• Aucun résultat trouvé

Governance, Corruption and Trade : A North-South Approach.

N/A
N/A
Protected

Academic year: 2021

Partager "Governance, Corruption and Trade : A North-South Approach."

Copied!
26
0
0

Texte intégral

(1)

cahier n° 2005-04

par

Emmanuelle Lavallée

Governance, Corruption and Trade :

a North-South Approach

(2)

Governance, Corruption and Trade : A North-South Approach.

EMMANUELLE LAVALLEE

Abstract:

The purpose of this paper is to assess the effect on North South trade of the quality of governance in developing countries by applying a gravity trade model. I test the model for a sample of 21 OECD countries and 95 developing countries over the period 1984-1997. A panel data analysis is used to disentangle the time invariant country-specific effects and to capture the relationship between the relevant variables over time. The estimation results show that the better governed a developing country is, the more it imports goods from industrialised ones. Besides, thanks to a non linear approximation, it shows that the two traditional visions of the consequences of corruption on trade coexist.

Keywords: bilateral trade flows, panel econometrics, gravity model, corruption, governance

(3)

I I

NTRODUCTION

.

Nowadays, good governance is one of the first priorities of international organisations, such as the International Monetary Fund and the World Bank, in their fight for development. Governance is generally defined as the traditions and the institutions by which authority is exercised in a country for common good. Numerous multilateral actions aim at addressing corruption which is a particular side of bad governance. As a matter of fact, since the mid 90’s, a number of multilateral instruments have been developed in order to prevent and combat the abuse of public office for personal gain. For instance, one could quote the United Nations Convention against corruption signed in December 2003, the Convention on Combating Bribery of Foreign Public Officials in International Business Transactions, adopted by the Organisation for Economic Cooperation and Development on 21 November 1997, or the African Union Convention on Preventing and Combating Corruption, adopted by the Heads of State and Government of the African Union on 12 July 2003. The multiplication of such measures corresponds to the increasing awareness of the deleterious effects of this particular government failure on growth and development (Mauro,1995). Indeed, in his statement on the adoption of the United Nations convention against corruption, Kofi Anan (United Nations Secretary-General) reminds that “Corruption hurts the poor disproportionately – by diverting funds intended for development, undermining a government’s ability to provide basic services, feeding inequality and injustice, and discouraging foreign investment and aid”. The objective of the following paper is two fold. Firstly, it aims at testing whether, as suggested by Romer (1994), bad governance deters rich countries

(4)

from trading with developing ones by raising the cost of doing business. Secondly, it intends to assess the effects on trade of a particular outward sign of poor governance, corruption.

The effect of corruption on trade, and more generally of the quality of national institutions is not much explored. Anderson and Marcouiller (2002) first demonstrate the importance of strong institutions on international trade. They develop a model of import demand in an insecure world, where insecurity is meant as hijacking, corruption and incomplete contract enforcement. Their structural model shows that insecurity constrains trade by raising the price of traded goods.

In order to estimate the impact on North South trade of the quality of governance, and of the level of corruption in developing countries, I test a gravity model of international trade for a sample of 21 OECD countries and 95 developing countries over the period 1984-1997.

II G

OVERNANCE

,

CORRUPTION AND INTERNATIONAL TRADE

.

The literature about the consequences of bad governance on international trade is relatively scarce. Nevertheless, low quality of governance is generally regarded as an obstacle to international trade.

Anderson and Marcouiller (2002) consider corruption and imperfect contract enforcement as elements of the insecurity of international trade. They build a model of import demand in an insecure world. They show that predation by thieves or by corrupt officials generates a price mark-up equivalent to a hidden tax or tariff, which extent depends on the quality of institutions for the defence of

(5)

trade. This price mark-up significantly constrains trade by two channels: a substitution effect between traded goods and non traded goods, and a real income effect. In order to estimate their model, the authors use data provided by the World Economic Forum (WEF) to evaluate the strength of national institutions for the defence of trade. They test the model for the year 1996 for a sample of 48 importing countries, half of them being developed countries. They find that trade increases dramatically when it is supported by strong institutions. They conclude that: “If the seven Latin American countries of the sample were to enjoy the same transparency and enforceability scores as the mean scores of the members of the European Union, predicted Latin American imports volume would rise by 30%” (p349). However, the relevance of their results can be questioned by the fact that industrialised countries trade disproportionately with each others, and that they also enjoy strong institutions.

