independent variables can produce biased coeﬃcients in OLS and GLS estimations. As already stated in section 2.5, production and wage variables are one possible source of en-dogeneity. Larger imports can favor domestic production when the latter relies heavily on foreign intermediate inputs, but can also have a harmful eﬀect if imported products crowd out local producers through increased competition on the domestic market. Wages too are aﬀected by a country’s performance on foreign markets. This can be visualized within a country through the gap between employees’ remuneration by exporting companies and by local market oriented firms. Last but not least, the quality of institutions can change with respect to country’s participation in international trade. The exchange of large amounts of goods with foreign partners brings the firms more often in contact with deficiencies in the institutional framework, and can determine them to lobby for reforms in the system.
Consequently, one calls for appropriate econometric proceedures when estimating the relative demand for foreign goods. The solution is given by instrumental variable (IV) and generalized method of moments (GMM) estimations. However, these approaches rely heavily on the availability of good instruments for each endogenous variable, i.e. variables that are important determinants of a particular endogenous variable but have no direct impact on the others. The literature acknowledges the diﬃculty of finding good instruments for all three variables mentioned above.
In econometric terms, what counts is good instruments for right hand side endogenous variables in order to find exogenous variations in production, wages and institutions, and explain relative trade. From a theoretical point of view, truly exogenous variations are unlikely to exist; it is necessary therefore to find sources of variation that are orthogonal to other determinants of trade. The theoretical model predicts a unitary coeﬃcient on production. One can thus easily correct for endogeneity between trade and production in (2.17) by imposing β1 = 1. Wages in a country are determined by demand and oﬀer conditions on the labor market: the size of the labor force, the number of employers (firms) operating on the market, unions’ bargaining power, and the productivity of labor. Of all these variables, only the last one passes the Sargan over-identification test and is used to instrument variation in wage ratio in first-stage estimations.
Endogenous institutions have been treated in the literature almost exclusively in devel-opment related issues, such as to demonstrate their strong causality on per-capita income (Acemoglu, Johnson and Robinson (2001)). They use mortality rates of colonial settlers as an instrument for institutional quality and find that the adopted instrument is strong. From this paper it is possible to say that the characteristic of the environment found by the colonialists is one of the most important reasons for the decision to establish them-selves in a determined area rather than in another. Moving from the idea that geography can explain institutions’ variations, Easterly and Levine (2003) and others instrumented institutions with diﬀerent geographic variables. They include diﬀerent sets of instruments for endowments like latitude, landlocked and ten diﬀerent dummy variables representing minerals and crops.
However, all theses studies adopt an approach specific to the breakdown of the world into industrialized and developing countries, with past colonial ties. Therefore, instruments they found are irrelevant for institutions of European countries. In our case instrumental vari-ables need to account for specific diﬀerences between East and West-European economies. None of the countries in our sample has been colonialized in the last centuries. Purely geographic aspects cannot explain diﬀerences in the functioning of institutions across the continent either. Although countries from the North tend to have better institutions, mov-ing to the South, the passage to poorer institutions is far from being smooth. Moreover, one should be able to provide an economic justification of the causality between the chosen instruments and the endogenous explanatory variable.
We have considered a entire set of possible instruments for institutions, starting from the mortality rate, and the share of non-tax revenue, passing through patents deposed by residents and foreigners, the share of rural population, market capitalization, the percentage of listed domestic companies, the share of traded stocks in the national product, and finishing with the per capita central government’s consumption. Plausible stories about the eﬀect on institutions’ quality can be told for any of these variables, but only three qualify as good instruments according to Sargan test.
The first one is the mortality rate. The intuition behind is that a high probability of dying tomorrow diminishes people’s valuation of future benefits, including the ones in terms of well-functioning institutions. Therefore, there is less incentive for institutional reforms in countries with high death rates. Building better institutions is a long-term investment, and those who make the main eﬀort may simply not be able to enjoy its future advantages. High mortality has the same eﬀects as political instability for government oﬃcials, who continue to accept bribery and corruption as ordinary things.
