Growth and Trade: Will A Liberalization of Trade Policy Lead to Higher Economic Growth?
An Empirical study of how trade liberalization affects the growth rate
Espen Brandt Fjeld
Master Thesis
Department of Economics Faculty of Social Sciences
UNIVERSITY OF OSLO
May 2019
Growth and Trade; Will A Liberalization of Trade Policy Lead to Higher Economic
Growth?
An Empirical study of how trade liberalization affects the growth rate
Espen Brandt Fjeld
Copyright Espen Brandt Fjeld
2019
Growth and Trade: Will a liberalization of trade policy lead to higher economic growth? An Empirical study of how trade liberalization affects the growth rate.
Espen Brandt Fjeld
http://www.duo.uio.no
Trykk: Reprosentralen, Universitetet i Oslo
Abstract
In this thesis I ask the question if countries that liberalize their trade, accelerates their rate of growth, compared to those countries that do not liberalize their trade policy. To investigate this question empirically I take advantage of the Uruguay round of trade talks, that focused on the lowering of tariff rates between countries. Using the results from this round of trade negotiations have the advantage that a large number of countries participated, and enough time have gone since the lowering of tariffs, to pick up on any effect this liberalization could have on the growth rate of a country. To investigate if there is a relation between the
economic growth and the lowering of tariffs, I use a difference-in-difference approach, where I compare the growth rate of the liberalizer in the post-treatment period to the growth rate of the non-liberalizers. The results I arrive at indicates that the liberalizers in fact accelerated their growth in comparison to those countries that did not liberalize their trade policy. All my calculations are done in STATA.
Acknowledgments
Writing this master thesis has been a rewarding, but also challenging journey. It is the
culmination of two years of master studies, at the department of economics, at the University of Oslo. Here I have been able to study topics in economics that I find both interesting and important, this thesis incorporate two of these subjects: Economic Growth and Trade.
I want to thank my supervisor Andreas Moxnes for the excellent advices he have given me through the entire process of writing this thesis. From the initial stage of choosing a topic to the final stages of writing. I also want to thank my parents for all the support they have given me throughout my studies, and for supporting the choices that I have done. I also want to thank Ingunn Krag-Rønne for taking time to read the draft for this thesis, and give me feedback on written mistakes, and for how to improve upon the text. I also want to thank everyone that has been around me in the process. To those who have helped me in times when my motivation has been at a low and put up with me at my most frustrating. Last, but not least, I want to thank my partner Nicole, thank you for all your support, I know that I have not been easy to be around for the last months. Thank you for all your support and the motivation you have given me.
All the mistakes, errors and inaccuracies in this thesis are my responsibility alone.
Espen Brandt Fjeld Vikersund
May 2019
table of contents
1 Introduction ... 1
2 Theory ... 5
2.1 Economic Growth in the Heckscher-Ohlin Model ... 5
3 Empirical Approach ... 12
3.1 The Difference-in-Difference Method ... 12
3.2 Application of the Difference-in-Difference method ... 16
4 Description of Data Sources ... 21
4.1 Growth rate ... 21
4.2 Tariffs ... 21
4.3 Controls ... 21
5 Summary Statistics ... 22
5.1 Tariff rate ... 22
5.2 GDP ... 24
5.3 GDP Vs. Tariffs ... 26
6 Empirical Results ... 29
6.1 Robustness tests ... 31
6.1.1 Dropping Variables ... 32
6.1.2 Placebo Test ... 33
6.1.3 Outliers ... 35
6.1.4 Regions ... 36
6.1.5 Continuous Variable ... 38
7 Summary ... 40
Biblography ... 42
Appendix: Sample Countries ... 45
List of Tables
Table 1; Grouping of possible observations using the difference- in-difference-method 15
Table 2; Average tariff rates 24
Table 3; Growth rates for Liberalizers and Non-Liberalizers 26 Table 4; Correlation between change in growth and change in tariff 28
Table 5; Mean change in growth rates and tariff rate 28
Table 6; Main regression results 30
Table 7; Dropping variables in the regression 32
Table 8; placebo test 33
Table 9; Outliers 35
Table 10; Controlling for regions 37
Table 11; Number of observations in each region 38
Table 12; Regression with a continuous variable 38
Table 13; Robustness test of the continuous variable using Huber weights 39
List of Figures
Figure 1; Tariffs before and after liberalization 22
Figure 2; Tariff development over time 23
Figure 3: Development of GDP 25
Figure 4; Development in GDP Liberalizers 25
Figure 5; Development GDP Non-Liberalizers 26
Figure 6: Change in Growth Vs. Change in tariffs 27
Figure 7; Common trend log GDP per capita growth relative to 1975-1990 trend 34
1 Introduction
The question if more trade leads to higher economic growth has been one of the most central questions in economics since Adam Smith wrote the Wealth of Nations. The question is possibly more relevant today than it has been in a long time. In the United States president Donald Trump has raised tariffs against foreign trade and threatens to withdraw from international trade agreements which he argues take advantages of the United States. In Europe one sees the rise of Eurosceptic and so called alternative right parties, that is skeptical to both the European Union and other international agencies. One sees a chaotic separation between the European Union and the United Kingdom, and no one really knows what will happen if it the United Kingdom leaves the European Union without an agreement in a so- called hard Brexit.
Today we can separate between two different views on what leads to economic growth: The policy view and the institutional view. The institutional view support that economic growth happens because of the quality of institutions and the historical development of these (Easterly, 2005, p. 1054). The policy view is a collection of many different approaches that incorporates many different political tools for how one might achieve a higher rate of growth.
According to the institutional view, historical factors are important for the development of the institutions in the country. The policy view, on the other hand, emphasizes that
institutions and geography play a role, but the history of how they developed is not important.
The changes in policy will lead to the necessary changes in institutions, regardless of historical factors (Easterly, 2005, p. 1054). The collection of policies known as the
“Washington Consensus” can be seen as being a part of the policy point of view.
In 1989, the term “Washington consensus” was coined and in 1990 John Williamson used the term in its written form for the first time. The term was used in a background paper for a conference that the Institute for International Economics in Washington D.C. held about Latin America. Here he made a list of ten polices that more or less everyone in Washington could agree upon was needed in the region and labeled the ten proposition the “Washington Consensus” (Williamson, 2008, p. 14)”. One of these ten polices was that some sort of trade liberalization was needed as a way of promoting growth and higher incomes.
