Intellectual Property Rights and International Tradeof ... · Intellectual Property Rights and...

Intellectual Property Rights and International Trade of

Agricultural Products. Evidence for Latin America

Marco Duenas

[email protected]

Sant'Anna School of Advanced Studies

Mercedes Campi

Resumo/Resumen

This paper studies the effect of strengthening IPRs after the signing of theTRIPS on agricultural trade and bilateral trade links, for the period 1995-2011. Ituses data of agricultural exports and an index of intellectual property (IP)protection that considers specificities of this sector, for a set of 60 countries whichincludes both developed and developing countries. Given the relevance of agriculturaltrade for Latin American countries, we estimate the effect of the recent tightening of IPsystems in these countries and compare it with the estimated effect for the full sample.The estimates show that stronger IP systems do not affect trade volumes ofagricultural products, but they do for some sub-sectors. Regarding trade links, anincrease in the IP protection levels in Latin America is expected to affect negativelythe creation of new markets for their agricultural products. However, LatinAmerican countries benefit from trading with countries with higher IP levels.Palavras Chaves/ Palabras Claves: Intellectual Property Rights; International Trade;Agriculture; Gravity Model; Latin America.JEL Codes: O1; O34; F14

________PhD Candidate. Institute of Economics - Sant’Anna School of Advanced Studies, Piazza Martiri dellaLiberta 33, 56127, Pisa, Italy. E-mail: [email protected]

†International Trade Program - Universidad de Bogota Jorge Tadeo Lozano, Carrera 4 # 22-61, Bogota,Colombia. Institute of Economics - Sant’Anna School of Advanced Studies, Piazza Martiri dellaLiberta 33, 56127, Pisa, Italy. E-mail: [email protected]

mailto:m.duenasesterling:@sssup.it

mailto:m.duenasesterling:@sssup.it

Conferência Internacional LALICS 2013 “Sistemas Nacionais de Inovação e Políticas de CTI para um DesenvolvimentoInclusivo e Sustentável”

11 e 12 de Novembro, 2013 – Rio de Janeiro, Brasil

1 INTRODUCTION

The signing of the Trade-Related Aspects on Intellectual Property Rights (TRIPS)

Agreement in 1994 has derived in a process of global diffusion and tightening of intellectual

property rights (IPRs) systems. While developed countries increased the level of existing

intellectual property (IP) protection, many developing countries have adopted new systems with

already strong levels of protection or adapted existing systems to the minimum standards

demanded by the TRIPS.

This process has implications for innovation, productivity, international trade and

economic development. Theoretically, IPRs are said to be incentives to innovate, which in

turn spur economic growth. Changes in IPRs may influence returns to innovation, affecting

decisions of firms to trade in different markets. However, the role of IPRs as incentives to

innovate leading to economic growth has been both theoretically and empirically criticized.

The impact of strengthening IPRs was proved to be, in general, country and technology

specific (Teece,

1986; Dosi et al., 2006). Meanwhile, the link between IPRs and trade flows has been less

studied. Theoretically, the net effect of increasing patent protection is not clear. Maskus and

Penubarti (1995) discussed that, in principle, stronger IPR regimes are expected to have

different and contrary effects on trade. On the one side, firms should be encouraged to export

patented goods to countries with stronger IP protection, since that reduces the risk of imitation.

On the other side, stronger IPRs increase the market power of the importing firm, which may

encourage the firm to behave in a monopolistic way, increasing prices and reducing sales. Then,

the net result will depend on the product sectors and the level of development in trading

partner countries. Therefore, empirical analysis are needed to disentangle the effect of

stronger IPRs on trade volumes and bilateral trade links of different sectors and countries.

Among other things, the signing of the TRIPS Agreement demanded IP protection for

plant varieties either by patents or a sui generis system. This is likely to affect the development

of the agricultural sector. Like other developing economies, Latin American countries have

recently adopted stronger IPRs systems for plant varieties. Historically, agriculture and

industries that use vegetables and grains as inputs have been very relevant for economies of Latin

American and the Caribbean (Perez, 2010). For many of them, a high proportion of the exports

is constituted by products that derive from the use of natural resources (excluding mineral

and fuels). This includes agricultural products both as raw material or having gone through

some kind of, in general minor, industrial transformation.

Therefore, taking advantage of the existence of a new IP index for plant varieties

recently created by Campi and Nuvolari (2013), this paper explores the effect of

strengthening IP protection in the agricultural sector after the signing of the TRIPS on



traded quantities and bilateral trade flows, for a group of 60 countries, which include both

developed and developing countries. Then, taking into account possible heterogeneities, we

also check the robustness of the findings dividing the sample according to development level.

Next, we investigate the same effect for a group of Latin American countries and compare

possible divergent effects.

The remaining of the paper is organized as follows. The next section briefly

discusses how

IPRs may affect trade among countries, reviewing both theoretical and empirical

approaches. In the third section, we explain the data. The forth section presents the

econometric estimations for the effect of IPRs on trade volumes. The fifth section explores the

effect of IPRs on bilateral trade. Finally, the main conclusions are presented.

2 HOW ARE IPRS AND TRADERELATED?

The effect of stronger IPRs on total trade flows and bilateral links has been marginally

studied until recently. The question of how are they related is difficult to be addressed

because contradictory effects have been identified by economic theory and, ultimately, the

answer seems to be an empirical matter.

Theoretically, different models were developed to study this issue. In models of

dynamic general equilibrium of two regions, North and South, where innovation takes place in

the North while the South imitates technologies that have been invented in the North,

Helpman (1993) identified four channels through which IPRs are likely to affect trade between

countries: terms of trade; interregional allocation of manufacturing; product availability; and

R&D investment patterns. He concludes that whether the strengthening of IPRs are

desirable or not can not be answered theoretically. However, his model predicts that “if

anyone benefits, it is not the South” (Helpman, 1993, 1274).

Maskus and Penubarti (1995) have showed that it is possible to expect contradictory

effects of stronger IPRs on trade. Considering a price-discriminating firm deciding on the

distribution of their exports to different countries, they argue that there is a trade-off between

the enhanced market power for the firm created by the stronger IPRs and the larger effective

market size generated by reduced abilities of local firms to imitate the patentable product.

The first one derives from the “market-power effect”, which would reduce the elasticity of

demand facing the foreign firm and would induce the firm to export less of its patentable

product to the market with stronger IPRs. A contrary effect is related with the “market-

expansion effect”, which would increase the demand curve facing the firm and attract larger

sales. In addition, in larger markets, it is possible to find a “cost-reduction effect” that would

raise exports if a stronger patent law reduces the need of the foreign firm to undertake private



expenditures to deter local imitation.

Market power and market size effects may be affected also by other factors.

