1

The Impact of Patent Protection on U.S. Pharmaceutical

Exports to Developing Countries

By Anne Boring1,2

LEDa-Dial, OFCE-Sciences Po

October 2014

Abstract

This paper provides evidence that patent protection can have a positive effect on trade, by

analyzing the impact of the implementation of intellectual property rights in developing

countries on the United States’ exports of pharmaceutical products, following intense

lobbying efforts from the U.S. pharmaceutical industry to have the Trade-related aspects

of intellectual property rights (TRIPS) agreement included in the creation of the World

Trade Organization (WTO). A gravity model using panel data from 1995 to 2010 suggests

that the implementation of minimum standards of patent protection has increased U.S.

exports of pharmaceuticals to 108 non-advanced countries.

Keywords: pharmaceutical trade, intellectual property rights, patents, TRIPS agreement.

JEL: F13, F14, L65, O19, O34

1 I especially wish to thank Bernard Guillochon, Margaret Kyle, Pierre-Guillaume Méon, and Jean-Marc

Siroën for great advice, as well as the members of DIAL for constructive discussions and support. I also

would like to thank participants of the 3rd Annual Conference on the Political Economy of International

Organizations at Georgetown, participants of the globalization and development research group seminar

from the University of Paris Dauphine, the OFCE-DRIC, participants of the 2010 annual congresses of the

Association française de sciences économiques (AFSE), the European Trade Study Group (ETSG), and

the 2013 ADRES Conference. 2 Contact: anne.boring@dauphine.fr or anne.boring@sciencespo.fr.

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1. Introduction Trade agreements, whose goal is to facilitate trade, generally require member

countries to protect patents, suggesting that the implementation of patent protection

increases trade. Yet the impact of intellectual property rights (IPR) protection on trade

remains unclear. The direct goal of patent protection is to create incentives for research

and development efforts, which could have a negative impact on trade as patents provide

a monopoly power to producers (Smith, 2001).

Some economists have even argued that multilateral trade agreements, such as

the World Trade Organization’s (WTO) 1995 Trade-Related Aspects of Intellectual

Property Rights (TRIPS) agreement, should not include strong IPR protection clauses, as

these clauses do not promote trade (e.g. Bhagwati, 2004).3 Empirical studies that have

investigated the impact of IPR protection on trade tend to show that IPR protection can

either increase, decrease or have no significant impact on trade depending on the degree

of patent protection (Bernieri, 2006), the types of countries that apply IPR protection

(Ferrantino, 1993; Smith, 2001; Rafiquzzaman, 2002; Blyde, 2006; Ivus, 2010; Delgado,

Kyle & McGahan, 2013; Foster, 2014), and the types of goods traded (Maskus &

3 The TRIPS agreement originally stated that IPRs were to create incentives for new ideas which could

benefit the whole of society. This objective is detailed in Article 7: “The protection and enforcement of

intellectual property rights should contribute to the promotion of technological innovation and to the

transfer and dissemination of technology, to the mutual advantage of producers and users of technological

knowledge and in a manner conducive to social and economic welfare, and to a balance of rights and

obligations” (WTO, 2012).

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Penubarti, 1995; Fink & Primo-Braga, 1999; Falvey, Foster & Greenaway, 2009;

Delgado, Kyle & McGahan, 2013; Campi & Duenas, 2014).

In this paper, I analyze U.S. export data of pharmaceutical products between

1995 and 2010 to 108 non-advanced economies to provide evidence of the impact of the

patent protection on trade. I test the hypothesis that the monopoly power that patent

protection conveys has encouraged U.S. pharmaceutical producers to increase their

exports of pharmaceutical products to developing countries. To conduct this analysis, I

build a new index of patent protection, which takes into account the legal changes that

have occurred during the time period of the study in each individual country. With this

new index, I am able to use a gravity model of trade to study the impact of developing

countries’ implementation of TRIPS standards of patent protection on their imports of

pharmaceuticals from the United States. I specifically focus on U.S. exports of

pharmaceuticals because the U.S. pharmaceutical industry spearheaded the global

lobbying efforts for the inclusion of IPR protection in trade agreements (Drahos, 1995),

suggesting that they expected to benefit from foreign IPR protection. The U.S.

pharmaceutical industry is the world’s largest exporter of pharmaceutical products, as

well as the world leader in terms of production and research and development

expenditures in pharmaceuticals (Kiriyama, 2011).

The results of the empirical analysis show that patent protection increases U.S.

exports of pharmaceuticals overall, suggesting that U.S. pharmaceutical producers’

lobbying efforts aimed at expanding the size of the market for pharmaceutical products

have been successful. The finding appears to be robust to allowing the marginal impact

of TRIPS standards of patent protection on U.S. exports to differ across quartiles of

countries’ life expectancies.

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Section 2 discusses the literature on the impact of foreign patent protection on

trade. Section 3 details the data and econometric specification. Section 4 gives the

results. Section 5 concludes.

2. Theoretical background From a theoretical point of view, IPR protection can either have a positive or a

negative impact on exports, as foreign patent protection can lead to the market

expansion effect, the cost reduction effect or the market power effect (Maskus &

Penubarti, 1995). The market expansion effect tends to increase the volume of exports:

patent protection opens access to a wider market, which increases the demand curve

firms face, generating larger sales. The cost reduction effect reinforces the market

expansion effect, as a lack of patent protection forces firms to internalize the costs of

preventing imitation. With the introduction of patent protection, firms’ costs drop since

they do not have to design strategies to deter imitation anymore (Taylor, 1993). The

market power effect, on the other hand, can offset the positive impact on trade of the

market expansion and cost reduction effects (Maskus & Penubarti, 1995). The market

power effect tends to reduce exports: patent protection reduces competition by

conveying a monopoly power to firms that hold patents. Consumer demand becomes

less elastic, thus reducing the volume of exports.

Applied to the exports of pharmaceutical products from the United States to

developing countries, the market expansion effect and the cost reduction effect are likely

to dominate the market power effect. More specifically, the market expansion effect and

the cost reduction effect are likely to be large, while the market power effect is likely to

be small. Indeed, U.S. pharmaceutical firms are likely to start exporting drugs they were

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not exporting because of a lack of patent protection. Fear of imitation is the main reason

why U.S. pharmaceutical firms may choose to restrict trade with countries that do not

protect IPR. When countries do not protect patents, pharmaceutical firms may choose to

refrain from exporting drugs to limit the ability of generic manufacturers in these

countries to copy their drugs through reverse engineering (Smith, 2001).4 Exports

facilitate generic manufacturers’ access to innovative pharmaceuticals, and therefore

increase the probability that generic manufacturers copy and sell these innovative

pharmaceuticals.

