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---

### Financial Conditions and Financial Conditions Index (FCI) methodology and variables
- Methodology:
  - Two approaches: Principal components approach (PCA) and Weighted sum VAR (WSA-VAR).
  - PCA: extracts common factor F_t; Bai and Ng (2002) selection criteria used; F_t purged of current and lagged non-hydrocarbon growth by regressing F_t on y_t (year-on-year non-hydrocarbon GDP growth rates).
  - WSA-VAR: weights derived from cumulative impulse-response functions of real non-oil GDP growth following a one-standard deviation shock to each variable; framework follows Swiston (2008).
  - Weights estimated as cumulative 18 - 24 months impulse response of real non-hydrocarbon GDP growth to a one-unit shock to x_jt for the period 2009 to 2023.
  - Data converted to monthly frequency using Chow and Lin (1971); nominal variables deflated with the GDP deflator; variables demeaned and standardized.
  - Identification via Cholesky decomposition with assumption that domestic financial conditions do not have contemporaneous effects on growth and domestic developments do not contemporaneously affect external variables.
  - Lag length of one selected for the VAR based on Schwartz Bayesian Criterion (SBC).
  - Stationarity confirmed using Augmented Dickey-Fuller tests.
- Variables included:
  - Global (external) factors: VIX (global financial market uncertainty), Nominal effective exchange rate (NEER), Oil prices.
  - Domestic factors: Real policy deposit rate, Real market interest spreads (difference between real short-term lending and deposit rates), Growth rates of credit to the private sector, Broad money (M2), 5-year CDS spread (sovereign risk premia), Stock price index, Real estate price index.
- Interpretation and sign expectations:
  - Increase in index = tightening of financial conditions; decrease = loosening.
  - Expected positive contributors to FCI: real policy interest rate, real market spread, risk premium.
  - Expected negative contributors to FCI: money supply, private sector credit, domestic asset prices.
  - For Qatar’s dollar peg, an appreciation in NEER expected to correlate with tightening financial conditions.

### FCI construction performance and decomposition
- WSA-VAR versus PCA:
  - Correlation between WSA-VAR FCI and PCA FCI: about 0.86.
  - WSA-VAR produced more consistent signs and better out-of-sample prediction of major shocks: oil price shock in 2015, Covid-19 in 2020, post-pandemic easing.
  - WSA-VAR predicts a tightening of financial conditions in 2023 more distinctly than PCA.
- FCI decomposition (WSA-VAR):
  - Significant contributors to aggregate FCI fluctuations: real policy deposit rate, monetary conditions (credit and broad money), and external factors driven mainly by oil prices.
  - External factors (oil price cycles) strongly shape Qatar’s external financial conditions.
- Historical dynamics:
  - Eased financial conditions: 2010 to 2014.
  - Tightening beginning in 2014 with the oil price collapse; persistent tight conditions through 2020 (2017 regional blockade and Covid-19).
  - Post-pandemic easing: 2021–2022 due to elevated hydrocarbon prices and increased liquidity; 2022 nominal policy deposit rate increases partly offset by higher inflation related to the World Cup.
  - Tightening again in 2023 as the real policy deposit rate increased (with lower inflation), hydrocarbon prices softened, and global financial conditions deteriorated.
  - 2017 diplomatic rift: wider sovereign risk spreads, downturn in stock market prices, overall tightening of financial conditions.

### FCI as a leading indicator and macroeconomic impacts (Growth-at-Risk link)
- Correlations and signals:
  - FCI correlation with real non-hydrocarbon GDP growth: about -0.66.
  - Credit conditions component closely mirrors the Qatar Central Bank (QCB) bank lending survey index.
- Impulse response magnitudes:
  - Tightening FCIs can reduce inflation on average by 0.3 and up to 0.5 percentage points after 2 years.
  - Tightening FCIs can lead to a contraction in output by 0.8 to 1.0 percentage points.
- Policy signal:
  - GaR framework assessment: current downside risks to Qatar’s baseline non-hydrocarbon growth projections are relatively mild (as assessed in the note).
- Policy implication:
  - Monitor real policy deposit rate, monetary aggregates (credit and M2), sovereign risk premia, and oil-price-driven external conditions for early warning on non-hydrocarbon growth prospects.

---

### Growth-at-Risk (GaR) approach, methodology, and key quantitative results
- GaR overview:
  - Links macro-financial conditions to the probability distribution of future GDP growth, assessing downside and upside risks.
- GaR methodology:
  - Partition financial conditions indicators into subgroups using linear discriminant analysis (LDA).
  - Estimate future output growth as a function of current economic conditions and partitioned financial conditions indicators using quantile regressions.
  - Transform conditional quantile function into a probability density function by fitting a skewed t distribution.
  - Quantile regression specification: regress future GDP growth on current growth (yt), domestic conditions (dom_cond), credit conditions (credit_cond) and external conditions (ext_cond).
  - Quantiles: 0.10, 0.25, 0.50, 0.75, 0.90. Forecast horizons (h): 4, 8 and 12 quarters.
- Financial conditions components (partitioned):
  - Domestic: real policy deposit rate; real market interest spread; Broad money (M2); stock prices; real estate prices; sovereign risk premia.
  - Credit: credit growth (y/y); credit-to-non-hydrocarbon GDP gap / credit-to-non-hydrocarbon impulse (credit-to-non-hydrocarbon GDP gap defined via Hodrick-Prescott filter).
  - External: oil price; VIX; NEER.
- Empirical findings:
  - GaR FCIs align closely with WSA-VAR and PCA FCIs, with bias toward WSA-VAR.
  - Decomposition: domestic and external conditions dominate the FCI since 2009, with credit conditions also contributing.
  - Quantile regression results:
    - Tight domestic conditions (high short-term interest rates) lower lower quantiles of GDP growth distribution over next 4 to 8 quarters, shifting distribution left of the median; effect diminishes over longer horizons.
    - Deterioration of credit conditions (high funding costs and subdued demand) caused credit growth to fall below potential, contributing to short-term risks; effects anticipated to lessen in medium term.
    - Tight external conditions (commodity price fluctuations) significantly worsened non-hydrocarbon growth outlook.
- Baseline calibration and downside quantification:
  - Distributions fitted using a t-skewed distribution calibrated so the mode is consistent with IMF staff baseline.
  - Baseline non-hydrocarbon growth: [1.5] percent for 2024 and [1.9] percent for 2025.
  - Severely adverse scenario (GDP growth falls below the 5th percentile) maximum expected non-hydrocarbon growth rate:
    - 0.5 percent (4 Quarters ahead)
    - 0.1 percent (8 Quarters ahead)
  - Interpretation: relatively mild short- to medium-term downside risk outlook.
- Scenario analyses:
  - Monetary policy easing:
    - Assumption: 100 basis-point reduction in the policy deposit rate by end of 2024, holding other factors constant.
    - QCB action noted: QCB reduced the policy rate by 0.55 bp following the 0.5bp cut in the US Fed rate in September 2024.
    - Result: 100 basis-point reduction improves average future growth; maximum near-term impact around 0.4 percentage points higher; significant reduction in GaR at the 5% percentile; effects dissipate over long term.
  - External shocks:
    - Oil price negative shock: could cost about 0.3-0.4 percentage points of non-hydrocarbon growth for Qatar; GaR at 5 percent could decline to about -0.1.
    - Increase in VIX: marginal negative impact; leftward shift in peak of future growth distribution and slight worsening of GaR.

