TRADING UP
Gernot Wagner and Richard J. Zeckhauser*
Harvard University
April 12, 2006
We construct a comprehensive measure of trade-weighted income per capita for the
trading partners of 157 countries from 1960 to 2000. We find that the vast majority of
countries trade up, which is not surprising once we recognize that with notable exceptions
of India and China richer countries are the biggest traders. We also link trading up with
increased domestic growth. A likely pathway are positive externalities associated with
increased trade relations between relatively poor and rich countries, which enable the
poor to grow faster.
*
The authors are, respectively, a Ph.D. student in Political Economy and Government at Harvard University, and
the Frank Plumpton Ramsey Professor of Political Economy at Harvard University’s John F. Kennedy School of
Government. Gernot Wagner worked on this paper in residence under the Repsol YPF-Harvard Kennedy School
Fellows Program, and acknowledges financial support from the Austrian Academy of Sciences. Without any
implications, we thank participants in the Repsol YPF-Harvard Kennedy School Fellows Seminar and the Harvard
Trade Seminar for helpful comments and discussions. Special thanks to Ana-María Herrera, Kai Guo and Brent
Neiman, and Ian Sue Wing.
See http://gwagner.net/research/trade for data and Stata code.
Contact: [email protected] and [email protected].
1
1. Trade Partners
Canada and Singapore are at similar levels of economic development. In 2000, their
respective per capita GDP levels were $28,700 and $29,000. While they differ along many other
dimensions, one striking difference is in their trade partners. While Canada trades mostly with
the United States, Singapore trades mostly with countries in South East and East Asia.
Geographic differences explain much of this variation. Countries tend to trade with each other, if
they share a border, are located close to each other, and have similarly sized economies (Frankel
and Romer 1999).
Location matters for who one trades with. We take these determinants of trade as given
and look at their implications. In particular, we focus on the average income of one’s trade
partners. To that end, we construct a measure of trade-weighted GDP per capita of one’s trading
partners and attempt to determine its relationship with economic growth.
1.1.
Trade-weighted GDP per capita of one’s trading partners, Act
We calculate the average income of a country’s trading partners weighted by the level of
trade flows, giving the same weight to a dollar of exports or imports. More specifically, we use
measures of bilateral imports of country c from country d at a given time t across all industrial
sectors, M cdt , and equivalent measures for exports from c to d, Xcdt . We denote the absolute
income per capita of a country’s trading partners as Ydt , and use that to calculate the average
income of country c’s trading partners,
Y M + X cdt
Act dt cdt
M cdt + X cdt
d
d
(1)
.
Table I depicts the results for 2000.
Interestingly, the absolute levels of Act are relatively high compared to per capita income
levels, Yct . The average ratio of Act to Yct in 2000 is 6.5, and the median is 4.0, whereas one
2
might naively expect a value of 1.0. This pattern reflects the often observed fact that most trade
is done by developed countries. Thus, the 30 richest countries in our sample of 157 account for
91% of the total trade. Both developed and developing countries tend to trade with rich trading
partners, which explains why 135 countries in our sample trade up.
Trade does not merely depend on whether one is rich or poor. Location in the world
matters a great deal, as does the possession of and need for raw materials. Thus it is to be
expected that Act exhibits considerable variability, holding
Yct fixed. In 2000, Canada and
Singapore had very similar per capita GDP levels; yet their trading partners’ incomes differed
widely. Canada, which mostly trades with the United States, had an Act ,of $30,900. For
Singapore, with most trade focused on South East and East Asia, the figure was $18,100. Similar
country-pairs are Mexico and Uruguay, or Honduras and Bhutan. Mexico and Honduras both
have significant trade relations with the United States, while Uruguay and Bhutan conduct
relatively more of their trade with nearby poorer neighbors. See Table I for these and other
comparisons.
The disparities in Act values for these three country-pairs are much greater than the norm.
