PAT

Introduction to Graphical Analysis

Session 2.1.

WFP Markets Learning Programme 2.1.1Price Analysis Training

Learning ObjectivesBy the end of this session, participants should be able to:

Describe the advantages of different types of graphs and charts and which is best used with particular types of data

Scale graphs in accordance with units used, and explain the meaning and intent of each of the axes on a graph

Identify the trends (dispersion, volatility, increasing/ decreasing) depicted by a graph

Identify the breaks in a data series and explain whether they are real or indicate problems with the data

Explain how to deal with missing data

WFP Markets Learning Programme Price Analysis Training 2.1.2

Graphical Analysis: Why?

“One picture is worth a thousand words.”

Graphs:

can portray much valuable information

useful tools for summarizing data

are efficient means of communicating numerical info

Research shows people retain info presented in graphs more than the same info written as prose

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Exercise 2.1.a.Charts and Graphs – Strengths & Limitations

Analyse the charts assigned to your team and discuss:

1. What types of data are being presented (e.g. discrete events or trends)?

2. What are the main messages the chart is trying to communicate to senior management?

3. What are the chart’s strengths/weaknesses in communicating these data/messages?

4. What recommendations can you make for improving presentation of this information?

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Debriefing

Review charts in order: 1, 2, 3…etc.• Data types? Messages?• Strengths/weaknesses?• Recommendations?

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What lessons have you learned from this exercise?

What will you do differently in the future?

Strengths of Graphical Analysis

Visual rather than numeric: provides for relatively clear communication of complex phenomena

With Excel charts: easy to visualize effects of changes in quantities of particular variables

Previously unseen patterns – e.g., seasonal price patterns – can emerge and help with (cautious!) forecasting

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Limitations: Graphical analysis…

Graphical analysis shows relationship between prices but does not quantify degree of this relationship

Graphical analysis doesn’t give clear understanding of direction of relationship (i.e. direction of price transmission)

Apparent relationship between prices on a graph (convergence, divergence) does not necessarily indicate meaningful relationship between them

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Limitations: Graphical analysis… Co-movement of price series in different

locations at same time could be due to common factors affecting prices – e.g., seasonality, inflation, drought, war, prohibition, trade barriers – rather than to meaningful causal relationship in trade between the different locations

Interpretation of graph may need additional info: relationship between variables (prices) could be lagged, instantaneous, linear, non-linear, symmetric, or asymmetric

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Example: “Other Factors”

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1. Jan 05 Ban on private trade in grain (revive state dist system)

2. Jul 06 Floods 3. Jan 06 Nuclear test, UN sanctions imposed4. Apr 07 Trading restrictions imposed5. Aug 07 Floods 6. Dec 07 Chinese export controls, NK trading activity ban7. Apr 08 Trading activity controls tightened8. May 08 Military stocks reportedly ordered released

May 08 US aid announcement9. Jun 08 1st US aid arrives at Nampo

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Price index

North Korean Grain Prices

The key messages…

Use caution in extrapolating from price series!

Know the underlying conditions / factors

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Sample Price Analysis Graph: Increasing/Decreasing Trends

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Real millet prices in regions of Niger, 1995-2005

Sample Price Analysis Graph: Increasing/Decreasing Trends

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Sample Price Analysis Graph: Dispersion

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Corn

Rice

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Coefficient Coefficient of variation of grain prices across provinces, 2004-08

Sample Price Analysis Graph: Volatility

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Volatility of Average National Sorghum Prices across time

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Volatility of average national sorghum prices across time

Charting with Excel: Points to ponder

Choice of chart type & orientation: Keep it simple!

Data labels & markers

“Appropriate imprecision”

Make the data table available for complex graph (as annex)

What about missing data…?

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3.0869437%?

or 3.09%

or 3.1%?

or 3%

1 2 3 ? ? 6 7 ? 9 ? 11 1213 ? 15 ? 17

Dealing with Missing Data

Do nothing: Delete missing data records

Data Imputation:

Mean substitution: Replace missing value with mean value (of previous and subsequent data values) for that particular attribute

Case Substitution: Replace missing value with historical value from similar cases. (We can not use value from current sample for case substitution; it must be from previous observations.)

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Questions about using Excel?

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Exercise 2.1.b. The Marketastan File: Excel Charts for Senior Managers

Aim of the exercise: Learning to communicate clearly to senior managers

Turn to Workbook Exercise 2.1.b.

Read the statements and then, with your partner, using Excel file (“2.1. b. Charts for WFP-Marketastan Senior Managers – Excel File.xls”), create a chart/graph for each statement, depicting the key message(s) implied by each statement

WFP Markets Learning Programme Price Analysis Training 2.1.18

Marketastan 2.1.b. Debriefing

1. Northern HHs food expenses

2. Wheat price trends by province

3. Seasonal patterns of wheat prices

4. Real wage trend

5. Food insecurity by livelihood group

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Wrap-up: Graphical Analysis

Data Quality Review & clean data first: then decide how

you will deal with missing data

Presentation: Image should clarify the message – not

require additional effort by the user to understand what you are presenting

…and remember: keep it simple, please!

WFP Markets Learning Programme Price Analysis Training 2.1.20