Market capitalization, expressed as a share of domestic product, is the second instru-ment employed. It reflects simultaneously the share of listed companies and the market value of their stock. A high level of market capitalisation illustrates a large participation of firms on the stock exchange, a high value of firms’ assets, or both. All three situations correspond to increased needs (and pressure) for strong institutions. The stock exchange alone can be viewed as an institution, and its condition can shed light on the state of other institutions in the country.
The last eligible instrument for institutions is the government’s tax revenue. It is generally accepted that a country’s institutions can change notably only with suﬃcient support from the government. Support can come in two forms: political and financial. The ability of the government to collect taxes determines the amount of its funds, and as a consequence its capacity to finance institutional reforms. A severe limitation in funds on which the government can draw to finance its actions reduces not only the scale of these actions, but also their popularity among the electorate.
Endogeneity compatible estimates of (2.17) are displayed in table 2.5. To control for the panel structure of our data, country pair eﬀects have been included in all second-stage estimations. Wu-Hausman and Durbin-Wu-Hausman test statistics for endogeneity are constructed and reported in the lower part of the table. The null hypothesis associated with this tests recognizes all the variables on the right hand side of (2.17) to be exogenous. The alternative hypothesis requires wages and institutions to be endogenous. As the results of these tests rely on the instruments selected, the Sargan validity of instruments test is conducted to determine whether the instruments selected are appropriate.
The IEF is used in column 1 as a measure of institutions’ quality. Note, that lim-ited availability of data on selected instruments reduces the number of observations and the variablility in other exogenous variables. Thus, insuﬃcient variation in import tariﬀs makes impossible the estimation of β7. The gain in statistical significance of coeﬃcients on institutional variables is very small, but zero on wages. And the large p-value for both Wu-Hausman (F = 8.67, p = 0.00) and Durbin-Wu-Hausman (χ2 = 29.99, p = 0.00) tests reveals that controlling for endogeneity is unnecessary. The absence of endogeneity also suggests that OLS and GLS estimations are reliable in this case.
Column 2 shows results with the EBRD composite institutional index. Both large partial R2 in first-stage regressions and low Sargan statistic (0.01, p = 0.91) testify the relevance of chosen instruments. The two endogeneity tests justify the use of instrumental variables estimator. All coeﬃcients are significant at the 1% level. The coeﬃcient on wage ratio is equal to -1.03, yielding a more credible value of the elasticity of substitution σ. Compared to OLS and GLS estimations presented in table 2.3, the eﬀect of NTB and the quality of institutions on cross border trade obtained with IV is much larger. The impact of import tariﬀs and institutional quality, on the contrary, is lower with a IV estimator. The elimination of tariﬀs for European trade, amounting at the beginning of the period to a 5% average, generates according to estimates in column 2 a 43%= [5 ∗(−8.62)] increase in trade. Diﬀerent from OLS and GLS results, the eﬀect of the quality of institutions on trade is 5.5 times larger than the eﬀect of institutional distance separating the trading countries. Still, in the case of the EBRD, the two measures are not orthogonal: Any improvement in the functioning of CEE countries’ institutions reduces the institutional distance separating them from EU members. Results are very similar if the NTB coverage ratio is substituted by the NTB frequency ratio.
Columns 3a and 3b, use the KKM general index to construct the two institutional variables, and the last two columns employ the Fraser index. The χ2 score of the Sargan test (equal to 3.42 with the KKM index, and to 4.25 with the Fraser index) is below the 1 percent critical value, suggesting that the null hypothesis, stating that the selected instruments are appropriate, should be accepted in both cases. The value of the Wu-Hausman test statistic shown in table 2.5 (32.09, and respectively 8.81) is well above the 1 percent critical value of the χ2 distribution, indicating that wage and institutions are truly endogenous variables in (2.17). IV estimations reveal institutions as stronger determinants of trade. However, for both KKM and Fraser indices, the IV estimator does not correct for heteroscedasticity in the system of equations estimated in the first and second stage. This is reflected by the significant Pagan-Hall statistic shown in the bottom of table 2.5. The generalized method of moments (GMM) estimator is used to fix this problem. Again, the GMM equivalent of the Sargan test, also called the J statistic of Hansen or Jansen, confirms that selected instruments are appropriate. Switching to the GMM estimator reduces the statistical significance of institutional eﬀects. However, we do not consider this loss to be important as we base our main conclusions on IEF and EBRD index. Results in column 3a, the only one yielding significant estimates for both institutional variables, show that the contraction of institutional distance separating two countries generates about as much trade as an equal upward shift in the quality of both countries’ institutions.