### GaR conclusions and policy implications
- FCIs from WSA-VAR, PCA, and GaR are closely aligned and highly correlated.
- FCI is essential for assessing current financial conditions and links between financial indicators and future growth distribution—useful for implementing the Third Financial Sector Strategy.
- Domestic conditions provide the strongest short-term signal for non-hydrocarbon GDP growth; external conditions are significant in both short and medium term.
- Alternative scenarios indicate non-hydrocarbon growth could improve following a reduction in the policy deposit rate; growth is primarily influenced by oil prices, with minimal effects from global financial market uncertainty.

---

### Fiscal multipliers, spending allocation, and supporting NDS3 goals (GCC panel and Qatar-specific evidence)
- Estimation strategy and data:
  - Baseline linear model regresses real growth rate of non-hydrocarbon output on real growth rates of central government spending (total, current, capital; j=1,2,3), controls for global output and oil prices, country fixed effects; parameters α, β, γ; country fixed effects δi.
  - Regressions estimated with and without the UAE.
  - Data: annual for 6 GCC countries spanning 1990–2023; Qatar single-country annual data span 1983–2023.
  - Capital stock from Penn World Table up to 2019; 2020–23 extrapolated.
  - Elasticities converted to fiscal multipliers by dividing estimated elasticity by ratio of spending level to non-hydrocarbon output level (average over estimation horizon).
- Key empirical patterns (GCC panel):
  - Real growth rates of non-hydrocarbon output and central government total spending moved more in tandem up to late-2000s; co-movement rose since COVID-19 pandemic.
  - Correlation coefficients for entire sample between output growth and total spending statistically significant for GCC.
  - Country-level: correlations most significant for Qatar, followed by Oman and Saudi Arabia; correlation negative for the UAE.
  - Current spending: more immediate effects. Capital spending: effects emerge with lag and last longer.
- Estimated results (GCC panel baseline):
  - Current spending implied fiscal multiplier: 0.2 (model 2).
  - Capital spending implied long-term fiscal multiplier: close to 0.9 (model 3).
  - When both current and capital included, capital multiplier falls to 0.7 (model 4).
  - Excluding the UAE strengthens effects of current and capital spending on non-hydrocarbon output (models 6–9).
- Capital stock interaction and time variation:
  - Growth impact of capital expenditure smaller when capital stock (capital to GDP) is high (Table 3).
  - Capital stock scaled by GDP, ranked and categorized into low, medium, high; “high” for top 1/3 of observations.
  - Coefficients on lagged capital spending become statistically insignificant when capital stock to GDP ratio is high (model 10); broadly unchanged when threshold lowered to top 1/2 (model 11).
  - Growth impact of capital spending weakens during second half of sample period (model 12).
- Qatar-specific findings:
  - Qatar long-run fiscal multiplier (elasticity without capital stock distinction): close to 1.5 (model 16).
  - When capital stock is relatively high, capital spending does not have significant growth effects in Qatar (model 17).
  - Excluding the UAE makes GCC fiscal spending more impactful generally; Qatar-specific elasticities broadly unchanged (models 18 and 19).
- Single-country (Qatar) approach findings:
  - Static and dynamic regressions confirm important growth effects of lagged capital spending in Qatar.
  - 12-year rolling regressions show growth effects of capital spending waned in recent decade, coinciding with maturation of the infrastructure investment cycle associated with the World Cup.
  - World Cup-related capital spending in the 2010s had renewed growth effects with greater persistence (particularly second lag); effects moderated as investment cycle matured.
  - Rolling approach reduces sample size and model does not control for all determinants; interpretations suggestive.
- Quantitative magnitudes preserved:
  - Panel data span: 1990–2023 for 6 GCC countries.
  - Qatar single-country data span: 1983–2023.
  - Implied fiscal multipliers: current spending 0.2; capital spending close to 0.9; combined capital falls to 0.7; Qatar long-run multiplier close to 1.5 (model 16).
  - Conversion example ratios: average capital spending to non-hydrocarbon output ratio of 0.12; average current spending to non-hydrocarbon output ratio of 0.47 (footnote).
- Policy implications and recommendations:
  - Qatar’s strong capital expenditure multiplier becomes less impactful when stock of capital is high—consistent with GCC panel results and Fouejieu et al. (2018).
  - Reorient public spending toward non-wage items, particularly education that builds human capital.
  - Focus public investment on improving human capital (education and health) for nationals and expatriates.
  - Provide a more conducive environment for businesses to crowd in private investment.
  - Enhance climate sustainability and invest in climate adaptation.
  - Facilitate decarbonization and promote renewables to reach emission reduction targets and smooth energy transition.
  - Improve spending efficiency and target investments with higher expected multipliers (knowledge economy, green investments).
  - Use efficient public spending to crowd in private sector investment and accelerate transition to private sector–driven growth.

---

### The Third National Development Strategy (NDS3), economic complexity, and export diversification
- NDS3 objectives:
  - Build a knowledge-based economy; focus on export diversification as pillar of broader economic diversification.
  - Strengthen business environment, labor markets, fiscal and institutional frameworks; identify “diversification clusters”; harmonize economic zones to attract FDI; promote PPP.
- Qatar’s diversification status:
  - Progress in services exports (transport, tourism, ICT); goods export diversification more limited.
  - Product space for goods exports remained broadly unchanged; main export minerals relatively isolated from products with higher complexity.
  - Qatar’s global shares of relatively complex products (electronics and machinery) are low compared to GCC peers.
- Economic complexity and empirical evidence:
  - Economic complexity positively associated with medium- and high-tech exports; human capital important for export diversification; physical capital not a significant factor for diversification.
  - Quantitative impact (using coefficients from model 6): a one unit improvement in economic complexity (one standard deviation) would increase medium- to high-tech manufacturing exports by 8–9 percentage points of total goods exports (from around 2 percent in 2019).
  - Increasing economic complexity to level of Malaysia would imply increase of 12–13 percentage points in Qatar’s export share of medium- to high-tech manufacturing.
  - Interactions: complexity’s association with per-capita real income amplified by physical and human capital.
  - Simulation illustrative impacts: one standard deviation increase in complexity → 20 percentage points on per-capita real output at average human and physical capital; impact rises to 30 percentage points when human and physical capital one standard deviation above average.
  - Human capital largest contributor to per-capita real output; physical capital important but marginal impact smaller for Qatar due to already large/high-quality physical capital.
- Technology and Innovation Policy (TIP) lessons:
  - TIP can boost economic complexity and per-capita income; support domestic sophisticated industries with export orientation, strict accountability, competition.
  - Comparative typology: Korea (“Moonshots”), Malaysia (“Leapfrog”), Chile (“Snail crawl”); Korea achieved largest gains.
  - Policy insights: pursue strategies supporting domestic sophisticated industries with export orientation; strengthen human capital and regulatory frameworks; attract and retain higher-skilled expatriate workers; improve innovation ecosystem.
- Korea case lessons and implications for Qatar:
  - Korea’s ECI rose from 0.9 in 1995 to 1.9 in 2021; total goods trade rose from $150 billion in 1995 to $803 billion in 2021.
  - Feasible opportunities evolve as capabilities improve; Qatar’s opportunities are relatively distant (close to 1 in the framework) and NDS3 focuses on downstream hydrocarbon products (petrochemical).
  - Complementarity matters: Qatar’s logistics and connectivity assets suggest sectors leveraging them could deliver greater gains.
- Policy priorities:
  - Intensify reforms in human capital, FDI, and competitive environment.
  - Attract and retain skilled expatriate workers (examples: TalentCorp programs).
  - Boost financial depth, institutions, labor market flexibility to attract FDI; maximize domestic spillovers from FDI.
  - Increase R&D spending; Qatar’s patent applications jumped in last decade.
  - Use targeted, time-bound, cost-effective, transparent vertical policies with exit clauses and impact evaluation; avoid policies that harm macroeconomic, fiscal, or external sustainability or generate negative cross-border spillovers.
  - Leverage services selectively (highly productive services) to support high value-added manufacturing exports.

