| PARIS21
[pic]
A Scoring System to Measure the Use of Statistics in the Policy Making Process
Summary Report
Paris, 30 January 2015
Table of Contents
Introduction 3
Methodology 5
Part 1: Upstream Use of Statistics 5
Section 1: Basic Facts /50 5
Section 2: Disaggregated Data /15 6
Section 3: Further Analysis /5 8
Part 2: Downstream Use of Statistics 8
Section 4: Monitoring and Evaluation /15 8
Section 5: Institutional Arrangements /5 9
Part 3: Statistical Capacity Development 10
Section 6: Data Production and Use /5 10
Section 7: Statistical Capacity Development Programs /5 11
Conclusion 12
Findings as of 2012 13
ANNEX 16
Scoring Sheet 16
Sample Scoring Sheet 19
Keyword List 23
APPENDICES 30
Introduction
This scoring system has been developed to assess the use of statistics in the policy making of developing countries. It is based on a review of the most recent poverty reduction strategy paper, medium-term strategy, or national development plan for Lower Middle Income countries (as defined by the OECD Development Assistance Committee List of Recipients of Official Development Assistance[1]). The current results cover 45 countries; 22 countries from Africa, 19 from Asia and the Pacific, three from Latin America and one from Europe:
|Afghanistan |East Timor |Moldova |
|Armenia |Gambia, The |Mongolia |
|Azerbaijan |Georgia |Nepal |
|Bangladesh |Guinea |Nicuragua |
|Benin |Guinea-Bissau |Niger |
|Bhutan |Kiribati |Pakistan |
|Bolivia |Kyrgyz Republic |Papua New Guinea |
|Burundi |Lao PDR |Rwanda |
|Cambodia |Lesotho |Samoa |
|Central African Republic |Liberia |Senegal |
|Comoros |Madagascar |Sudan |
|Congo, Dem. Rep |Maldives |Tajikistan |
|Côte d’Ivoire |Mauritania |Tanzania |
|Djibouti |Malawi |Togo |
|Dominica |Mali |Vanuatu |
The scoring system provides an analytical framework for the assessment of national policy documents, and allows for a quantitative ranking based on each country’s use of statistics, which falls into three categories: upstream policy use, downstream policy use, and statistical capacity development. Upstream refers to the extent to which statistics and statistical analysis have contributed materially to policy and decision-making (measured in sections 1-3). Downstream refers to the responsiveness of policy and decision-making to monitoring and evaluation (sections 4-5). Statistical capacity development involves taking steps to ensure that statistics are sufficiently available and of adequate quality to underpin policy processes (sections 6-7). In general, a “statistic” is considered to be any measurable value given in the report including absolute values, percentages, or fractions, used to assess a country's past, current, or future levels of development in a given field. This assessment limits the definition of a statistic to internal development indicators, and therefore does not include exogenously-determined variables (world interest rates, etc.), given geographical features (land area, coastline, etc.), or government financing figures.
It is important to note that the index is just one way to measure statistical use in the policy-making process. This is not an exact science. While it can be useful to quantify ‘statistical use’ there is no single correct way to measure the use of statistics and the design of this index and its scoring system are one of many ways to quantify this. While we have tried to make this assessment as objective and transparent as possible, there remains an irreducible degree of subjectivity in the report.
First, the basic design of the scoring system – with three subcategories - is just one of many scoring systems that could have been constructed. Second, the selection of the weighting attached to the different component scores in the index is also, essentially, arbitrary and many other schemes are possible. Indeed those interested in using this index are encouraged to consider using other weights that might better reflect their own priorities. Third, the guidelines setting the calculation of each component score are open to debate. And fourth, although the interpretation of those guidelines is set out in section II, there are inevitably a number of ‘judgement calls’ inherent in interpreting the guidelines to set each country’s score.
The report, and scores, should be used with all this in mind. Because calculating a score is not an exact science, small differences between countries should not be taken as significant. However, large differences can be taken to suggest genuine differences in the PRSP process and its use of evidence. The index, therefore, should be used to provoke debate and pave the way to a more detailed investigation. It seeks to generate debate and raise questions, rather than rank each country in a strict hierarchy.
Challenges in creating such a scoring system exist due to the difficulty of comparing numerous policy documents published in a variety of formats and with differing development objectives. Furthermore, a proper analysis must contain a consistent mechanism to produce quantifiable results and must not rely solely on subjective assessment. Thus, a large portion of the scoring system is based on the frequency of statistical references throughout the text, while another measurement addresses the scope of its coverage.
The next section provides a detailed breakdown of the methodology behind the scoring system, with calculations and reasoning described. This is followed by an analysis of the findings obtained as of 2013 (including baseline and milestone indicators for indicator G2 of the PARIS21 logical framework). The master scoring sheet and one country example (Benin) are included in the Annex. Finally, an appendix illustrates the scoring process using annotated pages from Burundi’s PRSP to help explain how the scoring system was applied.
Methodology
The scoring system is broken down into seven sections: three relating to upstream use of statistics, two to downstream use of statistics, and two to statistical capacity development. Each section is given a score according to its weight; these scores are then added together to obtain a final score with a maximum of 100.
Part 1: Upstream Use of Statistics
Section 1: Basic Facts /50
Current Statistics /25
Historical Trends /12.5
Forecasts /12.5
Rationale:
Section 1 measures the frequency of statistical use throughout the policy document, with the aim of determining how often statistics are quoted to present current situations, past trends, or forecasts for future development (“forecasts”, in this sense, refer to any statistic given for future years, whether it is simply a projection based on current conditions, or a specific target that the government aims to achieve.) This section is the most highly-weighted in the scoring system, as it provides a quantifiable way to measure the intensity of statistics usage in the development plan. Countries obtaining a high score in this regard must show that they have the means to measure current and past development indicators or to develop reasonable targets for future progress and must use these statistics appropriately. On the other hand, if a country states its indicators in less quantifiable terms, the measurement of its development and progress becomes less effective.
