Valoración sensorial de las recetas degustadas

The gravity model approach to analyze cross-border trade was originally developed by

Isard.1 _{Walsh was the first to use a gravity model to estimate cross-border trade in}

individual services sectors.2 _{In his models, Walsh employed explanatory variables}

commonly used to analyze trade in goods, such as distance, adjacency, population, and

GDP. Stern concentrated similar research on an individual country, South Africa.3 _He

used the ratio of South Africa’s net exports to world net exports as the dependent variable, regressing this on less traditional variables such as domestic patents registered per adult and expenditure on research and development. The Commission extended this modeling to U.S. trade in insurance.

Brainard was one of the first to investigate bilateral trade with respect to affiliate sales.4

She determined that there is a robust relationship between traditional gravity model variables and affiliate sales. Bergstrand and Egger analyzed foreign direct investment and

foreign affiliate sales between multiple countries using gravity models.5 The Commission

extended this research to examine U.S.-owned affiliate sales of insurance.

Description of Data and Model

The Commission’s models use panel data, with the export model including data on U.S. bilateral exports to 31 countries from 2001 through 2005, and the affiliate sales model using data for 34 countries from 1999 to 2005. Export and affiliate data come from the Bureau of Economic Analysis. Due to data limitations, the Commission’s models are not able to isolate P&C insurance. The export data include all types of primary insurance (including life insurance), although P&C is believed to account for the majority of cross-

border trade in primary insurance.6 _{The affiliate data reflect sales of both insurance and}

other nonbank financial services. In addition to the ITRI, both models include as independent variables the importer’s GDP, unemployment, English language, and relative distance. The importer’s GDP is measured in constant 2000 U.S. dollars, as reported in

1 _{Isard, “Location Theory and Trade Theory,” May 1954.} 2 _{Walsh, “Trade in Services,” October 2006.}

3 _{Stern, “Predicting South African Trade in Services,” 2002.}

4 _{Brainard, “An Empirical Assessment of the Proximity-Concentration Trade-off,” September 1997.} 5 _{Bergstrand and Egger, “A Knowledge-and-physical-capital Model,” 2007.}

6 _{Harold Skipper and Robert Klein (professors, Georgia State University),interview by Commission}

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the World Bank’s World Development Indicators (WDI). The WDI is also the source of the other macroeconomic variable, importer’s unemployment rate. A dummy variable is included to capture ease of communication. This variable equals one if English is an

official language, or if a minimum of 20 percent of the country speaks English.7 _The

model also includes a measurement of distance from the trading partner’s capital to Washington, DC, calculated using the greatest circle formula developed by the Centre D'Etudes Prospectives et D'Informations.

The second model, focusing on affiliate transactions, includes two additional explanatory variables: foreign direct investment and so-called tertiary labor. Tertiary labor is the percentage of the labor force with at least a bachelor’s degree, and is reported in the WDI. Foreign direct investment is measured as a proportion of the country’s GDP and is also sourced from the WDI.

The data are generally well distributed, with good variation (tables E.1 and E.2). The ITRI is right skewed, because most countries in the model are not highly restricted. The number of observations is restricted slightly in the first model, because of the paucity of information on the importer’s unemployment rate, and significantly in the second model because of the paucity of information on the percentage of tertiary labor. There is no significant correlation between the independent variables in either model (tables E.3 and E.4).

TABLE E.1 Exports—data summary

Variable Observations Mean Standard Deviation Minimum Maximum

Exports 154 37.81 91.17 0.00 606

ITRI 155 0.43 0.28 0.00 0.93

Importer's GDP 155 6.39E11 9.13E11 5.46E10 4.98E12

Distance 155 373.83 336.83 9.56 1188.00

Importer's unemployment 144 7.52 5.21 1.3 31.2

English speaking 155 0.29 0.46 0.00 1.00

Year 155 2003 1.42 2001 2005

Source: Compiled by the Commission.

