International Trade and Economic Growth:Evidence from Singapore

Clarence Jun Khiang Tan

Submitted in partial fulfillment of therequirements for the degree of

Master of Artsin the Graduate School of Arts and Sciences

COLUMBIA UNIVERSITY

2012

Abstract

International Trade and Economic Growth:Evidence from Singapore

Clarence Jun Khiang Tan

One of the frequently cited, though hotly contested, determinants of eco-

nomic growth is exposure to international trade. This study focusses on

Singapore, a country with high per capita GDP growth rates and high trade

exposure to explore the relationship between these two variables. I use a

cross-country dataset to gain initial insight into the trade-growth relation,

then use Singapore time series data covering the period 1965 - 2009 to look

at how Singapore’s trade exposure has contributed to its economic growth.

I find some support that trade exposure has led to increased growth for

Singapore. Other important determinants of growth include educational ex-

penditure, inflation, and technological progress.

Contents

1 Introduction 1

2 Theoretical Framework 4

2.1 Defining trade openness . . . . . . . . . . . . . . . . . . . . . 4

2.2 Is trade openness the actual driving force behind growth? . . . 6

2.3 How does trade openness promote growth? . . . . . . . . . . . 11

3 Methodology 16

3.1 Cross-country data . . . . . . . . . . . . . . . . . . . . . . . . 16

3.2 Singapore time series data . . . . . . . . . . . . . . . . . . . . 17

3.2.1 Unit root tests . . . . . . . . . . . . . . . . . . . . . . 19

3.2.2 A note about cointegration . . . . . . . . . . . . . . . . 21

3.2.3 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4 Data and Variables 27

4.1 Cross-country data . . . . . . . . . . . . . . . . . . . . . . . . 27

4.2 Singapore time series data . . . . . . . . . . . . . . . . . . . . 30

5 Results and Discussion 35

5.1 Cross-country data . . . . . . . . . . . . . . . . . . . . . . . . 35

5.2 Singapore time series data . . . . . . . . . . . . . . . . . . . . 40

5.2.1 Regression diagnostics . . . . . . . . . . . . . . . . . . 42

5.3 Limitations of the Study . . . . . . . . . . . . . . . . . . . . . 45

i

6 Conclusion 48

A List of countries ranked by γi 54

ii

List of Tables

1 Unit root tests . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2 Descriptive statistics for cross-country regression . . . . . . . 29

3 Bivariate correlations for cross-country regression . . . . . . . 29

4 Descriptive statistics for Singapore time series OLS regressions 33

5 Bivariate correlations for Singapore time series OLS regressions 34

6 OLS estimates using cross-country data . . . . . . . . . . . . 39

7 OLS estimates using Singapore time series data . . . . . . . . 43

8 List of countries included in cross-country regression, ranked

by γi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

iii

List of Figures

1 Partial Association between Mean Trade-to-GDP Ratio and

Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

2 Leverage-vs-squared residual plot for cross-country regression . 38

iv

Acknowledgements

I thank Christopher Weiss, Gregory Eirich, Christy Baker-Smith, Vanessa

Ohta and my colleagues in the Quantitative Methods in the Social Sciences

program at Columbia University for insightful discussions and helpful com-

ments. All errors are mine.

I also thank Yi Han Chong, Chai Wun Goh, Yi Ning Lim and my family

for their unwavering support.

v

1

1 Introduction

Economic growth has always been a central concern for governments and

people all over the world. Real Gross Domestic Product (GDP) per capita,

that is, GDP per capita adjusted for inflation, is often used as a proxy for

the standard of living in different countries. Because the level of GDP per

capita only measures the average income of the typical individual at the

given point in time, a more salient indicator may be the growth rate of real

GDP per capita. Positive growth rates indicate cumulative increases in real

income, implying that two countries with similar levels of GDP but small

differences in growth rates can over time build up large differences in levels

of real GDP per capita. This phenomenon is illustrated by the following

example: In 1965, Uruguay and Peru had similar real GDP per capita levels

at $4,486.49 and $4,613.71, respectively. Average annualized growth rates

(γi) over the 44-year period from 1965 to 2009 were also seemingly close, at

2.053% for Uruguay and 1.037% for Peru. However, this 1.016% difference

increased GDP per capita in Uruguay to $11,069.23 in 2009 as opposed to

only $7,279.81 for Peru1

One factor frequently cited as a possible source of economic growth is

international trade. However, the ways in which a country’s exposure to1Real GDP per capita data was extracted from the Penn World Tables Version 7.0

compiled by Heston et al. (2011). The figures are expressed in 2005-chained dollars andadjusted for Purchasing Power Parity (PPP) so they can be compared across countriesand time periods. The average annualized growth rate, (γi), is used to measure growthrates over a given period. The rate for the period from 1965 to 2009 was calculated usingthe formula 1

44 ln(yi,2009

yi,1965), where i is a country index.

2

international trade, also referred to as its “trade openness”, promotes growth,

remains a contested subject in economic literature.

Singapore is an example of a country with a very open economy and high

levels of per capita GDP growth rates. Over the 44-year period from 1965 to

2009, the Singapore economy grew at an average annualized rate of 5.253%

per year, with real GDP per capita increasing from $4,694.44 in 1965 to

$47,357.27 in 2009, exceeding the real GDP per capita levels of the United

Kingdom, Australia, and even the United States (Heston et al., 2011). Such a

startling growth rate has led Singapore to be labeled an East Asian Miracle.

Significantly, international trade has long been a major part of the Sin-

gapore economy. Data from the Penn World Tables Version 7.0 (Heston

et al., 2011) show that even in 1965, Singapore already had an trade-to-

GDP ratio of 206.73%; by 2009, this number had almost doubled, increasing

to 408.51%. Indeed, Singaporean economists Abeysinghe and Choy (2007)

affirm that “Singapore began its economic history as an entrepôt for South-

East Asia, importing commodities from the regional hinterland and then

re-exporting them . . . and vice versa” (p.46). Furthermore, the Singapore

government makes intentional efforts to increase the country’s exposure to

international trade. This is achieved mainly through a statutory board un-

der the Ministry of Trade and Industry, International Enterprise (IE) Sin-

gapore. This agency has been assigned the mission of “driving Singapore’s

external economy”. IE Singapore seeks to “spearheads the overseas growth

3

of Singapore-based companies and promote[s] international trade” (Interna-

tional Enterprise Singapore, 2010), and also negotiates free trade agreements

(FTA) for Singapore.

Considering Singapore’s trade openness and consistently high real GDP

per capita growth rates, it seems logical to use Singapore to examine the link

between exposure to international trade and growth. This study will test

the hypothesis that Singapore’s exposure to international trade has been a

significant contributor to its economic growth.

4

2 Theoretical Framework

A general review of extant literature shows that the link between trade open-

ness and growth remains unclear.

2.1 Defining trade openness

The ambiguity begins with the definition of trade openness itself. Many

authors define trade openness as the ratio of the sum of imports and ex-

ports (trade) to GDP (see Chang et al., 2009; Lee et al., 2004). Others

use trade-to-GDP ratios as the basis for defining indicators of openness that

include other variables (see Frankel & Romer, 1999; Irwin & Terviö, 2002;

Noguer & Siscart, 2005). Unfortunately, the trade-to-GDP ratio is definitely

not a perfect measure, as trade openness is not a one-dimensional concept

measured solely by volume of trade. The number and effectiveness of trade

barriers and protection, and the presence of preferential trading agreements,

are examples of other factors that may affect a country’s trade openness.

Furthermore, these definitions of trade openness suffer from endogeneity be-

cause both the volume of exports and imports and the GDP level are directly

related to GDP growth rates. This has led critics to point out that while it

is tempting to conclude that greater trade openness leads to higher growth,

the causality may also be reversed; for example, countries with low growth

rates may choose to close their borders to trade in order to limit competition.

5

Hallak and Levinsohn (2004) note that such countries may also impose tariffs

as a means of increasing government revenue.

Several approaches have been proposed to help mitigate this endogeneity

problem. For example, Chang, Kaltani and Loayza (2009) introduce inter-

action terms between trade openness, and a series of factors such as educa-

tional investment, financial depth, and inflation stabilization. Lee, Ricci and

Rigobon (2004) point out the simultaneity bias in the trade-growth regression

since all the terms entering into the calculation of a country’s trade open-

ness are directly related to income level. They propose a technique called

identification through heteroskedasticity (IH), described as follows:

“In the standard simultaneous equations problem the instru-mental variables approach searches for a variable that shifts thedemand (for example) to estimate the supply. In other words,we need a variable that moves the mean of the demand curveto estimate the supply. [. . .W]e could also solve the problem ofidentification if instead of moving the mean we find a variablethat increases the variance of one of the equations to infinity.. . . This is known today as ‘near identification’. In 1929, Leontiefindicated that we did not have to move the variance to infinity,only knowing that it has changed implies that the distribution ofthe residuals rotate along the different schedules [. . . and] this isenough to achieve identification.” (p. 4)

The IH method can be employed to overcome endogeneity thanks to high

sample heteroskedasticity in country growth rates and trade openness levels.

Recognizing the limitations of the trade-to-GDP ratio definition of open-

ness, other economists have proposed different indicators for measuring open-

6

ness. For example, Sachs and Warner (1995) measure openness by look-

ing at factors like the existence of nontariff barriers to trade, average tariff

rates, black market premiums, and existence of state monopolies on exports.

