I. Introduction

This paper presents a framework for analyzing the effectiveness of technical assistance provided by the West Africa Regional Technical Assistance Center (AFRITAC West or AFW) in the area of national accounts using the Technical Assistance Information Management System (TAIMS), which is the Fund’s archive of core technical assistance information and documentation (see Box 1). AFW beneficiary countries are Benin, Burkina Faso, Côte d’Ivoire, Guinea, Guinea-Bissau, Mali, Mauritania, Niger, Senegal, and Togo. National accounts technical assistance projects are in place for all AFW countries, except Mauritania, thus providing a suitable country group for analysis. Also, for these countries, the national accounts projects have benefited from a systematic use of TAIMS; a practice that it is still lagging in most IMF regional technical assistance centers around the world.

The goal is to respond to the request by the AFW Steering Committee to report on technical assistance “ultimate outcomes” rather than on “inputs” (i.e., the number of national accounts missions fielded by the regional technical assistance center), which has been the practice to date. We proceed in two steps towards that goal. First, we use the Data Reports on the Observance of Standards and Codes (Data ROSCs) prepared for 13 African countries, including 3 AFW countries, as well as input from IMF country teams² to AFW countries, to assess the data dimensions in need for improvement and thus, the “ultimate outcome” of the national accounts’ technical assistance project. Second, we reorganize the information contained in TAIMS using the Fund’s Data Quality Assessment Framework (DQAF) to make it comparable/consistent with the Data ROSCs’ data quality assessments. In principle, a strong consistency between the actual provision of technical assistance (as documented in TAIMS) and the reported data weaknesses (as reported by the Data ROSCs and IMF country teams) should increase the likelihood that the project’s “ultimate outcomes” would be attained within a reasonable time frame and budget.

Our analysis of TAIMS data concludes that the “ultimate outcome” of producing and disseminating robust national accounts is work in progress, with AFW’s technical assistance efforts mainly focusing on source data assessments and methodological issues underpinning the compilation of national accounts. The pending challenge, however, is to broaden the missions’ reach/scope and further support a more timely production and dissemination of national accounts data, which are pending challenges according to information contained in the Data ROSCs and input collected from IMF mission teams to AFW member countries. Striking the balance between data production and dissemination would be a challenge for data producers and users in AFW member countries. No doubt, without appropriate data sources, there is no technical assistance on methodology that could be successful. Yet, the international experience suggests that releasing data on a timely basis, with due reference to the data limitations, can raise the credibility of the national statistical offices and trigger a virtuous cycle, in which more data eventually results in better data, as data producers respond to pressures from data users.³

The Project Framework Summaries (PFSs) stored in TAIMS also suggest that the implementation status of measurable targets included in the national accounts technical assistance projects has varied across countries. Implementation has been strong in AFW countries which either have a relatively developed statistical system that gives countries a good starting point (Senegal), are rapidly catching up in terms of data production and dissemination after years of political crisis (Côte d’Ivoire), or are prompt to complete the updating of their national accounts supported by an array of new data sources (Niger). For other AFW country cases, the implementation status of major project targets has been weak and may remain a major technical challenge for years to come.

Moreover, the analysis points to a diverse technical assistance implementation record, not closely linked to the intensity of mission allocation. Several countries have managed to advance towards achieving their ultimate outcomes with relatively little support from AFW. This strengthens the case for enhanced TAIMS reporting on the circumstances surrounding the provision of AFW technical assistance, such as, for example, the role of other technical assistance providers and donors (e.g., multilateral/bilateral support from AFRISTAT, the African Development Bank, the World Bank, and other development partners).

This paper contributes to the debate on management by results regarding the provision of technical assistance by the Africa Regional Technical Assistance Centers (AFRITACs). It focuses on national accounts statistics, an area for which there exists a simple, yet rigorous, framework for data quality analysis: the Fund’s DQAF. However, for most other areas of IMF technical assistance (e.g., fiscal and financial markets’ development), standardized definitions of objectives/outcomes/indicators are still missing. Zeroing on a definitive approach to assess the effectiveness of the AFRITACs’ technical assistance delivery is also likely to require input and feedback from main stakeholders involved.

The structure of this paper is as follows. Section II analyzes the data users’ survey results and the IMF expert ratings on national accounts statistics, as reported in Data ROSCs prepared for 13 Sub-Saharan African countries, including 3 AFW member countries. It also appraises the IMF country teams’ replies to a questionnaire on national accounts issues. The analysis confirms that data users, IMF compilation experts, and IMF mission teams view the production and timely dissemination of national accounts data as the ultimate objective of technical assistance. Section III sketches the proposed framework to monitor progress towards achieving that objective using TAIMS. The framework is applied using AFW’s mission documentation stored in TAIMS for FY06-09. Section IV summarizes our findings and elaborates briefly on possible next steps.

II. Defining the National Accounts Project’s “Ultimate Outcome”

Two main sources were consulted to assess the “ultimate outcome” of AFW’s technical assistance program in the area of national accounts: (i) the Data ROSC prepared for African countries in recent years; and (ii) the IMF mission teams’ replies to a questionnaire on national accounts’ issues. Canvassing views from main data users and stakeholders is critical for determining the scope of technical assistance, its implementation timeline and the intensity of the technical support to AFW member countries. As shown below, all partner groups agreed on the need to work on the production of national accounts data with a minimum standard of accuracy and reliability, while, at the same time, ensuring timely data dissemination.

The information derived from the Data ROSCs is rich in two ways. First, it includes the IMF statistical experts’ assessments on the various aspects/dimensions of data collection, processing and dissemination in the respective countries, which are measured against best international practices. In essence, the experts’ assessments highlight the data dimensions in need of improvement, and thus the scope of future technical assistance. Second, for a selected number of cases, the reports include information on surveys that were launched by the Data ROSC missions to collect information/views from principal local users (i.e., private sector businesses, embassies, public agencies, and national and regional institutions) on the official statistics.

A survey of IMF country teams updates the Data ROSCs’ user surveys and highlights the concerns of economists working closely with policymakers in the context of surveillance and/or Fund programs. Currently, all AFW member countries, except Mauritania, have active PRGF programs. Financial programming exercises underpinning those programs rely, to a significant degree, on the available national accounts data.