TABLE E.2 Affiliate sales—data summary

Variable Observations Mean Standard Deviation Minimum Maximum

Affiliate sales 303 3.81E3 1.06E4 0 7.58E4

ITRI 315 0.35 0.27 0.00 0.91

Importer's GDP 319 4.14E11 7.67E11 22.44E12 4.98E12

Distance 322 8.91 0.46 8.02 9.70 Importer's unemployment 308 8.11 4.88 1.3 31.2 English speaking 322 0.28 0.45 0.00 1.00 Tertiary labor 214 25.08 9.11 7.20 51.50 FDI 319 8.85 40.65 -15.13 522.22 Year 322 2002 2.00 1999 2005

Source: Compiled by the Commission.

7 _{The 20 percent threshold is intended to recognize that English need not be the official or predominant language of a}

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TABLE E.3 Exports—correlation matrix

ITRI Ln(Importer's GDP) Ln(Distance) Ln(Importer's unemployment) English speaking Year ITRI 1.00 Ln(Importer's GDP) -0.23 1.00 Ln(Distance) 0.21 -0.31 1.00 Ln(Importer's unemployment) -0.09 -0.05 -0.07 1.00 English speaking -0.17 -0.23 0.15 0.14 1.00 Year -0.03 0.05 0.05 0.02 0.01 1.00

Source: Compiled by the Commission.

TABLE E.4 Affiliate sales—correlation matrix

ITRI Ln(Importer's GDP) Ln(Distance) Ln(Importer's unemployment) English speaking Tertiary

labor FDI Year

ITRI 1.00 Ln(Importer's GDP) 0.18 1.00 Ln(Distance) 0.07 0.13 1.00 Ln(Importer's unemployment) -0.16 -0.14 0.03 1.00 English speaking 0.06 0.01 0.51 -0.19 -0.06 1.00 Tertiary Labor -0.06 0.11 0.23 -0.13 0.00 0.41 1.00 FDI -0.04 -0.20 -0.04 -0.16 1.00 Year 0.07 0.00 0.08 0.00 -0.02 0.09 0.14 1.00

Source: Compiled by the Commission.

A log-log specification is used for all continuous variables except the ITRI. The year variable is a categorical representation of each year to control for an upward linear trend of insurance exports and affiliate sales over time. Neither the year nor the English dummy variable is logged. The models are as follows:

ln(exports) = α0 + β1 (ITRI) + β2 ln(GDP) + β3 ln(unemployment) + β4

ln(distance) + β5 (English) + β6 (year) + ε

ln(sales) = α0 + β1 (ITRI) + β2 ln(GDP) + β3 ln(unemployment) + β4 ln(distance)

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The foreign direct investment and the tertiary labor variables are in the level form because the data are reported in percentages. This constructs a constant elasticity model, where the percentage increase of an explanatory variable has a direct or inverse effect on the percent change of the dependent variable.

The ITRI variable is expected to have a negative sign because the more restrictive a country is, the more difficult it is for U.S. firms to export, enter, and operate. GDP is expected to have a positive sign because the larger the economy, the more incentive there is for U.S. companies to operate in the market. An increase in GDP also indicates a greater need for P&C insurance by the foreign country. The unemployment rate is expected to have a negative sign because it is an indication of the overall health of the trading partner’s economy. The distance from capital to capital is expected to have a negative sign because it is easier to trade with a country that is proximate. Although insurance is not physically transported like goods, there is travel involved with setting up and overseeing a new company. This variable may also capture cultural similarities and general familiarity with the foreign country. Foreign direct investment is expected to be positive because it captures both how open a country is to allowing foreign investment, and how desirable it is to invest in the country. The tertiary labor ratio is expected to have a positive sign because it is an indicator of a country’s level of development. The year variable is expected to have a positive sign because insurance trade has been increasing at a steady rate over the past decade.

Using results from both models, the total effect of removing the trade barriers of all included countries was estimated by taking the actual value for 2005 and adding what the model predicted the increase would be if the country’s ITRI equalled zero. The total growth is the summation of each country’s predicted liberalized value less the fitted values.

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