Frankel and Romer (1999) introduce geographical factors such as country

size, distance from other countries and access to maritime ports. They fo-

cus on geographical factors, as these factors are unlikely to be affected by

income, or government policies that affect income. Also, they note that the

main channel through which geographical factors may affect income, is trade.

2.2 Is trade openness the actual driving force behind

growth?

Notwithstanding the fact that openness is a difficult concept to define, the

economics profession remains divided about the influence of international

trade on growth. Some economists have concluded that the idea that open-

ness promotes growth is no more than an illusion. For example, Hallak and

Levinsohn (2004) criticize the research methodology used in the majority of

trade-growth studies conducted before their paper was written in January

2004. They are of the view that the positive relationship arises out of a

series of biases, and that “once [these] are properly addressed, the positive

relationship between openness and growth seems to vanish” (p. 9). They cite

endogeneity and omitted variable bias as the two major problems. Omitted

variable bias occurs if “variables omitted from the regression are those really

7

driving the relationship between openness and growth” (p. 6) . Essentially,

they maintain that this problem is often a result of the use of an inappropriate

dependent variable in the regression.

Hallak and Levinsohn also criticize the predominant use of country-level

macro data in the regressions. They explain that trade policy affects eco-

nomic growth through multiple channels, such as trade volume, market com-

petition, and growth of specific sectors of the economy. There are also many

different instruments affecting trade, for example, import tariffs, export sub-

sidies and subsidized credit. In view of this, many trade-growth regressions

consisting of linear or log-linear regressions of a single trade openness variable

fail to capture the complexity of trade policy described above. They advo-

cate the use of microeconomic data instead, reflecting the fact that firms and

consumers are the ones that trade with each other, and not the countries per

se. They contend that the use of country-level data results in an aggrega-

tion problem because it fails to accurately reflect the degree of sensitivity of

specific industries or consumers to changes in trade policy.

Perhaps one of the most cited papers questioning the positive relationship

between trade and growth is Rodriguez and Rodrik’s (2000) Trade Policy and

Economic Growth: A Skeptic’s Guide to the Cross-National Evidence. In this

work, Rodriguez and Rodrik eschew the simple question, “Does international

trade raise growth rates of income?”, in favor of a more specific one: “Do

countries with lower barriers to international trade grow faster, once other

8

relevant country characteristics are controlled for?” (p. 264) They justify the

use of this precise research question by saying that since trade restrictions

were put in place in response to “real or perceived market imperfections

or, at the other extreme, are mechanisms for rent extraction”, they should

be evaluated on different grounds than natural or geographical barriers to

trade. Also, they draw attention to the fact that in evaluating openness and

trade liberalization, the focus should be on the effect of trade policy and not

volume, thus also questioning the definition of the openness indicator using

trade volume.

Rodriguez and Rodrik also challenge the prevailing assumptions held by

intergovernmental organizations. One of the main activities of the World

Trade Organization (WTO) is “negotiating the reduction or elimination of

obstacles to trade (import tariffs, other barriers to trade) and agreeing on

rules governing the conduct of international trade (e.g. antidumping, subsi-

dies, product standards, etc.)” (World Trade Organization, n.d.a). Inherent

in this mission is the assumption that tariffs are necessarily bad for a coun-

try’s economy. Rodriguez and Rodrik (2000) challenge this assumption by

showing that the effect of a tariff is two-fold, using the example of a small2

open economy made up of manufacturing and agriculture, which in the manu-

facturing sector exhibits learning-by-doing behavior. The economy starts off

with a comparative disadvantage in manufacturing. Within this framework,2In literature relating to international trade, a country is considered “small” when it is

a price-taker on the world market. Changes in demand and supply in the country for agiven good do not affect the world price.

9

when a small tariff is imposed on manufacturing, it creates a distortion in

the allocation of resources on the production side, because the gap between

the manufacturing share of output at world prices is lower than the labor

share in manufacturing. This is known as the static efficiency loss. On the

other hand, the tariff also “[pulls] resources into the manufacturing sector,

[enlarging] the scope for dynamic scale benefits, thereby increasing growth”

(p. 271). Thus, depending on which two of these opposite effects dominate,

a small tariff may actually be beneficial to growth. This echoes Hallak and

Levinsohn’s (2004) view that there may be reason for some level of trade

protectionism in underemployed and underproductive economies. Rodriguez

and Rodrik (2000) eventually conclude that “the nature of the relationship

between trade policy and economic growth remains very much an open ques-

tion[, and] suspect that the relationship is a contingent one, dependent on a

host of country and external characteristics”.

Nobel Laureate Paul Krugman (1994) takes a strong stand as he flatly

denies that the high growth rates observed in East Asia were the result of any

growth miracle. Krugman argues that the sustained growth in real per capita

GDP observed in advanced economies was a result of technological progress

causing “continual increase[s] in total factor productivity”, thus improving the

efficiency of inputs used in the production process. For him, this rise in out-

put per unit of input is key. Specifically in reference to Singapore, Krugman

opines that the country’s spectacular growth rate is a result of “perspiration

rather than inspiration”, achieved through a more efficient mobilization of

10

resources, such as a doubling in the proportion of the population gainfully

employed, improved quality of the workforce through education, and a dra-

matic increase in the stock of physical capital, largely financed by domestic

saving. For Krugman, these are one-time increases that cannot be repeated,

and more importantly, the efficiency of the economy did not increase signifi-

cantly.

Of course, in exploring the link between economic growth and trade,

one must not lose sight of the fact that there are many other determinants

of economic growth besides international trade and openness. One of the

leading growth economists today, Robert Barro, elicited these determinants

in his 1996 paper, Determinants of Economic Growth: A Cross-Country

Empirical Study. Using panel data for 100 countries spanning 1960 to 1990,

Barro (1996) finds that “for a given starting level of real per capita GDP, the

growth rate is enhanced by higher initial schooling and life expectancy, lower

fertility, lower government consumption, better maintenance of the rule of

law, lower inflation, and improvements in the terms of trade" (p. 2).

Two of the factors cited by Barro were higher initial schooling and bet-

ter maintenance of the rule of law. With regards to higher initial schooling,

Barro finds that “an extra year of male upper–level schooling3 is . . . estimated

to raise the growth rate by a substantial 1.2 percentage points per year”. This

seems to support Krugman’s (1994) point that Singapore’s growth can be at-

tributed in part to the improved quality of the workforce through education.3At secondary levels and above.

11

Specifically, Krugman (1994) found that “while in 1966 more than half the

workers had no formal education at all, by 1990 two-thirds had completed

secondary education” (p. 10).

The other factor of interest is better maintenance of the rule of law, and

in the present case, this refers to the successful enforcement of contracts

and intellectual property rights by an impartial judiciary as a support for

greater innovation in the economy. Barro (1996) explains that the efficient

enforcement of the rule of law creates a favorable environment for potential

investors. Using an index created by the International Country Risk Guide,

he finds that “greater maintenance of the rule of law is favorable to growth”,

and that “an improvement by one rank in the underlying index . . . is estimated

to raise the growth rate on impact by 0.5 percentage points” (p. 20).

2.3 How does trade openness promote growth?

Economists who support the view that trade openness is beneficial to growth

have identified different channels through which trade openness may result

in growth.

Andersen and Babula (2008) study the openness-growth link by looking

at the channels through which international trade affects economic growth.

They identify capital accumulation and productivity growth as two sources of

growth in GDP per capita. They cite studies showing that the main source of

growth is not capital accumulation, and conclude that the effects of trade on

12

growth mainly operate through productivity growth. In a framework where

growth is driven by innovation, they attribute the influence of international

trade on growth to three factors. First, trade provides access to foreign

intermediate inputs and technologies. This can take the form of importing

foreign inputs not produced in the home country that are used directly in

production, or as inputs for research and development. Second, trade also

increases market size for new product varieties, incentivizing research and

development to continually produce new products. Finally, trade allows for

diffusion of general knowledge across geographical boundaries. The non-rival

nature of knowledge allows trading partners to share information, leading to

knowledge spillovers that further facilitate the R&D process and subsequent

innovation.

Chang, et al. (2009) consider the effect of complementary policies that

accompany a country’s decision to liberalize trade, and show that these poli-

cies are important in bringing about subsequent growth. They note that

although opening up to trade often has a positive effect in the initial stages,

“the aftermath of trade liberalization varies considerably across countries and

depends on a variety of conditions related to the structure of the economy

and its institutions” (p. 6). Examples of these complementary conditions

include the level of human capital investment, the amount of public infras-

tructure, the quality of governance and labor market flexibility. Chang et al.

(2009) find that per capita GDP only increases with more free trade when

there are minimal labor market distortions. Thus, there might be a case for

13

protectionism if the country is in a state of underemployment or underpro-

duction. This lends support to the idea that trade liberalization is not a

“one size fits all" prescription for economic growth, and that country-specific

factors should enter the decision whether or not to liberalize trade.

Chang, et al. then go on to discuss the complementary policies that

should accompany trade liberalization. Two examples of these include in-

vestment in human capital, and the protection of intellectual property rights.

In their opinion, the need to adopt new technologies and be able to employ

capital and labor efficiently is crucial to the success of the trade liberalization

process as they help a country face foreign competition. Taylor (1998) and

Wacziarg (2001) also support the view that sound investment policies are

crucial in order for countries to reap the full benefits of trade.