A. The Data ROSCs: Experts’ and Users’ Views

Data ROSCs are one of the IMF instruments to support countries’ efforts to compile macroeconomic statistics according to best international practices.⁴ To date, Data ROSCs have been undertaken for about 90 IMF member countries. Data ROSCs contain IMF statistical experts’ assessments on main macroeconomic statistics within six data quality dimensions (Box 2). The data quality dimensions review institutional issues—such as transparency and professionalism in the data compilation process—as well as the professional rigor of the existing data compilation methods and dissemination practices.⁵

The Data ROSC experts’ assessments on each data quality dimension are further summarized in the form of ratings depending on the degree of adherence to internationally-accepted best compilation practices. An “O” rating indicates that best practices are observed in the country; “LO” means that best practices are largely observed; “LNO” indicates that best practices are largely not observed; and “NO” that best practices are not observed.

A total of 13 African countries, including 3 AFW member countries, have undertaken Data ROSCs to date. The data quality ratings assigned to national accounts data in all African countries point to a number of data weaknesses, which are further heightened in the case of AFW member countries (Table 1). In particular, data accuracy and reliability (DQAF 3) shows the highest need for improvement, followed by shortcomings with methodological soundness (DQAF 2) and data serviceability (DQAF 4):

• National accounts’ data accuracy and reliability (DQAF 3) is impaired by poor quality of the source data used in the national accounts’ compilation. Limited information about the informal sector and the use of often-obsolete household budget and consumption surveys undermine the source data quality. There are also problems with the soundness of statistical techniques applied to derive main aggregates (e.g., use of outdated input-output tables for obtaining value added estimates, lack of adequate price indices to deflate different macroeconomic aggregates) and ad-hoc practices regarding data revisions (i.e., timing and analysis of the factors explaining the revised data).

• Methodological soundness (DQAF 2) is weakened by a limited application across countries of concepts and definitions recommended by 1993 System of National Accounts (1993 SNA), in part because of source data limitations.

• Data serviceability (DQAF 4), particularly data timeliness and periodicity, is hampered by the existence of long lags between the publication of different versions of national accounts (i.e., provisional, revised, and final estimates). This data weakness is particularly severe in the case of AFW member countries, whose data quality ratings for periodicity and timeliness are poor relative to other African countries. Because of the long delays in producing and publishing data, AFW users tend to resort to national accounts estimates prepared by data producers other than the national statistical offices using more aggregate (and usually nontransparent) compilation and estimation techniques.

Table 1.

Africa: Country ROSC Ratings–National Accounts

(By DQAF Data Quality Dimensions)

Source: ROSC reports; www.imf.org.

Table 1.

Africa: Country ROSC Ratings–National Accounts

(By DQAF Data Quality Dimensions)

	AFRITAC West Countries			Other African Countries
DQAF	Burkina Faso	Niger	Senegal	Botswana	The Gambia	Kenya	Mauritius	Mozambique	Namibia	South Africa	Tanzania	Uganda	Zambia
0. Prerequisites of quality
0.1 Legal and institutional environment	LO	LO	LO	O	O	LO	O	…	LNO	O	O	O	LNO
0.2 Resources	LNO	LNO	LNO	LO	LNO	LNO	LO	LNO	LO	LO	LO	LO	LNO
0.3 Relevance	LO	LO	LO	O	LNO	LO	O	…	LO	O	O	O	LO
0.4 Other quality management	…	LO	…	LO	LNO	O	O	LO	…	…	…	O	LO
1. Assurances of integrity
1.1 Professionalism	LO	LO	O	O	LO	O	O	…	LO	O	O	O	LO
1.2 Transparency	LO	LO	LO	LO	LO	LO	O	LO	LNO	O	LO	LO	LO
1.3 Ethical standards	LO	LO	O	O	LO	O	O	…	LO	O	O	O	O
2. Methodological soundness
2.1 Concepts and definitions	LO	LO	LO	LO	LNO	O	O	…	O	O	LO	LO	LO
2.2 Scope	LNO	LO	LO	LO	LNO	LNO	LO	LO	O	LO	LO	LO	LO
2.3 Classification/sectorization	LNO	LNO	LO	LNO	O	LNO	O	…	O	LO	LO	O	LO
2.4 Basis for recording	LO	LO	LNO	LO	LO	LO	LO	LO	LO	LO	LO	LO	LO
3. Accuracy and reliability
3.1 Source data	LNO	LNO	LNO	LO	LNO	LNO	LO	LNO	LNO	LO	LNO	LO	LNO
3.2 Assessment of source data	LO	LO	…	LO	O	LO	O	LO	LO	O	O	O	LO
3.3 Statistical techniques	LNO	LNO	LNO	LNO	LNO	LNO	LNO	LO	LO	O	LNO	LO	LNO
3.4 Assess. validation of interm. data/outputs	LO	LO	LNO	LO	LO	LO	LO	…	LO	O	LO	O	LNO
3.5 Revision studies	LNO	NO	LNO	LO	NO	LNO	O	LO	O	O	LO	O	LNO
4. Serviceability
4.1 Periodicity and timeliness	LNO	LNO	LO	O	O	O	O	O	O	O	O	O	O
4.2 Consistency	LO	LO	LO	LO	O	O	LO	LO	LO	O	O	O	O
4.3 Revision policy and practice	LNO	LO	LO	LNO	LO	LO	O	LO	O	O	LO	LO	LO
5. Accessibility
5.1 Data accessibility	LNO	LO	LO	LO	LNO	LO	LO	LO	LO	O	O	LO	LNO
5.2 Metadata accessibility	LO	LNO	LO	LO	LNO	O	O	LNO	LO	O	LO	LO	O
5.3 Assistance to users	LO	LO	O	O	LNO	LO	O	LNO	LO	O	LNO	O	LO

Source: ROSC reports; www.imf.org.

View Table

Table 1.

Africa: Country ROSC Ratings–National Accounts

(By DQAF Data Quality Dimensions)