Nevertheless, average ranges for Act are still sizeable. Figure 1 plots Act versus Yct for 1980 and
2000. Act increases with Yct for every year in the sample. Standard deviations around these
means decrease slightly with increasing Yct . Finally, the variability in Act is much smaller than
for Yct . This last observation is, in part, by construction since
Act is a weighted average of
different Ydt s. However, standard deviations of the Act s are still relatively large, and it is these
differences that we exploit in our analysis below. We split the sample into five equal sized
groups of 31 or 32 countries, grouped according to Yct for the year 2000. Act increases from the
lowest to highest income-group, from a low of $15,700 to a high of $22,000. Standard deviations
around these means range from $2,500 to $4,000.
3
The conclusion that Act increase with Yct is at odds with some recent trade theory.
Helpman, Melitz, and Rubinstein (2005) would predict a downward-sloping trend.1 In their
model, firms face fixed costs of trade. Hence, firms would choose the most attractive export
destinations. Poorer countries, with relatively fewer export-oriented firms, would trade with the
richest possible trade partner. As countries become richer, the number of trade partners increases,
decreasing Act in the process. We do not observe this pattern in
Act , which may indicate that
other factors are more influential in determining a country’s trade partners.
1.2.
Trade-weighted relative GDP per capita, Wct
To measure income relative to one’s trading partners, we weight each country’s GDP per
capita by the average GDP of its trading partners,
Wct (2)
Yct
.
Act
Table I shows Yct , Act , and Wct for all 157 countries in the sample and their respective rankings.
Singapore and Canada provide yet again an instructive example. Both are relatively rich in
absolute levels, ranked third and fifth in terms of Yct , respectively. Yet Canada conducts most of
its trade with the United States, an even richer trading partner. Its Act is second only to Mexico.
Singapore, on the other hand, trades primarily with its much poorer neighbors.2 Its Act is among
the lowest in the world. Taking the ratio of Yct to Act results in a Wct of 0.93 for Canada and
1.60 for Singapore. Using trade-weighted relative GDP per capita as the metric for ranking
economies, Singapore comes in third, while Canada ranks 24th.
We calculate the world average per capita GDP by weighing country-specific income
N
levels by population, Yct = c=1 (Yct Popct )
N
c=1
Popct . We then graph absolute GDP per capita
1
Thank you to Brent Neiman for pointing out this fact.
2
We use per capita world average GDP. The discussion would not change significantly, if we used world average
GDP by country,
Yct
1
N
N
Y . Only the vertical line in
c=1 ct
decreasing the number of countries in the lower right quadrant.
4
Figure 2 would move slightly to the right,
relative to that world average, Yct Yct , which we will subsequently refer to as Yctrelative . Figure 2
depicts Yctrelative and Wct for 1960 to 2000, by decade. The vertical and horizontal lines display the
respective world averages. Unambiguously rich countries, relative to the world average, are in
the top right quadrants. Poor countries are on the bottom left.
1.3.
A first pass at “Trading Up”
Countries tend to trade up. Most countries are poorer than their trading partners, even
those that have above average income. The only unambiguously rich economies are the top 15 to
20 economies, including the USA, several Western European countries, and in later years also
Singapore. The group of countries in the bottom right quadrants is relatively stable across time,
with some notable exceptions highlighted in the first four panels of Figure 2. Singapore, which
gained independence from Malaysia in 1965, starts out in the lower right quadrant, but gradually
moves out and is now clearly above the world average in both dimensions.3 Ireland experienced
a similar growth spurt over the last four decades. A few other nations, such as South Korea,
started out being in the lower left quadrant but moved up significantly.
A defining characteristic of most countries in the lower right quadrant is that they are
richer than the world average, but poorer when compared to the club of their developed trading
partners. Usually that is because they have wealthy neighbors, with whom they do most of their
trading. Greece, Portugal, and Spain, for example, are the three poorest countries in the EMU.
The same goes for Mexico and – to a lesser extent – Canada. Both are part of the North
American Free Trade Agreement, but are poorer than the United States. A final important subgroup are the newly industrializing countries of Eastern Europe, such as the Czech Republic,
Hungary, Poland, Slovakia, Slovenia. They are well integrated into the EU in terms of trade
volume, but are by far the poorest members of that market. They trade up in a major fashion.
