## 1bwaea2020001 ## Source details **Canonical URL:** [1bwaea2020001](https://www.imf.org/-/media/files/publications/cr/2020/english/1bwaea2020001.pdf) ## Other formats - [Markdown version](/-/media/files/publications/cr/2020/english/1bwaea2020001.pdf.md) - [Structured JSON version](/-/media/files/publications/cr/2020/english/1bwaea2020001.pdf.json) --- ### Summary of mission outcomes and priority recommendations - A technical assistance (TA) mission was conducted by IMF’s Regional Technical Assistance Center for Southern Africa (AFS) during August 19–29, 2019 to assist Statistics Botswana (SB) in improving the quality of the national accounts statistics. - Rebasing of national accounts to a base year of 2016 is underway; rebasing is recommended every five years and ideally requires Supply and Use Tables (SUT) to support coherence checking. - SB revised the release date for the rebase of national accounts estimates from March 2020 to December 2020 and updated the workplan accordingly. - SB has progressed in finalizing the statistical frame and associated weights to compile gross value added (GVA) estimates for industries covered by the economic survey and in estimating GVA for other industries. Areas identified for improvement: Public Administration, Public Health and Public Education; Finance and Insurance; Agriculture; Mining; and Owner-Occupied Dwellings (OOD). - The mission emphasized ongoing work with the Ministry of Finance (MoF) to resolve discrepancies between the Annual National Accounts (ANA), the Statement of Government Operations (SGO), and the Balance of Payments (BoP). The macro-fiscal framework workbook (created in 2014) includes worksheets outlining these discrepancies; MoF indicated the framework could and should be done quarterly and will provide it to SB once developed. - SB finalized industry and product classifications for the SUT (subject to minor changes depending on data availability). Specialized surveys for manufacturing and trade services are being finalized with data collection planned for October 2019. - SB obtained more disaggregated BoP data from the Bank of Botswana (BoB) for 12 key products; mission recommended SB provide the BoB with the SUT product classification (including correspondence to the Harmonized System) to harmonize BoP and national accounts data. Priority recommendations (from Table 1) - September 2019 — Determine the statistical frame and weights for the economic survey. (Responsible: SB) - October 2019 — Undertake additional specialized surveys to collect missing product data. (Responsible: SB) - December 2019 — Compilation of rebased GDP by production estimates. (Responsible: SB) ### Annual National Accounts (ANA) rebase — key findings and recommended actions Findings - Economic census: business listing exercise (2016) identified 14,452 establishments (excluding branches). Economic census (2017) had low response: 3,145 establishments responded. - Initial frame supplemented with approximately 500 large establishments identified from BURS large taxpayer unit; no further reconciliation with taxpayer list initially. - SB identified 27, 248 active taxpayers and have matched to 25 400 using the Tax Identification Number (TIN); SB are attempting to match the remaining using name and address. SB aims to complete reconciliation by the end of September 2019. - SB has compiled GVA estimates for industries not reliant on the economic survey: Public Administration, Public Health and Public Education; Finance and Insurance; Agriculture; Mining; and OOD. - Compensation of employees (COE) data obtained to estimate Public Administration, Public Health and Public Education GVA separately; SB will obtain data to compile a Wage Cost Index (WCI) to deflate these GVA estimates. - SB have data necessary to compile Financial Intermediation Services Indirectly Measured (FISIM) for Finance industry; methods for Insurance and Pension Funds output will follow 2008 SNA sections 6.175 to 6.206 and SB will consult Non-Bank Financial Institutions Regulatory Authority for data availability. - Agriculture GVA: crops, livestock and horticulture subcomponents discussed. Ministry of Agriculture is main data source; harvesting cycles need determining to allocate output across the year. Subsistence farming to be sourced from the Multi-Topic Household Survey (MTHS); base-year data from Agriculture Census. - Mining dominated by diamond mining; data come from establishments and Ministry of Mines but do not necessarily align. BoB and MoF also hold mining-related data; recommended coordination to ensure consistency. - OOD estimates will use Population Census (2011) stock data by dwelling types and MTHS rent data for same dwelling types; adjustments required to move estimates from 2011 to 2016 using more recent MTHS and residential building license data (if available). Recommended actions (exact wording preserved where applicable) - SB to determine the statistical frame and weights