---

### AI strategies, labor market exposure, and productivity scenarios
- AI strategy and initiatives:
  - National Skilling Program (with Microsoft) aims to train 50,000 individuals in advanced digital competencies, including AI and data science by 2025.
  - Digital Talent Development and Retention Program; fast-track visa system for global experts.
  - National HPC Program planned; data centers grew at an annual rate of 45 percent over last decade, reaching total capacity of 23.5 Megawatts (MW).
  - TASMU Smart Qatar program uses AI for food security and infrastructure; Labor Market Information System (LMIS) employs AI for job matching; Hukoomi portal integrated OpenAI GPT.
  - $2.5 billion incentive package announced in May 2024 to boost investment in AI and digital innovations.
  - QIA emphasized AI-related investments; Ooredoo secured a QR 2 billion financing deal in September 2024 to accelerate data center and AI business targeting 120MW data center capacity in five years; Ooredoo partnership with Nvidia.
- Digital access and readiness:
  - Qatar’s digital infrastructure, quality and affordability relatively advanced and comparable to AEs.
  - Digital knowledge lags GCC peers and AEs; Oxford Insights: Qatar outperforms AE average in government vision and data representativeness; close to AEs on digital capacity, infrastructure and human capital; gaps in data availability, governance and ethics, adaptability, maturity and innovation capacity.
- Labor market exposure and preparedness (key statistics):
  - As of 2023, around 3 7 percent of Qatar’s labor force see their jobs exposed to AI applications (note formatting preserved from source), representing an 18-percentage-point increase from 2014.
  - Qatar’s AI exposure level comparable to EM average (33 percent) but lower than AE average (58 percent).
  - Among jobs with high exposure to AI, more than 75 percent also have high ‘complementarity’ with AI.
  - Qatar’s labor force AI preparedness (ratio of high complementarity jobs within the high AI exposure group) is 75 percent compared to EM ( 43 percent) and AE (45 percent).
  - Nationality differentials: About 93 percent of Qatari workers have jobs exposed to AI, compared with 33 percent among non-Qatari workers.
  - Among high AI-exposure jobs held by Qatar nationals, 65 percent have high AI-complementarity and 35 percent are at risk of being replaced by AI.
  - Clerical support workers: large share of at-risk jobs among Qatari nationals; over 80 percent of Qatari clerical workers employed by public sector; 75 percent hold a secondary degree.
  - Workforce trends 2014–2023: total workforce increased by 30 percent; share of high-skilled, AI-benefiting occupations increased by 87 percent on average, driven mainly by private sector employment.
- Productivity scenario analysis:
  - Framework: Labor productivity = gross value added per worker; impact depends on speed of global AI advancement, Qatar’s AI exposure, and domestic AI adoption speed.
  - Baseline scenario assumes gradual gains from comprehensive reforms.
  - Scenarios:
    - Pessimistic: lower-bound global AI productivity impact; below historical trend increase in Qatar’s AI exposure (+4 ppts cumulatively by 2030).
    - Optimistic: upper-bound productivity impact; sustained AI exposure increase (+7 ppts cumulatively by 2030).
    - Very Optimistic: top-range global AI productivity impact; very fast AI exposure increase (+11 ppts cumulatively by 2030, reaching average AE’s AI exposure in 2023).
  - Estimation formula: Estimated Productivity Boost = Qatar’s AI Exposure and Penetration × Global AI Development and Productivity Impact.
  - Key quantitative findings:
    - Optimistic scenario: labor productivity growth could be lifted by an additional 1 percentage point annually (above baseline).
    - Scenario range of potential labor productivity growth differentials from baseline: - 0.8 (pessimistic) to 1.7 (very optimistic) percentage points.
  - Literature context:
    - Firm-level studies report productivity gains up to 6.8% in sales per worker for AI adopters.
    - Goldman Sachs projects AI could add between 0.3 and 3 percentage points to annual productivity growth.
    - EMDE estimates of AI-driven productivity gains range from 0.7 to 1.3 percentage points.
- Sectoral implications:
  - NDS3 “enabling clusters” (IT & Digital, Financial Services, Education) lie in high AI exposure–high complementarity quadrant.
  - Growth clusters with limited AI exposure: Manufacturing, Logistics, Tourism.
- Policy recommendations:
  - Enhance digital skills via targeted upskilling and reskilling programs.
  - Implement digital talent attraction schemes for expatriate workers.
  - Facilitate job transitions (including public to private sector) to mitigate displacement risks.
  - Develop adequate and well-targeted social safety nets to support vulnerable groups.

*Source: International Monetary Fund staff calculations and analysis as presented in the content unit.*

### References ____________________________________________________________________________ 16

### Financial Conditions and Their Growth Implications for Qatar

### Methodology for Constructing the Financial Conditions Index (FCI)
- Two approaches employed:
  - Principal components approach (PCA): extracts common factor F_t from a set of financial indicators using principal components; Bai and Ng (2002) selection criteria used to determine the optimal number of common factors. The factor F_t is purged of current and lagged non-hydrocarbon growth by regressing F_t on y_t (year-on-year non-hydrocarbon GDP growth rates).
  - Weighted sum VAR (WSA-VAR): weights derived from cumulative impulse-response functions of real non-oil GDP growth following a one-standard deviation shock to each variable; weights based on the VAR framework following Swiston (2008).
- Key implementation details:
  - Weights estimated as cumulative 18 - 24 months impulse response of real non-hydrocarbon GDP growth to a one-unit shock to x_jt for the period 2009 to 2023.
  - Data converted to monthly frequency using Chow and Lin (1971).
  - Nominal variables deflated with the GDP deflator; variables demeaned and standardized.
  - Identification via Cholesky decomposition with the assumption that domestic financial conditions do not have contemporaneous effects on growth and domestic developments do not contemporaneously affect external variables.
  - Lag length of one selected for the VAR based on Schwartz Bayesian Criterion (SBC).
  - Stationarity confirmed using Augmented Dickey-Fuller tests.

### Variables Included
- Global (external) factors:
  - VIX (global financial market uncertainty)
  - Nominal effective exchange rate (NEER)
  - Oil prices
- Domestic factors:
  - Real policy deposit rate
  - Real market interest spreads (difference between real short-term lending and deposit rates)
  - Growth rates of credit to the private sector
  - Broad money (M2)
  - 5-year CDS spread (sovereign risk premia)
  - Stock price index
  - Real estate price index

### Main Findings on FCI Construction and Performance
- WSA-VAR vs PCA:
  - Correlation between WSA-VAR FCI and PCA FCI is about 0.86.
  - WSA-VAR yielded more consistent signs and better out-of-sample prediction of major shocks (oil price shock in 2015, Covid-19 in 2020, post-pandemic easing).
  - The WSA-VAR method predicts a tightening of financial conditions in 2023 more distinctly than the PCA.
- Interpretation:
  - An increase in the index indicates tightening of financial conditions; a decrease indicates loosening.
  - Prior expectations: real policy interest rate, real market spread, and risk premium positively related to the FCI; money supply, private sector credit, and domestic asset prices negatively related to the FCI.
  - For Qatar’s dollar peg, an appreciation in NEER is expected to correlate with tightening financial conditions.
- FCI decomposition (WSA-VAR):
  - Significant contributors to aggregate FCI fluctuations: real policy deposit rate, monetary conditions (credit and broad money), and external factors driven mainly by oil prices.
  - External factors (oil price cycles) strongly shape Qatar’s external financial conditions.

