Jurisprudencia Internacional y Nacional

When it comes to the dependent variable, proliferation of zero trade flows may bias log-linear estimation of the gravity model (Santos Silva & Tenreyro, 2006). Zero trade flows can stand for trade flows that are too small to report which renders them economically insignificant relative to the non-zero observations. Second, if economically meaningful selection generates zero trade flows; for example, due to fixed costs of entry caused by trade barriers (Anderson, 2011). Zero trade flows occur in 197 of the 4,320 observed export relations, amounting to 4.6 per cent of all trade flows. Additionally, the pattern of diplomatic representation across the dataset is akin to that observed in Chapter 4, where diplomatic relations (in the form of a representation) exist in only 12.8 per cent of zero trade flows. In this cross-sectional dataset, this is even lower; only 13 out of 197 zero trade flows are accompanied by diplomatic representation, amounting to less than half a percent in the dataset. As such, the low level of zero trade flows in the dataset presents no heteroscedasticity in the log-linearisation of export flows that warrants the use of Santos Silva and Tenreyro's (2006) Poisson pseudo-maximum likelihood estimator.

The institutional variables mentioned in Section 5.2.1 above are possibly prone to measurement error. To check the consistency of the results, robustness checks using alternative variables from the Heritage Foundation (for institutional quality) and

100 internet penetration (in the form of broadband subscriptions) take place in Section 5.3.5.

Besides endogeneity, an additional problem that relates to the trade flows in this cross- sectional setting is of selection bias; the exporters in the dataset are largely developed countries. Not accounting for the potential systematic absence of trade flows in relation to particular countries or country-pairs may bias the commercial diplomacy variable upwards as its effect on exports can only be observed when there are nonzero export flows. Appendix B addresses this selection bias problem by applying a Heckman selection model (Heckman, 1979) to the dataset. It indicates that while there is a slight downwards adjustment in the coefficients and significance levels of the commercial diplomacy variable, this has no bearing on the results which are discussed next.

5.3.

Results and Discussion

5.3.1. Descriptive statistics

The countries in Rose's (2007) dataset consist of 22 exporters and 200 importers; almost all countries in the world are included as destination countries. Table 5.1 below lists only those countries which appear both as exporters and as importers.

Table 5.1: List of exporters in dataset

Australia Italy Spain

Belgium Japan Sweden

Brazil Korea Switzerland

Canada Mexico Turkey

China (Mainland) Netherlands United Kingdom

France Poland United States

Germany Russia

Indonesia India

The summary statistics of all variables, including those used to check the robustness of the institutional constraints variables, are in Table 5.2.

Missing values for log of exports are due to the 197 zero trade flows observations which disappear when taking the log of the level of exports. Missing values occur for the institutional variables as information is not available for all countries. Around half of all observations are missing for the broadband subscriptions variable, used for robustness. Countries that have no observations on any of the institutional variables are removed from the dataset, leaving 182 importers, down from 200.

Across the dataset the number of diplomatic missions in a partner country is just under one, and the median is one. Only from the top 90th percentile does this number increase,

101 with only 76 out of 4,320 observations, less than 2 per cent, involving foreign missions with more than 5 personnel. For the institutional variables, both linguistic and religious distance indicate that distance is generally large, with 1 being the maximum distance in the dataset, and both variables' averages being above 0.84 with low standard deviations. Considering the heterogeneity of the importers, this is to be expected.

Table 5.2: Summary statistics

Obs. Mean Std.

Dev.

Min. Max.