There is two conflicting visions of the consequences of corruption on trade. On the one hand corruption is seen, in the same way as poor governance, as an obstacle to international trade. On the other hand, corruption is regarded as “beneficial grease” for trade. Indeed, my problematic can be related to second best analysis that see corruption as a way to bypass rigidities imposed by governments. Bhagwati (1982, p. 993) suggests that corruption must be analysed as a Directly Unproductive Profit-seeking activity (DUP), that is to say a way “of making profits by undertaking activities that are directly unproductive”(p989). In the area of international trade, corruption can be compared to others DUP activities such as tariff evasion or smuggling. As long as these activities occur in initially distorted situations, second best analysis applies and DUP should enhance

(6)

welfare. Although, such theories do not directly study the effect of corruption on the volume of international trade, they present corruption as a mean of greasing the wheels of commerce.

Our study is inspired by Anderson and Marcouiller’s work. However, I focus on the impact on exports from North to South of the quality of governance and of the level of corruption in southern countries. Indeed, widespread corruption and poor governance seem to be an issue specific to southern countries. Moreover, the quality of institutions particularly matters for trade on long distances and in complex products (Greif, 1993; Berkowitz, Meonuis and Pistor; 2003). Export goods in distant countries requires fixed costs. New financial relationships, new shipping and communication links have to be set up. The legal, regulatory and tax environment has to be learnt. Corruption, red tape, improper contract enforcement can deter firms from undertaking such expenditures by lowering or making uncertain the revenue stream.

III S

PECIFICATION OF THE GRAVITY EQUATION AND DATA

.

A Specification of the gravity equation.

So as to estimate the effects on North South trade of the quality of the institutions of developing countries I use the gravity model as a standard modelling tool. It is based on the idea that opposite forces explain the intensity of trade between two countries. Income and size constitute attraction forces, whereas distance and trade barriers act as resistance ones. The theoretical foundations of the gravity equation

(7)

are often to be found in new international trade theories based upon the hypothesis of product differentiation. Nevertheless, Deardoff (1998) shows that the gravity equation can be justified by classical trade theories. Hence, it can be applied in order to estimate North South trade. The gravity model has been empirically used to explore the effects on trade of regional trade blocks and currency unions (Frankel and Romer, 1999; Glick and Rose, 2002; Rose, 2000; Rose & Van Wincoop, 2001).

I add to the traditional gravity equation a relative economic distance index1 and it square. Economic distance could have an ambiguous effect on North South trade. On the one hand, it captures distance in standard of living likely to decrease trade of horizontally differentiated goods. On the other hand, it reflects differences in capitalistic intensities which favour inter industry trade linked to differences in factor endowments. I introduce the economic distance variable and its square in the gravity equation in order to capture both effects.

The model is of the following form:

∑ + + + + + + + + + + = ijt ε kijt Z k β ² ij deco 8 α ij deco 7 α colony 6 α ij D log 5 α jt POP log 4 α it POP log 3 α jt Y log 2 α it Y log 1 α 0 α ijt LogEXP (1) Where:

o EXPijt are the exports from the country i to country j at time t;

o Yit is the income of country i at time t, and the same for Yjt for the

country j;

o POPit and POPjt are respectively the population of the exporting country

and of the importing country at time t;

(8)

o Colony is a binary variable which is equal unity if i ever colonized j, o Decoij measures differences in real income per capita between exporters

and importers;

o Decoij² is the square of deco;

o Zkij are dummy variables indicating a common element (frontier,

language, belonging to the same free trade agreement); o {Tt} is a comprehensive set of time “fixed effects”,

o εijt is a independent identically distributed random error term.

The coefficients of importer and exporter incomes are expected to be positive whereas the geographic distance coefficient must be negative since it is a proxy of all trade cost sources. The coefficient estimates of exporter and importer populations may be negative as they captures the economies’ size. Coefficient estimates of dummy variables for trading partners sharing a common language or a common border, as well as trading blocs dummies are expected to be positive.

The standard model is augmented with measures of the level of corruption or of the quality of governance for each importing country in order to analysis the impact on North South exports of corruption or weak national institutions.

B Data.

The aim of this study is to test whether weak national institutions and especially corruption deters rich countries from trading with developing ones. The availability of data on governance constrains the sample’s composition. Indeed,

(9)

few governance indicators are available and usable in time series and cross section. In the sample, Northern countries correspond to the 21 high income OECD countries and Southern countries to 95 developing countries. I adopt a broad definition of South since I group together countries belonging to Eastern Europe, Middle East, Latin America or Sub Saharan Africa.