Particularly relevant is that decisions of firms to export new patentable products or processes to

a particular market will depend not only on IPRs systems, but also on decisions of licensing

and foreign direct investment (FDI). Having stronger IP protection in a market could enhance

licensing agreements or FDI instead of trade. In addition, imitating is costly, time consuming and

depends on capabilities that vary across countries. Therefore, a weak IP system in a country

with low imitation ability not necessarily removes incentives of an innovative firm to enter that

market.

Even theoretical models predict that changes in IPRs would be affected and interact

with local market parameters, such as demand, the efficiency of local imitative production, and

the structure of trade barriers. Moreover, a critical issue is related with the reaction of foreign

firms, facing the change in IPRs, in terms of increasing exports or FDI.

There are some empirical studies addressing the question of how IPRs affect trade

volumes and trade bilateral links. Using trade data for a single year, Maskus and Penubarti

(1995) investigated whether the distribution of bilateral trade across nations depends on the

importing country’s patent regime. They found that exporting firms discriminate in their sales

decisions across export markets, taking account of local patent laws, across the range of

developing countries bilateral imports. Then, changes in international patent laws influence

international trade depending on the sector and development level.

Many empirical studies found evidence supporting the hypothesis that the effects

of IP protection on import flows vary by different product sectors and are strongest in

the knowledge-intensive sectors.

For the case of the US, Smith (1999) found that the link between IP protection

and international trade depends on the ability of the importer to imitate the exporter’s

technologies. She found evidence of the existence of both market market expansion effect and

market power effect for the US manufacturing exports, but found the latter to be more

relevant for exports to countries with weak capacity of imitation. In the same direction, Co

(2004) studied how sensitive are US exports to importing countries’ patent right regimes.

Using a trade gravity equation, she found that patent rights regimes per se do not matter;

but they matter with importing countries’ imitative abilities.

For a panel of countries, Fink and Braga (1999) found that stronger protection of

IPRs increases bilateral trade flows of manufactured non-fuel imports. But the results do not

hold for trade flows in high technology, where the effect of protecting IPRs was found to be

insignificant.

Ivus (2010) studied how stronger patent rights in developing countries have affected



the innovating developed world’s exports into their markets. She found that the strengthening

of IPRs in developing countries raises the value of developed countries’ exports in patent-

sensitive industries. The results are consistent before and after the signing of the TRIPS. This is

consistent with the predictions of Helpman (1993) model, in which higher IPRs produce

benefits for the North.

Regarding the effect on total trade and trade links of strengthening IPRs in

developing countries, less analyses are available. For the case of China, Awokuse and Yin

(2010) found that the strengthening of patent laws in the country had a strong market

expansion effect in China for trade with both developed and developing countries, leading to

an increase in its import flows, particularly in knowledge-intensive goods. In turn, for the

post-TRIPS period, Lesser (2001) found that the effect of stronger IPRs on both FDI and

imports is positive and significant for a group of developing countries.

Even less evidence exists for the case of agricultural products and the effect of stronger

IPRs on their trade. Recently, Yang and Woo (2006) studied whether and how national

differences in IPRs affect the trade flow of planting seeds imports from the United States.

They found that whether or not a country adheres to IPRs agreements has no discernible

impact on planting seeds imported from the US, implying that the strengthening of IPRs

seems not to induce more agricultural trade. Confirming these results, Eaton (2009) found

no evidence that the adoption of the UPOV-approved system of plant breeders’ rights

positively influences seed imports. However, this evidence was recently challenged by

Galushko (2012) who found that stronger IPRs can foster international seed exchange.

This paper contributes to the current debate by providing evidence for a group of

countries, both developed and developing, as well as Latin American countries, using an

indicator of IP protection specially built for the agricultural sector.

3 DATA

The paper studies the effect of strengthening IPRs on total agricultural exports and

trade relations for 60 countries during the period 1995 to 2011. The list of countries,

which can be seen in the Appendix A, includes 29 developed countries, 31 developing

countries and 14 countries from Latin America and the Caribbean.

Using data from Gaulier and Zignago (2010) (BACI), total trade of agricultural products

is computed adding trade of sectors 1 to 24 of the HS Product Classification, excluding

sectors 3 and 16, which are related with fishery. We consider as agricultural products

grains and vegetables, but also animal products and food that use vegetable products as

inputs. This is used as a the dependent variable to assess the effect of stronger IPRs on total

trade. For total links we use bilateral trade flows of agricultural products.



As a measure of IPRs systems, we use a recently developed index by Campi and

Nuvolari (2013), which quantifies the strength of IP protection for plant varieties. It consists

of five components that, as a whole, indicate the strength of each country’s IP system for plant

varieties. The index shows that the mean of protection has been steadily increasing over time,

specially after the signing of the TRIPS Agreement, developing countries have adopted

strong IPRs systems.

As controls, we include four independent variables which are usually included in

trade regressions. These variables are constructed using data from Feenstra and Timmer (2013).

The first one is GDP per capita, which is the real GDP at constant 2005 national prices (in

millions of 2005 US dollars). Then, we include an indicator of human capital per person, which

is based on years of schooling of Barro and Lee (2012) and returns to education from

Psacharopoulos (1994). This variable is expected to have a positive effect on the productivity

of a given country and, therefore, is will be expected to have a positive impact on trade. This

indicator is relevant for our case since we consider both developed and developing countries that

are heterogeneous in terms of human capital. The third independent variables is remoteness,

which is the output-weighted average of the distance of a country from all the rest (see, e.g.,

Melitz, 2007). Finally, the last independent variable is openness to trade, computed as the

sum of total exports and total imports, divided by the total GDP. Table A.1, in the Appendix

A, summarizes variables and sources. Table 1 displays the correlation matrix of the

independent variables.

Vari log( hc log(remot)indlog( 1hc 0

log(remot) -0.250 -0.297 -0.225 1log(open) 0.343 0.558 0.371 -0.332 1

Table 1: Correlation Matrix of Independent Variables

4 IPRS AND TOTAL TRADE. ECONOMETRIC ESTIMATIONS

In order to investigate the possible effect of the strengthening of IPRs on trade, first, wecarry on a multivariate regression. Taking advantage of the panel structure of the data, weperform a fixed effect estimation using the following model,

log(texpai(t)) = xi(t) · + µi(t) ; (1)

where,xi = {1, indai, log(gdppci), hci, log(remoti), log(openi)}. (2)



Table 2 displays the results of the fixed effects estimations. In the first model, estimated

using the full sample, the index of IP protection does not have a significant effect on the total

trade of agricultural products. In order to check the robustness of the results, we divide the

sample according to the development level in two groups, developed and developing

countries.1 In addition, the last model shows the estimations for Latin American countries. The

index of IP protection has no significant effect for any of the samples considered. The rest of the

variables, when they turn out significant, present the expected signs.

