characteristics of the trading partners: country size and GDP per capita. ...... Anderson, J.E and E. van Windcoop (2003b) 'Trade Costs', Mimeo, Boston College.
INSTITUTIONS, INFRASTRUCTURE, TRADE POLICY AND TRADE FLOWS
Marion Jansen (WTO, CEPR) and Hildegunn Kyvik Nordås (WTO, SNF) *
Abstract This paper analyses to which extent trade policy restrictiveness, the quality of institutions and the quality of infrastructure affect trade flows. We use two complementary approaches, one focusing on the size of total trade flows and one focusing on bilateral trade patterns (gravity equation). We find that the quality of roads and "rule of law" have a significant and positive effect on the ratio or trade to GDP and that lower tariffs only increase this ratio in countries where the rule of law is considered to be strong. The results from our regressions focusing on bilateral trade are less clear-cut. The tendency seems to be, though, that trade policies at home and abroad matter most for home imports, together with foreign institutions and home infrastructure.
* We want to thank Lurong Chen for excellent research assistance. This paper represents the opinion of the authors and is not meant to represent the position or opinions of the WTO or its Members.
1
I.
INTRODUCTION
Trade as a share of world GDP has increased from 25 per cent in 1960 to 46 per cent in 1999. This reflects deeper international specialization, which has probably led to an increase in the number of international transactions per dollar of world GDP. The increased number of transactions per unit of world GDP has coincided with a reduction in transaction costs. Tariffs have come down substantially since the 1960s, and the same goes for transport costs. Both phenomena are supposed to have contributed to the observed global increase in trade. Not all countries have experienced the same growth in trade, though, and the impression may arise that the elasticity of trade flows with respect to changes in tariffs differ across countries. One possible explanation for this phenomenon would be that tariff reductions increase trade flows only to the extent that other domestic factors create an environment that is favourable for trade. This paper will focus on two potential factors that can play a role in this respect: domestic institutions and domestic infrastructure. The existing empirical literature on the determinants of trade flows to a large extent focuses on factors that affect the international part of transaction costs, like tariffs, international transport costs or adjacency. Variables used to reflect a shared historical, political and cultural background can also be seen in this light. The measures that are most commonly used for this purpose are dummies that indicate the presence of a common language, a common dominant religion and/or a common colonial history. The three variables are likely to be related and each of them is in its own way likely to affect international transaction costs. A common language facilitates communication in personal contact. The same may be true for a common religion and the latter may besides increase mutual trust and thus reduce the perceived risk of transactions. A common colonial history has been considered to increase the similarity between country's institutions and through this channel affect international transaction costs. Two additional variables that have frequently been used in the relevant literature refer to domestic characteristics of the trading partners: country size and GDP per capita. Those variables do not reflect transaction costs, but are for other reasons considered to be relevant for trade flows. It is well-known that smaller economies trade more, in the sense that they are characterized by a high ratio of trade to GDP. This is due to the fact that small size limits the country's possibilities to diversify production. Smaller economies therefore rely to a larger extent on imports to satisfy their domestic demand than their larger counterparts.1 GDP per capita is a variable that tends to perform well in regressions that try to explain trade flows, in particular in gravity models. A recent paper (Anderson and van Wincoop, 2003a) has cast doubt on the justifications to include GDP per capita in gravity models. Our interpretation of the good performance of GDP per capita in gravity equations, is that GDP per capita is a proxy for other domestic variables that are strongly correlated with GDP per capita. This paper will argue that the two most obvious variables are domestic institutions and domestic infrastructure and it will show that these variables are indeed statistically significant when introduced into a gravity equation. Our approach may also explain certain contradictions in the current empirical literature on the determinants of trade flows. In a paper that focuses on the determinants of growth Rodrik et al. (2002) show that institutional quality has a positive effect on a country's trade shares. Yet, in a paper on the trade performance of Sub-Saharan Africa (SSA) Rodrik (1998) finds that trade liberalization has the same effect of increasing trade in SSA as in other countries. This finding appears surprising when one takes into account that SSA countries are in general characterized by an institutional set-up of low quality (Haber et al., 2003). The findings in Rodrik (1998) would therefore suggest that institutions do
1
See for instance Commonwealth Secretariat and World Bank (2000) and Easterly and Kraay (2000).
2
not matter for the effect of trade policy on trade. It should be noted though that Rodrik (1998) uses the standard approach of including GDP per capita (and country size) as one of the determinants of trade flows. Rodrik et al. (2002) do not include GDP per capita in their regression but do include a measure for institutional quality. Given that institutional quality and GDP per capita tend to be highly correlated across countries, this may explain why institutions do matter in one approach and (implicitly) do not in another. The rest of this paper will proceed as follows. Section II discusses how our approach can be related to the existing literature. Section III presents the results of two empirical exercises. Section III.A analyses the effect of institutions, infrastructure and trade policy on countries' trade to GDP ratio in a simple OLS framework. Section IIIB, instead, uses gravity equations to analyse the role of the same variables in bilateral trade. Section IV concludes. II.