Lack of patent protection stimulates competition from generic manufacturers,

which force pharmaceutical firms to lower prices. These lower prices increase the

probability that the drugs initially intended for developing countries make their way to a

developed country where prices are higher, with parallel traders making a profit through

arbitrage. Furthermore, price differentials might cause pressures in the United States to

reduce prices, as U.S. consumers become increasingly aware that prices are much lower

in other countries (Barton, 2004). A lack of patent protection in a foreign country may

therefore prompt U.S. firms to refrain from exporting to that country, so as to avoid

competition from generic manufacturers in developing countries and downward

pressures on prices in developed countries.5

4 Generic manufacturers use reverse engineering to develop generic formulations.

5 There is some evidence that pharmaceutical firms tend to set prices above purchasing power in

developing countries. Firms fear that if the difference in prices between poorer and richer countries is too

high, rich countries will import drugs from poorer countries (such as Mexico) to benefit from lower prices

(Danzon & Furukawa, 2003).

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The market expansion effect is also likely to prevail over the market power effect

as pharmaceutical firms are seeking to increase sales in emerging markets, with markets

in developed countries presenting low growth opportunities due to the expiration of the

patent protections of many of the top-selling drugs.6 Pharmaceutical firms generate large

revenues on very specific categories of pharmaceuticals: those that treat chronic diseases

(e.g. heart diseases, cancers, Alzheimer, obesity, etc.) that are prevalent in developed

countries. While populations in developing countries still suffer mainly from

communicable (infectious) diseases (e.g. tuberculosis, HIV/AIDS, malaria, measles,

etc.), chronic diseases are becoming an increasing health problem in developing

countries (Nugent, 2008), U.S. pharmaceutical firms may want to prolong the longevity

of their drugs as blockbusters by selling them in emerging markets.7

The market power effect, on the other hand, is likely to be small. Because U.S.

pharmaceutical firms tend to limit their exports in the absence of patent protection, the

market power effect is likely to be small for U.S. exports when developing countries do

start to implement patent protection. Whereas the market power effect may be high for

generic producers of developing countries who become restricted in their ability to trade

generic versions of pharmaceuticals protected by patents under trade agreements, such

6 For example, the U.S. patents for Lipitor and Plavix, the two drugs which generated the largest sales

worldwide in 2011 (IMS Health, 2011), expired in 2011.

7 A major health concern for developing countries is that patent protection limits the access of developing

countries to inexpensive essential drugs that could be traded by generic manufacturers when no IPR

protection measures are enforced (e.g. Akaleephan et al., 2009; Chaudhuri, Goldberg, & Jia, 2006; El-Said

& El-Said, 2007; Shaffer & Brenner, 2009).

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as the TRIPS agreement (Westerhaus & Castro, 2006), U.S. exports of pharmaceuticals

to developing countries are likely to increase with IPR protection.

In this paper, I test the hypothesis that the market expansion effect and the cost

reduction effect dominate the market power effect in terms of U.S. exports of

pharmaceutical products. Two related empirical studies by Ivus (2010) and Delgado,

Kyle & McGahan (2013) also suggest that the implementation of patent protection is

likely to increase the value of the United States’ exports of pharmaceuticals to

developing countries. These studies use difference-in-difference analyses to evaluate the

impact of patent protection on exports of technologically advanced products from

developed countries to developing countries. In contrast, I use a gravity model approach

to answer my research question. I am able to do so using a new index of patent

protection that takes into account the implementation of minimum standards of patent

protection required by the TRIPS agreement on a yearly basis. The following section

details my approach and the data I use.

3. Data and Methodology In the following sections, the gravity model of trade serves as a basis to

determine the impact of foreign patent protection on the United States’ exports of

pharmaceuticals.

3.1.The gravity equation of trade

The gravity equation suggests that trade flows increase with the economic size of

two areas, and decrease with distance. More generally, proximity between two countries

increases trade. In the basic form of the gravity equation, proximity generally refers to

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economic, geographic and cultural proximity. In this paper, a measure of legal proximity

is added to determine the impact of foreign IPR protection on U.S. trade. If a country

implements a minimum standard of patent protection that makes its IPR standards closer

to those of the United States, then the United States will increase its exports to that

country.

The gravity model is generally used to explain flows among a group of countries

for several goods. The theoretical foundations of the gravity equation developed by

Anderson (1979) suggest that two countries are likely to trade more with each other if

there are large resistances to trade with other countries. Trade flows between two

countries therefore depend on the multilateral resistance (the average trade barrier) of

other countries (Anderson & van Wincoop, 2003). Nonetheless, the gravity equation has

served as the basis of studies on flows between one country and the rest of the world

(e.g. Davies & Kristjánsdóttir, 2010), among a group of countries for one type of good

(e.g. Olper & Raimondi, 2008), or between one country and a group of other countries

for one type of good (e.g. Zhang & Li, 2009). This paper follows this literature to study

the impact of patent protection on U.S. exports of pharmaceutical products to 108 non-

advanced economies, from 1995 to 2010 (see Appendix 1 for a list of the countries.).

3.2.The variables

In the following estimations, the dependent variable is 𝑋𝑗𝑡. It represents the

United States’ exports to country j in year t. Export data come from the USA Trade

Online database published by the U.S. Census Bureau (2012a). The data is expressed in

constant 2005 dollars by deflating the original current dollar data using the U.S. Bureau

of Labor Statistics Import and Export Price Indexes (2012) for pharmaceutical products.

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3.2.1. The basic gravity model variables

According to the gravity model of trade, the United States is likely to trade more

with larger economies. In the following estimations, economic size is measured by the

trading partner’s gross domestic product per capita (𝐺𝐷𝑃𝑃𝐶𝑗𝑡). This variable is likely to

be highly significant in this context for at least two reasons. First, poor quality of

infrastructure can reduce trade (Nordås & Piermartini, 2004), and countries with higher

GDP per capita tend to benefit from higher quality of infrastructure.8 Second, poverty in

itself is an important factor explaining lack of access in developing countries (Attaran

2004; Watal, 2000; Westerhaus and Castro, 2006). Because GDP per capita is a measure

of poverty, it captures to some extent the effect of poverty on access to pharmaceuticals.

The following analysis also includes a variable that measures the trading

partner’s population (𝑃𝑜𝑝𝑗𝑡). Whereas GDP per capita controls for a country’s poverty

level, population controls for the size of the foreign country’s market. GDP data come

from the United Nations Statistics Division (2012) and population data come from the

International Data Base of the U.S. Census Bureau (2012b).

