## _wp1562

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---

### I. Introduction — Context and regional performance
- The Middle East and Central Asia’s (MENAP and CCA) strong growth has weakened since the global financial crisis.
- Oil exporters’ non-oil growth:
  - averaged over 8 percent during 2003-07.
  - since the crisis (2008-14), it almost halved to 4½ percent and is expected to pick up by only 1 percentage point over the medium-term.
- Oil importers:
  - pre-crisis growth of 5½ percent.
  - growth fell to 3½ percent during 2008-14 with expectations of a modest 1 percentage point increase over the next five years.
- Unemployment and living standards:
  - The global financial crisis reversed a gradually declining unemployment trend.
  - GCC number of unemployed nationals is projected to exceed 1 million over the next five years (IMF 2013a).
  - Absent job-creating economic growth outpacing population growth, standards of living, measured as per capita GDP, will drop from half to one third of the EMDC average over the next five years.
- Key objective and research questions:
  - What drives potential growth in the MENAP and CCA countries? Labor, physical capital, or total factor productivity (TFP)?
  - How much can these drivers boost growth potential?

### II. Main findings — Potential growth levels, trends, and drivers
- Cross-country variation:
  - Potential growth rates vary greatly across the MENAP and CCA.
  - Oil importers’ growth potential is substantially below the EMDC average.
  - Oil exporters — particularly in the GCC and the Caucasus and Central Asia (CCA) — have among the world’s highest non-oil potential growth.
- Post-crisis slowdowns:
  - Since the global financial crisis, MENAP and CCA potential growth rates are slowing by more than in other EMDCs.
  - The declines are projected to exceed EMDC averages by ¾ of a percentage point over the next five years.
  - CCA oil importers experienced a slowdown of about 3 percentage points.
  - In the MENAP region (except the GCC), potential growth dropped just after 2010 by almost 1 percentage point (Arab Spring effects compounded crisis effects).
  - In the GCC, non-oil potential growth is expected to slow by over 1½ percentage points over the next five years despite continued infrastructure investment.
- Drivers of the slowdown (2008-14 versus 2003-07):
  - Lower TFP growth has driven the decline in the CCA and contributed across MENAP, especially the GCC.
  - Lower labor contributions were the main driver of the slowdown in MENAP, reflecting lower public spending resulting in lower employment and, in oil importers, the discouraging effect of high unemployment and large remittance inflows on workforce participation.
  - Lower investment-to-GDP ratios across the ACTs and the CCA oil importers reduced physical capital’s contribution to potential growth.
- Decomposition highlights (production function results):
  - Oil exporters:
    - Largely driven by physical capital accumulation due to high global oil prices and government infrastructure spending.
    - In the GCC, labor contributes significantly because of abundant low-skilled foreign workers.
    - TFP contributes negatively in the GCC.
    - CCA oil exporters show strong positive contributions from TFP.
  - Oil importers:
    - Physical capital is a major driver in CCA oil importers and important in MENAP oil importers.
    - Labor contributes significantly in many oil importers owing to fast-growing populations, except in CCA economies where aging presents a challenge.
    - TFP has the lowest contribution to potential growth in MENAP and CCA oil importers.
- Robustness:
  - Findings are robust to a battery of robustness tests.