This section focuses on textual references to statistics, with an effort to avoid tallying repeated statistics. Most tabular data, unless used to sum up a section of text, is not included in the frequency count, but is considered later in the Further Analysis section (3).
Calculations:
The total frequency (F) of each type of statistic (current statistics, historical trends, forecasts) is tallied separately in the scoring sheet, and given a score out of five, with the scoring intervals based on the average distribution of statistical use among the countries analysed. The breakdown by sector is not relevant to this part, but will be of importance to Section 2.
The scoring is as follows:
|Current Statistics |Score | |Score |
|F = 0 |0 |75 < F < 89 |3 |
|0 < F < 14 |0.5 |90 < F < 104 |3.5 |
|15 < F < 29 |1 |105 < F < 119 |4 |
|30 < F < 44 |1.5 |120 < F < 134 |4.5 |
|45 < F < 59 |2 |F > 135 |5 |
|60 < F < 74 |2.5 | | |
| | | | |
|Historical Trends | | | |
|F = 0 |0 |40 < F < 47 |3 |
|0 < F < 7 |0.5 |48 < F < 55 |3.5 |
|8 < F < 15 |1 |56 < F < 63 |4 |
|16 < F < 23 |1.5 |64 < F < 71 |4.5 |
|24 < F < 31 |2 |F > 72 |5 |
|32 < F < 39 |2.5 | | |
| | | | |
|Forecasts | | | |
|F = 0 |0 |60 < F < 71 |3 |
|0 < F < 11 |0.5 |72 < F < 83 |3.5 |
|12 < F < 23 |1 |84 < F < 95 |4 |
|24 < F < 35 |1.5 |96 < F < 107 |4.5 |
|36 < F < 47 |2 |F > 108 |5 |
|48 < F < 59 |2.5 | | |
Once a score out of 5 is obtained for each of the three categories, they are weighted together: – the score for Current Statistics is multiplied by 5, while those for Historical Trends and Forecasts are each multiplied by 2.5. These three scores are then added together to obtain a Section 1 score out of a maximum of 50.
Section 2: Disaggregated Data /15
Rationale:
Having assessed the sheer frequency of statistical use in the previous part, Section 2 takes into account the scope of data used throughout the document. Therefore, it assigns higher scores to policy documents that use data to measure a wide range of indicators. This means that a country with a very high frequency of data use will not necessarily obtain a high score if it relates to only one or a few indicators or topic areas. To analyse this, the data from Section 1 is broken down into 25 topics and 5 divisions, which were based on those areas that most frequently arose in PRSP reports.
Topics:
• Poverty ( Farming & Agriculture
• Economic Growth ( Fisheries
• Other Macroeconomic Data (inflation, unemployment, etc.) ( Forestry
• Demographics ( Mining
• Trade ( Tourism
• Health Statistics ( Culture
• Nutrition ( Social Security
• HIV ( Banking & Credit
• Water & Sanitation ( Telecommunications
• Energy ( Housing & Land Ownership
• Education ( Environment & Conservation
• Literacy ( Governance (corruption, security, etc.)
• Infrastructure (roads, transportation, bridges, etc.)
Divisions:
• Geography
• Rural/Urban
• Income inequality
• Gender
• Age
The disaggregated data measurement assigns only one score to a topic that is broken down by sector. To take an example, assume current poverty rates are given for five regions (that is, they are broken down geographically). While this would count as five separate scores under the Basic Facts section (Poverty ( Current Statistics), only one score would be added to the Disaggregated Data section under “Geographical” (Current). In effect, one score is given in this section for each measurement that is disaggregated based on a single sector, regardless of how many separate statistics are given within this sector.
Calculations:
Based on the total number of sectors covered (topics + divisions), each type of statistic (current statistics, historical trends, forecasts) is first given a score out of five. These three scores are then averaged and the result multiplied by 3 to obtain the final score for Section 2. The scoring is as follows:
|Number of Sectors Covered |Score |
|0 |0 |
|1 to 5 |1 |
|6 to 11 |2 |
|12 to 17 |3 |
|18 to 23 |4 |
|24 to 30 |5 |
Section 3: Further Analysis /5
Rationale:
This section gives credit for additional analysis that goes beyond stating simple facts, trends or goals. It rewards policy documents that draw links between statistical trends, that use tables and regressions to provide more complex predictions, or develop alternate scenarios for development based on future indicators. This section is scored subjectively out of five, but roughly adheres to the following guidelines:
|Guidelines |Score |
|No analysis present. |0 |
|One or two correlations between indicator data. |1 |
|Data linkages; some predictions; models not present. |2 |
|Modelling graphs and tables present; correlations between data trends outlined; future predictions for a variety of |3 |
|indicators. | |
|Many graphs, tables, and predictions; data provided for several possible future scenarios for a variety of indicators; |4 |
|correlations and linkages between data trends outlined. | |
|Data correlations; growth models developed; advanced graphs, tables, regressions and/or categorical analyses included; |5 |
|detailed growth predictions for a variety of future scenarios. | |
Part 2: Downstream Use of Statistics
Section 4: Monitoring and Evaluation /15
Rationale:
A monitoring and evaluation framework is vital to measuring the success of the development plan and ensuring that policy-making is based on indicators that are measurable and usable. This section contains four assessments that measure the responsiveness of decision-making to monitoring and evaluation activities. The final score for this section (a maximum of 15), is obtained by averaging four individual scores (out of 5) and multiplying the result by 3.
