in 2020.
  - TFP growth averaged 0.02 percent over the last decade and contributed negatively to GDP growth over the last 25 years.
  - Physical capital grew by 5.5 percent on average from 1994 to 2020 and contributed 3 percentage points to potential growth.
  - MFMOD structural model: potential output growth 3.5–5.7 percent (2015–2019), average 4.3 percent; 2020 down to 0.7 percent; under current policies potential growth expected to increase to 5.1 percent in 2026 and to 6 percent in 2029; decelerate to 5.6 percent by 2040.
- Static Cobb-Douglas simulation:
  - Increasing TFP growth by 2 percent per year and accelerating physical capital from 5.5 to 6 percent per year would lead to a 2.5 percentage point increase in real GDP growth over the medium-to-long term (to 8.2 percent).
- Public investment efficiency and PIMA:
  - 2020 PIMA: Cameroon’s relative efficiency gap with respect to the efficiency frontier is 60 percent (compared to 40 percent for SSA countries).
  - DIG model: scaling up public investment by 2 percentage points of GDP with favorable financing yields by 2034: private consumption increase of 26 percent; investment increase of 16 percent; GDP increase of 4.8 percent.
- Policy priorities to boost TFP and potential growth:
  - Implement accelerated, continuous, and well-sequenced structural reforms.
  - Improve macroeconomic stability (inflation, government debt, public employment).
  - Enhance openness, technology transfer, labor quality and allocation, female labor force participation, sectoral diversification, monetary and financial development, and institutional/regulatory quality.
  - Improve public investment efficiency: streamline procedures, sign performance contracts, specify project selection criteria, clarify land expropriation and compensation, and improve governance and budgetary discipline.

### Infrastructure, logistics, innovation, gender, and competitiveness
- Infrastructure and logistics constraints:
  - Perception of infrastructure quality in Cameroon lags behind SSA average.
  - Roads along regional corridors poor, poorly maintained, weak axle weight enforcement; many segments dirt/gravel and impassable during rainy season.
  - Checkpoints and roadblocks occur every 20 kilometers on average, increasing transit times and transport costs.
  - Existence of a trucking cartel operating in CEMAC leading to excessive markups.
- Recommended infrastructure and logistics reforms:
  - Scale up efficient road investment and maintenance with sufficient financing.
  - Remove or reduce road checkpoints and informal payments.
  - Strengthen port management and establish data-based performance monitoring.
  - Deregulate and liberalize trucking industry; facilitate financing for transporters to renew fleets.
- Gender equality:
  - Closing gender gaps: bringing Gender Inequality Index score to SSA average could increase GDP per capita growth by 0.25 percentage points; bringing it to averages of 5-main emerging Asian countries or 5 leading Latin American economies could raise GDP per capita growth by more than 1 percentage point.
  - Recommended measures: strengthen gender budgeting, translate treaties into domestic law, modify laws to facilitate female entrepreneurship, ease women’s access to property, enhance education, and support increased women’s political participation.
- Innovation and economic complexity:
  - Economic complexity linked to growth; Cameroon diversified but industrial production weak.
  - Sectoral composition: primary 19 percent, secondary 25 percent, tertiary 56 percent of economic activity.
  - Cameroon scores poorly on Economic Complexity Index due to dependence on primary commodities.
  - Global Innovation Index (2020): Cameroon performs better than SSA average in Human capital & research, Business sophistication, Knowledge & technology outputs; worse in Institutions, Infrastructure, Market sophistication, Creative outputs.
  - Policy implications: strengthen education and professional training, promote R&D, on-the-job training, and university/industry collaboration to boost enterprise innovation.

*Source: Cameroonian Authorities and IMF staff estimates (February 10, 2022); IMF staff calculations and model description contained in the chapter.*

### 1. Sectoral Distribution of SOEs _________________________________________________________ 6

### 1. Sectoral Distribution of SOEs

### A. Key background and objectives
- Cameroon’s National Development Strategy (SDN 2030) aims to raise average annual growth from 4½ percent to 8 percent over 2020-2030, increase employment, reduce poverty, and improve governance.
- Fiscal objectives: reduce inefficient spending, increase non-oil revenues, and sustain public debt below 50 percent of GDP while providing resources to raise human and physical capital and improve social safety nets.
- SOEs are identified as large economic actors that generate significant direct fiscal costs and indirect growth distortions through differential taxation, administered prices, and other preferential sectoral policies.

### B. SOEs: scale and fiscal exposure
- SOE assets: CFAF 4,895 billion (20 percent of GDP).
- SOE employment: around 40 thousand people, about half employed in the agricultural sector.
- Direct subsidies to SOEs: CFAF 40 billion (0.2 percent of GDP) (not including indirect subsidization such as fiscal arrears).
- Net results 2020: SOEs in which the government has majority ownership (>50 percent) lost CFAF 33 billion in 2020.
- SOE total debt: 12.6 percent of GDP in 2020.
- SONARA’s debt: 67 percent of total SOE's public and publicly guaranteed debt.
- Tax arrears by SOEs: amounting to 1.5 percent of GDP in 2021.
- Administered prices without compensating public subsidies negatively affect performance of some SOEs (examples cited: CDC, SODECOTON, SEMRY and CAMPOST).