Whenever endogeneity is present, IV and GMM estimates of the wage ratio coeﬃcient are larger than 1 in absolute value, complying with predictions of the theoretical model. It can thus be concluded that wages can be employed as proxies for mill prices only if one corrects for the introduced endogeneity. Using either of the four institutional measures described above yields similar results: International trade increases when national insti-tutions work better, and are more alike. Trade liberalization, translated by lower import tariﬀs and share of trade exposed to NTB, has a strong trade-boosting eﬀect. Nevertheless, the amelioration of institutions is expected to increase trade between European partners, suggesting that trade integration can continue even when complete trade liberalization is reached.
Trade Policy vs. Institutional Reforms
There are two possible ways in which one can judge about the relative importance of trade policy instruments and reforms of national institutions for regional trade integration. The first method consists in determining the share of the overall border eﬀect explained by each determinant. The second regards the volume of additional bilateral trade generated by complete trade liberalization and fully accomplished institutional reforms. For both approaches estimates of imports relative to domestic trade mij /mjj are used.
The contribution of each factor to the total border eﬀect is actually equivalent to the share of trade policy and institutional costs in the total border-specific trade costs. The overall border eﬀect between two countries i and j is an expression of total trade costs between i and j in terms of lost trade volume. It reflects the loss in trade caused by all border-related barriers, and is obtained by taking the exponential of the opposite of the constant terms of equation (2.15). We denote it by BE ≡ exp(Bij ). Equation (2.15) is reached by dropping all border-specific trade cost terms from trade specification (2.17), assuming a uniform eﬀect of the quality of national institutions on trade within and across national borders. Shall institutions be assumed to matter for cross-border trade alone, the institutional term should be dropped from (2.15) in order to estimate total border-related costs.
Table of contents :
I Economic integration, trade policies, and institutional reforms
1 Border Effects and East-West Integration
1.2 Border Effects and Trade Potential
1.3 Theoretical Discussions
1.3.1 A differentiated-good trade structure
1.3.2 The fixed-effects specification
1.3.3 The odds specification
1.3.4 The friction specification
1.3.5 Differences in consumer preferences
1.4 Estimating Border Effects Across Europe
1.4.1 Estimated trade equations
1.4.2 Estimations for the manufactured sector
1.4.3 Estimations with industry level data
1.5 Trade Potential and East-West European Integration
1.6 National Product Differentiation vs. Monopolistic Competition
2 Trade Liberalization and Institutional Reforms
2.2 European Trade Liberalization and Institutional Changes
2.3 The Trade Model
2.4 The Data
2.5 Baseline Estimations and Results
2.6 Endogeneity Issues
2.7 Trade Policy vs. Institutional Reforms
2.8 Distinguishing among Institutions
II The role of social and business networks in international trade patterns
3 Trade in Cultural Goods and Social Networks
3.2 Cultural Goods and Social Networks
3.3 The Gravity Model and Data
3.4 International Trade and Social and Cultural Ties
3.5 A Measure of the Intensity of Social Networks
3.6 Information and Preference Effects
4 Migrant Associations, Trade, and FDI
4.2 Networks, Immigrants, and Migrant Associations
4.3 The Model
4.3.1 A Trade Model with Social and Business Networks
4.3.2 A Model of FDI with Network Effects
4.4 The Data and Stylized Facts on Migrant Associations
4.5 Foreign Trade in the Presence of Ethnic Networks
4.6 French Subsidiaries and Cross-border Networks