The model takes into account geographic and cultural distance. A country trades

more with neighboring countries to reduce transport costs. Distance (𝐷𝑗) is calculated

between country j’s largest city and New York City, using data from the CEPII (2009).

Countries that have similar cultures are also expected to trade more with each other. A

8 Proponents of patent protection argue that access to drugs in developing countries would not be secured

even if intellectual property rights did not exist, because inefficient health systems and infrastructure

(transportation, electricity, clean water supply) are major impediments to sustainable health care in

developing countries (PhRMA, 2003; Watal, 2000).

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language variable measures cultural proximity (𝐿𝑎𝑛𝑔𝑗), using data from the CEPII

(2009) and the CIA World Factbook (2010). It equals one if English is an official

language.

3.2.2 The variables of interest

In addition to the gravity model’s basic measures, the following estimations

include two variables of interest. They separate the effects of strong IPR protection

through free trade agreements, from less stringent IPR protection contained in the TRIPS

agreement.

The main variable of interest (𝑇𝑅𝐼𝑃𝑆𝑗𝑡) is a dummy variable equal to one if a

country has implemented minimum standards of IPR protection such as those specified

by the TRIPS requirements, regardless of whether the country belongs to the WTO.

According to the TRIPS agreement, developed countries had until 1996 to implement

the TRIPS agreement’s full requirements. Developing countries had until 2000 to do so,

but Article 65.4 enabled them to postpone the implementation of the requirements for

pharmaceuticals until 2005. Finally, the WTO requires least developed countries to have

implemented the TRIPS requirements by 2016 for pharmaceutical products. Some

countries, however, have implemented TRIPS-like requirements before the deadline.

The TRIPS variable was built using information from the WTO, the World Intellectual

Property Organization and national patent offices. When no information was available,

the deadline year to implement the TRIPS requirements was used as a default (see

Appendix 1 for more information on the year during which countries implemented the

minimum patent protection requirements).

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This TRIPS variable is an addition to the existing literature. Its main advantage is

that it covers a wider range of countries than previous studies (e.g. Co, 2004; Ivus, 2011,

Smith, 2002). It also covers the period during which many developing countries

implemented minimum standards of patent protection through the TRIPS agreement’s

requirements, i.e. after 2000 and 2005. Most papers on the impact of patent protection

on trade cover the period pre-2000, before most countries implemented the TRIPS

agreement’s requirements (e.g. Fink & Primo Braga, 1999; Ivus, 2010, 2011; Maskus &

Penubarti, 1997; Rafiquzzaman, 2002).

However, the fact that a country has implemented the TRIPS agreement’s

requirements does not guarantee that it actually enforces the new rules. Other papers

have generally used the IPR index created by Ginarte and Park (1997), which provides

data for 110 countries for every five years from 1960 to 1990. The index was then

updated by Park (2008) to include 122 countries (including both developed and

developing countries), from 1960 to 2005. The main advantage of the Ginarte and Park

index is that it takes into account several factors that can influence the effective

implementation of IPR protection: the laws’ coverage (including patentability of

pharmaceuticals), a country’s membership in international treaties, the duration of

protection, a country’s enforcement mechanisms, and restrictions on patent rights.

The TRIPS variable presents three main advantages compared to the Ginarte and

Park index for the purpose of this paper. First, it provides data for every year of the

study from 1995 to 2010, which considerably increases the number of observations

available for panel data estimation compared to the Ginarte and Park index. This feature

is essential to study the impact of the implementation of IPR protection in developing

countries, taking into account the fact that many developing countries started

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implementing IPR protection after 2000. Second, the TRIPS variable covers all countries

for which U.S. trade flows of pharmaceuticals are available. Third, the TRIPS variable

does not present an endogeneity problem, since it takes into account only minimum

requirements of patent protection, which apply to all WTO countries (almost all

countries in the sample).9 Throughout the analysis, the TRIPS variable is therefore

preferred, but I nonetheless provide estimations in which I replace the TRIPS variable

with the Ginarte and Park index as a robustness check. Indeed, the TRIPS variable does

not take patent protection enforcement into account, which could be a drawback

compared to the Ginarte and Park index.

The second variable of interest is the free trade agreement variable (𝐹𝑇𝐴𝑗𝑡),

which is a dummy variable equal to one if the country j is enforcing a free trade

agreement with the United States in year t. This variable is meant to capture the “TRIPS-

Plus” effect of patent protection on U.S. exports: over the past decade, the United States

has encouraged the implementation of strict IPR protection in foreign countries through

bilateral and regional trade agreements (Krikorian & Szymkowiak, 2007). These U.S.

free trade agreements require the trading partner to implement stronger measures to

protect intellectual property rights than the TRIPS agreement’s rules (see Appendix 2 for

a list of U.S. free trade agreements).

9 The use of the Ginarte and Park index can generate a problem of endogeneity, because it indicates the

strength of a country’s patent laws. The index therefore does not take into account some countries that

might have laws generated by pressure from the United States. Some papers also used the index created by

Rapp and Rozek (1990), which rates countries’ legislations according to minimum standards established

by the US Chamber of Commerce in 1987. This index does not cover the period studied in this paper.

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3.2.3. The control variables

In addition to the basic gravity model variables and variables of interest, four

control variables try to take into account other factors that might affect the United

States’ exports of pharmaceuticals. Protests against the implementation of patent

protection in developing countries generated global awareness of the access to essential

drugs problem in developing countries. As the United States started to push for stronger

patent protection clauses in free trade agreements, the persistent lack of access of poor

countries to pharmaceuticals prompted the United States to launch a major initiative to

increase access. In 2003, the United States launched PEPFAR, which initially committed

15 billion dollars over a five year period to prevent, provide care and treat populations

with HIV/AIDS in 15 focus countries.10 The program appears to be significantly

associated with a decrease in HIV-related deaths in the Sub-Saharan African focus

countries (Bendavid & Bhattacharya, 2009).11 This program may have increased U.S.

exports to the focus countries, since part of its funds are dedicated to antiretroviral

(ARV) drug procurement. In 2003, Congress required that 55% of the funds dedicated to

PEPFAR be for the treatment of people with HIV/AIDS. PEPFAR has increasingly used

generic ARVs: while generic ARVs represented only 9.2% of total expenses on ARVs in

2005, they represented 76.4% of total expenses in 2008. Thanks to this widespread use

of generics, 2.4 million adults and children were receiving treatments through PEPFAR

10 PEPFAR’s original 15 focus countries are Botswana, Cote d’Ivoire, Ethiopia, Guyana, Haiti, Kenya,

Mozambique, Namibia, Nigeria, Rwanda, South Africa, Tanzania, Uganda, Vietnam and Zambia.