for the economic survey. - SB to obtain data to compile a WCI in order to deflate Public Administration, Public Health and Public Education. - SB to determine what data is available to calculate GVA for insurance and pension funds. - SB needs to investigate what data is available on an ongoing basis to improve the estimate of GVA for agriculture. - SB work with the BoB and MoF to ensure the diamond mining data is consistent. - SB to extract stock data by the types of dwellings from the Population Census and rent data from the MTHS for the same dwelling types to estimate OOD. - SB and the MoF work together to resolve the discrepancies between the national accounts and the SGO now and on an ongoing basis. ### Supply and Use Tables (SUT) — findings and actions Findings - SB plans to compile a SUT as part of the rebasing exercise; SUT will enable balancing measures of GDP and produce input-output ratios for ongoing GVA compilation. - Current classifications: 60 industries and 93 products (may change marginally when populating the SUT). - Product-level data are not widely available administratively; specialized surveys are required for trade services (margins), manufacturing, transport, and construction. - Survey instruments for manufacturing and trade services will be finalized shortly; comments provided on the draft manufacturing survey instrument. Data collection expected in October 2019. Construction and transport specialized survey instruments still need drafting. - SB planned to use product-level Foreign Trade data for imports and exports, but this can create discrepancies with BoP and NA totals. Disaggregated BoP data for 12 key products aligned with Foreign Trade data; BoB may have more detailed product-level data. Recommended actions - SB to undertake specialized surveys for manufacturing and retail trade during October 2019. - SB to draft specialized survey instruments for the construction and transport industries. - SB provide the BoB with the SUT product classification and discuss with the BoB the possibility of obtaining the BoP data at that level. ### Strategic issues and timetable - SB revised the release date for rebased estimates from March 2020 to December 2020; the rebasing will include the SUT. - SB revised the workplan and timetable to reflect the new release date; dates considered achievable if the team commits to them. - SB indicated the revised backseries will most likely be released during 2021. The backcast series may be released in two parts — first 2006-2016 and then 1994 to 2005. ### Staffing and institutional capacity - Two temporary staff in national accounts extended for a further two years after approximately 18 months with the national accounts. - Assessment: Very positive — they demonstrated a good understanding of the national accounts and a willingness to build their capacity. - Recommendation: SB could consider keeping them permanently as the resource level in the national accounts is low for the work required to compile ANA and quarterly national accounts on an ongoing basis. ### Detailed technical assessment and recommendations — Results Based Framework Objective: Strengthen compilation and dissemination of data on macroeconomic and financial statistics decision making according to the relevant internationally accepted statistical standard, including developing/improving statistical infrastructure, source data, serviceability and/or metadata. Outcome: Data are compiled and disseminated using the concepts and definitions of the latest manual/guide. - Verifiable indicator: The general framework, concepts and definitions broadly follow the 2008 SNA. - Milestones, targets, comments: - Statistical frame and weights determined and applied to economic survey. - Target Completion Date: May 31, 2019 - Comments: Ongoing. Matching the SB list of establishments with the taxpayer list has progressed but not finalized. Change date to 30 September 2019. - Compilation of rebased GDP by production estimates. - Target Completion Date: December 31, 2019 - Comments: Ongoing. Mining, Finance and Public Administration, Health and Education have been worked on. Estimates for Finance and Insurance, and Agriculture reviewed and some improvements to be made. - Compilation of rebased GDP by expenditure estimates. - Target Completion Date: January 31, 2020 - Comments: Ongoing. Trade data has been aligned to the BoP data. HFCE data is currently being analyzed. - SUT populated and balanced. - Target Completion Date: June 30, 2020 - Comments: Ongoing. Product and Industry classifications have been finalized. - Dissemination of rebased GDP estimates. - Target Completion Date: To be determined. Outcome: Higher frequency data has been compiled and disseminated internally and/or to the public. - Verifiable indicator: National accounts compiled and disseminated on a quarterly or monthly basis. - Milestones, targets, comments: - Review quarterly GDP methodologies and data sources. - Target