To investigate if a trade liberalization has an effect, I am asking the question; “Dose the rate of growth accelerate more for a liberalizing country than a non-liberalizing country”. This is the same question as Estevadeordal and Taylor asked in their article; “Is the Washington Consensus Dead? Growth, Openness, and the Great Liberalization” from 2013. This way of asking the question has the benefit of being closer to what todays debate of openness and economics is about. The view of those who are skeptical to today’s international agreements and the international trading system, is that they are being taken advantage of. They claim that they do not compete on the same terms and thus the economic performance of the country is not enhanced by international trade but hampered by what they see as unfair competition. In their view, a more closed home economy will enhance their economic growth, meaning higher barriers to trade actually will accelerate growth. But what if the opposite is true? If the shift to a more open economy in fact accelerates the growth
performance of the liberalizers? Then a more restrictive politics towards international trade, could also mean that the growth rate should deaccelerate. The results that I arrive at, in fact indicate that the growth rate dose accelerate for a liberalizing economy, something that could indicate that the opposite is true for an economy that goes from being open to closed.
In testing the claim made in the previous paragraph will follow the approach of
Estevadeordal and Taylor (2013), which uses a difference-in-difference method in their article. The approach they developed seems, as of now, to be the most robust way of testing if there is a connection between growth and openness. As they do, I look at the growth rate before and after the liberalization took place in the 1990´s. The first period is from 1975 to 1989 and the second period is running from 1990 to 2014. My second period is running for ten extra years compared to Estevadeordal and Taylor, this means that I will be able to pick up on effects that they, for obvious reasons, could not. By using the difference-in-difference approach one also avoids some of the problems related to the omitted variable bias that has plagued some earlier studies. As Estevadeordal and Taylor (2013) I use both a discreet and a continuous measure to avoid one of the problems addressed with an paper by Sachs and Warner (1995): That a binary variable will throw away too much information. I am also looking at different regions in the world to see if any of them could drive a potential growth acceleration for the liberalizers in the second period.
At around the same time as the term “the Washington consensus” was used for the first time a number of empirical studies was published. These supports the view that trade liberalization
has an effect on growth. One of the most cited papers is by Dollar (1992) that develop an index for real-exchange distortions. He then uses this index to investigate if there was a relation between the outward orientation of a country and growth. He found that there is a significant, negative relation between distortion in the real-exchange rate and the growth of per capita GDP. Dollar suggested that there were large gains to be made for Latin-American and African economies to be more outward orientated. Another study that is widely cited is by Sachs and Warner (1995). In their paper they use five criteria to asses if a country is open or closed, the country will be classified as closed if it qualifies for one or more of the five criteria’s. Then they use a dummy variable in their regression, the variable will be on if the country liberalized between 1955 and 1970 and off if it did liberalize after 1970. After this exercise they conclude that there is evidence for convergence among economies that was open in the time period 1970-89, and evidence for accelerated growth among countries that did undertake market reforms. Ben-David (1996) examines the relationship between groups of major trade partners. He finds that within the trade groups it is evidence of income convergence. When he groups countries randomly, he does not find the same income convergence as when the countries are staying within their trade group. Edwards (1998) analyses the robustness of the relationship between openness and total factor productivity growth. Here he uses nine indexes of trade policy to investigate the claim that the total factor productivity growth is faster in more open economies. The result that Edwards (1998) arrives at seems to indicate that more open economies in fact grow faster. Frankel and Romer (1999) measures the geographic component of countries trade. They then use those measures to obtain an instrumental variable estimate of trades effect on income. They conclude that trade has a quantitatively, large and robust, but only moderately statistically significant positive effect on income.
Many of the studies above are contested by Rodríguez and Rodrik (2000). They point out that the results often are weak and not robust to testing, and shows that one easily can contest the results if one change the method slightly or control for other factors. Slaughter (2001) uses a difference-in-difference approach to analyze if trade liberalization contributed to per capita convergence across countries. In the analysis he investigates trade liberalizations after the second world war, and he arrives at the result that there is no evidence for convergence among the liberalizer. On the contrary, the results suggest that the trade liberalization in fact causes divergence in income among the liberalizers. In a self-review by the World bank, Rodrik (2006) claims that the “Washington Consensus” in fact is dead. He here presents a
new set of reforms that Washington now can agree on, these objectives can be separated into two groups. The first group are the institutional reforms, and the second group puts faith to foreign aid, such as the U.N. millennium report. Easterly (2005) is also giving support to the institutional view that is promoted by Rodrik (2006). He shows that the policy view in fact has a reasonable theoretical foundation. The statistical results he arrives at when testing the theory seems to not support the theoretical foundations of the policy view.
Estevadeordal and Taylor (2013) contest the view that is taken by Rodrick, that the Washington Consensus is dead. In their article they point out that most of the earlier work that is criticized by Rodríguez and Rodrik (2000), and the paper by Slaughter (2001), uses data from before the liberalization in the 1990s. This means that they do not pick up on any growth effects that could have happened because of this policy change. In their paper they use the reduction in tariffs that was agreed upon after the Uruguay round of trade talks to investigate the relationship between the growth rate and the openness of a country. They look at the growth rate before and after the liberalization, by using a difference-in-difference approach, where they use a dummy variable for the liberalizers. They find that there in fact seems to be a connection between openness and trade.
In the following sections I will first discuss a theoretical approach for what one might expect happens with the growth rate in a country, that is going form autarky to an open economy, in the short to medium run. After this I will turn to a description of my empirical approach.
Before I give a short description and summary of the data that I employ. I then will present the result of the empirical exercises I have done. In the end I will give a summary.
2 Theory
To be able to say something about the gains from trade one also needs a theory. This theory needs to both incorporate international trade and economic growth. Here I will not go deeply into the theories of why countries trade in the first place but focus on the effects that trade has on growth. The two main different approaches one has in modeling why countries trade in the first place is the Heckscher-Ohlin model, here trade is driven by differences in factor
abundance between countries that faces similar production technologies. The second approach is to use a Ricardian type model where trade is driven by differences in technological comparative advantages (Acemoglu, 2009, p. 648). The main difference between these two approaches lies in whether the prices of the goods that a country supply to the world market are affected by its own production and accumulation decisions (Acemoglu, 2009, p. 648). It turns out that when using the Heckscher-Ohlin-model the prices are not affected, under the assumption that countries are small relative to the world. In the Ricardian case on the other hand, even under the assumption that the country is small it will be able to influence the price. In the following I will just concentrate on the first approach; this is because it helps describe what one might expect happen with the growth rate in the short to medium run in a simple and intuitive way. It will turn out that growth is driven by a Solow like capital accumulation. I start with a short description of the model, and discuss some implications, before turning to some problems of using this model.