As other authors, such as Awokuse and Yin (2010), have found for industrial products, the

effect of strengthening IPRs may be different when considering products at a more disaggregated

level. Therefore, we performed fixed effects estimations, in which the dependent variable is the

quantity of exports of each of the 24 products sub-sectors (excluding 3 and 16, related with

fishery) at the first level of the HS Classification Product; and the independent variables are the

same included in Eq. (1).

As we are mainly interested in the effect of the strengthening of IPRs on trade, Table

3 displays the coefficients of the IP index for the estimations performed for each sub-sector. once

again, we check the results for countries grouped according to the development level and for

Latin American countries.

Unlike the model for the aggregated trade of agricultural products, the index of IP

protection has different effects at a more disaggregated level. The first observation is the

heterogeneity. For some cases, the IP index results no statistically significant, while for other

cases in which the effect is significant, we observe both a positive and a negative effect on trade

of a certain type of products.



1 See Appendix A for the list of countries.

Model (1) (2) (3) (4)Sample FS DC LDC LAIP Index 0.015 -0.031 0.001 0.015

(0.015) (0.025) (0.020) (0.027)log GDP per capita 0.228*** 0.784*** -0.012 -0.366**

(0.074) (0.119) (0.095) (0.154)human capital 0.505*** -0.034 1.111*** 1.153***

(0.126) (0.161) (0.186) (0.245)log remotness 1.421*** 2.311*** 0.593 4.324***

(0.332) (0.481) (0.447) (1.091)log openness 1.062*** 1.061*** 1.015*** 0.705***

(0.034) (0.047) (0.047) (0.072)constant -7.717*** -19.180*** 0.466 -28.650***

(2.895) (4.183) (3.924) (8.818)Observations 1,020 493 527 238R-squared 0.837 0.871 0.825 0.867Number ofcountries

60 29 31 14Note : The dependent variable is the log of total exportsofthe agricultural sector. Standard errors are inparenthesis. Significance level: *** p<0.01, ** p<0.05, *p<0.10. FS: Full Sample; DC: Developed Countries; LDC:Developing Countries; LA: Latin American Countries.

Table 2: Total Exports. Fixed Effects Estimations for the Agricultural Sector

For the full sample, we observe that the index of IPRs is positively and statistically

significant correlated with the exported quantities for 7 sectors out of 22; a negative ans

statistically significant correlation for 3 sectors; and a non significant correlation for the rest of

the sectors, 14 sectors out of 22.

For the case of developed countries, we observe a positive and statistically significant

effect for only 3 sectors, while a negative and significant effect is observed for 6 sectors.

Meanwhile, for developing countries, when the coefficients are statistically significant, the effect

is positive for 5 sectors and negative for 3 sectors.

For the Latin American case, the effect of strengthening IP protection for plant varieties

has a significant and positive impact for sectors in 7 sectors out of 22; a significant and

negative impact on sectors 2 sectors; and a no statistically significant effect for the rest of the

sectors (13 sectors out of 22).

5 DO STRONGER IPRS ENHANCE BILATERAL TRADE?

As we presented, IPRs might impact bilateral trade in different ways that will

ultimately be determined by sector or countries specificities. A natural framework to explore



the possible implication of IPRs on bilateral trade is the Gravity Model (GM) of trade, which has a

relevant empirical success at explaining an important extent of the observed trade flows. Initially

proposed by Tinbergen (1962), the GM has became the baseline model to explain bilateral trade

flows among countries, taking as explanatory variables the GDP of both the importer and the

exporter, as well as the distance between them. The modern economic interpretation of the gravity

expression generalizes the original idea by including proxies of possible trade barrier–aspects

related with geography, culture, bilateral trade agreements, among others. The GM emerges from a

wide set of theoretical models, including monopolistic competition (see,Fratianni, 2009, for a

comprehensive survey) and Heckscher–Ohlin model with specialization (see, e.g., Anderson, 1979;

Bergstrand, 1985).

Model (1) (2) (3) (4)Sample FS DC LDC LA1 Live Animals 0.080 0.009 0.151 0.153

(0.067) (0.0611) (0.105) (0.208)2 Meat and Edible Meat Offal 0.156*** -0.136** 0.244*** -0.047

(0.057) (0.053) (0.091) (0.168)4 Dairy, Eggs, Honey, and Edible Products 0.282*** 0.096*** 0.344*** 0.213*

(0.041) (0.036) (0.064) (0.120)5 Products of Animal Origin -0.004 0.012 0.033 0.034

(0.042) (0.038) (0.068) (0.118)6 Live Trees and Other Plants 0.075** -0.050 0.116** 0.246***

(0.037) (0.057) (0.051) (0.089)7 Edible Vegetables 0.010 -0.104*** 0.064 0.178***

(0.029) (0.038) (0.042) (0.065)8 Edible Fruits and Nuts, Peel of Citrus/Melons -0.038 -0.007 -0.069* -0.050

(0.028) (0.045) (0.038) (0.059)9 Coffee, Tea, Mate and Spices -0.085** -0.153*** -0.080* -0.142**

(0.036) (0.052) (0.048) (0.061)10 Cereals 0.142* 0.059 0.086 0.127

(0.074) (0.094) (0.111) (0.177)11 Milling Industry Products 0.044 0.066 -0.016 -0.016

(0.052) (0.064) (0.078) (0.109)12 Oil Seeds/Misc. Grains/Med. Plants/Straw 0.113*** 0.074 0.073 0.272**

(0.038) (0.052) (0.055) (0.113)13 Lac, Gums, Resins, etc. -0.047 -0.173** 0.031 -0.095

(0.049) (0.073) (0.068) (0.120)14 Vegetable Planting Materials -0.190*** -0.217** -0.214*** 0.119

(0.062) (0.103) (0.082) (0.148)15 Animal or Vegetable Fats, Oils and Waxes 0.035 0.007 0.049 0.339***

(0.040) (0.044) (0.063) (0.069)17 Sugars and Sugar Confectionery 0.122*** 0.112** 0.046 -0.060

(0.040) (0.051) (0.058) (0.079)18 Cocoa and Cocoa Preparations 0.087** 0.172*** 0.074 -0.172**

(0.044) (0.045) (0.069) (0.083)19 Preps. of Cereals, Flour, Starch or Milk 0.137*** 0.041 0.190*** 0.098

(0.038) (0.047) (0.056) (0.089)20 Preps. of Vegetables, Fruits, Nuts, etc. -0.085*** -0.151*** -0.028 0.038

(0.022) (0.036) (0.029) (0.038)21 Misc. Edible Preparations 0.082** 0.008 0.117** 0.219***

(0.033) (0.037) (0.051) (0.068)22 Beverages, Spirits and Vinegar 0.025 -0.033 0.046 0.018

(0.036) (0.050) (0.052) (0.078)23 Residues from Food Industries, Animal Feed 0.031 -0.005 0.002 0.196**

(0.039) (0.037) (0.062) (0.088)24 Tobacco and Manuf. Tobacco Substitutes 0.037 0.143 -0.038 0.027

(0.060) (0.094) (0.080) (0.102)Note : The dependent variable is the log of exports for each selected sub-sector of the HS ProductClassification. Standard errors are in parenthesis. Significance level: *** p<0.01, ** p<0.05,* p<0.10. FS: Full Sample; DC: Developed Countries; LDC: Developing Countries; LA: LatinAmerican Countries.