### III. Estimating potential growth — Definitions, methodology, and data
- Definition:
  - Potential or trend growth is the highest level of sustainable real GDP growth during a long period without stoking inflation; technically, it is the difference between actual growth and the change in output gap.
- Uncertainties:
  - Estimates are subject to significant uncertainty for MENAP and CCA due to incomplete statistics and lags.
- Approaches applied:
  - Statistical filters:
    - Hodrick Prescott (HP) linear filter (1997).
    - Christiano and Fitzgerald (CF) (2003) band-pass filter.
    - Baxter and King (BK) (1999) band-pass filter.
  - Production function approach:
    - Standard Cobb-Douglas production function following Solow (1957) decomposing output into labor, physical capital, and TFP.
- Key methodological parameters and data:
  - Sample of 19 MENAP and CCA countries spanning 1991-2019 (earliest available year used when 1991 data unavailable).
  - Production function assumptions:
    - Physical capital depreciation rate δ = 0.1.
    - Physical capital’s share in output α = 0.50 for oil exporting countries and α = 0.35 for oil importing countries.
    - Employed labor force is used to represent labor.
  - Statistical filter specifics:
    - HP: baseline λ = 100 (annual baseline varied to 6.25 in sensitivity checks; standard Ravn-Uhlig rule noted: λ = 1600 for quarterly, 6.25 for annual).
    - CF filter chosen band for business cycle: 8 to 32 quarters.
    - BK filter passes components between 6 and 32 quarters; uses 8 quarters leads/lags in this implementation.
  - Data sources:
    - Real GDP in USD for oil importers and non-oil real GDP in USD for oil exporters converted using period average exchange rate; WEO database used.
    - Employment from WEO; where unavailable (Lebanon, Qatar, Yemen), ILO GIT used for 1991-2018 and a 3-year moving average for 2019.
    - Initial investment proxied by gross fixed capital formation in 1991; capital growth the average growth rate of gross fixed capital formation during 1991-2019.

### IV. Sensitivity and robustness checks
- HP filter:
  - Baseline λ of 100 varied to 200 and 6.25; main findings do not change significantly.
- CF filter:
  - Baseline bands of 2 to 8 varied to 3 to 7; main findings robust.
- Production function:
  - Physical capital’s share of output varied from 0.2 to 0.5 for oil importers and 0.3 to 0.8 for oil exporters.
  - Depreciation rate δ varied from 0.05 to 0.10 (and elsewhere 0.05 to 0.15 noted).
  - Different smoothing parameters and CF filter applied to underlying variables.
- Aggregation and filters:
  - BK filter produces the most extreme annual estimates and is excluded from some aggregates due to shorter series coverage.
  - Production function approach provides the least variation in potential growth estimates.
- Overall conclusion: main findings are robust across methodological choices.

### V. Prospects for raising potential growth — Policy levers and illustrative scenarios
- Primary levers:
  - Elevating lagging TFP growth — critical across oil exporters and importers because TFP carries fewer constraints than other factors.
    - Policy focus: worker talent, modernization of production methods, re-orienting public sector roles, political stability and security (MENAP), reversing slowdown in structural reforms (CCA).
  - Accelerating physical capital accumulation in oil importers — address outdated and insufficient physical capital via government infrastructure investment and private investment.
    - Structural reforms to improve business environment and financial market development are critical.
- Labor prospects:
  - Medium-term gains from labor are limited; plausible medium-term workforce growth is slow even with high population growth.
  - In some CCA economies aging population, already low unemployment, and high participation rates restrict labor contributions.
  - Over the long run in MENAP, greater female labor force participation can significantly boost labor contribution.
- Scenario examples and modeling assumptions:
  - ACTs reaching EMDC average:
    - ACTs could reach average EMDC potential growth in five years with current investment-to-GDP ratio of 22 percent combined with increasing annual productivity growth from zero to 1½ percent.
    - Even then, standard of living would only rise by one percentage point — remaining at two thirds of EMDC living standards.
    - Long-run catch-up example: if ACTs sustained growth potential of 8 percent over the next 40 years, they would catch up to projected EMDC living standards.
  - Medium-term modeling assumptions used for scenarios:
    - Medium-term growth targets set at 5 percent (weighted average for EMDCs).
    - Interim paths imputed by linear interpolation between 2014 growth and the medium-term target; adjustments if 2019 growth exceeds 5 percent.
    - Investment-to-GDP ratios increased by 0.1 percentage point increments to arrive at medium-term ratios between 0.1 and 10 percentage points higher than 2014 ratios.
    - Male and female unemployment rates both reduced by 2 percentage points between 2014 and 2019.
    - Male labor force participation rates increased by 2 percentage points between 2014 and 2019; female rates increased by 4 percentage points.

