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---

### Preface — mission overview and team
- Mission: Monetary and Capital Markets (MCM) Department mission to Nairobi, Kenya during April 17–21, 2023 to assist the Central Bank of Kenya (CBK) in improving the Forecasting and Policy Analysis System (FPAS).
- Mission team: Ms. Jianping Zhou (Mission Chief, MCM); Messrs. Edward Anthony Chernis and Taylor Webley (Short-term experts (STXs), Bank of Canada).
- Meetings: CBK forecasting team in the Real Sector Analysis Division (overseen by Dr. Maureen Were), MPC Secretariat, External Sector and Fiscal Analysis, Monetary Policy Analysis, and Statistics Divisions. Mr. Raphael Otieno joined opening and concluding sessions; Ms. Zhou met Dr. Patrick Njoroge (CBK Governor) and Prof. Robert Mudida (Director of the Research Department) at IMF HQ during the 2023 Spring Meetings.
- Note: Dr. Kamau Thugge has been the new Governor since June 19, 2023.

### Executive Summary — objectives and completed tasks
- Two specific objectives:
  - Review and improve the current MPC survey framework, customize the MPC surveys, enhance data management and analysis, and integrate the survey framework more closely with the FPAS.
  - Improve the nowcasting framework for GDP to provide better inputs for the Quarterly Projection Model (QPM).
- Completed tasks:
  - (i) Reviewed three MPC business surveys and proposed detailed changes to survey questions to streamline surveys and enhance monetary policy relevance.
  - (ii) Created EViews programs for more efficient survey data management and for converting qualitative survey data to quantitative data using the concept of “balance of opinion (BoO)”.
  - (iii) Reviewed existing models for nowcasting CPI and GDP, operationalized a forecast evaluation system, and created a disaggregated nowcasting framework for enhancing narrative-building.
  - (iv) Started the process of integrating survey results into nowcasting.
- Deliverables: detailed concluding presentation summarizing recommendations (Attachment I) and three mock-up MPC presentations prepared by CBK participants (Attachment II).

### Key messages on surveys and nowcasting
- Surveys:
  - Continue to improve and streamline survey questions and use.
  - Further develop tools created during the mission for more efficient survey data management and analysis.
  - Consider changing survey timing so surveys better inform nowcasts and QPM projections.
- Nowcasting:
  - Integrate quantified survey results into nowcasting.
  - Continue refining the nowcasting framework for GDP at a disaggregated level to improve understanding of current developments and storytelling.
  - Continue expanding the database of leading indicators and assess new nowcasting models using the forecast evaluation framework.

### Capacity and next mission focus
- CBK forecasting team is skilled but could become more specialized; current practice has team members handling nowcasting, QPM-based projections, and other tasks to avoid disruption from staff departures, but deeper modeling specialization is recommended.
- Agreed next mission focus: improve the QPM model, considering significant developments in the economy and data rebasing since the original model development.

### High-level recommendations (recap)
- Integrate quantified survey results into nowcasting.
- Refine the disaggregated nowcasting framework for GDP to enhance narrative-building.
- Expand database of leading indicators.
- Assess new nowcasting models leveraging the forecast evaluation framework.

---

### Survey correlation analysis and findings
- Finding: TA team found a strong correlation between Balance of Opinion (BoO) indicators and GDP (Figure 1).
- Caveat: Due to the short sample the results are suggestive and not conclusive; should be reviewed with a longer sample size after CBK compiles past results and gathers new responses.
- Training: Mission team provided training on how to conduct this analysis.
- Result implication: Current results show strong correlations between survey questions and quarterly growth in real GDP, suggesting significant value added given the significant lag in most economic data.
- Formal BoO definition (preserved exactly): "In this case, BoO = (the number of respondents with positive opinion – the number responses with negative opinion) / (total number of respondents)."

### Illustrative example and interpretation
- Example (MPS): question on expected change in the Ksh/USD exchange rate with five possibilities: weaken, weaken slightly, same level, strengthen slightly, and strengthen.
- Observations:
  - Top panel (BoO indices): both banks and non-banks expect further depreciation and their views have changed over time.
  - Lower panel: firms’ sector prospect expectations (BoO indices) closely traced actual quarterly GDP growth and CBK forecasts in the last 2-3 years (when survey data is available).
- Implication: timely survey-based indicators provide high value for nowcasting and narrative building given lags in official economic data. Recommendation: CBK should continue exploring relationships between survey indicators and key macroeconomic time series.

### Automating survey data ingestion
- Proof-of-concept: process to import raw Survey Monkey data into EViews to reduce manual entry time and transcription errors.
- Capabilities delivered:
  - Import survey data efficiently.
  - Generate summary statistics and quantitative indicators such as BoO indices.
- Training: CBK staff received hands-on tutorial covering ingesting data, adjusting code for new surveys, and writing small algorithms to generate analytical survey series (e.g., shares of firms providing responses and BoO indices).
- Outstanding tasks (preserved numbering and wording):
  - iii. Establishing a well-defined and persistent mnemonic structure for each survey so data ingestion and analysis is repeatable.
  - iv. Writing a program to gather cleaned data and computations from multiple surveys to stack as a time series.
  - v. Writing a program to export key time series of interest to a database for easy access by analysts and the nowcasting tools.
- Note: Further work required by CBK or experts in future missions to complete the system.

### Improving the nowcasting framework — current platforms and immediate work
- CBK currently has two separate nowcasting platforms for both GDP and CPI producing forecasts for one year ahead (either four quarters or twelve months); only the first quarterly nowcast is used as the starting point for QPM.
- GDP platforms:
  - Platform 1 (Matlab): A PCA and BVAR model using a variety of domestic and external indicators. The BVAR is currently not functional and the PCA is the primary nowcast tool.
  - Platform 2 (EViews): Estimates a large set of bridge equations and VARs using domestic indicators. The code had deteriorated and was not being used actively at the start of the mission.
- Inflation platforms:
  - Platform 1 (EViews): A disaggregated nowcasting tool that forecasts each component of CPI and adds them up to a CPI nowcast.
  - Platform 2 (EViews): Same as Platform 2 for GDP, though using only lagged monthly price data for CPI sub-components. CBK staff can choose whether this platform targets inflation or GDP by identifying the target variable.
- Mission focus and code improvements:
  - Decision: focus on fixing and developing the EViews-based platform because (i) EViews provides more flexibility and scope for enhancement; (ii) it generates nowcasts for both GDP and CPI; (iii) the efficacy and usefulness of the BVAR required more time to determine.
  - Updated EViews code now runs without errors regardless of the variable (CPI or GDP) being predicted. Additional static factor-based models added (e.g., PC, DFM, FAVAR to be reviewed in next TA missions). Bugs producing non-descriptive errors were fixed.
- Automated forecast evaluation system:
  - The EViews platform now includes an automated system that, for every nowcasting model, generates RMSEs and other summary statistics (e.g., adjusted R-squared, residual plots), creates summary tables comparing statistics, and exports results to excel files.
  - Benefits: simple and transparent comparison of model performance; empowers CBK staff to conduct efficient model development and instantaneously compare predictive performance. CBK staff received hands-on workshops and successfully customized datasets, specified custom models, and examined forecast evaluation results in real-time.