In spite of the earlier noted concern that trade liberalization is not “one

size fits all”, Chang et al. (2009) find that in general, trade does improve

growth. Without considering the interaction terms, the general finding is

that trade liberalization leads to faster growth on average. With the interac-

tion terms included, the relation still holds, except in countries where there

are strong distortions in complementary areas, as described earlier. Also,

continued policy reforms after liberalization help a country to reap the full

benefits of its decision to liberalize.

Dollar and Kraay (2004) explore the relation between trade and growth by

looking at the “top one-third of developing countries in terms of increases in

14

trade-to-GDP over the past 20 years” (p. F23), labeling this the “globalizing

group” of developing countries. In the 20-year period, Dollar and Kraay find

that the ratio of trade-to-GDP doubled in this group, and that the group also

significantly reduced tariff levels. By comparison, the remaining two thirds of

developing countries actually saw a reduction in trade openness in the same

period, and the degree to which they cut tariffs was also significantly smaller

than the globalizing group. They find that growth rates increased for these

“recent globalisers”, from 2.9% per year in the 1970s to 3.5% in the 1980s, and

5.0% in the 1990s, while rich country growth rates slowed over this period.

In the other group of non-globalizing developing countries, they observed a

“decline in the average growth rate from 3.3% per year in the 1970s to 0.8%

in the 1980s and recovering to only 1.4% in the 1990s” (p. F24).

In order to test the generalizability of their results, Dollar and Kraay

look at “decade-over-decade changes in the volume of trade as an imperfect

proxy for changes in trade policy”. (p. F24) This eliminates the possibility

that the results they obtained were driven by geography or some unobserved

characteristic affecting both growth and trade, thus addressing some of the

criticism leveled at trade-growth studies by Rodriguez and Rodrik (2000). In

their study of 100 countries, Dollar and Kraay (2004) found that changes in

growth rates showed a strong correlation with increases in trade volume.

Frankel and Romer (1999), as well as further studies building on the

same methodology using geographical variables to measure openness, such

15

as Irwin and Terviö (2002) and Noguer and Siscart (2005), all yield encour-

aging results. They all find that trade raises income, even after controlling

for endogeneity. Frankel and Romer (1999) attribute this to the increasing

accumulation of physical and human capital, as well as increasing levels of

income at each level of capital. Irwin and Terviö (2002) find that this re-

sult is consistent across time. Noguer and Siscart (2005) arrive at the same

conclusion that trade increases income using a better dataset than the previ-

ous two studies with less missing data, and sensitivity analyses confirm the

robustness of their results across geographical and institutional controls.

16

3 Methodology

This section details the methods that will be used to study the trade-growth

relationship, and specifies the models that will be tested. Cross-country

data is used for an initial examination of how trade and population size

affect growth in 112 different countries. Focussing on the Singapore case,

time series data is used to estimate models incorporating various covariates.

Hereafter in this study, Y refers to real GDP, while y is used for real GDP

per capita.

3.1 Cross-country data

First, I use cross-country data to estimate Equation (1) using Ordinary Least

Squares (OLS):

(1)γi = β0 + β1openi6509 + β2 ln(popi6509) + εi

where γi is the average annualized growth rate in real GDP per capita (yi,t)

for country i over the period from 1965 to 2009 that satisfies the equation

(2)yi,2009 = yi,1965 · e[2009−1965]γi

openi6509 is is the mean level of trade-to-GDP ratio for the period 1965-

2009 for country i, calculated at 2005-chained prices, and ln(popi6509) is the

natural logarithm of the mean population level (expressed in millions) for

the period 1965-2009 for country i.

Looking at Equation (2), I see that γi shows the average rate at which

real GDP per capita had to grow every year between 1965 and 2009, in order

17

to reach the level in 2009. Using the average annualized growth rate, γi,

instead of year-on-year (y-o-y) changes in real GDP per capita is helpful

as it removes the short-term fluctuations in γi This reflects that economic

growth occurs over a long period of time and that increases in income level

are a result of small, repeated additions over time. Short-term reductions in

y-o-y growth rates in response to business cycle fluctuations are expected of

every economy, and are unlikely to be indicative of any long run or structural

problems. Thus, γi is expected to be positive for most countries, even with

short-run fluctuations. However, troubled economies with persistently low or

negative y-o-y growth rates will also have low, or even negative, values of γi

, if log per capita GDP in 2009 is lower than in 1965.

ln(popi6509) is included to control for the size of the country’s domestic

market. Noguer and Siscart (2005) note that lower trade-to-GDP ratios in

larger countries are possibly reflective of greater intra-country trade and a

larger domestic market, rather than reduced trade openness as determined

by policy.

3.2 Singapore time series data

The decision to focus on one country, Singapore, implies that I can no longer

consider average growth rates over the period of study, but instead have to

use time series data and look at y-o-y growth rates instead. From this point

18

forward, ∆ is used to denote the difference operator, such that for a variable

gt,∆gt = gt − gt−1.

When working with time series data, it is important to ensure that the

data is stationary, and weakly dependent. In a highly influential 1974 paper,

Spurious Regression in Econometrics, Granger and Newbold show that OLS

regressions using the levels of non-stationary time series data often produce

spurious results, with significant t-statistics and high degree-of-fit (as mea-

sured by R2 or R̄2), coupled with low Durbin Watson statistics, indicating

high serial correlation in the error terms. It is clear that such regressions

do not in any way represent the true relationship between the variables, re-

gardless of the value of the t-statistics or R2. Granger and Newbold run 100

OLS regressions on two independent random walks and show that “[u]sing

the traditional t test at the 5% level, the null hypothesis of no relationship

between the two series would be rejected (wrongly) on approximately three-

quarters of all occasions” (p. 5).

Ensuring stationarity of time series data becomes especially crucial when

I consider that “[with] economic time series, one typically finds a very high se-

rial correlation between adjacent values,. . . with changes being small in mag-

nitude compared to the current level” (Granger & Newbold, 1974, p. 2).

This implies that many economic time series are not stationary in the levels

and often contain unit roots.

19

3.2.1 Unit root tests

A time series Yt is covariance stationary if it has a constant mean µ < ∞,

a constant variance σ2 < ∞, and the covariance between any two terms Yt

and Yt+h depends only on h and not t, for all t and h ≥ 1 (Woolridge, 2010).

Given the following general AR(1) model of Yt:

(3)Yt = ρYt−1 + ut

Yt is stationary if and only if |ρ| < 1. If |ρ| = 1, Yt contains a unit root

and is non-stationary. If |ρ| > 1, Yt is also non-stationary, and contains an

explosive root.

To test for the presence of unit roots in the various time series, I use

the Augmented Dickey-Fuller (ADF) test in Said and Dickey (1984), which

expands on the original Dickey-Fuller (DF) test proposed by Dickey and

Fuller (1979) to allow for the inclusion of trends, and lags to correct for

serial correlation in the error terms. Lag selection is particularly important

in the ADF test. When lag length k is too small, the errors may still be

autocorrelated, biasing the test statistic, while an overly large value of k

reduces the power of the test. In order to avoid arbitrary lag selection, I rely

on the Akaike Information Criterion (AIC), selecting k to minimize the AIC.

I include a trend term when a visual inspection of the time series plot reveals

an upward or downward trend. The ADF tests the null hypothesis that the

series contains a unit root (i.e. H0 : |ρ| = 1) against the alternative that the

20

series is stationary (H1 : |ρ| < 1). If a unit root is detected in the levels, I

difference the series and run the ADF test again.

Table 1: Unit root tests

Variable Zta Conclusionb

ln( ytyt−1

) level -4.737∗∗∗ I(0)

opentlevel -2.945

I(1)1st diff. -3.238∗

ln(edexpt)level -2.459

I(1)1st diff. -5.369∗∗∗

inft level -3.377∗ I(0)

∆mfptc 1st diff. -3.518∗∗ I(1)

usrect level -6.027∗∗∗ I(0)

ttit level -3.837∗ I(0)

reert level -2.955∗∗ I(0)

ftatlevel -0.942

I(1)1st diff. -5.895∗∗∗

pctlevel -1.329

I(1)1st diff. -5.224∗∗∗

mktcaptlevel -2.195

I(1)1st diff. -5.127∗

a∗∗∗ p<0.001, ∗∗ p<0.01, ∗p<0.05bA series is integrated of order n if it must be differenced n times before it becomes

stationary. I(1) series are difference-stationary; stationary series are trivially, I(0).cData collected for multi-factor productivity measure was “Change in Multi-factor Pro-

ductivity”, and is thus reflected here in first differences. Data for levels is not available

21

Table 1 shows the series that are I(1) in the levels become stationary

when differenced, at the 5% level. The series of first differences can now be

used in regressions without the risk of spurious results.

3.2.2 A note about cointegration

It is important to note that not all regressions between non-stationary time

series are spurious. For example, consider the case of two time series Yt and Xt

that are integrated of order 1, denoted I(1). In general a linear combination

of I(1) variables Yt−βXt is also expected to be I(1). However, if there exists

a value of β such that Yt−βXt is I(0), Yt and Xt are said to be cointegrated.

This implies that that while the two series vary when considered individually,

when taken together, the path that the two series take over time are closely

related, and this is usually indicative of some long-term relationship between

the two variables. In this case, since the dependent variable ∆ ln(yt−1) is

I(0), cointegration analysis would not be appropriate.

3.2.3 Models

In the ADF tests for unit roots reported in Table 1, I confirm that the

series that are I(1) in the levels become stationary when differenced. With

stationary data, OLS can be used without the risk of spurious results detailed

in Granger and Newbold (1974). In addition, OLS is also the Best Linear

Unbiased Estimator (BLUE) if the error terms are homoskedastic and are

not serially correlated. I will test for heteroskedasticity and serial correlation

22

following the estimations4. To mitigate the endogeneity problem posed by

the trade-to-GDP ratio definition of trade openness, I include interaction

terms between ∆opent and the appropriate explanatory variables.