DISCUSSION OF RELATED LITERATURE
In order to analyse the determinants of trade flows, one has in principle the choice between two approaches: analyse a country's total trade with the rest of the world or analyse its bilateral trade flows. It is reasonable to believe that domestic trade policy, institutions and infrastructure affect a country's overall propensity to trade. This assumption is best tested with the first approach, as the analysis of bilateral trade flows focuses on the distribution of trade among partners rather than on changes in the volume of total trade. It is a-priori less clear to which extent domestic institutions/infrastructure and foreign institutions/infrastructure affect the distribution of trade flows among trading partners. In order to have a closer look at this question, we introduce the variables we are interested in into a gravity equation. Trade policy, institutions and infrastructure are all likely to affect transaction costs. Tariffs and non tariff barriers to trade increase the cost of importing goods from abroad. The quality of institutions will for instance affect information costs, contract enforcement costs and legal and regulatory costs. Domestic infrastructure determines the transportation costs necessary to bring imported goods from the border to the end user in the country or to bring goods meant for exports from the producer to the border. Economic literature, in particularly growth literature, has recently increased its interest in the role of institutions for the functioning of markets.2 The notion of an institution embodies several elements – formal and informal rules of behaviour, ways and means of enforcing these rules, procedures for mediation of conflicts, and sanctions in the case of breach of the rules.3 Institutions are more or less developed, depending on how well these different features operate. Well developed institutions are likely to decrease the transaction costs for market participants and thus increase the efficiency of markets. They do this through a number of channels:4 • They decrease information asymmetries as they channel information about market conditions, goods and participants; • They reduce risk as they define and enforce property rights and contracts, determining who gets what and when; and • They increase competition in markets – or decrease it.
2
See for instance Frankel and Romer (1999), Acemoglu et al (2001) and Rodrik et al (2002) for contributions to the relevant growth literature. The instrument used for property rights institutions in those papers is the settler mortality rate in the colonial era, while an instrument for the legal system and hence transaction costs is colonial history. Acemoglu and Johnson (2003) find that property rights have an impact on economic growth, while transaction costs do not. 3 See North (1994). 4 World Bank (2002)
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The availability of information and the assessment of risk are particularly important concerns for foreigners trading with a country. Even if a country lowers its trade barriers, outsiders may therefore be reluctant to trade with the country if, for instance, they do not believe contracts can be enforced or are not sure whether payments will be made. It makes therefore sense to assume that the quality of domestic institutions matters for international trade. Anderson and Young (2000) present a theoretical framework, in which the absence of rule of law has a negative effect on trade, which has to some extent been confirmed in recent empirical literature. Rodrik et al. (2002) show that the quality of institutions indeed has a significant and positive impact on country's total trade flows. De Groot et al. (2003) introduce a measure for institutional quality into a gravity equation and find that a better quality of formal institutions tends to coincide with more trade. They also find that similarity between trading partners in the quality of their institutions promotes trade. De Groot et al. (2003) however do not control for trade policy barriers in their paper, nor do they include measures for domestic infrastructure. In order to measure institutional quality they use the database on governance indicators developed by Kaufmann et al. (2002). This is also the database employed in this paper. Anderson and Marcouiller (2002) use survey data from businessmen by the World Economic Forum on contractual enforcement and corruption as an index of institutional quality. They find that lower institutional quality has a substantially negative effect on trade. This paper concentrates on three indicators of institutional quality included in Kaufmann et al. (2002) database: 1. 'Governance Effectiveness' refers to the quality of public service provision, the quality of the bureaucracy, the competence of civil servants, the independence of the civil service from political pressures, and the credibility of the government's commitment to policies. It is therefore a measure for the quality of government inputs. 2. 'Rule of Law' is based on several indicators which measure the extent to which agents have confidence in and abide by the rules of society. These include perceptions of the incidence of crime, the effectiveness and predictability of the judiciary, and the enforceability of contracts. 3. 'Control of Corruption' measures perceptions of corruption, conventionally defined as the exercise of public power for private gain. The reason to focus on these three variables is that we expect them to closely affect the uncertainty involved in trade and thus transaction costs. Domestic infrastructure is likely to have an important impact on a country's propensity to trade, as it will, for instance, affect its supply response to any changes in trade policy. In this respect the study by Minten and Kyle (1999) on transport costs in the region surrounding Kinshasa (Republic of Congo) is quite illuminating as it gives us an idea of the dimensions involved. Small-scale farmers in the Kinshasa region trade their surplus output in Kinshasa. The region is characterized by large distances between villages and roads are often of poor quality. As a result a 300 km journey by road will take around 4 days and transport costs account for as much as 30 per cent of the wholesale price for goods transported by road and sold in Kinshasa. These 30 per cent are likely to have a significantly negative effect on the possibilities of Congolese farmers to compete in international markets. It has been quite common in the literature to use geographical distance as a proxy for transport costs. Few papers have used direct measures for transport costs as is the case in Baier and Bergstrand (2001). They also include tariffs in their regressions and find that income growth accounted for 67 per cent of trade growth among 16 OECD countries during the period 1958-1988, tariff reductions accounted for 25 per cent and transport cost reductions 8 per cent. As a measure for transport costs they use c.i.f./f.o.b ratios from the IMF's International Financial Statistics, a measure that has been
4
criticized in the literature for its low quality.5 C.i.f./f.o.b. ratios also have the disadvantage that they only take into account the international part of transport costs and ignore the transport costs incurred within the destination country. The approach taken in Limão and Venables (1999) allows for such a distinction. They argue that bilateral imports depend on GDP's, in the standard way, and on transport costs, which they model in terms of a number of geographical and infrastructure measures. As geographical measures they include the commonly used distance variable and a border dummy. In addition they include island dummies. To measure the impact of infrastructure they include both an infrastructure variable for each trading partner and for the countries that each trading partner has to transit. Their infrastructure measure is constructed as an average of the density of the road network, the paved road network, the rail network, and the number of telephones per person. In their regression most variables have the expected sign and are highly significant. The approach taken in Section III.B of this paper is related to the one in Limão and Venables (1999) when it comes to the role of infrastructure in trade. The main difference is that we also include the impact of trade policy and institutions in our regressions.6 III.