### VI. Conclusions and policy implications
- Magnitude of slowdown:
  - The recent slowdown of MENAP and CCA potential growth rates exceeds that of most other EMDC regions.
  - Over the next five years, the region’s potential growth is estimated to be ¾ of a percentage point lower than other EMDCs.
- Drivers vary by subregion:
  - Lower TFP growth drove the decline in the CCA.
  - Lower labor contributions were the main driver in the rest of the MENAP region.
  - Reduced capital contributions played an important role in non-GCC MENAP oil exporters, the ACTs, and CCA oil importers.
- Policy priorities:
  - Foster TFP growth across MENAP and CCA as a key challenge and priority; removing constraints to TFP could bring potential growth to EMDC levels over several years.
  - Oil importers should also raise investment-to-GDP ratios to bolster potential growth.

*Source: _wp1562 - Bibliography (excerpt). PDF chapter/section.*

### Bibliography ...........................................................................................................

### _wp1562 - Bibliography ...........................................................................................................

### I. INTRODUCTION — Context and regional performance
- The Middle East and Central Asia’s (MENAP and CCA) strong growth has weakened since the global financial crisis.
- Oil exporters’ non-oil growth:
  - averaged over 8 percent during 2003-07.
  - since the crisis (2008-14), it almost halved to 4½ percent and is expected to pick up by only 1 percentage point over the medium-term.
- Oil importers:
  - pre-crisis growth of 5½ percent.
  - growth fell to 3½ percent during 2008-14 with expectations of a modest 1 percentage point increase over the next five years.
- Unemployment and living standards:
  - The global financial crisis reversed a gradually declining unemployment trend.
  - GCC number of unemployed nationals is projected to exceed 1 million over the next five years (IMF 2013a).
  - Absent job-creating economic growth outpacing population growth, standards of living, measured as per capita GDP, will drop from half to one third of the EMDC average over the next five years.

### Key objective and research questions
- Elevating the region’s medium-term economic prospects hinges on raising potential growth.
- Central questions:
  - What drives potential growth in the MENAP and CCA countries? Labor, physical capital, or total factor productivity (TFP)?
  - How much can these drivers boost growth potential?

### Main findings
- Potential growth rates vary greatly across the MENAP and CCA:
  - Oil importers’ growth potential is substantially below the EMDC average.
  - Oil exporters – particularly in the GCC and the Caucasus and Central Asia (CCA) – have among the world’s highest non-oil potential growth.
- Since the global financial crisis, MENAP and CCA potential growth rates are slowing by more than in other EMDCs:
  - The declines are projected to exceed EMDC averages by ¾ of a percentage point over the next five years.
- Drivers of the slowdown differ by subregion:
  - Lower TFP growth has driven the decline in the CCA and contributed across MENAP, especially the GCC.
  - Lower labor contributions to potential growth have been the main driver of the slowdown in MENAP, reflecting lower public spending resulting in lower employment and, in oil importers, the discouraging effect of high unemployment and large remittance inflows on workforce participation.
  - Lower investment-to-GDP ratios across the ACTs and the CCA oil importers reduced physical capital’s contribution to potential growth.
- Prospects for raising potential growth:
  - Boosting potential growth will depend on raising TFP across the region and, in the oil importers, raising physical capital accumulation.
  - MENAP and CCA TFP growth has lagged other EMDCs both before and after the global financial crisis.
  - TFP growth carries fewer constraints than other factors of production — making it critical for raising potential growth in both oil exporters and importers.
  - Given oil importers’ relatively low and eroding physical capital stock, a wide range of plausible annual TFP and investment-to-GDP combinations can raise potential growth.
  - The medium-term contribution of labor is limited since even in countries with high population growth, plausible medium-term workforce growth is slow.
- Robustness:
  - These findings are robust to various data challenges as demonstrated by a battery of robustness tests.

### II. ESTIMATING POTENTIAL GROWTH — Definitions and uncertainties
- Potential or trend growth is defined as the highest level of sustainable real GDP growth during a long period without stoking inflation; technically, it is the difference between actual growth and the change in output gap.
- Uncertainties surrounding potential growth estimates are significant for MENAP and the CCA due to incomplete statistics and lags.
- To ensure robustness, several techniques are applied.