I start off by using OLS to estimate Equation (4), that does not include

any interaction terms. This will serve as the benchmark for comparison with

subsequent models:

(4)∆ ln(yt) = β0 + β1∆ ln(yt−1) + β2∆opent + β3∆ ln(edexpt)+ β4inft + β5∆mfpt + β6year + εt

where ∆ ln(yt) is the y-o-y growth rate of real GDP per capita (yt) at time

t5, ∆opent is the change in trade-to-GDP ratio at time t, ∆ ln(edexpt) is the

change in the natural logarithm of total education expenditure at time t,

inft is inflation at time t as measured using the Consumer Price Index (CPI,

2005 = 100), ∆mfpt is the change in multi-factor productivity at time t and

year incorporates a linear time trend.

The lagged dependent variable ∆ ln(yt−1) is included to account for de-

pendence in the ∆ ln(yt) series, making Equation (4) a Lagged Dependent

Variable model. ∆ ln(edexpt) is included to study the effect of human capital

investment on economic growth, and proxies for the quality of the workforce.

A better-educated workforce is expected to be able to assimilate, and react

to, the “knowledge spillovers”6 resulting from trade more effectively, leading4See Section 5.2.1 Regression diagnostics on page 42.5To see why this is so, consider that ∆ ln(yt) = ln(yt)− ln(yt−1) = ln( yt

yt−1)

6See Andersen and Babula (2008)

23

to greater innovation. Thus, I expect the coefficients on ∆ ln(edexpt) and

the associated interaction term to be positive.

inft shows how changes in the price level (i.e. inflation) affect economic

growth. The effect of inflation on growth is another hotly contested topic

in economics. On one hand, inflation can be good for growth when the

excess of aggregate demand over aggregate supply is driven by factors such

as high export levels or strong investment. On the other hand, inflation can

be detrimental if it is the result of the money supply increasing faster than

the economic growth.

As for multi-factor productivity (MFP), within a standard neoclassical

growth model framework, MFP is represented by the At term, and is often

used as a measure of technological progress. Assuming that production at

time t, Yt, is defined by the following Cobb-Douglas function with constant

returns to scale,(5)Yt = Kα

t (AtLt)1−α 0 < α < 1

At is labor-augmenting, and can be interpreted as the contribution of tech-

nological progress to improving the “effectiveness of labor” (Romer, 2005, p.

9). I expect the coefficient on mfpt to be positive.

It goes without saying that trade openness, human capital investment, in-

flation, and technological progress are not the only factors affecting economic

growth. Many other factors such as political stability, good governance and

maintenance of the rule of law can also be related to growth. However, in

24

view of the limited number of available observations7, for various reasons,

it would be impractical to include all possible covariates in one model. For

example, the dataset used for this time series analysis was assembled from

different sources, and the date range covered differs from one data source

to the next. Thus, including all covariates in one model may significantly

decrease the number of observations entering the regression. Furthermore, if

I include every explanatory variable and the associated interaction terms, I

would be estimating a model with 21 regressors. With only 28 observations,

this would do little more than increase the R̄2 without revealing much about

the relationship between the variable.

To mitigate this problem, I include additional explanatory variables one

at a time, along with the appropriate interaction term and compare the

resulting estimates and R̄2 with those obtained using the benchmark model.

For example, in Model (5) of Table 7, I look at how a recession in the US

affects Singapore’s economic growth and trade openness level. Thus, I add

the terms usrect and ∆opent × usrect into the model.

The additional variables I include in the regressions are a US recession

dummy variable(usrect), indices for the terms of trade (ttit) and real effective

exchange rate (REER), the number of countries with at least one FTA with

Singapore (ftat), total claims on the private sector held by deposit money

banks (pct), and stock-market capitalization (mktcapt).7The benchmark Equation (4) only has 28 observations.

25

The US recession dummy variable studies the effect of a recession in the

United States on Singapore. As a small open economy with a high level

of trade exposure, I expect that Singapore would be particularly sensitive

to changes in the world economy. When a large country like the US goes

into a recession, general global demand can be expected to drop, associated

with decreased economic growth, and also a decrease in demand for exports.

Data from the IMF’s (n.d.a) Direction of Trade Statistics (DOTS) database

shows that in 2010, Singapore’s total trade with the US (in nominal terms)

was US$58.6 billion, making the US Singapore’s third-largest trading partner

after Malaysia and Mainland China. Thus, I expect the coefficient on this

variable to be negative.

reert is an index of the REER, itself a measure of the value of the Singa-

pore dollar against a weighted basket of foreign currencies (Nominal Effective

Exchange Rate, NEER), adjusted for inflation using the CPI.

The terms of trade is defined as a ratio of the price of exports to the price

of exports, and the terms of trade are said to improve if this ratio rises (i.e.

if the price index of exports rises or the price index of imports falls). This is

termed an improvement because intuitively, exports are “sold” by a country

to a trading partner, and a rise in export prices results in an increase in the

revenues from trade, if the quantity of exports remains unchanged. However,

an increase in export prices also decreases the price competitiveness of a

country’s exports vis-à-vis those of its competitors, possibly reducing the final

26

quantity exported as a result. The overall effect of this change in the terms

of trade will depend on which one of these two opposing forces dominates.

Free trade agreements encourage trade by reducing or eliminating the

import tariffs (a barrier to trade) incurred. This reduction in tariffs increases

the price competitiveness of the products sold by the country with an FTA

as compared to competitor countries without FTAs. The indicator chosen

here is the number of countries with at least one FTA with Singapore, and

not the number of FTAs of which Singapore is a signatory. This is because

Singapore has multiple FTAs with certain countries. If the premise that freer

trade leads to greater growth is true, the correlation between growth and the

number of countries covered by at least one FTA should be positive. Also,

while a more informative indicator may be the volume of goods that were

imported using an FTA, unfortunately that data is not publicly available.

The financial variables pct and mktcapt are included to see how the fi-

nancial system impacts growth. mktcapt is the stock market capitalization

as a percentage of GDP, and measures the size and importance of the stock

market in relation to the entire economy, while pct shows the claims on the

private sector held by deposit money banks at time t, indicating the efficiency

of deposit money banks in credit allocation (Beck et al., 2009).

27

4 Data and Variables

This next section provides descriptions of the datasets and variables that will

be used to conduct my analysis. I will also cover the coding of variables and

provide descriptive statistics and bivariate correlations for them. Descriptive

statistics give a first view of the dataset: the mean is a measure of central

tendency, while the variance and range measure the dispersion of the data.

Bivariate correlations, on the other hand, show how strongly the two vari-

ables under consideration are related to each other: the greater the absolute

value, the stronger the relationship, while the associated p-value shows the

likelihood the correlation occurred by chance.

4.1 Cross-country data

The cross-country data used to estimate Equation (1) was drawn from the

Penn World Tables Version 7.0 (PWT 7.0) compiled by Heston, Summers

and Aten (2011) at the University of Pennsylvania. The PWT 7.0 data has

been adjusted for inflation and Purchasing Power Parity (PPP) in order to

make it comparable across countries and time periods. The original dataset

comprised data for 189 countries, and reported two versions of data for China.

The first version was based on official data provided in the 2005 edition of

the United Nations International Comparisons Project (ICP). The second

version adjusts the official growth-rate and price data to account for the

28

sheer size of China’s population, and differences in price levels between cities

(Heston, 2011). Data for China Version 1 were included in this regression.

Countries for which real GDP per capita was missing in either 1965 or

2009 were dropped, and γi was calculated using Equation (2). The final

regression included 112 countries. γi and openi6509 were expressed as a

percentages, and popi6509 was expressed in millions.

Table 2 reports descriptive statistics for this set of data. popi6509 has

a very large standard deviation and range, with a maximum of 1074.9 mil-

lion (for China). This suggests a right-skewed distribution, and thus, a log

transformation was used to help normalize the data. Table 3 provides bi-

variate correlation between the variables. I note that the correlation be-

tween gammai and openi6509 is positive, and has a low p-value, indicating

that I should reject the null hypothesis that the correlation between the two

variables occurred by chance. On the other hand, the correlation between

gammai and ln(popi6509), while positive, has a high p-value, and thus, I fail

to reject the null hypothesis that this correlation occurred by chance. This

indicates that using this dataset, the relationship between openi6509 and γi

is probably worthy of attention.

29

Table 2: Descriptive statistics for cross-country regression

Variable Mean Std. Dev. Min. Max.

γi 0.019 0.017 -0.033 0.082openi6509 67.058 42.951 14.025 321.947popi6509 38.597 128.832 0.069 1074.943ln(popi6509) 2.156 1.677 -2.679 6.98N 112

Table 3: Bivariate correlations for cross-country regression

Variable γi openi6509 ln(popi6509)

γi 1.000

openi6509 0.279 1.000(0.003)

ln(popi6509) 0.013 -0.465 1.000(0.895) (0.000)

p-values in parentheses

30

4.2 Singapore time series data

No single dataset contained all the variables required for this study; thus ,the

data was assembled from various sources.