	AFRITAC West Countries			Other African Countries
DQAF	Burkina Faso	Niger	Senegal	Botswana	The Gambia	Kenya	Mauritius	Mozambique	Namibia	South Africa	Tanzania	Uganda	Zambia
0. Prerequisites of quality
0.1 Legal and institutional environment	LO	LO	LO	O	O	LO	O	…	LNO	O	O	O	LNO
0.2 Resources	LNO	LNO	LNO	LO	LNO	LNO	LO	LNO	LO	LO	LO	LO	LNO
0.3 Relevance	LO	LO	LO	O	LNO	LO	O	…	LO	O	O	O	LO
0.4 Other quality management	…	LO	…	LO	LNO	O	O	LO	…	…	…	O	LO
1. Assurances of integrity
1.1 Professionalism	LO	LO	O	O	LO	O	O	…	LO	O	O	O	LO
1.2 Transparency	LO	LO	LO	LO	LO	LO	O	LO	LNO	O	LO	LO	LO
1.3 Ethical standards	LO	LO	O	O	LO	O	O	…	LO	O	O	O	O
2. Methodological soundness
2.1 Concepts and definitions	LO	LO	LO	LO	LNO	O	O	…	O	O	LO	LO	LO
2.2 Scope	LNO	LO	LO	LO	LNO	LNO	LO	LO	O	LO	LO	LO	LO
2.3 Classification/sectorization	LNO	LNO	LO	LNO	O	LNO	O	…	O	LO	LO	O	LO
2.4 Basis for recording	LO	LO	LNO	LO	LO	LO	LO	LO	LO	LO	LO	LO	LO
3. Accuracy and reliability
3.1 Source data	LNO	LNO	LNO	LO	LNO	LNO	LO	LNO	LNO	LO	LNO	LO	LNO
3.2 Assessment of source data	LO	LO	…	LO	O	LO	O	LO	LO	O	O	O	LO
3.3 Statistical techniques	LNO	LNO	LNO	LNO	LNO	LNO	LNO	LO	LO	O	LNO	LO	LNO
3.4 Assess. validation of interm. data/outputs	LO	LO	LNO	LO	LO	LO	LO	…	LO	O	LO	O	LNO
3.5 Revision studies	LNO	NO	LNO	LO	NO	LNO	O	LO	O	O	LO	O	LNO
4. Serviceability
4.1 Periodicity and timeliness	LNO	LNO	LO	O	O	O	O	O	O	O	O	O	O
4.2 Consistency	LO	LO	LO	LO	O	O	LO	LO	LO	O	O	O	O
4.3 Revision policy and practice	LNO	LO	LO	LNO	LO	LO	O	LO	O	O	LO	LO	LO
5. Accessibility
5.1 Data accessibility	LNO	LO	LO	LO	LNO	LO	LO	LO	LO	O	O	LO	LNO
5.2 Metadata accessibility	LO	LNO	LO	LO	LNO	O	O	LNO	LO	O	LO	LO	O
5.3 Assistance to users	LO	LO	O	O	LNO	LO	O	LNO	LO	O	LNO	O	LO

Source: ROSC reports; www.imf.org.

Box 2:

Data Quality Dimensions Assessed in Data ROSCs

Data quality dimensions assessed under the Data ROSCs review institutional issues, as well as the rigor of the existing data compilation methods and dissemination practices. All areas of analysis are classified under the DQAF, using specific codes (0 to 5). Institutional issues cover the legal environment underpinning the compilation of macroeconomic statistics and the professional quality of the individuals involved in the compilation process. Mythological rigor covers aspects of data concepts and definitions, source data quality, data serviceability and access to the public. Specific coverage under the various codes is presented below:

DQAF Code	Data Quality Dimension	Specific Elements under Review
0	Prerequisites of data quality	Legal and institutional environment is supportive of statistics; resources are commensurate with the needs of statistical programs; quality is recognized as a cornerstone of statistical work.
1	Assurances of integrity	Professionalism in statistical policies and practices is a guiding principle; statistical policies & practices are transparent; statistical processes are guided by ethical standards.
2	Methodological soundness	Data concepts and definitions, data scope/classification/sectorization are in accord with international statistical practices. Flows and stocks are valued and recorded according to intl. accepted standards.
3	Accuracy and reliability	Source data available provide an adequate basis to compile statistics and portray the reality of the economy (i.e., assessment of source data); statistical techniques conform to sound statistical procedures; assessment and validation of intermediate data and statistical outputs are in place.
4	Serviceability	Statistics cover relevant information on the subject field; data timeliness and periodicity follow internationally accepted dissemination standards; data are consistent over time/across major data systems; data revision policies and practices follow regular procedures.
5	Accessibility	Statistics are presented in a clear and understandable manner; forms of dissemination are adequate; data and metadata are easily available by users.

View Table

DQAF Code	Data Quality Dimension	Specific Elements under Review
0	Prerequisites of data quality	Legal and institutional environment is supportive of statistics; resources are commensurate with the needs of statistical programs; quality is recognized as a cornerstone of statistical work.
1	Assurances of integrity	Professionalism in statistical policies and practices is a guiding principle; statistical policies & practices are transparent; statistical processes are guided by ethical standards.
2	Methodological soundness	Data concepts and definitions, data scope/classification/sectorization are in accord with international statistical practices. Flows and stocks are valued and recorded according to intl. accepted standards.
3	Accuracy and reliability	Source data available provide an adequate basis to compile statistics and portray the reality of the economy (i.e., assessment of source data); statistical techniques conform to sound statistical procedures; assessment and validation of intermediate data and statistical outputs are in place.
4	Serviceability	Statistics cover relevant information on the subject field; data timeliness and periodicity follow internationally accepted dissemination standards; data are consistent over time/across major data systems; data revision policies and practices follow regular procedures.
5	Accessibility	Statistics are presented in a clear and understandable manner; forms of dissemination are adequate; data and metadata are easily available by users.

Data users’ surveys included in selected Data ROSCs add information to the statistical experts’ assessments. ⁶These surveys indicate that, except for South Africa, African users were mainly concerned about data serviceability and to a lesser extent about source data and methodology issues (Table 2). Timeliness was considered too long for GDP, fiscal and balance of payments data. There was also widespread criticism about the lack of information on official publication calendars for these statistics. Users’ views about source data and methodology issues—which were central to the Data ROSC experts’ assessments—were generally constrained by the reported poor data and metadata dissemination practices in all countries, except South Africa. Users’ comments and suggestions for improving data scope and detail varied across African countries depending on their stage of development of their statistical frameworks.

Table 2.

Data ROSCs: Statistics Users’ Survey–Summary

Source: IMF Data ROSC Reports.

Table 2.

Data ROSCs: Statistics Users’ Survey–Summary

	Data ROSC Date	Timeliness	Scope	Degree of Detail	Frequency		Advanced Publication Calendar	Data/Metadata Accessibility
								ranking	comment
AFW Countries
Burkina Faso	Mar-04	too long	inadequate	insufficient	inadequate		little info. available	satisfactory	should broaden dissemination
Niger	Jun-06	too long	inadequate	insufficient	inadequate		little info. available	unsatisfactory	should broaden dissemination
Senegal	Nov-02	too long	broadly adequate	inadequate	inadequate		little info. available	…	methodological notes are not very descriptive
Other African countries
Kenya	Oct-05	too long	adequate	could be improved	adequate	little info. available		…		should broaden dissemination
Mozambique	Aug-05	too long	inadequate	insufficient	inadequate	little info. available		unsatisfactory		methodological notes are not very descriptive
South Africa	Oct-01	adequate	adequate	adequate	adequate	satisfactory		satisfactory		seasonal adjustments & data revisions could be better explained

Source: IMF Data ROSC Reports.

