ITRN 501: Fall 2008

Methods of Analysis for International Commerce

and PolicyClass 2

Instructor: Danilo Pelletieredpelleti@gmu.edu

Objectives of this classStudents 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.

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

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.

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.

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.

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.

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.

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.

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)

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 or unconscious.

– Need not be “on purpose.”

– Bias creates an association that is untrue.– Confounding error creates an association that

is true but potentially misleading.

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.

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?

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

Types of Models

1. 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

2. Schematic Capital

Labor

Econ Growth

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.

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

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

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.

Source: World Bank (2006) Moldova Poverty Update

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

Source: World Bank (2006) Moldova Poverty Update

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.

Source: World Bank (2006) Moldova Poverty Update

Source: World Bank (2006) Moldova Poverty Update

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.

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

Shares in bars: better for comparison

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

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.