∆ ln(yt) is y-o-y growth rate in PPP-adjusted GDP per capita, calculated

using the rgdpch8 series in Heston et al. (2011), expressed in 2005 interna-

tional dollars per person:

(6)∆ ln(yt) = ln(yt)− ln(yt−1)= ln(rgdpcht)− ln(rgdpcht−1)

∆opent is the first difference of the openk 9 series in the same dataset, which

gives the ratio of the sum of imports and exports expressed as a percentage

of real GDP. Both ∆ ln(yt) and ∆opent have 44 annual observations for each

year between 1966 and 2009.

∆ ln(edexpt) is the growth rate of total educational expenditure, ex-

pressed as a percentage. The original data series,“Government Expenditure

on Education - Total”, expressed in thousands of Singapore dollars, was pro-

vided by the Singapore Ministry of Education (2010), and contained 28 an-

nual observations from 1981 to 2009.

∆mfpt is the y-o-y percentage change in multi-factor productivity pro-

vided by the Singapore Department of Statistics. The series includes data

from 1976 to 2009.8PPP Converted GDP Per Capita (Chain Series), at 2005 Constant Prices9Openness at 2005 Constant Prices (%)

31

usrect is a dummy variable that takes on the value 1 during a recessionary

year in the United States, and 0 otherwise. Monthly data was collected from

Federal Reserve Bank of St. Louis (2011), and for any given year t, usrect

was coded 1 if for more than 6 months during the year the US economy

was in recession, and 0 otherwise. All 45 years between 1965 and 2009 were

included.

Two time series were extracted from the IMF’s (n.d.b) International Fi-

nancial Statistics (IFS) database : reert and ttit. reert is an index of the

real effective exchange rate, while ttit is a terms of trade index, calculated

as the ratio of the export price index to the import price index at time t.

All three indices take the value 100 in 2005. Data for reert ran from 1976 to

2009, while that for ttit covered 1979 to 2009.

ftat tracks the number of countries that have at least one FTA with

Singapore at time t. It is constructed using the Regional Trade Agreements

Database from the World Trade Organization (n.d.b). If a country has more

than one FTA with Singapore, that country is included as of the year of the

earliest FTA, as determined using the date of entry into force as opposed to

the date of signing.

The two financial variables pct andmktcapt were taken from Beck, Demirgüç-

Kunt and Levine (2009). pct shows the claims on the private sector held by

deposit money banks at time t, andmktcapt is the stock market capitalization

at time t. Both variables are expressed as percentages of GDP, and include

32

data from 1965 - 2009, and 1989 - 2009 respectively. Tables 4 and 5 provide

descriptive statistics and bivariate correlations for this dataset respectively.

Focussing on the first column of Table 5, I note that the signs for most

of the correlations obtained between ∆ ln(yt) and the various independent

variables are consistent with the extant literature. Of particular note are

the correlations between ∆ ln(yt) and ∆ftat and ∆pct. As explained in the

previous section, the correlation between growth and the number of countries

covered by at least one FTA should be positive. The negative correlation

observed here may indicate that the ∆ftat variable (number of FTAs) does

not effectively capture the influence of FTAs on trade openness and growth.

As for ∆pct, the negative correlation is expected, as recessions are associated

with reduced liquidity as banks and other financial institutions are more

cautious about taking risks, and choose to cut back on loans and financing,

thus also reducing the efficiency of credit allocation.

33

Table 4: Descriptive statistics for Singapore time series OLS regressions

Variable Mean Std. Dev Min. Max. N

∆ ln(yt) 5.253 4.62 -7.388 11.867 44∆opent 4.586 24.557 -57.7 64.836 44∆ ln(edexpt) 0.063 0.098 -0.103 0.327 28inft 1.765 2.142 -1.062 9.710 44∆mfpt 0.811 3.441 -7.600 6.2 36usrect 0.156 0.367 0 1 45ttit 123.751 17.599 95.238 152.124 31reert 109.271 8.124 92.815 125.53 34∆ftat 0.545 1.606 0 9 44∆pct 1.135 5.055 -12.263 15.765 44∆mktcapt 2.458 32.439 -34.432 58.542 20

34

Table 5: Bivariate correlations for Singapore time series OLS regressions

Variable ∆ ln(yt) ∆opent ∆ ln(edexpt) inft ∆mfpt usrect ttit reert ∆ftat ∆pct ∆mktcapt

∆ ln(yt) 1.000

∆opent 0.115 1.000(0.458)

∆ ln(edexpt) 0.253 -0.549 1.000(0.194) (0.003)

inft 0.210 0.202 0.317 1.000(0.171) (0.188) (0.100)

∆mfpt 0.675 0.278 -0.171 -0.153 1.000(0.000) (0.101) (0.384) (0.373)

usrect -0.241 -0.009 0.212 0.404 -0.548 1.000(0.115) (0.952) (0.279) (0.007) (0.001)

ttit 0.159 -0.207 0.272 0.150 0.064 -0.092 1.000(0.394) (0.265) (0.161) (0.419) (0.732) (0.624)

reert -0.121 -0.329 0.415 0.002 -0.514 0.036 0.307 1.000(0.497) (0.057) (0.028) (0.991) (0.002) (0.839) (0.093)

∆ftat -0.258 0.011 -0.230 -0.044 -0.047 0.150 -0.943 -0.242 1.000(0.091) (0.944) (0.238) (0.775) (0.785) (0.324) (0.000) (0.168)

∆pct -0.495 -0.065 -0.252 -0.165 0.013 -0.001 -0.572 -0.155 0.604 1.000(0.001) (0.674) (0.195) (0.284) (0.939) (0.993) (0.001) (0.381) (0.000)

∆mktcapt 0.351 0.384 -0.267 -0.104 0.591 -0.383 0.194 -0.212 -0.150 -0.003 1.000(0.129) (0.094) (0.255) (0.663) (0.006) (0.096) (0.412) (0.371) (0.528) (0.990)

p - values in parentheses

35

5 Results and Discussion

5.1 Cross-country data

The OLS estimation of Equation (1) yielded a positive and highly statistically

significant coefficient on mean trade openness (openi6509) of 0.0142. A 1%

increase in mean trade-to-GDP ratio was associated with a 1.42% increase

in average annualized growth rate, ceteris paribus. This is consistent with

the findings in Noguer and Siscart (2005), who estimate a similar model

using log per capita GDP levels, and including an additional control for

land area. They, too, find a positive and statistically significant relation

between trade openness and real GDP per capita. A Breusch-Pagan test for

heteroskedasticity in the error term produced a χ2 statistic of 0.52 (p-value

= 0.469); thus, I fail to reject the null hypothesis that the error term has the

same variance given any value of openi6509 and ln(popi6509), and conclude

that the error is homoskedastic.

Figure 1 shows the partial association between mean trade-to-GDP ratio

and growth. It is much like a “scatter plot” of gammai against openi6509,

showing the visual distribution of points around the “regression line”10. Look-

ing at Figure 1, one might be tempted to conclude that the statistically sig-10The terms “scatter plot” and “regression line” are in quotation marks because they are

only valid when used in association with a simple linear regression with one dependentvariable and one independent variable. However, the Frisch-Waugh-Lovell theorem (Lovell,2008) provides a means to focus on one explanatory variable, in this case, opent, by“removing” the effects of other variable(s), in this case, ln(popi6509). This is the theoreticalbasis for the partial association plot.

36

nificant coefficient on mean trade openness was driven by a few potentially

influential data points like Singapore (SGP) or Equatorial Guinea (GNQ).

However, a plot of leverage against the squared residuals provided in Figure

2 shows that none of the data points are highly influential11. Furthermore,

a robust regression of Equation (1) (reported as Model (2) in Table 6) pro-

duces largely similar results, and included all 112 countries. The coefficient

on mean trade openness increased from 0.0142 to 0.0163, and the associated

t-statistic rose from 3.55 to 5.25.

However, as explained earlier, one should be careful when considering

the statistical significance of these coefficients, as the trade-to-GDP ratio

definition of trade openness suffers from endogeneity. Furthermore, Equation

(1), including only two dependent variables, is most probably underspecified,

and suffers from omitted variable bias, as there are many other variables that

may be related to economic growth, such as education, political stability and

inflation. Regardless, it still provides some initial insight into the relationship

between trade openness and growth.

This cross-country dataset also lends support to the view that real GDP

per capita is at best an imperfect proxy of welfare and standard of living.

A ranking of all countries by average annualized growth rate γi (provided at

Appendix A) sees Equatorial Guinea (GNQ) at the top of the list, with an11Leverage measures deviation from the mean. An observation with high leverage is far

from the mean value of the sample. An outlier is an observation with a large residual, or alarge difference between the observed and predicted value. A highly influential observationhas both high leverage and a large residual. Thus it would be found in the upper-right-hand or “north-east” corner of the graph. Figure 2 shows no such points.

37

annualized growth rate of 8.21%. This reflects the rise in real GDP per capita

from $593.16 in 1965 to $22,016.84 in 2009 (Heston et al., 2011). However,

in its 2011 edition of the Human Development Report (HDR), the United

Nations Development Program (UNDP) ranked Equatorial Guinea 136th out

of 187 countries based on the Human Development Index12. Even though

the Gross National Income (GNI) per capita13 for Equatorial Guinea, at

$17,608, is well above the average for Sub-Saharan Africa of $1,966, its life

expectancy at birth of 51.1 years trails the average of 54.4 years, as does the

number of expected years of schooling. While an in-depth treatment of the

Equatoguinean case is beyond the scope of this paper, it is a good example

of how welfare is not an unidimensional concept measured by income alone.