11 PEPFAR has been extended to a new five-year period (2009-2013). It has benefited from increased

funding to 48 billion dollars, and its scope has widened (PEPFAR, 2012).

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by 2009 (Holmes et al., 2010). PEPFAR is therefore likely to increase the United States’

exports of pharmaceuticals to the program’s focus countries, at least in terms of

quantities sold. A dummy variable is equal to one for the countries that are part of the

PEPFAR program (𝑃𝐸𝑃𝐹𝐴𝑅𝑗𝑡), starting in 2004.

The other control variables are 𝐿𝑎𝑛𝑑𝑙𝑗 (a dummy variable equal to 1 if the trade

partner is a landlocked territory), 𝐷𝑜𝑙𝑙𝑎𝑟𝑗𝑡 (a dummy variable equal to 1 if the trade

partner uses U.S. dollars as its official currency or if the local currency is

interchangeable 1:1 with U.S. dollars), and 𝑂𝑝𝑒𝑛𝑗𝑡 (a measure of economic openness).

The data for the two former variables come from the CEPII (2009) and the CIA World

Factbook (2010). Data for the economic openness variable come from the actual flows

measure of the KOF Index of Economic Globalization (Dreher, 2006). This indicator is a

measure of a country’s degree of economic globalization, by taking into account a

country’s actual economic flows in terms of total trade (imports plus exports), FDI and

portfolio investment (normalized by GDP).12

The following function summarizes the United States’ exports to developing

countries:

𝑋𝑗𝑡 = 𝑓(𝐺𝐷𝑃𝑃𝐶𝑗𝑡, 𝑃𝑜𝑝𝑗𝑡 , 𝐷𝑗 , 𝐿𝑎𝑛𝑔𝑗 , 𝐹𝑇𝐴𝑗𝑡, 𝑇𝑅𝐼𝑃𝑆𝑗𝑡, 𝑃𝐸𝑃𝐹𝐴𝑅𝑗𝑡 , 𝐿𝑎𝑛𝑑𝑙𝑗 , 𝐷𝑜𝑙𝑙𝑎𝑟𝑗𝑡, 𝑂𝑝𝑒𝑛𝑗𝑡).

Table 1 describes the summary statistics for each variable, for 108 non-advanced

countries, from 1995 to 2010. U.S. exports are likely to increase towards countries that

have implemented patent protection, either through the TRIPS agreement or through a

12 The components of the actual flows variable are as follows: trade (in percent of GDP) for 23%, FDI in

percent of GDP) for 29%, portfolio investment (in percent of GDP) for 27%, and income payments to

foreign nationals (in percent of GDP) for 22%.

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free trade agreement with the United States. Countries participating in the PEPFAR

program will also probably see an increase in their imports of pharmaceuticals from the

United States. An increase in GDP per capita and sharing a common language or

currency are also likely to increase U.S. exports. An increase in distance and smaller

countries in terms of population, on the other hand, are likely to have a negative impact

on U.S. exports, and the United States is likely to display lower trade levels with

countries that tend to not be very open to trade and capital flows, or that are landlocked

since having no access to the sea tends to increase transportation costs.

[TABLE 1]

3.3.Estimation strategy

A large part of the literature that applies the gravity equation to estimate trade

flows uses ordinary least squares (OLS) as the baseline specification, the dependent

variable being the natural logarithm of some measure of trade. However, estimating the

gravity equation with OLS is a problem because the estimation does not take into

account the fact that the United States does not trade with all countries.13 Furthermore,

13 Since 𝑙𝑛(0) is undefined, log-linearization leads to the estimation of a truncated sample. A common

way of solving this problem in the literature has been to use an ad hoc correction for the presence of zeros

with OLS: the dependant variable is the natural logarithm of the value of trade plus a small constant, such

as 1 or 0.01 (e.g. Eichengreen & Irwin, 1995). Another solution has been the use of a Tobit estimator.

However, both of these solutions generate inconsistent estimates of the parameters of interest (Silva &

Tenreyro, 2006).

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log-linearization leads to inconsistent estimates in case of heteroskedasticity (Silva &

Tenreyro, 2006). To solve both problems, Silva and Tenreyro (2006) suggest that a

Poisson pseudo-maximum likelihood (PPML) method be used instead of OLS. The

gravity equation is then estimated in levels.14 Since Silva and Tenreyro (2006), count

estimators have increasingly become the benchmark specification. Burger, van Oort and

Linders (2009) have argued that negative binomial and zero-inflated methods can

replace PPML in estimations of the gravity equation in case of excess zero flows and

overdispersion.15 A few authors have started using these alternatives to the PPML. For

example Gassebner and Méon (2010) have used a negative binomial model to estimate

cross-border M&A flows in the context of a gravity model.

In this paper, the dependent variable exhibits over-dispersion: the variance is

much larger than the mean (see Table 1.). Therefore, the following analysis uses a

negative binomial estimator as its benchmark specification. Year time dummies are

included to take into account common shocks to all countries. To keep in line with the

literature on gravity equations, country fixed effects are also included. The PPML and

OLS estimators are used as robustness tests.

Finally, I test the robustness of the impact of patent protection on U.S. exports of

pharmaceuticals, by allowing the marginal impact of TRIPS to differ across quartiles of

14 Martin and Pham (2008) have argued that the PPML estimator is appropriate if zero trade values are not

frequent. However, Silva and Tenreyro (2010) have justified their approach even in the case of large zero-

trade values.

15 Although Poisson and negative binomial models are originally count data models, Wooldridge (2002)

shows that they can be used to analyze models with non-negative continuous variables (see Burger et al.,

2009).

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life-expectancy. If U.S. firms are interested in expanding their presence abroad to sell

their blockbuster drugs for chronic diseases, then patent protection is likely to matter for

the countries with higher life-expectancy levels. If U.S. firms are interested in expanding

their market for communicable diseases as well, then patent protection is also likely to

matter for the countries with lower life-expectancy levels. Dividing the sample into

quartiles is more appropriate than the introduction of an interaction term, because it can

be quite difficult to interpret interaction effects in non-linear models (see Ai & Norton,

2003; Gassebner & Méon, 2010). Table 2 shows the four groups of countries divided by

quartiles of life-expectancy (according to life-expectancy in 2007).

