Objectives of this class

1. ITRN 501: Fall 2008Methods of Analysis for International Commerce and PolicyClass 2Instructor: Danilo Pelletieredpelleti@gmu.edu

2. Objectives of this class Students will: • be proficient in the most basic quantitative methods used in international commerce policy. • have experience retrieving and formatting quantitative data from standard sources. • be familiar with multivariate analysis methods. • understand some of the most prevalent practical problems and ethical issues confronting policy analysis. • complete a project drawing on their knowledge of these elements.

3. What are data? • A representation of facts, concepts, or instructions in a formalized manner suitable for communication, interpretation, or processing by humans … • Characteristics of data determine the possible methods of analysis

4. The subjective/objectivenormative/positive debate In popular usage: • Objective matters can be observed and quantified and all must reach the same basic result in assessing them. • Subjective matters are open to individual interpretation. • Positive statement - a statement of “fact” without indication of approval. • Normative statement - expresses whether a situation is desirable or undesirable.

5. The qualitative/quantitative debate • Quantitative data can be counted and the results of statistical analysis are meaningful. • Basic interpretation is clear, i.e. x>y. • Qualitative data are meaningful to humans but can not be counted or manipulated with statistical methods. • Researcher/reader must be relied on for basic interpretation.

6. The practical synthesis • The debate is limiting to the policy analyst. Data and methods should be assessed as they are useful and necessary to address a problem. • There is plenty that can be subjective, normative and qualitative in quantitative analysis. • Sometimes qualitative data are the best or only data there are • Mixed methods often lead to better questions and stronger, more persuasive results, reaching broader audiences. • Case studies and anecdotes can motivate, explain, support, or raise questions about quantitative results. • With coding, qualitative data can be introduced to quantitative models.

7. Types of data used in this class (scales)‏ • Numeric variables – • Interval data have meaningful intervals between measurements, but there is no true starting point (zero). • 20 C is twice 10  C but 68 F is not twice 50 F • Ratio data have the highest level of measurement. Ratios between measurements as well as intervals are meaningful because there is a starting point (zero). • Basic policy research mostly assumes ratio scales.

8. Types of data used in this class (scales)‏ cont. Ordered Categorical Variables – or ordinal data allow the ranking of the data, e.g. bigger/smaller, healthier/less healthy etc., but the interval is meaningless • Unordered Categorical Variables – or nominal data are categorical data where the order of the categories is arbitrary, e.g. race, religion, colors.

9. Types of statistical data (scales)‏ • Discrete Variable – Has a limited number of known values, e.g. number of automobiles imported by Ghana can not be 100,000.2. • Continuous Variable – Can take any value, weight tends to have this quality and currency does to a great extent. Ghana could import 1,000,000.25713 KG worth of automobiles, • even if it is not likely significant at this level of precision.

10. The use of statistical methods to analyze data does not (necessarily) make a study more “scientific”, “rigorous”, or “objective.” 1) The wrong method 2) The wrong data 3) The wrong question 4) Just wrong (error)‏

11. Error in data and analysis • Random error • Sampling error • Random misclassification • Systematic error/bias • Systematic non-random deviation from the true values. • Can be conscious orunconscious. • Need not be “on purpose.” • Bias creates an association that is untrue. • Confounding error creates an association that is true but potentially misleading.

12. Ideally, problem determines methods and data, and these in turn your conclusions… • You should not assemble data to prove your point. (Sometimes we can be selective to make a point.)‏ • Method choice or data availability should not determine problem definition, • i.e. if you have a hammer you should not make every problem a nail. (We are unaware of all possibilities and they are not always at our disposal.)‏ In sum, we try not to use statistics as a drunk might use a street lamp: • For support rather than illumination, or • To decide where (or what) to look for.

13. Thinking in models • What is a model? • Explains which elements relate to each other and how. • Describing Relationships in a model • Covariation – move in the same direction • Direct or Positive • Inverse or Negative • Nonlinear • False of spurious • Control (confounding) variables • Are you looking for the best model or testing someone else’s?

14. Developing models • Where does a model come from? • From your own assessment and observation of the problem, or from talking to others. • From the literature. • Elements others include or consider important • Definitions of these elements • Descriptions of the “expected” relationships among variables • Results and explanations • Sources and strategies for data • Suggestions of models or variations to be tested in the future

15. Types of Models • Symbolic • Economic growth is a function of changes to the amount of capital (K) and changes to the amount of Labor (L). • G=f(K,L)‏ • G=α+β1K+β1L+e • Schematic Capital Econ Growth Labor

16. The importance of writing • Policy writing is a fundamental form of analysis: • Written results must “track” and be accessible. • If it does not make written sense, and the argumentation does not follow, the analysis is suspect. • Writing helps the researcher and not just the reader understand the results. • Results without a well written analysis will generally have less policy influence. • Bad results with good writing often have a greater impact than they deserve.

17. The importance of critical thinking and logic • Received wisdom is not always right… • But if want to say that it isn’t you need to recognize it, and address its failings. • Familiarize yourself with common fallacies • http://www.unc.edu/depts/wcweb/handouts/fallacies.html • http://www.nobeliefs.com/fallacies.htm • http://www.nizkor.org/features/fallacies/ • Hasty generalization • Unrepresentative sample • Post Hoc • Straw man • Category errors • Non sequitor

18. Tables and figures • Must also include anything necessary for proper interpretation. Exhibits must be able to stand ALONE. • Titles – tells reader what is going on, what they are looking at, may provide some interpretation. • All relevant data, no irrelevant data must be included. • Clear labels titling data and units • Sources

19. Tables • Tables are used to present many data series or variables or when details are important. • Columns should be fewer than rows in most instances. • Nested tables, crosstabs etc.

20. Source: World Bank (2006) Moldova Poverty Update

21. Line graph • Often best to show change over a series of points in time, or any continuous change (i.e. income distribution)‏ • X axis (time) series variable • Y axis variable of interest

22. Source: World Bank (2006) Moldova Poverty Update

23. Bar graph • Can be used with just two data points • More visually striking when fewer data points are expressed. • For comparisons of multiple observations over a few years it can overcome the spaghetti problem of line charts. • Can be combined with line charts to good effect.

24. Source: World Bank (2006) Moldova Poverty Update

25. Source: World Bank (2006) Moldova Poverty Update

26. Pie chart • Used to show proportions and shares at a point in time • Must add up to a meaningful total • Often used for comparisons when other charts would be preferable.

27. Figure 2.1 Types of Drugs Used by Past Month Illicit Drug Users Aged 12 or Older: 2003 D Source: US DHHS (2004) 2003 National Survey on Drug Use & Health: Results

28. Shares in bars: better for comparison

29. Issues and tricks • Scale and origin • Using indexes to compare variables with different scales. • Normalize by a variable such as population. • Show only the most important relationships. • Provide full data in appendix tables • Titles can lead reader as long as subtitles, and all other required information are clear and complete

30. The final project • What are others saying about trade and your country? • What is your model? • What is happening and why? • Do you have the data you need? • Can you get them? • What do you think they say? • Is the data ready for presentation? • Start writing and be ready to reiterate these steps.