### C. Institutional and data challenges
- SOE monitoring: progress in data collection but consistency and reliability remain limited.
- Oversight: fragmented institutional setup with overlapping mandates; two centralized agencies exercise ownership functions while ministries perform technical oversight, creating potential conflicts of interest.
- Reforms launched: 2016 SOE platform; 2017 legal framework with two laws for SOEs and EPs; 2018 comprehensive reform strategy; new decree on remuneration and ministerial circular on financial/operational information; diagnostic studies launched but not finalized.
- CEMAC Directive 06/11 on Transparency and Governance issued, but implementation has been weak.

### D. Macroeconomic implications
- SOE inefficiencies produce unreliable provision of goods and services and raise input costs for firms.
- Direct fiscal costs (subsidies, recapitalizations, guarantees, on-lending) and indirect costs (lost tax revenues due to lower GDP and misallocation) are emphasized.
- Differential factor prices in SOEs: model calibration indicates in all main sectors (primary, secondary, tertiary) the labor cost faced by SOEs is higher than in non-SOEs, whereas the return to capital is lower — interpreted as above-market wages and below-market returns on capital in SOEs when compared to non-SOEs.

### E. Simulated policy impact (summary)
- Reform scenario: gradual elimination, over a 5-year horizon, of about ½ of direct SOEs subsidization and ¼ of sectoral distortions.
- Simulated outcomes over a 15-year horizon:
  - GDP increase: about 25 percent relative to baseline.
  - Fiscal impact: improvement in the fiscal primary balance of about 6 percentage points of baseline GDP.
  - Sectoral rebalancing: substantial expansion of tertiary output, and to a lesser extent secondary output; primary sector output changes little relative to baseline.
- Note: The increase in GDP leads to a significant rise in the real wage. Although this effect is not considered in the simulation, the higher market wage would very likely lead to an increase in formal employment.

### F. Data needs and caveats
- Results are illustrative and depend on calibration; more detailed information on SOE investment, labor composition, and sectoral policies would sharpen the analysis.
- Better SOE data would allow assessment of productivity impacts from managerial reforms; systematic overview of sectoral policies would help identify where government intervention can be rolled back or strengthened.

---

### G. Model structure (high-level technical description)
- Economic structure:
  - Three sectors: primary, secondary, tertiary (indices i = 1,2,3).
  - Two firm types within each sector: SOEs (j = s) and non-SOEs (j = n).
- Production:
  - Firm production uses Cobb-Douglas technology with labor share denoted a_i; labor share can differ across sectors.
  - Firm-level TFP denoted A_ij.
  - Sectoral output y_ij (SOEs and non-SOEs) aggregated into homogeneous sectoral output Y_i using technology with constant elasticity of substitution 1/φ.
  - GDP Y aggregates sectoral outputs with constant elasticity of substitution 1/σ and sector weights ω_i.
- Distortions (wedges):
  - Within-sector distortions: SOE factor costs equal τ_i^w w (labor) and τ_i^r r (capital). Example: if τ_i^w > 1 and τ_i^r < 1 then SOEs face above-market labor costs and below-market rental rates.
  - Cross-sector distortions: sectoral outputs Y_i sold at price P_i but aggregated into GDP at price τ_i P_i; example: if τ_1 > τ_2 then output is distorted in favor of the primary sector relative to the secondary sector.
  - Aggregate output written as Y = f(K, L, tau-bar), where tau-bar collects within- and cross-sector distortions.
- Fiscal sector:
  - Government revenue: tax on GDP at rate χ (revenue-to-GDP ratio).
  - Government spending: covers SOE subsidies and public good G. Per-worker and per-unit-of-capital transfers to SOEs in sector i are (τ_i^w − 1) w and (1 − τ_i^r) r respectively.
  - Interpretation: G can represent the government’s net fiscal balance if government balances its budget each year.
- Dynamics:
  - Capital accumulation: K_{t+1} = (1 − δ) K_t + X_t with δ ≥ 0 as depreciation rate.
  - Representative household: allocates net output (1 − χ) Y_t to consumption C_t and investment X_t; supplies inelastic labor L_t = 1; period utility additively separable between private consumption C_t and public good G_t; intertemporal elasticity of substitution 1/γ; discount factor β < 1.