2.1 Economic Growth in the Heckscher-Ohlin Model
The intuition when using the Heckscher-Ohlin model to describe what happens with growth when opening up for trade is the following. When countries open up to the world after being in a state of autarky, it will face a return to capital that is not influenced by the decisions of how much capital to accumulate. This will only be the case as long as itself is small relative to the world. Since the country cannot influence the price of capital it will now be as if they face a production function without diminishing returns to capital. This in turn means, that the return on the last unit of capital employed by the liberalizer will be higher, compared with the return the world receives for the same unit of capital. This means that a liberalizing country can increase the saving rate relative to the world and accelerate the growth rate. This will
only happen in the short to medium run since there will be a point where the country becomes so big that it starts to influence the world price of capital. When this happens, it will no longer act as it has constant returns to capital, but again follow the standard neo-classical production function with diminishing returns to capital. The growth rate relative to the world will fall as it converges towards the new steady state level. Since all countries here face the same production technologies, the reason that they trade at all is because of the difference in factor abundance.
The Heckscher-Ohlin theorem says that each country will export the good that uses its factor insensitively in production (Feenstra, 2016, p. 26). This means that countries that is abundant in labor will tend to export goods that uses this factor intensively in production and those intensive in capital will tend to export capital intensive goods. If one test if these prediction holds empirically it turns out that the model dose a quiet bad job in predicting the actual trade patterns (Feenstra, 2016, p. 28). Another feature of this model is that the opening up of trade should lead to an equivalization of factor prices. In a dynamic model of trade as shown by Stiglitz (1970) this might not be the case if countries has different rates of time preferences, then factor prices actually could diverge. Samuelson (1971) on the other hand shows that it is very likely that the factor prices will converge, under the standard specific framework. Thus, there is no clear answer to the prediction that the Heckscher-Ohlin model do about factor prices, actually holds. To show how economic growth might look like in a Heckscher-Ohlin world I will mostly follow Acemoglu (2009, pp. 655-663) who in turn bases his treatment on Ventura (1997).
The Heckscher-Ohlin model assumes that countries have access to the same technology, but they differ in the number of factors that it uses for production. Some countries will be more abundant in labor and others in capital and it is this difference that will drive the trade. The production of the final output in a country is a function of labor-intensive inputs and capital- intensive inputs. It is assumed that countries can trade in inputs of production, but not in the final good. The production function for the different countries will follow the standard assumptions, meaning it displays constant return to scale and diminishing returns to capital.
The Inada conditions is also assumed to hold, and the production is competitive. An
assumption done by Ventura (1997) is that that there can be production differences between countries in production of labor intensive goods but not in capital goods. This is due to the form of the production function for the intermediate goods. The production of the labor-
intensive input contains a labor augmenting term that can differ between countries while the production function for capital is the same for all countries. It is also assumed that there is no technological progress. Another important feature is that one unit of the final good can be transformed into one unit of consumption good or one unit of capital good, since both goods are produced using the same technology (Acemoglu, 2009, p. 658). This means that one can give up on one unit of consumption to receive one more unit of the capital good.
Since one assume that there is free-trade in the intermediate goods, the prices between these goods will equalize to their world price as determined from the world supply and demand.
Then as long as a country is small, the decision about how much capital to accumulate or how much of the good that it should supply to the world market will not affect the factor price. Since we have a competitive market, the wage rate and the rental rate on capital are given by their marginal product.
In Heckscher-Ohlin model with growth it turns out that the rental rate on capital will be equal to the price of the capital good on the world market. Wages will be equal to the price of the labor-intensive good times the labor augmenting term. Factor prices will equalize since they are determined by world markets (Acemoglu, 2009, p. 657). The rental rate on capital across countries will be the same as the world price of the capital-intensive input. The wage rate will not be equalized across countries because the model allows for different labor augmenting technologies. What will equalize is the effective wage rates, this is the wage rate dived on the labor augmenting term (Acemoglu, 2009, p. 657). Since one does not allow for technological change this means that it is the wage that has to change, for the effective wage rate to be equalized across countries. This condition is weaker than if there had been no difference in productivity of labor, which had implied that the wage rate would have equalized across countries (Acemoglu, 2009, p. 657). In essence this is a closed-economy model, so the intensive to accumulate capital is shaped by the capital-labor ratio. Since the world now trades this is not determined by prices at home but in the world (Acemoglu, 2009, p. 657).
In this world we do not consider intertemporal trade, meaning that countries cannot trade in financial assets just real commodities. Since one do not allow for lending or borrowing all countries must run a balanced trade. If it is importing the majority of its labor-intensive goods, it must export capital intensive-goods. Or said differently, if a country uses more labor-intensive goods in production than it produces its self it needs to import the extra labor-
intensive goods. This has to be made up by an equal export of the capital-intensive good, which will be produced in a greater amount than what is used in production at home. On the consumer side of the economy we allow for a representative consumer that maximizes her utility over a given set of preferences. A world equilibrium in this economy is a phat of consumption, capital accumulation and capital intermediate intensity decisions for each country, and paths of world prices, such that the representative consumer maximizes her utility according to the price, and that the world markets clear (Acemoglu, 2009, p. 659). In the steady state these sizes do not change, meaning that the derivative with respect to time is zero.
So far nothing out of the ordinary has happen within the framework of the Heckscher-Ohlin model with growth, it looks like the neoclassical growth model applied to the world as a whole. When solving the model one finds that the world is converging towards a unique steady state equilibrium. This should not be a surprising result, since as mentioned, this model has the feature of a closed economy. The interesting point of this model is due to the assumption that each country by itself is small and thus do not affect the world price of capital. If a country is in autarky it will run into diminishing returns to capital, because the concavity of the production function. A small country that is integrated into the world economy on the other hand will face a somewhat different production technology than the world as a whole. Since it is small, the decision on how much capital it wants to accumulate will not influence the world price of capital. This in turn means that the country not will face diminishing returns to capital, in fact it will receive a return on capital that is independent form its capital accumulation decisions. They will face a production with constant return to capital.