### A. Methodology and Data — Approaches applied
- Two common techniques are applied:
  - Statistical Filters:
    - Decompose raw GDP data into cyclical/noise and trend components.
    - Methods used: Hodrick Prescott (HP) linear filter (1997), Christiano and Fitzgerald (CF) (2003) band-pass filter, and Baxter and King (BK) (1999) band-pass filter.
    - Notes on HP: it is a univariate filter estimating potential output as the series that minimizes the deviation of actual output from its trend, subject to sensitivity adjustment via the penalty parameter λ.
    - Band-pass filters differ in weights assigned to moving averages to extract cycles in a “band”.
  - Production Function Approach:
    - Decomposes output growth into contributions from labor, physical capital, and total factor productivity (TFP) in a growth accounting framework following Solow (1957).
    - TFP is calculated as the residual contribution to GDP growth once contributions of physical capital and labor (adjusted for unemployment) are taken into account.
    - Typically specifies a simple Cobb-Douglas production function with an assumption on the share of physical capital and labor in output.

### Methodological challenges and implementation details
- Main drawbacks of statistical filters:
  - As purely statistical techniques, they estimate trends without regard to other macroeconomic variables; the relationship between output gaps and inflation is not exploited.
  - End-point problem: instability of estimates near the end of the sample period; common remedy is to extend the sample by using forecast data (applied in this paper).
- Model-based macro approaches cannot be applied due to data limitations: lack of high-frequency and sufficiently long time series of inflation, unemployment, and capacity utilization.
- Data and sample:
  - Both statistical filter and production function approaches are applied to data for 19 MENAP and CCA countries spanning from 1991-2019.
  - When data was unavailable from 1991, the earliest year of available data was used. Appendix 1 provides details.
- Specific assumptions for the production function approach:
  - Physical capital depreciation rate of 0.1.
  - Physical capital’s share in output of 0.50 for oil exporting countries and 0.35 for oil importing countries.
  - These assumptions are consistent with past research on MENAP and CCA oil exporters (IMF 2012, IMF 2013b, IMF 2013d, and the Total Economy Database (TED) from Chen and others 2010) and on oil importers (IMF 2014, TED from Chen and others 2010, and Gollin 2002) which assume physical capital’s share of output to range from 0.4 to [text truncated in source].

### Paper scope and structure
- This paper:
  - (i) estimates past and future potential growth for a broad group of emerging market and low income countries applying a consistent methodology across countries based on statistical filters and standard (Solow-style) growth accounting methodologies;
  - (ii) examines the supply-side drivers of potential growth in the MENAP and CCA;
  - (iii) assesses which drivers could be most effective in raising potential growth for the region.
- Structure:
  - Section II: estimation of potential growth, methodology and results.
  - Section III: explores drivers of potential growth through a growth decomposition exercise.
  - Section IV: assesses prospects for raising potential growth.
  - Section V: conclusion.

*Source: _wp1562 - Bibliography (excerpt). PDF chapter/section.*

### 0.67 for oil exporters and from 0.25 to 0.4 for oil importers and the physical capital

### _wp1562 - 0.67 for oil exporters and from 0.25 to 0.4 for oil importers and the physical capital

### Results: regional potential growth patterns and trends
- Potential growth varies greatly across MENAP and CCA countries; relative rates and direction of change over time are consistent across techniques.
- The average of estimates across the statistical filters (HP and CF) and production function approaches indicates:
  - Economically less developed oil importers have the lowest potential growth in the region — well below the EMDC average.
  - Oil exporters — particularly in the GCC and CCA — have among the world’s highest non-oil potential growth, comparable to emerging and developing Asia.
- Pre-global financial crisis (2003-07) context:
  - GCC and CCA oil exporters’ non-oil potential growth exceeded 7 percent.
- Post-crisis declines and projected medium-term evolution:
  - The crisis reversed pre-crisis gains; potential growth in advanced and emerging economies declined after 2008.
  - MENAP and CCA potential growth rates are slowing by more than in other EMDCs — decline exceeds the EMDC average by ¾ of a percentage point over the next five years.
  - CCA oil importers experienced a slowdown of about 3 percentage points.
  - In the MENAP region (except the GCC), potential growth dropped just after 2010 by almost 1 percentage point (Arab Spring effects compounded crisis effects).
  - In the GCC, non-oil potential growth is expected to slow by over 1½ percentage points over the next five years despite continued infrastructure investment.
- Methodological notes:
  - Annual estimates differ across approaches; BK filter produces most extreme annual estimates and is excluded from some aggregates due to shorter series coverage.
  - Production function approach provides the least variation in potential growth estimates.