Data on governance are extracted from the IRIS III data set that contains annual indicators of the quality of governance over the period 1982-1997. It is computed by Stephen Knack2 and the IRIS Centre (University of Maryland) on the basis of monthly International Country Risk Guide’s data which are stemming from a poll of experts.

The corruption index ranges in value from 0 to 6 (six meaning low level of corruption). Lower scores indicate that "high government officials are likely to demand special payments" and that "illegal payments are generally expected throughout lower levels of government" in the form of "bribes connected with import and export licenses, exchange controls, tax assessment, police protection, or loans."

The global governance indicator is built on the basis of five IRIS indexes : Corruption, Rule of Law, Quality of the Bureaucracy, Repudiation of Contracts, and Risk of Expropriation.Following Chong and Zanforlin (2000), I defined the governance index as the simple average of the five individual measures re-scaled from zero to six (six meaning better quality of institutions).

Data on 1984-1997 exports in current US dollars are taken from the Direction of Trade Statistics (DOTS) published by the International Monetary Fund. Exports flows are deflated by the American Consumer Price Index (World Development

(10)

Indicators, 1995=100). Data on population, and GDP in constant US dollars (1995=100) are taken from the World Bank’s World Development indicators (WDI). Distance from capital city to capital city on the basis of geographical coordinates are taken from the CEPII database as well as the adjacency and common language dummies. I compose a dummy variable so as to capture common membership in a free trade agreement.

IV E

STIMATION RESULTS

.

The gravity equations are estimated in a number of different ways. First, they are performed on the pooled data set to which I add country-specific effects, one for each exporter and importer, in order to take into account “multilateral resistance to trade”( Wei & Subramanian, 2003; Anderson & Van Wincoop, 2003). Then, I use the fixed effects and the random effects models in order to take into account exports flows’ heterogeneity. The fixed effects model includes a set of country-pair specific intercepts, while the random effects one treats the latter as random. The random effects model has the advantage of keeping the explanatory variables which have no temporal dimension such as distance, common language, or adjacency dummies. But, it is likely to give inconsistent parameters estimates because of correlation between explanatory variables and the unobserved bilateral effects.

I present the results obtained by these three estimation techniques for comparison purposes, but I place more confidence in the fixed effects estimator. Indeed, Egger and Pfaffermayr (2003) demonstrate that the specification with importer and exporter effects is a restricted version of a more general one which includes

(11)

bilateral interaction effects. Moreover, my study focuses on exports flows between an ex-ante predetermined selection of Nations ( from North to South). In this case, Egger (2000) shows that the fixed effects model is a better choice than the random effects model.

A Governance and North-South exports.

Estimations show that the gravity model performs well. Coefficient estimates are in accordance with expectations. Geographic distance reduces trade, while real GDP or a common colonial past expand it. The effects are economically and statistically significant. For instance, in the model with country specific effects, distance reduces trade with an elasticity of –2,97 and is significant at the 1 per cent level. However, a result is worthy to note. The existence of a non linear effect for economic distance is confirmed in the fixed effects model. Indeed, difference in standard of living appears as a good proxy both for similarity of supply and demand structures, and for distance in capitalistic intensities.

The coefficients of interest are related to the impact of the quality of governance in Southern countries on North-South exports. The estimations show that the better governed a developing country is, the more it imports goods from industrialised countries. Indeed, whatever the model used, the point estimates are positive and significant at the one per cent threshold.

(12)

Although my gravity equation are estimated through various ways, I test the sensitivity of these results extensively. Firstly, the model is estimated over various samples. I discard outlying observations. Specifically, I discard values of the dependant variable that are respectively three and two standard deviation away from the mean. Secondly, I add to the model measures of the quality of governance in the exporting countries.

The key result seems quite robust. Indeed, the coefficient estimates of the importing countries’ quality of governance are always positive and highly significant.

Table 2

B Corruption in the importing countries and North-South exports. From a theoretical point of view, corruption has an ambiguous effect on trade. It can act as a beneficial grease or, on the contrary, as an obstacle for international trade. That is why, I test the effect on North South exports of the level of corruption of the importing countries.

First, I add to the reference model a proxy of the level of corruption in the importing countries. Coefficient estimates of importer corruption are positive, indicating that less corruption increases North South exports, but they are never significant at the ten per cent threshold.