Table 3: IP Index Coefficients. Fixed Effects Estimations for Sub-sectors

Our aim at using the GM is twofold: we are interested in the extensive and the

intensive margins of IPRs on bilateral trade. In other words, we are not only interested in

testing the hypothesis that strong IPRs systems promote export and import volumes (the

intensive margin), but also we want to investigate whether strong IPRs facilitate the creation

of bilateral trade relationships (the extensive margin). Additionally, we split the data in two

groups of analysis. In the first group, we consider all trade relationships present in our data

base. Meanwhile, the second group considers all those trade relationships in which a Latin

American country is the exporter.

More generally, let Wij,k (t) be the export from country i to country j, in sector k, of

the year t. Therefore, the gravity equation in its standard specification can be written as,

Wij,k(t) = exp{xij(t) · k } ij,k(t), (3)

where,xij = {log(Yi), log(Yj), log(Xi), log(Xj), Zi, Zj , dij, Dij, i, j}; (4)

i, j = 1, ..., N ; Yi is the annual GDP for country i; dij the geographical distance betweenboth countries; Xi = {AREAi, POPi} vector of country-specific macro variables; Dij ={contig,comlang off, comcol, colony} the vector of link-specific variables indicating barriers totrade; Zi = {landl, IP Index}country-specific dummies; and it is assumed that E[ ij |Yi, Yj , dij, ...] =1. See Table A.1, in the Appendix A, for a complete description of variables and sources.

The estimation of Eq. (3) is not straightforward. It requires an special treatment

of heteroskedasticity (non-linearity), zero-valued flows, endogeneity and omitted-term biases

(see, e.g., Santos Silva and Tenreyro, 2006). The GM can be fitted to data using different

econometric techniques, ranging from simple Ordinary Least Squares (OLS) applied to the

log-linearized equation (cf., for instance Glick and Rose, 2002; Subramanian and Wei, 2007),

the two-stage Poisson estimations where probability of having zero trade flows is also

estimated (cf., for example Burger et al., 2009), and even panel data techniques with

instrumental variables (cf. Awokuse and Yin, 2010). A common feature of most estimation

techniques is that they all achieve high R-squared coefficients of determination, i.e. a quite

satisfactorily goodness of fit. This largely explains the success of the gravity model.

With the aim of studying the effect of IPRs on bilateral trade, we expand the standard

GM specification, which contains common explanatory variables, by adding IP protection

indexes represented in two country specific variables, related to exporters and importers: IP

index e and IP index i. This enriches our analysis, allowing to explore whether trade

volumes are strengthened when the exporter and/or the importer have strong IP protection. In



addition, we can use the GM specification to explore how IPRs contribute to the formation of

bilateral trade relationships. To achieve this, we implement a Logit estimation on the observed

bilateral trade relationships.



We estimate the GM under three different econometric techniques: i) panel data,

assuming fixed effects (FE), ii) PPML with time dummies, i.e. in this case we pool all cross-

sections, and iii) Logit with time dummies. Notice that in the first two models the dependent

variable proxies the observed trade volumes, while in the last model the dependent variable is a

binary variable representing the observed bilateral trade relationships.2 Therefore, as

mentioned above, with the first two models we are allowed to study the intensive margins of

trade, while in the last one we investigate the extensive margins.

Table 4 presents the estimation results for the aggregated bilateral trade in

agricultural products.

Sample FS LAModel FE PPML Logit FE PPML LogitIP Index e 0.002 -0.001 -0.032* -0.048** -0.388*** -0.301***

(0.010) (0.019) (0.018) (0.021) (0.056) (0.060)IP Index i -0.081*** 0.020 0.307*** -0.076*** 0.259*** 0.246***

(0.010) (0.021) (0.019) (0.022) (0.045) (0.043)log GDP e 1.146*** 0.807*** 1.391*** 0.388*** 0.680*** -0.935***

(0.038) (0.023) (0.020) (0.100) (0.080) (0.124)log GDP i 1.642*** 1.002*** 1.023*** 2.217*** 0.780*** 1.583***

(0.036) (0.022) (0.018) (0.092) (0.039) (0.044)log POP e -0.576*** -0.261*** -0.622*** 1.133*** -0.862*** 0.506***

(0.097) (0.023) (0.021) (0.320) (0.102) (0.138)log POP i -0.300*** -0.175*** -0.607*** -0.025 0.150*** -1.191***

(0.095) (0.025) (0.022) (0.225) (0.049) (0.055)log AREA e 0.062*** 0.203*** 0.851*** 1.052***

(0.011) (0.012) (0.040) (0.052)log AREA i -0.150*** -0.018 -0.125*** 0.027

(0.010 (0.012) (0.025) (0.035)Land-locked e -0.819*** -0.619*** -1.036*** -4.068***

(0.035) (0.032) (0.121) (0.165)Land-locked i -0.516*** -0.149*** -0.398*** -0.792***

(0.036) (0.036) (0.057) (0.076)log Distance -0.561*** -0.857*** -0.654*** -1.354***

(0.017) (0.019) (0.053) (0.090)Contiguity 0.931*** 1.143*** 0.805***

(0.046) (0.236) (0.090)Common Language 0.187*** 1.515*** 0.080 0.508***

(0.041) (0.094) (0.076) (0.140)Common Colonizer 0.271*** 2.075*** -2.949*** 0.878

(0.085) (0.120) (0.344) (0.611)Colony -0.025 0.375 0.376***

(0.036) (0.276) (0.101)Time-dummies yes yes yes yesConstant yes yesObservations 61,129 70,720 70,720 13,241 15,232 14,331Note : The dependent variable is the total bilateral exports of the agriculturalsector. Significance level: *** p<0.01, ** p<0.05, * p<0.10. FS: Full Sample;LA: Latin American Countries.