EMPIRICAL ESTIMATES
The empirical estimates of the impact of trade policy on trade takes two complementary approaches. First, we regress openness as measured by exports + imports over GDP on a range of trade barriers, institutions and infrastructure that are likely to affect openness, controlling for country size. Openness is a commonly used indicator of how well a country is integrated in international markets and it is also found to be a significant determinant of economic growth in a number of studies.7 A better understanding of the determinants of openness is therefore valuable for policy making. However, since the dependent variable of the openness regression contains both trade and income, we cannot distinguish whether the independent variables affect exports, imports or income. Thus, if an independent variable has no significant impact on the openness indicator this can either be because it does not affect trade or income or it can be because it affects trade and income proportionally. We therefore use the gravity equation in order to further investigate the determinants of trade flows. The gravity equation determines the geographical distribution of trade, given production patterns and expenditure patterns. We follow Hummels (2001) and focus on the determinants of the geographical distribution of imports. A.
OPENNESS AND TRADE POLICY - A SIMPLE OLS REGRESSION
We start the empirical investigation of the impact of trade policy on openness by a simple OLS regression applied to a cross-section dataset. We use period averages for the period 1995-2001 and include all observations for which data are available for at least one year during this period. The measure of openness is trade (exports + imports) over GDP. Our aim is to explore the correlations between actual trade flows and tariffs. However, as shown by Hummels (2001) and Andersen and Wincoop (2003b), tariffs constitute only a fraction of trade costs, and they are usually not even the largest. Transport costs and other transaction costs related to finding a trade partner, entering and enforcing contracts between trading partners are found to be significantly higher than tariffs when 5
See for instance Hummels (2001). It should also be noted that the IMF has in the meantime stopped publishing the relevant data. 6 Like Limão and Venables (1999) we use the paved road network and the number of telephones per person as indicators for institutional quality. Limão and Venables also include the density of the road and the rail network in their analysis. Whereas they construct and average index based on all four infrastructure variables, we insert different infrastructure variables separately into the regressions. 7 See for instance Frankel and Romer (1999), Edwards (1993a) and Levine and Renelt (1992). Edwards (1993b) surveys the literature.
5
tariff equivalents are calculated. Finally, trade flows are accompanied by information flows and financial flows whose costs should also be counted as trade costs. We therefore explore the impact of tariffs relative to these other trade costs on openness and also possible interactions between various components of trade costs. The cost of entering and enforcing contracts are not directly observable and we hypothesize that they are closely correlated to the quality of institutions as measured by the rule of law, government efficiency and control of corruption. We use the indices developed by Kaufman et al (2003), which take values between -2.5 and 2.5 – the higher the value the better the institution. Transport costs are in principle observable, but data are not readily available and a number of proxies have been applied in the literature. We have chosen the quality of roads measured by the share of paved roads.8 Finally, we assume that the relevant costs of finance and information are closely related to the depth of financial services as measured by credit to private sector relative to GDP and the penetration of telephones (mobile and fixed lines) respectively. Here one could argue that more direct cost measures could be used such as the cost of making a telephone call, the real interest rate on borrowing or fees for payment services. However, the cost of information flows are not only related to the cost of making a telephone call, but also to how many people can be reached by telephone and the ease at which local, long-distance and international calls can be placed, and not least the opportunity cost of not being able to place a call or access information on the internet. These aspects are likely to be more closely correlated with telephone density than with the cost of making a call. Large countries are found to trade less than small countries relative to income. This is because consumers prefer a broad range of goods and services and large countries produce a broader range than small countries.9 We control for the effect of country size by including the population size in the regressions. We start by taking an "a-theoretical" approach in order to explore how the indicators of transport and other transaction costs are correlated. In the next section we add more structure to the empirical estimations by applying the gravity equation. Annex Table A.1. presents the correlation matrix between the variables included in the regressions. The institutional variables are highly correlated with a coefficient of correlation of about 0.95 all combinations or pairs of the three indicators. They are also relatively rough and imprecise measures of various aspects of the same thing – the quality of a country's institutions. Nevertheless, corruption, uncertainties related to entering and enforcing contracts and inefficiencies in government provision of services probably account for separate cost elements that where each adds to the trade costs incurred by companies that engage in international trade. We therefore regress openness on tariffs and each of the institutional indices separately while controlling for country size in each case. The tariff data are taken from the TRAINS database, where we apply the simple average tariff rate.10 The results are presented in Table 1. T-statistics are in parenthesis and significance at a one per cent level is indicated by three stars, significance at a 5 per cent level is indicated with two stars and at 10 per cent level with one star.