### Decomposing potential growth: contributions of labor, capital, and TFP
- Growth accounting uses trend components of labor, physical capital, and TFP from the production function approach.
- Oil exporters (MENAP and CCA):
  - Largely driven by physical capital accumulation due to high global oil prices and government infrastructure spending.
  - In the GCC, labor contributes significantly because of abundant low-skilled foreign workers.
  - TFP contributes negatively in the GCC, reflecting less emphasis on productivity improvements.
  - CCA oil exporters show strong positive contributions from TFP, supported by reform implementation.
- Oil importers (MENAP and CCA):
  - Physical capital is a major driver in CCA oil importers and important in MENAP oil importers.
  - Labor contributes significantly in many oil importers owing to fast-growing populations, except in CCA economies where aging presents a challenge.
  - TFP has the lowest contribution to potential growth in MENAP and CCA oil importers.
- Drivers of recent declines (changes in contributions, 2008-14 versus 2003-07):
  - Oil exporters: continued infrastructure investment drives non-oil potential growth but is offset by declining labor and productivity contributions.
  - Oil importers: outdated physical capital, inefficiency in using energy/capital/talent, weak global ties, strained public finances, political instability, and cumbersome regulations have lowered investment and TFP.
- Comparison with EMDCs:
  - Oil exporters’ lower productivity contributions to non-oil potential growth are offset by larger physical capital and worker contributions relative to EMDCs.
  - Oil importers’ lower productivity compounded by lower physical capital contributions relative to EMDCs.

### Robustness tests and sensitivity
- HP filter sensitivity:
  - Baseline λ of 100 varied to 200 and 6.25; main findings do not change significantly.
- CF filter sensitivity:
  - Baseline bands of 2 to 8 varied to 3 to 7; main findings robust.
- Production function sensitivity:
  - Physical capital’s share of output varied from 0.2 to 0.5 for oil importers and 0.3 to 0.8 for oil exporters.
  - δ, depreciation rate of physical capital, varied from 0.05 to 0.10 (and elsewhere 0.05 to 0.15 noted).
  - Different smoothing parameters and CF filter applied to underlying variables.
- Overall: main findings do not change significantly across robustness checks.

### Prospects for raising potential growth: policy levers and scenarios
- Main levers to raise potential growth:
  - Elevating lagging TFP growth — critical across oil exporters and importers because TFP carries fewer constraints than other factors.
    - Policy focus: worker talent, modernization of production methods, re-orienting public sector roles, political stability and security (MENAP), reversing slowdown in structural reforms (CCA).
  - Accelerating physical capital accumulation in oil importers — addressing outdated and insufficient physical capital via government infrastructure investment and private investment.
    - Structural reforms to improve business environment and financial market development are critical.
- Labor force prospects:
  - Medium-term gains from labor are limited; plausible medium-term workforce growth is slow even with high population growth.
  - In some CCA economies aging population, already low unemployment, and high participation rates restrict labor contributions.
  - Over the long run in MENAP, greater female labor force participation can significantly boost labor contribution.
- Example scenario to reach EMDC average potential growth within five years:
  - ACTs could reach average EMDC potential growth in five years with current investment-to-GDP ratio of 22 percent combined with increasing annual productivity growth from zero to 1½ percent.
  - Even then, standard of living would only rise by one percentage point — remaining at two thirds of EMDC living standards.
  - Long-run catch-up example: if ACTs sustained growth potential of 8 percent over the next 40 years, they would catch up to projected EMDC living standards.
- Medium-term modeling assumptions used for scenarios:
  - Medium-term growth targets set at 5 percent (weighted average for EMDCs).
  - Interim paths imputed by linear interpolation between 2014 growth and the medium-term target; adjustments if 2019 growth exceeds 5 percent.
  - Investment-to-GDP ratios increased by 0.1 percentage point increments to arrive at medium-term ratios between 0.1 and 10 percentage points higher than 2014 ratios.
  - Male and female unemployment rates both reduced by 2 percentage points between 2014 and 2019.
  - Male labor force participation rates increased by 2 percentage points between 2014 and 2019; female rates increased by 4 percentage points.

