Measuring and Presenting Jigawa CDF KPIs

Transcription

1 Measuring and Presenting Jigawa CDF KPIs February 2011

2 The opinions expressed in this report are those of the authors and do not necessarily represent the views of the Department for International Development

3 Content List Abbreviations and Acronyms Section One: Introduction... 1 Section Two: Defining Formulas for Calculating Indicators... 2 Section Objectives... 2 Overview of Data Tables... 2 How to Develop Data Tables for Indicators... 2 Section Three: Manipulating Data and Entering Data into Formulas... 5 Section Objectives... 5 Overview of Data Manipulation... 5 How to Manipulate and Aggregate Data for Indicators... 5 Section Four: Creating Charts for Indicators... 9 Section Objectives... 9 Overview of Excel Charts... 9 How to Create Charts for Indicator Values... 9 How to Create Charts for the Change Against Baseline Section Five: Creating Information Products Section Objectives Overview of Information Products How to Create Information Products Appendix One: Example of a Completed Briefing Note... 15

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5 Abbreviations and Acronyms BEPD CDF KPI M&E MDA Budget an Economic Planning Directorate Comprehensive Development Framework Key Performance Indicator Monitoring and Evaluation Ministry, Directorate or Agency

6 Section One: Introduction The purpose of this manual it to help Statistical Officers in BEPD and other sector MDAs enhance their knowledge and skills related to: Data processing techniques. Calculating Key Performance Indicators (KPIs) listed in the CDF M&E Results Framework. User friendly presentation and interpretation of KPI results. The manual is based on materials prepared for a practical workshop held in Dutse on 24 th 25 th February 2011 for statistical officers from BEPB and other sector MDAs. The manual is split into four sections covering: a) developing KPI formulas, b) collating data and entering into KPI formulas, c) developing charts which show KPI results and d) presenting these charts and analysing key features. A practical worked example for a specific KPI is used in these sections. Through building up an understanding of this worked example, readers can apply the knowledge gained to other KPIs. The manual is also intended to serve as a reference guide for readers as they come to measure KPIs in the CDF M&E framework once the latest data sets have been collected. 1

7 Section Two: Defining Formulas for Calculating Indicators Section Objectives The objective of this section is to familiarise participants with approaches for setting up well ordered data tables and calculating formulas. By the end of this section participants will be able to: Develop well ordered tables which detail the data needed to calculate indicators. Define basic formulas for calculating indicators. The guidance given below can be applied to any indicator. Within the guidance below, a specific example is used to help demonstrate the steps. When applying this guidance to other indicators, you should simply adapt the guidance to the needs of the indicator. Overview of Data Tables The CDF M&E framework contains over 100 KPIs, split across 7 pillars and 17 themes. Most of these KPIs require two or more data sources. With this much data, it is potentially harder for users to locate the information they require and there is an increased risk that errors will be made. Consequently, it is important to store data in well labelled and well formatted tables. These make it easier for users to locate and use the correct data. Getting into the discipline of managing data in this way is an essential skill to develop when using Excel. How to Develop Data Tables for Indicators 1. Create Spreadsheet: Create a new Excel spreadsheet, give it an appropriate name and save it in an appropriate folder. 2. Setup Worksheets: Create a worksheet for each CDF Pillar and name the worksheets accordingly (e.g. Pillar 1, Pillar 2). 3. Setup Indicator Titles: Select the pillar worksheet with which the indicator is associated and define titles for the Indicator data table: 2