12The Human Development Index is a summary measure of human development incorpo-rating indicators for life expectancy, education, and income. (United Nations DevelopmentProgramme, 2011)

13GNI per capita is the income measure used in the HDR. The OECD provides a cleardefinition of GNI and its relation to GDP: “Gross national income (GNI) is GDP less nettaxes on production and imports, less compensation of employees and property incomepayable to the rest of the world plus the corresponding items receivable from the rest ofthe world” (Organization for Economic Cooperation and Development (OECD), 2001).

38

Figure 1: Partial Association between Mean Trade-to-GDP Ratio andGrowth

COM

ISL

BDI

HTI

URY

ZMBCAF

NZL

CPV

SYC

GMB

SLECMR

AUSGRC

SLV

NIC

ARGUGA

GNQ

ROM

FIN

PERNPL

COL

BENTCD

TUR

PRT

BFAMWI

CRI

BOL

BRAJPNESP

ZWE

ECU

RWA

TZA

CHL

TGO

DNKMLIMOZ

NER

GIN

CYP

MEX

NOR

BGD

ISR

KOR

FRAUSA

ETHCHESWEGTM

MAR

VEN

DOM

GBRITAAUT

NAM

PAKCAN

KEN

ZAF

SEN

BWA

PRYPNGSYR

JAM

FJI

IRL

GAB

GNB

IND

NGACIV

LKA

MRT

NLDBRB

TWN

IDN

MDG

JOR

CHN

IRNDZA

TTOTUNMUS

ZAR

EGY

PHLCOG

THA

BELLSO

HND

GHA

PRI

MYS

PANLUX

HKG

SGP

−5

05

10

Avera

ge a

nnualis

ed g

row

th in r

eal G

DP

per

capita (

PP

P)

−100 0 100 200 300Mean Trade/GDP,1965 − 2009

coef = .01418734, se = .00400158, t = 3.55

Figure 2: Leverage-vs-squared residual plot for cross-country regression

GNQ

CHN

BWAKORTWN

SGP

SYC

THAMYS

HKG

IDN

LKA

CPV

LUX

CYPROM

IND

DOMTTOMUS

IRLEGY

PRT

PAN

JPN

NORTUNGRCCHL

PRIESPAUTFINCOLMARTURAUSBELISR

ISL

BRA

NLD

LSOITACOG

BRB

URYFRAPAK

PNGCANGBRMLI

USA

DNKGHAFJI

SWETZAECUMEXGNBSYRCRIMOZGTM

PHLPRYNPL

IRNDZACHE

BGD

BFANZLARGZAFSLVETHPERUGA

HNDMRT

GMB

TCDGAB

MWINGA

RWABENBDICMRJAMJORBOL

NAMCIVVENKENSEN

SLE MDG

COM

ZMBGIN

HTITGO

CAFNICNERZWEZAR

0.1

.2.3

.4Levera

ge

0 .05 .1 .15Normalized residual squared

39

Table 6: OLS estimates using cross-country data

Variable γi γi

Model (1) (2)

openi6509 0.0142∗∗∗ 0.0163∗∗∗

(0.00400) (0.00311)

ln(popi6509) 0.182 0.316∗∗∗

(0.103) (0.0796)

constant 0.527 -0.0319(0.446) (0.346)

N 112 112R̄2 0.087 0.205

∗∗∗ p<0.001, ∗∗ p<0.01, ∗p<0.05Standard errors in parentheses

40

5.2 Singapore time series data

I begin by looking at the benchmark equation (4) without interaction terms,

reported as Model (1) in Table 7. All else held constant, a 1 unit increase in

the change of trade-to-GDP ratio is associated with a 0.089% increase in the

current y-o-y growth rate of real GDP per capita. This result is significant

at the 5% level. A 10% increase in total education expenditure is associated

with a 1.7% increase in the growth rate 14, all else held constant. This is

also significant at the 5% level. A 1 unit increase in inflation leads to a

1.59% increase in the growth rate, ceteris paribus. All else held constant, a 1

unit increase in the change of multi-factor producitivity is associated with a

1.076% increase in the growth rate. The coefficient on the lagged dependent

variable ∆ ln(yt−1) is positive, and statistically significant at the 5% level,

suggesting that there is indeed some dependence in the ∆ ln(yt) series, and

that effects from the previous time period t−1 persist into the current period

t.

Surprisingly, only two out of the nine interaction terms included were

statistically significant, ∆opent × ttit and ∆opent × ∆ftat. In Model (6),

both the coefficient on ttit and the interaction term are negative. The latter

is also statistically significant at the 5% level. This shows that all else held

constant, an increase in the ratio of export prices to import prices has a

negative effect on the growth rate of GDP per capita, lending support to the14Calculated using (1.100.17736)− 1 = 0.0170.

41

view that a rise in export prices hurts the price competitiveness of Singapore’s

exports, thus reducing growth.

In Model (8), the coefficients on ∆ftat and ∆opent × ∆ftat are both

positive, and the interaction term is statistically significant at the 10% level.

This indicates that an increase in the number of countries covered by an

FTA with Singapore has helped helped increase encourage economic growth

through an increase in Singapore’s trade openness.

In addition, the coefficient on the usrect variable is also statistically sig-

nificant, at the 10% level. However, the interaction term is not statistically

significant, suggesting that recessions in large economies such as the US neg-

atively affect growth in Singapore, but trade openness may not be the main

channel through which this negative effect influences the economy.

Overall, in the ten models estimated, several variables were consistently

statistically significant. These are educational expenditure, inflation, and

multi-factor productivity, indicating that these are important determinants

of Singapore’s growth for the period of study from 1981 - 2009. Specifically,

the positive coefficients on inft indicate that inflation was beneficial for Sin-

gapore’s growth, and this occurs when aggregate demand exceeds aggregate

supply, driven by factors such as high export levels or strong investment.

Data from Heston et al. (2011) supports this claim, showing that Singa-

pore’s mean trade openness for the period was 347% of GDP, and the mean

investment share of GDP was 40.3%. The trade openness variable was statis-

42

tically significant in five of the ten models estimated, showing some support

for the positive relation between trade openness and Singapore’s economic

growth.

5.2.1 Regression diagnostics

To ensure that OLS is the BLUE, I test the residuals in the OLS regres-

sions for serial correlation and heteroskedasticity. Since a lagged dependent

variable is included as one of the regressors, the Durbin-Watson test for first-

order autocorrelation is no longer valid. Instead, I use Durbin’s h statistic,

and the Breusch-Godfrey Lagrange multiplier (LM) test for higher order se-

rial correlation. For each of the 10 models estimated, the h and χ2 statistics

from the Durbin’s h and LM test were low and not statistically significant,

indicating that I cannot reject the null hypothesis that there is no serial

correlation in the error terms.

The Breusch-Pagan test for heteroskedasticity in the error terms produced

low χ2 statistics in each of the 10 models estimated. The highest χ2 value

recorded was 0.78 in Model (4), corresponding to a p-value of 0.377. As

such, I fail to reject the null hypothesis that the error terms have constant

variance given any value of the independent variables, and conclude that the

error terms are homoskedastic.

The homoskedastic and serially uncorrelated error terms prove that OLS

is the BLUE, making it an appropriate estimation method.

43

Table 7: OLS estimates using Singapore time series data

Variable (1) (2) (3) (4) (5)

∆ ln(yt−1) 0.571∗∗ 0.648∗∗∗ 0.558∗∗ 0.458∗ 0.379(0.208) (0.222) (0.217) (0.261) (0.231)

∆opent 0.089∗∗ 0.102∗∗ 0.077 0.103∗∗ 0.066(0.035) (0.038) (0.054) (0.04) (0.039)

∆ ln(edexpt) 17.736∗∗ 14.059 19.093∗∗ 21.893∗∗ 14.172∗

(7.885) (8.713) (9.158) (9.791) (8.186)

inft 1.590∗∗∗ 1.542 ∗∗∗ 1.574∗∗∗ 1.609∗∗∗ 2.101∗∗∗

(0.505) (0.508) (0.519) (0.512) (0.571)

∆mfpt 1.076∗∗∗ 1.142∗∗∗ 1.071∗∗∗ 1.023∗∗∗ 0.801∗∗∗

(0.209) (0.22) (0.215) (0.224) (0.253)

year -.099 -.066 -.109 -.117 -.045(0.081) (0.088) (0.089) (0.085) (0.084)

usrect -5.970∗

(3.314)

∆opent ×∆ ln(edexpt) -.294(0.296)

∆opent × inft 0.013(0.042)

∆opent ×∆mfpt -.010(0.014)

∆opent × usrect -.095(0.099)

constant 196.422 128.963 216.855 233.063 90.086(161.035) (174.824) (177.153) (170.373) (166.829)

N 28 28 28 28 28R̄2 0.597 0.597 0.579 0.588 0.622Durbin’s h 0.06 -.086 0.117 0.183 -.073LM test χ2 0.227 0.018 0.502 0.924 0.803Breusch-Pagan test χ2 0.42 0.29 0.57 0.78 0.26∗∗∗p < 0.01,∗∗ p < 0.05,∗ p < 0.1

(Continued. . . )Standard errors in parentheses

44

Variable (6) (7) (8) (9) (10)

∆ ln(yt−1) 0.539∗∗ 0.576∗∗∗ 0.585∗∗∗ 0.415∗ 0.856∗∗∗

(0.221) (0.21) (0.198) (0.226) (0.27)

∆opent 0.489∗∗∗ 0.21 0.047 -.100 0.145∗∗∗

(0.184) (0.39) (0.047) (0.274) (0.044)