---

*Source: Cameroonian Authorities and IMF staff estimates (February 10, 2022).*

### 15.      Calibration of technological and preference parameters. As emphasized by Brandt, Tombe

### 1cmrea2022002 - 15.      Calibration of technological and preference parameters. As emphasized by Brandt, Tombe

### Calibration of technological and preference parameters
- Technology
  - Baseline calibration set to 휙 = 휎 = 0.5.
  - Sectoral weights vector 휔 = (0.15, 0.80, 0.25) chosen so pre-reforms employment distribution matches empirical counterpart.
  - Sectoral labor shares vector 푎 = (0.30, 0.47, 0.65) set to match sectoral ratios of labor compensation to value added from US data (assumes US economy relatively undistorted).
  - Capital depreciation rate 훿 = 0.1.
- Preferences
  - Discount factor 훽 = 0.95.
  - Risk-aversion/utility curvature 훾 = 2.

### Calibration of fiscal and distortion parameters, SOEs measurement
- Tax and sectoral distortion benchmarks
  - Tax rate 휒 = 0.1 (set to match tax revenue ratio in Cameroon).
  - Sectoral distortions vector calibrated to 휏 = (1,5,2) using ratio of sectoral product taxes to sectoral value added.
    - This reflects product tax ratio for secondary sector five times larger than primary; agricultural sector receives highest preferential treatment among the three.
- Within-sector distortions and TFP
  - 휏^l (labor-price distortions at SOEs) equals ratio of labor productivity (nominal value added / employment) of SOEs to non-SOEs.
  - 휏^k (capital-price distortions at SOEs) derived from ratios of firms’ nominal value added to capital stocks.
  - Derived values (by matching equilibrium equations to data):
    - Vector of labor distortions 휏^l = (1.7, 3.4, 11.6).
    - Vector of capital distortions 휏^k = (0.06, 0.23, 0.01).
  - TFPs computed as Solow residuals; firm output prices are inferred from equilibrium conditions (price of output not directly observed).
- Empirical inputs for SOEs and non-SOEs
  - Calibration based on 21 SOEs with partial time series of employment and nominal investment over 2009-2019 provided by authorities.
  - SOEs assigned to three sectors (Primary, Secondary, Tertiary) as in Table 1.
  - Employment uses firm-year values for 2016 (or closest available year).
  - Capital stocks calculated from investment data:
    - For each sector, average nominal investment of SOEs during 2009-2009 divided by average nominal gross capital formation (from national accounts) over same period to infer SOEs’ share of sector capital.
    - Total capital stock for sector in 2019 calculated using permanent inventory model with sectoral real capital formation time series back to 1993.
  - SONARA excluded from calibration to be conservative.

### Policy reform scenarios
- Definitions
  - Efficient benchmark: distortions optimal value 휏* = 1 at firm and sectoral levels (휏^l = 휏^k = 휏 = 휏* = 1).
- Scenario 1: Partial removal of SOEs subsidization
  - Remove 1/2 of SOEs subsidization schemes gradually over a 5-year horizon.
  - Each sector i: initial calibrated vector (휏^l, 휏^k) changes linearly and reaches 1/2(휏^l, 휏^k) + 1/2(휏*, 휏*) = 1/2(휏^l + 1, 휏^k + 1) in year 5 (described in text as reaching (1/2 휏 + 1/2 휏*) in year 5).
- Scenario 2: SOEs subsidy removal plus partial sectoral policy reform
  - In addition to Scenario 1, remove 1/4 of sectoral distortions.
  - Sectoral distortions in year 5 equal 3/4 휏 + 1/4 휏* (i.e., 3/4 of calibrated value plus 1/4 of optimal value).

### Simulation results and key quantitative outcomes
- Aggregate GDP and dynamics
  - GDP increases by about 15 percent over the first five years while reforms are gradually carried out.
  - One-third of the initial five-year GDP increase is caused by SOEs reform alone; remainder mostly from reduction of sectoral distortions.
  - By year 15 (10 years after completion of reforms), GDP has grown by about 25 percent.
  - At reform announcement (year 0), consumption jumps and investment falls temporarily due to forward-looking consumption behavior; by year 2 investment grows beyond pre-reform level and continues to increase through the simulation horizon.
- Fiscal effects
  - Fiscal balance improves by about 6 percentage points (of baseline GDP).
  - Two-thirds of the fiscal improvement attributed to the SOEs subsidy reform alone (conservative calculation recognizing offsetting forces on government spending).
- Sectoral rebalancing and factor reallocations
  - Economy strongly rebalances toward tertiary sector and, to a lesser extent, secondary sector.
  - Relative expansion of tertiary sector largely due to large productivity gains from SOEs subsidy reform.
  - Expansion of secondary sector driven entirely by rebalancing of sectoral demand after reduction in sectoral distortions.
  - After 15 years:
    - Primary sector value-added and capital stocks largely unaffected.
    - Primary sector employment falls by as much as 20 percent, offset by increase in sectoral TFP from SOEs subsidy reform.
- Summary simulation headline
  - Gradual elimination of 1/2 of estimated subsidies to SOEs and removal of 1/4 of cross-sectoral distortions would raise GDP by about 25 percent over a 15-year horizon and improve the fiscal deficit by about 6 percentage points of baseline GDP.
- Notes on fiscal accounting of subsidy reform
  - Calculation is conservative because reducing SOE distortion parameters toward 1 lowers per-worker wage premium costs but higher market wages following reforms can raise government spending on residual premium rates still in place.