View Table

Table 2.

Data ROSCs: Statistics Users’ Survey–Summary

	Data ROSC Date	Timeliness	Scope	Degree of Detail	Frequency		Advanced Publication Calendar	Data/Metadata Accessibility
								ranking	comment
AFW Countries
Burkina Faso	Mar-04	too long	inadequate	insufficient	inadequate		little info. available	satisfactory	should broaden dissemination
Niger	Jun-06	too long	inadequate	insufficient	inadequate		little info. available	unsatisfactory	should broaden dissemination
Senegal	Nov-02	too long	broadly adequate	inadequate	inadequate		little info. available	…	methodological notes are not very descriptive
Other African countries
Kenya	Oct-05	too long	adequate	could be improved	adequate	little info. available		…		should broaden dissemination
Mozambique	Aug-05	too long	inadequate	insufficient	inadequate	little info. available		unsatisfactory		methodological notes are not very descriptive
South Africa	Oct-01	adequate	adequate	adequate	adequate	satisfactory		satisfactory		seasonal adjustments & data revisions could be better explained

Source: IMF Data ROSC Reports.

In the users’ view, improved data timeliness and periodicity according to a preannounced dissemination calendar is critical to develop a richer exchange between users and data producers, and an eventual quality improvement in national accounts compilation. A virtuous cycle would develop: a timely supply of data would create a demand for it that would, in turn, create pressures for better and more data production.

III. A Monitoring Framework Using TAIMS

This section builds on the information collected for the Data ROSCS and the users’ surveys, as reported in section II. It sketches a framework to monitor progress towards producing and disseminating national accounts along best international practices, which is considered the “ultimate outcome” of the technical assistance project under review. Inputs for the analysis are the projects’ documentation stored in TAIMS, which we reorganize according to the six data quality dimensions assessed in the Data ROSCs (see Box 2 above) for reporting on the effectiveness of the actual technical assistance delivery. We also use the mission information contained in STA’s Regional Allocation Plans (RAPs) for FY06-09 to supplement TAIMS in assessing the intensity of the technical assistance effort—as measured by the number missions and nights spent in the field—in the AFW region.

The analysis proceeds in three stages:

• First, we check the AFW missions’ scope (tasks and reported outcomes) against the data quality shortfalls identified by the statistical experts in the context of the Data ROSCs and by national accounts’ data users, including the IMF mission teams. A strong consistency between the missions’ scope (as reported in TAIMS) and the data quality shortfalls identified in the Data ROSCs and data users’ surveys should warrant a greater likelihood of achieving the project’s ultimate objectives. The latter being the production and dissemination of national accounts along best international practices.

• Second, we review the available PFSs to assess the challenges in meeting the current technical assistance implementation deadlines for the various data quality dimensions assessed in the Data ROSCs.

• Third, we report on the intensity of AFW’s technical assistance effort and the countries’ success in meeting the project’s “ultimate outcomes.”

Checking missions’ scope against reported data weaknesses

Upgrading a country’s national accounts is a major technical endeavor. The undertaking involves (i) the collection/updating of source data and (ii) changes/improvements in the national accounts’ compilation methodology in line with the 1993 SNA. According to the international experience, the combined implementation of changing the national accounts’ base year and implementing the 1993 SNA demands intense work (usually undertaken over a period of 5-6 years) and significant budgets. ⁸ Many data source upgrades⁹ usually evolve in tandem, while the implementation of methodological changes requires a careful consultation among main data producers and specialists, including international advisory groups. The latter provide input on other countries’ experiences on best ways to transit from outdated compilation methodologies embodied in the 1968 System of National Accounts (1968 SNA) to those recommended under the 1993 SNA.¹⁰

Overall, it appears that AFW has responded effectively to the challenge of developing robust national accounts in its member countries. A dedicated resident statistical advisor on national accounts, together with short-term experts, provided technical support to national statistical offices in the region. Also, the AFRITAC independent external evaluation concluded that the overall relevance of AFW statistics technical assistance effort was broadly “good”, notwithstanding the remaining compilation weaknesses, staffing issues in key statistical agencies, and challenges with donor coordination.

The information stored in TAIMS help us go beyond that general assessment. Specifically, we reorganize the information contained in TAIMS using the Fund DQAF to make it comparable/consistent with the data quality dimensions assessed under the Data ROSCs. The overall picture that emerges is that national accounts missions focused mainly on source data assessments (data accuracy and reliability) and data concepts/definitions issues (methodological soundness), while data timeliness (i.e., serviceability) and dissemination (i.e., accessibility) issues generally lagged behind in the missions’ agendas. On average, about 70 percent of the missions’ tasks dealt with source data and methodological issues (Table 3 and Annex III). Mali is an extreme case in which all missions recorded in TAIMS for FY06-09 focused on those two data quality aspects alone. By contrast, missions to Burkina Faso and Senegal emphasized issues of data serviceability and accessibility, possibly reflecting the countries’ efforts to address the low ratings received in these data categories during their Data ROSCs. Senegal’s upgrading efforts may also relate to the country’s objective of subscribing to the IMF Special Data Dissemination Standard (SDDS).¹¹

Table 3:

AFRITAC West: Cumulative Number of Mission Tasks, FY06-FY09

(Percentage distribution of total mission tasks during period)

Source: TAIMS and AFR staff estimates.

Table 3:

AFRITAC West: Cumulative Number of Mission Tasks, FY06-FY09

(Percentage distribution of total mission tasks during period)

Data aspect\country	Benin	Burkina Faso	Cote d’Ivoire	Guinea	Guinea Bissau	Niger	Mali	Senegal	Togo	All AFW Countries
0. Prerequisite of quality	41.2	37.5	0.0	0.0	20.0	4.8	0.0	17.6	33.3	16.0
1. Assurance of integrity	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2. Methodological soundness	5.9	0.0	62.5	31.6	0.0	28.6	16.7	35.3	0.0	21.0
3. Accuracy & reliability	52.9	12.5	25.0	63.2	50.0	52.4	83.3	29.4	66.7	47.9
4. Serviceability	0.0	12.5	0.0	0.0	5.0	9.5	0.0	0.0	0.0	3.4
5. Accessibility	0.0	37.5	12.5	5.3	25.0	4.8	0.0	17.6	0.0	11.8
Total mission tasks	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0

Source: TAIMS and AFR staff estimates.