Calculations:
The first of the four scores is obtained simply by verifying that a monitoring and evaluation framework is in place. This is often found near the end of a policy document in a section entitled “Mechanisms for Implementation, Monitoring-Evaluation and Risks” or something similar. A score of 5 is awarded if this framework is in place – otherwise, a score of 0 is given.
The second score depends on the indicator table(s) present in the document, which often provide baselines and targets for development in line with the Millenium Development Goals and national development strategies. This score takes into account 20 subject areas that are commonly addressed in these tables, and assigns a score of 0-5 based on the scope of its coverage:
|Scope of Coverage (Topics) |Score |
|0 |0 |
|1 to 4 |1 |
|5 to 8 |2 |
|9 to 12 |3 |
|13 to 16 |4 |
|17 to 20 |5 |
The third and fourth scores look at the baselines and targets, respectively, provided in the indicator table. A score of 0-5 is determined for each depending on the percentage of indicators missing baselines or targets. This provides an idea as to the mechanisms in place for measuring data.
|Percentage of Indicators Missing Baseline/Target |Score |
|100% |0 |
|30% ≤ X < 100% |1 |
|20% ≤ X < 30% |2 |
|10% ≤ X < 20% |3 |
|0% ≤ X < 10% |4 |
|0% |5 |
Once these four scores are obtained, they are averaged, providing a maximum result of five. This result is then multiplied by three to obtain the sectional score out of 15.
Section 5: Institutional Arrangements /5
Rationale:
This section determines a simple score out of five based on whether or not institutional arrangements are in place for reporting as part of the monitoring and evaluation framework. It considers three elements of the institutional arrangement: the responsibilities delegated to the reporting parties; the details of the reporting processes; and the timing and periodicity of reports.
Calculations:
Each of these three elements is given a score based on whether they are outlined in the document (5 for yes, 0 for no). These three scores are averaged to obtain the score for Section 5.
Part 3: Statistical Capacity Development
These final sections on statistical capacity development do not measure the actual usage of statistics in each policy document, but reflect the underlying attitude towards statistics within the document. They reward the identification of statistical problems, proposals for data improvement, and overall acknowledgement of statistics as key to the development process.
Section 6: Data Production and Use /5
Rationale:
Section 6 assigns a score out of five based on the extent to which the policy document identifies weaknesses in current data collection, as well as its discussion of specific initiatives designed to improve data in a particular sector or for a particular purpose (for example, improvement of the health information system or funding for an environmental database and management information system). Examples from the policy documents include:
“…information systems, particularly for data on the economy, still have significant gaps that make conventional decision-making tools unworkable.”
-Mauritania, p. 55
“The NIGERINFO databank will be used to stock and present the indicators that are required for monitoring the various sector strategies and the DPRS. The databank will receive sector data and data from the various surveys. Sector data bases will therefore have to be upgraded.”
-Niger, p. 115
“Develop reliable agricultural statistics and an effective early warning system.”
-Malawi, p. 80
Calculations:
To obtain a score for this section, individual references to the aforementioned criteria are noted throughout the document. They are then tallied and scored according to the following system:
|Number of References |Score |
|0 |0 |
|1 to 5 |0.5 |
|6 to 10 |1 |
|11 to 15 |1.5 |
|16 to 20 |2 |
|21 to 25 |2.5 |
|26 to 30 |3 |
|31 to 35 |3.5 |
|36 to 40 |4 |
|41 to 45 |4.5 |
|46 or greater |5 |
Section 7: Statistical Capacity Development Programs /5
Rationale:
This section is similar to Section 6, but deals with statistical capacity development in a broader sense, that is, with the institutions and programs that entrench statistical analysis in the policy-making process and promote access and dissemination of statistics as vital to the achievement of a developed and democratic society. Like the previous section, it applies a score out of five based on a tally of individual references to statistical capacity development programs throughout the document. In addition, it rewards the policy document for containing a section devoted to discussion of the national statistical office or agency, either proposed or already in place, and its achievements, successes, and challenges.
Examples from the policy documents:
“DGSCN will see to broad dissemination of the quantitative data necessary for monitoring and evaluation of the poverty reduction strategy by making use of appropriate channels, particularly the PRSP website and Togo Info. It will publish analyses of poverty in Togo on a regular basis.”
-Togo, p. 99
“Design national statistical Classifiers aligned to European Union standards.”
-Moldova, p. 64
“As regards gender, it is important that both men and women have equal access to accurate, timely and relevant information. This will allow them to participate fully in democratic decision-making, such as voting and contributing to planning processes, and provide them with an evidence base for evaluating government performance at local and national level.”
-Rwanda, p. 89
Calculations:
The final Section 7 score is marked out of a maximum of 5. The score is based on two parts, A and B.
(Part A) If the document contains a section on the national statistical office or agency it receives mark of 1.67. It receives 0 for part otherwise.
(PART B) The score comes from adjusting by two-thirds the score out of five based on the following:
|Number of References |Score |
|0 |0 |
|1 to 2 |0.5 |
|3 to 4 |1 |
|5 to 6 |1.5 |
|7 to 8 |2 |
|9 to 10 |2.5 |
|11 to 12 |3 |
|13 to 14 |3.5 |
|15 to 16 |4 |
|17 to 18 |4.5 |
|19 or greater |5 |
Conclusion
The index we have calculated is just one way to measure statistical use in the policy-making process. This is not an exact science. While it can be useful to quantify ‘statistical use’ there is no single correct way to measure the use of statistics and the design of this index and its scoring system are one of many ways to quantify this. While we have tried to make this assessment as objective and transparent as possible, there remains an irreducible degree of subjectivity in the report. The report, and scores, should be used with all this in mind. Because calculating a score is not an exact science, small differences between countries should not be taken as significant. However, large differences can be taken to suggest genuine differences in the PRSP process and its use of evidence. The index, therefore, should be used to provoke debate and pave the way to a more detailed investigation, It seeks to generate debate and raise questions, rather than rank each country in a strict hierarchy.