### Model structure (concise)
- Firm and sector production
  - Firms produce according to CES-like production functions with sectoral aggregators and GDP aggregator as specified in Table A.1 and Annex equations.
  - Relative distortions of SOEs input prices calibrated using equilibrium equations relating prices, value added, and inputs.
  - Aggregate output Y expressed as a function f(K,L,τ̅) that aggregates sectoral outputs with TFPs endogenously derived given distortions.
- Factor allocation and equilibrium
  - Equilibrium conditions yield sectoral capital and labor allocations K_i and L_i via equations (1)-(7) in Annex.
  - For given aggregate K and L and vector τ̅ of SOE and sectoral distortions, sectoral TFP A_i and outputs Y_i are derived.
- Households and capital accumulation
  - Household problem solved via standard value function iteration with modified aggregate production function (1-χ) f(K,L,τ) to account for fraction of output not appropriated by household.

### Main conclusions and policy recommendations
- Reforms of SOEs subsidization and reductions of sectoral distortions can:
  - Substantially increase GDP and investment.
  - Rebalance the economy toward secondary and tertiary sectors.
  - Improve the fiscal balance without additional fiscal cost by reducing inefficient subsidization and improving quality of sectoral policies.
- Identified distortions and impacts
  - SOEs pay higher wages than non-SOE firms and earn lower returns to capital, implying labor and capital subsidies that distort within-sector allocation and impose direct fiscal costs.
  - Cross-sector distortions skew the economy toward the primary sector, lowering aggregate productivity and discouraging investment.
- Recommended priority measures for a successful reform strategy
  - Improve governance and transparency of SOEs and public enterprises.
  - Collect complete information on financial relations between public budget and SOEs to assess direct and indirect subsidization.
  - Strengthen and consolidate institutional framework for SOEs governance and oversight to improve management practices and align SOE operations with financial and non-financial policy priorities.
  - Over the medium term, open capital of commercially-oriented SOEs to private sector participation to reduce fiscal risks and refocus public action on goods and services with clear public utility rationale.
- Data and analysis improvements suggested
  - Better and more reliable data on SOEs gross capital formation to improve calibration and enable sharper assessment of SOEs performance.
  - More disaggregated time series on gross capital formation to capture complementarities or substitutability between SOE-produced goods and non-SOE goods.
  - Potential to calibrate comprehensive SOE reform scenarios including managerial improvements and SOE productivity increases.

*Source: IMF staff calculations and model description contained in the chapter.*

### 3. Financial inclusion plays an important role in economic development. The broad

### 3. Financial inclusion plays an important role in economic development. The broad

### Role of financial inclusion and financial development
- Financial development promotes economic growth, enhances productivity growth and capital accumulation at macroeconomic and microeconomic levels (Beck et al. (2020)).
- Financial development has been shown to reduce income inequality and poverty.
- Financial inclusion is an aspect of financial development and is potentially associated with the benefits derived from financial development.
- Barajas et al. (2020a): greater financial inclusion is associated with lower income inequality; inclusion of women further reduces inequality.
- Provided quality of regulation and supervision is high, financial inclusion and stability can be pursued simultaneously.
- Structural and policy-related factors affecting financial inclusion include information asymmetries, moral hazard, bank competition, and channeling government payments through bank accounts (Barajas et al. (2020b)).
- Financial inclusion is particularly important for:
  - Households’ access and use of financial services to manage risks and smooth consumption.
  - Easing financing constraints of micro, small, and medium-sized enterprises (MSMEs).
- Fouejie et al. (2020): financial inclusion for MSMEs is favored by economic and institutional stability, competition, public sector size and government effectiveness, credit information infrastructure (e.g., credit registries), the business environment (e.g., legal frameworks for contract enforcement), and financial supervisory and regulatory capacity.
- Mengistu and Perez-Saiz (2018): bank competition helps financial inclusion.
- IMF (2018a): access to finance is low in Cameroon and depends on socio-economic conditions, including low education levels; the micro-finance sector plays an important role as a source of financial services.