View Table

Table 3:

AFRITAC West: Cumulative Number of Mission Tasks, FY06-FY09

(Percentage distribution of total mission tasks during period)

Data aspect\country	Benin	Burkina Faso	Cote d’Ivoire	Guinea	Guinea Bissau	Niger	Mali	Senegal	Togo	All AFW Countries
0. Prerequisite of quality	41.2	37.5	0.0	0.0	20.0	4.8	0.0	17.6	33.3	16.0
1. Assurance of integrity	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2. Methodological soundness	5.9	0.0	62.5	31.6	0.0	28.6	16.7	35.3	0.0	21.0
3. Accuracy & reliability	52.9	12.5	25.0	63.2	50.0	52.4	83.3	29.4	66.7	47.9
4. Serviceability	0.0	12.5	0.0	0.0	5.0	9.5	0.0	0.0	0.0	3.4
5. Accessibility	0.0	37.5	12.5	5.3	25.0	4.8	0.0	17.6	0.0	11.8
Total mission tasks	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0

Source: TAIMS and AFR staff estimates.

Several factors explain the emphasis on source data assessments and methodological issues as reported in TAIMS. First, without appropriate data sources, there is no technical assistance on methodology and data dissemination that can be successful. Second, analysis of source data and methodology has responded to the completion of household and business surveys by the country authorities, which has not been easy to predict at times, and has required several consecutive missions to address different aspects of the source data collection and compilation processes. Third, the emphasis on source data and methodological issues is consistent with AFW’s country pages in the IMF’s General Data Dissemination System (GDDS; see Table 4). The GDDS country pages—usually produced by the national statistical offices—emphasize improvement plans for data collection and compilation, albeit without a commitment to disseminate those data along a strict calendar.

Table 4.

AFRITAC West: GDDS Country Pages–Short-term Plans for Statistical Improvement

(Number of tasks per data quality dimension)

Source: IMF GDDS country pages.

Table 4.

AFRITAC West: GDDS Country Pages–Short-term Plans for Statistical Improvement

(Number of tasks per data quality dimension)

Country\Data dimension	Prerequisites of Quality	Assurance of Integrity	Methodological Soundness	Accuracy & Reliability	Serviceability	Accessibility
Benin	X		X X X X	X X X X	X X
Burkina Faso	X X		X	X X	X	X
Cote d’Ivoire	X		X	X X X
Guinea			X X		X
Guinea Bissau			X X	X X X X
Mali			X X	X		X
Niger			X X	X X X X X X	X	X
Senegal	X X		X X	X
Togo	X		X X X

Source: IMF GDDS country pages.

View Table

Table 4.

AFRITAC West: GDDS Country Pages–Short-term Plans for Statistical Improvement

(Number of tasks per data quality dimension)

Country\Data dimension	Prerequisites of Quality	Assurance of Integrity	Methodological Soundness	Accuracy & Reliability	Serviceability	Accessibility
Benin	X		X X X X	X X X X	X X
Burkina Faso	X X		X	X X	X	X
Cote d’Ivoire	X		X	X X X
Guinea			X X		X
Guinea Bissau			X X	X X X X
Mali			X X	X		X
Niger			X X	X X X X X X	X	X
Senegal	X X		X X	X
Togo	X		X X X

Source: IMF GDDS country pages.

Yet, the need to simultaneously address source data and methodological issues, as well as timely data dissemination is supported by the Data ROSC experts’ assessments, input from main data users, and the international experience. Main arguments in this regard are the following:

• AFW member countries need to catch up with data serviceability practices in other African countries. In particular, data periodicity and timeliness elsewhere in Africa proceeds along best international practices (Table 1), while best practices in data serviceability in the AFW region are “largely not observed.” Notably, data periodicity and timeliness were main concerns for data users, including the Fund mission teams.

• The international experience suggests that timely data dissemination narrows the scope for data manipulation. Published data, supported by a clear methodology that emphasizes the scope and limitations of the data, raises the credibility of the data producing agencies, reduces faulty data interpretations, and increases the usefulness of the data for economic analysis.¹²

• Also, timely data release triggers data users’ demands that put pressure for more and better data. This is the virtuous cycle of data production and dissemination observed in countries around the world.

• Finally, there is scope for country differentiation. The timeliness of the data publication should be consistent with the data producers’ resource envelope, although a data publication calendar should be in place and binding in all cases. Experience suggests a trade-off regarding publication lags: unduly short lags may lead to inaccurate/unreliable data and trigger numerous data revisions. Unduly long lags generate obsolete data that are not useful for decision-making.

Checking implementation deadlines

In this section we compare the expected implementation deadlines for technical assistance, as reported in the available PFSs, against the actual status of the various tasks. While the TAIMS information about the current standing of key milestones is limited, a number of preliminary conclusions based on those data and our earlier analysis are warranted:

• According to the PFSs, deadlines for data serviceability and accessibility are mostly due in the second half 2009 (Table 5), a time bound that appears tight for all AFW member countries, except Côte d’Ivoire and Senegal. Côte d’ Ivoire is rapidly catching up in terms of national accounts data production and dissemination after years of domestic political crisis. Technical assistance to Senegal has mainly focused on the development of quarterly national accounts. According to the information stored in TAIMS, the Senegal authorities are broadly on target to deliver a timely publications of these accounts, which is consistent with their intention to subscribe to the Fund’s SDDS.

• Publicly available information, and the evidence reported in TAIMS, highlights the “catching up” effort needed to meet national accounts’ data dissemination challenges in AFW member countries. According to our preliminary assessment (see Box 3 and Table 7), by end-2009, except for Senegal, all other 9 AFW countries would need to publish two or three years of final or semi-final national accounts data (2006 to 2007) and one year of preliminary national accounts (2008). This catching up effort would be consistent with the “rule of thumb” proposed by IMF country teams of producing within year t+1 reasonably sound preliminary estimates for year t, sound semi-final estimates for year t-1, and final estimates for year t-2.