Findings as of 2013
The scoring system has been applied to a selection of IDA countries[2] that have published poverty reduction strategy papers (PRSPs), medium-term strategies, or national development plans since 2001. As of 2013, both the baseline and the milestone indicators have been calculated, reaching a score of 54.86 and 70.89 out of 100 respectively.
32 country documents that were produced during the period 2001-07 were used to calculate the baseline indicator and the milestone indicator was calculated using 33 country documents published between 2008 and 2013. 18 countries were covered both by the baseline and the milestone evaluation, resulting in 45 countries overall that were covered by the scoring system as of 2013.
Due to vast differences in statistical capacities and layouts of PRSPs, medium-term strategies and national development plans, the results vary greatly both for the baseline and milestone country scores. Regarding the baseline measurements, Mongolia has the lowest (17.83) and Mauritania the highest score (86.00). Moreover, Kiribati has the lowest score measured for the milestone indicator (9.58) while Nicaragua has the highest (93.25).
Figure 1 and 2 are based on data given in the table below (“Scores as of 2013”). Figure 1 shows baseline and milestone scores for African countries. As evident from the figure, all countries that were covered both by baseline and milestone measurements improved their scores – except for Rwanda – and the overall average for Africa increased from 61.94 to 74.51. Some countries, such as Guinea-Bissau and Tanzania, made impressive progress in terms of data use in their PRSPs, medium-term strategies, or national development plans over the 2000s. On the other hand, countries such as Mauritania, Malawi and Senegal only marginally increased their data use.
Figure 1. Africa: baseline and milestone scores
[pic]
Figure 2 shows the results for countries in the Asia-Pacific region. Compared to the sample of African countries, the Asia-Pacific region has lower average scores for both baseline and milestone measurements (47.46 and 65.02 respectively). This may be explained by the extremely low scores of Kiribati, Mongolia and (the baseline score of) Papua New Guinea. Despite a number of very low scores, other countries including Bangladesh and Lao PDR achieved impressive milestone scores and all countries which were part of both baseline and milestone measurements in the region showed progress over the period.
Figure 2. Asia-Pacific: baseline and milestone scores
[pic]
Note that no table was made for the European region nor Latin America & Carribean due to limited sample size (one and three respectively). For country scores in these regions, please see the table below. It should be noted that not a single country covered in this exercise saw a significant decrease from the baseline to milestone measurements.
|Scores as of 2013 |
| | |Baseline: ≤2007 |Milestone: ≥2008 |Score |
|Region |Country |Year of |
| | |publication |
| | | | |
|Country: | | | |
|Region: | | | |
|Title: | | | |
|Period: | | | |
|Publication Date: | | | |
| | | | |
|Section One: Basic Facts | | | |
| | | | |
|Current Statistics (Weight: 25) | | | |
|Historical Trends (Weight: 12.5) | | | |
|Forecasts (Weight: 12.5) | | | |
| |Current |Historical |Forecast |
|Poverty | | | |
|Economic Growth | | | |
|Other Macroeconomic Data | | | |
|Demographics | | | |
|Trade | | | |
|Health Statistics | | | |
|Nutrition | | | |
|HIV | | | |
|Water & Sanitation | | | |
|Energy | | | |
|Education | | | |
|Literacy | | | |
|Infrastructure | | | |
|Farming & Agriculture | | | |
|Fisheries | | | |
|Forestry | | | |
|Mining | | | |
|Tourism | | | |
|Culture | | | |
|Social Security | | | |
|Banking & Credit | | | |
|Telecommunications | | | |
|Housing & Land Ownership | | | |
|Environment & Conservation | | | |
|Governance | | | |
|Total Frequency | | | |
| | | | |
|Scores: |Out of 5 |Weighted | |
|Current Statistics | | | |
|Historical Trends | | | |
|Forecasts | | | |
| | | | |
|Total Section One (50) | | | |
| | | | |
| | | | |
|Section Two: Disaggregated Data | | | |
|Weight: 15 | | | |
| |Current |Historical |Forecast |
|Geographical | | | |
|Rural/Urban | | | |
|Inequality | | | |
|Gender | | | |
|Age | | | |
|Total Sectors Covered | | | |
| | | | |
|Scores: |Out of 5 | | |
|Current Facts | | | |
|Historical Trends | | | |
|Forecasts | | | |
|Average | | | |
| | | | |
|Total Section Two (15) | | | |
| | | | |
| | | | |
|Section Three: Further Analysis | | | |
|Weight: 5 | | | |
| |Out of 5 | | |
|Score: | | | |
| | | | |
|Total Section Three (5) | | | |
| | | | |
| | | | |
|Section Four: Monitoring and Evaluation | | | |
|Weight: 15 | | | |
| | | | |
|Monitoring Framework Included (0 or 1) | | | |
| | | | |
|Development Indicators Covered | | | |
|Poverty | | | |
|Economic Growth | | | |
|Other Macroeconomic Data | | | |
|Demographics | | | |
|Health Statistics | | | |
|Nutrition | | | |
|HIV | | | |
|Water & Sanitation | | | |
|Energy | | | |
|Education | | | |
|Literacy | | | |
|Infrastructure | | | |
|Farming & Agriculture | | | |
|Social Security | | | |
|Banking & Credit | | | |
|Telecommunications | | | |
|Housing & Land Ownership | | | |
|Environment & Conservation | | | |
|Governance | | | |
|Other | | | |