### Fintech and digital finance: drivers, benefits, and risks
- Khera et al. (2021b): access to infrastructure, financial and digital literacy, and quality of institutions are key drivers of digital financial inclusion; digital financial inclusion can help foster economic growth.
- Sahay et al. (2020): digital finance tends to increase financial inclusion, complementing or substituting traditional finance; rapid growth of digital financial services could present financial stability risks if regulation and supervision do not keep pace.
- IMF (2018b): mobile money in Kenya fostered financial inclusion; innovations in mobile technology allowed greater access, diversified providers, helped smooth consumption, reduce poverty and stimulate growth.
- Gershenson et al. (2021): no evidence of association between Fintech development and greater financial inclusion in Latin America; barriers to entry and constraining regulation may hinder Fintech adoption.
- Sy et al. (2020): Fintech is a major force shaping the financial industry in Sub-Saharan Africa and may carry significant gains for financial inclusion by improving financial sector efficiency.
- Policy needs to fill infrastructure gaps (electricity and internet) and ensure regulations keep up with Fintech innovations.

### Financial access and digital money in Cameroon: stylized facts
- Mobile money access improvements:
  - Number of active mobile agent outlets per km2 increased from 4 on average in 2014 to 230 in 2020.
  - The level per km2 is about the median for Sub-Saharan countries but below the Sub-Saharan average of 503 active mobile agent outlets per km2.
  - Number of active mobile agent outlets per capita grew from 35 agents in 2014 to 709 in 2020; above the Sub-Saharan Africa average of 528 in 2020.
- Infrastructure usage:
  - Use of mobile money infrastructure (percent of population with a subscription to mobile cellular phones) and use of internet infrastructure (percent of population using internet) have improved and are at the Sub-Saharan Africa average but well below the world average.
- Mobile money transaction growth:
  - Total value of annual mobile money transactions grew from below ½ percentage points in 2014 to reach about 55 percent of GDP in 2020.
  - Number of transactions per year for 1,000 adults increased from about 700 in 2014 to about 70,000 in 2020.
  - Number of transactions per active account rose from about 5 per year in 2014 to about 126 on average in 2020.
- Composite indicator of Digital Financial Inclusion (Khera et al. (2021)) suggests relatively low level of Digital Financial Inclusion in Cameroon despite good infrastructure and transaction volumes; low usage areas include mobile phone use for utility payments and wages.

### Access to traditional banking and micro-finance in Cameroon
- Commercial banking infrastructure is comparatively low:
  - Commercial bank branches per 100,000 adults: 2.15 on average in 2019 (Cameroon).
  - Commercial bank branches per 1,000 km2: 0.68 on average in 2019 (Cameroon).
  - These remain about ½ of the Sub-Saharan Africa median of 4.02 (branches per 100,000 adults) and 1.28 (branches per 1,000 km2) at end-2019.
- Micro-finance access fares relatively better:
  - All microfinance institution branches per 100,000 adults and per 1,000 km2 are at or above the Sub-Saharan Africa average.
  - Average and median indicators of micro-finance access improved for Sub-Saharan African countries but remained stable in Cameroon since 2014.
- Table indicators (selected values):
  - ATMs per 100,000 adults: Cameroon 0.97 (2014) → 1.45 (2019).
  - ATMs per 1,000 km2: Cameroon 0.54 (2014) → 0.68 (2019).
  - All microfinance institution branches per 100,000 adults: Cameroon 3.58 (2014) → 4.61 (2019).
  - All microfinance institution branches per 1,000 km2: Cameroon 0.97 (2014) → 1.45 (2019).
  - (Source: IMF Financial Access Data as presented in the chapter.)

### Empirical analysis: mobile money and micro-finance institutions (Box 1 and Table 2)
- Empirical approach:
  - Dynamic panel regressions for 23 Sub-Saharan countries, period 2011-2020.
  - Dependent variable: ratio of volume of loans issued by micro-finance institutions to GDP (Credit_GDP).
  - Explanatory variables include real GDP growth and two mobile money activity measures:
    - Ratio of value of mobile money transactions to GDP.
    - Ratio of number of mobile money transactions per capita.
  - Model includes a lagged dependent variable and country fixed effects.
- Key empirical finding:
  - Mobile banking activity is negatively associated with micro-finance institutions’ lending activities, suggesting digital money may substitute for some micro-finance lending.
  - Within countries, more mobile money activity is associated with lower credit by micro-finance institutions, after controlling for real GDP growth, past credit levels, and country fixed effects (columns 1 and 3).
  - Magnitude: a 1 standard deviation increase in mobile banking activity (relevant indicator in column (1) and (3)) is associated with lower credit by micro-finance institutions of 0.6 percentage of GDP (column (1)) and 0.8 percentage of GDP (column (3)).
  - The negative association remains but becomes statistically insignificant when country fixed effects are omitted, suggesting correlations are within-country rather than between-country.
- Model specification (Box 1, equation):
  - Credit_GDP_{i,t} = α ⋅ Credit_GDP_{i,t-1} + β ⋅ growth_{i,t} + δ ⋅ Mobile_Money_{i,t} + F_i + ε_{i,t}
  - Mobile_Money_{i,t} is either ratio of value of mobile money transactions to GDP or ratio of number of mobile money transactions per capita.
- Sample notes:
  - Sample covers 2011-2020 and includes 23 Sub-Saharan countries.
  - Robustness analysis includes dropping 2020 data and estimating Cameroon-specific slope.