8
As indicated in footnote 6 we are also working with other variables. The gravity model, for instance is based on the Armington assumption and the varieties produced in the world economy enter consumer preferences as CES aggregates in one-sector or multi-sector models. 10 The trade-weighted average tariff is used in several regressions on the impact of trade policy on growth, but this measure already capture some of the effect of tariffs on trade and we argue that for our purpose the simple average tariff rate is a better indication of trade costs. 9
6
Table 1: Regression results, institutions Dependent variable: trade/GDP Rule of law Constant 305.2*** (8.93) ln population -13.4***
Government efficiency 298.4***
Control of corruption 298.1***
(10.90) -13.0*** (-7.88)
-12.8***
-0.68
-0.90*
(3.79)
(-1.44) 10.91*** (2.84)
(-1.88) 6.68* (1.80)
140 0.29
151 0.34
151 0.32
(-6.31)
tariffs institution indicator n Adjusted R2
-0.73 (-1.40) 9.12***
(10.52) (-7.55)
It appears that the institutional variables do capture different aspects of trade cost since they yield somewhat different results. All the institutional indicators are positively and significantly related to openness, but corruption only at a 10 per cent level. Thus, countries with better institutions appear to be more integrated in world markets than countries with less well developed institutions. Tariffs have the expected negative sign in all the reported regressions, but are significant only in the regression whit control of corruption as the institutional variable. We next explore the possibility that the relation between trade policy and openness depends on the quality of institutions. In this regard we take a similar approach as several studies investigating the impact of development aid on economic growth. Aid is found to have no significant impact on its own, but when aid interacts with good governance it may have a significant and positive impact on growth (see Burnside and Dollar, 2000, and Easterly et al., 2003). In order to explore a similar interaction between trade policy and institutional development that affects the cost of trade, we introduce interaction terms between tariffs and the institution index in each regression. The result is reported in Table 2.
Table 2: Regression results institutions and interaction terms Dependent variable: trade/GDP Rule of law Constant 310.7***
Government efficiency 300.2***
ln population
(9.08) -13.9*** (-6.50)
tariffs
-0.73
institution indicator
(-1.41) 17.2*** (2.65)
(10.96) -13.1*** (-7.95) -0.80* (-1.65) 15.9*** (2.74)
-0.98 (-1.53) 140 0.30
-0.61 (-1.15) 151 0.34
Interaction term n Adjusted R2
Control of corruption 310.5*** (10.67)
-13.5*** (-7.77)
-1.15** (-2.30)
14.2** (2.46)
-1.01* (-1.69)
151 0.33
The interaction term improved R square slightly in all three regressions and in all regressions the institutional indicator remains significant while the parameter value increases quite substantially. Apparently, when allowing for interaction between trade policy and institutional quality, the measured impact of institutions on openness increases. The interaction term is, however only significant at the 7
10 per cent level in the control of corruption regression. Furthermore, both the absolute value of the coefficients and the level of significance of tariffs and institutions increases in this regression. The result suggests that the marginal impact of lowering tariffs is larger the better is the control of corruption. We next turn to a partial analysis of trade costs related to the quality of infrastructure. The indicators included are those that we believe affect transport and transaction costs the most directly; the quality of roads (percentage of total roads that is paved), number of fixed and mobile telephone lines per 100 inhabitants, access to credit (credit to private sector as a share of GDP). Data on telecoms and roads are taken from the World Development Indicators 2003, while data on financial indicators are taken from the IMF. The infrastructure variables are correlated, but not as strongly as the institutional indicators. Nevertheless, problems with multicolinearity should be expected and we start with regressing openness on size and tariffs and one infrastructure indicator at the time. The results are reported in Table 3. Table 3: Regression results, infrastructure Dependent variable: trade/GDP Roads Telecom Constant 269.6*** 271.0*** Population Tariffs Infrastructure n Adjusted R2
Finance 297.9***
(9.31) -12.1*** (-7.10) -0.97** (-2.23) 0.35*** (3.59)
(8.03) -12.8*** (-7.28)
(10.2) -14.0*** (-7.87)
-0.60
-0.43
(-1.19) 5.22** (2.46)
(-0.89) 0.37*** (3.99)
145 0.34
147 0.34
134 0.37
All the variables have the expected sign and all infrastructure variables are significant at least at a 2 per cent level. Tariffs are significant (at the 5 per cent level) only in the regression including the quality of roads. As in the case with institutions, we introduce interaction terms between tariffs and infrastructure in order to explore possible complementarities between trade policy and quality of infrastructure. The results are reported in Table 4.