8 a. In cell A1, enter the text Theme: >> In cell B1 enter the code and name of the CDF Theme with which the Indicator is associated (e.g. in the example: 1.1 Agriculture and Food Security ). b. In cell A2, enter the text Target: >> In cell B2 enter the code and text of the CDF Target with which the Indicator is associated (e.g. in the example: Increased agricultural productivity (higher farm yields) by 10% during the period 2010 to 2013 ). c. In cell A3, enter the text Indicator: >> In cell B3 enter the name of the CDF Indicator being calculated (e.g. in the example: Mean Farm yields for major crops in kg/ha per each year ). d. Format all cells as Ariel font >> Format cells A1 and A3 as bold >> Format cells A1 and B1 as 14 pt >> Format cells A2-B3 as 12 pt. 4. Create Indicator Data Input Table: Leave an empty row (e.g. row 4) underneath the row containing the indicator name and setup the Indicator data input table: a. In cell A5, enter the text Data Input >> In cells A6-E6 enter headings as shown in Table 1 below >> In cell B6 enter the baseline year (YYYY) >> In cell C6 enter the first year (YYYX) for which you have data >> In cells D6 and E7, enter the subsequent years (YYYX+1 and YYYX+2) for which you have data. b. Under the Data Description heading in column A, enter the text describing each source of data needed to calculate the Indicator >> Use one row for each source of data (e.g. there are two sources of data needed to calculate the Indictor in the Agriculture example given earlier see Table 1). c. Format cells A5-E6 as bold >> Format cells A5-E8 as Ariel 11 pt >> Fill cells A6-E6 with a colour of your choice >> Highlight cells A6-E8 and give all cells a border. d. Give the data in cells A7-E8 an appropriate format (e.g. number, percentage etc.) >> In the example shown in Table 1, cells A7-E8 should be formatted as a Number with 1 Decimal Place. Table 1: Example of the Data Input Table A B C D E 1 Theme: 1.1. Agriculture and Food Security 2 Target: Increased agricultural productivity (higher farm yields) by 10% during the period 2010 to Indicator: Mean Farm yields for major crops in kg/ha per each year 4 5 Data Input 6 Data Description Baseline (YYYY) 7 Production of Major Crops (Kg) 8 Total area under cultivation of major crops (Hectares) YYYX YYYX+1 YYYX+2 5. Create Indicator Calculation Table: Leave an empty column (e.g. Column F) next to the Data Input Table and setup the Indicator Calculation Table: 3

9 a. In cell G5, enter the text Indicator Value >> In cells G6-J6 enter headings as shown in Table 2 below >> In cell G6 enter the baseline year (YYYY) >> In cell H6 enter the first year (YYYX) for which to calculate the Indicator value for >> In cells I6 and J6, enter the subsequent years (YYYX+1 and YYYX+2) for which to calculate the Indicator value for. b. Format cells G5-J6 as bold >> Format cells G5-J7 as Ariel 11 pt >> Fill cells G6-J6 with a colour of your choice >> Highlight cells G6-J7 and give all cells a border. c. In cell G7, enter the formula =B7/B8. This formula divides the first data source by the second for the baseline year. d. In cell H7, enter the formula =C7/C8 >> In cell I7, enter the formula =D7/D8 >> In cell J7, enter the formula =E7/E8. e. Give the results in cells G7-J7 an appropriate format (e.g. number, percentage etc.) >> In the example shown below, cells G7-J7 should be formatted as a Number with 1 Decimal Place. Table 2: Example of the Indicator Calculation Table G H I J 5 Indicator Value 6 Baseline (YYYY) YYYX YYYX+1 YYYX+2 7 =B7/B8 =C7/C8 =D7/D8 =E7/E8 6. Create Baseline Comparison Table: Leave an empty column (e.g. column K) next to the Indicator Calculation Table and setup the Indicator Baseline Comparison Table: a) In cell L5, enter the text Change Against Baseline >> In cells L6-N6 enter headings as shown in Table 3 below >> In cell L6 enter the first year (YYYX) for which to calculate the amount by which the Indicator has changed compared to the baseline for >> In cells M6 and N6, enter the subsequent years (YYYX+1 and YYYX+2) for which to calculate the change compared to the baseline. b) Format cells L5-N6 as bold >> Format cells L5-N7 as Ariel 11 pt >> Fill cells L6-N6 with a colour of your choice >> Highlight cells L6-N7 and give all cells a border. c) In cell L7, enter the formula =(H7-G7)/G7. This formula calculates the difference in the indicator value between the baseline year (YYYY) and first year of data (YYYX) and expresses it as a percentage of the baseline indicator value. d) In cell M7, enter the formula =(I7-G7)/G7 >> In cell N7, enter the formula =(J7-G7)/G7. e) Give the results in cells L7-N7 an appropriate format (e.g. number, percentage etc.) >> In the example shown in Table 3, cells L7-N7 should be formatted as a Percentage with 1 Decimal Place. Table 3: Example of the Baseline Comparison Table L M N 5 Change Against Baseline 6 YYYX YYYX+1 YYYX+2 7 =(H7-G7)/G7 =(I7-G7)/G7 =(J7-G7)/G7 7. Complete for all Indicators: Repeat steps 3-6 above for all required indicators. At the end of this process you should have an Indicator Calculation Spreadsheet which allows you to enter data and calculate the value of indicators for multiple years. 4