Table 4: Gravity Model Estimation

The first three columns relate to the full sample, while the last three columns relate to thesample restricted to exports from Latin American countries. The first point to notice is thatFE and PPML provide statistically different results, suggesting heteroscedasticity.3 We

2 The FE and PPML estimations might be comparable, considering that to implement FE it is necessary tolog-linearize Eq. (3) and, therefore, zero trade flows are omitted, while in the PPML case we use all observationsavailable in the sample.

3Obviously, these differences are observed for time varying variables only because of FE estimator.



find that almost all country specific variable estimates are asymmetric: the null hypothesis that

importer and exporter variables affect proportionally trade flows is rejected for both FE and

PPML.

As expected, the gravity structure of trade is mirrored by the signs of the countries’ size

and distance regressors: positive for countries’ GDP and negative for countries’ distance. The

country size effect is slightly discounted from GDPs by other country specific variables as area

and population. It is interesting to highlight that, in the case of the PPML estimations, bilateral

trade volumes of agricultural products face decreasing returns of scale with respect to country

sizes, specially in the multiplier related to the GDP of the exporter which takes the value

0.807(0.023). Regarding the effect of IPRs on bilateral trade, we observe significance in the

multiplier related to the index of the importer just for the FE model, which is actually very low

and negative, -0.81(0.010). These results agree and complement our findings in the previous

section.

Looking at the Logit estimations (third column), we can also check the expected outcome of

the gravity model: for a couple of countries the probability of creating a bilateral relationship

increases with the countries’ sizes and decreases with the distance between them. All other

regressors have expected signs. Interestingly enough, the country specific IP indexes end up

to be significant for the creation of trading channels. This effect is low and negative for the

exporter taking the value -0.032(0.018), and positive and high for the importer 0.307(0.019).

This evidence shows that, at the aggregated level, IPR systems do not affect trade volumes,

however they might be very important for the creation of new markets.

Many interesting differences arise if we instead sample on the restricted group of Latin

American exporters (see columns 4-6 in Table 4). First, the estimation of the size effects,

captured by country specific variables as GDP, Populations and Area, reveal diverse results

among models. For instance, the effect associated with the GDP of the importer under the

FE model is almost three times the one estimated under the PPML model. It is interesting to

stress that in this regression the area of the exporter, i.e. the area of a Latin American country,

starts playing an important role at explaining the variability of trade volumes, and actually

the network market formation. Unfortunately, being a constant parameter, the area can not be

estimated under the FE estimation. However, the PPML model shows that comparing both

samples (restricted and unrestricted to Latin America) the effect of the area in Latin American

countries is expected to be much greater than the one predicted using all countries. This result

is certainly not strange since an important extent of the Latin American exports are devoted to

agriculture and ultimately related to the use of land. The effect of IPR systems turns out to be

negative for Latin American export volumes, i.e. an increase in the IP protection levels in Latin

America is expected to affect negatively exports flows. However, the fact that the importer IP



index is positive reveals that Latin American countries benefit from trading with countries with

higher IP levels.

Again, looking at the Logit estimations (sixth column in Table 4), we check that for Latin

American countries the size effect is better captured by the area of the exporter (cf. third

column in Table 4). The effect of the IP index of the exporter is also negative –Latin American

countries with higher IP indexes are expected to have a lower likelihood for the creation of new

markets for their agricultural products– and positive for importers.

Interestingly enough, the effect of countries’ IP indexes is quite asymmetric for Latin

American countries, not only in the extensive but also in the intensive margin. If we assume that

Latin American countries have similar protection levels, then, the effect of the IPRs systems

on the within trade might be ambiguous –since both indexes’ coefficients might cancel out.

However, if we instead consider the heterogeneity of the IP indexes, then it is expected that Latin

American countries are more likely to create trade or to foster strongly trade with countries

with stronger IP protection schemes.

In order to investigate possible divergent effects, Tables 5 and 6 summarize the

estimation results of the index of IP protection for different sub-sectors. Unlike the GM for the

aggregated bilateral trade of agricultural products, IPRs systems show heterogeneous effects when

considering the disaggregated level. Notice that the estimation results of the other regressors

were omitted in the tables, since we focus on the effect of IPRs on trade.4 The results might be

classified in four groups according to the sign, size and significance of the the importer and

exporter IP index regressors. We propose to classify sectors as non, positively, negatively, and

ambiguously affected. Therefore, there is no effect when both indexes are non statistically

significant; a positive effect is considered when both estimated parameters are positive and

significant (or may be one negative, but considerably lower than the other one in absolute

value); a negative effect can be analogously defined but for negative estimates; and, a result is

considered ambiguous when both indexes estimations are equivalent in absolute values but with

different sings. In order to do this classification we shall trust on the PPML estimator. However,

most of our observations are robustly observed under the fixed effects estimator (see Table A.2

in the Appendix A).

According to the results reported in the Table 5 (first two columns), we observe, for

the full sample, that the expected effect of strengthening IP protection for plant varieties in

both countries, for a bilateral trade volume, is positively and statistically significant for 10

sectors; negatively and statistically significant for 8 sectors; and significant but ambiguously

for 4 sectors. For the Latin American case, according to Table 5 (last two columns), strengthening

IP protection affects bilateral trade volumes: positively and statistically significant for 7

sectors; negatively and statistically significant for 9; significant but ambiguously for 4 sectors;



and not statistically significant one sector. It is worth noticing that in the Latin American case

most of the IP index e significant estimated parameters, across the agricultural sector, are

negative. In other words, exports from Latin American countries are affected by increasing their

levels of IP protection.

Finally, Table 6 (first two columns) reports that the strengthening of IP protection,

for the full sample, is expected to affect bilateral trade relationships: positively and

statistically significant in 20 sectors; and negatively in just 2 sectors. For the Latin American

case, according to Table 5 (last two columns), strengthening IP protection is expected to affect

bilateral trade relationships: positively and statistically significant in 6 sectors; and negatively

and statistically significant in 16 sectors.