|Total Sectors Covered | | | |
| | | | |
|Percent of Indicators Missing Baseline | | | |
| | | | |
|Percent of Indicators Missing Target | | | |
| | | | |
|Scores: |Out of 5 | | |
|Monitoring Framework | | | |
|Sector Coverage | | | |
|Baselines | | | |
|Targets | | | |
|Average | | | |
| | | | |
|Total Section Four (15) | | | |
| | | | |
| | | | |
|Section Five: Institutional Arrangements | | | |
|Weight: 5 | | | |
| | | | |
|Responsibilities | | | |
| | | | |
|Processes | | | |
| | | | |
|Timing | | | |
| | | | |
|Scores: |Out of 5 | | |
|Responsibilities | | | |
|Processes | | | |
|Timing | | | |
|Average | | | |
| | | | |
|Total Section Five (5) | | | |
| | | | |
| | | | |
|Section Six: Data Production and Use | | | |
|Weight: 5 | | | |
| | | | |
|Frequency | | | |
| | | | |
|Score (Out of 5): | | | |
| | | | |
|Total Section Six (5) | | | |
| | | | |
| | | | |
|Section Seven: Statistical Capacity Development | | | |
|Weight: 5 | | | |
| | | | |
|Frequency | | | |
|Section on National Statistical System | | | |
| | | | |
|Scores: |Out of 5 | | |
|Frequency | | | |
|NSS | | | |
|Average | | | |
| | | | |
|Total Section Seven (5) | | | |
| | | | |
|Final Score | | | |
|Sample Scoring Sheet | | | |
| | | | |
|Country: Benin | | | |
|Region: Africa | | | |
|Title: Benin: Poverty Reduction Starategy Paper | | | |
|Period: 2011-2015 | | | |
|Publication Date: October 2011 | | | |
| | | | |
|Section One: Basic Facts | | | |
| | | | |
|Current Statistics (Weight: 25) | | | |
|Historical Trends (Weight: 12.5) | | | |
|Forecasts (Weight: 12.5) | | | |
| |Current |Historical |Forecast |
|Poverty |51 |17 | |
|Economic Growth |10 |7 |11 |
|Other Macroeconomic Data |44 |25 |24 |
|Demographics |8 |8 | |
|Trade |1 | | |
|Health Statistics |7 |5 |3 |
|Nutrition |4 |1 | |
|HIV | | | |
|Water & Sanitation |7 |5 | |
|Energy |6 |1 |1 |
|Education |14 |12 |1 |
|Literacy |4 |3 | |
|Infrastructure |5 |9 | |
|Farming & Agriculture |8 |5 |4 |
|Fisheries | | | |
|Forestry | | | |
|Mining | | | |
|Tourism | | |1 |
|Culture | | | |
|Social Security |1 | | |
|Banking & Credit | | | |
|Telecommunications | | | |
|Housing & Land Ownership | | | |
|Environment & Conservation |5 | | |
|Governance |15 |17 | |
|Total Frequency |190 |115 |45 |
| | | | |
|Scores: |Out of 5 |Weighted | |
|Current Statistics |5 |25 | |
|Historical Trends |5 |12.5 | |
|Forecasts |2 |5 | |
| | | | |
|Total Section One (50) |42.5 | | |
| | | | |
| | | | |
|Section Two: Disaggregated Data | | | |
|Weight: 15 | | | |
| |Current |Historical |Forecast |
|Geographical |3 | |1 |
|Rural/Urban |8 |1 | |
|Inequality |3 |1 | |
|Gender |3 |1 | |
|Age |3 |4 | |
|Total Sectors Covered |21 |17 |8 |
| | | | |
|Scores: |Out of 5 | | |
|Current Facts |4 | | |
|Historical Trends |3 | | |
|Forecasts |2 | | |
|Average |3 | | |
| | | | |
|Total Section Two (15) |9 | | |
| | | | |
| | | | |
|Section Three: Further Analysis | | | |
|Weight: 5 | | | |
| |Out of 5 | | |
|Score: |5 | | |
| | | | |
|Total Section Three (5) |5 | | |
| | | | |
| | | | |
|Section Four: Monitoring and Evaluation | | | |
|Weight: 15 | | | |
| | | | |
|Monitoring Framework Included (0 or 1) |1 | | |
| | | | |
|Development Indicators Covered | | | |
|Poverty |1 | | |
|Economic Growth |1 | | |
|Other Macroeconomic Data |1 | | |
|Demographics | | | |
|Health Statistics |1 | | |
|Nutrition | | | |
|HIV |1 | | |
|Water & Sanitation |1 | | |
|Energy |1 | | |
|Education |1 | | |
|Literacy | | | |
|Infrastructure |1 | | |
|Farming & Agriculture |1 | | |
|Social Security |1 | | |
|Banking & Credit | | | |
|Telecommunications | | | |
|Housing & Land Ownership | | | |
|Environment & Conservation |1 | | |
|Governance |1 | | |
|Other |1 | | |
|Total Sectors Covered |14 | | |
| | | | |
|Percent of Indicators Missing Baseline |30 | | |
| | | | |
|Percent of Indicators Missing Target |30 | | |
| | | | |
|Scores: |Out of 5 | | |
|Monitoring Framework |5 | | |
|Sector Coverage |4 | | |
|Baselines |1 | | |
|Targets |1 | | |
|Average |2.75 | | |
| | | | |
|Total Section Four (15) |8.25 | | |
| | | | |
| | | | |
|Section Five: Institutional Arrangements | | | |
|Weight: 5 | | | |
| | | | |
|Responsibilities |1 | | |
| | | | |
|Processes |1 | | |
| | | | |
|Timing |1 | | |
| | | | |
|Scores: |Out of 5 | | |
|Responsibilities |5 | | |
|Processes |5 | | |
|Timing |5 | | |
|Average |5 | | |
| | | | |
|Total Section Five (5) |5 | | |
| | | | |
| | | | |
|Section Six: Data Production and Use | | | |
|Weight: 5 | | | |
| | | | |
|Frequency |2 | | |
| | | | |
|Score (Out of 5): |0.5 | | |
| | | | |
|Total Section Six (5) |0.5 | | |
| | | | |
| | | | |
|Section Seven: Statistical Capacity Building | | | |
|Weight: 5 | | | |
| | | | |
|Frequency |6 | | |
|Section on National Statistical System |1 | | |
| | | | |
|Scores: |Out of 5 | | |
|Frequency |1.5 | | |
|NSS |5 | | |
|Average |2.666666667 | | |
| | | | |
|Total Section Seven (5) |2.6666667 | | |
| | | | |
|Final Score |72.9167 | | |
Keyword List
The following words are intended to facilitate the search and homogenize the extent of it in order to make the results as comparable as possible over time. Even though this list has been compiled carefully, it is not exhaustive. However, to ensure comparability, it is advised to stick to this list, since large deviations might lead to misleading results.