10 Section Three: Manipulating Data and Entering Data into Formulas Section Objectives The objective of this section is to familiarise participants with approaches for: a) collating data, b) dealing with missing data points, c) aggregating data where required and d) entering this data into the Indicator Calculation Spreadsheet developed in the previous session. By the end of this section participants will be able to: Collate, clean and aggregate data sources needed for calculating indicators. Use this data to calculate relevant indicators. The guidance given below can be applied to any data sources. Within the guidance below, a specific example is used to help demonstrate the steps. When applying this guidance to other data sources, you should simply adapt the guidance to the needs of the data. Overview of Data Manipulation Information required by users should determine what data you need to collate; which is why it is important to setup the KPI calculation tables first. Once you have determined what data is needed, this should be collected from the most appropriate sources. Wherever possible, data should be taken from existing sources (e.g. sector MDAs, State Statistical Yearbook), rather than attempting to collect new data. It is important to go through a data cleaning process in order to identity any data gaps or problems with the data. If this is not done, it is possible you may over or under-estimate some KPI values. Ideally, where gaps or problems are encountered, you should follow up with the relevant MDA or data source and attempt to correct the problems directly. If this is not possible, then estimates should be made and used to fill gaps or address problems. How to Manipulate and Aggregate Data for Indicators 1. Identify the data required: Within the Indicator Calculation Spreadsheet, select the Indicator you want to collate data for and review the Data Sources listed in the Data Input Table developed previously. 5

11 2. Locate the data required: In order to calculate the indicator, you should have gone through an exercise of data collection. This data should either be in an Excel spreadsheet/s or as a paper copy. In the case of historic data, you should be able to locate this in the Jigawa State Statistical Yearbook, DHS or other survey data. 3. Collate the data required: Once you have identified the required data sources, you should now collate this data within an Excel spreadsheet. Where the data has already been provided in an Excel spreadsheet, simply make a copy of this spreadsheet (you may need to edit the data and should preserve the original copy) and move to Step 4. Where the data has been provided as a paper copy or is contained within the Jigawa State Statistical Yearbook (or similar), do the following: a) Create a new Excel spreadsheet in which to store the data (not the Indicator Calculation Spreadsheet) - give it an appropriate name and save it in an appropriate folder. b) As you did within the Indicator Calculation Spreadsheet, define titles for the Source Data. The simplest way to do this is to open the Indicator Calculation Spreadsheet select the cells containing the Indicator Titles (e.g. A1-B3) >> Copy these cells >> Switch back to the spreadsheet in which you are entering data >> Select cell A1 and paste cells. a) In cell A5, enter the text Data Source 1: >> In cell B5, enter the name of the first data source (e.g. in the example: Production (Kg) ). Table 4: Example of Source Data A B C D E 1 Theme: 1.1. Agriculture and Food Security 2 Target: Increased agricultural productivity (higher farm yields) by 10% during the period 2010 to Indicator: Mean Farm yields for major crops in kg/ha per each year 4 5 Data Source 1: Estimated Production of Major Crops (Tons) 6 Data Description Millet 206, , , ,730 8 Sorghum 115,400 92, , ,273 9 Cowpea 0 49,730 63,609 10, Groundnut 27,700 21,230 22,224 4, Rice 16,000 47,230 75,360 56, Maize 4,200 8,230 10,684 30, Tomatoes 58, Nil Pepper 31,400 Nil Nil Wheat 7,700 Nil Nil 7, Sesame 31,600 13,640 16,614 20, Onions 33,100 Nil Nil 5, Carrots 15,000 Nil Nil 1, Lettuce 1,070 Nil Nil Sugarcane 735,000 Nil 80 Nil 6