### Financial development and economic growth in Cameroon (time series analysis)
- Objective: investigate relationship between economic growth and credit to the private sector using cointegration techniques.
- Variables: real GDP per capita, ratio of gross investment to GDP, trade openness (exports + imports to GDP), credit to the private sector.
- Data frequency and period: quarterly, 2013-2020.
- Statistical tests:
  - Unit root tests (ADF and PP) conclude variables are non-stationary and integrated of order 1.
  - Johansen (1988) multivariate cointegration model finds at least one long-run relationship among variables.
  - Residual of regression is stationary (I(0)).
- Long-run cointegration results (Table 3 and discussion):
  - Positive and statistically significant long-run relationship between GDP per capita and credit to the private sector with elasticity 0.36.
    - Interpretation: a 1 percent increase in the ratio of credit to the private sector to GDP results in a 0.36 percent increase in GDP per capita in the long run.
    - Confidence interval for elasticity: minimum 0.05 and maximum 0.68.
  - Positive and statistically significant long-run relation between GDP per capita and investment with elasticity 0.11.
  - The long-run relationship between private credit and GDP appears to operate through channels other than investment (e.g., productivity growth), though investment also provides a transmission channel since it partly relies on credit from the financial sector.

### Policy implications for Cameroon
- Despite rapid mobile money development, access to financial services in Cameroon remains low; reforms to improve financial inclusion for individuals and firms should be accelerated.
- Experience suggests a sound banking system is a prerequisite for further progress in financial inclusion, which in turn supports financial deepening and economic growth.
- Mobile banking supports financial inclusion—particularly money transfers and payments—but could further support intermediation activities of microfinance institutions.

*Prepared by Mokhtar Benlamine (IMF) and Thierry Tressel (IMF).*

### 14. The new National Strategy for Inclusive Finance 2021-2025 creates an opportunity for

### 1cmrea2022002 - 14. The new National Strategy for Inclusive Finance 2021-2025 creates an opportunity for

### Financial inclusion: opportunities and policy actions
- The National Strategy for Inclusive Finance 2021-2025 could include policy actions to:
  - improve financial literacy and land registries;
  - create credit bureaus and strengthen existing institutions that facilitate information gathering and sharing among banks and microfinance institutions;
  - further strengthen mobile banking, including through phone network infrastructure, and reinforce complementarities with the banking system including regarding microconsumer loans.
- New taxes in the 2022 budget law that should be reviewed and phased out because they are inconsistent with financial inclusion and financial development objectives:
  - the income tax on non-profit micro-finance institutions;
  - the tax on mobile transactions.

### Banking-sector reforms to support financial inclusion and intermediation
- Key reform actions recommended:
  - (i) Restructuring of the two failed banks;
  - (ii) Continue implementation of the 2019 law allowing a clean-up of past NPLs from bank balance sheets; hire and train judges and clerks and make specialized commercial courts operational;
  - (iii) Review tax policies (including VAT on interest revenues, and the taxation of provisions at the time of write-off of bank legacy NPLs) in the context of the upcoming tax diagnostic to facilitate financial intermediation activities of banks and microfinance institutions;
  - (iv) Further strengthen SRC resources and its organizational structure consistent with its mandates.

### Potential growth: measurement results and key statistics
- Statistical filters (HP, Christiano-Fitzgerald, Baxter-King) on 1993–2018 output growth:
  - potential output growth varying between 3.9 to 4.4 percent over the period 1993-2018;
  - average of 4.8 percent over the last decade;
  - maximum of 5.5 percent in 2014 and 2015.
- Production-function (Cobb-Douglass) estimates over 1993–2019:
  - potential output growth varying between 3.2 percent and 5.4 percent over the period 1993-2019;
  - average of 4.5 percent over the last decade;
  - COVID-19 pandemic reduces potential output growth to 2.2 percent in 2020;
  - TFP growth averaged 0.02 percent over the last decade and contributed negatively to GDP growth over the last 25 years;
  - physical capital grew by 5.5 percent on average from 1994 to 2020 and contributed to potential growth by 3 percentage points.
- MFMOD structural model results:
  - potential output growth varied between 3.5 and 5.7 percent from 2015 to 2019, with an average of 4.3 percent;
  - in 2020, the model indicates potential output growth decreased to 0.7 percent due to the pandemic;
  - simulation consistent with current policies: potential growth expected to increase to 5.1 percent in 2026 and to 6 percent in 2029;
  - starting from 2030, growth expected to gradually decelerate to 5.6 percent by 2040.
- Static Cobb-Douglas simulation on increasing TFP and physical capital:
  - an increase of TFP growth by 2 percent per year and an acceleration of physical capital from 5.5 to 6 percent per year would lead to a 2.5 percentage point (to 8.2 percent) increase in real GDP growth over the medium-to-long term.