Let us assume that country 𝑗 goes from being in autarky to be an integrated part of the world economy. Also make the assumption that this country is small relative to the rest of the world. When a country is in autarky the equations that determines the laws of motion in the economy will be the same for the world and for country 𝑗. The difference will be the
parameter values that enter these equations (Ventura, 1997, p. 67). The law of motion for the average consumption of the world, with preferences given by ∫ (-, $%&'(*)+)*𝑒)/0)𝑑𝑡, turns out to be $̇$%
% = *
+(𝑟0− 𝜌). 𝑐0 is per capita consumption, 𝜌 is the discount rate and *
+ is the elasticity of intertemporal substitution and 𝑐̇0 is the time derivative. Since the rental rate on
capital, in this model will be equal to the world price on capital, we get 𝑅0 = 𝑃0=. Since 𝑅0 = 𝑟0+ 𝛿, the above Euler equation can be written as $̇$%
% =+*(𝑃0=− 𝛿 − 𝜌) where 𝛿 is the depreciation rate on capital. In the steady state it must be that 𝑐̇0 = 0. This in turns gives a price on capital in the steady state of 𝑃0= = 𝛿 + 𝜌. Assuming now that country 𝑗 has a lower discount rate than the world. Also, assume that the other parameters are the same as for the world. It turns out that for a time interval [0, 𝑇) the growth rate of country 𝑗 will be given by 𝑔F = $̇$G%
G% =+*( 𝜌 − 𝜌F), for 𝜌 > 𝜌F. This is a result that is attributed to the AK-production function.
Rebelo (1991) introduces a simple AK-model of endogenous growth, here he suggests that output is proportional to capital. There is worth mentioning that Rebelo´s concept of capital is broad and includes both human and physical capital. In principle it includes all stocks of knowledge, technology or organizational techniques that can be built up over time by
sacrificing some of today’s consumption (Easterly, 2005, pp. 1017-1018). The simplest form of the relationship between output and capital that one can write down, is the following linear relation, 𝑌 = 𝐴𝐾, which it is why it is called the AK-production function.
Using this production function one can see that the return on capital will be 𝑅F0= 𝐴 and 𝑟F0= 𝐴 − 𝛿. The law of motion for consumption given the preferences above will be
$̇G%
$G% =+*(𝑟F0− 𝜌F) inserting for 𝑟F0, gives $̇$G%
G% =+*(𝐴 − 𝛿 − 𝜌F) . Since the return on capital for country 𝑗 now will be equal to the world price of capital, one actually have that 𝐴 = 𝑃0= thus we have that $̇$G%
G% =+*(𝑃0=− 𝛿 − 𝜌F) this is the same as for the world in general, except for the difference in the discount rate. Now observing that 𝑃0= = 𝛿 + 𝜌 in the world steady state, it is possible to write $̇$G%
G% =+*(𝛿 + 𝜌 − 𝛿 − 𝜌F) which gives, $̇$G%
G% =+*(𝜌 − 𝜌F). This means as long as 𝜌 ≠ 𝜌F and assuming that 𝜌 > 𝜌F, country 𝑗 will grow faster compared to the rest of the world with 𝑔F =$̇$G%
G%. This will happen until the country is so big that it will influence the world price in its decision on how much capital to accumulate. When it reaches this point, it will again follow the neoclassical growth function with diminishing returns to capital. If on the other hand, 𝜌 < 𝜌F then the growth rate of the liberalizer will fall relative to the world
equilibrium, and the country will in fact have no intensive to liberalize at all. This is because the return on capital in that case will be higher in country 𝑗 than in the world.
This illustrates a problem with using the Heckscher-Ohlin model for describing what happens with growth when opening up for trade. To arrive at a well-behaved equilibrium it must happen that the discount rates equalize across countries, if not the model do not admit a well behaved equilibrium (Acemoglu, 2009, p. 663). When the discount rate, or the
deprecation rate of capital, is higher in the non-liberalizing country it has no reason to liberalize at all. But if country 𝑗 is assumed to have a lower discount rate and the return on capital is pinned down by the rest of the world, then it has a motive for liberalizing. The liberalizer will save at a higher rate than the rest of the world, because of the lower discount rate. This means that they can achieve positive growth per capita compared the rest of the world (Acemoglu, 2009, p. 663). This is the explanation Ventura (1997) gives for how the Asian “growth miracles” ,that started in the 1970s, could grow without facing diminishing return to capital. It is also shown that East Asian countries was more open than other developing economies at this time, and they accumulated capital at a more rapid rate (Acemoglu, 2009, p. 663).
Another reason that I use this framework is that it shows how growth possibly can accelerate for liberalizers in the medium run compared to the world, even though there are many weak points. The weakness is a result from how the Heckscher-Ohlin model assumes that trade between countries happens. For the growth acceleration to take place one needs the same discount factor between the countries and that factor prices in fact equalizes. If factor prices diverge as Stiglitz (1970) proposes, because of differences in the discount rates, we will not end up with a well-defined world equilibrium.
The Heckscher-Ohlin model, as mentioned, fails to actually predict trading patterns, one of the explanations given for this is that technologies differ across countries. This is probably a more realistic assumption than what the Heckscher-Ohlin model assumes. It is probably more likely that a producer in a developing country do not have access to the same means of
production as a producer in a developed country. At the same time opening up for trade could lead to the spread of technology, such that, producers in different countries with time will have access to the same technology. If one allows for technological progress that differs between countries it will take longer before the level of technology will equalize. If assuming
that the technology is developed in one part of the world and adopted in another as a result of trade. This is not unreasonable, since most of the technology we use today is made in
developed countries and spread to developing countries. Trough the spreading of
technologies one can imagine that production technology of some goods actually become standardized and can be used in any country. The Heckscher-Ohlin model could then apply since the technology of production will be the same.
Another drawback with the Heckscher-Ohlin model, that incorporates growth, is that it is not useful when studying the long run effects of economic growth on trade, it only says
something about the short to medium run effects. When a liberalizer converges towards the world equilibrium the growth effect disappears, and one will be in the world steady state. If one wants to account for differences in technology and investigate the long-term effects, one could use a model based on Ricardian comparative advantages in technologies. This model set up allows for differences in technology and for studying the long-term growth effects.