∆ ln(edexpt) 16.777∗∗ 19.111∗∗ 16.865∗∗ 18.832∗∗ 14.774∗

(8.386) (8.214) (8.222) (7.827) (8.697)

inft 1.378∗∗∗ 1.552∗∗∗ 1.381∗∗∗ 0.963 1.510∗∗∗

(0.485) (0.514) (0.49) (0.593) (0.474)

∆mfpt 1.056∗∗∗ 1.044∗∗∗ 1.094∗∗∗ 0.899∗∗∗ 1.233∗∗∗

(0.206) (0.214) (0.198) (0.224) (0.279)

year -.726 -.098 -.290 0.006 0.052(0.747) (0.089) (0.248) (0.099) (0.143)

ttit -.337(0.376)

reert -.102(0.092)

∆ftat 0.223(0.246)

∆pct -.166∗

(0.09)

∆mktcapt 0.036(0.034)

∆opent × ttit -.003∗∗

(0.001)

∆opent × reert -.001(0.004)

∆opent ×∆ftat 0.006∗

(0.003)

∆opent ×∆pct 0.002(0.003)

∆opent ×∆mktcapt -.002(0.002)

constant 1487.134 204.797 574.856 3.408 -106.760(1536.232) (180.001) (492.953) (191.919) (286.651)

N 28 28 28 28 20R̄2 0.66 0.587 0.648 0.624 0.717Durbin’s h 0.09 0.076 -.013 -.122 -.218LM test χ2 0.183 0.143 0.028 0.233 0.545Breusch-Pagan test χ2 0.02 0.36 0.17 0.12 0.06∗∗∗p < 0.01,∗∗ p < 0.05,∗ p < 0.1 Standard errors in parentheses

45

5.3 Limitations of the Study

One of the greatest challenges associated with this study is the limited avail-

ability of time series data. While data for real GDP per capita and trade-to-

GDP ratio were available for all 44 years between 1965 and 2009, other se-

ries such as educational expenditure and multi-factor productivity were only

available starting from 1981. For some datasets, such as Barro-Lee (2010),

observations are not available for every year in a date range15. As such, even

though educational attainment and, by extension, the quality of human capi-

tal, may be more closely related to growth than educational expenditure, the

latter is chosen only because of data availability. Also, while interpolation

may be able to fill in missing values in a time series, the interpolated values

are not “true” data, and introduce (or increase) serial correlation into the

data by virtue of the interpolation process.

In addition, the small sample size limits the ability to draw generalizations

from the results of this study. The dataset used included selected years

between 1965 and 2009. Even if data were available for every year in this

period, the total number of observations would total only 45. Conclusions

drawn from such a small sample may not always be generalizable. Of course,

one way to increase the number of observations is to increase the frequency

of the data, for example, using quarterly instead of annual data16. However,15There are a variety of possible reasons for this, for example, that the data is not

collected on a yearly basis. An obvious example of this would be the decennial census.16This approach was explored initially, but found to be unfeasible.

46

this introduces greater seasonality into the data, which has to be controlled

for in turn. This may also exacerbate the data availability problem.

The two issues cited above are probably rooted in the decision to focus on

one country. Including more countries in the dataset would easily overcome

the small sample problem, as well as mitigate the issue of data availability.

With more countries included, the time period under study could be short-

ened, while maintaining a sizable sample, thus easing the data collection

process. Furthermore, working with cross-country data, one can use means

and average changes over a given period instead of looking to y-o-y changes,

which are often prone to short-run fluctuations. As mentioned before, us-

ing average changes reflects that economic growth occurs over a long period

of time, and that increases in income level are a result of small, repeated

additions over time. Unfortunately, with a small sample size, averaging ob-

servations is a luxury I cannot afford.

Focussing on the Singapore case also limits the types of explanatory vari-

ables that can be included in the growth regressions. For example, following

independence in 1965, the People’s Action Party has been the incumbent

party in government. This has naturally led to political stability for the

country, and while political stability is likely to be positively correlated with

growth, the Singapore time series data can neither prove nor disprove this

claim, simply because a dataset including data only from Singapore pro-

47

vides no basis for comparison with another country that has had changes in

political leadership during the period of study.

48

6 Conclusion

This study looked at the channels through which international trade af-

fects economic growth. I used a panel dataset of 112 countries and find

a strong cross-country relationship between trade openness and economic

growth. Even though the regression equation was probably underspecified

and suffers from endogeneity, the panel dataset provided initial insight into

the trade-growth relation. I then examined time series data from Singapore,

a country with high per capita GDP growth rates and trade openness levels,

to see if the country’s economic growth is indeed driven by its level of trade

exposure.

In sum, I find some support for the hypothesis that exposure to inter-

national trade has been beneficial to Singapore’s growth. Other important

determinants of Singapore’s growth over the period of study include educa-

tional expenditure, inflation and technological progress.

However, as noted in Section 5.3, limited availability of time series data

was the main reason for a small sample size of 28. Thus, for greater general-

izability of results, and to allow for comparison between countries, the study

can be repeated with a cross-country dataset so that the sample is enlarged,

and other covariates can be added.

49

References

Abeysinghe, T. & Choy, K.M. (2007). The Singapore economy: an econono-

metric perspective. Taylor & Francis.

Andersen, L. & Babula, R. (2008). The link between openness and long-run

economic growth. Journal of International Commerce and Economics, pp.

1–20.

Barro, R. (1996). Determinants of economic growth: a cross-country empir-

ical study.

Barro, R. J. & Lee, J.-W. (2010). A New Data Set of Educational Attain-

ment in the World, 1950Ð2010. Working Paper 15902, National Bureau of

Economic Research.

Beck, T., Demirgüç-Kunt, A., & Levine, R. (2009). Financial institutions

and markets across countries and over time. World Bank, Policy Research

Working Paper, 4943.

Chang, R., Kaltani, L., & Loayza, N. (2009). Openness can be good for

growth: The role of policy complementarities. Journal of Development

Economics, 90 (1), pp. 33–49. ISSN 0304-3878.

Dickey, D. A. & Fuller, W. A. (1979). Distribution of the Estimators for

Autoregressive Time Series With a Unit Root. Journal of the American

Statistical Association, 74 (366), pp. 427–431. ISSN 01621459.

50

Dollar, D. & Kraay, A. (2004). Trade, Growth, and Poverty*. The Economic

Journal, 114 (493), pp. F22–F49. ISSN 1468-0297.

Federal Reserve Bank of St. Louis (2011). NBER based Recession Indi-

cators for the United States from the Period following the Peak through

the Trough (USREC). URL https://research.stlouisfed.org/fred2/

series/USREC/downloaddata?cid=32262.

Frankel, J. & Romer, D. (1999). Does trade cause growth? American

Economic Review, 89 (3), pp. 379–399. ISSN 0002-8282.

Granger, C. & Newbold, P. (1974). Spurious regressions in econometrics.

Journal of Econometrics, 2 (2), pp. 111–120. ISSN 0304-4076.

Hallak, J. & Levinsohn, J. (2004). Fooling ourselves: evaluating the global-

ization and growth debate. NBER Working Paper.

Heston, A. (2011). Description of PWT 7.0. Center for International Com-

parisons of Production, Income and Prices at the University of Pennsyl-

vania.

Heston, A., Summers, R., & Aten, B. (2011). Penn World Table Version 7.0.

Center for International Comparisons of Production, Income and Prices

at the University of Pennsylvania.

International Enterprise Singapore (2010). About IE Singa-

pore. URL http://www.iesingapore.gov.sg/wps/portal/

51

!ut/p/c5/04_SB8K8xLLM9MSSzPy8xBz9CP0os3gDf4PQMFMD_

1A3g2BDI0MPHzcDKND388jPTdUvyHZUBADUfu5u/dl3/d3/

L3dDb0EvUU5RTGtBISEvWUZSdndBISEvNl9DR0FINDdMMDAwODlGMElLQUE_

zT0UzMjhUMA!!/.

International Monetary Fund (n.d.a). Direction of Trade Statistics

(DOTS). URL http://elibrary-data.imf.org/FindDataReports.

aspx?d=33061&e=170921.

International Monetary Fund (n.d.b). International Financial Statistics

(IFS). URL http://elibrary-data.imf.org/FindDataReports.aspx?

d=33061&e=169393.

Irwin, D. & Terviö, M. (2002). Does trade raise income?: Evidence from the

twentieth century. Journal of International Economics, 58 (1), pp. 1–18.

ISSN 0022-1996.

Krugman, P. (1994). The myth of Asia’s miracle. Foreign Affairs, 73 (6), pp.

62–78. ISSN 0015-7120.

Lee, H., Ricci, L., & Rigobon, R. (2004). Once again, is openness good for

growth? Journal of Development Economics, 75 (2), pp. 451–472. ISSN

0304-3878.

Lovell, M. (2008). A Simple Proof of the FWL Theorem. The Journal of

Economic Education, 39 (1), pp. 88–91.

52

Ministry of Education, Singapore (2010). Government Expenditure on

Education - Total, Singapore Dollars (’000). URL http://data.gov.sg/

Metadata/SGMatadata.aspx?id=0302010000000005762P&mid=14276&t=

TEXTUAL.

Noguer, M. & Siscart, M. (2005). Trade raises income: a precise and robust

result. Journal of International Economics, 65 (2), pp. 447–460. ISSN

0022-1996.

Organization for Economic Cooperation and Development (OECD) (2001).