12 b) In cells A6-E6 enter headings as shown in Table 4 >> In cell B6 enter the baseline year (YYYY) >> In cell C6 enter the first year (YYYX) for which you have data >> In cells D6 and E7, enter the subsequent years (YYYX+1 and YYYX+2) for which you have data. c) Under the Data Description heading in column A, enter text which describes the Data Points associated with the Data Source >> Use one row for each Data Point (e.g. there are Data Points for each major crop in the example see Table 4). d) Under each year in columns B-E, enter the value of each data point for each year. e) Format cells A5-E6 as bold >> Format cells A5-E6 as Ariel 11 pt >> Fill cells A6-E6 with a colour of your choice. f) Highlight all cells in the Data Source Table (e.g. A6-E20 in the example see Table 4) and give all cells a border and format these cells as Ariel 11pt. g) Give the data in the Data Source Table an appropriate format (e.g. number, percentage etc.) >> In the example shown in Table 1, cells A7-E20 should be formatted as a Number with 0 Decimal Places. 4. Fill data gaps: It is possible the data will contain gaps for some Data Points for some years (e.g. for Pepper and Wheat in 2006 and 2007 see Table 4). If these gaps are not filled then the value of the related Indicator will be less than it should. There are various ways to fill a data gap, but one of the simplest is to use the value for the previous year for which data is available. To fill data gaps, go through each row of Data Points in turn and check the values for each year. If you find a data gap (e.g. Nil) you should enter a formula that will display the value for the previous year for which data is available as shown in Table 5. Do this for all Data Points until you have filled all data gaps. You should make a note of any corrections you make to data gaps as you may have to make reference to this within any analysis based on the indicator calculations. Table 5: Example of How to Fill Data Gaps A B C D E 14 Pepper 31,400 =B14 =B Wheat 7,700 =B15 =B15 7, Calculate Data Totals: In order to calculate the overall value of the Indicator, it may be necessary to total the Data Points values for each year. The Indicator in the example is: Mean Farm yields for major crops in kg/ha. Therefore we need to calculate the total production across all major crops we are not interested in the amount of individual crops. To create totals, do the following: a) In the row under the last Data Point (e.g. row 21 in the example see Table 6) enter the text Totals in the Data Description Column (e.g. cell A21) >> Calculate the total amount of crops produced in the baseline year by entering the formula =sum(b7:b20) in the Baseline column (e.g. cell B21). This formula adds together (or sums) all the values in cells B7-B20. b) Calculate the total amount of crops for all other years by entering the formula s as shown in row 21 in Table 6. Table 6: Example of How to Calculate Totals A B C D E 21 Totals (Tons) =sum(b7:b20) =sum(c7:c20) =sum(d7:d20) =sum(e7:e20) 22 Totals (Kgs) =B21*1000 =C21*1000 =D21*1000 =E21*1000 7