Sample FS LAVariable IP index e IP index i IP index e IP index i1 Live Animals -0.010 -0.324*** 0.407 -0.028

(0.067) (0.056) (0.281) (0.232)2 Meat and Edible Meat Offal 0.543*** 0.144*** -0.081 0.845***

(0.043) (0.045) (0.122) (0.151)4 Dairy, Eggs, Honey, and Edible Products 0.553*** -0.044 0.427** -0.043

(0.045) (0.033) (0.177) (0.103)5 Products of Animal Origin -0.004 0.247*** -0.524*** 0.529***

(0.028) (0.041) (0.102) (0.086)6 Live Trees and Other Plants 0.400*** 0.261*** 0.761*** 0.933***

(0.036) (0.073) (0.165) (0.135)7 Edible Vegetables -0.573*** 0.103** -0.394*** -0.802***

(0.038) (0.052) (0.076) (0.089)8 Edible Fruits and Nuts, Peel of Citrus/Melons -0.450*** 0.430*** -0.337*** 0.555***

(0.031) (0.046) (0.057) (0.061)9 Coffe, Tea, Mate and Spices -0.179*** 0.333*** -0.038 0.473***

(0.034) (0.064) (0.103) (0.079)10 Cereals -0.040 -0.266*** 0.588*** -0.096

(0.042) (0.030) (0.127) (0.070)11 Milling Industry Products 0.185*** -0.208*** 0.624*** -0.491***

(0.040) (0.054) (0.206) (0.099)12 Oil Seeds/Misc. Grains/Med. Plants/Straw -0.255*** -0.163*** -0.815*** 0.045

(0.054) (0.045) (0.232) (0.083)13 Lac, Gums, Resins, etc. 0.142*** 0.085*** -0.673*** 0.249**

(0.032) (0.032) (0.103) (0.112)14 Vegetable Planting Materials -0.432*** -0.134*** -0.299*** -0.044

(0.045) (0.039) (0.103) (0.083)15 Animal or Vegetable Fats, Oils and Waxes 0.007 -0.144*** -0.123 0.053

(0.032) (0.031) (0.161) (0.057)17 Sugars and Sugar Confecionery 0.046 0.202*** -0.457*** 0.793***

(0.036) (0.062) (0.108) (0.147)18 Cocoa and Cocoa Preparations 0.089*** 0.130*** -0.077 -0.129*

(0.030) (0.029) (0.066) (0.066)19 Preps. of Cereals, Flour, Starch or Milk -0.055** -0.043 -0.210** -0.238***

(0.028) (0.032) (0.095) (0.062)20 Preps. of Vegetables, Fruits, Nuts, etc. -0.292*** 0.172*** -0.974*** 0.277***

(0.022) (0.033) (0.091) (0.053)21 Misc. Edible Preparations -0.005 0.106*** -0.027 0.408***

(0.022) (0.029) (0.096) (0.066)22 Beverages, Spirits and Vinegar -0.002 0.112*** -0.359*** 0.115

(0.034) (0.041) (0.089) (0.076)23 Residues from Food Industries, Animal Feed 0.036 0.196*** -0.557*** 0.515***

(0.030) (0.036) (0.139) (0.062)24 Tobacco and Manuf. Tobacco Substitutes 0.122** -0.285*** -0.917*** 0.575***

(0.051) (0.045) (0.171) (0.074)Note : The dependent variable is the bilateral export for each sub-sector of the HS ProductClassification. Significance level: *** p<0.01, ** p<0.05, * p<0.10. PPML: Poisson PseudoMaximum Likelihood. FS: Full Sample; LA: Latin American Countries.

Table 5: IP Index Regressors from Gravity’s Estimation, PPML Estimations for the FullSample and for the Sample of Bilateral Trade Flows Restricted to Latin American Exporters_4 All estimation results are available upon request.



6 CONCLUDING REMARKSThe effect of strengthening IPRs on trade volumes and bilateral links is not clear.

Since contradictory effects may be expected, the final result turns out to be an empirical

question.Sample FS LAVariable IP index e IP index i IP index e IP index i1 Live Animals 0.369*** 0.109*** -0.692*** 0.176***

(0.018) (0.017) (0.064) (0.046)2 Meat and Edible Meat Offal 0.188*** 0.223*** -0.685*** 0.255***

(0.015) (0.015) (0.055) (0.035)4 Dairy, Eggs, Honey, and Edible Products 0.337*** 0.012 -0.256*** 0.175***

(0.015) (0.015) (0.065) (0.041)5 Products of Animal Origin 0.171*** 0.111*** -0.546*** 0.164***

(0.016) (0.016) (0.051) (0.037)6 Live Trees and Other Plants 0.102*** 0.262*** -0.106** 0.250***

(0.015) (0.015) (0.044) (0.034)7 Edible Vegetables -0.063*** 0.193*** -0.395*** 0.137***

(0.015) (0.015) (0.044) (0.036)8 Edible Fruits and Nuts, Peel of Citrus/Melons -0.255*** 0.286*** -0.323*** 0.213***

(0.013) (0.014) (0.039) (0.030)9 Coffe, Tea, Mate and Spices -0.338*** 0.189*** -0.590*** 0.179***

(0.014) (0.015) (0.043) (0.033)10 Cereals 0.208*** 0.143*** -0.059 0.170***

(0.016) (0.015) (0.068) (0.037)11 Milling Industry Products 0.265*** -0.009 -0.220*** 0.011

(0.017) (0.016) (0.060) (0.043)12 Oil Seeds/Misc. Grains/Med. Plants/Straw 0.149*** 0.156*** -0.918*** 0.121***

(0.015) (0.014) (0.049) (0.035)13 Lac, Gums, Resins, etc. 0.205*** 0.096*** -0.727*** 0.128***

(0.016) (0.016) (0.054) (0.042)14 Vegetable Planting Materials -0.199*** 0.115*** -0.265*** 0.066

(0.018) (0.019) (0.056) (0.045)15 Animal or Vegetable Fats, Oils and Waxes -0.045*** 0.105*** -0.090* 0.170***

(0.015) (0.015) (0.051) (0.037)17 Sugars and Sugar Confecionery 0.023 0.156*** 0.044 0.028

(0.015) (0.015) (0.048) (0.034)18 Cocoa and Cocoa Preparations 0.051*** 0.053*** 0.089** 0.013

(0.015) (0.015) (0.044) (0.035)19 Preps. of Cereals, Flour, Starch or Milk 0.052*** 0.045*** -0.002 -0.163***

(0.016) (0.016) (0.055) (0.039)20 Preps. of Vegetables, Fruits, Nuts, etc. -0.087*** 0.228*** -0.762*** 0.135***

(0.015) (0.015) (0.045) (0.034)21 Misc. Edible Preparations 0.008 0.250*** -0.010 0.123***

(0.015) (0.016) (0.043) (0.035)22 Beverages, Spirits and Vinegar 0.250*** 0.174*** -0.014 0.184***

(0.015) (0.015) (0.045) (0.035)23 Residues from Food Industries, Animal Feed 0.267*** 0.155*** -0.305*** 0.141***

(0.015) (0.015) (0.050) (0.036)24 Tobacco and Manuf. Tobacco Substitutes -0.030** 0.107*** -0.380*** 0.079**

(0.015) (0.014) (0.040) (0.033)Note : The dependent variable is the bilateral relationship for each sub-sector of the HS ProductClassification. Significance level: *** p<0.01, ** p<0.05, * p<0.10. FS: Full Sample; LA: LatinAmerican Countries.