### Historical Dynamics and Recent Developments
- Timeline and episodes:
  - Eased financial conditions: 2010 to 2014 (post-global financial crisis recovery).
  - Tightening beginning in 2014 with the oil price collapse.
  - Persistent tight conditions through 2020, compounded by the 2017 regional blockade and the Covid-19 shock.
  - Post-pandemic easing: 2021–2022 due to elevated hydrocarbon prices and increased liquidity; 2022 nominal policy deposit rate increases partly offset by higher inflation related to the World Cup.
  - Tightening again in 2023 as the real policy deposit rate increased (with lower inflation), hydrocarbon prices softened, and global financial conditions deteriorated.
- Sovereign and regional events:
  - The 2017 diplomatic rift resulted in wider sovereign risk spreads, a downturn in stock market prices, and an overall tightening of financial conditions.

### FCI as a Leading Indicator and Impact on Macroeconomy (Growth-at-Risk framework)
- Correlations and predictive relationships:
  - FCI has a negative correlation of about -0.66 with real non-hydrocarbon GDP growth, indicating potential leading indicator properties for non-hydrocarbon activity.
  - The credit conditions component of the FCI closely mirrors the Qatar Central Bank (QCB) bank lending survey index.
- Impulse response results:
  - Tightening FCIs can reduce inflation on average by 0.3 and up to 0.5 percentage points after 2 years.
  - Tightening FCIs can lead to a contraction in output by 0.8 to 1.0 percentage points.
- Policy signal:
  - The GaR framework suggests that overall, the current downside risks to Qatar’s baseline non-hydrocarbon growth projections are relatively mild (as assessed in the note).

### Conclusions and Policy Implications (extracts from analysis)
- The WSA-VAR FCI is preferable for Qatar for capturing deep recessions and predicting historical developments in financial conditions compared with PCA.
- Monitoring the FCI provides useful early warning signals for non-hydrocarbon growth prospects and helps assess the impact of domestic and external financial shocks.
- Key channels to monitor include the real policy deposit rate, monetary aggregates (credit and M2), sovereign risk premia, and oil-price-driven external conditions.

*Source: International Monetary Fund staff calculations and analysis as presented in the content unit.*

### 16.      Financial conditions can provide important signals about risks to future economic

### 16.      Financial conditions can provide important signals about risks to future economic performance.

### Overview of the Growth-at-Risk (GaR) approach
- Links macro-financial conditions to the probability distribution of future GDP growth, encompassing the full spectrum of growth distribution and allowing assessment of both downside and upside risks.
- Enables quantification of the impact of risk on future GDP growth.
- Prior evidence: financial conditions are significant predictors of GDP growth risks over short and medium term, with predictive effects diminishing over longer horizons (IMF GFSR 2017b; Prasad et al.,2019; Busch and others, 2022).

### GaR methodology implemented
- Three key steps:
  - Partition financial conditions indicators into a predetermined number of subgroups using linear discriminant analysis (LDA).
  - Estimate a model of future output growth as a function of current economic conditions and the partitioned financial conditions indicators using quantile regressions.
  - Transform the conditional quantile function into a probability density function by fitting a skewed t distribution to quantify downside tail risks to future GDP growth.
- Quantile regressions specification:
  - Regress future GDP growth on current growth (yt), domestic conditions (dom_cond), credit conditions (credit_cond) and external conditions (ext_cond).
  - Quantile levels: 0.10, 0.25, 0.50, 0.75, 0.90.
  - Forecast horizons (h): 4, 8 and 12 quarters.

### Financial conditions components (partitioned variables)
- Domestic conditions:
  - real policy deposit rate
  - real market interest spread.
  - Broad money (M2)
  - stock prices
  - real estate prices
  - sovereign risk premia
- Credit conditions:
  - credit growth (y/y)
  - credit- to - non-hydrocarbon GDP gap / credit-to -non-hydrocarbon impulse
  - Note: Credit to non-hydrocarbon GDP gap defined as the deviation of the credit-to-non-hydrocarbon GDP ratio from trend; computed using Hodrick-Prescott filter. Results robust to using the credit impulse.
- External conditions:
  - oil price
  - VIX
  - nominal effective exchange rate (NEER)

### Empirical findings from quantile regressions and FCI construction
- FCIs estimated via GaR align closely with FCIs from WSA-VAR and PCA, showing a bias toward the WSA-VAR FCI.
- Decomposition of GaR FCI indicates domestic and external conditions dominate the FCI since 2009, with credit conditions also contributing.
- Quantile regression results:
  - Tight domestic conditions, primarily driven by high short-term interest rates, have a more pronounced effect on non-hydrocarbon growth by lowering the lower quantiles of the GDP growth distribution over the next 4 to 8 quarters, shifting the distribution left of the median. This effect tends to diminish over longer periods as conditions improve.
  - Deterioration of credit conditions (high funding costs and subdued demand) has led to credit growth falling below potential, contributing to short-term risks; these effects are anticipated to lessen in the medium term as conditions improve.
  - Tight external conditions, mainly caused by fluctuations in commodity prices, had a significant negative impact on the overall non-hydrocarbon growth outlook.
- Notes on estimation visuals:
  - Coefficients reported with 95% confidence intervals across quantiles and horizons (4-, 8-, 12-Quarters Ahead).

### GaR distributions, baseline calibration, and quantified risks
- Distributions fitted using a t-skewed distribution calibrated so the mode is consistent with IMF staff baseline:
  - Baseline non-hydrocarbon growth: [1.5] percent for 2024 and [1.9] percent for 2025.
- GaR downside quantification:
  - Under IMF staff’s baseline distribution, in a severely adverse scenario (GDP growth falls below the 5th percentile), the maximum expected non-hydrocarbon growth rate would be:
    - 0.5 percent (4 Quarters ahead)
    - 0.1 percent (8 Quarters ahead)
  - Interpretation: relatively mild short- to medium-term downside risk outlook.

### Scenario analyses (shocks and their effects on the growth distribution)
- Impact of monetary policy easing:
  - Assumption: 100 basis-point reduction in the policy deposit rate by end of 2024, holding other factors constant (policy rates in Qatar generally follow US Fed rates consistent with the peg to the US dollar).
  - QCB action noted: The QCB reduced the policy rate by 0.55 bp following the 0.5bp cut in the US Fed rate in September 2024.
  - Results:
    - A 100 basis-point reduction improves average future growth with a rightward shift in the peak of the future growth distribution and a significant reduction in GaR at the 5% percentile.
    - Maximum impact of accommodative conditions realized in the near term (around 0.4 percentage points higher), with effects dissipating over the long term.
- Impact of external conditions:
  - One-standard deviation shocks applied separately to oil prices and VIX, holding other factors constant.
  - Oil price negative shock:
    - Could cost about 0.3-0.4 percentage points of non-hydrocarbon growth for Qatar.
    - GaR at 5 percent could decline to about -0.1, implying elevated downside risks.
  - Increase in VIX (global financial market uncertainty):
    - Marginal negative impact; leads to a leftward shift in the peak of the future growth distribution and a slight worsening of GaR.
    - Marginality partly attributed to relatively less developed financial markets in Qatar and VIX capturing only global financial market uncertainty.