### Enhancements to bottom-up disaggregate nowcasting
- Expansion: platform now predicts disaggregated components of GDP; input-output structure adjusted to streamline generation of nowcasts for individual components.
- Features:
  - Create datasets for each industry component (or custom sub-industries) and run platform.
  - Program automatically estimates specified models, evaluates historical performance, and outputs nowcast predictions alongside evaluation results.
  - New tool aggregates disaggregated nowcasts into a bottom-up nowcast for headline GDP growth and produces/exports contributions to growth for analysis and presentation.
- Implementation note: Current industry set determined jointly with CBK staff using expert judgement.
- Recommendation: future work should identify optimal level of disaggregation by examining nowcasting accuracy across various cuts of the data using the forecast evaluation platform.
- Survey integration: Due to limited sample size of most survey-based indicators created during the mission, they were not integrated systematically into the nowcasting platform. Near-term recommendation: actively use survey-based indicators to enhance narratives and inform judgement-based forecasts; once sufficient history exists, leverage the evaluation platform to explore nowcasting benefits of adding survey-based indicators.
- Recommendation: evaluate benefits of forecast combination in future work (forecast combination likely to yield gains in accuracy for simple bridge equations, VARs, or static factor models).

### Next steps and policy recommendations (summary)
- Surveys:
  - Continue refining and improving MPC surveys; streamline questionnaires to reduce response burden and increase policy relevance.
  - Priority: CEO survey due to relatively short sample size.
  - Consider creating a firm registry to manage and track survey respondents/systematically manage the survey sample.
  - Consolidate historical survey results into a time series and use new analysis tools (e.g., BoO) to inform policy.
  - Consider moving the survey period at least one week earlier to allow more time to integrate results into the policy process.
- Nowcasting and models:
  - Continue to improve nowcasting framework at a disaggregated level to enable better storytelling and policy communication.
  - Use forecast evaluation framework (Appendix IV) to explore relationships between GDP/inflation and key economic indicators (e.g., exchange rates, Purchasing Managers’ Index (PMIs)) and refine tools by trimming worst-performing models and exploring forecast combination.
  - Further refinements: monitor framework performance, resolve issues, upscale where possible, revisit BVAR framework, consider dynamic factor models, factor augmented VARs, averaging bridge, and regARIMAs.
- Team structure and QPM:
  - Recommend greater specialization within CBK forecasting teams while maintaining contingency planning.
  - QPM: rebuild the QPM framework afresh rather than piecemeal due to rebasing of National Accounts and CPI in 2014 and 2021, which rendered many trends, steady states, and parameters out of sync. The current framework will run until a new upscaled QPM framework is launched. Next mission should be in-person and focus on improving the QPM model.

---

### Central Bank Business Surveys — summary (Box 1)
- CBK conducts three main surveys to support MPC decisions:
  - (1) Market Perception Survey (since 2009)
    - Focus: perceptions on inflation, economic growth, demand for credit, growth in credit to private sector, exchange rate, optimism on economic prospects and business environment, current and expected economic conditions, focusing on economic activity and employment.
    - Targets: commercial banks, micro-finance banks, and a sample of non-bank private sector firms from major towns: Nairobi, Mombasa, Kisumu, Eldoret, Nakuru, Nyeri, Meru and Kisii, in sectors that account for about 78 percent of GDP.
    - Administered via a direct online survey.
  - (2) CEOs’ Survey (since March 2021)
    - Focus: CEOs’ views on business confidence and optimism, current business activity, outlook for near-term business activity, key drivers and threats to firms’ growth, and strategic priorities over the medium-term.
    - Targets: CEOs of key private sector organizations including members of KAM, KNCCI and KEPSA.
    - Administered via a direct online survey.
  - (3) Agriculture Sector Survey (since July 2022)
    - Focus: high frequency agriculture sector data on agricultural prices and output expectations across the country.
    - Targets: wholesale markets, retail markets, and farms in major towns including Nairobi, Nairobi Metropolitan area, Naivasha, Gilgil, Nakuru, Narok, Bomet, Nyandarua, Nyahururu, Kisumu, Mombasa, Kisii, Eldoret, Kitale, Meru and Nyeri.
    - Conducted via face-to-face interviews.
- Source: CBK and IMF staff calculations.

---

### Annex I — Mission Concluding Presentation (summary)
- Mission context: Kenya-FPAS Mission, April 17-21, 2023; Concluding Meeting with CBK Staff and Management, APRIL21,2023.
- Team: Taylor Webley (STX, Bank of Canada), Tony Chernis (STX, Bank of Canada), Jianping Zhou (IMF HQ, Mission Chief).
- FPAS focus: organization of the forecast process and forecast team; nowcasting framework including CBK expectations surveys; core macro model QPM; survey data management; nowcasting tools and models; communication.
- Key achievements:
  - Streamlined surveys and enhanced monetary policy relevance.
  - Improved survey data management and analysis via EViews programs and BoO transformation.
  - Reviewed nowcasting models for GDP and CPI; operationalized forecast evaluation system; created disaggregated nowcasting framework.
  - Started integrating survey results into nowcasting; CBK staff prepared MPC presentations.
- Survey operational suggestions:
  - Move survey timing earlier (example: surveys were sent on March 1, responses back on March 15, too late for analytical meeting on March 20 and MPC meeting on March 29). Start by sending surveys one week earlier; consider moving earlier (two weeks) after review.
  - Extend existing survey data further into the past (currently compiled to around ~2017).
  - Provided templates for firm registry and ETL pipeline; demonstrations of converting qualitative responses to quantitative indicators (BoO).
- Nowcasting improvements delivered:
  - Reviewed and updated the database with metadata and formalized indicators.
  - Updated EViews platform to run without errors; added static factor–based models; operationalized model forecast evaluation system producing RMSEs and summary statistics.
  - Built disaggregated bottom-up nowcasting framework to construct industry nowcasts and combine into headline GDP growth forecast on a nay/ybasis for judgment-based forecasts and MPC storytelling.
- Integration of surveys into nowcasting:
  - Preliminary pipeline work and template for firm registry and ETL provided.
  - Demonstrated conversion of qualitative to quantitative indicators and examples of policy use.
  - Initial analysis: survey data broadly tracks economic situation (optimism after COVID; pessimism during election; pessimism around KSH depreciation) and shows some predictive power, though sample is extremely short. CBK to assess information content as sample grows and is extended backwards.
- Next steps and timeline:
  - Concluding Presentation finalized and shared with CBK (Monday).
  - Team to prepare TA report based on the concluding presentation.
  - TA report sent to CBK for comment in early May.
  - Planning of the next mission (in-person) and follow-up on other issues.

