Adding the institutional constraint variables

Before the analysis using interaction variables and subsamples takes place, it is necessary to study the behaviour of the institutional variables in this dataset. First, the relevant institutional variables are added separately from each other into equation (5.1), then the institutional variables are added hierarchically into equation (5.1).

Separate estimation of institutional variables in equation (5.1)

Table 5.4 contains the results to equation (5.1). Columns (1) and (6) provide OLS and IV estimates with no institutional variable added; these are the benchmark estimations that Rose (2007) uses. They differ slightly because of the exclusion of the binary common language variable that Rose (2007) uses, and because this study takes the product of GDP per capita across two countries as well as of population sizes. The results for the commercial diplomacy proxy, the Number of Diplomatic Missions, therefore change slightly. In columns (1) and (6), the exports-increasing effect is now 12 and 8 per cent respectively, whereas in Rose (2007) this is 10 and 6 per cent. The sign, coefficient size, and behaviour of the control variables in both columns, as well as in all other columns, is fully consistent with the empirical gravity model literature. Columns (2) to (5) separately add the institutional variables to equation (5.1). Columns (7) to (10) do the same for the instrumental variable estimation. Upon the inclusion of each institutional variable, the coefficient of the commercial diplomacy variable decreases. This decrease is relatively minor in the OLS estimations when compared with the IV estimations, where the inclusion of each individual institutional variable decreases the effect of the number of diplomatic missions by about half, and reduces its significance from the 1 per cent level to the 5 per cent level. In addition, the addition of each institutional variable increases the total variance explained slightly with respect to the estimations that do not include institutional variables.

The signs and coefficients of the institutional variables are all significant at the 1 per cent level, and the change in estimates between the OLS and IV specifications hardly impacts their estimates. Importantly, the signs and effect sizes are as expected from the empirical literature. The effects of Linguistic Distance and Religious Distance are negative, and about equal to those in Melitz and Toubal (2014). The result for Institutional Quality (WGI) is almost identical in size to the results obtained by studies that use the same variable (de Groot et al., 2004; Meon & Sekkat, 2008). The result for Log of Internet Use is also consistent with the empirical literature (e.g. Freund & Weinhold, 2004; Lin, 2015; Bojnec & Ferto, 2009).

104

Table 5.4: Separate addition of institutional constraint variables into equation (5.1)

OLS IV (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Number of Diplomatic 0.111*** 0.080*** 0.084*** 0.101*** 0.096*** 0.076*** 0.044** 0.044** 0.038** 0.034** Missions (0.022) (0.017) (0.017) (0.021) (0.020) (0.021) (0.017) (0.017) (0.018) (0.017) Linguistic Distance -1.448*** -1.474*** (0.127) (0.127) Religious Distance -0.404*** -0.416*** (0.109) (0.109)

Institutional quality (WGI) 0.185*** 0.193***

(0.035) (0.036)

Log of Internet Use 0.094*** 0.097***

(0.021) (0.021)

Log of Distance -0.666*** -0.626*** -0.700*** -0.665*** -0.644*** -0.671*** -0.631*** -0.706*** -0.673*** -0.653***

(0.045) (0.045) (0.046) (0.046) (0.047) (0.044) (0.045) (0.046) (0.046) (0.047)

Log of GDP p/c product 0.856*** 0.833*** 0.871*** 0.811*** 0.800*** 0.867*** 0.845*** 0.885*** 0.829*** 0.818***

(0.015) (0.015) (0.015) (0.018) (0.020) (0.016) (0.016) (0.016) (0.018) (0.020)

Log of Population product 0.978*** 0.993*** 0.979*** 0.961*** 0.961*** 0.989*** 1.006*** 0.993*** 0.982*** 0.982***

(0.018) (0.017) (0.018) (0.017) (0.018) (0.018) (0.018) (0.018) (0.018) (0.018)

Landlocked -0.709*** -0.767*** -0.777*** -0.721*** -0.715*** -0.709*** -0.766*** -0.776*** -0.720*** -0.715***

(0.053) (0.053) (0.054) (0.054) (0.057) (0.053) (0.053) (0.054) (0.054) (0.057)