Completion Date: September 30, 2020 - Comments: Ongoing. Will be undertaken as part of the rebasing exercise. - Dissemination of revised quarterly GDP estimates based on the rebased annual national accounts. - Target Completion Date: September 30, 2020 ### A. Methodology for Deriving a Wage Cost Index - Concept: A wage index for government is calculated by weighting salary levels for each grade using total payments—numbers multiplied by average rate—for the grade. - Key statement: Wages = the price of labor. So a wage index is equivalent to a price index weighted by the total wages paid in each grade. - Example (Hypothetical GSS example): - Table rows reproduced with original numbers and headings as in source: - 2005 2006 Wage index - Number in grade / Average Wage / Total volume Wage bill / Average number / Total Wage bill / Deflated 2005=100 / Weight / Weight % - GS1 10,000 10,000 111,000 11,000 10,000 3.3 3.3 3.6 - DGS2 9,000 18,000 29,900 19,800 18,000 5.9 5.9 6.5 - 8 48,000 32,000 48,800 35,200 32,000 10.5 10.5 11.5 - 7 66,000 36,000 76,600 46,200 36,000 11.8 11.8 13.0 - 6 64,000 24,000 54,400 22,000 24,000 7.9 7.9 8.6 - 5 203,000 60,000 203,300 66,000 60,000 19.6 19.6 21.6 - 4 302,000 60,000 302,200 66,000 60,000 19.6 19.6 21.6 - 3 231,200 27,600 231,320 30,360 27,600 9.0 9.0 9.9 - 2 101,000 10,000 101,100 11,000 10,000 3.3 3.3 3.6 - 1 40,700 28,000 40,770 30,800 28,000 9.2 9.2 10.1 - TOTAL 1,423 05,600 1,423 38,360 307,600 305,600 100.0 100.0 110.0 - Growth results from example: - Real Growth 0.7% - Nominal Growth 10.7% - Scenario 1 described: - All wages up 10% - No change in total numbers, but 1 person promoted from grade 6 to grade 7. This promotion is equivalent to growth as it is assumed that persons at a higher grade have greater output, as a result of their greater experience. - Note: The example shows annual index with wages increased on January 1; a monthly index can be calculated similarly. Example monthly series (if increase occurs beginning of July): - Jan 100 - Feb 100 - Mar 100 - Apr 100 - May 100 - Jun 100 - Jul 110 - Aug 110 - Sep 110 - Oct 110 - Nov 110 - Dec 110 - Annual Average 105 ### B. Simple Method for Estimating FISIM - Context: Using reference rates to determine level and allocation of FISIM is theoretically attractive and expected (for example in the IMF’s Data Quality Assessment Framework) but measurement is not straightforward. - Practical considerations: - For many smaller developing countries the procedure complicates measurement and typically makes little difference to the level of GDP. - Allocation is highly sensitive to choice of reference rate, with unpredictable results when rates are varying. - In many smaller developing countries, a service-free, risk-free, market-determined rate is not observable (e.g., inter-bank lending market small or unrepresentative or at an artificial or controlled rate). - Simple, objective, transparent alternative: - Calculate reference rate rr as the simple average of the actual lending and borrowing rates: - Definitions: - yL = stock of loans - yD = stock of deposits - rL = rate on Loans - rD = rate on Deposits - rr = (rL + rD)/2 - The Output of FISIM = (rL - rr) yL + (rr - rD) yD - Data requirements and allocation: - Required data are readily available from banks' profit and loss accounts. - Allocation to sectors and activities requires additional data, such as on the distribution of lending across activities. - Important to obtain data for loans and deposits of the household sector, which are usually available. - By convention, government could be exempt from such allocation. - Note: The 2008 SNA advises against this approach, but it is often used and gives sensible results where the reference rate is unavailable or gives unsuitable results. ### C. Officials Met During the Mission - Dr. Burton Mguni — Statistics Botswana (Statistician General) - Malebogo Kerekang — Statistics Botswana (Deputy Statistician General) - Ketso Makhumalo — Statistics Botswana (Acting Director of Economic Statistics) - Lekoko Simako — Statistics Botswana - Winsten Kabo — Statistics Botswana - Chandler Madisa — Statistics Botswana - Boitumelo Kobua — Statistics Botswana - Phemelo Ntwayapelo — Statistics Botswana - Godiraone Gaolaolwe — Statistics Botswana - Michael Andina — Statistics Botswana - Tapologo Baakile — Statistics Botswana - Grace Mphetolang — Statistics Botswana - Phetogo Zambezi — Statistics Botswana - Eden Onyadile — Statistics Botswana - Kebonyethebe Johane — Statistics Botswana - Banabo Tshupeng — Statistics Botswana - Mavis Mogami — Statistics Botswana - Temba Sibanda — Statistics Botswana - Jimmy George — Statistics Botswana - Susan Matroos — Statistics Botswana - Golebaone David — Statistics Botswana - Nametso Kgosiyame — Statistics Botswana - Doulphy Nkele — eBotswana - Junior Mooketsi — eBotswana - Keneilwe Lephoi — eBotswana/Yaronafm *IMF Country Report No. 20/53, Technical Assistance Report — Report on National Accounts Mission, October 2019.