---

### CBK Surveys — detailed findings and operations (selected statistics and items)
- Inflation drivers and relief expectations:
  - Relief: improved supply and distribution of food following onset of the rains and food imports expected to reduce cost of food items (57 percent); positive spillovers from lower global crude oil prices on local fuel cost (43 percent).
  - Persistently elevating factors: high food prices due to prolonged dry weather and below average rain (82 percent); pass through effects of foreign exchange rate depreciation, low dollar liquidity, expensive imports (43 percent); high local energy prices and upward review of power tariffs (39 percent).
- Market Perception Survey — expected inflation (selected months and group averages):
  - March '22 (Mar-Apr 2022): Large banks 5.4; Medium banks 5.7; Small banks 5.7; All banks (weighted by size of bank) 5.5; MFBs 5.5; Non-bank private firms 5.9
  - May '22 (May-Jun 2022): Large banks 7.1; Medium banks 6.9; Small banks 6.8; All banks (weighted by size of bank) 7.1; MFBs 6.7; Non-bank private firms 7.2
  - July '22 (Jul-Aug 2022): Large banks 8.2; Medium banks 8.1; Small banks 7.9; All banks (weighted by size of bank) 8.1; MFBs 7.9; Non-bank private firms 7.5
  - September '22 (Sep-Oct 2022): Large banks 8.6; Medium banks 8.7; Small banks 8.7; All banks (weighted by size of bank) 8.7; MFBs 8.5; Non-bank private firms 8.2
  - November '22 (Nov-Dec 2022): Large banks 9.8; Medium banks 9.7; Small banks 8.7; All banks (weighted by size of bank) 9.7; MFBs 9.7; Non-bank private firms 9.4
  - January '23 (Jan-Feb 2023): Large banks 8.9; Medium banks 9.1; Small banks 9.0; All banks (weighted by size of bank) 9.0; MFBs 9.0; Non-bank private firms 8.8
  - March '23 (Mar-Apr 2023): Large banks 9.2; Medium banks 9.2; Small banks 9.2; All banks (weighted by size of bank) 9.2; MFBs 9.2; Non-bank private firms 9.2
  - Reference: February 2023 listed as actual inflation in the source.
- Agriculture and food price expectations (April 2023):
  - 63 percent expect retail prices of cereals and grains in April 2023 to either decline (16 percent) or remain unchanged (47 percent).
  - Retail prices of animal products, non-vegetables and some vegetable items expected to remain unchanged or decline; vegetable prices expected to remain elevated in April except for kales and traditional vegetables.
  - Retail prices of most food items increased in March 2023; wheat, loose maize and milk moderated in March 2023 relative to January 2023 following recent harvests and previous short rains.
- Farmers’ constraints to accessing inputs:
  - High cost of inputs (67 percent); Lack of finances (18 percent); Lack of knowledge of inputs/Extension officers (8 percent); Poor fertilizer/seeds variety (5 percent); Others (3 percent).
- Economic activity and optimism:
  - Factors supporting moderate to strong activity: onset of long rains boosting agricultural activity and lowering food costs (60 percent); increased private sector credit growth supported by improved private sector performance and resilient services (30 percent).
  - Risks: high cost of living (92 percent); weak domestic currency and shortage of dollars (44 percent).
  - Optimism drivers: government strategies to improve agricultural production and hustler fund (49 percent); resilience of private sector supported by growth in industry and services including tourism (43 percent).
  - Main risks to optimism: high cost of living (60 percent); unfavorable weather conditions (50 percent); weakening of local currency (45 percent).
- CEOs’ Survey concerns:
  - Primary concerns for Kenyan economy: inflation, weakening Shilling, drought, low consumer spending.
  - Primary global concerns: lingering war in Ukraine; interest rate hikes in advanced economies; effects of the collapse of two banks in the U.S.
- Survey operations:
  - Timing: First two weeks of every MPC month.
  - Platform: Survey Monkey for electronic responses; some questionnaires received in Word format.
  - Follow-up: Reminders sent by email typically one week after launch; phone call follow-ups.
  - Data analysis: Excel worksheets; data stored in individual worksheets.
  - Issues: response rate; data storage centralization; target audience clarity; suitable software; presentation.

---

### Market Perceptions Survey questionnaire (selected structure and key items)
- Non-bank Private Sector sections:
  - Respondent identification: Name of establishment; Location (Town); Sector; Total number of employees.
  - Q1: Demand for credit/funds/financing next 12 months vs last 12 months (very high / high / moderate / low / very low) plus reasons and current access to bank credit.
  - Q2: Perception of economic activity over last three months (March, April and May 2024) — Very strong / Strong / Moderate / Weak / Very weak; expectations for next three months (June – August 2024).
  - Q3: Employment comparison with similar period in 2023 and hiring expectations for 2024 vs 2023.
  - Q4: Company's economic growth rate expectations for 2024 and 2025 - 2027.
  - Q5: Inflation expectations: April 2024 inflation rate was 5.0 percent; expected inflation rates for May-24, Jun-24, Jul-24; expected average inflation over Next 1 year (May 2024 - April 2025), Next 2 years (May 2024 - April 2026), Next 5 years (May 2024 – April 2029).
  - Q6: Exchange rate expectations for Ksh/USD for Next two months (June – July 2024) and Next 12 months (June 2024 – May 2025) with options: Weaken / Weaken slightly / Same Level / Strengthen slightly / Strengthen.
  - Q7: Sentiment on Kenya’s economic prospects in the next 12 months: Very optimistic / Optimistic / Pessimistic / Very pessimistic.
  - Q8–Q10: Suggestions to enhance business environment and contact fields.
- Commercial and Microfinance Banks sections:
  - Q1: Expected change in bank’s lending rates in next 1 year (June 2024 – May 2025): Increase / Remain the same / Decline.
  - Q2a: By what percentage does your bank expect to grow (+ve) or shrink (-ve) credit to the private sector in 2024 relative to 2023? (numeric percentage field).
  - Q3–Q10: Demand for credit, inflation expectations (April 2024 inflation rate was 5.0 percent), exchange rate expectations, perceptions of economic activity, employment comparison, economic growth expectations for 2024 and 2025 - 2027, sentiment on Kenya’s economy in the next 12 months, suggestions to enhance bank’s business environment, contact fields.

*IMF Technical Assistance Report — Preface; Sections 5 and 12; Annex I.*

### Preface ................................................................................................................