To summarize, In the long-run it will turn out that one ends up with a model that is equal to the neoclassical models of growth in a closed economy. The reason for this is that when the world is viewed as an integrated economic entity it will behave as a closed economy. Prices will be set as a result of world supply and demand, and no small country will be able to influence these prices by themselves. Since countries not are able to influence the prices given by the world economy, then in the short to medium run each country could act as if they faced an AK production function. Since this is the case, they are able to accumulate capital without influencing the return on capital. A higher return to capital means that the country wants to save more, in turn this effect will mean that a liberalizer will accelerate the growth rate compared to the rest of the world.
3 Empirical Approach
In this thesis I use the empirical approach used by Estevadeordal and Taylor (2013), where they ask if the rate of growth accelerate more in a liberalizing country than in a non-
liberalizing country. In the older literature, that is criticized by Rodríguez and Rodrik (2000) the dominant research question is: Do liberalized countries grow faster than the non-
liberalizers in a given period all else equal? This is mostly likely the wrong question to ask since it probably is impossible to include all the proper controls. This means, as Rodríguez and Rodrik (2000) showed, that the results are fraught with omitted variable bias and leaves little hope of a precise and definitive answer to the question asked in the older literature (Estevadeordal & Taylor, 2013, p. 1675). The advantage of asking the question in the way Estevadeordal and Taylor (2013) dose is that it is closer to the question asked by the policy makers before the liberalization, and it also corresponds closer to the claim made in the Washington Consensus that the liberalizing countries will grow faster (Estevadeordal &
Taylor, 2013, p. 1675).
To be able to answer the question: Dose the liberalizers accelerate the growth in comparison to the non-liberalizers we need to be able to conduct some sort of experiment. What can be used as a natural experiment her is the GATT Uruguay round that started in 1986 and concluded in 1994. This round of trade negotiations included 125 countries and focused strongly on tariff reductions both in developing and developed countries (Estevadeordal &
Taylor, 2013, p. 1676). The tariff cut done in this round of negotiation can then be used as a natural experiment to investigate if the countries that lowered their tariff levels -the
liberalizers- did have a higher growth rate compared to the non-liberalizers. To test for this one can, use a difference-in-difference regression, which is designed to control for
unobservable but fixed omitted variables (Angrist & Pischke, 2009, p. 221). Thus, one can by this method avoid some of the problems associated with omitted variables.
3.1 The Difference-in-Difference Method
The difference-in-difference set up is a research design that is popular in empirical economics and it is often used to investigate the impact of policy design and reforms that do not affect
everybody at the same time and in the same way (Lechner, 2011, p. 167). The method itself was probably first used by the physician John Snow which studied how cholera spread (Angrist & Pischke, 2009, p. 227). The prevailing view at the time was that it spread through
“bad air”. Snow developed an alternative hypothesis that it spread through contaminated water. To test this hypothesis, he used two water companies that got their water from the same source in the Thames river, which at the time was heavily contaminated by sewer, before one of the companies moved up river, and got water from a source that had less sewage in it. He then compared the areas that received water from the old source and the new sources. It turned out that the death rates fall sharply in the areas that got supplied by the water sources with less sewage (Angrist & Pischke, 2009, p. 227). In economics the
difference-in-difference method is widely used, among other, in the field of labor economics.
John Snow took advantage of the change in water supplies when he first used the difference- in-difference method. In labor economics one often takes advantage of variations in
minimum wages between states in the USA to investigate the slope of the labor-demand curve. Here the hypothesis is: If a state increases its minimum wage, the demand for labor should fall, since an increase in the minimum wage will move us upward on the downward sloping labor demand curve, and thus employment should fall. A widely cited study that looks into this question is by Card and Krueger (1994) where they investigate fast-food employment in New Jersey and Pennsylvania, before and after New Jersey raised the
minimum wage. Surprisingly they find that employment in the New Jersey fast-food business rose after the increase. Over time there has been developed a large literature in empirical economics using this method. The common factor in this literature, and in the study by Snow, is that there is some sort of natural variations between the groups that can be taken advantage of. The language and conceptual framework of these types of studies are the same as in experiments using true randomization. These studies are called natural experiments (Meyer, 1995, p. 151). The studies that are classified as natural experiments are comparing outcomes of groups that are not randomly assigned (Meyer, 1995, p. 151). In a laboratory study or a true experiment, the groups would have been randomized and the researcher would have been in full control over the variables. This is not the case in a natural experiment.
To investigate if countries that liberalized its trade regime in fact accelerate their growth towards those who do not one need some sort of natural experiment. In this case the event or policy change that is used as a natural experiment are the liberalization that happen after the
GATT Uruguay round. As mentioned, this qualify as a natural experiment because of the large number of participants, and enough time should have passed for us to identify any variation that might have occurred because of the treatment. The liberation also happens in a time frame that is approximately similar between countries. This point is important according to Slaughter (2001, p. 211) since many of the effects of the liberalization happens as the trade barrier is reduced. As the barriers falls the product prices will change, this could in turn alter investments in the economy (Slaughter, 2001, p. 211). Thus, the GATT liberalization seems like a good natural experiment for investigating if we actually can observe a growth
acceleration if we liberalize for trade.
The question if more trade will lead to higher economic growth is almost as old as the science of economic itself and thus it is a large literature that investigate this question. The problem with much of this literature as noted by Rodríguez and Rodrik (2000), is that it is fraughted with omitted variable bias, meaning that results from these papers easily can be contested.
The problem is also that many factors might be unobservable, such as cultural factors that can affect the attitudes towards trade. This attitude is different for different countries, but they are also fixed over time, so one call them state fixed effects. By using the difference-in-
difference estimator it is possible to control for state fixed effects by averaging out the data (we require panel data, since averaging requires observations of the same object at different points in time), this means that the analysis becomes similar to a difference in means analysis. The deviations from the mean kills the unobserved state fixed effects (Angrist &
Pischke, 2009, pp. 223-224). Thus, by using the difference-in-difference method it is possible to circumvent many of the problems that older studies faced with omitted variables. The next question then become how we identify the effect from a liberalization using the difference-in- difference estimator.