* ### Section 1 ### 1bwaea2020001 - Section 1 ### Summary of mission outcomes and priority recommendations - A technical assistance (TA) mission was conducted by IMF’s Regional Technical Assistance Center for Southern Africa (AFS) during August 19–29, 2019 to assist Statistics Botswana (SB) in improving the quality of the national accounts statistics. - Rebasing of national accounts to a base year of 2016 is underway; rebasing is recommended every five years and ideally requires Supply and Use Tables (SUT) to support coherence checking. - SB revised the release date for the rebase of national accounts estimates from March 2020 to December 2020 and updated the workplan accordingly. - SB has progressed in finalizing the statistical frame and associated weights to compile gross value added (GVA) estimates for industries covered by the economic survey and in estimating GVA for other industries. Areas identified for improvement: Public Administration, Public Health and Public Education; Finance and Insurance; Agriculture; Mining; and Owner-Occupied Dwellings (OOD). - The mission emphasized ongoing work with the Ministry of Finance (MoF) to resolve discrepancies between the Annual National Accounts (ANA), the Statement of Government Operations (SGO), and the Balance of Payments (BoP). The macro-fiscal framework workbook (created in 2014) includes worksheets outlining these discrepancies; MoF indicated the framework could and should be done quarterly and will provide it to SB once developed. - SB finalized industry and product classifications for the SUT (subject to minor changes depending on data availability). Specialized surveys for manufacturing and trade services are being finalized with data collection planned for October 2019. - SB obtained more disaggregated BoP data from the Bank of Botswana (BoB) for 12 key products; mission recommended SB provide the BoB with the SUT product classification (including correspondence to the Harmonized System) to harmonize BoP and national accounts data. Priority recommendations (from Table 1) - September 2019 — Determine the statistical frame and weights for the economic survey. (Responsible: SB) - October 2019 — Undertake additional specialized surveys to collect missing product data. (Responsible: SB) - December 2019 — Compilation of rebased GDP by production estimates. (Responsible: SB) ### Annual National Accounts (ANA) rebase — key findings and recommended actions Findings - Economic census: business listing exercise (2016) identified 14,452 establishments (excluding branches). Economic census (2017) had low response: 3,145 establishments responded. - Initial frame supplemented with approximately 500 large establishments identified from BURS large taxpayer unit; no further reconciliation with taxpayer list initially. - SB identified 27, 248 active taxpayers and have matched to 25 400 using the Tax Identification Number (TIN); SB are attempting to match the remaining using name and address. SB aims to complete reconciliation by the end of September 2019. - SB has compiled GVA estimates for industries not reliant on the economic survey: Public Administration, Public Health and Public Education; Finance and Insurance; Agriculture; Mining; and OOD. - Compensation of employees (COE) data obtained to estimate Public Administration, Public Health and Public Education GVA separately; SB will obtain data to compile a Wage Cost Index (WCI) to deflate these GVA estimates. - SB have data necessary to compile Financial Intermediation Services Indirectly Measured (FISIM) for Finance industry; methods for Insurance and Pension Funds output will follow 2008 SNA sections 6.175 to 6.206 and SB will consult Non-Bank Financial Institutions Regulatory Authority for data availability. - Agriculture GVA: crops, livestock and horticulture subcomponents discussed. Ministry of Agriculture is main data source; harvesting cycles need determining to allocate output across the year. Subsistence farming to be sourced from the Multi-Topic Household Survey (MTHS); base-year data from Agriculture Census. - Mining dominated by diamond mining; data come from establishments and Ministry of Mines but do not necessarily align. BoB and MoF also hold mining-related data; recommended coordination to ensure consistency. - OOD estimates will use Population Census (2011) stock data by dwelling types and MTHS rent data for same dwelling types; adjustments required to move estimates from 2011 to 2016 using more recent MTHS and residential building license data (if available). Recommended actions (exact wording preserved where applicable) - SB to determine the statistical frame and weights for the economic survey. - SB to obtain data to compile a WCI in order to deflate Public Administration, Public Health and Public Education. - SB to determine what data is available to calculate GVA for insurance and pension funds. - SB needs to investigate what data is available