• The PFSs indicate that very few measurable targets within the grand national accounts’ technical assistance project have been completed to date (Annex IV). These successful cases generally apply to AFW countries that have relatively advanced statistical systems (Senegal and Côte d’ Ivoire) or are very close to revise their national accounts’ base year (Niger). Examples of these accomplishments include staff training in the area of quarterly national accounts (Senegal), completion of data survey questionnaires (Niger), availability of robust annual (Niger) and higher frequency (Senegal) source data for national accounts compilation, and completion of household expenditure surveys to support a direct estimate of household final consumption in the national accounts (Côte d’ Ivoire). The remaining measurable targets are work in progress, mainly affecting those countries with significantly outdated national accounts base years. Pending common challenges across countries include the allocation of sufficient human and financial resources to produce national accounts data (Benin, Guinea, Guinea-Bissau),¹³ staff training on a software accounting platform developed by the European Commission and the government of France (Benin, Côte d’Ivoire, Guinea-Bissau, and Mali), and the strengthening of compilation and data validation techniques–including adjustments for missing observations and undercoverage of production activities–for international trade data and the government sector statistics (Benin, Guinea-Bissau, Niger, and Mali). A measurable target that is still pending in Niger, Guinea-Bissau and Mali refers to the development of data surveys on their mineral and energy sectors.

Table 5.

AFW: Technical Assistance Implementation Deadlines By Data Quality Dimensions ^a/

(As reported in Project Framework Summaries)

Source: Annex IV.

^a/No PFSs are available in TAIMS for Burkina Faso and Togo.

Table 5.

AFW: Technical Assistance Implementation Deadlines By Data Quality Dimensions ^a/

(As reported in Project Framework Summaries)

	Prerequisite of Quality	Methodological soundness	Accuracy & Reliability	Serviceability	Accessibility
Benin	Mid-09	Dec. 09	Q4-09	Q4-09	…
Côte d’Ivoire	…	Mid-09	Q2 & Q4-09	Q3-09	June 09
Guinea	Mid- 2010	…	June 2010	June 2010	…
Guinea- Bissau	April 2010	Dec. 2010	Q2 & Q4-09	Q3/Q4-09	…
Niger	…	Dec. 2010	II half 09; end-2010	Q4-09	Oct. 09
Mali	…	Dec. 08	Dec. 08	June 09	…
Senegal	Dec. 09	May 09	Q2 & Q3-09	Dec. 09	…

Source: Annex IV.

^a/No PFSs are available in TAIMS for Burkina Faso and Togo.

View Table

Table 5.

AFW: Technical Assistance Implementation Deadlines By Data Quality Dimensions ^a/

(As reported in Project Framework Summaries)

	Prerequisite of Quality	Methodological soundness	Accuracy & Reliability	Serviceability	Accessibility
Benin	Mid-09	Dec. 09	Q4-09	Q4-09	…
Côte d’Ivoire	…	Mid-09	Q2 & Q4-09	Q3-09	June 09
Guinea	Mid- 2010	…	June 2010	June 2010	…
Guinea- Bissau	April 2010	Dec. 2010	Q2 & Q4-09	Q3/Q4-09	…
Niger	…	Dec. 2010	II half 09; end-2010	Q4-09	Oct. 09
Mali	…	Dec. 08	Dec. 08	June 09	…
Senegal	Dec. 09	May 09	Q2 & Q3-09	Dec. 09	…

Source: Annex IV.

^a/No PFSs are available in TAIMS for Burkina Faso and Togo.

Table 6:

EIU–Country Reports: National Accounts’ Data’s Timeliness & Information Source

Source: EIU Country reports, various.

Table 6:

EIU–Country Reports: National Accounts’ Data’s Timeliness & Information Source

	Date of EIU Report	Latest “actual” GDP data (year)	“Estimated GDP data” (years, source)	Other referred sources on national accounts data
Benin	April 2009	(2005)	(2006-08, EIU)	Association interprofessionelle du coton; IMF
Burkina Faso	May 2009	(2006)	(2007-08, EIU)	IMF
Côte d’Ivoire	May 2009	(2007)	(2008-10, EIU)	WAEMU; Direction Générale des Douanes; Direction de la Conjoncture et de la Prévision économique; IMF
Guinea	June 2009	(2005)	(2006-08, EIU)	IMF
Guinea-Bissau	April 2009	(2006)	(2007-08, EIU)	IMF
Mali	May 2009	(2006)	(2007-08, EIU)	BCEAO; Ministry of Agriculture; IMF
Mauritania	April 2009	(2006)	(2007-08, EIU)	IMF
Niger	May 2009	(2006)	(2007-08, EIU, IMF)	IMF
Senegal	May 2009	(2008)	(2009-10, EIU)	Agence Nationale de la Statistique et de la Démographie, Ministry of Economy and Finance; IMF
Togo	April 2009	(2006)	(2007-08, EIU)	BCEAO; WAEMU; Afristat; IMF
Botswana	May 2009	(2008)	(2008-10, EIU)	Central Statistics Office; Bank of Botswana, Botswana Financial Statistics, Annual Report; IMF
The Gambia	April 2009	(2007)	(2008, EIU)	IMF
Kenya	May 2009	(2007)	(2008-10, EIU)	Central Bank of Kenya; IMF
Mauritius	May 2009	(2008)	(2009-10, EIU)	Central Statistics Office; Ministry of Tourism, Leisure and External Communications; Ministry of Finance and Economic Development; IMF
Mozambique	May 2009	(2007)	(2008-10, EIU)	UN Food and Agriculture Organization; IMF
Namibia	May 2009	(2008)	(2009-10, EIU)	Bank of Namibia; Central Bureau of Statistics; Irwin, Jacobs, Greene/Institute for Public Policy Research, Windhoek; IMF
South Africa	May 2009	(2007)	(2008-10, EIU)	Statistics South Africa, South African Reserve Bank; IMF
Tanzania	May 2009	(2007)	(2008-10, EIU)	Barrick Gold; UN Food and Agriculture Organization; Bank of Tanzania; IMF
Uganda	May 2009	(2007)	(2008-10, EIU)	Bank of Uganda; Uganda Bureau of Statistics; IMF
Zambia	June 2009	(2007)	(2008-10, EIU)	Ministry of Agriculture and Cooperatives, Bank of Zambia; IMF

Source: EIU Country reports, various.