### Conclusions and policy implications
- The recent slowdown of MENAP and CCA potential growth rates exceeds that of most other EMDC regions:
  - Over the next five years, the region’s potential growth is estimated to be ¾ of a percentage point lower than other EMDCs.
  - It also exceeds the advanced economies average by 1¾ percentage points (not reported).
- Drivers of the slowdown vary:
  - Lower TFP growth drove the decline in the CCA.
  - Lower labor contributions were the main driver in the rest of the MENAP region.
  - Reduced capital contributions played an important role in non-GCC MENAP oil exporters, the ACTs, and CCA oil importers.
- Policy priorities:
  - Foster TFP growth across MENAP and CCA as a key challenge and priority; removing constraints to TFP could bring potential growth to EMDC levels over several years.
  - Oil importers should also raise investment-to-GDP ratios to bolster potential growth.

*Source: IMF staff estimates and analysis in _wp1562 (MENAP and CCA potential growth decomposition and projections).*

### BIBLIOGRAPHY

### _wp1562 - BIBLIOGRAPHY

### BIBLIOGRAPHY
- Anand,  R.,  K.  C.  Cheng,  S.  Rehman,  and  L.  Zhang,  (2014)  “Potential  Growth  in  Emerging  Asia”, IMF Working Paper No. 14/2. (Washington, D.C.: International Monetary Fund).
- Andrle,  M.,  (2013)  “What  is  in  Your  Output  Gap?  Unified  Framework  and  Decomposition  into  Observables”,  IMF  Working  Paper  No.  13/105.  (Washington,  D.C.:  International  Monetary Fund).
- Baxter,  M.,  and  R.  G.  King,  (1999)  “Measuring  Business  Cycles:  Approximate  Band-Pass  Filters for Economic Time Series”, Review of Economics and Statistics, 81 (4): pp. 575–593.
- Benes,  J.,  K.  Clinton,  R.  Garcia-Saltos,  M.  Johnson,  D.  Laxton,  P.  Manchev  and  T.  Matheson,   (2010)   “Estimating   Potential   Output   with   a   Multivariate   Filter”,   IMF Working Paper No. 10/285. (Washington, D.C.: International Monetary Fund).
- Beveridge, S. and C.R. Nelson, (1981) “A New Approach to the Decomposition of Economic Time Series into Permanent and Transitory Components with Particular Attention to Measurement of the Business Cycle”, Journal of Monetary Economics 7 (2): pp. 151−174.
- Chen, V., A. Gupta, A. Therrien, G. Levanon, and B. V. Ark, (2010) “Recent Productivity Developments in the World Economy: An Overview from the Conference Board Total Economy Database,” International Productivity Monitor, 19 (Spring), pp. 3–19.
- Christiano, L. J. and T. J. Fitzgerald, (2003) “The Band Pass Filter”, International Economic Review 44 (2): pp. 435–465.
- Clark, P. K., (1987) “The Cyclical Component of U.S. Economic Activity", The Quarterly Journal of Economics 102 (4): pp. 797-814.
- Cubeddu, L., A. Culiuc, G. Fayad, Y. Gao, K. Kochhar, A. Kyobe, C. Oner, R. Perrelli, S. Sanya, E. Tsounta, and Z. Zhang, (2014) “Emerging Markets in Transition: Growth Prospects and Challenges,” IMF Staff Discussion Note 14/6. (Washington, D.C.: International Monetary Fund).
- El-Ganainy,  A.  and  A.  Weber,  (2010)  “Estimates  of  the  Output  Gap  in  Armenia  with Applications   to   Monetary   and   Fiscal   Policy”,   IMF   Working   Paper   No.   10/197.   (Washington, D.C.: International Monetary Fund).
- Gollin, D., (2002) “Getting Income Shares Right,” Journal of Political Economy, 110 (2): pp. 458–74.
- Harvey, A. C., (1985) “Trends and Cycles in Macroeconomic Time Series", Journal of Business and Economic Statistics 3 (3): pp. 216-227.
- Hodrick, R.J. and E.C. Prescott, (1997) “Postwar U.S. Business Cycles: An Empirical Investigation”, Journal of Money, Credit, and Banking 29 (1): pp. 1–16.