Table 3

Second, as I suspect non linearity with respect to the importer corruption variable, I add to the reference model the importer corruption variable and its square in order to capture some of the non linearity3. In terms of the coefficients, I find that

(13)

up to the score of 2.85 an improvement in the corruption index (meaning less corruption) increases North South exports and after decreases them.

Table 4

The sensitivity of these results is tested in the same ways as for governance. Control the effects on trade of corruption by the level of distortions in southern countries should be interesting. Unfortunately, no data on taxes or tariffs are available for southern countries and this time period. Nevertheless, I use the “bureaucratic quality indicator” as a proxy of red tape. Adding this variable does not change nor the signs or the significativity of the corruption variable’s parameter estimates (table 5, column 4).

Table 5

V C

ONCLUSION

The aim of my study is two fold. On the one hand, it focuses on the impact on exports from North to South of the quality of national institutions in southern countries. On the other hand, it aims at testing the effects on trade of a particular outward sign of poor governance, corruption. I use the gravity model in order to estimate the effects of developing countries’ quality of institutions on North South trade. The model is estimated for a sample of 21 OECD countries and 95 developing countries over the period 1984-1997. Using a non linear approximation, I show that the traditional visions of the consequences of corruption on international trade can coexist. Indeed, estimation results show that corruption can act both as an obstacle, and as a beneficial grease for international trade. Moreover, the estimation results show that the worse governed a developing

(14)

country is, the less it imports goods from industrialised countries. Indeed, whatever the estimation method used, the point estimates are positive and significant. As for developing countries exports from industrialised ones are a way to acquire new goods, this trade diversion might have very large negative effects on the developing countries’ aggregate output by preventing them of adopting new technologies, or exploiting new productive activities. That is why testing the impact of corruption and poor governance on the North-South exports of various types of goods and especially capital goods appears of great interest.

(15)

Table 1:Governance and North South exports: core regressions, panel 1984-1997 MODEL WITH COUNTRY

SPECIFIC EFFECTS FIXED EFFECTS MODEL RANDOM EFFECTS MODEL INDEPENDENT VARIABLES (1) (1’) (2) (2’) (3) (3’) Importer governance - 0.104*** (0.022) - 0.088*** (0.023) - 0.093*** (0.021) Log Real GDP exporter 1.102*** (0.230) 1.135*** (0.231) 1.291*** (0.239) 1.309*** (0.243) 1.218*** (0.033) 1.218*** 0.033 Log Real GDP importer 0.804*** (0.086) 0.719*** (0.09) 0.691*** (0.098) 0.616*** (0.101) 0.799*** (0.056) 0.754*** 0.056 Log population exporter -4.367** (1.433) -4.727*** (1.456) -3.165** (1.437) -3.172** (1.456) -0.304*** (0.043) -0.304*** 0.0427 Log population importer -0.525** (0.242) -0.422* (0.245) -3.524*** (0.504) -3.458*** (0.510) 0.086 (0.116) 0.155 0.116 Log geodesic distance -2.972*** (0.155) -2.968*** (0.155) - - -2.271*** (0.093) -2.248*** (0.094) Common language dummy 0.419*** (0.109) 0.419*** (0.109) - - 0.533*** (0.110) 0.513*** (0.111) Colony 1.164*** (0.150) 1.157*** (0.149) - - 1.133*** (0.143) 1.141*** (0.142) Adjacency dummy 0.993** (0.473) 0.996** (0.472) - - -0.081 (0.262) -0.071 (0.785) Free trade agreement dummy 0.513*** (0.071) 0.513*** (0.072) 0.189*** (0.043) 0.186*** (0.043) 0.320*** (0.039) 0.312*** (0.039) Economic distance (2.429) 2.663 2.504 ( 2.428) 12.584*** (2.989) 12.862*** (3.04) 5.691*** (1.733) 5.500*** (1.753) Square of economic distance (6.184) -1.697 -1.329 (6.182) -23.07*** (6.590) -23.64*** (6.724) -6.064 (4.369) -5.826 (4.426)

Time effects YES YES YES YES YES YES

Importer and exporter specific effects YES YES NO NO NO NO Number of observations 25636 25636 25636 25636 25636 25636 R-Square 0.83 0.83 0.93 0.93 - -

Dependent variable: log real exports. Robust standard errors (clustered by country-pairs) reported below the coefficient estimates. Intercepts not reported for ease of presentation.