[TABLE 2]

4. Results In this section, the first part of the discussion deals with the impact of foreign

patent protection on the value of U.S. exports. The second part tests the robustness of the

results by dividing countries by quartiles of life-expectancy.

4.1.Main findings: patent protection and the value of total U.S. pharmaceutical

exports to non-advanced economies

Table 3 shows the results of the main estimation of the impact of foreign patent

protection on U.S. exports to 108 non-advanced countries, from 1995 to 2010. The basic

gravity variables are significant and vary in the predicted way. A country’s GDP per

capita tends to be positively correlated with an increase in exports. The larger a country

is in terms of population, the more it imports pharmaceuticals from the United States.

The more distant a country is to the United States, the less it imports pharmaceuticals.

Finally, English-speaking countries trade more with the United States. The control

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variables also vary as expected in the negative binomial regressions. The United States

tends to export more pharmaceuticals to countries that are part of PEPFAR when

country conditional fixed effects are included (column (3)). It exports more to countries

that use the dollar as their currency, and that are generally more open to trade and

foreign investments.

[TABLE 3]

The three estimators generate similar results, but, as expected, the negative

binomial regressions generate the most robust and coherent results with regard to the

literature on the gravity equation applied to trade. GDP per capita remains significant at

the 99% level in the negative binomial regression with country conditional fixed effects,

but the variable is not statistically significant in the PPML regression with year and

country fixed effects, and only significant at the 90% level in the OLS regression with

year and country fixed effects. The OLS and PPML estimators do not yield coherent

results in terms of the population variable with year and country fixed effects (columns

(6) and (9)). The PPML estimator finds that countries with larger populations tend to

import much fewer drugs than smaller countries.16 Furthermore, the only significant

16 As robustness tests, the negative binomial, ordinary least squares and Poisson pseudo-maximum

likelihood estimators were applied to an equation in which GDP per capita was replaced by GDP and the

population variable was kept, and another equation in which GDP per capita was replaced by GDP but the

population variable was dropped. In both cases, there was no major change regarding the results of the

other variables.

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variables in the OLS estimation with year and country fixed effects are the PEPFAR

dummy (99% significance level) and log GDP per capita (90% significance level). These

results support the argument that the OLS estimator is not appropriate in this context.

Finally, the PPML estimator finds that countries that are more open to trade and foreign

investments tend to import significantly fewer drugs from the United States.

Because the results confirm that the negative binomial regression is in fact the

appropriate estimator to evaluate the impact of foreign patent protection on U.S. exports,

the following analysis will discuss the results obtained thanks to this estimator.

According to the results in Table 3, a country that implements at least the minimum

requirements included in the TRIPS agreement will benefit from an increase in its

imports of pharmaceutical products from the United States. This result is statistically

significant at the 99% level. However, the TRIPS variable may be underestimating the

extent of the impact of patent protection on the United States’ exports of

pharmaceuticals for at least two reasons. First, because the U.S. has been increasingly

offshoring its production of pharmaceuticals to foreign countries, it is likely that some

U.S. pharmaceuticals are reaching the markets of non-advanced economies through

exports from the countries in which U.S firms offshore production. The TRIPS variable

may therefore underestimate the impact of foreign patent protection on access to U.S.

pharmaceuticals in these non-advanced economies. Second, although some countries

have in fact changed their laws to include patent protection, not all countries are actually

enforcing patent protection. Despite these causes for underestimation, the fact that the

variable is statistically significant suggests that the impact of implementing the TRIPS

agreement is strong.

20

Free trade agreements do not seem to have a statistically significant impact on

U.S. exports when country conditional fixed effects are taken into account (column (3)).

While implementing minimum standards of patent protection does seem to have a

significant statistical and economic impact on U.S. exports of pharmaceuticals to non-

advanced economies, free trade agreements do not seem to have an influence on exports

when country fixed effects are taken into account. This result suggests that FTAs are

correlated with unobserved country specific characteristics. FTAs therefore do not seem

to increase the United States’ exports of pharmaceuticals.17

[TABLE 4]

Table 4. shows the results for negative binomial regressions in which the Ginarte

and Park index replaces the TRIPS dummy. In columns (1), (2) and (3), the level of

patent protection is tested using the Ginarte and Park Index. The number of observations

drops because there are now only three years included (1995, 2000 and 2005) and only

78 countries. Columns (4), (5) and (6) show the results of regressions using the same

database of countries, but the TRIPS variable replaces the Ginarte and Park index. Using

the Ginarte and Park index does not change the conclusion regarding the impact of

patent protection on U.S. exports when conditional country fixed effects are included:

stronger patent protection increases U.S. exports of pharmaceuticals (column (3)). The

TRIPS variable is still statistically and economically significant when applied to this

database when conditional country fixed effects are included (column (6)). In both cases,

17 Free trade agreements tend to be signed with countries that are candidates to becoming production

platforms for U.S. firms. These FTAs are likely to have a larger impact on U.S. imports than exports.

21

free trade agreements are statistically insignificant when country fixed effects are

included.

4.2. Robustness check: the impact of patent protection across quartiles of life-

expectancy

In ths section, I study the impact of protection across quartiles of life-expectancy

as a robustness check. I assume that market similarity is likely to generate a market

expansion effect, with people from countries with higher life expectancies being more

likely to suffer from the same types of diseases as in developed countries. Most of the

largest selling drugs worldwide are used to treat chronic diseases, such as cardiovascular

conditions, asthma, psychotic disorders and autoimmune disorders (IMS Health, 2011),

which suggests that U.S. pharmaceutical firms would be able to expand the markets for

these drugs when countries with higher life-expectancy implement patent protection.

The market expansion effect may also prevail for countries with lower life-expectancy

levels, if U.S. pharmaceutical firms start exporting more drugs to treat communicable

diseases.

Table 5 shows the results of estimations that test whether patent protection

increases the exports of pharmaceuticals to countries with higher life-expectancy. The

first quartile (columns (1), (5) and (9)) includes countries in which the population’s life-

expectancy was lower than or equal to 57.1 years in 2007. The second quartile (columns

(2), (6) and (10)) includes countries with life-expectancies between 57.1 and 68.6 years,

the third quartile (columns (3), (7) and (11)) between 68.6 and 72.6 years, and the fourth

quartile (columns (4), (8) and (12)) above 72.6 years.