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Table 4: Regression results, infrastructure and interaction terms Dependent variable: Trade/GDP Roads Telecom Constant 251.5*** 259.8***
Finance 293.4***
(8.65) -12.7*** (-7.59)
(7.60) -13.6*** (-7.52)
(10.08) -14.3*** (-8.06)
1.15
Population Tariffs Infrastructure Interaction n Adjusted R2
(1.31) 0.82*** (4.19) -0.04*** (-2.75)
1.31 (1.07) 10.5***
0.48 (0.67) 0.57***
(2.82) -0.44* (-7.72)
(3.85) -0.02* (-1.70)
145 0.38
147 0.35
134 0.38
The parameter values on the infrastructure variables are substantially higher when including an interaction term between infrastructure and tariffs. However, tariffs become positively correlated with openness when we control for quality of infrastructure and the interaction term, albeit not significantly so. The interaction term takes the expected negative sign and is significant at least at a 10 per cent level in all the regressions. The quality of infrastructure thus has a positive direct impact on openness while it also appears to improve the marginal impact of lower tariffs. A tentative conclusion from the analysis so far is that the impact of trade policy on integration into international markets depends on the control of corruption and the quality of infrastructure, including access to credit. Hence, for countries with a low degree of integration, better infrastructure and the rooting out of corruption appear to be complementary to trade policy. We finally explore the relative importance of infrastructure and institutions for trade performance. Again we run into problems of multicollinearity due to the fact that good infrastructure and good institutions go together. Nevertheless, we expect them to have distinct impacts on openness and it is important to identify their relative importance in order for developing countries to be able to identify the most effective policy measures when the objective is to become better integrated in world markets. We chose the infrastructure variable the least correlated with the institutional variables, roads, and combine it with each of the institutional variables and an interaction term between institutions and tariffs as follows:
( X + M ) / GDP = a 0 + a1 ln pop + a 2 t + a3 infr + a 4 inst + a5 t * inst The results are presented in Table 5.
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Table 5: Roads, institutions and interaction terms Dependent variable: trade/GDP Variable Rule of law Constant Population Tariffs
285.5*** (8.40) -13.6*** (-6.57) -0.58 (-1.16) 0.37***
Road
Interaction inst./tariffs
(3.26) 17.5*** (2.65) -1.64**
n Adjusted R2
(-2.48) 136 0.35
Institution
Government efficiency 270.0***
Corruption
(9.46) -12.2*** (-7.27) -0.91* (-1.90) 0.29*** (2.76) 15.3** (2.49) -1.05* (-1.92)
283.4*** (9.65) -13.0*** (-7.51) -1.25*** (-2.57) 0.36*** (3.41) 13.6** (2.31) -1.54*** (-2.54)
145 0.36
145 0.36
The interaction term between tariffs and institutions turned out to yield the best results in terms of significance of the individual variables and the overall explanatory power of the regressions. In all three regressions the quality of roads and the institutional variable are both significant at least at a 5 per cent level. Tariffs are significant at a 1 per cent level when controlling for the corruption variable and at a 10 per cent level when controlling for government efficiency. When controlling for rule of law, tariffs are not significant, but tariffs take the expected negative sign in all regressions. The interaction term between tariffs and the institutional variable is, however, significant in all regressions, it takes the expected negative sign and the value of the parameter is well above the one obtained when including institutions and infrastructure separately. The relation between a change in openness and a change in tariff rates can be expressed as follows:
∆( X + M ) / GDP = ∆t (a 2 + a5 inst ) Notice, however, that given the linear functional form and the fact that the institutional variables can take the values -2.5 to 2.5, the interaction term actually becomes negative for negative values of the institutional quality indicators. Hence, in the most corrupt countries with the least efficient governments and the lowest quality of the legal system, trade liberalization in the form of lowering tariffs may not yield the desired integration into international markets.
B.
THE GRAVITY EQUATION
The workhorse of empirical trade research is the gravity equation. As pointed out by Anderson and van Wincoop (2003a), such estimates are often not based on a theoretical foundation and estimations suffer from omitted variable bias. A theoretically founded gravity model is based on a CES expenditure system where goods are differentiated by region of origin. Bilateral trade is then a function of the two trading partners' respective shares in world income and the trade barriers between them relative to the trade barriers between them and all other countries. Bilateral trade is in other words determined by relative trade barriers, not absolute trade barriers between the two countries in question, which is typically assumed in empirical work. An important insight from Andersen and Wincoop (2003a) is that a uniform increase or reduction in world trade barriers; for example an improvement in transport technology does not affect bilateral trade. Hence the robustness of the 10
distance parameter in gravity equation estimates over time in spite of declining transport costs over time. Another feature of the theory-based gravity equation is unitary income elasticities, stemming from the assumption that consumers have identical homothetical preferences in all countries. Thus the parameters on both trading partners' incomes (GDP) should be the same and restricted to unity in the regressions. Many gravity regressions add common language and other factors affecting trade barriers. Since distance is a proxy for aggregated trade costs, one would expect that the coefficient on distance would decline as elements of this aggregate are included as independent variables. The regressions presented in this paper seek to unbundle the trade cost aggregate and in particular investigate the possibility of complementarities between institutions, infrastructure and policy-induced barriers to trade, using the same variables as in the previous section. The first equation estimated is the following:
ln M ij = a0 + a1 ln y i + a 2 ln y j + a3 dist + a5 borderij + a 6 tariff i + a 7 tariff j + a 7 inst i + a 7 inst j We include the usual measures and proxies for trade costs (distance and a common border) and then add proxies for institutional quality, one at a time, and finally the home (importing) country's average tariff rate, the foreign country's average tariff rate and finally both of them. The average most favoured nation applied tariff should be interpreted as an indicator of the country's trade restrictiveness rather than bilateral trade costs, since we have at this stage not included bilateral tariff rates.11 We also included a similar interaction term between tariffs and institutions as in the previous section, but the interaction term turned out to be insignificant without affecting other variables. The results of our regressions are reported in Table 6. It is noticeable that in all the regression it appears that what matters most for bilateral trade is the foreign (exporting country) institutions and trade policy. Thus, a country tends to import more the lower its own trade barriers, and its sourcing of imports from a trading partner depends positively on the trading partner's institutions and negatively on its trade barriers. It is also noticeable that including the trading partners' trade restrictiveness strengthens both the economic and statistical significance of the trading partner's institutions. These findings suggest that good institutions reinforce the effect of having low trade barriers on exports, although the interaction term between partner institutions and partner tariffs (not reported) was insignificant in all three regressions. Turning to infrastructure, Table 7 reports the results of including measures of home and foreign infrastructure quality in the regression. Before including trade policy measures in the regressions, quality of infrastructure both in home and foreign country is significant at a one per cent level in all regressions, and the coefficient on home infrastructure is larger. The introduction of trade restrictiveness reduces the economic and statistical significance of infrastructure in the cases of roads and finance, while telecommunications in the exporting country are less sensitive to whether or not tariffs are included.