### Conclusions and policy implications
- FCIs from WSA-VAR, PCA, and GaR are closely aligned and highly correlated.
- A financial conditions index is essential for assessing current financial conditions and evaluating links between financial indicators and future growth distribution—useful for implementing the Third Financial Sector Strategy.
- The FCI is an important leading indicator of Qatar’s non-hydrocarbon growth and closely follows QCB’s bank lending survey.
- Domestic conditions offer the strongest short-term signal for non-hydrocarbon GDP growth; external conditions are significant in both short and medium term.
- Current downside risks to Qatar’s baseline non-hydrocarbon growth projections are relatively mild.
- Alternative scenarios indicate non-hydrocarbon growth could improve following a reduction in the policy deposit rate; non-hydrocarbon growth is primarily influenced by oil prices, with minimal effects from global financial market uncertainty.

*Source: IMF staff calculations and analysis as presented in the chapter.*

### 4.      To inform spending allocation decisions to support NDS3 goals, this paper estimates

### 4.      To inform spending allocation decisions to support NDS3 goals, this paper estimates

### Estimation strategy and data (GCC panel approach)
- Baseline linear model (equation (1)) regresses real growth rate of non-hydrocarbon output on real growth rates of central government spending (total, current, and capital; j=1,2,3), controls for global output and oil prices (k=1,2), country fixed effects, and error term. Parameters to be estimated: α, β, γ; country fixed effects δi.
- Regressions estimated with and without the UAE.
- Baseline model extensions:
  - Include indicator of capital stock to assess whether fiscal multiplier on capital spending declines as capital stock rises.
  - Estimate Qatar-specific coefficients via a Qatar country dummy while exploiting GCC panel information.
- Data:
  - Annual data for 6 GCC countries spanning 1990–2023 for output, central government spending, and oil prices; some 2023 data are estimates from IMF WEO.
  - Stock of capital from Penn World Table available up to 2019; 2020–23 data are extrapolated.
- Conversion: Elasticities converted to fiscal multipliers by dividing estimated elasticity by the ratio of spending level to non-hydrocarbon output level (average over estimation horizon).

### Key empirical patterns and correlations (GCC)
- Visual and statistical patterns:
  - Real growth rates of non-hydrocarbon output and central government total spending moved more in tandem up to the late-2000s than afterwards; degree of co-movement rose since the COVID-19 pandemic.
  - Correlation coefficients for the entire sample period between output growth and total spending are statistically significant for the GCC.
- Country-level correlations:
  - Correlation coefficients most significant for Qatar, followed by Oman and Saudi Arabia.
  - Correlation is negative for the UAE.
- Spending component timing:
  - Current spending tends to have more immediate effects.
  - Capital spending effects tend to emerge with a lag and last longer.

### Estimated results (GCC panel baseline)
- Baseline regressions (Table 2) findings:
  - Real growth of central government total spending significantly impacts non-hydrocarbon output in the GCC with lags (model 1).
  - Current spending: coefficients significant with one-year lag; implied fiscal multiplier is 0.2 (model 2).
  - Capital spending: significant effects with longer lags; implied long-term fiscal multiplier is close to 0.9 (model 3).
  - When both current and capital spending included, current spending loses significance and fiscal multiplier of capital spending falls to 0.7 (model 4).
  - Oil prices significant when introduced without global output growth (model 4), indicating close association between oil prices and global output growth.
  - Global output growth affects non-hydrocarbon activity in the GCC (models 1–3, 5).
  - Excluding the UAE strengthens effects of current and capital spending on non-hydrocarbon output (models 6–9).

### Capital stock interaction and time variation (GCC)
- Growth impact of capital expenditure is smaller when capital stock (capital to GDP) is high (Table 3):
  - Capital stock scaled by GDP, ranked and categorized into low, medium, high; “high” for top 1/3 of observations.
  - Coefficients on lagged capital spending become statistically insignificant when capital stock to GDP ratio is high (model 10).
  - Results broadly unchanged when threshold for “high” lowered to top 1/2 (model 11).
  - Growth impact of capital spending weakens during the second half of the sample period (model 12).
  - Results generally unchanged when the UAE is dropped, though current spending becomes more impactful.

### Qatar-specific findings within GCC panel
- Qatar-specific estimates (Table 4):
  - For Qatar, estimated elasticity without distinguishing capital stock level implies long-run fiscal multiplier is close to 1.5 (insignificant for GCC countries) (model 16).
  - When capital stock is relatively high, capital spending does not have significant growth effects in Qatar (model 17).
  - Excluding the UAE makes GCC fiscal spending more impactful generally; Qatar-specific elasticities broadly unchanged (models 18 and 19).

### Single-country approach (Qatar)
- Data and methods:
  - Annual data spanning 1983–2023 (including estimates).
  - Regress non-hydrocarbon output on central government spending (total, current, capital), global output, and oil prices, all in real growth rates.
  - Estimate static regressions and dynamic regressions (vector autoregression and local projections).
  - Time-varying growth effects of lagged capital spending estimated using 12-year rolling regressions.
- Findings:
  - Static and dynamic regressions confirm important growth effects of lagged capital spending in Qatar (Annex I).
  - 12-year rolling regressions show growth effects of capital spending waned in the recent decade, coinciding with maturation of the infrastructure investment cycle associated with the World Cup.
  - World Cup-related capital spending in the 2010s appears to have had renewed growth effects with greater persistence (particularly second lag); these effects moderated as the investment cycle matured.
  - Rolling approach reduces sample size and the model does not control for all potential determinants; interpretations are suggestive.

### Quantitative magnitudes and data coverage (preserved values)
- Panel data span: 1990–2023 for 6 GCC countries.
- Qatar single-country data span: 1983–2023.
- Implied fiscal multipliers reported:
  - Current spending implied fiscal multiplier: 0.2 (model 2).
  - Capital spending implied long-term fiscal multiplier: close to 0.9 (model 3).
  - When both current and capital included, capital multiplier falls to 0.7 (model 4).
  - Qatar long-run fiscal multiplier (elasticity without capital stock distinction): close to 1.5 (model 16).
- Capital spending to non-hydrocarbon output ratio used in conversion example: average capital spending to non-hydrocarbon output ratio of 0.12 (footnote).
- Current spending to non-hydrocarbon output example: average current spending to non-hydrocarbon output ratio of 0.47 (footnote).

### Conclusions and policy implications
- Main conclusion:
  - Qatar’s strong capital expenditure multiplier becomes less impactful when the stock of capital reaches a high level—consistent with GCC panel results and Fouejieu et al. (2018) that fiscal multipliers declined over time as capital stock generally rose.
- Policy-relevant insights from literature and empirical results:
  - Reallocation within current spending toward non-wage items, particularly education that builds human capital, tends to boost growth (Gupta et al., 2005; Acosta-Ormaechea and Morozumi, 2013).
  - Higher capital spending relative to current spending tends to boost growth, but excessive use of seemingly productive expenditures can become unproductive (Devarajan et al., 1996).
  - Investments in new areas to support a knowledge-based economy, higher value-added sectors, and climate sustainability (green growth) could produce significantly larger multipliers than traditional infrastructure investment (IMF, 2023b).
  - Growth effects of public spending are more sizable, long-lasting, and stable when institutional quality is higher (Avellan et al., 2020).
- Policy recommendations for Qatar to support NDS3 goals:
  - Reorient public spending to support knowledge-based growth by:
    - Focusing public investment on improving human capital (education and health) for nationals and expatriates.
    - Providing a more conducive environment for businesses to crowd in private investment.
    - Enhancing climate sustainability and investing in climate adaptation to mitigate vulnerabilities to climate stressors.
    - Facilitating decarbonization and promoting renewables to reach emission reduction targets and smooth the energy transition.
  - Improve spending efficiency and target investments with higher expected multipliers (knowledge economy, green investments).
  - Use efficient public spending to crowd in private sector investment and accelerate transition to private sector–driven growth.