### Policies and reforms to boost potential growth and productivity (TFP)
- Broad policy priorities to accelerate TFP and growth:
  - implement accelerated, continuous, and well-sequenced structural reforms;
  - address factors affecting productivity, including macroeconomic stability (inflation, government debt, public employment), openness and technology transfer, quality and allocation of labor inputs, female labor force participation, sectoral composition and diversification, monetary and financial development, and institutional/regulatory quality.

### Public investment efficiency and expected gains
- 2020 Public Investment Management Assessment (PIMA):
  - Cameroon’s relative efficiency gap with respect to the efficiency frontier is 60 percent, compared to 40 percent for SSA countries.
- Debt-Investment-Growth (DIG) model simulation:
  - scaling up public investment by 2 percentage points of GDP with favorable financing options yields by 2034:
    - private consumption increase of 26 percent;
    - investment increase of 16 percent;
    - GDP increase of 4.8 percent.
- PIMA recommended sequencing to improve public investment:
  - reduce time and cost of public investments and strengthen controls by reviewing and streamlining texts, processes and actors at each project stage;
  - sign performance contracts and specify project selection criteria;
  - better-defined land expropriation procedures and landowner compensation strategies;
  - improve governance and budgetary discipline by eliminating cumbersome procedures and unscheduled investments.

### SOEs, market competitiveness, and policy simulation outcomes
- Constraints: market concentration, crowding out by large state-owned commercial firms, and government regulations limit competition.
- Policy simulation (gradual reforms):
  - gradual elimination of half of estimated subsidies to SOEs and removal of one-fifth of cross-sectoral distortions would:
    - raise GDP by about 25 percent over a 15-year horizon;
    - reduce the fiscal deficit by about 6 percentage points of baseline GDP;
    - growth driven by expansion of the tertiary and secondary sectors.
- Recommended reforms to promote competitiveness and productivity:
  - reduce formal firms’ entry cost by simplifying administrative procedures, facilitate access to financing, promote fair market competition, and strengthen anti-corruption measures;
  - improve governance and transparency of SOEs by enhancing monitoring, improving disclosure of contingent liabilities, and adopting performance contracts;
  - adopt a stronger and less fragmented institutional framework for SOE governance and oversight;
  - consider liberalizing the capital of commercial SOEs to foster competitiveness and refocus SOEs on clear public utility functions.

### Infrastructure, logistics, and regional trade
- Infrastructure quality and logistics constraints:
  - perception of infrastructure quality in Cameroon lags behind the average for Sub-Sahara Africa;
  - roads along regional corridors are poor, suffer from poor maintenance and weak axle weight regulation enforcement, contributing to high transport costs;
  - large segments of the nine road corridors connecting Cameroon with Nigeria are dirt and gravel and are often impassable during the rainy season;
  - checkpoints and roadblocks occur every 20 kilometers on average, increasing transit times and transport costs;
  - existence of a trucking cartel operating in CEMAC with freight allocation schemes and collusion among a few large freight forwarders, trucking companies, and shippers leading to excessive markups.
- Implication: reducing infrastructure gaps and improving logistics efficiency are critical to lower transport costs and promote regional trade competitiveness.

*IMF staff summary based on chapter content.*

### 14. Reforms aiming at improving the quality of infrastructure and tackling the logistic

### 14. Reforms aiming at improving the quality of infrastructure and tackling the logistic

### Infrastructure and logistics reforms
- Policy objective: Improve quality of infrastructure and tackle logistic services key bottlenecks to promote regional trade and competition.
- Recommended reforms:
  - Scale up efficient road investment and maintenance by ensuring sufficient financing for new projects and the rehabilitation of existing roads.
  - Remove or reduce road check points to reduce informal payments.
  - Strengthen management of Port operations and establish a data-based performance monitoring system.
  - Deregulate and liberalize the trucking industry to improve quality of service and reduce transport prices.
  - Facilitate access to adequate financing for transporters to renew their fleet.

### Financial development and inclusion
- Key finding: Financial development and inclusion are associated with economic development and act as engines of economic growth.
- Empirical evidence for Cameroon:
  - Testing cointegration between credit to the private sector and GDP per capita growth highlights a statistically significant, positive long-run relationship.
  - A one percent increase in the ratio of credit to the private sector to GDP results in a 0.36 percent increase in GDP per capita in the long run, with a coefficient confidence interval varying between 0.05 and 0.68.
- Recommended reforms to accelerate financial and banking soundness and access:
  - Strengthen the soundness of the banking and financial sectors.
  - Improve financial awareness and financial literacy.
  - Facilitate access to financing and mobile money through a review of the key bottlenecks.

