Asymmetries in Exports and Imports in the Americas:

1980 to 2003

Mary E. Malliaris

Loyola University Chicago

Data

• World Trade OrganizationMerchandise trade by commodity 1980 through 2003

• Commodity Areas

agricultural products iron and steel

automotive products machinery

chemical products manufactures

clothing mining products

food office equipment

fuels textiles

Countries in the Americas

• Canada, the United States, Mexico

• Argentina, Brazil, Chile, Ecuador, Nicaragua, Peru, and Venezuela

Data Split into 4 Groups

• 2 Time Periods– 1980 through 1993– 1994 through 2003

• 2 Country Groups– North America

• Canada, the United States, Mexico

– Latin America• Argentina, Brazil, Chile, Ecuador, Nicaragua, Peru,

and Venezuela

Portion of WTO data set

Reporter_desc

Flow Indicator

_desc

Partner Unit_desc Year Value

Argentina Export Agricultural products World US dollar at

current prices 1980 5,708,928,674

Argentina Export Food World US dollar at

current prices 1980 5,209,290,482

Argentina Export Mining products World US dollar at

current prices 1980 453,867,614

Argentina Export Manufactures World US dollar at

current prices 1980 1,854,639,930

Association Analysis Data Transformation

Country_Year

Agri Exports

This Year

Auto Exports

This Year

Chem Exports

This Year

Cloth Exports

This Year

Food Exports

This Year Can00 1 1 1 1 1 Can01 0 0 1 0 1 Can02 0 1 1 1 0 Can03 1 1 1 0 1 Can94 1 1 1 1 1 Can95 1 1 1 1 1

Association Analysis Results

Net Exports BNNA

-200,000,000,000

-150,000,000,000

-100,000,000,000

-50,000,000,000

0

50,000,000,000

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993

Agricultural products Automotive products Chemical productsClothing Food FuelsIron and steel Machinery and transport equipment ManufacturesMining products Office and telecom equipment Textiles

Net Exports ANNA

-500,000,000,000

-400,000,000,000

-300,000,000,000

-200,000,000,000

-100,000,000,000

0

100,000,000,000

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Agricultural products Automotive products Chemical productsClothing Food FuelsIron and steel Machinery and transport equipment ManufacturesMining products Office and telecom equipment Textiles

Net Exports BNLA

-30,000,000,000

-20,000,000,000

-10,000,000,000

0

10,000,000,000

20,000,000,000

30,000,000,000

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993

Agricultural products Automotive products Chemical products

Clothing Food FuelsIron and steel Machinery and transport equipment Manufactures

Mining products Office and telecom equipment Textiles

Net Exports ANLA

-80,000,000,000

-60,000,000,000

-40,000,000,000

-20,000,000,000

0

20,000,000,000

40,000,000,000

60,000,000,000

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Agricultural products Automotive products Chemical productsClothing Food FuelsIron and steel Machinery and transport equipment ManufacturesMining products Office and telecom equipment Textiles

Net Exports Totaled for 12 Commodities 80 to 93, LA vs NA

(400,000,000,000)

(350,000,000,000)

(300,000,000,000)

(250,000,000,000)

(200,000,000,000)

(150,000,000,000)

(100,000,000,000)

(50,000,000,000)

-

50,000,000,000

100,000,000,000

LA

NA

Net Exports Totaled for 12 Commodities 94 to 03, LA vs NA

(1,200,000,000,000)

(1,000,000,000,000)

(800,000,000,000)

(600,000,000,000)

(400,000,000,000)

(200,000,000,000)

-

200,000,000,000

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

LA

NA

Association Analysis within theCurrent Year

The first set of findings was common to all sets of the data.

• Agriculture Imports were up when Food Imports and Imports of either Fuel, Mining or Auto were up in the same year.

• Manufacturing Imports were up when Machine Imports and Imports of one of: Agriculture, Chemicals, or Food were up in the same year.

• Manufacturing Exports were up when Textile Exports and Fuel Imports were up in the same year.

Latin America, 80 to 93

• Agriculture Imports were up when imports in Food and either Clothing, Office, Mining, Textiles or Manufacturing were up.

• Food Exports were up when Exports in Agriculture were up and Imports in either Textiles, Clothing or Office were up.

Latin America, 94 to 03

• Agriculture Imports were up when imports in Food and imports in one of the following were up: Auto, Chemical, Clothing, Office, Manufacturing, Fuel, Machines or Textiles.

• Food Exports were up when Exports in Agriculture were up and Imports in either Textiles, Mining or Fuel were up.

• Mining Imports were up when Fuel Imports and one of the following imports were up: Auto, Machine, Chemicals, Food, Agriculture.

North America, 80 to 93

• Manufacturing Exports were up on years when Agriculture Exports, Textile Exports or Food Exports were also up.

• In years when Imports of Clothing, Mining, Office, or Textiles were up, Manufacturing Imports were also up.

North America, 94 to 03

• Chemical Imports were up in years where Imports were also up in Agriculture, Food, or Manufacturing.

• Textile Exports were up in years where manufacturing Exports were also up

• Manufacturing Imports were up in years where Automobile Imports and either Agriculture or Chemical Imports were also up.

Association Analysis withFollowing Year Consequents

• The first set of results held across all four divisions with a confidence of at least 91%.

• Machine Exports are up next year when Auto Imports, Office Exports and either Textile Exports or Mining Imports are up this year.

• Chemical Imports were up the following year when Clothing Exports and one of the following pairs were up this year: Auto Imports – Office Imports, Auto Imports-Auto Exports, or Auto Exports- Machine Imports.

Latin America, 80 to 93

• The results with the highest confidence (85% to 89%) were:

• If Clothing Exports and either Textile Export or Manufacturing Exports were up this year, then Manufacturing Exports were up the following year.

• When Textile Exports and Clothing Imports increased this year, then Office Imports increased the following year.

• When Manufacturing Imports and Textile Exports increased this year, it was followed by an increase of Clothing Exports the following year.

Latin America 94 to 03

• The only result concerned Food Exports

• Food Exports were up the following year when one of these pairs happened in one year: Iron Exports and Office Exports increased, or Mining Exports and Food Imports increased.

North America, 80 to 93

• The following were up with 90% confidence: Manufacturing Imports and Manufacturing Exports

• When either Food Imports or Clothing Imports increased this year, Office Exports increased the following year (96%).

• When Textile Imports were up in a given year, Clothing Imports were up the following year (92%).

North America, 94 to 03

• Chemical Imports next year were up 87% of the time.

• If Automobile Imports were also up, the confidence for Chemical Imports increasing the following year increased to 92%.

• When Automobile Imports increased in a given year, we also saw increases in Agriculture Imports (88%), and Food Imports (88%) in the following year.

SUMMARY

• Association analysis is a data-driven mining technique that uncovers relationships between co-existence of occurrences in data.

• It is highly dependent upon the particular set of data it is applied to and is not considered a method to determine causality.

• This study used association analysis to uncover relationships that held between the 12 commodities listed over a period of years.

Summary, continued

• Several of the relationships were stable across country groups and over the entire period from 1980 to 2003.

• Seven rules held across all groups for the analysis using only same year data.

• Using the data set with consequents from the following year, five rules held across all groups.

• Clementine’s association analysis found some distinct sets of rules for each group and some that applied overall to the entire dataset.