### Preface

### Mission overview and team
- At the request of the Central Bank of Kenya (CBK) and in accordance with the AFRITAC East (AFE) workplan, a Monetary and Capital Markets (MCM) Department mission visited Nairobi, Kenya during April 17–21, 2023, to assist the authorities in improving their Forecasting and Policy Analysis System (FPAS).
- Mission team: Ms. Jianping Zhou (Mission Chief, MCM) and Messrs. Edward Anthony Chernis and Taylor Webley (Short-term experts (STXs), Bank of Canada).
- The mission met with the entire CBK forecasting team in the Real Sector Analysis Division (overseen by Dr. Maureen Were), staff from the MPC Secretariat, External Sector and Fiscal Analysis, Monetary Policy Analysis, and Statistics Divisions.
- Mr. Raphael Otieno (Deputy Director, Monetary Policy Analysis Division) joined the opening and concluding sessions; Ms. Zhou met Dr. Patrick Njoroge, the CBK Governor, and Prof. Robert Mudida, Director of the Research Department, at IMF headquarters during the 2023 Spring Meetings.
- Note: Dr. Kamau Thugge has been the new Governor since June 19, 2023.

### Executive Summary — objectives and completed tasks
- Two specific objectives:
  - Review and improve the current MPC survey framework, customize the MPC surveys, enhance data management and analysis, and integrate the survey framework more closely with the FPAS.
  - Improve the nowcasting framework for GDP to provide better inputs for the quarterly projection model (QPM).
- Completed tasks:
  - (i) Reviewed three MPC business surveys and proposed detailed changes to survey questions to streamline surveys and enhance monetary policy relevance.
  - (ii) Created EViews programs for more efficient survey data management and for converting qualitative survey data to quantitative data using the concept of “balance of opinion (BoO)”.
  - (iii) Reviewed existing models for nowcasting CPI and GDP, operationalized a forecast evaluation system to empower future development, and created a disaggregated nowcasting framework for enhancing narrative-building.
  - (iv) Started the process of integrating survey results into nowcasting.
- The mission gave a detailed concluding presentation summarizing recommendations (Attachment I) and CBK participants prepared three mock-up presentations to the MPC (Attachment II).

### Key messages on surveys and nowcasting
- Surveys:
  - Continue to improve and streamline survey questions and use.
  - Further develop tools created during the mission for more efficient survey data management and analysis.
  - Consider changing survey timing so surveys better inform nowcasts and QPM projections.
- Nowcasting:
  - Integrate quantified survey results into nowcasting.
  - Continue refining the nowcasting framework for GDP at a disaggregated level to improve understanding of current developments and storytelling.
  - Continue expanding the database of leading indicators and assess new nowcasting models using the forecast evaluation framework.

### Capacity and next mission focus
- CBK forecasting team is skilled but could become more specialized; current practice has team members handling nowcasting, QPM-based projections, and other tasks to avoid disruption from staff departures, but deeper modeling specialization is recommended.
- Agreed next mission focus: improve the QPM model, considering significant developments in the economy and data rebasing since the original model development.

### Recommendations (high-level)
- Integrate quantified survey results into nowcasting.
- Refine the disaggregated nowcasting framework for GDP to enhance narrative-building.
- Expand database of leading indicators.
- Assess new nowcasting models leveraging the forecast evaluation framework.

*IMF Technical Assistance Report — Preface*

### 12. The TA team assessed the correlation between Balance of Opinion indicators and GDP,

### 12. The TA team assessed the correlation between Balance of Opinion indicators and GDP,

### Survey correlation analysis and findings
- The TA team found a strong correlation between Balance of Opinion (BoO) indicators and GDP (Figure 1).  
- Due to the short sample the results are suggestive and not conclusive; they should be reviewed with a longer sample size for their survey (after CBK compiles past results and gathers new responses).  
- The mission team provided training on how to conduct this analysis.  
- The current results show strong correlations between survey questions and quarterly growth in real GDP, suggesting significant value added given the significant lag in most economic data.  
- Formal BoO definition preserved exactly from source:  
  - "In this case, BoO = (the number of respondents with positive opinion – the number responses with negative opinion) / (total number of respondents)."

### Illustrative example (Figure 2) and interpretation
- Example based on the MPS: specific question on expected change in the Ksh/USD exchange rate with five possibilities: weaken, weaken slightly, same level, strengthen slightly, and strengthen.  
- Top panel (BoO indices): both banks and non-banks expect further depreciation and their views have changed over time.  
- Lower panel: firms’ sector prospect expectations (BoO indices) closely traced actual quarterly GDP growth and CBK forecasts in the last 2-3 years (when survey data is available).  
- Implication: timely survey-based indicators provide high value for nowcasting and narrative building given lags in official economic data. Recommendation: CBK should continue exploring relationships between survey indicators and key macroeconomic time series.

### Automating survey data ingestion
- The TA team developed a proof-of-concept process to import raw Survey Monkey data into EViews to reduce manual entry time and transcription errors.  
- With the program, CBK staff can import survey data more efficiently and generate summary statistics and quantitative indicators such as BoO indices.  
- CBK staff received training and an extensive hands-on tutorial covering: ingesting data, adjusting code for new surveys, and writing small algorithms to generate analytical survey series (e.g., shares of firms providing responses on each question and balance of opinion indices).  
- Key outstanding tasks (preserved numbering and wording):  
  iii. Establishing a well-defined and persistent mnemonic structure for each survey so data ingestion and analysis is repeatable.  
  iv. Writing a program to gather cleaned data and computations from multiple surveys to stack as a time series.  
  v. Writing a program to export key time series of interest to a database for easy access by analysts and the nowcasting tools.  
- Further work will be required by the CBK (or by experts in future missions) to complete the system.

### Improving the nowcasting framework — current platforms and immediate work
- CBK currently has two separate nowcasting platforms for both GDP and CPI producing forecasts for one year ahead (either four quarters or twelve months), though only the first quarterly nowcast is used as the starting point for QPM.  
- For GDP:  
  - Platform 1 (Matlab): A PCA and BVAR model using a variety of domestic and external indicators. The BVAR is currently not functional and the PCA is the primary nowcast tool.  
  - Platform 2 (EViews): Estimates a large set of bridge equations and VARs using domestic indicators. The code had deteriorated and was not being used actively at the start of the mission.  
- For Inflation:  
  - Platform 1 (EViews): A disaggregated nowcasting tool that forecasts each of the components of CPI and adds them up to a CPI nowcast.  
  - Platform 2 (EViews): Same as Platform 2 for GDP, though using only lagged monthly price data for the various sub-components of CPI. CBK staff can choose whether this platform targets inflation or GDP by identifying the target variable of interest.  
- Mission focus and code improvements:  
  - Decision to focus on fixing and developing the EViews-based platform for reasons: (i) EViews provides more flexibility and scope for enhancement; (ii) it generates nowcasts for both GDP and CPI; (iii) the efficacy and usefulness of the BVAR required more time to determine.  
  - Updated EViews code now runs without errors regardless of the variable (CPI or GDP) being predicted. Additional static factor-based models were added (e.g., PC, DFM, FAVAR to be reviewed in next TA missions). Bugs that produced non-descriptive errors were identified and corrected.  
- Automated forecast evaluation system:  
  - The EViews platform now includes an automated system for evaluating nowcasting accuracy for all models generated by the platform. For every nowcasting model, the evaluation system generates RMSEs and other key summary statistics (e.g., adjusted R-squared, residual plots), creates summary tables comparing the statistics, and exports results to excel files for charting and manipulation.  
  - Benefits: (i) simple and transparent comparison of model performance to identify informative predictions; (ii) empowers CBK staff to conduct efficient and flexible model development and to instantaneously compare predictive performance of new models with existing models.  
  - CBK staff received hands-on workshops and successfully customized datasets, specified custom models, and examined forecast evaluation results in real-time.