Island -0.243*** -0.315*** -0.255*** -0.262*** -0.258*** -0.242*** -0.315*** -0.254*** -0.262*** -0.259***

(0.052) (0.052) (0.053) (0.053) (0.053) (0.052) (0.052) (0.053) (0.054) (0.053)

Log of Area product -0.148*** -0.174*** -0.156*** -0.146*** -0.154*** -0.149*** -0.175*** -0.157*** -0.147*** -0.155***

(0.012) (0.013) (0.013) (0.012) (0.013) (0.012) (0.013) (0.013) (0.012) (0.013) RTA 0.823*** 0.828*** 0.796*** 0.856*** 0.880*** 0.833*** 0.835*** 0.804*** 0.870*** 0.894*** (0.079) (0.076) (0.079) (0.079) (0.077) (0.078) (0.075) (0.079) (0.078) (0.076) Currency -0.058 -0.399** -0.475** -0.414** -0.470*** -0.030 -0.367** -0.442** -0.369** -0.425*** (0.206) (0.186) (0.198) (0.186) (0.155) (0.202) (0.182) (0.194) (0.180) (0.149) Contiguity 1.065*** 0.850*** 1.021*** 1.035*** 1.109*** 1.128*** 0.920*** 1.101*** 1.153*** 1.226*** (0.156) (0.148) (0.157) (0.153) (0.150) (0.157) (0.149) (0.159) (0.154) (0.151) Observations 4,123 3,539 3,560 3,808 3,695 4,123 3,539 3,560 3,808 3,695 R-squared 0.765 0.793 0.790 0.775 0.774 0.765 0.792 0.790 0.774 0.773 Notes:

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Intercepts included but not recorded. As per Rose (2007), in columns (6) to (10) the Number of Diplomatic Missions variable is instrumented with the log of proven oil reserves, the number of Zagat guides, the number of Condé-Nast Top 100 destinations, the number of Lonely Planet guides, and the number of Economist city guides.

105 Table 5.5 shows the first-stage regression results for the IV estimations in Table 5.4. Three of the five excluded instruments in the second stage regression have strongly significant effects on the number of diplomatic missions, the proxy variable for commercial diplomacy. The No. Condé-Nast Top 100 destinations and No. Economist city guides variables are significant at the 1 per cent level in all estimations, while the significance of the Proven Oil Reserves (bbl) variable lies between the 10 and 1 per cent levels. The coefficients for these instruments are consistent across the board.

Table 5.5: First-stage regression results for IV estimations Dependent variable: No. of

Diplomatic Missions (1) (2) (3) (4) (5)

No. of Zagat's guides 0.052 0.043 0.044 0.041 0.058

(0.053) (0.053) (0.053) (0.054) (0.055)

No. of Condé-Nast Top 100 0.247*** 0.238*** 0.242*** 0.256*** 0.233***

destinations (0.061) (0.060) (0.061) (0.062) (0.064)

No. of Lonely Planet guides 0.179* 0.165 0.163 0.152 0.112

(0.106) (0.107) (0.107) (0.106) (0.108)

No. of Economist city guides 0.611*** 0.671*** 0.671*** 0.712*** 0.691***

(0.149) (0.159) (0.158) (0.159) 0.160)

Proven Oil Reserves (bbl) 1.50e-13*** 1.28e-13** 1.18e-13** 9.93e-14* 1.07e-13*

(5.33e-14) (5.57e-14) (5.61e-14) (5.43e-14) (5.60e-14))

Observations 4,123 3,539 3,560 3,808 3,695 R-squared 0.464 0.465 0.465 0.464 0.460 Under-identification test 0.000 0.000 0.000 0.000 0.000 Kleibergen-Paap Wald F- statistic 18.733 17.281 17.535 18.199 14.180 Over-identification 0.000 0.000 0.000 0.000 0.000 Notes:

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Included but not recorded control variables are: Log of Distance, Log of GDP p/c product, Log of Population product, Landlocked, Island, Log of Area product, RTA, Currency, Contiguity, the institutional variables (Linguistic Distance, Religious Distance, Institutional Quality, Log of Internet Use), and the intercept. The Kleibergen-Paap Wald F-statistic is used rather than the Cragg-Donald Wald F-statistic due to the inclusion of robust standard errors.