The keywords listed below either describe certain indicators or, in case of a diversity in wording of indicators, lead to more general findings. In any case, it is recommended to take a closer look at every finding in regard to relevance.
For Section I
Poverty:
• Growth in poverty
• Headcount poverty
• Perceived poverty
• Perception of poverty
• Poverty gap
• Poverty headcount
• Poverty line
• Poverty ratio
• Reduction in poverty
• Severity of poverty
Economic Growth:
• Nominal GDP Growth
• Real GDP Growth
Other Macroeconomic Data:
• Assets
• Balance of payments
• Borrowing
• Budget deficit
• Capital account
• Capital-labour ratio
• CPI
• Credit-to-GDP ratio
• Current Account
• Debt-to-GDP ratio
• Debt-to-export ratio
• Domestic debt
• Exchange rate
• Expenditures
• External debt
• External financing
• FDI-to-GDP ratio
• Foreign debt
• Foreign direct investment
• Foreign grants
• Foreign portfolio investment
• GDP deflator
• Inflation
• Investments
• Labour productivity
• Lending
• Liabilities
• Remittance
• Reserves
• Savings
• Tax revenue
• Unemployment
Demographics:
• Birth rate
• Disability
• Employment status
• Family planning
• Fertility rate
• Population growth
• Race
• Social mobility
Trade:
• Domestic trade
• Exports
• Export market penetration
• Export performance
• Imports
• Net trade
• Trade balance
Health Statistics:
• Child mortality
• Contraceptive
• Disease
• Health personnel
• Immunization
• Infant mortality
• Insecticide Treated Net (ITN)
• Life expectancy
• Malaria
• Maternal mortality
• Mortality rate
• Neonatal mortality
• Tuberculosis (TB)
• Vaccination (VAC)
Nutrition:
• Anemia
• Body weight
• Caloric intake
• Food reserve
• Food security
• Malnutrition
• Nutrition rate
• Prevalence of underweight
• Stunting
HIV:
• Access to medication
• Prevalence rate
• Mother-to-child transmission
• Sero-positivity
Water & Sanitation:
• Access to sanitation
• Drinking water
• Sanitary facilities
• Sanitation facility
• Water supply
Energy:
• Access to electricity
• Coal reserves
• Consumption of electricity
• Energy use
• Gas reserves
• Generation capacity
• Oil reserves
• Sources of energy
Education:
• Child labour
• Completion ratio
• Enrolment ratio
Literacy:
• Literacy rate
Infrastructure:
• Access to services
• Bridges
• Freight
• Gravel road
• Km of railways
• Km of roads
• Km of waterways
• Passenger
• Paved road
• Police
• Postal service
• Road network
• Shipping
• Traffic
Farming & Agriculture:
• Agricultural GDP
• Agricultural productivity
• Crop yield
• Irrigation
• Sustainability
Fisheries:
• Fish farming
• Fish production
Forestry:
• Deforestation
• Reforestation
Mining:
• Exploration
• Extraction
• Quarry
Tourism:
• Hotel beds
• Income from tourism
• Number of tourists
Culture:
• Archive
• Arts
• Craft
• Culture
• Dancing
• Folk
• Heritage
• History
• Language
• Library
• Literature
• Music
• Painting
• Religion
• Tribe
Social Security:
• Disability benefit
• Social empowerment
• Social protection
• Social safety net
• Social security
• Unemployment benefit
Banking & Credit:
• Access to credit
• Access to finance
• Access to financial services
• Cash transfer
• Microcredit
• Micro finance
• Payment
• Transactions
Telecommunications:
• Access to telephone
• Broadband
• Internet
• Radio
• Television
Housing & Land Ownership:
• Land ownership
Environment & Conservation
• Shelter
• Degradation
• Disaster resilient habitats
• Forest coverage
• Pollution
Governance:
• Access to government information
• Access to public information
• Accountability
• Citizen
• Civil liberties
• Corruption
• Hearings held by parliament
• Human rights
• Political rights
• Rule of law
• Transparency
For Section VI
• Capacity
• Data base
• Development in statistics
• Gaps
• Lack of data
• Little data
• Missing data
• No data
• Plans for improvement
• Poor data
• Problem of data
• Problem with data
• Reliable
• Statistical data
• Statistical development
For Section VII
• Decision-making
• National statistical office
• National statistical system
• National statistics development strategy
• NSDS
• NSS
• Production of statistical data
• SNDS
APPENDICES
The following pages contain full-page excerpts from the Poverty Reduction Strategy Paper of Burundi (September 2006), with footnotes explaining the highlighted sections. They are intended to illustrate the scoring process by giving direct examples of its application and clarifying the more complicated, or subjective, aspects of the system.