- IMF, (2012) “Saudi Arabia: Selected Issues Paper”, IMF Country Report No. 12/272. (Washington, D.C.: International Monetary Fund).
- IMF, (2013a) “Labor Market Reforms to Boost Employment and Productivity in the GCC,” Annual Meeting of the Gulf Cooperation Council Ministers of Finance and Central Bank Governors, Riyadh, October 5 (Washington).
- IMF, (2013b) “Saudi Arabia: Selected Issues Paper”, IMF Country Report No. 13/230. (Washington, D.C.: International Monetary Fund).
- IMF, (2013c) “Republic of Armenia—Sixth Reviews Under the Extended Fund Facility Arrangement and the Extended Credit Facility Arrangement”, EBS/13/75 (Washington, D.C.: International Monetary Fund).
- IMF, (2013d) “Algeria: Selected Issues Paper”, IMF Country Report No. 13/48. (Washington, D.C.: International Monetary Fund).
- IMF, (2015) (forthcoming) “World Economic Outlook, April 2015: A Survey by Staff of the International Monetary Fund”, World Economic and Financial Surveys. (Washington, DC: International Monetary Fund).
- IMF, (various issues) “Middle East and Central Asia Regional Economic Outlook, (Washington, DC: International Monetary Fund).
- Mitra, P., A. Hosny, and G. Minasyan, (2015) (forthcoming): “Structural Reforms to Raise Potential Growth in the Middle East and North Africa,” IMF Working Paper (Washington, D.C.: International Monetary Fund).
- Mitra, P., A. Hosny, G. Minasyan, G. Abajyan and Mark Fischer, (2015) (forthcoming): “Avoiding the New Mediocre? Policies to Strengthen Potential Growth in the Middle East and North Africa,” IMF Staff Discussion Note (Washington, D.C.: International Monetary Fund).
- Morley, J., C. Nelson and E. Zivot, (2003) “Why are the Beveridge-Nelson and Unobserved-Components Decompositions of GDP so Different?”, Review of Economics and Statistics 85 (2): pp. 235-243.
- Ravn, M. O., and H. Uhlig, (2002) “On adjusting the Hodrick–Prescott filter for the frequency of observations”, Review of Economics and Statistics 84 (2) pp.: 371–376.
- Solow, R. M., (1957) “Technical Change and the Aggregate Production Function”, Review of Economics and Statistics 39 (3): pp. 312–320.
- Sosa,  S.,  E.  Tsounta,  and  H.  S.  Kim,  (2013)  “Is  the  Growth  Momentum  in  Latin  America  Sustainable?” IMF   Working   Paper   No.   13/109.   (Washington,   D.C.:   International Monetary Fund).
- World Bank, (2009) “From Privilege to Competition: Unlocking Private-Led Growth in the Middle East and North Africa”, MENA Development Report. (Washington, D.C.: World Bank).

### Appendix I. Methodology

#### Overview
- The paper estimates potential growth using two approaches:
  - Statistical filters (rely only on statistical properties of GDP; do not impose structural restrictions).
  - Production function approach (estimates production capacity given factor endowment and total productivity level).
- Using both approaches increases robustness of results.

#### A. Statistical Filters

- Hodrick-Prescott (HP) Filter
  - Minimizes the difference between actual and potential output while constraining the rate of change in potential output for the whole sample of T observations.
  - y is the logarithm of real GDP and y* is the logarithm of potential real GDP.
  - λ is a weighting factor that determines the degree of smoothness of the trend.
  - Standard procedure: set λ equal to 1600 for quarterly data, and 6.25 for annual data, following the Ravn-Uhlig (2002) rule which sets λ to 1600*p^4, where p is the number of periods per quarter.
  - T is the length of the time series.