[TABLE 5]

22

The results suggest that the market expansion effect and the cost reduction effect

dominate the market power effect for at least the two highest quartiles of life-

expectancy, confirming the results of the previous section. Regarding the two highest

quartiles of life-expectancy (columns (3-4), (7-8) and (11-12)), the market expansion

effect may concern pharmaceutical products that treat chronic conditions. It is harder to

conclude for the second quartile compared to the two highest quartiles, as the TRIPS

variable is never statistically significant for the second quartile (columns (2), (6) and

(10)). However, the countries in this second quartile do appear to benefit from an

increase in imports of U.S. pharmaceuticals caused by the President’s Emergency Plan

for AIDS Relief (PEPFAR). For the lowest quartile (columns (1), (5), and (9)), the

implementation of patent protection may correspond to drugs that treat communicable

diseases, but the results are not significant when country and year fixed effects are

included. Having signed a free trade agreement with the United States does not have a

consistently significant impact on U.S. exports, confirming the results of the previous

section.

5. Conclusion The TRIPS agreement seems to have had a positive impact on the United States’

exports of pharmaceuticals to non-advanced economies. This result suggests that U.S.

pharmaceutical firms lobbied heavily for the implementation of patent protection abroad

to benefit from market expansion and cost reduction effects. While the data used in this

paper does not permit to disentangle whether U.S. pharmaceutical firms increase their

extensive or intensive margins of trade, there is some evidence in the literature which

23

suggests that patent protection increases at least their extensive margins of trade (Ivus,

2011; Foster, 2014).

The results presented in this paper are likely to underestimate the actual rise in

access to U.S. pharmaceuticals since American pharmaceutical firms are increasingly

offshoring production following the implementation of patent protection abroad, using

foreign markets as export platforms for their pharmaceutical products. This increase in

offshoring explains to some extent the different results found in this paper regarding the

impact of the TRIPS requirements on U.S. exports, versus the impact of free trade

agreements on U.S. exports. Indeed, free trade agreements are signed by the United

States with a few specific countries where U.S. pharmaceutical firms are interested in

implementing production facilities. Stronger patent protection obtained through free

trade agreements does not necessarily lead to more exports, but actually to more

offshoring of production. Free trade agreements are therefore more likely to increase the

United States’ imports of pharmaceuticals if U.S. firms decide to take advantage of

foreign facilities to produce pharmaceuticals at a lower cost.

Non-governmental organizations have been very critical of U.S. pharmaceutical

firms for forcing patent protection on developing countries. While U.S. exports to

developing countries have increased following the implementation of the TRIPS

agreement, developing countries have not necessarily benefitted from an increase in

access to pharmaceuticals. Actually, although PEPFAR has led to an increase in U.S.

exports of pharmaceuticals to program participants, poorer developing countries might

have suffered from a drop in access to pharmaceuticals overall. Indeed, patent protection

limits these countries’ ability to purchase cheap generics from other countries such as

India.

24

The impact on pharmaceutical innovation in developing countries following this

increase in U.S. exports due to patent protection would be an interesting extension to

this paper. Implementation of patent protection in developing countries could stimulate

innovation and research and development spillover effects in developing countries

through trade. Theory suggests that IPR protection in the South should increase

technology transfers from multinational firms from the North (Dinopoulos &

Segerstrom, 2010). And some empirical evidence suggests that trade generates

technology transfers and research and development spillover effects (e.g. Almeida &

Fernandes, 2008; Ciruelos & Wang, 2005; Haruna, Jinji, & Zhang, 2010; Keller, 2004;

Parameswaran, 2009; Xu & Chiang, 2005). An increase in U.S. exports of

pharmaceuticals following patent protection implementation in developing countries

could lead to stimulating innovation in pharmaceuticals in at least some developing

countries.

25

APPENDIX 1: LIST OF TRADING PARTNERS

Albania (2000)

Algeria (2005)

Angola (NA)

Argentina (2000)

Armenia (1999)

Azerbaijan (1997)

Bangladesh (NA)

Barbados (2001)

Belarus (2003)

Belize (2001)

Benin (2002)

Bolivia (2000)

Botswana (1997)

Brazil (1997)

Bulgaria (1996)

Burkina (2002)

Burundi (NA)

Cambodia (NA)

Cameroon (2002)

Cape Verde (2009)

Central African Rep.

(2002)

Chad (2002)

Chile (2005)

Colombia (2000)

Costa Rica (2000)

Cote d'Ivoire (2002)

Djibouti (2009)

Dominican Rep. (2000)

Ecuador (1996)

Egypt (2002)

El Salvador (2005)

Equatorial Guinea (2002)

Estonia (1999)

Fiji (NA)

Gabon (2002)

Georgia (1999)

Guatemala (2000)

Guinea (2002)

Guinea-Bissau (2002)

Guyana (NA)

Haiti (NA)

Honduras (2000)

Hungary (1996)

India (2005)

Indonesia (2001)

Jamaica (NA)

Jordan (1999)

Kazakhstan (1999)

Kenya (2002)

Kyrgyzstan (1998)

Laos (NA)

Latvia (1995)

Lebanon (2000)

Lesotho (NA)

Liberia (NA)

Libya (NA)

Lithuania (2000)

Madagascar (NA)

Malawi (NA)

Malaysia (2000)

Maldives (NA)

Mali (2002)

Mauritania (2002)

Mauritius (2003)

Mexico (2000)

Moldova (2000)

Morocco (2004)

Mozambique (2006)

Namibia (NA)

Nicaragua (2001)

Niger (2002)

Nigeria (NA)

Oman (2000)

Pakistan (2005)

Panama (1997)

Papua New Guinea (2002)

Paraguay (2005)

Peru (2000)

Philippines (1998)

Poland (2000)

Rep. Yemen (NA)

Romania (2000)

Russia (2003)

Rwanda (NA)

Saudi Arabia (2004)

Senegal (2002)

Seychelles (NA)

Sierra Leone (NA)

Slovenia (2001)

Solomon Islands (NA)

South Africa (1997)

Sri Lanka (2003)

Sudan (NA)

Swaziland (NA)

Syria (NA)

Tanzania (NA)

Thailand (1999)

Togo (2002)

Trinidad and Tobago

(1997)

Tunisia (2000)

Turkey (1999)

Uganda (NA)

Ukraine (2003)

Uruguay (2001)

Vanuatu (NA)

Venezuela (2000)

Zambia (NA)

Zimbabwe (NA) Note: the date in parentheses indicates the year that a country has implemented some type of patent

protection similar to the TRIPS requirements for pharmaceuticals. Some countries have implemented

rules similar to the TRIPS agreement before the deadline. For example, lesser developed countries that

have signed the Bangui Agreement Relating to the Creation of an African Intellectual Property

Organization have implemented rules in 2002, despite the fact that the TRIPS requirement will only apply

in 2016.