13 6. Converting from one unit to another: It is possible that the values entered in the Data Source Table may not be of the same unit as that required by the Indictor. If the units are not the same you will need to enter a formula which converts the totals from one unit to another. In the example, the data unit in the Data Source Table is Tons but the unit required by the Indicator is Kg. To make the conversion, for each year of data simply divide the total in Tons by 1,000. Calculate the total amount of crops in Kg by entering the formula s as shown in row 22 in Table 6. Note that, for other indicators, more complex conversions may be required. 7. Complete for all Data Sources: Repeat steps 3-6 above for all Data Sources required by the Indicator. You should create a Data Source Table for each Data Source and leave an empty row between each. In the example, you would now create a Data Source Table for Data Source 2: Total area under cultivation of major crops (Hectares). 8. Enter data into Indicator Calculation Spreadsheet. At the end of this process you should have a set of data which you can now enter into the Indicator Calculation Spreadsheet you created earlier. Simply copy the calculated data totals (e.g. cells B22-E22 in the example) and paste (or enter by hand if you prefer) into the Data Input cells in the Indicator Calculation Spreadsheet (e.g. cells B7-E7 in the example). 9. Complete for all Indicators: Repeat steps 3-8 above for all required indicators. At the end of this process you should have a completed Indicator Calculation Spreadsheet which now has calculated values for all required indicators. 8

14 Section Four: Creating Charts for Indicators Section Objectives The objective of this section is to familiarise participants with approaches to displaying information within charts. By the end of this section participants will be able to: Create charts which display information calculated in previous sessions. Format and customise the way in which information in charts is displayed. The guidance given below can be applied to any information sources. Within the guidance below, a specific example is used to help demonstrate the steps using information developed in previous sessions. When applying this guidance to other information sources, you should simply adapt the guidance to the needs of the information. Overview of Excel Charts Charts can be a powerful aid to presenting data in a way that is assessable to users. Patterns and trends are often much easier to see when presented in a chart, especially when using multiple years of data. Different users have different information needs. For example, a researcher may be interested in tables of raw data which they can analyse and make conclusions about. However, senior managers may require a summarised analysis of data which fulfil their priority information needs. How to Create Charts for Indicator Values 1. Select Data and Create Chart: Within the Indicator Calculation Spreadsheet Locate the Indicator Value data to be displayed in a chart and do the following: a) Select the Indicator Value data for multiple years (e.g. cells G7-J7 in the example). b) In the Excel toolbar, select the Insert tab (see Figure to right) >> Under the Chart options, click on the Line chart option >> Click on the first line chart type displayed in the drop down list. A basic line chart will now be created. 9

15 2. Add a Legend Entry: Click on the chart just created and do the following: a) Click on the right mouse button and click on the Select Data option the Select Data Source box will be displayed >> Find the Legend Entries (Series) list and click on the series with the name Series1 (see Figure to right). b) Click the Edit button and the Edit Series box will be displayed >> Under the Series Name label, enter the name of the Indicator you are creating a chart for (e.g. Mean Farm yields for major crops in kg/ha ) >> Click OK and the title and legend text of the chart will be updated. 3. Add Category Labels: Click on the chart you just created and do the following: a) Click on the right mouse button and click on the Select Data option the Select Data Source box will be displayed (see Figure above) >> Find the Horizontal (Category) Axis Labels and click on the Edit button the Axis Labels box will be displayed. b) Click on the button to the right of the text entry box to select the cells which contain the axis labels >> Highlight the cells containing the axis labels (e.g. G6-J6 in the example) >> Click on the button to the right of the text entry box again and then click OK to close the Axis Labels box >> Click OK to and the Axis Labels will be updated. 4. Move and Name the Chart: Click on the chart just created and do the following: a) Click on the right mouse button and select the Move Chart option the Move Chart box will be displayed (see Figure to right) >> Select the New Sheet option this will create a new worksheet which displays the chart only. b) Give the chart a name in the box next to the New Sheet option the name should include the associated CDF Target code and the number of the Indicator. Also add an a to differentiate this chart from one the one you will be producing in the next section (e.g Indicator 1a in the example) >> Click OK to and the chart will be moved to a new worksheet. 5. Add Axis Titles: Select the Chart worksheet just created and do the following: a) In the Excel toolbar, select the Layout tab (see Figure to right) >> Under the Labels options, click on the Axis Titles option >> In the drop down list displayed, select the Primary Horizontal Axis Title option >> Select the Title Below Axis option the text Axis Title will appear below the horizontal axis >> Click on this text and rename the axis title to Year. b) In the Excel toolbar, select the Layout tab >> Under the Labels options, click on the Axis Titles option >> In the drop down list displayed, select the Primary Vertical Axis Title option >> Select the Rotated Title option the text Axis Title will appear next to the vertical axis >> Click on this text and give the axis an appropriate name (e.g. Crop Production in Kg/ha in the example). 10