### Enhancements to bottom-up disaggregate nowcasting
- The nowcasting framework was expanded to predict disaggregated components of GDP. The platform input-output structure was adjusted to streamline generation of nowcasts for individual components. CBK staff can create datasets for each industry component (or custom sub-industries) and run the platform.  
- The program can automatically estimate specified models, evaluate historical performance, and output nowcast predictions alongside evaluation results.  
- A new tool aggregates disaggregated nowcasts into a bottom-up nowcast for headline GDP growth and produces/export contributions to growth for analysis and presentation (Figure 4 example).  
- Current industry set was determined jointly with CBK staff using expert judgement. Recommendation: future work should identify the optimal level of disaggregation by examining nowcasting accuracy across various cuts of the data using the new forecast evaluation platform.  
- Due to limited sample size of most survey-based indicators created during the mission, they were not integrated systematically into the nowcasting platform. Near-term recommendation: actively use survey-based indicators to enhance narratives and inform judgement-based forecasts; once sufficient history exists, leverage the evaluation platform to explore nowcasting benefits of adding survey-based indicators.  
- Recommendation: evaluate benefits of forecast combination in future work (forecast combination likely to yield gains in accuracy for simple bridge equations, VARs, or static factor models).

### Next steps and policy recommendations (summary)
- MPC surveys should continue to be refined and improved to inform forecasting. Questionnaires should be streamlined to reduce response burden and increase policy relevance. The CEO survey, due to relatively short sample size, should be a priority for revision. Consider creating a firm registry to manage and track survey respondents/systematically manage the survey sample.  
- Continue to improve and automate the survey data pipeline, consolidate historical survey results into a time series, and use new analysis tools developed during the mission (for example, balance of opinion, see Appendix IV) to inform the policy process. Explore relationships between survey-based indicators and key macroeconomic time series. Consider moving the survey period at least one week earlier to allow more time to integrate results into the policy process.  
- CBK forecasting teams should continue to improve the nowcasting framework at a disaggregated level to enable better storytelling and policy communication. Use the forecast evaluation framework (Appendix IV) to explore relationships between GDP/inflation and key economic indicators (e.g., exchange rates, Purchasing Managers’ Index (PMIs)) and refine disaggregated nowcasting tools by trimming worst-performing models and exploring forecast combination.  
- Further refinements during the TA arrangement: monitor current framework performance, resolve emerging issues, upscale where possible, revisit the BVAR framework to resolve challenges and improve efficacy or upscale it. Consider additional frameworks: dynamic factor models, factor augmented VARs, averaging bridge, and regARIMAs.  
- Team structure and capacity: current team members perform all forecasting tasks; greater specialization is recommended to encourage deeper modeling skills while maintaining contingency planning.  
- QPM framework: rebuild the QPM framework afresh rather than piecemeal due to rebasing of National Accounts and CPI in 2014 and 2021, which rendered many trends, steady states, and parameters out of sync. The current framework will run until the new upscaled QPM framework is launched. Next mission should be in-person and focus on improving the QPM model.

### Central Bank Business Surveys (Box 1 — summary)
- CBK conducts three main surveys to support MPC decisions:  
  (1) Market Perception Survey (since 2009)  
    - Captures perceptions on inflation, economic growth, demand for credit, growth in credit to private sector, exchange rate, optimism on economic prospects and business environment, current and expected economic conditions, focusing on economic activity and employment.  
    - Targets commercial banks, micro-finance banks, and a sample of non-bank private sector firms from major towns where CBK has presence: Nairobi, Mombasa, Kisumu, Eldoret, Nakuru, Nyeri, Meru and Kisii, in sectors that account for about 78 percent of GDP.  
    - Administered via a direct online survey.  
  (2) CEOs’ Survey (since March 2021)  
    - Captures CEOs’ views on business confidence and optimism, current business activity, outlook for near-term business activity, key drivers and threats to firms’ growth, and strategic priorities over the medium-term.  
    - Targets CEOs of key private sector organizations including members of KAM, KNCCI and KEPSA.  
    - Administered via a direct online survey.  
  (3) Agriculture Sector Survey (since July 2022)  
    - Generates high frequency agriculture sector data on agricultural prices and output expectations across the country.  
    - Targets wholesale markets, retail markets, and farms in major towns including Nairobi, Nairobi Metropolitan area, Naivasha, Gilgil, Nakuru, Narok, Bomet, Nyandarua, Nyahururu, Kisumu, Mombasa, Kisii, Eldoret, Kitale, Meru and Nyeri.  
    - Conducted via face-to-face interviews.

*Source: CBK and IMF staff calculations.*

### Annex I. Mission Concluding Presentation

### Annex I. Mission Concluding Presentation

### Context and mission
- Mission: Kenya-FPAS Mission, April 17-21, 2023; Concluding Meeting with CBK Staff and Management, APRIL21,2023.
- Team: Taylor Webley (STX, Bank of Canada), Tony Chernis (STX, Bank of Canada), Jianping Zhou (IMF HQ, Mission Chief).
- FPAS focus: organization of the forecast process and forecast team; nowcasting framework including the CBK expectations surveys; core macro model Quarterly Projection Model (QPM); CBK surveys and survey data management; CBK nowcasting tools and models; communication. Emphasis: ONLY the interaction between ALL these key components will provide for a strong internal framework to support policymaking!

### Mission objectives
- Review the current MPC business expectations surveys to improve content and design.
- Improve survey data management and data analysis.
- Start developing a nowcasting framework for GDP.
- Integrate survey results into nowcasting to provide better initial conditions for the QPM.
- Prepare a presentation for MPC.

### Key achievements of the mission
- Streamlined the surveys and enhanced their monetary policy relevance: reviewed three MPC business surveys and proposed changes to survey questions.
- Improved survey data management and data analysis:
  - Created an EViews program for more efficient data management.
  - Used the concept of "balance of opinion" to convert qualitative survey data to quantitative data.
- Reviewed existing models for nowcasting GDP and CPI, operationalized a forecast evaluation system, and created a disaggregated nowcasting framework to enhance narrative-building.
- Started integrating survey results into nowcasting.
- CBK staff prepared presentations for MPC.