*Sources: Haver, IMF WEO, Penn World Table, and IMF staff calculations (as presented in the source PDF).*

### 1.      The Third National Development Strategy (NDS3) lays out plans to build a

### The Third National Development Strategy (NDS3) lays out plans to build a knowledge-based economy

### NDS3 objectives and strategic focus
- NDS3 emphasizes building a knowledge-based economy to boost growth potential, with a focus on export diversification as an important pillar of broader economic diversification given the small size of domestic markets in Qatar.
- Key NDS3 directions highlighted:
  - Strengthening the business environment, labor markets, fiscal and institutional frameworks to close gaps in innovation, human capital, and the regulatory environment.
  - Identifying “diversification clusters” with ambitious sectoral and macroeconomic targets.
  - Harmonizing economic zones to attract FDI, boosting international trade through additional agreements with key target markets, and promoting Public-Private partnership (PPP) to facilitate private sector participation.
- Contextual impediments noted:
  - The growth rate of total factor productivity over the past 4 decades were negative in Qatar, similar to its peer GCC countries.
  - Other impediments include barriers to market entry and FDI, various regulatory impediments, an immature innovation ecosystem, and insufficient human capital.

### Qatar’s progress in export diversification
- Services versus goods:
  - Qatar has made progress in diversifying services exports (including transport, tourism, and ICT), while goods export diversification has been more limited.
  - ICT is highlighted as a services area that could generate high value added and sustained productivity gains.
- Product space and goods exports:
  - The “product space” for goods exports has remained broadly unchanged; Qatar’s main export product minerals are relatively isolated from other products and especially from products with higher complexity.
  - Qatar’s global shares of relatively complex products (electronics and machinery) are low compared to GCC peers and other selected economies.
- Comparative indicators (text figure summaries):
  - Economic Diversification: reported percent non-hydrocarbon sector's share of total for 2000 and 2022 (figure context).
  - Productivity Improvements: average annual growth rate of TFP, 1974-2019 showed negative growth rates for Qatar and some GCC peers.
  - PISA Test Scores: OECD period average = 100; OECD and Qatar time series shown.
  - Expatriate Workers in Qatar by Skill: shares in 2006, 2013, 2020 (unskilled, semi skilled, skilled, highly skilled).

### Role of economic complexity for diversification and growth
- Conceptual link:
  - Producing goods with higher complexity matters for boosting export diversification and non-hydrocarbon output because greater economic complexity requires advanced knowledge and technology and is associated with greater economic growth.
  - Producing and exporting more complex goods generates learning and raises productivity of investment and labor, potentially creating a virtuous cycle with higher R&D investment.
- Qatar’s standing:
  - Qatar and other GCC countries are in the middle of the pack for economic complexity.
  - Given their economic complexity, technology-intensive manufacturing exports are relatively low while per-capita income is relatively high.
- Measurement:
  - The economic complexity index is calculated from equations for diversity and ubiquity to express the recursion (Hidalgo and Hausmann, 2009).
  - The distance in product space represents relatedness measured by the probability of co-export between products.

### Econometric evidence: determinants of export diversification and per-capita income
- Data and models:
  - Dependent variables: medium and high-tech exports in percent of total goods exports, and per-capita real income (both in logarithm).
  - Explanatory variables: indicators of economic complexity, human capital, physical capital, and employment-to-population ratio; interaction terms between economic complexity and human capital or physical capital included for per-capita real income.
  - Periods: annual data 1960–2019 for export regressions; non-overlapping 5-year averages for per-capita real income regressions (1960-64, 1965-1969, ..., 2010-19).
  - Panel: 133 countries.
  - Estimation: pooled, fixed-effects panel, and random-effects panel models; Hausman test suggests fixed-effects models are preferred.
- Key empirical findings:
  - Economic complexity is positively associated with medium- and high-tech exports (pooled, fixed-effects, and random-effects regressions).
  - Human capital is important in supporting export diversification; physical capital does not emerge as an important factor for export diversification.
  - Quantitative impacts for Qatar (using statistically significant coefficients from model 6):
    - If Qatar’s economic complexity improves by one unit (equal to one standard deviation in the sample), medium- to high-tech manufacturing exports would increase by 8–9 percentage points of total goods exports (from around 2 percent in 2019).
    - Increasing economic complexity to the level of Malaysia would imply an increase of 12–13 percentage points in Qatar’s export share of medium- to high-tech manufacturing.
- Interactions and per-capita income:
  - Economic complexity’s association with per-capita real income is amplified by physical and human capital.
  - Simulation results (illustrative for a hypothetical average economy):
    - The impact of a one standard deviation increase in economic complexity is 20 percentage points on per-capita real output when human and physical capital are at the average levels across country and time.
    - The growth impact of economic complexity increases to 30 percentage points when starting levels of human and physical capital are above the average by one standard deviation.
  - Relative importance:
    - Human capital is the largest contributor to per-capita real output.
    - Physical capital is important but its marginal impact is smaller for Qatar due to already large and high-quality physical capital stock.

### Technology and Innovation Policy (TIP) and country experience lessons
- TIP role:
  - Technology and Innovation Policy can help boost economic complexity and per-capita income durably, complementing Qatar’s existing top-notch capital stock and strategy to boost human capital.
  - TIP emphasis: support domestic sophisticated industries (sometimes beyond comparative advantage with export orientation), strict accountability, and competition to support productivity gains.
- Comparative case typology and lessons:
  - Three case studies used as illustrative benchmarks: Korea, Malaysia, and Chile, characterized as:
    - Korea: “Moonshots” — jumped into high value-added sectors and delivered the greatest growth gains.
    - Malaysia: “Leapfrog” — created higher value-added exports and growth but economic complexity rose less than Korea’s.
    - Chile: “Snail crawl” — main exports include copper and agricultural products; economic complexity stagnated.
  - High-level outcomes:
    - Korea lifted its real per-capita income from less than 10 percent of the US to nearly 70 percent of the US and registered 8 percent average growth over and above the US over the period analyzed.
    - Malaysia’s average annual growth rate was 6 percent over the US.
    - Chile’s average annual growth rate was 4.5 percent over the US.
  - Relevance to Qatar:
    - Malaysia and Chile share characteristics with Qatar: both export commodities, and Malaysia relies on expatriate workers, making their experiences relevant to Qatar’s diversification challenges.
- Policy insights to inform Qatar’s NDS3-aligned TIP:
  - Pursue strategies that support domestic sophisticated industries with export orientation, recognizing that diversification opportunities may be relatively “distant” for Qatar even compared with Malaysia.
  - Strengthen human capital and regulatory frameworks to amplify the growth impact of increased economic complexity.
  - Use targeted TIP measures alongside approaches to attract and retain higher-skilled expatriate workers and to improve the innovation ecosystem.

### Korea case: evidence of successful goods export diversification
- Product space evolution (1995–2021):
  - Korea’s export mix moved away from lower complexity products (textiles, agriculture) toward higher complexity products (chemicals, machinery, electronics).
  - Korea’s economic complexity more than doubled from 0.9 in 1995 to 1.9 in 2021.
  - Total goods trade rose from $150 billion in 1995 to $803 billion in 2021.
- Feasible opportunities:
  - In 1995, export diversification into products with lower complexity was relatively easier for Korea; by 2021, diversification into higher complexity products became easier for Korea as capabilities evolved.
- Analytical tools:
  - The Atlas of Economic Complexity and the “product space” visualization are used to trace capability proximity and feasible diversification paths.