11
We are working on incorporating bilateral tariffs in a next draft of the paper.
11
Table 6: Trade policy and Institutions Dependent variable: bilateral trade Rule of law Variable Core home t foreign t Constant -23.8*** -23.9*** -21.4*** (-54.7) (-43.8) (-39.6) GDPi 0.92*** 0.91*** 0.91*** (81.0) (62.3) (67.7) GDPj 1.11*** 1.09*** 1.01*** (99.2) (82.7) (70.3) distance -1.22*** -1.16*** -1.20*** (-44.8) (-36.5) (-38.5) Border 0.89*** 0.89*** 0.94*** (5.68) (4.76) (5.07) Institutioni 0.15*** 0.11*** 0.14*** (5.93) (3.25) (4.75) Institutionj 0.15*** 0.12*** 0.28*** (6.08) (3.85) (8.21) ti -0.01** (-2.33) tj -0.02*** (-4.77) n 10488 7123 6871 adj R2 0.68 0.70 0.70
both t -20.6*** (-30.7) 0.89*** (51.5) 0.99*** (58.5) -1.18*** (-33.0) 0.81*** (3.82) 0.08* (1.95) 0.24*** (5.90) -0.01*** (-3.04) -0.02*** (-3.54) 4664 0.72
Core -23.6*** (-53.6) 0.91*** (78.39) 1.11*** (98.6) -1.22*** (-45.0) 0.88*** (5.60) 0.18*** (6.75) 0.14*** (5.29)
10488 0.68
Government efficiency home t foreign t -23.8*** -21.3*** (-43.2) (-39.0) 0.91*** 0.90*** (61.0) (65.3) 1.10*** 1.02*** (82.1) (70.1) -1.16*** -1.19*** (-36.6) (-38.3) 0.89*** 0.94*** (4.74) (5.03) 0.13*** 0.18*** (3.69) (5.70) 0.11*** 0.23*** (3.32) (6.76) -0.01** (-2.43) -0.02*** (-5.87) 7123 6871 0.70 0.70
12
both t -20.6*** (-30.6) 0.88*** (50.3) 1.00*** (58.4) -1.17*** (-32.8) 0.81*** (3.83) 0.11*** (2.71) 0.19*** (4.65) -0.01*** (-2.94) -0.02*** (-4.39) 4664 0.71
Control of corruption Core home t foreign t -24.0*** -24.1*** -21.6*** (-55.5) (-44.5) (-40.3) 0.92*** 0.92*** 0.91*** (81.8) (63.9) (68.1) 1.11*** 1.09*** 1.02*** (103.0) (85.5) (72.5) -1.22*** -1.16*** -1.20*** (-44.9) (-36.5) (-38.4) 0.87*** 0.88*** 0.93*** (5.52) (4.68) (4.97) 0.11*** 0.06** 0.12*** (4.92) (2.01) (4.33) 0.13*** 0.11*** 0.22*** (5.66) (3.97) (7.14) -0.01*** (-2.92) -0.02*** (-5.22) 10488 7123 6871 0.68 0.70 0.70
both t -20.8*** (-31.4) 0.90*** (52.5) 1.00*** (60.2) -1.18*** (-33.0) 0.80*** (3.78) 0.05 (1.46) 0.20*** (5.23) -0.02*** (-3.32) -0.02*** (-3.79) 4664 0.71
Table 7: Trade policy and infrastructure. Dependent variable: bilateral trade Roads Variable Core home t foreign t Constant -25.4*** -24.8*** -23.0*** (-58.63) (-45.8) (-42.5) GDPi 0.93*** 0.93*** 0.93*** (85.4) (66.8) (70.6) GDPj 1.15*** 1.12*** 1.08*** (106.8) (87.7) (77.2) distance -1.23*** -1.18*** -1.22*** (-41.7) (-33.6) (-35.1) Border 0.88*** 0.94*** 0.95*** (5.33) (4.71) (4.77) Infrastri 0.003*** 0.00 0.002*** (4.18) (0.81) (2.73) Infrastrj 0.00 0.00 -0.00 (0.51) (0.62) (-0.97) ti -0.01*** (-3.70) tj -0.03*** (-8.55) n 9648 6520 6329 2 adj R 0.68 0.69 0.69
both t -21.7*** (-32.4) 0.91*** (54.7) 1.05*** (63.5) -1.21*** (-29.7) 0.85*** (3.79) -0.00 (-0.38) -0.00 (-0.05) -0.02*** (-4.30) -0.03*** (-6.25) 4286 0.71
Core -24.1*** (-55.9) 0.92*** (78.0) 1.11*** (95.5) -1.22*** (-44.6) 0.85*** (5.36) 0.0002*** (3.91) 0.0003** (5.32)
10566 0.68