Gross National Income (GNI). URL http://stats.oecd.org/glossary/

detail.asp?ID=1176.

Rodriguez, F. & Rodrik, D. (2000). Trade policy and economic growth:

a skeptic’s guide to the cross-national evidence. NBER Macroeconomics

Annual, 15, pp. 261–325. ISSN 0889-3365.

Romer, D. (2005). Advanced Macroeconomics, 3rd edition. McGraw HIll

Irwin, Boston. ISBN 0072877308.

Sachs, J. & Warner, A. (1995). Economic reform and the process of global

integration. Brookings papers on economic activity, 1995 (1), pp. 1–118.

ISSN 0007-2303.

Said, S. E. & Dickey, D. A. (1984). Testing for unit roots in autoregressive-

moving average models of unknown order. Biometrika, 71 (3), pp. 599–607.

53

Taylor, A. (1998). On the costs of inward-looking development: price distor-

tions, growth, and divergence in Latin America. The Journal of Economic

History, 58 (01), pp. 1–28. ISSN 0022-0507.

United Nations Development Programme (2011). Human Development Re-

port 2011 Sustainability and Equity: A Better Future for All.

Wacziarg, R. (2001). Measuring the dynamic gains from trade. The World

Bank Economic Review, 15 (3), p. 393. ISSN 0258-6770.

Woolridge, J. (2010). Introductory Econometrics: A Modern Approach, 4th

Edition. Cengage Learning.

World Trade Organization (n.d.a). About the WTO — a statement by

the Director-General. URL http://www.wto.org/english/thewto_e/

whatis_e/wto_dg_stat_e.htm.

World Trade Organization (n.d.b). Regional Trade Agreements Database.

URL http://rtais.wto.org/UI/PublicSearchByCrResult.aspx.

54

Appendix

A List of countries ranked by γi

Table 8: List of countries included in cross-country regression,ranked by γi

Rank Country CodeReal GDP per capita(US$)

γi1965 2009

1 Equatorial Guinea GNQ 593.16 22,016.84 8.21

2 China CHN 356.71 7,011.57 6.77

3 Botswana BWA 628.11 8,868.16 6.02

4 Republic of Korea KOR 2,052.92 25,029.01 5.68

5 Taiwan TWN 2,465.12 28,693.00 5.58

6 Singapore SGP 4,694.44 47,357.27 5.25

7 Seychelles SYC 3,504.13 23,801.85 4.35

8 Thailand THA 1,174.62 7,793.65 4.3

9 Malaysia MYS 1,770.17 11,295.08 4.21

10 Hong Kong HKG 5,905.33 36,286.26 4.13

11 Indonesia IDN 675.03 4,075.05 4.09

12 Sri Lanka LKA 869.22 4,034.42 3.49

13 Cape Verde CPV 835.67 3,779.20 3.43

14 Luxembourg LUX 19,554.27 84,510.57 3.33

15 Cyprus CYP 4,445.69 18,981.09 3.3

16 Romania ROM 2,290.51 9,736.75 3.29

17 India IND 775.07 3,237.98 3.25

18 Dominican Republic DOM 2,412.52 9,912.04 3.21

19 Trinidad & Tobago TTO 7,648.11 30,990.67 3.18

20 Mauritius MUS 2,358.23 9,483.60 3.16

(Continued. . . )

55

Table 8 – Continued

Rank Country CodeReal GDP per capita(US$)

γi1965 2009

21 Ireland IRL 8,376.27 33,347.45 3.14

22 Egypt EGY 1,247.60 4,955.85 3.13

23 Portugal PRT 5,373.30 19,889.91 2.97

24 Panama PAN 2,771.27 10,196.15 2.96

25 Japan JPN 9,043.20 31,957.85 2.87

26 Norway NOR 15,017.26 49,939.94 2.73

27 Tunisia TUN 1,936.56 6,300.35 2.68

28 Greece GRC 8,948.34 27,285.50 2.53

29 Chile CHL 3,950.78 11,998.79 2.52

30 Puerto Rico PRI 7,813.63 23,661.09 2.52

31 Spain ESP 9,335.66 27,631.96 2.47

32 Austria AUT 12,651.62 37,399.77 2.46

33 Finland FIN 11,247.38 32,160.07 2.39

34 Colombia COL 2,636.93 7,528.69 2.38

35 Morocco MAR 1,210.02 3,293.97 2.28

36 Turkey TUR 3,649.44 9,910.42 2.27

37 Australia AUS 15,234.40 41,298.05 2.27

38 Belgium BEL 12,810.17 34,620.50 2.26

39 Israel ISR 9,453.90 25,548.96 2.26

40 Iceland ISL 13,742.65 37,112.36 2.26

41 Brazil BRA 3,564.26 9,352.81 2.19

42 Netherlands NLD 15,608.19 40,565.93 2.17

43 Lesotho LSO 514.29 1,311.47 2.13

44 Italy ITA 11,029.18 27,691.75 2.09

45 Republic of Congo COG 894.31 2,219.80 2.07

46 Barbados BRB 9,272.30 22,913.57 2.06

47 Uruguay URY 4,486.49 11,069.23 2.05

(Continued. . . )

56

Table 8 – Continued

Rank Country CodeReal GDP per capita(US$)

γi1965 2009

48 France FRA 12,554.56 30,821.49 2.04

49 Pakistan PAK 971.80 2,353.25 2.01

50 Papua New Guinea PNG 1,173.85 2,746.31 1.93

51 Canada CAN 15,554.18 36,208.07 1.92

52 United Kingdom GBR 14,357.56 33,385.98 1.92

53 Mali MLI 438.74 999.38 1.87

54 United States USA 18,350.65 41,101.86 1.83

55 Denmark DNK 15,146.67 33,906.11 1.83

56 Ghana GHA 565.22 1,239.19 1.78

57 Fiji FJI 1,960.40 4,284.47 1.78

58 Sweden SWE 16,404.59 35,224.10 1.74

59 Tanzania TZA 557.50 1,188.89 1.72

60 Ecuador ECU 2,909.68 6,170.96 1.71

61 Mexico MEX 5,486.60 11,629.61 1.71

62 Guinea-Bissau GNB 390.10 818.38 1.68

63 Syria SYR 1,996.98 4,001.89 1.58

64 Costa Rica CRI 5,605.43 11,216.67 1.58

65 Mozambique MOZ 383.54 759.48 1.55

66 Guatemala GTM 3,279.88 6,284.95 1.48

67 Philippines PHL 1,484.29 2,838.63 1.47

68 Paraguay PRY 1,943.76 3,704.76 1.47

69 Nepal NPL 655.55 1,211.48 1.4

70 Iran IRN 5,790.50 10,621.92 1.38

71 Algeria DZA 3,375.73 6,067.59 1.33

72 Switzerland CHE 22,069.02 39,619.45 1.33

73 Bangladesh BGD 780.98 1,397.05 1.32

74 Burkina Faso BFA 513.83 902.44 1.28

(Continued. . . )

57

Table 8 – Continued

Rank Country CodeReal GDP per capita(US$)

γi1965 2009

75 New Zealand NZL 15,920.82 27,864.80 1.27

76 Argentina ARG 6,871.52 11,961.30 1.26

77 South Africa ZAF 4,625.95 7,588.43 1.12

78 El Salvador SLV 3,931.99 6,339.00 1.09

79 Ethiopia ETH 430.68 683.66 1.05

80 Peru PER 4,613.71 7,279.81 1.04

81 Uganda UGA 731.50 1,151.75 1.03

82 Honduras HND 2,301.59 3,605.01 1.02

83 Mauritania MRT 1,014.32 1,573.77 1

84 The Gambia GMB 947.57 1,464.76 .99

85 Chad TCD 829.22 1,276.40 .98

86 Gabon GAB 6,832.00 10,280.37 .93

87 Malawi MWI 440.35 653.04 .9

88 Nigeria NGA 1,410.75 2,034.15 .83

89 Rwanda RWA 731.97 1,031.09 .78

90 Benin BEN 800.50 1,115.71 .75

91 Burundi BDI 264.40 368.06 .75

92 Cameroon CMR 1,327.28 1,811.00 .71

93 Jamaica JAM 6,510.51 8,794.49 .68

94 Jordan JOR 3,576.84 4,643.94 .59

95 Bolivia BOL 3,066.43 3,793.85 .48

96 Namibia NAM 3,873.57 4,732.94 .46

97 Côte d’Ivoire CIV 1,166.68 1,343.22 .32

98 Venezuela VEN 8,014.00 9,115.23 .29

99 Kenya KEN 1,063.28 1,206.17 .29

100 Senegal SEN 1,349.66 1,492.04 .23

101 Sierra Leone SLE 854.41 872.68 .05

(Continued. . . )

58

Table 8 – Continued

Rank Country CodeReal GDP per capita(US$)

γi1965 2009

102 Madagascar MDG 815.33 753.22 -.18

103 Comoros COM 1,036.00 915.71 -.28

104 Zambia ZMB 2,044.41 1,764.72 -.33

105 Guinea GIN 970.70 826.33 -.37

106 Haiti HTI 1,774.81 1,444.55 -.47

107 Togo TGO 1,056.28 734.20 -.83

108 Central African Republic CAF 992.61 647.71 -.97

109 Nicaragua NIC 3,574.97 2,191.36 -1.11

110 Niger NER 884.20 534.34 -1.14

111 Zimbabwe ZWE 267.92 142.56 -1.43

112 Dem. Rep of the Congo ZAR 1,004.29 231.28 -3.34