PAGE 11
91. The aim of the economic program for 2006 is therefore to boost real economic growth to 6.1 percent[3], hold average inflation to 2.5 percent, and cap the current external balance at -17.1 percent of GDP[4].
92. In the budget area, social and poverty reduction expenditures will be given priority. Furthermore, improved revenue collection and domestic debt reduction will be pursued. Fresh measures have been taken in this connection under the budget law for fiscal 2006 to rehabilitate fiscal management, expand the tax base, and strengthen the monitoring of budget execution.
93. On the monetary and foreign exchange front, the government intends to curb significantly the expansion of liquidity and pursue liberalization of the exchange regime.
94. Generally speaking, 2006 is a decisive year for accelerating the implementation of structural reforms, notably the government’s withdrawal from the productive sectors, economic liberalization through an effective policy of support for the private sector, and swifter progress in the areas of governance and transparency.
CHAPTER IV: OVERVIEW OF POVERTY IN BURUNDI
95. Burundi is one of the world’s poorest countries, with per capita incomes at US$83 at end-2004[5]. The seriousness of poverty poses a major risk to the country’s economic and social recovery.
96. While some progress has been made in just a few years, thanks to tangible progress in the political arena and in the implementation of economic reforms, the social situation remains difficult because of: (i) widespread poverty; (ii) the large number of disaster victims; (iii) the shortfall in basic social services coverage, and (iv) the proportions of the HIV/AIDS problem.
97. It is difficult to ascertain fully the scope and structure of poverty in Burundi, given the shortage of reliable and detailed statistical data.[6] The poverty analysis was carried out by using the 1998 household survey, the polls conducted in 2002 and 2004, and the social indicators.
4.1. The perception of poverty
99. The findings of a 2004 opinion poll of 3,000 persons yielded a glimpse of how poverty is perceived and the priority measures that should be taken. The results of the poll showed that around 40 percent of those polled are subjectively poor.[7]
100. More than 80 percent of those surveyed feel that poverty remains unchanged or has increased over the past five years, with 50 percent of them citing a sharp increase. Some 40 percent of respondents predict a drop in poverty[8] over the next five years. A similar proportion foresees a worsening of the situation.
PAGE 32
significant role in child mortality in Burundi. For this reason, it poses a major problem from a public health and developmental standpoint.
201. During the 1980s, the epidemic spread rapidly in urban areas while rural areas remained relatively unscathed. During the 1990s, the prevalence of the epidemic stabilized somewhat in urban areas; however, it made major inroads in rural areas.
202. The second national seroprevalence survey, conducted in 2002, pointed to a seroprevalence rate among persons age 15 and over of 9.4 percent in urban areas, 10.5 percent in semi-urban areas, and 2.5 percent in rural areas.[9] The latter rate more than tripled between 1990 and 2002[10] (in the course of a decade).
203. In late 2004, the number of persons living with HIV/AIDS was estimated at 250,000, with 230,000 of this number corresponding to persons between the ages of 15 and 49[11]. In 2001, the UNAIDS estimated the number of children who had lost at least one parent to AIDS at 237,000.[12]
204. HIV is exerting tremendous pressure on the health facilities of some services, largely the internal medical, pediatric, and out-patient consultation services. AIDS patients account for more than 70 percent of hospitalized persons[13] in the internal medicine services of hospitals in Bujumbura, while tuberculosis, the other major disease in Burundi, afflicts 56 percent of hospitalized persons who are HIV-positive.[14]
205. The cost of treating opportunistic diseases, the shrinkage of the labor force wrought by these diseases, and the paucity of facilities to manage the situation and conduct awareness- building activities are all impediments to economic poverty reduction and growth. HIV/AIDS has become one of the factors stifling Burundi’s development.
206. Increased seroprevalence is attributable to: (i) the effects of war (population displacement and regrouping and the increase in the number of widows and widowers); (ii) the rising incidence of poverty among the population; and (iii) lack of access to means of communication.
5.5. Gender and equity constraints
207. Despite significantly heightened awareness among women and numerous initiatives benefiting them, much remains to be done in the area of gender equality.
208. In Burundi, as in other post-conflict countries, a host of factors impinge upon the integration of gender into the country’s socio-economic development. One major factor is ingrained cultural habits, which stand in the way of gender equality and the representation of women on decision-making entities as well as their involvement in the economy.
209. The socio-economic problems facing women are additional factors exacerbating their poverty and vulnerability. Take the following, for example: the widowhood rate—
PAGE 49
301. In collaboration with the World Bank, the government has recently undertaken a new study to complete the work already done in identifying rural (agricultural) sources of growth. A recent study commissioned by the government of Burundi highlighted the potential importance of traditional and nontraditional export crops, as well as food crops and livestock. The study carried out in partnership with the Bank should serve to confirm the areas of greatest potential growth and, most importantly, identify and prioritize the actions to be taken to fulfill this potential.
6.2.2.1.1. Stimulate the agricultural, livestock, fisheries, and fish farming sectors
302. The agricultural sector is the foundation of the Burundian economy. It occupies 94 percent of the working population, provides 95 percent of the food supply, and accounts for more than 90 percent of foreign exchange earnings.[15] The rural sector is thus currently the principal source of growth of the economy and the foundation on which efforts to strengthen and improve the Burundian economy must be based.
303. To increase the sector’s contribution to the creation of wealth and fight poverty more effectively, rural development must target three objectives for improving food and export crops, livestock, and fish farming: (i) improve volume of output and productivity; (ii) improve cost control; and (iii) improve and stabilize income from sales.
a. Develop and improve food production
304. The objective is to raise yields in this sector and thereby increase local market output, which remains insufficient overall to satisfy the needs of a growing population. This also involves finding the means, by monetizing trade in food products, to increase the income of populations living in rural areas. In addition, special emphasis will be placed on crops likely to improve food security and crops for which the country possesses a comparative advantage and which could find better outlets in the context of regional integration and globalization. This principally comes down to the following crops: paddy rice, wheat, maize, sorghum, beans, cassava, and bananas.