View Table

Table 6:

EIU–Country Reports: National Accounts’ Data’s Timeliness & Information Source

	Date of EIU Report	Latest “actual” GDP data (year)	“Estimated GDP data” (years, source)	Other referred sources on national accounts data
Benin	April 2009	(2005)	(2006-08, EIU)	Association interprofessionelle du coton; IMF
Burkina Faso	May 2009	(2006)	(2007-08, EIU)	IMF
Côte d’Ivoire	May 2009	(2007)	(2008-10, EIU)	WAEMU; Direction Générale des Douanes; Direction de la Conjoncture et de la Prévision économique; IMF
Guinea	June 2009	(2005)	(2006-08, EIU)	IMF
Guinea-Bissau	April 2009	(2006)	(2007-08, EIU)	IMF
Mali	May 2009	(2006)	(2007-08, EIU)	BCEAO; Ministry of Agriculture; IMF
Mauritania	April 2009	(2006)	(2007-08, EIU)	IMF
Niger	May 2009	(2006)	(2007-08, EIU, IMF)	IMF
Senegal	May 2009	(2008)	(2009-10, EIU)	Agence Nationale de la Statistique et de la Démographie, Ministry of Economy and Finance; IMF
Togo	April 2009	(2006)	(2007-08, EIU)	BCEAO; WAEMU; Afristat; IMF
Botswana	May 2009	(2008)	(2008-10, EIU)	Central Statistics Office; Bank of Botswana, Botswana Financial Statistics, Annual Report; IMF
The Gambia	April 2009	(2007)	(2008, EIU)	IMF
Kenya	May 2009	(2007)	(2008-10, EIU)	Central Bank of Kenya; IMF
Mauritius	May 2009	(2008)	(2009-10, EIU)	Central Statistics Office; Ministry of Tourism, Leisure and External Communications; Ministry of Finance and Economic Development; IMF
Mozambique	May 2009	(2007)	(2008-10, EIU)	UN Food and Agriculture Organization; IMF
Namibia	May 2009	(2008)	(2009-10, EIU)	Bank of Namibia; Central Bureau of Statistics; Irwin, Jacobs, Greene/Institute for Public Policy Research, Windhoek; IMF
South Africa	May 2009	(2007)	(2008-10, EIU)	Statistics South Africa, South African Reserve Bank; IMF
Tanzania	May 2009	(2007)	(2008-10, EIU)	Barrick Gold; UN Food and Agriculture Organization; Bank of Tanzania; IMF
Uganda	May 2009	(2007)	(2008-10, EIU)	Bank of Uganda; Uganda Bureau of Statistics; IMF
Zambia	June 2009	(2007)	(2008-10, EIU)	Ministry of Agriculture and Cooperatives, Bank of Zambia; IMF

Source: EIU Country reports, various.

Table 7.

AFW: Number of National Accounts’ Missions & Mission Nights, FY06-FY09 ^1/

Source: STA Regional Allocation Plans (FY06-FY09).

^1/Includes missions undertaken by long-term and short-term experts.

^2/Currently not eligible for IMF technical assistance.

Table 7.

AFW: Number of National Accounts’ Missions & Mission Nights, FY06-FY09 ^1/

	Number of		Distribution by
	Missions	Mission Nights	Missions	Mission Nights
Benin	3	31	6.1	7.6
Burkina Faso	4	25	8.2	6.1
Cote d’Ivoire	4	30	8.2	7.3
Guinea	8	86	16.3	21.0
Guinea-Bissau	7	65	14.3	15.9
Mali	6	53	12.2	13.0
Mauritania 2/	…	…	…	…
Niger	3	27	6.1	6.6
Senegal	10	60	20.4	14.7
Togo	4	32	8.2	7.8
Total	49	409	100.0	100.0
Average	5.4	45.4

Source: STA Regional Allocation Plans (FY06-FY09).

^1/Includes missions undertaken by long-term and short-term experts.

^2/Currently not eligible for IMF technical assistance.

View Table

Table 7.

AFW: Number of National Accounts’ Missions & Mission Nights, FY06-FY09 ^1/

	Number of		Distribution by
	Missions	Mission Nights	Missions	Mission Nights
Benin	3	31	6.1	7.6
Burkina Faso	4	25	8.2	6.1
Cote d’Ivoire	4	30	8.2	7.3
Guinea	8	86	16.3	21.0
Guinea-Bissau	7	65	14.3	15.9
Mali	6	53	12.2	13.0
Mauritania 2/	…	…	…	…
Niger	3	27	6.1	6.6
Senegal	10	60	20.4	14.7
Togo	4	32	8.2	7.8
Total	49	409	100.0	100.0
Average	5.4	45.4

Source: STA Regional Allocation Plans (FY06-FY09).

^1/Includes missions undertaken by long-term and short-term experts.

^2/Currently not eligible for IMF technical assistance.

Box 3:

Tracking the National Accounts’ Data Timeliness & Information Source in EIU Country Reports

We reviewed the latest available country reports produced by the Economic Intelligence Unit (EIU) for a group of African countries, including all AFW member countries and those undertook Data ROSC examinations in recent years. The EIU country reports contain regular and up-to-date analysis of economic and financial developments for countries around the world. The EIU’s publication cycle is almost identical for all country reports, thus proving a helpful benchmark for cross country comparisons for data availability at a point in time.

The country reports’ review confirms the difficult situation with data timeliness in AFW member countries (see Table 6):

• As of May/June 2009, except for Senegal and Côte d’Ivoire, “actual” annual national accounts data for AFW member countries were significantly outdated, dating from 2005 (2 countries) or 2006 (6 countries).

• Data timeliness for the rest of African countries reviewed was much better, with “actual” GDP data dating from 2007 (7 countries) or 2008 (3 countries).

• GDP “estimates” prepared by the EIU covered any data gaps (for the recent past or short-term future). No information on the EIU’s national accounts estimation methodology was included in the country reports.

• For most AFW member countries, the IMF (i.e., IFS or the Fund’s Direction of Trade Statistics) was the only additional data source referred in the EIU country reports. This contrasts with the multiple domestic data sources referred in other African cases.

Checking for the intensity of TA delivery

Mission data compiled in TAIMS and the RAPs for FY06-09 confirm AFW’s vast effort to support countries’ initiatives to upgrade their national accounts (Table 7). During the last four fiscal years, there were 49 national accounts missions to the region, with a total of staff nights in the field reaching about 410. However, the mission distribution was not even across countries, with Benin, Burkina Faso, Côte d’Ivoire, Niger, and Togo receiving relatively less missions than the rest of AFW countries.

There seems to be no clear consistency between the intensity of the project (as measured by the number of missions and mission nights) and the reported effectiveness of the technical assistance delivery. Countries like Côte d’Ivoire and Niger received limited technical assistance support from AFW, but are reportedly prompt to attain a timely production and dissemination of national accounts (Côte de Ivoire) or update the national accounts’ base year (Niger). Senegal is a case in which an intense technical assistance effort (10 missions and 60 nights in the field) has delivered important results in the production of quarterly national accounts and the development of institutional sectors accounts. By contrast, the outcome has been much limited in countries like Guinea, Guinea-Bissau and Mali, notwithstanding AFW’s intense technical assistance effort. Mali has had the added advantage of being AFW’s host country, which, in principle, should have speeded up mission delivery processes and eased feedback between the country authorities and the local technical assistance experts.