- Band Pass (BP) Filters
  - Define business cycles as fluctuations of a certain frequency; filter extracts frequencies within a specified frequency range.
  - Two BP filters applied: Christiano-Fitzgerald (CF) and Baxter-King (BK).
  - Both approximate the ideal infinite BP filter assuming a cycle lasts from 1.5 to 8 years.

  - Christiano-Fitzgerald (CF) filter
    - Full sample asymmetric filter; weights on leads and lags are allowed to differ and are time-varying.
    - Data must be made stationary before applying filter (linear trend in real GDP is removed).
    - Chosen band for business cycle: 8 to 32 quarters.
    - Uses whole time series for each filtered data point; designed to work better than BK on a larger class of time series.

  - Baxter-King (BK) filter
    - Fixed length symmetric filter; no phase shifts in resulting filtered series.
    - Weights for lags and leads of the same length are the same and time-invariant.
    - Passes components with fluctuations between 6 and 32 quarters.
    - Moving average weights depend on the band specification; 8 quarters in this case.
    - Shortens the time series; choosing lower leads/lags results in poor approximation to the ideal high pass filter.

- Data for All Statistical Filters
  - STATA is used to apply all the filters on:
    - real GDP in USD for oil importing countries, and
    - non-oil real GDP in USD for oil exporting countries.
  - Data range: 1991-2019.
  - Data source: WEO database.
  - Where data was unavailable from 1991, the earliest year of available data was used.
  - Footnote: To ensure compatibility across the statistical filter and production function approaches, the earliest year is defined as the first year for which data is available for output, labor, and capital.

#### B. Production Function Approach

- Concept
  - Describes functional relationship between output and factor inputs.
  - Calculates potential output as level of output given ‘normal’ rates of capacity utilization (labor and capital consistent with non-accelerating wages and inflation; TFP at its trend level).

- Production function used
  - Standard Cobb-Douglas form:
    - Y_t = A_t * K_t^α * L_t^(1-α)
    - Where Y_t represents real GDP in period t, K_t is the stock of capital, L_t is the labor force, A_t represents TFP, and α is the share of capital in output.

- Capital stock construction (perpetual inventory method)
  - Initial capital stock: K_0 = I_0/(g + δ), where I_0 is initial investment expenditure, g is growth rate of capital, and δ is capital’s depreciation rate.
  - Subsequent series: K_t = (1-δ) K_{t-1} + I_t.

- Steps to estimate potential GDP
  1. Obtain historical TFP with the formula: A_t = Y_t / (K_t^α * L_t^(1-α))
  2. Apply the HP filter to K, L, and A, which gives the trends of each variable.
  3. Derive potential growth by applying trend K, L, and A to the Cobb-Douglas production function.
  4. Calculate the growth rates of potential output.

- Data for Production Function Approach
  - All variables span 1991-2019. Where data was unavailable from 1991, the earliest year of available data was used.
  - Real GDP (for the oil importing countries) and non-oil real GDP (for the oil exporting countries) are converted to U.S. dollars using the period average exchange rate. All three of these variables are from the WEO database.
  - The initial investment expenditure is approximated by gross fixed capital formation in 1991.
  - The growth of capital is the average growth rate of gross fixed capital formation during 1991-2019.
  - The capital depreciation rate is assumed to be 0.1.
  - The employed labor force is used to represent the labor force (instead of the entire stock of labor available) to ensure that changes in the unemployment rate are not reflected into changes in TFP.
  - Employment series are sourced from the WEO database. When this data is not available (Lebanon, Qatar, and Yemen), ILO Global Employment Trends (GIT) databases were used for 1991-2018 and a 3-year moving average for 2019.
  - Initial investment expenditure and gross fixed capital formation are from the WEO database.
  - The share of capital is assumed to equal 0.50 for oil exporting countries, and 0.35 for oil importing countries.

*Source: _wp1562 - BIBLIOGRAPHY (PDF chapter/section).*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2015/_wp1562.pdf_