Log of Total Exports (US million $) 4,123 3.478 3.065 -11.152 12.327

Number of Diplomatic Missions 4,320 0.961 1.727 0 43

Linguistic Distance 3,632 0.843 0.169 0 1

Religious Distance 3,678 0.847 0.21 0.079 1

Institutional Quality (WGI) 3,950 -0.091 0.911 -1.942 1.954

Institutional Quality (Heritage overall score)

3,950 49.674 24.147 0 87.8

Log of Internet Users (per 100 people) 3,821 1.148 2.035 -7.76 4.371

Log of Broadband Subscriptions (per 100 people)

2,108 -1.957 3.018 -8.502 3.11

Log of Geographic Distance 4,320 7.998 0.704 4.606 9.157

Log of Product of GDP per Capita 4,320 17.091 2.079 10.745 21.730

Log of Product of Population Sizes 4,320 33.257 2.686 23.770 41.752

Landlocked 4,320 0.24 0.446 0 2

Islands 4,320 0.374 0.547 0 2

Log of Product of Areas 4,320 24.767 3.448 12.198 32.769

RTA 4,320 0.097 0.297 0 1

Currency 4,320 0.019 0.136 0 1

Contiguity 4,320 0.024 0.154 0 1

The two proxies for an importer's formal institutional quality exhibit normally distributed behaviour. The average of the six WGI indicators is standardised, with scores ranging between negative and positive 3. The overall score from the Heritage foundation, whose measure focuses on economic institutions, reaches the lowest score on the scale in 14 per cent of all observations. The last two indicators, those relating to internet infrastructure as a measure of economic institutions as well as of ease of communication (Freund & Weinhold, 2004), are log variables. Across all importers, internet use varies from 4 100th _{of a per cent to 79 per cent of the population. Similarly,}

broadband subscriptions range between 2 100th of a percent and 22 per cent.

The correlation matrix in Table 5.3 is of special interest. Institutional quality is highly correlated with the internet infrastructure variables, as well as with the product of GDP per capita. Also highly correlated are the two internet infrastructure variables themselves, which is neither surprising nor concerning. Whether the multicollinearity between GDP per capita, internet infrastructure, and institutional quality poses a problem in terms of estimation is considered next.

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Table 5.3: Correlation matrix

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17)

(1) Log of Total Exports (US$) 1

(2) Number of Diplomatic

Missions

0.48 1

(3) Linguistic distance -0.16 -0.13 1

(4) Religious distance -0.09 -0.07 0.45 1

(5) Institutional quality (WGI) 0.39 0.19 -0.20 -0.09 1

(6) Institutional quality (Heritage overall score)

0.48 0.18 -0.02 -0.07 0.39 1

(7) Log of Internet Users (per 100 people)

0.44 0.21 -0.24 -0.08 0.83 0.31 1

(8) Log of Broadband

Subscriptions (per 100 people)

0.41 0.22 -0.22 -0.14 0.76 0.23 0.84 1

(9) Log of Geographic Distance -0.34 -0.20 0.22 0.15 -0.19 -0.13 -0.19 -0.14 1

(10) Log of Product of GDP per Capita

0.45 0.23 -0.30 -0.12 0.62 0.24 0.64 0.58 -0.16 1

(11) Log of Product of Population Sizes

0.51 0.32 0.19 0.07 -0.14 0.31 -0.07 -0.06 0.04 -0.34 1

(12) Landlocked -0.20 -0.05 0.03 -0.01 -0.18 -0.01 -0.17 -0.27 -0.11 -0.09 -0.11 1.00

(13) Islands -0.15 -0.11 -0.10 0.12 0.17 -0.14 0.14 0.15 0.19 0.22 -0.32 -0.17 1.00

(14) Log of Product of Areas 0.28 0.21 0.03 0.02 -0.16 0.27 -0.11 -0.11 0.09 -0.29 0.77 -0.08 -0.35 1.00

(15) RTA 0.36 0.18 -0.18 -0.14 0.27 0.18 0.25 0.21 -0.57 0.32 -0.10 0.01 -0.11 -0.16 1.00

(16) Currency 0.25 0.21 -0.15 -0.22 0.21 0.09 0.15 0.16 -0.36 0.23 0.01 -0.04 -0.11 -0.06 0.35 1.00

103