16 6. Add a Data Table and/or a Legend: In order to help users of the charts you may wish to include a data table which displays the value of each data point. To create a data table, select the Chart worksheet and do the following: a) In the Excel toolbar, select the Layout tab >> Under the Labels options, click on the Data Table option >> In the drop down list displayed, select the Show Data Table With Legend Keys option a data table will be displayed below the horizontal axis. b) If you have displayed a data table with a legend key, there is no need to display the legend as well. To turn off the legend, in the Excel toolbar, select the Layout tab >> Under the Labels options, click on the Legend option >> In the drop down list displayed, select the None option. c) If you have not displayed a data table you should position the legend in the most appropriate place (often underneath the chart). To position the legend, in the Excel toolbar, select the Layout tab >> Under the Labels options, click on the Legend option >> In the drop down list displayed, select the Show Legend at Bottom option. 7. Format Titles and Labels: In order to make the chart as readable as possible you may wish to alter the formatting of some of the text. To format the text, select the Chart worksheet and do the following: a) In the Excel toolbar, select the Layout tab >> Under the Current Selection options, click on the drop down box displaying the various chart elements >> In the drop down list displayed, select the Chart Title option the chart title will be displayed >> In the Excel toolbar, switch to the Home tab and set the Font of the title to Ariel 20pt in Bold. b) Repeat the previous step for the Horizontal (Category) Axis Title and Vertical (Value) Axis Title options and set the Font for each of these to Ariel 16pt in Bold. c) Repeat the previous step for the Data Table (if included), Horizontal (Category) Axis, Legend (if included) and Vertical (Value) Axis options and set the Font for each of these to Ariel 16pt (not Bold). 8. Complete for all Indicators: Repeat steps 1-7 above for all required indicators. At the end of this process you should have a worksheet displaying values for each indicator within the Indicator Calculation Spreadsheet. How to Create Charts for the Change Against Baseline 1. Select Data and Create Chart: Locate the Change Against Baseline data to be displayed in a chart and do the following: c) Select the Change Against Baseline data for multiple years (e.g. cells L7-N7 in the example). d) In the Excel toolbar, select the Insert tab >> Under the Chart options, click on the Column chart option >> Click on the first 2-D Column chart type displayed in the drop down list. A basic column chart will now be created. 2. Move, Name and Format Chart: Follow steps 2-8 for the Indicator Value charts as detailed in the previous section. The name of this chart should include the associated CDF Target code and the number of the Indicator. Also add a b to differentiate this chart from the one you produced in the previous section (e.g Indicator 1a in the example). 3. Complete for all Indicators: Repeat steps 1-2 above for all required indicators. At the end of this process you should have a worksheet displaying the Change Against Baseline for each indicator within the Indicator Calculation Spreadsheet. 11