*Prepared by Ken Miyajima; analysis and figures based on IMF staff calculations and sources cited in the text.*

### 13.      Korea’s feasible opportunities suggest that the country’s capabilities to diversify into

### 13.      Korea’s feasible opportunities suggest that the country’s capabilities to diversify into 

### Korea’s evolution in feasible diversification opportunities
- The figure described uses:
  - Horizontal axis: distance, measuring ease of diversification. Empirically the minimum value is around 0.4-0.5 (e.g., for Germany) and the shortest distance of around 0.7 for Korea is relatively short.
  - Vertical axis: product complexity, rising from textile and agriculture to electronics and machinery.
- 1995 (ECI = 0.85):
  - Korea’s economic complexity was lower.
  - Low complexity products (textile, agriculture) were easier to diversify into and less distant.
  - More complex products (electrical, machinery) were less easy to diversify into and more distant.
  - The feasible opportunity was tilted to the right.
- 2021 (ECI = 1.93):
  - Korea’s economic complexity rose (the broken horizontal line moved up).
  - More complex products became less distant than less complex products.
  - The dots were tilted to the left.
  - Existing product mix, know-how and production capabilities were more conducive to diversify into electronics and machinery.

### Key lessons for Qatar from cross-country experiences
- Emphasis on human capital and high value-added manufacturing exports while leveraging existing comparative advantage.
- Human capital:
  - Korea invested in human capital development through general, vocational education, and health care.
  - Malaysia created the Human Resources Development Fund and programs to study abroad.
  - Malaysia implemented policies to attract and retain skilled expatriate workers (e.g., TalentCorp programs including a 10-year renewable visa for highly skilled expatriate workers).
- TIP/economic zones:
  - Korea required strict accountability for receiving government support; intense competition and export pressure fostered productivity improvements and local technology creation over several decades.
  - Malaysia emphasized technology transfers, upgrades, and international competition; MNCs supported export sophistication but technology diffusion was not always extensive and linkages between FDI/free trade zones and the onshore economy were not very strong.
  - Malaysia was less successful in developing comparative advantage in high value-added goods.
- Services:
  - Some services (e.g., tourism) can bolster the non-hydrocarbon economy but may not generate sustained productivity and growth gains.
  - Highly productive services (telecommunications, logistics and delivery, financial services) can reduce transaction costs, provide infrastructure for digital transactions, and elevate growth potential when supported by competition-enhancing policies.
  - Chile’s diversification focused on agriculture and worked as intended but long-term growth dividends are more limited.
  - In Qatar, visitor arrivals have risen structurally; tourism can leverage existing high-quality infrastructure and make the country more attractive to high-skilled expatriate workers but does not remove the need to diversify into more complex manufacturing and service exports. Existing logistics infrastructure would support manufacturing exports.

### Qatar: Feasible goods export opportunities (comparison and implications)
- Malaysia’s feasible opportunities (plotted for comparison) are less distant than Qatar’s from their own export mix, and somewhat tilted to the left—diversifying into products with higher complexity can be easier than into lower complexity products.
- For Qatar:
  - Opportunities to diversify goods exports are relatively distant, close to 1 (the maximum in the framework).
  - NDS3 focuses on downstream products from hydrocarbon, such as petrochemical (a “Snail crawl” or at most “Leapfrog” type of strategy).
  - Given most products are similarly distanced, efforts required are similarly significant; reaching for products with higher complexity (akin to “Moonshots”) would generate greater productivity and growth gains.
  - Complementarity matters: Qatar’s comparative advantage in logistics and connectivity through seaports and airports suggests sectors that leverage them could deliver greater gains.
  - Regionally, diversifying into sectors that are somewhat different from, or that complement, sectors targeted by other GCC countries could generate greater gains.

### Policy priorities for Qatar to diversify goods exports
- Main areas to intensify reforms: foster human capital, FDI, and a competitive environment.
- Human capital:
  - Qatar has progressed in enhancing education attainment but has room to enhance human capital relative to top-notch physical capital.
  - Korea and Malaysia’s success hinged on strong focus on human capital and aggressive diversification strategies.
  - Given expatriate workers represent a large share of total labor in Qatar, measures to attract and retain skilled expatriate workers should be intensified, similar to Malaysia’s case.
- FDI:
  - FDI in Qatar’s non-hydrocarbon economy is low overall though inflows in high value-added sectors (Chemicals and ICT) are rising.
  - Intensifying efforts to boost financial depth, human capital, institutions, and labor market flexibility would continue to attract FDI.
  - Qatar emphasizes public-private partnership (PPP) to attract FDI—PPP needs to be mindful of fiscal risk.
  - Maximizing domestic spillovers from FDI is key; building local technology over several decades contributed to Korea’s success.
  - Qatar’s patent application started to increase rapidly and a boost to R&D spending could continue the strong trend.
- Competitive environment:
  - Qatar’s manufacturing exports are relatively low and may be below potential, leaving significant upside to grow.
  - Reducing the state’s footprint, upgrading the regulatory environment, and ensuring a level playing field would facilitate private investment and help Qatar boost production of more complex products.
  - Intense domestic and international competition amid export pressure underpinned Korea’s success.
  - Qatar’s conditions for trade are favorable, with many trade and other agreements and generally low trade barriers.
  - Further fostering trade diversification would involve reducing remaining tariff and non-tariff barriers, exploring new trade corridors (Middle Corridor from China to Europe; India-Middle East-Europe Corridor), and investments in the digital economy.

### Vertical policies: DOs and DO NOTs
- DOs:
  - Vertical policies should be targeted, time-bound, cost-effective, transparent, and deliver on objectives.
  - Social goals should be clearly identified (e.g., emission reductions).
  - Domestic knowledge spillovers from innovation in targeted sectors should be strong.
  - Sufficient administrative capacity should be in place.
  - Proper cost-benefit analysis and impact evaluation, together with strict exit criteria, claw-back mechanisms, and sunset clauses, would help minimize risks and phase out policies as needed.
  - Targeted interventions should not substitute for broad-based structural reforms, which should continue to be rolled out.
- DO NOTs:
  - Avoid creating negative effects on macroeconomic, fiscal, and external sustainability.
  - Avoid generating negative cross-border spillovers, including harming trading partners or violating international commitments.

### Catalysts and indicators for Qatar (as presented)
- Annual FDI commitments by sector (US$ billions, period average) and by industry (percent of total non-hydrocarbon) are shown to indicate:
  - Overall and non-hydrocarbon annual FDI commitments over periods: 2003-09, 2010-16, 2017-23.
  - Industry shares include Food, Chemicals, IT/software, Tourism, Communications.
- R&D and complexity:
  - Qatar’s patent applications jumped in the last decade (Total Patent Applications per one million population series from 1980 to 2020 compared to GCC ex-Qatar and World).
  - R&D Spending and Economic Complexity plotted with R&D spending (%GDP, log, 2020 or 2021) vs Economic complexity index, 2021.
- Trade and manufacturing gaps:
  - Trade barriers are relatively low in Qatar (Indicators of Trade Barriers, 2021, deviation from 165 country-mean in standard deviation).
  - IMF internal analysis suggests room to boost manufacturing exports for the GCC, potentially for Qatar (Estimated Manufacturing Export Gaps, Percent of potential).