***, **, * define 1 per cent, 5 per cent and 10 per cent significance level respectively.

(1), (2), (3) estimations of the reference model;

(16)

Table 2: Governance and North South exports: robustness check, Fixed effects model, 1984-1997

INDEPENDENT VARIABLES (1) (2) (3) Importer Governance 0.077*** (0.021) 0.0799*** (0.02) 0.0887*** (0.020)

Log Real GDP exporter 1.223***

(0.235)

1.093***

(0.218)

1.037***

(0.248)

Log Real GDP importer 0.615***

(0.101)

0.593***

(0.105)

0.598***

(0.099)

Log Population exporter -2.504*

(1.389)

-4.344***

(1.296)

-0.457

(1.524)

Log Population importer -3.422***

(0.498)

-3.265***

(0.476)

-3.416***

(0.504)

Free trade agreement

dummy (0.043) 0.182 0.224*** (0.044) 0.178*** (0.043) Economic distance 12.404*** (3.005) 11.649*** (3.021) 14.316*** (3.002) Square of economic distance -22.882*** (6.650) -21.049** (6.725) -26.280*** (6.717) Exporter governance - - 0.3402*** (0.057)

Time specific effects YES YES YES

Number of observations 25528 24489 25636

Dependant variable: log real exports. Robust standard errors (clustered by country-pairs) reported below the coefficient estimates. Intercepts not reported for ease of presentation.

***, **, * define 1 per cent, 5 per cent and 10 per cent significance level respectively.

(1), (2) values of the dependant variable that are respectively three and two

standard deviations away from the mean are discarded.

(17)

Table 3: Corruption and North South exports: core regressions, panel 1984-1997 INDEPENDENT VARIABLES MODEL WITH

COUNTRY SPECIFIC EFFECTS FIXED EFFECTS MODEL RANDOM EFFECTS MODEL Importer corruption 0.016 (0.013) 0.007 (0.014) (0.013) 0.015

Log Real GDP exporter 1.131***

(0.231)

1.293***

(0.241)

1.217***

(0.033)

Log Real GDP importer 0.802***

(0.087)

0.693***

(0.099)

0.802***

(0.056)

Log population exporter -4.688***

(1.453)

-3.099**

(1.456)

-0.304***

(0.043)

Log population importer -0.541**

(0.242)

-3.718***

(0.513)

0.071

(0.117)

Log geodesic distance -2.966***

(0.155) -

-2.267***

(0.093)

Common language dummy .4194***

(0.109) - 0.535*** (0.111) Colony 1.1578*** (0.150) - 1.131*** (0.143) Adjacency dummy 0.996** (0.472) - -0.081 (0.262)

Free trade agreements dummy 0.518***

(0.072) 0.189*** (0.043) 0.321*** (0.039) Economic distance 2.374 (2.428) 12.242*** (3.024) 5.35*** (1.755)

Square of economic distance -1.073

(6.179)

-22.545***

(6.646)

-5.438

(4.395)

Time effects YES YES YES

Importer and exporter specific

effects YES NO NO

Number of observations 25636 25636 25636

R-Square 0.83 0.92 -

Dependent variable: log real exports. Robust standard errors (clustered by country-pairs) reported below the coefficient estimates. Intercepts not reported for ease of presentation.

(18)

Table 4: Corruption and North South exports: core regressions, panel 1984-1997 INDEPENDENT VARIABLES MODEL WITH

COUNTRY SPECIFIC EFFECTS FIXED EFFECTS MODEL RANDOM EFFECTS MODEL Importer corruption 0.120*** (0.038) 0.126*** (0.038) 0.105*** (0.036)

Square of importer corruption -0.019***

(0.006)

-0.022***

(0.006)

-0.017*** (0.006)

Log Real GDP exporter 1.131***

(0.230)

1.309***

(0.240)

1.219*** (0.033)

Log Real GDP importer 0.794***

(0.000) 0.676*** (0.099)

0.798*** (0.056)

Log population exporter -4.697***

(1.45)

-3.146**

(1.452)

-0.306*** (0.043)

Log population importer -0.533 **

(0.242)

-3.778***

(0.515)

0.076 (0.117)

Log geodesic distance -2.964***

(0.155) -

-2.267*** (0.093)

Common language dummy 0.419***

(0.109) - 0.534*** 0.111 Colony 1.158*** (0.149) - 1.129*** (0.144) Adjacency dummy 0.997** (0.473) - -0.072 (0.261)

Free Trade agreement dummy 0.524***

(0.071) 0.197*** (0.000) 0.328*** (0.039) Economic distance 2.412 (2.427) 12.791*** (3.024) 5.599*** (1.759)

Square of economic distance -1.136

(6.176) -23.423*** (6.615)

-5.725*** (4.387)

Time effects YES YES YES

Importer and exporter specific

effects YES NO NO

Number of observations 25636 25636 25636

R-Square 0.83 0.93 -

Dependent variable: log real exports. Robust standard errors (clustered by country-pairs) reported below the coefficient estimates. Intercepts not reported for ease of presentation.