17 Section Five: Creating Information Products Section Objectives The objective of this section is to familiarise participants with approaches to presenting charts within either: a) short briefing notes for senior management or b) more comprehensive reports containing a range of analysis. By the end of this section participants will be able to: Export charts to Word and format these charts. Draft short analytical descriptions of the information contained within the charts. The guidance given below can be applied to any charts for inclusion in any document. The guidance below focuses on the specific example of preparing a briefing note for a specific CDF pillar and theme in order to demonstrate the steps using charts developed in previous sessions. When applying this guidance to other charts or documents, you should simply adapt the guidance to the needs of the information. Overview of Information Products In order for the KPI analysis to be used, it is important to present this analysis in the most appropriate way. The way in which you present this analysis will depend on the audience it is aimed at. For example, if you are presenting the analysis to senior decision makers, you should make it as short as possible and only focus on the key features (senior decision makers are very busy and will not have time to read a detailed analysis). However, if the audience is more technical, the analysis should be more comprehensive in order to support detailed planning and budgeting decisions. How to Create Information Products 1. Create a Briefing Note: Create a new Word document, give it an appropriate name and save it in an appropriate folder >> Give the document a title which refers to the fact that it is a briefing note and include a reference to the relevant CDF Pillar and CDF Theme see Table 7 below for the current Agriculture example. 12

18 Table 7: Example of Briefing Note Title Briefing Note: CDF Pillar 1 Agriculture and Environmental Protection CDF Theme: 1.1 Agriculture and Food Security 2. Prepare an Introduction: Underneath the title, enter a heading: Introduction >> Under this heading introduce and list the Targets and Indicators associated with the current CDF Theme within a table see Table 8 below for the current Agriculture example. Table 8: Example of Briefing Note Introduction Introduction The CDF theme of Agriculture and Food Security has four targets as follows: Target Increased agricultural productivity (higher farm yields) by 10% during the period Reduce post-harvest losses by 25% for both cereals and vegetables by 2011 Key Performance Indicators Mean Farm yields for major crops in kg/ha per each year Mean quantity (kg) of crops sold and consumed in household compared to quantity harvested (kg) for major crops and vegetables Increase profitability of agricultural production by 20-30% by year Increase the number of each stock of livestock by 10% by 2011 Gross and Net income from major crops and livestock enterprises per annum Number of all major livestock types per annum 3. Add the Analysis Section: Underneath the introduction, enter a heading: Analysis of Progress Towards Targets 4. List the Target to Analyse data For: Under the analysis heading, list the first target given in the introduction >> Under the target insert a table with 1 row and 2 columns into which charts prepared in previous sessions can be pasted - see Table 9 below for the current example. 5. Export Charts from Excel to Word: Open the Indicator Calculation Spreadsheet which should contain charts developed in previous sessions and do the following: a) Select the worksheet containing the Indicator Values chart you want to export to word >> Click the right mouse button and select the Copy option. b) Switch back to the Word document you have just created >> Position the cursor in the first column of the table inserted in the previous step (3) >> In the Word toolbar, select the Home tab >> Under the Clipboard options click on the Paste option and select Paste Special >> The Paste Special box will be opened select Picture (Enhanced Metafile) and click OK the Chart will now be pasted into your document as an image - see Table 9 for the current example. Note: The reason for pasting the chart into Word in this special format is to ensure that the chart displays properly. c) Click on the chart in the Word document and reduce its size so that it fits into the table >> In the Word toolbar, select the Format tab >> Under the Size options set the Width of the chart to 7.5 cm. d) Repeat steps a-c for the Change Against Baseline chart but paste this chart into the second column of the table created in the previous step (3). 13