Three tests apply to assess the strength of these instruments. First, it is a requirement that the equation is identified. Given that there are more excluded instruments than endogenous regressors, the significant coefficient for the under-identification test is not surprising. Second, the null hypothesis of weak instruments is rejected in all estimations; following Stock and Yogo's (2005) rule of thumb, the Kleibergen-Paap Wald F-statistic more than satisfies this threshold in all estimations. Lastly, the null hypothesis of the Sargan-test is rejected in all estimations, suggesting that the equations are over-identified – given the consistent insignificance of two of the instruments this is expected. While retaining only the Condé-Nast and Economist city guide instruments does yield a non-rejected null hypothesis for the Sargan test, in those equations the weak identification test is less strong as well. Because the results in the

106 second stage when using only two instrumental variables are similar in significance the larger set of instruments is preferable as they are a stronger set.

Hierarchical addition of institutional variables in equation (5.1)

The results for the sequential addition of the institutional variables are in Table 5.6. It again uses OLS and IV estimations of equation (5.1) as a baseline in columns (1) and (6). In the OLS estimations, column (2) adds linguistic distance, column (3) religious distance column (4) formal institutional quality, and column (5) internet use. This same pattern applies to the IV estimations in columns (7) to (10).

The inclusion of both the linguistic and religious distance variables causes religious distance to become insignificant, and the effect of linguistic distance to become slightly larger. This occurs in the OLS estimation in column (3), and the IV estimation in column (8) compared to columns (2) and (7), respectively. At the same time, the inclusion of both indicators of informal institutional distance causes the effect of the number of diplomatic missions on exports to rise from 0.08 in column (2) to 0.081 in column (3). Moreover, in the IV estimations the effect of this variable becomes more significant upon the inclusion of both informal institutional distance indicators; in column (7) it is significant at the 5 per cent level, and in column (8) it is significant at the 1 per cent level. Thus, this is an indication that there is some multicollinearity present which destabilises the variable of interest, although not greatly.

Adding formal institutional quality in column (4) yields a smaller estimate for this variable as compared to Table 5.4. Its inclusion also does little to change the linguistic distance and commercial diplomacy proxy variables, the effects of which decrease slightly compared to column (4). In the IV estimation, however, the inclusion of formal institutional quality renders the commercial diplomacy proxy variable to become insignificant. Thus, accounting for indicators of both the formal and informal institutional environment renders the commercial diplomacy proxy variable to become insignificant. This is in line with the expectation that commercial diplomacy is less relevant when communication is easier and formal institutions are better, yet does not mean that the effect of commercial diplomacy becomes insignificant for any values along the linguistic distance and formal institutional quality variables. The extent to which this is the case is explored later in this Chapter.

107

Table 5.6: Hierarchical addition of institutional constraint variables into equation (5.1)

OLS IV (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Number of Diplomatic 0.111*** 0.080*** 0.081*** 0.079*** 0.075*** 0.076*** 0.044** 0.044*** 0.025 0.018 Missions (0.022) (0.017) (0.017) (0.016) (0.016) (0.021) (0.017) (0.017) (0.016) (0.016) Linguistic Distance -1.448*** -1.477*** -1.450*** -1.412*** -1.474*** -1.501*** -1.484*** -1.447*** (0.127) (0.147) (0.146) (0.150) (0.127) (0.147) (0.147) (0.151) Religious Distance 0.052 0.054 0.022 0.049 0.049 0.015 (0.123) (0.122) (0.123) (0.123) (0.122) (0.123)

Institutional quality (WGI) 0.112*** 0.202*** 0.117*** 0.209***

(0.034) (0.047) (0.034) (0.047)

Log of Internet Use -0.060** -0.063**

(0.026) (0.026)

Log of Distance -0.666*** -0.626*** -0.625*** -0.613*** -0.616*** -0.671*** -0.631*** -0.631*** -0.620*** -0.624***

(0.045) (0.045) (0.045) (0.045) (0.046) (0.044) (0.045) (0.045) (0.045) (0.046)