on an ongoing basis to improve the estimate of GVA for agriculture. - SB work with the BoB and MoF to ensure the diamond mining data is consistent. - SB to extract stock data by the types of dwellings from the Population Census and rent data from the MTHS for the same dwelling types to estimate OOD. - SB and the MoF work together to resolve the discrepancies between the national accounts and the SGO now and on an ongoing basis. ### Supply and Use Tables (SUT) — findings and actions Findings - SB plans to compile a SUT as part of the rebasing exercise; SUT will enable balancing measures of GDP and produce input-output ratios for ongoing GVA compilation. - Current classifications: 60 industries and 93 products (may change marginally when populating the SUT). - Product-level data are not widely available administratively; specialized surveys are required for trade services (margins), manufacturing, transport, and construction. - Survey instruments for manufacturing and trade services will be finalized shortly; comments provided on the draft manufacturing survey instrument. Data collection expected in October 2019. Construction and transport specialized survey instruments still need drafting. - SB planned to use product-level Foreign Trade data for imports and exports, but this can create discrepancies with BoP and NA totals. Disaggregated BoP data for 12 key products aligned with Foreign Trade data; BoB may have more detailed product-level data. Recommended actions - SB to undertake specialized surveys for manufacturing and retail trade during October 2019. - SB to draft specialized survey instruments for the construction and transport industries. - SB provide the BoB with the SUT product classification and discuss with the BoB the possibility of obtaining the BoP data at that level. ### Strategic issues and timetable - SB revised the release date for rebased estimates from March 2020 to December 2020; the rebasing will include the SUT. - SB revised the workplan and timetable to reflect the new release date; dates considered achievable if the team commits to them. - SB indicated the revised backseries will most likely be released during 2021. The backcast series may be released in two parts — first 2006-2016 and then 1994 to 2005. *IMF Country Report No. 20/53, Technical Assistance Report — Report on National Accounts Mission, October 2019.* ### Section 2 ### 1bwaea2020001 - Section 2 ### Staffing and institutional capacity - Two temporary staff in national accounts extended for a further two years after approximately 18 months with the national accounts. - Assessment: Very positive — they demonstrated a good understanding of the national accounts and a willingness to build their capacity. - Recommendation: SB could consider keeping them permanently as the resource level in the national accounts is low for the work required to compile ANA and quarterly national accounts on an ongoing basis. ### Detailed technical assessment and recommendations — Results Based Framework Objective: Strengthen compilation and dissemination of data on macroeconomic and financial statistics decision making according to the relevant internationally accepted statistical standard, including developing/improving statistical infrastructure, source data, serviceability and/or metadata. Outcome: Data are compiled and disseminated using the concepts and definitions of the latest manual/guide. - Verifiable indicator: The general framework, concepts and definitions broadly follow the 2008 SNA. - Milestones, targets, comments: - Statistical frame and weights determined and applied to economic survey. - Target Completion Date: May 31, 2019 - Comments: Ongoing. Matching the SB list of establishments with the taxpayer list has progressed but not finalized. Change date to 30 September 2019. - Compilation of rebased GDP by production estimates. - Target Completion Date: December 31, 2019 - Comments: Ongoing. Mining, Finance and Public Administration, Health and Education have been worked on. Estimates for Finance and Insurance, and Agriculture reviewed and some improvements to be made. - Compilation of rebased GDP by expenditure estimates. - Target Completion Date: January 31, 2020 - Comments: Ongoing. Trade data has been aligned to the BoP data. HFCE data is currently being analyzed. - SUT populated and balanced. - Target Completion Date: June 30, 2020 - Comments: Ongoing. Product and Industry classifications have been finalized. - Dissemination of rebased GDP estimates. - Target Completion Date: To be determined. Outcome: Higher frequency data has been compiled and disseminated internally and/or to the public. - Verifiable indicator: National accounts compiled and disseminated on a quarterly or monthly basis. - Milestones, targets, comments: - Review quarterly GDP methodologies and data sources. - Target Completion Date: September 30, 2020 - Comments: Ongoing. Will be undertaken as part of the rebasing exercise. - Dissemination