26

APPENDIX 2: FREE TRADE AGREEMENTS (FTA) INVOLVING THE UNITED

STATES

Free Trade Agreement Status

The United States-Australia Free Trade

Agreement

entered into force on January 1, 2005

The United States-Bahrain Free Trade

Agreement

entered into force in August 2006

The North American Free Trade Agreement

(NAFTA) between the United States, Canada

and Mexico

entered into force on January 1, 1994

The United States-Chile Free Trade

Agreement

entered into force on January 1, 2004

The United States-Colombia Trade Agreement signed on October 21, 2011

The Dominican Republic-Central America-

United States Free Trade Agreement

(CAFTA-DR) with five Central American

countries (Costa Rica, El Salvador,

Guatemala, Honduras, and Nicaragua) and the

Dominican Republic

entered into force for El Salvador on

March 1, 2006, for Honduras and

Nicaragua on April 1, 2006, for

Guatemala on July 1, 2006, for the

Dominican Republic on March 1,

2007, and for Costa Rica on January

1, 2009.

The United States-Israel Free Trade Area

Agreement

entered into force August 19, 1985

The United States-Jordan Free Trade Area Agreement

entered into force on December 17, 2001

The United States-Korea Trade Agreement entered into force on March 15, 2012

The United States-Morocco Free Trade

Agreement

entered into force on January 1, 2006

The United States-Oman Free Trade

Agreement

entered into force on January 1, 2009

The Panama Trade Promotion Agreement signed on October 21, 2011

The United States-Peru Trade Promotion

Agreement

entered into force on February 1,

2009

The United States-Singapore Free Trade

Agreement

entered into force on January 1, 2004

Source: Office of the USTR (2012)

27

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35

TABLE 1. SUMMARY STATISTICS

Variable Obs. Mean Std. dev. Min Max

𝑋𝑗𝑡 1,728 18.3 73.4 0 1,188.2

𝑙𝑛(𝑋𝑗𝑡) 1,536 14.24 2.66 7.75 20.90

𝑙𝑛(𝐺𝐷𝑃𝑃𝐶𝑗𝑡) 1,728 7.40 1.17 4.44 9.94

𝑙𝑛(𝑃𝑜𝑝𝑗𝑡) 1,728 15.89 1.65 11.22 20.88

𝑙𝑛(𝐷𝑗) 1,728 8.99 0.46 7.81 9.69

𝐿𝑎𝑛𝑔𝑗 1,728 0.28 0.45 0 1

𝐹𝑇𝐴𝑗𝑡 1,728 0.04 0.19 0 1

𝑇𝑅𝐼𝑃𝑆𝑗𝑡 1,728 0.45 0.50 0 1

𝑃𝐸𝑃𝐹𝐴𝑅𝑗𝑡 1,728 0.07 0.25 0 1

𝐿𝑎𝑛𝑑𝑙𝑗 1,728 0.22 0.42 0 1

𝐷𝑜𝑙𝑙𝑎𝑟𝑗𝑡 1,728 0.03 0.18 0 1

𝑂𝑝𝑒𝑛𝑗𝑡 1,617 57.17 18.25 9.45 98.72

Note: exports in millions of dollars. Data unavailable in 2010 for 𝑂𝑝𝑒𝑛𝑗𝑡.

36

TABLE 2. GROUPS OF COUNTRIES BY QUARTILES OF LIFE-EXPECTANCY IN 2007

Below or equal to

57.1 years

Above 57.1 and

below or equal to

68.6 years

Above 68.6 and

below or equal to

72.6 years

Above 72.6 years

Angola

Botswana

Burkina

Burundi

Cameroon

Central African

Rep.

Chad

Djibouti

Equatorial Guinea

Guinea-Bissau

Kenya

Lesotho

Malawi

Mali

Mozambique

Namibia

Niger

Nigeria

Rwanda

Senegal

Sierra Leone

South Africa

Swaziland

Tanzania

Uganda

Zambia

Zimbabwe

Azerbaijan

Bangladesh

Benin

Bolivia

Cambodia

Cote d'Ivoire

Gabon

Guinea

Guyana

Haiti

India

Kazakhstan

Kyrgyzstan

Laos

Liberia

Madagascar

Maldives

Mauritania

Moldova

Pakistan

Papua New Guinea

Republic of Yemen

Russia

Solomon Islands

Sudan

Togo

Ukraine

Algeria

Belarus

Brazil

Cape Verde

Dominican

Republic

Egypt

El Salvador

Fiji

Georgia

Guatemala

Honduras

Indonesia

Jamaica

Jordan

Latvia

Lebanon

Lithuania

Mauritius

Morocco

Paraguay

Philippines

Romania

Sri Lanka

Thailand

Trinidad and

Tobago

Turkey

Vanuatu

Albania

Argentina

Armenia

Barbados

Belize

Bulgaria

Chile

Colombia

Costa Rica

Ecuador

Estonia

Hungary

Libya

Malaysia

Mexico

Nicaragua

Oman

Panama

Peru

Poland

Saudi Arabia

Seychelles

Slovenia

Syria

Tunisia

Uruguay

Venezuela

Source: World Bank (2010).

37

Table 3. The Impact of Patent Protection on the United States’ Exports of Pharmaceuticals Model: (1) (2) (3) (4) (5) (6) (7) (8) (9)

Estimator: NB NB NB OLS OLS OLS PPML PPML PPML

Log GDP p.c. 0.61***

(0.02)

0.59***

(0.03)

0.57***

(0.03)

1.21***

(0.10)

1.18***

(0.11)

0.75*

(0.38)

1.13***

(0.04)

1.17***

(0.05)

-0.20

(0.35)

Log population 0.62***

(0.01)

0.64***

(0.02)

0.78***

(0.02)

1.13***

(0.07)

1.12***

(0.08)

-0.60

(0.87)

0.91***

(0.04)

0.95***

(0.04)

-1.42**

(0.65)

Log distance -0. 93***

(0.06)

-0.90***

(0.06)

-1.73***

(0.23)

-1.63***

(0.22)

-1.07***

(0.08)

-0.92***

(0.07)

Language dummy 0.39***

(0.05)

0.35***

(0.06)

0.83***

(0.25)

0.69***

(0.26)

0.10

(0.10)

0.21**

(0.09)

FTA dummy 0.50***

(0.07)

0.51***

(0.08)

0.03

(0.12)

0.50

(0.41)

0.51

(0.42)

-0.08

(0.15)

0.01

(0.12)

0.09

(0.13)

-0.13

(0.12)

TRIPS dummy 0.28***

(0.05)

0.28***

(0.05)