Log of GDP p/c product 0.856*** 0.833*** 0.833*** 0.805*** 0.817*** 0.867*** 0.845*** 0.845*** 0.821*** 0.835***

(0.015) (0.015) (0.015) (0.018) (0.019) (0.016) (0.016) (0.016) (0.018) (0.019)

Log of Population product 0.978*** 0.993*** 0.992*** 0.987*** 0.997*** 0.989*** 1.006*** 1.006*** 1.006*** 1.018***

(0.018) (0.017) (0.017) (0.017) (0.018) (0.018) (0.018) (0.018) (0.018) (0.018)

Landlocked -0.709*** -0.767*** -0.767*** -0.740*** -0.775*** -0.709*** -0.766*** -0.766*** -0.737*** -0.774***

(0.053) (0.053) (0.053) (0.054) (0.056) (0.053) (0.053) (0.053) (0.054) (0.056)

Island -0.243*** -0.315*** -0.318*** -0.339*** -0.343*** -0.242*** -0.315*** -0.318*** -0.339*** -0.346***

(0.052) (0.052) (0.053) (0.053) (0.054) (0.052) (0.052) (0.053) (0.053) (0.054)

Log of Area product -0.148*** -0.174*** -0.174*** -0.171*** -0.181*** -0.149*** -0.175*** -0.176*** -0.173*** -0.183***

(0.012) (0.013) (0.013) (0.012) (0.013) (0.012) (0.013) (0.012) (0.012) (0.013) RTA 0.823*** 0.828*** 0.828*** 0.813*** 0.835*** 0.833*** 0.835*** 0.835*** 0.822*** 0.846*** (0.079) (0.076) (0.076) (0.076) (0.076) (0.078) (0.075) (0.075) (0.075) (0.076) Currency -0.058 -0.399** -0.390** -0.406** -0.626*** -0.030 -0.367** -0.359* -0.362* -0.582*** (0.206) (0.186) (0.189) (0.189) (0.163) (0.202) (0.182) (0.185) (0.185) (0.160) Contiguity 1.065*** 0.850*** 0.849*** 0.838*** 0.903*** 1.128*** 0.920*** 0.919*** 0.939*** 1.011*** (0.156) (0.148) (0.148) (0.147) (0.149) (0.157) (0.149) (0.149) (0.147) (0.149) Observations 4,123 3,539 3,539 3,539 3,453 4,123 3,539 3,539 3,539 3,453 R-squared 0.765 0.793 0.793 0.793 0.791 0.765 0.792 0.792 0.793 0.790 Notes:

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Intercepts included but not recorded. As per Rose (2007), in columns (6) to (10) the Number of Diplomatic Missions variable is instrumented with the log of proven oil reserves, the number of Zagat guides, the number of Condé-Nast Top 100 destinations, the number of Lonely Planet guides, and the number of Economist city guides.

108 The addition of the internet use variable in the OLS and IV estimations in columns (5) and (1) confounds the WGI formal institutional quality variable, the effect of which becomes almost twice as large. In addition, the R-square decreases as a result of adding the fourth institutional variable. The surest sign of multicollinearity concerns comes in the form of a negative and significant sign for the internet variable, where in Table 5.4 it was positive and significant in accordance with the empirical literature. The combination of these events indicates a strong multicollinearity issue between the indicators of formal institutional quality.

Due to the suspected but minor multicollinearity concern between the informal institutional variables and the major multicollinearity issues between the formal institutional indicators, the main analyses below utilise the institutional constraint variables independently.

One final note on Table 5.4 and Table 5.6 is that replacing the institutional quality from the WGI database for the Heritage Foundation's overall index does not change the outcomes and conclusions above. However, the other internet infrastructure variable, the percentage of broadband subscriptions, is insignificant when used in Table 5.4, and significant at the 5 and 10 per cent level in Table 5.6. Its behaviour in terms of signs remains the same. The less significant estimates are likely a result of this variable having observations for only about half of the importing countries. Nevertheless, the robustness of the formal institutional quality variables is satisfying enough to now turn to the main analyses.