305. Rice production rose from 4,500 metric tons in 1972 to 64,000 metric tons in 2005 and should reach 120,000 metric tons by 2010.[16] The output of wheat should also show a positive trend. Production had in fact stagnated at 8,000 metric tons[17] after the processing plant closed down, but it is now steadily climbing and could reach 16,000 metric tons by 2010.[18] This growth in output is supported by creditworthy demand from both processors (establishment of a second flour mill) and bread consumers in rural areas.
306. As for bananas, which account for the highest volume of output in the country by far, the departments of the Ministry of Agriculture estimate that banana yields, which are currently on the order of 20 metric tons per hectare[19], could rise to 30 metric tons per hectare.[20] From a practical standpoint, that would enable smallholders to increase their output of bananas from 1.6 million metric tons in 2005 to 2.3 million metric tons in 2010[21], a surplus which could be traded on the local market.
PAGE 66
426. As such, the objectives set by the government are to: (i) reduce the infant mortality rate from 114 deaths per 1,000 live births to 90 in 2010 and 65 in 2015; (ii) reduce the maternal mortality rate from 800 deaths per 100,000 live births to 560 in 2010 and 392 in 2015; (iii) raise the proportion of births assisted by health personnel from 17 percent in 2002 to 35 percent in 2010 and 60 percent in 2015; (iv) increase immunization coverage to 85 percent in 2010 and 90 percent in 2015[22]; (v) reduce the percentage of children with low body weight from 30 percent to under 10 percent in
2010; (vi) reduce the percentage of children with growth retardation from 52.5 percent to 35 percent and low body weight from 39.2 percent to under 26 percent in 2010[23].
6.2.3.3. Access to potable water, hygiene, sanitation, and decent housing
427. The crisis weighing on Burundi for more than a decade took a devastating toll on the infrastructure for supplying potable water, hygiene and sanitation systems, and housing. Furthermore, it has been noted that all this destruction has led to a rise in waterborne diseases and the spread of malaria.
428. The government’s strategy is to develop a water sector policy aimed at providing rural and urban populations with the minimum quantity of water necessary for their survival.
429. In the area of hygiene and sanitation, efforts will be undertaken in urban and rural centers to promote a sanitation program with the participation of organized communities and the private sector.
430. The principal objectives in this sector are to: (i) develop water sources and rehabilitate potable water supply systems; (ii) strengthen water production facilities; (iii) strengthen existing sanitation programs and expand them nationwide; (iv) promote community management of water supply; and (v) train and inform populations about hygiene and sanitation techniques appropriate to their environment.
431. With respect to housing, the destruction in the wake of the conflict has only worsened an already difficult situation. In rural areas, where more than 90 percent of the population lives, there is virtually no housing that meets standards of decency.
432. Even though Burundi is an overpopulated country, its urbanization rate of 9 percent[24] makes it one of the least urbanized countries of the world. All signs indicate that it will continue to be an essentially rural country for a long time. For this reason, it is important to bring coherence to community land development issues within the framework of village-based clustering.
433. Such a policy will have the greatest chance of success because displaced and repatriated populations are already accustomed to village life in host sites. Making villages viable, specifically by developing water supply systems, electric hookups, and road access, will make them more attractive, especially for youth to develop nonagricultural activities.
-----------------------
[1] dac/stats/daclist
[2] Note that the scoring system is applied to IDA classified countries as of 2009; while Azebaijan graduated from IDA status since then, it is still included in milestone calculations.
[3] Forecast – Economic Growth (aim of the economic program).
[4] Forecasts (2) – Other Macroeconomic Data (inflation, external balance).
[5] Current (one-time) Statistic – Economic Growth (measured by per-capita income).
[6] This acknowledgement of weakness in data collection is counted toward Section 6 – Data Production and Use.
[7] Survey data was also taken into account. In this case, the poll reveals the perception of poverty among respondents, and is thus counted under Current Statistics – Poverty.
[8] Although three values are given, they represent responses to the same survey question and are thus counted as one score for Current Statistics – Poverty.
[9] This is a good example of disaggregated data. While three scores are given for Current Statistics – HIV, only one score is given for Current Statistics – Rural/Urban in the disaggregated data section. Note: HIV data are separated from general health statistics due to their importance as a focus area in most PRSPs.
[10] Historical trend – HIV (the given rate tripled over a decade).
[11] Two Current Statistics – HIV, one disaggregation under Current Statistics – Age.
[12] Current Statistic – HIV.
[13] Current Statistic – HIV.
[14] Current Statistic – Health Statistics.
[15] Three current statistics given about the agriculgural sector.
[16] Historial Trend – Agriculture; Forecast – Agriculture.
[17] Historical Trend – Agriculture (stagnation in agricultural production).
[18] Forecast – Agriculture.
[19] Current Statistic Agriculture (yields).
[20] Forecast – Agriculture.
[21] Current Statistic (2005), Forecast (2010) – Agriculture.
[22] Four Forecasts (government objectives) – Health Statistics. This category covers mortality rates, vaccination levels, diseases (not including HIV/AIDS), and health personnel. To be distinguished from Nutrition (see below).
[23] Three Forecasts – Nutrition. This category covers malnourishment, body weight (especially children), nutrition-related diseases (anemia), nutritional practices and caloric intake.
[24] Two Current Statistics – Demographics. These measurements simply show the distribution of the population, and do not disaggregate other sectors with regard to Rural and Urban areas (hence not included in the Disaggregated Data section).
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