To identify any effects, we normally compare four different groups, and we can only observe the treatment effect in one of the four groups (Lechner, 2011, p. 167). First, one has the treatment group and the non-treatment group. Then one has two time periods before the treatment and after the treatment. Before the treatment, in the pre-treatment period, one cannot, for obvious reasons, observe any effect of the treatment. In the period after the treatment, in the post treatment period, it is possible to observe the effect of the treatment on the treatment group, but not the control group, again, for obvious reasons. This gives four
groups, two in the pre-treatment period and two in the post treatment period. This can be shown in the following way.
Table 1; Grouping of possible observations using the difference- in-difference-method
Group/Period Pre treatment Post treatment
Treatment Not possible to observe
effect of treatment
Effect of treatment possible observe
Control Not possible to observe
effect of treatment
Not possible to observe effect of treatment
As one can see, we have three groups where it is not possible to observe the effects of the treatment. Only in one group we can actually observe if the treatment had any effect. The core of the identification strategy of the difference-in-difference estimator is to compute the difference of the mean outcomes of the treatment and control group, and subtract the outcome difference that had been there already before the treatment had any effect (Lechner, 2011, p.
176). For identification using the difference-in-difference estimator we need a set of assumption to hold. These assumptions are: (Lechner, 2011, pp. 176-182)
• It is only possible to observe, one and only one, outcome for each member of the population.
• The factors that we are conditioning on will not be influenced by the treatment, this is the exogeneity assumption.
• The treatment should have no effect on the pre-treatment period.
• The constant bias assumption, if there is a bias in the data and this is constant it is possible to correct for this bias.
The two first assumption are basic assumptions one has in econometrics, while the third is specific for the difference-in-difference approach. The constant bias assumption is closely connected to what is the defining assumption of the difference-in-difference approach namely the common trend assumption(Lechner, 2011, p. 179).
The common trend assumption simply states that in the absence of treatment, the two groups should follow the same trend over time. In the absence of treatment this trend should not change. Stated differently, the difference in the expected nontreatment outcomes over time, conditional on some variable X are unrelated to belonging to the treatment or control group in
the post treatment period (Lechner, 2011, p. 179). This in turn implies that in the absence of the treatment the two groups should follow the same trend over time. Further it implies that we should control for factors that would lead to different time trends, at the same time these control factors should be purely exogeneous, thus the covariates should not be influenced by the treatment (Lechner, 2011, pp. 177-179), this is the exogeneity condition. If this
assumption holds together with the rest of the assumption and we observe a counterfactual trend break of the treatment group in the post treatment period, we have evidence for an effect of the treatment.
3.2 Application of the Difference-in-Difference method
To investigate if the counties that did liberalize under the GATT agreement actually had higher growth rates than the countries that did not liberalize, I follow the method of Estevadeordal and Taylor (2013). They are in turn inspired by the method of Sachs and Warner (1995) that uses a 0-1 openness indicator. Here I do not use an indicator for openness but as in Estevadeordal and Taylor (2013), I use a dummy variable for the liberalizing
countries. Since I only use two periods the equation that I estimate are in first difference and have the following form.
∆𝑦P = ∆𝛽 + 𝛼[∆𝑙𝑖𝑏𝑒𝑟𝑙𝑖𝑧𝑒𝑟P] + 𝜑∆𝑋P + ∆𝜀P
Where ∆𝑦P is the change in the average GDP growth per capita between period one and period two, this is the dependent variable of the regression. If a country is defined as a non- liberalizer the change in the average growth rate of the GDP between the two periods is given by ∆𝛽. The coefficient of interest is 𝛼 and gives the extra growth rate if the country is defined as a liberalizer in period two. The coefficient of control variables is given by 𝜑.
To see that 𝛼 is the coefficient that we are interested in estimating: There is only possible to observe any effect of the treatment in one group. This is the group of liberalizers in the post- treatment period. In all other groups there is impossible to register any effect of the treatment.
If one wants to estimate the average growth in GDP for group 𝑖, 𝑤ℎ𝑒𝑟𝑒 𝑖 ∈ (𝑙𝑖𝑏, 𝑛𝑜𝑛 − 𝑙𝑖𝑏) in the pre-treatment period or period 𝑡 − 1 we can estimate the equation (for simplicity I omit the vector of control variables)
𝑦P0)* = 𝛽0)*+ 𝜀P0)*
As can be seen this is simply the average GDP growth in period 𝑡 − 1. Since it is impossible to observe any effect of a liberalizing here, we do not need to account for any possible effect from a treatment. In the post-treatment period or period 𝑡 we can observe one group that gets the treatment and one that do not. This means that it is possible to observe an effect of the treatment if there is any. To identify if there is any effect of the treatment, we can add a dummy variable that is one if the group receives the treatment and zero if it is not receiving the treatment. the estimated equation for the average GDP growth in period 𝑡, becomes
𝑦P0 = 𝛽0+ 𝛼[𝑙𝑖𝑏𝑒𝑟𝑙𝑖𝑧𝑒𝑟P]+𝜀P0
Assuming that 𝑖 = 𝑙𝑖𝑏, taking the difference between the two periods gives
𝑦aPb0− 𝑦aPb0)* = 𝛽0+ 𝛼+𝜀P0− (𝛽0)*−𝜀P0)*)
∆𝑦aPb = ∆𝛽 + 𝛼 + ∆𝜀P
For a non-liberalizing country, we get, after taking the first difference
∆𝑦cdc = ∆𝛽 + ∆𝜀P
The difference in the growth rate between the two groups becomes
∆𝑦aPb− ∆𝑦cdc)aPb = ∆𝛽 + 𝛼 + ∆𝜀P− (∆𝛽 + ∆𝜀P)
∆𝑦aPb− ∆𝑦cdc)aPb = 𝛼
If there is any effect of a treatment in period two, this effect should be captured trough the coefficient 𝛼. As long as the requirement for identification holds, we can establish that there is an effect, or no effect if the coefficient turn out be significantly indifferent from zero, between the liberalization and the extra growth of the liberalizers.