Table 6: IP Index Regressors from Logit’s Gravity Estimation, for the whole Sample ofBilateral Relationships



Empirically, the issue has been investigated mostly for developed countries and

manufacturing sectors. This papers has investigated the effect of tightening IPRs systems since

the signing of the TRIPS on exports of agricultural products of 60 countries, including

developed, developing and Latin American countries.

We found that on the case of trade volumes evidence supporting the hypothesis

that strengthening IPRs does not affect the traded volume of agricultural products. The

robustness of the results were checked for countries according to development level and also

for Latin American countries, for which exports of agricultural products have a high economic

relevance. Next, we checked these results were still present at a more disaggregated level. In this

case, for all the samples, the effect of strengthening IPRs varied across sub-sectors. For some of

them the effect was not statistically significant; for others, a positive and significant effect was

found; and finally, for other products, the coefficients turned out statistically significant and

negative, meaning, for those cases, that stronger IPRs might decrease the traded quantities.

The estimations of the gravity confirmed this evidence and provided additional

evidence regarding the effect of strong IPRs on bilateral trade links. For a couple of

countries the probability of creating a bilateral relationship increases with the countries’ sizes

and decreases with the distance between them. For Latin American countries, the area plays

an important role at explaining the variability of trade volumes and the network market

formation.

Regarding the extensive margin, that is, whether strong IPRs facilitate the creation

of bilateral trade relationships, we found that an increase in the IP protection levels of

the exporters affects negatively exports flows. On the contrary, countries may benefit from

trading agricultural products with other countries with higher IP levels. This evidence is

stronger for the case of Latin American countries.

Finally, the analysis for a more disaggregated product level, showed that the effect is

sector specific. It can be classified in four groups according to the sign, size and significance of

the the importer and exporter IP index regressors. IPRs may have no effect at all, or when they

do, the effect can be positive, negative or ambiguous. Therefore, the evidence support previous

findings relating the effect of strengthening IPRs on trade as been sector and country specific.

Thus, further sectoral analysis is needed to disentangle the possible effects of strengthening

IPRs on trade and bilateral trade. However, in terms of policy implications, it allows to

conclude that there is no unique system that may fit all countries and sectors such as the one

advocated by TRIPS supporters.



REFERENCE

Anderson, J. E. (1979). A theoretical foundation for the gravity equation. AmericanEconomicReview 69 (1), 106–16.

Awokuse, T. O. and H. Yin (2010). Does stronger intellectual property rights protection inducemore bilateral trade? Evidence from China’s imports. World Development 38 (8), 1094–1104.Barro, R. J. and J.-W. Lee (2012). A new data set of educational attainment in the world,1950–2010. Journal of Development Economics .Bergstrand, J. H. (1985). The gravity equation in international trade: Some microeconomicfoundations and empirical evidence. The Review of Economics and Statistics 67 (3), 474–81.Burger, M., F. v. Oort, and G. Linders (2009). On the specification of the gravity model oftrade: Zeros, excess zeros and zero-inflated estimation. Research Paper ERS-2009-003-ORGRevision, Erasmus Research Institute of Management (ERIM).Campi, M. and A. Nuvolari (2013). Intellectual Property Protection in Plant Varieties. A NewWorldwide Index (1961-2011). LEM Papers Series 2013/03, Laboratory of Economics andManagement (LEM), Sant’Anna School of Advanced Studies, Pisa, Italy.Co, C. Y. (2004). Do patent rights regimes matter? Review of International Economics 12 (3),359–373.Dosi, G., L. Marengo, and C. Pasquali (2006). How much should society fuel the greed ofinnovators?: On the relations between appropriability, opportunities and rates of innovation.Research Policy 35 (8), 1110–1121.Eaton, D. J. (2009). Trade and intellectual property rights in the agricultural seed sector.Conference, August 16-22, 2009, Beijing, China, International Association of AgriculturalEconomists.Feenstra, Robert C., R. I. and M. P. Timmer (2013). The Next Generation of the Penn WorldTable, available for download at www.ggdc.net/pwt.Fink, C. and C. A. P. Braga (1999). How stronger protection of intellectual property rightsaffects international trade flows. World Bank, Science and Technology Thematic Group.Fratianni, M. (2009). The gravity model in international trade. In A. M. Rugman (Ed.), TheOxford Handbook of International Business. Oxford University Press, Oxford, U.K.Galushko, V. (2012). Do stronger intellectual property rights promote seed exchange: evidencefrom us seed exports? Agricultural Economics 43 (s1), 59–71.

Gaulier, G. and S. Zignago (2010). BACI: International Trade Database at theProduct-Level. The 1994-2007 Version, Working Paper, CEPII research center,http://ideas.repec.org/p/cii/cepidt/2010-23.html .Glick, R. and A. K. Rose (2002, June). Does a currency union affect trade? the time-seriesevidence. European Economic Review 46 (6), 1125–1151.Helpman, E. (1993). Innovation, imitation, and intellectual property rights. Econometrica 61 (6),1247–80.Ivus, O. (2010). Do stronger patent rights raise high-tech exports to the developing world?Journal of International Economics 81 (1), 38–47.Lesser, W. (2001). The effects of trips-mandated intellectual property rights on economicactivities in developing countries. World Intellectual Property Organization (WIPO) Studies 1,1–24.Maskus, K. E. and M. Penubarti (1995). How trade-related are intellectual property rights?Journal of International Economics 39 (3), 227–248.Melitz, J. (2007). North, south and distance in the gravity model. European Economic Review 51,971–991.Perez, C. (2010). Technological dynamism and social inclusion in latin america: a resource-basedproduction development strategy. CEPAL Review 100, 121–141.Psacharopoulos, G. (1994). Returns to investment in education: A global update. Worlddevelopment 22 (9), 1325–1343.Santos Silva, J. M. C. and S. Tenreyro (2006). The log of gravity. The Review of Economicsand Statistics 88 (4), 641–658.

http://www.ggdc.net/pwt.

http://ideas.repec.org/p/cii/cepidt/2010-23.html



Smith, P. J. (1999). Are weak patent rights a barrier to us exports? Journal of InternationalEconomics 48 (1), 151–177.Subramanian, A. and S.-J. Wei (2007). The WTO promotes trade, strongly but unevenly.Journal of International Economics 72 (1), 151 – 175.Teece, D. (1986). Profiting from technological innovation: implications for integration,collaboration, licensing and public policy. Research Policy 15 (6), 285–305.Tinbergen, J. (1962). Shaping the World Economy : Suggestions for an International EconomicPolicy. The Twentieth Century Fund.Yang, C.-H. and R.-J. Woo (2006). Do stronger intellectual property rights induce moreagricultural trade?: a dynamic panel data model applied to seed trade. Agriculturaleconomics 35 (1), 91–101.