### Improving CBK expectations surveys
- Current surveys: bi-monthly Market Perceptions Surveys (MPS) since 2009; CEO Surveys (CEO) since March 2021; Agriculture Sector Survey since July 2022.
- Key findings:
  - Surveys provide a rich source of information for the monetary policy process but need better integration into the policy process.
  - Design: could be streamlined and made more policy relevant.
  - Timing: currently surveys are sent about one month before MPC meetings (example: surveys were sent on March 1, surveys back on March 15, too late for the analytical meeting on March 20 and the MPC meeting on March 29).
  - Policy relevance: surveys could provide inputs for MPC meetings.
- TA recommendations:
  - Suggested changes to questions for Market Perceptions Surveys, CEO Surveys, and Agriculture Sector Survey to increase analytical power and reduce response burden.
  - Extend existing survey data further into the past (currently data compiled to around ~2017).
  - Start by sending out surveys one week earlier than current timelines; review experiences and consider moving earlier (two weeks, for example).
- Operational support provided: templates for a firm registry and ETL pipeline; demonstrations of converting qualitative responses to quantitative indicators (Balance of Opinion).

### Improving survey data management and analysis
- Demonstrated Balance of Opinions (BoO) transformation: ( #ofhigher – #oflower ) / (total #of answers ).
- Current practice: entirely manual data entry from SurveyMonkey to concatenation with existing data to generate timeseries.
- Proof-of-concept presented and agreed to be highly beneficial for survey data ingestion.
  - Key advantages: saves significant time on manual data entry and manipulation; reduces margin for error; boosts efficiency of transferring survey indicators/balances of opinion into CBK operational databases/nowcasting tools.
- Provided a template for a firm registry; CBK to consolidate firm contact information into a single database.

### Nowcasting system review and improvements
- Current nowcasting setup:
  - GDP and inflation each have two separate nowcasting platforms.
  - For GDP:
    - Platform 1 (Matlab): APCA and BVAR model using a variety of domestic and external indicators. The BVAR is currently not functional and the PCA is the primary nowcast tool.
    - Platform 2 (EViews): system of simple bridge equations and VARs based on domestic indicators (code had deteriorated and was not being used actively).
  - For Inflation:
    - Platform 1 (EViews): disaggregated nowcasting tool that forecasts each component of CPI and adds them up to a CPI nowcast.
    - Platform 2 (EViews): similar to GDP Platform 2, using only monthly price data.
  - Setup to produce forecasts for four quarters, though only the first quarterly nowcast is used as the starting point for QPM.
- Improvements delivered:
  - Reviewed and updated the database; CBK has added metadata and formalized the full suite of indicators; focus shifted entirely to nowcasting.
  - Thorough review and update of the EViews-based platform so it runs without errors.
  - Added additional static factor–based models to model outputs.
  - Designed and operationalized a model forecast evaluation system within the nowcasting tool that automatically generates RMSEs and other summary statistics and exports results as a table.
  - Built a disaggregated bottom-up nowcasting framework that constructs nowcasts for various industries within GDP and combines them into a headline GDP growth forecast on a nay/ybasis to guide judgment-based forecasts and aid narrative-building for MPC presentations.

### Integrating survey results into nowcasting
- TA focus: improving survey process and integrating survey outputs into nowcasting:
  - Preliminary work on the survey pipeline.
  - Template provided for firm registry and ETL of survey data.
  - Demonstrated conversion of qualitative data to quantitative indicators and examples of how survey data can inform policy processes.
  - Initial analysis shows survey data is informative about the Kenya economy; on average survey results broadly track the economic situation (optimism after COVID; pessimism during election; pessimism around KSH depreciation).
  - Shown that survey indicators have some predictive power, but sample is extremely short; CBK can begin assessing information content as the sample grows and is extended backwards.

### Recommendations (recap) and next steps
- Survey recommendations:
  - Consider changing survey timing so it can inform the nowcast and projection.
  - Create a firm registry to track survey respondents.
  - Further refine and automate the survey data pipeline.
  - Continue consolidating historical survey results into a timeseries.
  - Use the TA survey review as a guideline to revise the surveys.
  - Leverage new analysis tools (BoO) to inform the policy process.
  - Begin assessing the information content of survey indicators as the sample grows and is extended backwards.
- Nowcasting recommendations:
  - Continue to expand the database of leading indicators; look for additional (timely) quarterly series to bring into the dataset.
  - Use the forecast evaluation framework to continuously explore relationships between GDP/inflation and key economic indicators/new data sources (e.g., exchange rates, PMIs).
  - Assess performance of larger VAR models using the "additional models" part of the nowcasting platform.
  - Leverage the bottom-up framework to help tell the short-run economic narrative.
  - Refine the performance of the new disaggregated nowcasting tools by trimming the worst performing models and exploring forecast combination.
- Next steps:
  - The Concluding Presentation finalized and shared with CBK (Monday).
  - Team to prepare the TA report based on the concluding presentation.
  - TA report send to CBK for comment in early May.
  - Planning of the next mission (in-person).
  - Other issues for follow-up.

*Annex I. Mission Concluding Presentation — IMF Technical Assistance Report*

### 5.  CBK Surveys

### 5. CBK Surveys

### Survey types, objectives, targets, and methodology
- Market Perception Survey
  - Objective: Capture perceptions of different firms on selected economic indicators including inflation, economic growth, demand for credit, growth in credit to private sector and exchange rate; assess levels of optimism in the country’s economic prospects and business environment; perspectives on current and expected economic conditions, focusing on economic activity and employment.
  - Target: Commercial banks, micro-finance banks, and a sample of non-bank private sector firms selected from Nairobi, Mombasa, Kisumu, Eldoret, Nakuru, Nyeri, Meru and Kisii.
  - Sectors covered: sectors that account for about 78 percent of GDP.
  - Methodology: questionnaires administered via a direct online survey; responses weighted by market size for banks/microfinance banks and by sector weights for non-bank private firms based on latest sectoral contributions to GDP.
- CEOs’ Survey
  - Objective: Capture CEOs’ views/perceptions on business confidence and optimism, current business activity, outlook for business activity, key drivers and threats to firms’ growth, internal and external factors influencing business outlook, and strategic priorities.
  - Target: CEOs of key private sector organizations including members of KAM, KNCCI and KEPSA.
  - Methodology: questionnaires administered via a direct online survey; conducted every two months since March 2021.
- Agriculture Sector Survey
  - Objective: Generate high frequency agriculture sector data to support monetary policy decisions; obtain indicative information on recent trends in prices and output of agricultural commodities in various markets and farms across the country.
  - Focus: Prices of key agricultural commodities and expectations; agricultural output, acreage, and expectations; use of farm inputs; factors affecting agricultural production; proposals to improve agricultural production.
  - Target: wholesale markets, retail markets, and farms in major towns including Nairobi, Nairobi Metropolitan area, Naivasha, Gilgil, Nakuru, Narok, Bomet, Nyandarua, Nyahururu, Kisumu, Mombasa, Kisii, Eldoret, Kitale, Meru and Nyeri.
  - Methodology: face to face interviews.
- Other ad hoc surveys
  - Survey of Flower Farms (discontinued in March 2021 after evidence of post-COVID recovery).
  - Survey of Hotels (discontinued in May 2022 after evidence of post-COVID recovery; merged with Market Perceptions).