0.34***

(0.06)

0.09

(0.22)

0.16

(0.22)

0.10

(0.14)

0.73***

(0.11)

0.69***

(0.10)

0.22***

(0.06)

PEPFAR dummy 0.08

(0.10)

0.28***

(0.08)

0.81***

(0.23)

1.01***

(0.30)

-0.06

(0.16)

0.17

(0.13)

Landlocked dummy -0.09

(0.07)

-0.16

(0.30)

-0.82***

(0.14)

Currency dummy 0.59***

(0.11)

0.66***

(0.13)

1.24*

(0.63)

-0.10

(0.13)

1.35***

(0.14)

0.35***

(0.11)

Openness index 0.00***

(0.00)

0.01***

(0.00)

0.00

(0.01)

-0.00

(0.01)

0.01*

(0.00)

-0.01***

(0.00)

R² 0.66 0.67 0.88 0.79 0.80 0.98

Country fixed effect No No Yes No No Yes No No Yes

Observations 1,728 1,617 1,617 1,536 1,430 1,430 1,728 1,617 1,617

Notes: *, ** and *** denote significance at the 10%, 5% and 1% level. Standard errors are reported in parentheses. Estimations in columns (4)-(9)

include annual time dummies. For OLS estimations, the dependant variable is the log of exports, and robust standard errors clustering by country are

in parentheses. Column (3) displays the results of a negative binomial panel regression with country conditional fixed effects and annual time

dummies. Columns (1) and (2) display the results of negative binomial panel regressions with year conditional fixed effects.

38

TABLE 4. THE GINARTE AND PARK INDEX VS. THE TRIPS INDEX

Model: (1) (2) (3) (4) (5) (6)

Estimator: NB NB NB NB NB NB

Log GDP p.c. 0.69***

(0.09)

0.72***

(0.10)

0.33***

(0.12)

0.66***

(0.08)

0.68***

(0.09)

0.32***

(0.12)

Log population 0.57***

(0.04)

0.59***

(0.05)

0.71***

(0.09)

0.58***

(0.04)

0.59***

(0.05)

0.75***

(0.09)

Log distance -1.01***

(0.16)

-1.01***

(0.16)

-0.99***

(0.15)

-0.97***

(0.16)

Language dummy 0.37***

(0.14)

0.34**

(0.15)

0.36**

(0.14)

0.32**

(0.15)

FTA dummy 0.64***

(0.20)

0.60***

(0.20)

0.01

(0.34)

0.59***

(0.20)

0.57***

(0.19)

0.04

(0.35)

Ginarte & Park Index 0.04

(0.09)

-0.00

(0.10)

0.35***

(0.12)

TRIPS dummy 0.31**

(0.15)

0.29**

(0.15)

0.63***

(0.18)

PEPFAR dummy 0.39

(0.28)

0.60**

(0.27)

0.40

(0.27)

0.73***

(0.28)

Landlocked dummy -0.08

(0.20)

-0.14

(0.19)

Currency dummy 0.70***

(0.23)

0.25

(0.36)

0.64***

(0.23)

0.24

(0.37)

Openness index 0.00

(0.00)

0.01**

(0.01)

0.00

(0.00)

0.01**

(0.01)

Country fixed effects No No Yes No No Yes

Observations 234 231 231 234 233 233

Notes: *, ** and *** denote significance at the 10%, 5% and 1% level. Standard errors are reported

in parentheses. Columns (3) and (6) display the results of a negative binomial panel regression with

country conditional fixed effects and annual time dummies. Columns (1), (2), (4) and (5) display the

results of negative binomial panel regressions with year conditional fixed effects.

39

TABLE 5. U.S. EXPORTS ACCORDING TO QUARTILES OF LIFE-EXPECTANCY

Model (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Estimator NB NB NB NB NB NB NB NB NB NB NB NB

Log GDP p.c. 0.61***

(0.06)

0.47***

(0.05)

0.68***

(0.08)

0.52***

(0.06)

0.87***

(0.11)

0.36***

(0.06)

0.72***

(0.09)

0.48***

(0.07)

0.48***

(0.11)

0.31***

(0.08)

0.23*

(0.12)

-0.14

(0.09)

Log

population

0.81***

(0.04)

0.80***

(0.03)

0.67***

(0.04)

0.84***

(0.04)

0.90***

(0.06)

0.87***

(0.04)

0.74***

(0.05)

0.82***

(0.05)

1.06***

(0.06)

0.70***

(0.06)

0.59***

(0.04)

0.57***

(0.05)

Log distance -1.51***

(0.12)

-0.56***

(0.10)

-0.72***

(0.11)

-1.56***

(0.14)

-0.59***

(0.11)

-0.81***

(0.12)

Language

dummy

0.45***

(0.12)

0.48***

(0.12)

0.66***

(0.12)

1.80***

(0.26)

0.32**

(0.12)

0.36***

(0.12)

0.54***

(0.13)

1.43***

(0.27)

FTA dummy -0.20

(0.18)

0.45***

(0.10)

-0.19

(0.19)

0.43***

(0.11)

-0.31**

(0.15)

0.18

(0.16)

TRIPS

dummy

0.45***

(0.12)

0.04

(0.10)

0.38***

(0.10)

0.52***

(0.10)

0.32**

(0.13)

-0.05

(0.11)

0.37***

(0.11)

0.40***

(0.12)

-0. 11

(0.16)

-0.07

(0.13)

0.34***

(0.10)

0.77***

(0.12)

PEPFAR

dummy

0.24

(0.16)

0.37**

(0.18)

-0.57

(0.39)

0.20

(0.17)

1.11***

(0.22)

0.09

(0.16)

Landlocked

dummy

0.11

(0.13)

0.47**

(0.19)

-0.47***

(0.18)

Currency

dummy

Openness

index

-0.01***

(0.00)

0.01***

(0.00)

0.01***

(0.00)

0.01***

(0.00)

0.00

(0.00)

0.01**

(0.00)

0.01

(0.00)

0.02***

(0.00)

Country FE No No No No No No No No Yes Yes Yes Yes

Observations 432 432 432 432 405 405 402 405 405 405 402 405

Quartile Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Notes: *, ** and *** denote significance at the 10%, 5% and 1% level. Standard errors are reported in parentheses. Columns (1)-(8) display the results of negative binomial

panel regressions with year conditional fixed effects. Columns (9)-(12) display the results of a negative binomial panel regression with country conditional fixed effects and

annual time dummies. Estimations (1) and (5) omit distance, because all countries in this first quartile are in Africa and cultural proximity matters more than distance.