(19)

Table 5: Corruption and North South Exports: robustness check, Fixed effects model, 1984-1997 INDEPENDENT VARIABLES (1) (2) (3) (4) Importer corruption 0.117*** (0.037) 0.108** (0.035) 0.126*** (0.038) 0.11571*** (0.038) Square of importer corruption -0.022*** (0.006) -0.02*** (0.006) -0.022*** (0.006) -0.023*** (0.006)

Log Real GDP exporter 1.225***

(0.234) 1.098*** (0.216) 1.295*** (0.241) 1.309*** (0.241)

Log Real GDP importer 0.666***

(0.099) 0.648*** (0.103) 0.674*** (0.099) 0.657*** (0.099)

Log Population exporter -2.487**

(1.386) -4.316*** (1.293) -3.036** (1.451) -3.149** (1.451)

Log Population importer -3.719

(0.502) -3.55*** (0.478) -3.773*** (0.515) -3.775*** (0.512)

Free Trade agreement dummy 0.193*** (0.042) 0.239*** (0.043) 0.196*** (0.042) 0.192 (0.041) Economic distance 0.117** (0.037) 11.63*** (2.999) 12.987*** (3.019) 12.823*** (3.013) Square of economic distance -0.0216*** (0.0059) -20.81*** (6.589) -23.788*** (6.595) -23.617*** (6.592) Exporter corruption - - 0.052*** (0.023) - Importer quality of bureaucracy 0.045** (0.021)

Time specific effects YES YES YES YES

Number of observations 25528 24490 25636 25636

Dependent variable: log real exports. Robust standard errors (clustered by country-pairs) reported below the coefficient estimates. Intercepts not reported for ease of presentation.

***, **, * define 1 per cent, 5 per cent and 10 per cent significance level respectively.

(1), (2) values of the dependant variable that are respectively three and two standard deviations

away from the mean are discarded.

(3) Exporters corruption added; (4) Importing country’s quality of bureaucracy added

END NOTES:

1 It is calculated from a formula inspired by Balassa (1986) and Balassa and

Bauwens (1987) which gives an index contained between 0 (standard of living equality) and 1 (maximal difference in standard of living):

(20)

2 ln 2 ) z 2 ln( ) z 2 ( z ln z deco= + − − with             = jt y ; it y Max jt y ; it y Min

z where yit and yjt are

respectively the real income per capita of the exporter and of the importer at time t.

2 For a more detailed description of the measures see Knack and Keefer (1995). 3 I also add to the reference model the logarithm of the corruption index.

Coefficient estimates were positive but not significant. Results are available upon request.

REFERENCES:

Anderson, J.E. & Marcouiller, D. (2002) “Insecurity and the pattern of trade : an empirical investigation”. The Review of Economics and Statistics, 84, 2, 342-52. Anderson, J.E. and van Wincoop, E. (2003) “Gravity with Gravitas: A Solution to the Border Puzzle”. American Economic Review, 93, 1, 170–92.

Baghwati, J. N. (1982) “Directly Unproductive, Profit-seeking Activities” Journal of Political Economy, 90, 5, 988-1002.

Balassa, B. (1986). “Intra-Industry Trade Among Exporters of manufactured goods”. In D.Greenaway P.K.M. Tharakan (Ed.), Imperfect Competition and International Trade, New Jersey: Wheatsheaf Books, Sussex and Humanities Press, p.

Balassa, B. & Bauwens, L. (1987) “Intra-Industry Specialization in a Multi-Country and Multilateral Framework”. The Economic Journal, 97, 923-39.

(21)

Berkowitz, D., Moenuis, J. & Pistor K. (2003) “Trade, law and product complexity”. University of Colombia Law School, mimeo.

Deardorff, A. V. (1998) “Determinants of Bilateral Trade: Does Gravity Work in a Neoclassical World?” in. J.A. Frankel (ed.) The Regionalization of

the World Economy. Chicago: University of Chicago Press, 7–22.