of revised quarterly GDP estimates based on the rebased annual national accounts. - Target Completion Date: September 30, 2020 ### A. Methodology for Deriving a Wage Cost Index - Concept: A wage index for government is calculated by weighting salary levels for each grade using total payments—numbers multiplied by average rate—for the grade. - Key statement: Wages = the price of labor. So a wage index is equivalent to a price index weighted by the total wages paid in each grade. - Example (Hypothetical GSS example): - Table rows reproduced with original numbers and headings as in source: - 2005 2006 Wage index - Number in grade / Average Wage / Total volume Wage bill / Average number / Total Wage bill / Deflated 2005=100 / Weight / Weight % - GS1 10,000 10,000 111,000 11,000 10,000 3.3 3.3 3.6 - DGS2 9,000 18,000 29,900 19,800 18,000 5.9 5.9 6.5 - 8 48,000 32,000 48,800 35,200 32,000 10.5 10.5 11.5 - 7 66,000 36,000 76,600 46,200 36,000 11.8 11.8 13.0 - 6 64,000 24,000 54,400 22,000 24,000 7.9 7.9 8.6 - 5 203,000 60,000 203,300 66,000 60,000 19.6 19.6 21.6 - 4 302,000 60,000 302,200 66,000 60,000 19.6 19.6 21.6 - 3 231,200 27,600 231,320 30,360 27,600 9.0 9.0 9.9 - 2 101,000 10,000 101,100 11,000 10,000 3.3 3.3 3.6 - 1 40,700 28,000 40,770 30,800 28,000 9.2 9.2 10.1 - TOTAL 1,423 05,600 1,423 38,360 307,600 305,600 100.0 100.0 110.0 - Growth results from example: - Real Growth 0.7% - Nominal Growth 10.7% - Scenario 1 described: - All wages up 10% - No change in total numbers, but 1 person promoted from grade 6 to grade 7. This promotion is equivalent to growth as it is assumed that persons at a higher grade have greater output, as a result of their greater experience. - Note: The example shows annual index with wages increased on January 1; a monthly index can be calculated similarly. Example monthly series (if increase occurs beginning of July): - Jan 100 - Feb 100 - Mar 100 - Apr 100 - May 100 - Jun 100 - Jul 110 - Aug 110 - Sep 110 - Oct 110 - Nov 110 - Dec 110 - Annual Average 105 ### B. Simple Method for Estimating FISIM - Context: Using reference rates to determine level and allocation of FISIM is theoretically attractive and expected (for example in the IMF’s Data Quality Assessment Framework) but measurement is not straightforward. - Practical considerations: - For many smaller developing countries the procedure complicates measurement and typically makes little difference to the level of GDP. - Allocation is highly sensitive to choice of reference rate, with unpredictable results when rates are varying. - In many smaller developing countries, a service-free, risk-free, market-determined rate is not observable (e.g., inter-bank lending market small or unrepresentative or at an artificial or controlled rate). - Simple, objective, transparent alternative: - Calculate reference rate rr as the simple average of the actual lending and borrowing rates: - Definitions: - yL = stock of loans - yD = stock of deposits - rL = rate on Loans - rD = rate on Deposits - rr = (rL + rD)/2 - The Output of FISIM = (rL - rr) yL + (rr - rD) yD - Data requirements and allocation: - Required data are readily available from banks' profit and loss accounts. - Allocation to sectors and activities requires additional data, such as on the distribution of lending across activities. - Important to obtain data for loans and deposits of the household sector, which are usually available. - By convention, government could be exempt from such allocation. - Note: The 2008 SNA advises against this approach, but it is often used and gives sensible results where the reference rate is unavailable or gives unsuitable results. ### C. Officials Met During the Mission - List of officials and institutions (preserved exactly as in source): - Dr. Burton Mguni — Statistics Botswana (Statistician General) - Malebogo Kerekang — Statistics Botswana (Deputy Statistician General) - Ketso Makhumalo — Statistics Botswana (Acting Director of Economic Statistics) - Lekoko Simako — Statistics Botswana - Winsten Kabo — Statistics Botswana - Chandler Madisa — Statistics Botswana - Boitumelo Kobua — Statistics Botswana - Phemelo Ntwayapelo — Statistics Botswana - Godiraone Gaolaolwe — Statistics Botswana - Michael Andina — Statistics Botswana - Tapologo Baakile — Statistics Botswana - Grace Mphetolang — Statistics Botswana - Phetogo Zambezi — Statistics Botswana - Eden Onyadile — Statistics Botswana - Kebonyethebe Johane — Statistics Botswana - Banabo Tshupeng — Statistics Botswana - Mavis Mogami — Statistics Botswana - Temba Sibanda — Statistics Botswana - Jimmy George — Statistics Botswana - Susan Matroos — Statistics Botswana - Golebaone David — Statistics Botswana - Nametso Kgosiyame — Statistics Botswana - Doulphy Nkele — eBotswana - Junior Mooketsi — eBotswana - Keneilwe Lephoi — eBotswana/Yaronafm *Source: 1bwaea2020001 - Section 2 (PDF)* --- _Source: https://www.imf.org/-/media/files/publications/cr/2020/english/1bwaea2020001.pdf_