As then mentioned in the introduction I use two periods. The first period runs from 1975 to 1989 and the second periods runs from 1990 to 2014. I take the averages in each period such that the dependent variable, the growth in GDP, is the change in average growth between the
two periods. Within the difference-in-difference set up it is also possible to extend the analysis to more than two time periods. The problem with using more than two periods, as shown by Bertrand, Duflo and Mullainathan (2004), is that one runs the risk of biased
standard errors. One of the solutions they suggest for avoiding this bias is to average the data before and after the implementation of the treatment. This means that one now basically ignores the time series information and we do not run the same risk of serial correlation. By doing this one can of course argue that one misses some information that would not have been missed by using the full time series, but this loss is probably small compared to the gain one gets from more robust standard errors. Another drawback of differencing the averages is that it requires that the treatment happens at the same time for all the treated states (Bertrand et al., 2004, p. 267).
With a policy implementation that involves several countries this is nearly impossible to achieve, because of differences between the countries in terms of economic interests, political factors and the differences in institutions that allows for policy changes. The GATT Uruguay round is possibly the closest one can get to such a simultaneously change in policy from a large number of countries. The length of each period is hopefully long enough relative to the lags in policy implementation not to pose any problem. Another problem one hopefully avoids by using relatively long time periods is that natural business cycles are canceled out, so one only picks up the effect of the treatment. At the same time the time horizon should be short enough to pick up any medium term growth effects (Estevadeordal & Taylor, 2013, p.
1678).
When it comes to how to define a country as a liberalizer or a non-liberalizers I have chosen to follow Estevadeordal and Taylor (2013). In their article they define liberalizing countries as those countries that lowered the tariff rates in the post-treatment period by more than the median tariff level of the pre-treatment period. This is a natural way to proceed since the GATT Urugay round focused on lowering tariff rates between countries. Another argument for procedeing in this way is that the tariff rates refelcts how closed the country is towards the world. A country with a tariff rate of a 100 % is in ecense a country that is in autarky.
To illustarte the claime made above, a natural argument will be that the higher tariff a country have on its imports, the less likely it is that it will recive any imports and thus it will be a closed economy. Assuming that we have two countries home and foreign, two producers
of the same good which only differ in location, one is located home and one in foreign. I also assume that there are no transport costs for exports that need to be accounted for. If home have a tariff rate of a 100 % of the value on the importetd good, the foreign produced good will be twice as expensive in the home market, compared to the good produced at home.
Since the good produced in the foreign country is twice as expensive, the demand for the foreign good will be zero, in turn this means that the foregin producer not will enter the home market. The home country will not recive any imports, assuming futher that the forgeing will reply with the same tariff rate on imports from home, means that the home producer not will export. This gives a situation where home is in autarky. If now the home and foreign agrees that the home country should lower their tariff rates to zero and leave it there. The foreign and home producer will compete on the same terms and enter each others markets, and we go from a situation where one has autarky to a situation of open trade.
The way I have chosen to follow for classifying the groups of liberalizers and non-liberalizers is not the only approach one could take. Sachs and Warner (1995) uses five indicators to define if a country has an open or closed trade regime. If a country qualifies for one or more of the five criteria’s they classify the economy as closed. To use this method here will then mean that one has to investigate which countries that qualifies for one or more of the criteria to be defined as closed after the treatment took place. For a country to be defined as open it cannot qualify for any of the criteria for the entire post treatment period, so it cannot go from open to closed to open again. This method of classification is contested by Rodríguez and Rodrik (2000), which shows that the indicator that Sachs and Warner uses mainly are driven by two of the criteria they use for the classification of a country. These two criteria are the black-market premium on currency and a state monopoly on the main export of the country.
It seems like this way of classifying the countries overestimates the effect of trade liberaliztion. One also have a high correlation of the indicators with other effects such as macroeconomic unstaiblity and other instituional effects. Another drawback of theire indicator is that countires could open up at different points in time, thus it is breaking with one of the requierments for being used as a natural experiment.
Another problem with the the 0 one 1 indicator of Sachs and Warner is pointed out by Slaughter (2001). He points out that the indicator do not pick up on any behavioral changes that might be caused by switching from one catogory to the other. This could also be a valid critique of the way of classifiying using the tariff reduction. It is possible to imagin that a
country changes its legal system to the better because the economy now is open. The reason for this change one can imagin is to attract new investments or as a part of the deal for entring new markets such as the European Union. The institution view of economic growth empahsis that one of the main driving forces of economic growth is the strength and vailidity of
institutions of countries rather than openess. An argument against this is that if the country did not agree to open up, the legal system had been the same. This means that the opening up of the economy to the outside world indirectly affected the growth rate through the better institutions. Something that had not been the case if the country had remained a closed economy. This shows that there is in fact no perfectly good way of testing if liberalizing the economy has a direct effect on economic growth, or if it is an indirect effect of somthing else.
One then have to controll for all the factors that might affect the growth rate. These factors should be strictly exogeneous and those which could lead to differential trends between the control group and the treatment group (Lechner, 2011, p. 187), such as institutions.
I use the average tariff rate and the change in this to classify the country as either a liberlizer or a non-liberlizer. One could also choose to use tariffs for capital goods and intermediate goods, using these tariff measures could in theory change the results of the regression.
According to theory in among Estevadeordal and Taylor (2013) it is the the tariff reduction of capital goods and intermediate goods that is the most gorwth enhancing. This is a natural conclusion, if the tariffs on inputs used in production is lower, the costs to firms will be lower. Since the fims gets lower costs, this means that the profit will increase, assuming then that we are in a situation of perfect competiton more firms will enter the market until profit again is zero. If now, for simplicity,one assumes that the firms in the economy uses one unit of capital for each unit produced, more firms translates into a higher level of capital in the econmy, which in turn means a higher steady state level of capital. Said differently, the economy indirectly increases its savings rate when it is opening the economy. Assuming the Solow model is used, savings rates are constant, and total savings in the economy given by 𝑠𝑓(𝑘), more firms means more production, this gives a higher output, more savings.
According to this then it is important to use dissagergate tariffs, rather than just average tariffs. I have choosen not to do this because of time constraints, the tariff rates for capital and intermidate goods are not as aviabel as for the average tariffs.
4 Description of Data Sources
4.1 Growth rate
The growth rate is the dependent variable in the data set and is the growth in per capita GDP in constant 2010 USD. I have downloaded the data from the World Bank´s World
Development Indicators
(https://datacatalog.worldbank.org/search?search_api_views_fulltext_op=AND&query=NY.
GDP.PCAP.KD&nid=&sort_by=search_api_relevance&sort_order=DESC) (meta data:
NY.GDP.PCAP.KD), this data set spans from 1960 to 2018, the data was last updated on November 14, 2018. I have calculated the growth rate in a continuous fashion using difference in log levels divided by the years elapsed.