Chong, A. & Zanforlin, L. (2000) “Law Tradition and Institutional Quality: Some Empirical Evidence”. Journal of International Development, 12, 8, 1057-68. Egger, P. (2000) “A note on the proper specification of the gravity equation”. Economics Letters, 66, 1, 25-31.

Egger, P (2002) “An econometric view on the estimation of gravity models and the calculations of trade potentials”. The World Economy, 25, 2, 297-312.

Egger, P. and Pfaffermayr, M. (2003) “The proper econometric specification of the gravity equation: A three way model with bilateral interaction effects”. Empirical Economics, 28, 3, pp.571-81.

Frankel, J. & Romer, D. (1999) “Does Trade Cause Growth?”. American Economic Review, 89, 3, 379–99.

Glick, R. & Rose, A.K. (2002) “Does a Currency Union Affect Trade? Time-Series Evidence”. European Economic Review, 46, 6, 1125–51.

Greif, A. (1993) “Contract enforceability and economic institutions in early trade : the Maghribi traders’coalition”. American Economic Review, 83, 3, 525-48.

(22)

Knack, S. & Keefer, P. (1995) “Institutions and Economic Performance: Cross-Country Tests Using Alternative Institutional Measures”. Economics and Politics, 7, 3, 207-27.

Mauro, P. (1995) “Corruption and growth”. Quaterly Journal of Economics, 110, pp 681-712.

Romer, P. (1994) “New goods, old theory, and the welfare costs of trade restrictions”. Journal of Development Economics, 43, 5-38.

Rose, A.K. (2000) “One Money One Market: The Effect of Common Currencies on Trade”. Economic Policy, 30, 9–45.

Rose, A.K. (2004) “Does the WTO make trade more stable?” NBER Working Paper, No. 10027 (Cambridge, MA: NBER).

Rose, AK & van Wincoop (2001) “National money as a barrier to trade: the real case for currency union”. American Economic Review (Papers and Proceedings), 91, 2, 386-90.

Subramanian, A. & Wei, S-J (2003) “The WTO Promotes trade, Strongly but Unevenly”. NBER Working Paper, No. 10024 (Cambridge, MA: NBER). Wei, S.J. (1996) “Intra-National Versus International Trade: How Stubborn are Nations in Global Integration?” NBER Working Paper, No. 5531 (Cambridge, MA: NBER).

(23)

Appendix 1: List of countries in the sample.

EXPORTERS IMPORTERS

Australia A

FRICA ASIA CENTRAL

AND EASTERN EUROPE

Austria Algeria Bangladesh Albania

Belgium-Luxembourg Angola China,P.R Bulgaria

Canada Botswana Hong Kong Cyprus

Denmark Burkina Faso India Czech Republic

Finland Cameroon Indonesia Hungary

France Congo, Dem. Rep. of Korea Malta Germany Congo, Republic of Malaysia Poland

Greece Egypt Pakistan Romania

Ireland Côte d'Ivoire Philippines Russia

Italy Ethiopia Singapore Slovak Republic

Japan Gabon Sri Lanka Turkey

New Zealand Gambia, The Thailand

Netherlands Ghana Vietnam L

ATIN AMERICA AND CARRIBEAN

Norway Guinea Argentina

Portugal Guinea-Bissau MIDDLE EAST Bahamas, The

Spain Kenya Bolivia

Sweden Liberia Bahrain, Kingdom of Brazil Switzerland Madagascar Iran, I.R. of Chile United Kingdom Malawi Israel Colombia United States Mali Jordan Costa Rica

Morocco Kuwait Dominican Republic

Mozambique Lebanon Ecuador

Namibia Oman El Salvador

Niger Saudi Arabia Guatemala

Nigeria Syrian Arab Republic Guyana Papua New Guinea Yemen, Republic of Haiti Senegal Yemen, Republic of Honduras

Sierra Leone Jamaica

South Africa Mexico

Sudan Nicaragua

Tanzania Panama

Togo Paraguay

Tunisia Peru

Uganda Suriname

Zambia Trinidad and Tobago

Zimbabwe Uruguay

Venezuela Venezuela Venezuela

(24)

Appendix 2 : Data Sources and Description

Variable Name : Bilateral exports (Xij)

Description: Bilateral exports of the country i to the country

j, in F.O.B terms and in U.S. $ deflated by the American Consumer Price Index (1995=100), years 1984-1997.

Source: IMF, Direction of Trade Statistics.

Variable Name : GDP

Description: Total Gross Domestic Product in constant U.S.