### Key findings — inflation, expectations, and drivers
- Factors respondents expect to provide relief on inflation:
  - Improved supply and distribution of food following the onset of the rains, and food imports expected to reduce the cost of food items (57 percent).
  - Positive spillovers from lower global crude oil prices on the cost of local fuel (43 percent).
- Factors respondents expect to keep inflation elevated:
  - High food prices due to prolonged dry weather conditions and below average rain as forecasted by the weathermen (82 percent).
  - Pass through effects of foreign exchange rate depreciation, low dollar liquidity aggravating supply shocks hence prolonging price pressures, expensive imports (43 percent).
  - High local energy prices impacting transport costs despite declining international oil prices, and upward review of power tariffs (39 percent).
- Market Perception Survey—expected inflation (selected survey months and group averages)
  - March '22 (Mar-Apr 2022): Large banks 5.4; Medium banks 5.7; Small banks 5.7; All banks (weighted by size of bank) 5.5; MFBs 5.5; Non-bank private firms 5.9
  - May '22 (May-Jun 2022): Large banks 7.1; Medium banks 6.9; Small banks 6.8; All banks (weighted by size of bank) 7.1; MFBs 6.7; Non-bank private firms 7.2
  - July '22 (Jul-Aug 2022): Large banks 8.2; Medium banks 8.1; Small banks 7.9; All banks (weighted by size of bank) 8.1; MFBs 7.9; Non-bank private firms 7.5
  - September '22 (Sep-Oct 2022): Large banks 8.6; Medium banks 8.7; Small banks 8.7; All banks (weighted by size of bank) 8.7; MFBs 8.5; Non-bank private firms 8.2
  - November '22 (Nov-Dec 2022): Large banks 9.8; Medium banks 9.7; Small banks 8.7; All banks (weighted by size of bank) 9.7; MFBs 9.7; Non-bank private firms 9.4
  - January '23 (Jan-Feb 2023): Large banks 8.9; Medium banks 9.1; Small banks 9.0; All banks (weighted by size of bank) 9.0; MFBs 9.0; Non-bank private firms 8.8
  - March '23 (Mar-Apr 2023): Large banks 9.2; Medium banks 9.2; Small banks 9.2; All banks (weighted by size of bank) 9.2; MFBs 9.2; Non-bank private firms 9.2
  - February 2023: A ctual inflation (listed as a reference point in the source).
- Agriculture and food price expectations
  - Majority (63 percent) expect retail prices of cereals and grains in April 2023 to either decline (16 percent) or remain unchanged (47 percent).
  - Retail prices of animal products, non-vegetables and some vegetable food items expected to remain unchanged or decline in April 2023; vegetable prices expected to remain elevated in April except for kales and traditional vegetables.
  - Retail prices of most food items increased in March 2023; wheat, loose maize and milk moderated in March 2023 relative to January 2023 following recent harvests and previous short rains.
- Farmers’ constraints to accessing inputs (survey responses)
  - High cost of inputs (67 percent).
  - Lack of finances (18 percent).
  - Lack of knowledge of inputs/Extension officers (8 percent).
  - Poor fertilizer/seeds variety (5 percent).
  - Others (3 percent).

### Economic activity, optimism, and CEO perceptions
- Expectations for near-term economic activity (March–April vs January–February)
  - Factors supporting moderate to strong economic activity: onset of long rains to boost agricultural activity and lower the cost of food improving consumer purchasing power (60 percent); increased private sector credit growth supported by improved private sector performance and resilient services (30 percent).
  - Risks to expected economic activity: high cost of living (92 percent); weak domestic currency, expensive imports, shortage of dollars (44 percent).
- Optimism in Kenya’s economic prospects (next 12 months)
  - Respondents attributed optimism to strategies by the government to improve agricultural production by subsidizing cost of fertilizers and the hustler fund expected to support businesses (49 percent); resilience of Kenya’s private sector supported by growth in industry and services including tourism (43 percent).
  - Main risks to optimism: high cost of living (60 percent); unfavorable weather conditions (50 percent); weakening of local currency (45 percent).
- CEOs’ Survey — concerns about growth prospects and business environment
  - Reported primary concerns for the Kenyan economy: inflation, weakening Shilling, drought, low consumer spending.
  - Reported primary global concerns: lingering war in Ukraine; interest rate hikes in advanced economies; effects of the collapse of two banks in the U.S.
- Business activity snapshots from CEOs’ Survey
  - Q1 2023 vs Q4 2022: mixed results across sectors—higher activity for financial, security, and tourism sectors; largely the same for ICT and wholesale/retail trade; subdued activity for professional services, real estate, and manufacturing (inflation, forex availability, seasonal factors cited). Across sectors, firms reported persistence of elevated prices of goods and services purchased.
  - Q2 2023 outlook: demand/orders, production volumes and sales expected to increase or remain the same for majority; easing of inflation and increased government spending at fiscal year close expected to benefit some sectors; concerns include reduced consumer spending, cost of inputs (electricity, forex, farm inputs), and global conditions.

### Survey operations, data handling, and challenges
- Survey process and timing
  - Timing: First two weeks of every MPC month.
  - Platform: Survey Monkey for electronic responses; some questionnaires received in Word format.
  - Follow-up: Reminders sent by email typically one week after launch; phone calls to follow up.
  - Data analysis: Excel worksheets.
  - Reports: Finalized and formatted during the MPC week and shared with stakeholders by the end of the week.
- Data storage and structure
  - Excel worksheets with data stored in individual worksheets.
  - Time series data available for Market Perception and CEOs Surveys.
- Issues and challenges identified
  - Response rate.
  - Data storage — need for centralization.
  - Target audience — clarity on which survey to respond to.
  - Suitable software.
  - Presentation.

*IMF Technical Assistance Report — 5. CBK Surveys.*

### 5. What top three internal factors could strengthen your company’s outlook over the next 12 months?

### 5. What top three internal factors could strengthen your company’s outlook over the next 12 months?

### Internal factors — options for ranking (1, 2, 3)
- Improved efficiency/innovation
- Skills retention and talent development
- Strengthen product portfolio/develop new products
- Diversification of revenue streams
- Internal measures to contain costs (restructuring, outsourcing etc.)
- Digitization/increased automation
- Strong supply chains
- Union relationships
- Increased marketing/better branding
- Strengthen corporate governance
- Other (please specify)

### External factors — options for ranking (from Question 6)
- Diversification (expansion into new markets, developing new products etc.)
- Increased marketing/better branding
- Research and development
- Other (please specify)
- Containment of the Covid-19 pandemic
- Stable economic environment (controlled inflation, economic growth etc.)
- Enabling business environment/easing of the cost of doing business

### Funding sources in Quarter 2, 2024 (April – June) — reporting template
- Funding — Approximate Percentage
- Own resources
- Bank loans
- Private equity
- New share issue/IPO
- Other
- TOTAL