### Gender equality reforms
- Key finding: Gender equality is positively associated with higher income, faster growth, more equal income distribution, economic diversification, and better access to finance.
- Simulation results for Cameroon:
  - GDP per capita growth in Cameroon could increase by 0.25 percentage points if its Gender Inequality Index score is brought down to the SSA average.
  - GDP per capita growth could increase by more than 1 percentage point if its Gender Inequality Index score is brought to the average of the 5-main emerging Asian countries (Indonesia, Malaysia, Thailand, Philippines and Vietnam) or the 5 leading Latin American economies (Brazil, Chile, Colombia, Mexico and Peru).
- Recommended reforms to narrow participation gaps:
  - Strengthen ongoing efforts to adopt gender budgeting.
  - Translate approved international treaties that promote gender equality into domestic laws and issue implementation decrees to operationalize existing laws.
  - Modify laws and policies aimed at facilitating female entrepreneurship.
  - Intensify consultations with banks and technical and financial partners to facilitate women's access to financial services and credit.
  - Eliminate remaining legal barriers to women’s access to property and enhance women’s education.
  - Continue to support increased women’s political participation.

### Economic complexity
- Key findings:
  - Economic complexity is positively linked with economic growth; the structure of a country’s product portfolio influences future production capabilities (Hidalgo and Hausmann, 2009).
  - The Economic Complexity Index (ECI) offers an indirect measure of economic complexity by examining the diversity and ubiquity of a country’s exports basket.
  - Cameroon is diversified but industrial production remains weak:
    - Primary, secondary, and tertiary sectors comprise 19, 25, and 56 percent of economic activity, respectively.
    - Manufacturing growth over the last two decades remains centered on rudimentary processing of agricultural and mineral products and is hampered by poor infrastructure quality and an unfavorable business environment.
  - Because of dependence on primary commodities (crude oil and increasingly liquified natural gas), Cameroon scores poorly on the ECI relative to other countries in Africa and among comparably sized economies.
- Recommended policies:
  - Develop public infrastructure and foster private investment to promote dynamism and create opportunities for more sophisticated product production.

### Innovation
- Key findings:
  - Innovation is essential for sustaining and boosting potential growth (Solow, 1956; Schumpeterian concepts).
  - Innovation-led growth requires complementary policies to ensure coherence between structural reforms and measures to mitigate negative sectoral implications and help reallocate workers.
  - Drivers of enterprise innovation (Daksa et al., 2018): engagement in R & D, on-the-job training, and website ownership significantly influence enterprise innovation.
- Cameroon’s performance on the Global Innovation Index (GII) (2020):
  - Performs better than the SSA average in 3 of the 7 GII pillars: Human capital & research, Business sophistication, and Knowledge & technology outputs.
  - Performs worse than the SSA average in 4 pillars: Institutions, Infrastructure, Market sophistication, and Creative outputs.
- Policy implications:
  - Strengthen education and broaden professional formal training of skills, knowledge, and techniques to boost enterprise innovation.

### Innovation strengths and weaknesses (GII-based indicators)
- Strengths:
  - Graduates in science & engineering, %
  - Gross capital formation, % GDP
  - Microfinance gross loans, % GDP
  - Firms offering formal training, %
  - University/industry research collaboration
  - Scientific & technical articles/bn PPP$ GDP
  - Growth rate of PPP$ GDP/worker, %
  - ICT services exports, % total trade
  - Cultural & creative services exports, % total trade
- Weaknesses:
  - Rule of law
  - Global R&D companies, top 3, mn US$
  - QS university ranking, average score top 3
  - Information & communication technologies (ICTs)
  - ICT use
  - ICT services imports, % total trade
  - Market sophistication
  - Ease of protecting minority investors
  - Trade, competition, and market scale
  - Applied tariff rate, weighted avg., %
  - Intellectual property payments, % total trade
  - Creative outputs (intangible assets, global brand value, creative goods exports, Wikipedia edits)

### Conclusion and policy priorities
- Central finding: Cameroon’s growth is constrained by low productivity and inefficiencies; boosting TFP provides an opportunity to grow the economy more efficiently and sustainably.
- Policy package to yield growth dividends (selected):
  - Increase public investment efficiency in line with the recommendations of the 2020 PIMA.
  - Promote market competitiveness and productivity gains.
  - Improve the quality of infrastructure and tackle logistic services key bottlenecks to promote regional trade and competition.
  - Accelerate financial and banking soundness and access.
  - Narrow participation gaps between women and men.
  - Develop public infrastructure and foster private investment for more sophisticated product production.
  - Promote innovation.

*International Monetary Fund — Cameroon: Selected Issues (chapter content).*

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_Source: https://www.imf.org/-/media/files/publications/cr/2022/english/1cmrea2022002.pdf_