$.(1995=100)

Source: World Bank, World Development Indicators on line

Variable Name : Population Description: Population

Source: World Bank, World Development Indicators on line

Variable Name : Bilateral Distance (Dij)

Description: Great arc circle kilometric distance between the

two capitals of countries i and j.

Source: CEPII data base,

http://www.cepii.fr/francgraph/bdd/bdd.htm

Variable Name : Deco

Description: relative economic distance indicator

Source: author computation, on the basis on data on GDP per

capita in constant US$ (1995=100) taken from the World

Development Indicators on line

Variable Name : Common Language

Description: Dummy variable equals 1 if countries i and j

share the same language.

Source: CEPII data base,

http://www.cepii.fr/francgraph/bdd/bdd.htm

Variable Name : Adjacency

Description: Dummy variable equals 1 if countries i and j

(25)

Source: CEPII data base,

http://www.cepii.fr/francgraph/bdd/bdd.htm

Variable Name : colony

Description: Dummy variable equals 1 if countries i and j

share a common border.

Source: authors data base.

Variable Name : Agreement

Description: Dummy variable equals 1 if countries i and j are

both members of the same free trade agreement (APEC, EU, NAFTA)

Source: World Trade Organisation, http://www.wto.org/

Variable Name : corruptionm

Description: Level of corruption in the importing country Source: IRISIII dataset on the basis of ICRG data.

Variable Name : bureaucratic quality

Description: quality of the bureaucracy in the importing

country

Source: IRISIII dataset on the basis of ICRG data.

Variable Name : corruptionx

Description: Level of corruption in the exporting country Source: IRISIII dataset on the basis of ICRG data.

Variable Name : governance

Description: Quality of governance in the importing country Source: author calculation on the basis IRISIII dataset.

Variable Name : gouvX

Description: Quality of governance in the exporting country Source: author calculation on the basis IRISIII dataset.

(26)

Appendix 3: Descriptive Statistics VARIABLE NUMBER OF OBSERVATIONS MEAN STANDARD DEVIATION MINIMUM MAXIMUM Adjacency 29078 0.005 0.072 0 1 Common language 29078 0.078 0.268 0 1 Ln distance 29078 3.797 0.295 1.775 4.277 Agreement 29078 0.039 0.193 0 1 corruptionm 28077 2.930 1.158 0 6 corruptionx 29078 5.287 0.731 3 6 Bureaucratic quality 28077 2.787 1.144 0.2 6 gouvernance 28077 3.269 1.012 0.6 5.6 GouvX 29078 5.549 0.517 3.012 6 Ln exports 27647 3.118 2.566 -9.372 11.123 Ln pibx 29078 26.646 1.319 24.431 29.703 Ln pibm 26852 23.203 1.746 18.719 27.451 Ln popx 29078 7.111 0.846 4.124 8.434 Ln popm 29078 6.939 0.714 5.337 9.090 Colony 29078 0.038 0.191 0 1 deco 26852 0.043 0.061 0.0002 0.346

Figure

Table 1:Governance and North South exports: core regressions, panel 1984-1997  M ODEL WITH COUNTRY
Table 2: Governance and North South exports: robustness check,   Fixed effects model, 1984-1997
Table 3: Corruption  and North South exports: core regressions, panel 1984-1997  I NDEPENDENT VARIABLES M ODEL WITH
Table 4: Corruption and North South exports: core regressions, panel 1984-1997  I NDEPENDENT VARIABLES M ODEL WITH
+2

Références

Documents relatifs

A partir des résultats révisés du premier trimestre 2000, les estimations trimestrielles sont également diffusées à un niveau plus détaillé : pour 31 des 36 postes que compte

Vertical governance and corruption in urban India: The spatial segmentation of public food distribution... CSH-IFP Working Papers

LE GOFF G., CARNEVALE P., FONDJO E. These three methods were 1) the classical human landing catches where the man was in the same time bait and catcher, 2) the single-nets which

Recent optogenetic experiments com- bined with functional magnetic resonance imaging have revealed that light stimulation of neurons expressing the calcium binding protein

In order to overcome the maximum sensitivity imposed by the minimum coupling ratio that prevents mode aliasing, the functionalization of electrostatic nonlinearities is proposed for

wa = Pa.. Equations 1 and 5 result from the equality between nominal wage and the value of the marginal product in agricultural and manufactured formal sector. Equations 2 to 4

Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of

time spend since liberalisation and privatisation for electricity generation; capital and liquidity requirements, operational expenditures and interest rate volatility