### Company strengths (Question 8) — options for top three
- Global economic recovery
- Political stability
- Stability of the Kenyan Shilling
- Taxation issues (reduced taxation, tax refunds, tax incentives etc.)
- Reduced corruption
- Government stimulus programs
- Regulatory issues (licensing, county regulations, proportionate regulation, approvals etc.)
- Weather conditions
- Other (please specify)
- Technical capabilities/skilled workforce
- Trusted brands/product quality
- Corporate governance/board/management experience
- Effective supply chains
- Company values

### Strategic priorities (Question 9)
- What are the top 3 strategic priorities for your company over the next 3 years?

### Comments and additional fields
- Comments (if not applicable or other sources of funding)
- E. Any other comments

*IMF Technical Assistance Report | 59–60*

### 10.   Do you have any other comments that you would like to give?

### 10.   Do you have any other comments that you would like to give?

### Themes listed for open comments (checkbox items)
- Thought leadership
- Being customer-centric
- Diversified business
- Technological advancement
- Long presence in the market/history of operations
- Other (please specify)

### Strategic priorities (checkbox grid)
- Improved efficiency
- Cost optimization
- Diversification (market expansion/new products)
- Invest in talent/skills development
- Improve corporate governance
- Sustainable business growth
- Mergers and Acquisitions
- Mobilization of resources
- Digital transformation/technological advancements
- Partnerships/engagement
- Other (please specify)

### Non-bank Private Sector — questionnaire structure and key items
- Respondent identification fields:
  - Name of establishment; Location (Town); Sector; Total number of employees.
- Q1: Demand for credit/funds/financing next 12 months vs last 12 months (very high / high / moderate / low / very low).
  - Q1b: Reasons for demand (order of importance).
  - Q1c: Current access to bank credit (very easy / easy / moderate / difficult / very difficult) and reasons.
  - Q1d: Current sources of financing to be ranked 1–8:
    - Bank loans; Shares; Retained profits; Sale of assets; Family members; Credit suppliers; Government programs; Other (please specify).
- Q2: Perception of economic activity over last three months (March, April and May 2024) — categories: Very strong / Strong / Moderate / Weak / Very weak. Reasons requested.
  - Q2b: Expectations for economic activity in next three months (June – August 2024) with same response categories and reasons.
- Q3: Employment comparison with similar period in 2023 (Higher / About the same / Lower) for Permanent, Contract, Casual.
  - Q3b: Reasons check-list (Grow business/expand; Cost reduction; Attracting New talent; Improved efficiency; Diversify skills; Increase profits by reducing overheads; Replacing exiting staff; Technological advancements; Improve morale; Industry decline; Mergers and acquisition).
  - Q3c: Hiring expectations for 2024 vs 2023 (Definitely will; Probably will; Probably won't; Definitely won't).
- Q4: Company's economic growth rate expectations for:
  - 2024
  - 2025 - 2027
  - Reasons for both periods requested.
- Q5: Inflation expectations
  - April 2024 inflation rate was 5.0 percent.
  - a. Expected inflation rate in the next 3 months (percent): May-24; Jun-24; Jul-24. Reasons for each month requested.
  - b. Expected average inflation rate over:
    - Next 1 year (May 2024 - April 2025)
    - Next 2 years (May 2024 - April 2026)
    - Next 5 years (May 2024 – April 2029)
    - Reasons for each horizon requested.
- Q6: Exchange rate expectations for Ksh/USD
  - Q6a: Next two months (June – July 2024): Weaken / Weaken slightly / Same Level / Strengthen slightly / Strengthen. Reasons requested.
  - Q6b: Next 12 months (June 2024 – May 2025): same response options and reasons.
- Q7: Sentiment on Kenya’s economic prospects in the next 12 months: Very optimistic / Optimistic / Pessimistic / Very pessimistic. Reasons for optimism and pessimism requested.
- Q8: Suggestions to enhance business environment (open text).
- Contact fields: Name and position of person completing the questionnaire; Address; Date questionnaire filled; E-mail address; Telephone Number; Town/Location.

### Commercial and Microfinance Banks — questionnaire structure and key items
- Respondent identification fields: Name of establishment.
- Q1: Expected change in bank’s lending rates in next 1 year (June 2024 – May 2025): Increase / Remain the same / Decline. Reasons for expected direction requested.
- Q2a: By what percentage does your bank expect to grow (+ve) or shrink (-ve) credit to the private sector in 2024 relative to 2023? (numeric percentage field).
  - Q2b: Reasons requested.
- Q3: Demand for credit experienced in April and May 2024: Very high / High / Moderate / Low / Very low. Reasons requested.
  - Q3b: Expected demand for June and July 2024 with same categories and reasons.
- Q4: Inflation expectations
  - April 2024 inflation rate was 5.0 percent.
  - a. Expected inflation rate in the next 3 months (percent): May-24; Jun-24; Jul-24. Reasons for each month requested.
  - b. Bank’s expected average inflation rate over:
    - Next 1 year (May 2024 - April 2025)
    - Next 2 years (May 2024 - April 2026)
    - Next 5 years (May 2024 - April 2029)
    - Reasons for each horizon requested.
- Q5: Exchange rate expectations for Ksh/USD
  - Q5a: Next two months (June – July 2024): Weaken / Weaken slightly / Same Level / Strengthen slightly / Strengthen. Reasons requested.
  - Q5b: Next 12 months (June 2024 – May 2025): same response options and reasons.
- Q6: Perception of economic activity over last three months (March, April, May 2024) — Very strong / Strong / Moderate / Weak / Very weak. Reasons requested.
  - Q6b: Bank’s expectation of economic activity for June – August 2024 with same categories and reasons.
- Q7: Employment comparison with similar period in 2023 for Permanent and Contract (Higher / About the same / Lower).
  - Q7b: Reasons check-list (Grow business/expand; Cost reduction; Attracting New talent; Improved efficiency; Diversify skills; Increase profits by reducing overheads; Replacing exiting staff; Technological advancements; Improve morale; Industry decline; Mergers and acquisition).
  - Q7c: Hiring expectations for 2024 vs 2023 (Definitely will; Probably will; Probably won't; Definitely won't). Q7d: Reasons requested.
- Q8: Bank’s economic growth rate expectations for:
  - 2024
  - 2025 - 2027
  - Reasons requested for both periods.
- Q9: Sentiment on Kenya’s economy in the next 12 months: Very optimistic / Optimistic / Pessimistic / Very pessimistic. Reasons for optimism and pessimism requested.
- Q10: Suggestions to enhance the bank’s business environment (open text). Contact fields: Name and position; Date questionnaire filled; Address; E-mail address; Telephone Number.

*IMF Technical Assistance Report — Market Perceptions Survey Questionnaire (Central Bank of Kenya), May, 2024*

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_Source: https://www.imf.org/-/media/files/publications/tar/2024/english/tarea2024065-print-pdf.pdf_