### Concluding remarks
- Empirical analysis and experience from other countries provide lessons Qatar could consider in implementing NDS3:
  - Growth potential could be boosted by building a knowledge-based economy with focus on enhancing export diversification into higher technology manufacturing products.
  - Qatar could go beyond sectors where it already has comparative advantages by focusing more on developing sectors with high complexity.
  - Key ingredients for success: strong focus on attracting and developing human capital, technology, private sector investment into high value-added non-hydrocarbon activities, and a competitive environment.
  - The role of services needs careful assessment to best support high value-added manufacturing exports and long-term growth potential.
  - Benefits from vertical policies to complement horizontal policies need to be carefully weighed against a range of pitfalls.

*Source: 1qatea2025002-print-pdf - 13.      Korea’s feasible opportunities suggest that the country’s capabilities to diversify into (IMF).*

### 3.   Qatar’s AI strategies are underpinned by a range of initiatives in digital talent, digital

### 3.   Qatar’s AI strategies are underpinned by a range of initiatives in digital talent, digital

### AI strategies and major initiatives
- AI digital talent
  - National Skilling Program (in collaboration with Microsoft) aims to train 50,000 individuals in advanced digital competencies, including AI and data science by 2025.
  - Digital Talent Development and Retention Program encourages local talent through attractive opportunities and incentives.
  - A fast-track visa system is being introduced to attract global experts.
  - Qatar University is identified as the nation’s leading research institution on AI (Figure 1).
- AI computing infrastructure
  - Planning to establish a National HPC Program to expand cloud-based capabilities for AI.
  - Data centers grew at an annual rate of 45 percent over the last decade, reaching total capacity of 23.5 Megawatts (MW).
- AI for public and labor market services
  - TASMU Smart Qatar program uses AI and analytics for goals such as food security and infrastructure optimization.
  - Labor Market Information System (LMIS) employs AI for labor insights and job matching.
  - The Hukoomi portal integrated OpenAI GPT to streamline government interactions and enhance efficiency.
- AI investment
  - A $2.5 billion incentive package announced in May 2024 to boost investment in AI and digital innovations.
  - Qatar Investment Authority (QIA) emphasized continued focus on AI-related investments in data centers, data categorization, software applications and semiconductors.
  - Ooredoo secured a QR 2 billion financing deal in September 2024 to accelerate its data center and AI business, aiming to build 120MW data center capacity in five years.
  - Ooredoo has an ongoing partnership with Nvidia to develop AI-ready platforms across MENA.

### Digital access and AI readiness
- Qatar’s digital infrastructure, quality and affordability are relatively advanced and comparable to those in AEs.
- Digital knowledge lags GCC peers and AEs, but the Digital Agenda includes talent programs to close the gap.
- According to Oxford Insights, Qatar:
  - Outperforms AE average in government vision and data representativeness.
  - Is close to AEs on digital capacity, infrastructure and human capital.
  - Has gaps in data availability, governance and ethics, adaptability, maturity and innovation capacity.

### Labor market exposure and preparedness
- Aggregate exposure and trends
  - As of 2023, around 3 7 percent of Qatar’s labor force see their jobs exposed to AI applications, representing an 18-percentage-point increase from 2014.
  - Qatar’s AI exposure level is comparable to the EM average (33 percent) but lower than the AE average (58 percent).
  - Among jobs with high exposure to AI, more than 75 percent also has high ‘complementarity’ with AI.
  - Qatar’s labor force AI preparedness (ratio of high complementarity jobs within the high AI exposure group) is 75 percent compared to EM ( 43 percent) and AE (45 percent).
- Occupational and sector patterns
  - All high-skilled occupations—managers, professionals, and technicians—fall in the high-AI exposure–high-complementarity quadrant.
  - Over 2014–2023, the share of high-skilled occupations increased by 10 percentage points.
  - From 2014 to 2023, Qatar’s total workforce increased by 30 percent, while the share of high-skilled, AI-benefiting occupations recorded a much higher growth of 87 percent on average, driven mainly by private sector employment.
- Nationality, gender, education and age differentials
  - About 93 percent of Qatari workers have jobs exposed to AI, compared with 33 percent among non-Qatari workers.
  - Among high AI-exposure jobs held by Qatar nationals, 65 percent have high AI-complementarity and 35 percent are at risk of being replaced by AI.
  - Clerical support workers (e.g., general office clerks, numerical clerks, and material recording and transport clerks) constitute a large share of at-risk jobs among Qatari nationals.
  - Over 80 percent of Qatari clerical workers are employed by the public sector and 75 percent of them hold a secondary degree.

### Scenario analysis: estimating AI impacts on labor productivity
- Framework and drivers
  - Labor productivity defined as gross value added per worker.
  - Impact depends on speed of global AI technology advancement, Qatar’s labor force exposure to AI and speed of domestic AI adoption.
  - Baseline Scenario underpins staff’s macroeconomic projections and assumes gradual gains from comprehensive reforms (human capital, business environment, private sector growth, deepening AI penetration).
- Additional scenarios built around the baseline
  - Pessimistic Scenario: lower-bound global AI productivity impact and a below historical trend increase in Qatar’s AI exposure (+4 ppts cumulatively by 2030) and penetration.
  - Optimistic Scenario: upper-bound productivity impact estimates and sustained gains in Qatar’s AI exposure in line with historical trend (+7 ppts cumulatively by 2030) and penetration.
  - Very Optimistic Scenario: top-range global AI productivity impact and very fast increase in Qatar’s AI exposure (+11 ppts cumulatively by 2030, reaching an average AE’s AI exposure in 2023) and penetration.
- Estimation formula used
  - Estimated Productivity Boost = Qatar’s AI Exposure and Penetration × Global AI Development and Productivity Impact
- Key quantitative findings from scenarios
  - Optimistic scenario suggests labor productivity growth could be lifted by an additional 1 percentage point annually (above the baseline scenario).
  - The scenario range of potential labor productivity growth differentials (from the baseline) is from - 0.8 (pessimistic) to 1.7 (very optimistic) percentage points.
- Context from literature and firm-level studies
  - Some firm-level studies report increases in productivity, with gains of up to 6.8% in sales per worker for AI-adopting companies.
  - Goldman Sachs projects AI could add between 0.3 and 3 percentage points to annual productivity growth.
  - EMDE estimates of AI-driven productivity gains range from 0.7 to 1.3 percentage points.

### Sectoral implications
- NDS3 “enabling clusters” positioned to harness AI gains
  - IT & Digital, Financial Services, and Education concentrate in the high AI exposure–high complementarity quadrant and are better positioned to benefit from productivity gains.
- Growth clusters with limited AI exposure
  - Manufacturing, Logistics, and Tourism have limited exposure to AI and remain largely shielded from both risks and opportunities related to the technology.

### Conclusions and policy recommendations
- Overall assessment
  - Qatar is well positioned to benefit from AI-boosted productivity gains due to proactive digitalization and increasing AI exposure and labor force preparedness.
  - The country’s expatriate-dominated labor structure provides flexibility to adjust the labor force for swifter AI adoption.
  - The private sector is critical in creating high-skilled employment that can leverage AI to boost productivity.
- Vulnerabilities
  - Public-sector clerical positions held by Qatari nationals are more susceptible to job displacement risks.
- Policy recommendations
  - Enhance digital skills via targeted upskilling and reskilling programs.
  - Implement digital talent attraction schemes for expatriate workers to fill high-skilled roles.
  - Facilitate job transitions, including from the public to the private sector, to mitigate job displacement risks.
  - Develop adequate and well-targeted social safety nets to support vulnerable groups.

*Source: https://www.imf.org/-/media/files/publications/cr/2025/english/1qatea2025002-print-pdf.pdf*

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_Source: https://www.imf.org/-/media/files/publications/cr/2025/english/1qatea2025002-print-pdf.pdf_