Telecom home t foreign t -24.2*** -22.0*** (-45.0) (-41.2) 0.93*** 0.91*** (59.2) (64.6) 1.08*** 1.01*** (78.2) (65.0) -1.15*** -1.17*** (-35.7) (-37.0) 0.87*** 0.96*** (4.62) (5.14) 0.00 0.0002*** (0.70) (3.30) 0.0003*** 0.0005*** (4.47) (6.31) -0.01*** (-2.80) -0.02*** (-3.87) 7106 6881 0.69 0.70
13
both t -21.1*** (-32.2) 0.91*** (48.6) 0.98*** (53.1) -1.15*** (-31.7) 0.82*** (3.86)
Core -23.6*** (-53.9) 0.91*** (77.7) 1.10*** (96.4) -1.26*** (-45.6) 0.83*** (5.19) -0.00 0.003*** (-0.09) (6.62) 0.0005*** 0.004*** (5.21) (8.49) -0.02*** (-3.33) -0.01** (-2.45) 4619 10139 0.71 0.69
Finance home t foreign t -23.6*** -21.2*** (-42.3) (-38.2) 0.91*** 0.91*** (59.2) (64.9) 1.09*** 1.01*** (79.9) (66.3) -1.19*** -1.22*** (-36.9) (-38.5) 0.82*** 0.85*** (4.29) (4.46) 0.002*** 0.002*** (3.91) (4.17) 0.003*** 0.004*** (5.40) (7.11) -0.01*** (-3.43) -0.03*** (-8.47) 6845 6629 0.70 0.70
both t -20.5*** (-29.6) 0.89*** (48.9) 1.00*** (55.2) -1.20*** (-32.5) 0.71*** (3.27) 0.001 (1.54) 0.003*** (4.34) -0.02*** (-3.95) -0.03*** (-6.22) 4474 0.71
IV.
CONCLUSIONS
This paper focuses on transaction costs in international trade and analyses to which extent these costs affect trade flows. It presents two complementary approaches. First, we regress openness as measured by exports + imports over GDP on a range of trade barriers, institutions and infrastructure that are likely to affect openness, controlling for country size. Next we analyse bilateral trade flows with the use of a gravity equation. When taking the first approach we find that all institutional variables included have a significant and positive impact on trade flows. The regressions suggest that rule of law has the largest impact on the openness indicator. We also find that the interaction term between tariffs and institutions are negative and significant in all regressions, albeit only at a 10 per cent level in the regression including government efficiency. This means that the marginal impact of a reduction of tariffs on the openness indicator is larger the better the institutional quality. In a next step we present preliminary results of regressions based on the gravity equation that allow us to analyse bilateral trade flows. This analysis finds that the quality of institutions and infrastructure matters for trade flows, and it also provides some insights into the relative importance of institutions, for imports and exports. In the case of infrastructure, we find that it is the institutional quality and the level of tariffs in the exporting country that matter the most for the sourcing of imports. In the case of institutions, a similar pattern emerges. It is the institutional quality and tariff levels of exporters that matter the most for bilateral trade. To conclude the paper suggests that the impact of trade liberalization on actual trade flows depends on the quality of institutions and infrastructure. Ongoing research incorporating bilateral tariffs, nontariff barriers and better measures of transport costs will improve the analysis and allow us to draw better policy implications.
V.