A APPENDIX

List of Countries

Developed CountriesAustralia; Austria; Bulgaria; Canada; Croatia; Czech Republic; Denmark; Estonia; Finland;France; Germany; Hungary; Iceland; Ireland; Italy; Japan; Latvia; Lithuania; Netherlands; NewZealand; Norway; Poland; Portugal; Slovakia (Slovak Republic); Spain; Sweden; SwitzerlandLiechtenstein; United Kingdom; United States of America

Developing CountriesAlbania; Argentina; Azerbaijan; Belarus; Bolivia; Brazil; Chile; China; Colombia; Costa Rica;Dominican Republic; Ecuador; Georgia; Israel; Jordan; Kenya; Republic of Korea; Kyrgyzstan;The Former Yugoslav Republic of Macedonia; Mexico; Republic of Moldova; Morocco; Panama;Paraguay; Peru; Russian Federation; Singapore; Slovenia; South Africa; Trinidad and Tobago;Tunisia; Turkey; Ukraine; Uruguay; Uzbekistan; Viet nam

Latin American and the Caribbean CountriesArgentina; Bolivia; Brazil; Chile; Colombia; Costa Rica; Dominican Republic; Ecuador; Mexico;Panama; Paraguay; Peru; Trinidad and Tobago; Uruguay

Conferência Internacional LALICS 2013 “Sistemas Nacionais de Inovação e Políticas de CTI para umDesenvolvimento Inclusivo e Sustentável”


List of Variables

Label Related to Description Source

W Link Imports in U.S. Dollars by sectors BACI-CEPII(http://www.cepii.fr/) Gaulierand Zignago (2010)

texpa Country Total Exports of Agricultural Products BACI-CEPII(http://www.cepii.fr/) Gaulierand Zignago (2010)

open Country Openness to Trade Feenstra and Timmer (2013)

GDP Country Gross-domestic product Penn World Table (Feenstraand Timmer, 2013)

area Country Country area in Km2 citesubramanian-wei

pop Country Country population CEPII (http://www.cepii.fr/)

IP Index Country Index of IP protection for PlantVarieties

Campi and Nuvolari (2013)

hc Country Index of human capital per person Feenstra and Timmer (2013)

remot Country Remoteness Melitz (2007)

d Link Distance between two countries, basedon bilateral distances between thelargest cities of those two countries,weighted by the share of the city in theoverall country’s population

CEPII (http://www.cepii.fr/)

landl Country Dummy variable equal to 1 forlandlocked Countries


contig Link Contiguity dummy equal to 1 if twocountries share a common border


comlang off Link Dummy equal to 1 if both countriesshare a common official language


comcol Link Dummy equal to 1 if both countrieshave had a common colonizer


colony Link Dummy equal to 1 if both countrieshave ever had a colonial link


Table A.1: Variables Employed in the Estimation Exercises

http://www.cepii.fr/







GM Fixed-Effects Estimations Summary

Sample FS LAVariable IP index e IP index i IP index e IP index i1 Live Animals 0.055** -0.084*** -0.006 -0.032

(0.027) (0.024) (0.067) (0.075)2 Meat and Edible Meat Offal 0.139*** 0.043* 0.117 -0.074

(0.028) (0.023) (0.072) (0.066)4 Dairy, Eggs, Honey, and Edible Products 0.061*** -0.088*** 0.184** -0.038

(0.022) (0.019) (0.076) (0.069)5 Products of Animal Origin -0.048** 0.080*** -0.023 0.110*

(0.022) (0.020) (0.057) (0.066)6 Live Trees and Other Plants -0.054*** -0.002 -0.089** 0.060

(0.018) (0.018) (0.034) (0.044)7 Edible Vegetables -0.028* -0.000 -0.040 -0.022

(0.016) (0.016) (0.038) (0.043)8 Edible Fruits and Nuts, Peel of Citrus/Melons -0.061*** -0.037** -0.152*** -0.007

(0.014) (0.015) (0.029) (0.030)9 Coffe, Tea, Mate and Spices -0.161*** -0.078*** -0.109*** -0.109***

(0.016) (0.016) (0.032) (0.037)10 Cereals 0.167*** -0.145*** 0.199** -0.501***

(0.032) (0.029) (0.094) (0.081)11 Milling Industry Products -0.023 -0.150*** -0.262*** -0.145**

(0.026) (0.021) (0.063) (0.069)12 Oil Seeds/Misc. Grains/Med. Plants/Straw -0.020 -0.004 -0.048 -0.093*

(0.017) (0.016) (0.050) (0.051)13 Lac, Gums, Resins, etc. -0.131*** -0.041** -0.065 -0.202***

(0.024) (0.019) (0.059) (0.059)14 Vegetable Planting Materials -0.150*** -0.169*** -0.077 -0.124

(0.027) (0.029) (0.067) (0.078)15 Animal or Vegetable Fats, Oils and Waxes -0.096*** -0.165*** -0.119* -0.299***

(0.021) (0.019) (0.061) (0.059)17 Sugars and Sugar Confecionery -0.003 -0.095*** -0.276*** -0.113**

(0.020) (0.018) (0.050) (0.051)18 Cocoa and Cocoa Preparations 0.014 -0.071*** -0.208*** -0.091*

(0.020) (0.018) (0.047) (0.054)19 Preps. of Cereals, Flour, Starch or Milk 0.106*** -0.110*** -0.008 -0.080

(0.017) (0.015) (0.055) (0.055)20 Preps. of Vegetables, Fruits, Nuts, etc. -0.077*** -0.113*** -0.096*** -0.146***

(0.014) (0.014) (0.031) (0.035)21 Misc. Edible Preparations -0.004 -0.029** -0.021 -0.002

(0.015) (0.014) (0.039) (0.041)22 Beverages, Spirits and Vinegar -0.113*** -0.185*** -0.193*** -0.187***

(0.015) (0.014) (0.039) (0.040)23 Residues from Food Industries, Animal Feed -0.042** 0.048*** -0.137** -0.038

(0.021) (0.018) (0.058) (0.050)24 Tobacco and Manuf. Tobacco Substitutes -0.008 0.002 -0.027 0.141***

(0.026) (0.025) (0.045) (0.053)Note : The dependent variable is the bilateral export for each sub-sector of theHS Product Classification. Significance level: *** p<0.01, ** p<0.05, * p<0.10.PPML: Poisson Pseudo Maximum Likelihood. FS: Full Sample; LA: Latin AmericanCountries.

Table A.2: IP Index Regressors from Gravity’s Estimation, Fixed Effects for theFull Sample and for the Sample of Bilateral Trade Flows Restricted to LatinAmerican Exporters

Date post:	12-Jun-2020
Category:	Documents
Upload:	others
View:	1 times
Download:	0 times

Intellectual Property Rights and International Tradeof ... · Intellectual Property Rights and...

Documents