While the PFSs or other information stored in TAIMS provide limited information about the circumstances surrounding the provision of technical assistance, two factors may explain the diverse results across countries:

• Countries like Guinea, Guinea-Bissau, and Togo are currently considered fragile states in which civil war, political instability, and weak government institutions may have hampered technical assistance implementation. Côte d’ Ivoire is also a fragile state, although it has apparently managed to address data weakness following progress in resolving its civil conflict.

• Donor support may have played a key role in funding activities that complemented the technical advice provided by AFW. A timely funding of surveys, censuses and other data sources may have eased technical assistance implementation. Further country analysis in this regard seems warranted to highlight the multilateral/bilateral support from AFRISTAT, the African Development Bank, the World Bank, and other friendly development partners.

Looking to the future, a regular reporting of the external environment surrounding the provision of technical assistance may provide valuable signals on the likely record of technical assistance implementation. PFSs may benefit from a timely reporting/updating on the work of other technical assistance providers. This information may, in turn, influence the sequencing of AFW’s technical assistance provision to a country.

IV. Concluding Remarks

This note represents an effort to analyze the effectiveness of technical assistance provided by AFW in the area of national accounts using TAIMS. The challenge has been to report on “ultimate outcomes” (i.e., the production and dissemination of national accounts statistics along best international practices) rather than on “inputs” (i.e., the number of national accounts missions fielded by the regional technical assistance center), as has been the case to date.

The analysis has benefited from the national accounts’ mission documentation stored in TAIMS, information included in the Data ROSCs launched for African countries in recent years, as well as input received from the IMF country teams to AFW countries. The information collected from the Data ROSCs and the country teams helped us define the project’s “ultimate outcome,” which we then used as a benchmark to gauge the effectiveness of the actual technical assistance delivery.

Central to the analysis has been the application of the Fund’s DQAF to organize the information collected in six specific data quality dimensions. These data dimensions range from institutional factors surrounding the production of national accounts data to quality factors referring to data compilation/processing/dissemination processes in place in the various AFW countries. As such, the DQAF provided a flexible structure for our qualitative assessment of AFW’s technical assistance work on national accounts.

Our analysis of TAIMS data suggests that the “ultimate outcome” of producing and disseminating robust national accounts is work in progress, with AFW’s technical assistance efforts mainly focusing on source data assessments and methodological issues underpinning the compilation of national accounts. The pending challenge, however, is to further support a more timely production and dissemination of national accounts data, which are main data weaknesses according to information contained in the Data ROSCs and input from IMF mission teams to AFW member countries.

While the national accounts missions’ emphasis on source data assessments and methodological issues is indeed critical, the international experience recommends that the production and timely dissemination of national accounts should advance in parallel. This joint approach to statistical development would also help AFW member countries catch up with other African countries in terms of data periodicity and timeliness. Currently, all IMF country teams to AFW countries rely on the country authorities to collect latest national accounts data for purposes of surveillance and IMF lending operations. However, a timely availability of the same data to other data users is not guaranteed, with external analysts (e.g., The EIU country reports) resorting to their own national accounts calculations in the absence of official data.

Striking the balance between a timely production and dissemination of national accounts data would be a challenge for data producers in AFW member countries, albeit with a number of benefits. For one, published data, supported by a clear methodology that emphasizes the scope and limitations of the data, raises the credibility of the data producing agencies, reduces faulty data interpretations, and increases the usefulness of the data for economic analysis. A timely data release usually triggers data users’ demands that put pressure for more and better data. This is the virtuous cycle of data production and dissemination observed in countries around the world.

The analysis of TAIMS also suggests that the implementation status of measurable targets included in the national accounts technical assistance project appears to be stronger in AFW countries which either have relatively developed statistical system that improves the countries’ initial conditions (Senegal), are rapidly catching up in terms of data production and dissemination after years of political crisis (Côte d’Ivoire), or are prompt to complete an updating of their national accounts using newly-available data surveys and censuses (Niger). For other AFW country cases, the implementation status of major project targets may remain weak for years to come.

Finally, the analysis points at the diverse technical assistance implementation record, notwithstanding the intensity of the mission allocation. Several countries have managed to advance towards achieving their ultimate outcomes with relatively little support from AFW. This strengthens the case for enhanced TAIMS reporting on the circumstances surrounding the provision of technical assistance, such as, for example, the role of other technical assistance providers and donors in areas supporting the implementation of AFW technical assistance.

Looking to the future, a number of observations emerge from the analysis:

• Conducting a thorough analysis of technical assistance in all AFRITACs would require a solid effort from the centers to input the missions’ documentation in TAIMS. Currently, the information in TAIMS is rather limited or absent for a number of countries.

• AFW mission teams could do a better job in defining their tasks in the missions’ briefing papers. Mission tasks are currently recorded in rather general terms, thus hampering a clear-cut classification according to the DQAF proposed in this note. A possible strategy could be for the AFRITACs to develop/define a number of “menu tasks” based on their country/regional experiences. Standardizing the mission tasks would ease the tracking over time of the missions’ tasks and ultimate objectives, albeit with an initial time investment by the AFRITACs.

• PFSs need to be developed and updated on a regular basis for all AFW countries to convey information to monitor the evolution and assess the effectiveness of the national accounts projects in place. PFSs should include regular updates on other technical assistance providers and donors operating in the same topical area.

• Consolidating the TAIMS data into the proposed analytical format presented above may require cooperation with information technology experts to automatically extract the missions’ tasks information listed according to the DQAF. This would reduce the time spent in manual manipulations of the mission briefs’ information.

• Data ROSC provide important benchmarks on the various quality aspects of data collection, processing, and dissemination. To date, only a limited number of African countries have undergone these comprehensive data assessments. Increasing the number of Data ROSC for Africa would provide observes with valuable information to assess the effectiveness of technical assistance provided by the AFRITACs.

This paper has offered input to the debate on management by results regarding the provision of technical assistance in national accounts by the AFRITACs. The proposed framework is an effort to structure the debate within the rather simple, yet rigorous, context of the Fund’s DQAF. Zeroing on a definitive approach to assess the effectiveness of the AFRITACs’ technical assistance delivery in areas other than macroeconomic statistics (e.g., fiscal and financial sector management) would require the development of standardized definitions of objectives-outcomes-indicators, which are still missing. Such an approach will also require input and feedback from main stakeholders involved in the delivery and implementation of the technical advice.