COMPRESOR

In order to test our hypotheses, we utilize a straightforward extension of the standard dyadic gravity equation (Rose 2005):

( )

Estimation will be carried out with pooled ordinary least squares. The dependent varia-ble is ( ), the log of exports from country i to country j at time t (in current US dollars). We construct this variable from the Correlates of War dyadic trade dataset (Barbieri et al. 2008, 2009).

The vector contains explanatory variables from the standard gravity ap-proach:

( ), where GDP is real gross domestic product in constant 1990 dollars (Division 2009),

, which is a time-constant measure for the geodesic distance between countries i and j (Mayer and Zignago 2006).

The vector contains (1) proxies for the quality of formal contract enforcement, (2) proxies for the informal institution in the exporting or importing country and (3) all two- and three-way interactions of these variables, including the squared informal insti-tution. We estimate this equation separately for each dimension of informal institutions (uncertainty avoidance, universalism, patriotism). Furthermore note that, for each of those informal institutions, we estimate two separate equations: one for exporting coun-try informal institutions, one for importing councoun-try informal institutions. This is neces-sary because WVS data are available for a limited cross-section of countries. Investigat-ing both exporter and importer informal institutions in one equation requires data to be available for both countries, which would severely reduce sample size and introduce se-lection problems.

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A more detailed description of our estimation approach is now³:

( )

( )

( ) ( )

A straightforward implication is that – with the exception of and – the estimat-ed coefficients can no longer be directly interpretestimat-ed as (unconditional) marginal effects.

In order to arrive at inferences regarding the effect of the institutional variables, we need to calculate marginal effects conditional on specific values of the respective other covariates.

Because our unit of observation is the dyad (country pair), we have non-independent observations, which in turn leads to biased standard errors. In order to counter this, we employ a full set of time-varying exporter and importer dummies plus a full set of year dummies⁴. By controlling for this broad set of fixed effects, we can safely neglect in-cluding any further control variables, as most of those controls’ variation is already covered by our fixed effects. Furthermore, we adjust standard errors to account for clus-tering at the dyad level.

Our proxy for the strength of formal contract enforcement in the importing (exporting) country is ( ). For this, we employ the ICRG indicator for quality of government (Teorell et al. 2010). This is the mean value of several subjective measures provided by the International County Risk Guide: Corruption, Law and Order and Bu-reaucratic Quality. Each of these variables covers aspects that are relevant for the quali-ty of contract enforcement. The ICRG indicator is highly correlated with other potential measures, like the World Bank rule of law indicator or the Heritage Foundation’s indi-cator for protection of property rights, but available for a broader time

3 This is the equation for importing country informal institutions. For the exporting country, we substitute for .

4 The first best approach would be to year-varying exporter and importer dummies (Baldwin and Taglioni 2006), but due to the limited availability of our informal institutional variables, this is not feasible. Instead, we opt to vary the country dummies by wave of World Values Survey, which amounts to the five waves: 1981-1989, 1990-1994, 1995-1998, 1999-2004 and 2005-2008.

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range. Naturally, the most straightforward and objective measure for contract enforce-ment would be the check collection variable from the Courts project (Djankov et al.

2003), which is unfortunately not available as time-series. Note also that the ICRG indi-cator is a much broader measure than for example the Djankov indiindi-cator. Potential prob-lems of using such broad measures have recently been discussed (Voigt 2009).

In order to operationalize the concept of informal institutions, we employ data from the World Values Survey (WVS), an extensive survey that has been carried out for repre-sentative samples in up to 96 countries since 1981.

To construct an importing (exporting) country’s index of uncertainty avoidance ( ), we utilize a set of questions from the WVS which ask re-spondents to identify from various listed groups (e.g. people with a criminal record, homosexuals etc.) those groups that they would not like to have as neighbors. We as-sume that the more such groups were mentioned by the respondents, the more individu-als are bound by the institution of uncertainty avoidance. Calculating the mean over all mentioned groups results in a variable that lies between 0 and 1, with higher values in-dicating stronger uncertainty avoidance. In turn, this mean is averaged over all individu-als of a country-year to result in our aggregate measures of uncertainty avoidance.

In order to arrive at an index of universalism ( ), we consider two re-sponses from the WVS, both of which measure some distinct aspect of that institution:

(1) of two otherwise identical secretaries, the more productive one should be paid a higher wage, (2) larger income inequalities are needed for their incentive effect. Using factor analysis, we construct a weighted average of the two, invert the resulting variable and calculate the country mean to obtain our aggregate index of universalism.

Our measure for patriotism is derived from two WVS questions: (1) How proud are you of your nationality? (2) Are you willing to fight for your country? Using factor analysis, we calculate a weighted average. Again, aggregation to the country-year level is achieved by averaging.

Descriptive statistics for the variables employed in this study are in Table 6, pairwise correlations can be found in Table 7.

65 4 Estimation results

The presentation of our estimation results will proceed in two steps: First, we consider average effects, i.e. marginal effects (expressed as elasticities) holding constant other covariates at their respective means. In order to identify potential non-linearities and in-teractions, we will secondly consider several conditional effects plots. We will restrict our interpretation to parts of the respective distribution above the 50^th percentile. This is justified as we are interested in the effects of institutions when they actually matter.

In Table 8, we present the estimated average effects. “Average” means that we hold con-stant the values of respective other institutional variables at their means. For instance, the marginal effect of any importing country informal institution in the above statistical model is:

( )

In order to be able to draw inferences regarding the effect of , have to evaluate this expression at specific values of , and . In Table 8, we present the effects when inserting the respective mean.

First of all, we can see that the standard gravity explanatory variables, ( ) and , are estimated to have a significant impact, with the expected signs: Ceteris paribus, more economic mass (geographic distance) is associated with more (less) bilat-eral trade.