19 e) Underneath the table containing the charts: a) list the source of the data on which the analysis is based and b) If any data gaps were filled for the current Indicator Data Sources during previous sessions, add a note to this effect. Table 9: Example of Briefing Note Charts Analysis of Progress Towards Targets Increased agricultural productivity (higher farm yields) by 10% during the period 2010 to Mean Farm yields for major crops (kg/ha) 1,600 % Change in Yields Compared to Baseline 10% 1,400 0% Crop Production in Kg/ha 1,200 1, Change against Baseline -10% -20% -30% -40% -50% Mean Farm yields for major crops (kg/ha) 0 Baseline (2004) ,475 1, Year -60% % Change in Yields Compared to Baseline % -55% -41% Year Source: Analysis based on Jigawa State Statistical Yearbook 2008 Note: Data for some crops in some years estimated from previous year s data due to data gaps 6. Draft the Analysis: You should now draft a short analysis of some of the key findings which can be concluded from the Indicator charts pasted into to the document in the previous step (e.g. see Table 10 below for the current example). You should not attempt to analyse all information shown in the charts, but highlight (e.g. in bullets) some of the key features. Things to look for and analyse include: Examples of where there is a significant increase or decrease in the value of the indicator (e.g. between 2005 and 2006 in the example). Possible explanations for any significant changes (e.g. the reduction in Sugercane crops in the example). Any significant trends shown in the information (e.g. the most recent trend in production between 2006 and 2007 in the example) Extrapolating these trends forward and making an assessment of the extent to which the associated CDF Target will be achieved if these trends continue. Table 10: Example of Briefing Note Analysis The charts above show data for the Mean Farm yields for major crops in kg/ha KPI. Key findings include: Production of major crops increased slightly by 3% from a baseline of 1,475 Kg/Ha in 2004 to 1,516 Kg/Ha in Production fell sharply between 2005 and 2006 by over 50% to reach a low of 662 Kg/Ha. The main reason for this sharp decline in production was a massive decrease in the production of sugarcane (735,000 Tons in 2005 compared to only 80 Tons in 2006). There was growth in production of 31% between 2006 and 2007 and if this pattern continues then the outlook is good for achieving the CDF target for increased agricultural productivity. 7. Complete for all Targets: Repeat steps 4-6 above for all Targets listed in the introduction for the current CDF Theme. At the end of this process you should have a briefing note which provides overall analysis of Indicator associated with all Targets for the current CDF Theme. 14

20 Appendix One: Example of a Completed Briefing Note 15

21 Introduction Briefing Note: CDF Pillar 1 Agriculture & Environmental Protection CDF Theme Agriculture and Food Security The CDF theme of agriculture and food security has four indicators as follows: Target Increased agricultural productivity (higher farm yields) by 10% during the period 2010 to Reduce post-harvest losses by 25% for both cereals and vegetables by 2011; Increase profitability of agricultural production by 20-30% by year 2011; Increase the number of each stock of livestock by 10% by Analysis of Progress Towards Targets KPIs Mean Farm yields for major crops in kg/ha per each year Mean quantity (kg) of crops sold and consumed in household compared to quantity harvested (kg) for major crops and vegetables; Gross and Net income from major crops and livestock enterprises per annum; Number of all major livestock types per annum Increased agricultural productivity (higher farm yields) by 10% during the period 2010 to Mean Yields of Major Crops (Kg/Ha), Jigawa Annual Change in Yields of Major Crops Compared to Baseline, Jigawa 2,000 10% 1,500 0% 1, % -20% % Baseline (2004) % -50% -60% Source: Analysis based on Jigawa State Statistical Yearbook 2008 Note: Data for some crops in some years estimated from previous year s data due to data gaps The charts above show data for the Mean Farm yields for major crops in kg/ha KPI. Key findings include: Production of major crops increased slightly by 3% from a baseline of 1,475 Kg/Ha in 2004 to 1,516 Kg/Ha in Production fell sharply between 2005 and 2006 by over 50% to reach a low of 662 Kg/Ha. The main reason for this sharp decline in production was a massive decrease in the production of sugarcane (735,000 Tons in 2005 compared to only 80 Tons in 2006). There was growth in production of 31% between 2006 and 2007 and if this pattern continues then the outlook is good for achieving the CDF target for increased agricultural productivity. 16

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