REFERENCES:
Acemoglu, D. and S. Johnson (2003), 'Unbundling institutions', NBER working paper no 9934, August. Acemoglu, D., S. Johnson. and J.A. Robinson (2001), 'The colonial origins of comparative development: an empirical investigation', American Economic Review, 91, 5: 1369-1401. Anderson, J. E. and E. van Wincoop (2003a) 'Gravity with Gravitas: A Solution to the Border Puzzle', The American Economic Review 93,1:170-192. Anderson, J.E and E. van Windcoop (2003b) 'Trade Costs', Mimeo, Boston College. Anderson, J.E. and D. Marcouiller (2002), 'Insecurity and the Pattern of Trade: An Empirical Investigation', Review of Economics and Statistics, 84,2:345-352. Anderson, J.E. and Young (2000), 'Trade Implies Law: The Power of the Weak', NBER Working Paper No. 7702. Baier S.L: and J. H. Bergstrand (2001) 'The Growth of World Trade: Tariffs, Transport Costs, and Income Similarity', Journal of International Economics 53:1-27. Burnside, C. and D. Dollar (2000) 'Aid, Policies, and Growth', American Economic Review 90,4:847868. 14
Commonwealth Secretariat/World Bank Joint Task Force (2000), "Small States: Meeting Challenges in the Global Economy", report of the Commonwealth Secretariat, Commonwealth Secretariat/World Bank Joint Task Force. De Groot, H.L.F, G.J. Linders, P. Rietveld and U. Subramanian (2003) 'The Institutional Determinants of Bilateral Trade Patterns', Tinbergen Institute Discussion Paper 2003-044/3, Amsterdam: Tinbergen Institute. Easterly, W. and A. Kraay (2000), 'Small States, Small Problems? Income, Growth and Volatility in Small States', World Development 28,11:2013-2027. Easterly, W., R. Levine and D. Roodman (2003) 'New Data , New Doubts: A Comment on Burnside and Dollar's "Aid, Policies, and Growth" (2000)', NBER Working Paper 9846. Edwards, S. (1993a) 'Openness, Trade Liberalization, and Growth in Developing Countries', Journal of Economic Literature 31 (September): 1358-1393. Edwards, S. (1993b) 'Trade Policy, Exchange Rates and Growth', NBER Working Paper 4511. Frankel, J.A. and D. Romer (1999), 'Does trade cause growth?' American Economic Review, 89, 3: 379-399. Haber, S., North, D.C. and Weingast B.R. (2003) 'If Economists Are So Smart, Why Is Africa So Poor?', Wall Street Journal, July 31,2003. Hummels, D. (2001) 'Toward a Geography of Trade Costs', Working Paper, Purdue University. Kaufman, D., A. Kray and P. Zoido-Lobaton (2002), 'Governance matters II: Updated Indicators for 2000-01', World Bank Policy Research Working Paper 2772. Levine, R and D. Renelt (1992) 'A Sensitivity Analysis of Cross-Country Growth Regressions', American Economic Review 82: 942-963. Limão, N. and A. Venables (1999), 'Infrastructure, Geographical Disadvantage and Transport Costs', World Bank Working Paper 2257, The World Bank, Washington D.C. Minten, B. and S. Kyle (1999), 'The effect of distance on road quality and food collection, marketing margins and traders' wages: evidence from the former Zaire', Journal of Development Economics, 60, 467-495. North, D. (1994) 'Economic Performance Through Time', American Economic Review 84,3:359-368. Rodrik D., A. Subramainan and F. Trebbi, (2002), 'Institutions rule: primacy of institutions over geography and integration in economic development', CEPR working paper no 3643, November. Rodrik, D. (1998a), 'Trade Policy and Economic Performance in Sub-Saharan Africa', NBER Working Paper No. 6562. Rodrik, D., Subramanian, A. and Trebbi, F. (2002) 'Institutions Rule: The Primacy of Institutions over Geography and Integration in Economic Development', NBER Working Paper No. 8119. World Bank (2002) 'Building Institutions for Markets', World Development Report 2002, Washington D.C.: World Bank. 15
ANNEX
Correlation matrix 1 Open lnpop lngdpcap GDPcap ROL goveff corrupt tele finan roads tariffs IMF TRI index Port efficiency index Island dummy Landlocked dummy Credit to private sector Concentration Overhead Interest margin absolute latitude
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
1.00 -0.49
1.00
0.28
-0.15
1.00
0.25
-0.09
0.89
1.00
0.28
-0.12
0.83
0.88
1.00
0.28
-0.03
0.82
0.88
0.95
1.00
0.27
-0.11
0.82
0.89
0.95
0.96
0.25
-0.05
0.85
0.91
0.86
0.86
0.88
1.00
0.17
0.11
0.60
0.61
0.65
0.61
0.61
0.65
0.31
-0.04
0.58
0.52
0.48
0.46
0.44
0.55
0.36
1.00
-0.18
-0.06
-0.47
-0.51
-0.42
-0.47
-0.45
-0.53
-0.25
-0.25
1.00 1.00 1.00
-0.17
0.08
-.20
-0.17
-0.14
-0.19
-0.17
-0.23
-0.07
-0.17
0.61
1.00
0.33
-0.18
0.72
0.77
0.73
0.73
0.76
0.76
0.53
0.52
-0.43
0.08
0.20
-0.61
0.15
0.07
0.15
0.07
0.13
0.06
0.06
0.05
0.25
0.19
0.25
1.00
0.02
-0.01
-0.25
-0.15
-0.19
-0.20
-0.19
-0.15
-0.26
0.03
-0.06
-0.05
-0.18
-0.22
1.00
0.29
0.02
0.72
0.75
0.78
0.77
0.74
0.76
0.85
0.45
-0.36
-0.15
0.62
0.12
-0.22
1.00
1.00
0.10
-0.45
-0.46
-0.39
-0.37
-0.37
-0.30
-0.37
-0.42
-0.26
0.25
0.21
0.14
0.17
0.22
-0.44
1.00
-0.17
0.04
-0.44
-0.46
-0.55
-0.59
-0.56
-0.41
-0.50
-0.26
0.06
-0.23
-0.42
-0.15
0.29
-0.56
0.11
-0.18
0.04
-0.51
-0.53
-0.59
-0.63
-0.59
-0.49
-0.53
-0.30
0.10
-0.07
-0.40
-0.12
0.30
-0.60
0.15
0.83
1.00
0.04
0.12
0.56
0.55
0.50
0.51
0.50
0.61
0.28
0.58
-0.42
-0.28
0.42
-0.14
0.16
0.33
-0.25
-0.15
-0.20
16
1.00 1.00