4.2 Tariffs
The tariff rate is taken from the Fraser institute´s Economic Freedom of the World (EFW) dataset, section 4-A(ii) (https://www.fraserinstitute.org/economic-
freedom/dataset?geozone=world&year=2016&page=dataset&min-year=2&max- year=0&filter=0). The data is available every five year from 1970 until 2000, and then becomes available for every year from 2000 until 2016. In the same way as the for change in the growth rate, I have calculated the change in the tariff level in a continuous fashion using log levels divided by the year elapsed.
4.3 Controls
In the regression I include controls for institutional quality and schooling as a proxy for human capital. The measure for institutional quality is taken from the EFW data set (see link in section 4.2), Area 2, and is a composite index for the quality of the legal system and the protection of property rights. This measure contains among other a measure for the
impartialities of courts and for the legal protection of property rights. Schooling is taken from the Penn World Table version 9.1; additional data and programs, file; labor
(https://www.rug.nl/ggdc/productivity/pwt/). The data set gives the average years of schooling for the population aged 25 years or older.
5 Summary Statistics
5.1 Tariff rate
I am investigating if countries that liberalize increase their growth rate in comparison to countries that do not liberalize. As a proxy for openness I am using tariffs, for this to work a certain number of countries have to lower their tariff rates. One also needed that some countries do not change their tariff rate. There could be several reasons for this. Their tariff rates could already be so low that it is difficult to lower it even more. Some countries may not have participated in the Uruguay round of trade talks. Below I have made a scatter plot of the average tariffs (using my data) in the first and second period following the approach of Estevadeordal and Taylor (2013, p. 1676). The picture that one should expect when drawing a 45-degree line through the diagram, is that the countries that did not lower the tariffs should be on or close to the line. The countries that did liberalize on the other hand should be on the right side of the 45-degree line.
Figure 1; Tariffs before and after liberalization
As can be seen from the scatter plot we get the picture that is expected, the countries that are defined as liberalizers are to the right side of the 45-degree line. Some countries, such as Bangladesh and India lowered their tariffs substantially as can be seen from figure 1. The non-liberalizers are much closer to the 45-degree line, and some countries are seen already in period one to have tariffs close to zero. Any lowering of tariffs for these countries is not a practically possibility.
By looking at figure 2, it is possible to confirm that there was a lowering of tariffs for the countries that are in the liberalizer group. The graph shows the mean tariff rate of the
liberalizing countries, the non-liberalizing countries and the average tariffs in the world over time. We can see that the average tariffs of the liberalizing countries are falling towards the tariff rates of the non-liberalizing. The tariff rate of the non-liberalizing countries looks stable.
Figure 2; Tariff development over time
Table 2, below, confirms what the scatter plot and the graph indicate. The average tariff in the liberalizing group has fallen considerably when comparing to the non-liberalizing group.
Table 2; Average tariff rates
Liberalizers Non-
Liberalizers
Mean Standard
deviation
Mean Standard
deviation Average tariff period 1 .3162387 .1250097 .1006422 .0606698 Average tariff period 2 .0978186 .0397814 .0780406 .0562481 Change in average tariff
rate
-.2184201 .1243642 -.0226016 .0350007
Observations 41 42
5.2 GDP
Change in GDP is a standard measure for economic growth. If the economy grows, we should expect an upward sloping trend in GDP. If the tariff reductions had any effect, and according to theory: What should be observed is that countries that lowered their tariffs should accelerate their growth in comparison to the countries that did not change their tariff level. In the figure 3 below I plot the GDP growth of the two groups and the world in log levels. From this it is possible to see that we have a weak upward sloping trend in GDP, and the liberalizing countries are seen to have a lower level of GDP than the non-liberalizing countries. This reflects that the countries that did liberalize mainly were developing countries.
In the next two figures I plot the GDP development for the liberalizing and non-liberalizing countries. Again, the GDP growth is given in log level. From these two graphs it is possible to get a clearer picture of what might happen after the tariff reduction in the two groups. One can see that after the year 1990 it seems like the growth of GDP is faster for the liberalizers compared to before 1990. For the non-liberalizers, GDP growth seems to have been more constant for both periods. Table 3 below summarizes the results numerically.
Figure 3: Development of GDP
Figure 4; Development in GDP Liberalizers
Figure 5; Development GDP Non-Liberalizers
Table 3; Growth rates for Liberalizers and Non-Liberalizers
Liberalizers Non-
Liberalizers Mean GDP
Growth
Standard Deviation
Mean GDP Growth
Standard Deviation GDP growth Period 1 .0113055 .0251804 .0185718 .0267474
GDP growth Period 2 .02157 .0170273 .0159793 .0135583
Change in GDP growth .0096108 .0227289 -.0030351 .0257225
Observations 41 41
5.3 GDP Vs. Tariffs
A lower tariff rate would mean that a country should accelerate its growth rate towards those that do not liberalize. In the figure below I have a scatter plot with the change in tariff rate on the y-axis and the change in growth on the x-axis. What is expected is that the main mass of the liberalizers should be in the south-eastern part of the diagram, and the mass of the non-
liberalizers should be around zero. This is the picture that the figure gives. The mass of the liberalizer is closer to zero but have some spread. The mass of the countries that received the treatment are down and to the right in the diagram. From this picture it seems that there is a negative relation between the growth rate and the tariff rate, but not a clear relation. Cyprus, for instance can be seen as an outlier. It is defined as a liberalizer but have a change in growth of -0.05 percentage points.
Figure 6: Change in Growth Vs. Change in tariffs
By looking at the correlation, in the table 4 below, between change in tariff and the change in growth rate one can see that it is negative and statistically significant on a 1 % level. This is not to say that there is or is not a causality between lowering of tariffs and changes in the growth rate.
Table 4; Correlation between change in growth and change in tariff Change in Growth
Change in Tariff -0.297***
* p < 0.1, ** p < 0.05, *** p < 0.01
Table 5; Mean change in growth rates and tariff rate
Liberalizers Non-
Liberalizers
Mean Standard error Mean Standard error Change in growth rate .0096108 .0227289 -.0030351 .0257225 Change in tariff rate -.2793902 .1849957 -.025 0398626
Observations 41 42