3
Hong Kong would exhibit similar trends as Singapore. However, Gleditsch (2002) groups it as part of China’s
economy. See Appendix A for a detailed discussion.
5
1.4.
Using tonnage rather than dollar values
Trade flows used for calculating Act are in dollar values. Beginning in 1984, Feenstra et
al.’s (2005) trade data are also available in weight. Given the great disparities in dollar per ton
valuations across goods, and that some countries trade much more high value/weight goods than
others, we thought it worthwhile to recompute our values using tonnage. Calculating Act with
imports, M cdt , and exports, Xcdt , denoted in tons results in minor changes at the margin and
slightly different rankings in Table I. However, none of the main conclusions changes. Countries
in the bottom right quadrant of Figure 2 remains largely the same for the year 2000. The
magnitudes of Wct , for the most part, are not appreciably different. One notable exception is
Hong Kong, which was excluded from the preceding analysis based on Gleditsch’s (2002)
dataset but plays a prominent role in Feenstra et al.’s data. Calculated in value-terms, its Wct
equals 1.95, second only to the United States. Calculated in weight-terms, Hong Kong’s Wct
equals 5.15, second to none. Hong Kong conducts a significant portion of its high-value trades
with rich trading partners in the OECD. By weight, however, the vast majority of its trade goes
to and from much closer and much poorer China.4 Singapore would likely exhibit a similar
pattern as Hong Kong. However, while Hong Kong is excluded from Gleditsch’s dataset,
Singapore, in turn, is excluded from Feenstra et al.’s data. In general, we would expect countries
in poor regions to have higher Wct in tons than dollars, given transport costs.
1.5.
Imports versus Exports
Equation (1) calculates Act based on total trade flows. Trading partners’ per capita
incomes are weighted by the sum of imports plus exports between the two nations. We also
analyze trading partners based on imports and exports separately by defining
4
It would not be unreasonable to think of China as two countries, one quite developed with a relatively high per
capita income, and one quite poor. Hong Kong trades overwhelmingly with the first China, suggesting these
values would be more in line if we bifurcated China. India, also a major trading nation, also has a relatively rich
trading section and poor non-trading section.
6
Ydt M cdt A M cdt d
d
M
ct
(3)
and
Ydt X cdt ,
A X cdt d
d
X
ct
(4)
respectively. Figure 3 displays the results for the years 1980 and 2000. Both ActM and ActX
increase with per capita GDP. However, the fitted line for ActX is flatter than for ActM , and simple
F-tests reveal that the difference in slopes is highly significant, that both slopes are positive
(richer countries export and import from richer countries), and that both slopes are well below 1
(as discussed before, that poorer countries tend to have comparatively richer trading partners).
The significant difference in slopes implies that though poor countries tend to import from
relatively richer countries, this phenomenon is much more pronounced with exports.
Work not shown demonstrates that these relationships tend to be stable across time. There
are some notable exceptions. China, for example, went from importing from richer countries and
exporting to rich but comparatively poorer ones in 1980 to precisely the opposite pattern in 2000.
This shift was mainly caused by an enormous boost in exports to the USA and European
economies, which caused bilateral trade deficits from 1980 to turn into large surpluses by 2000.
China is also the chief reason why the U.S. ActX exceeds its ActM in 2000 by a much larger margin
than it did in 1980. During these twenty years, China moved from being America’s 23rd largest
trading partner to number four, only behind Canada, Mexico, and Japan. The United States runs
an enormous proportional trade deficit with China – exports were just 32% of imports in 2000 –
which implies China’s relatively low GDP per capita figures prominently in the calculation of
ActM , but less strongly in ActX .5
5
The U.S. proportional trade deficit, the ratio of exports over imports, with all trade partners was 67%.
7
Regardless of whether we calculate Act in terms of weight or value, or on imports or
exports alone or on the sum of the two, a focus on income relative to one’s trading partners may
prompt us to re-classify our understanding of countries’ prosperity relative to their trading
partners. Such an understanding is critical to formulating trade agreements around the world, and
also to answering the classic question of how trade affects growth. The next section will focus on
how a country’s trading partners affect its economic fortunes.
2. Trade and Performance
This section ties the GDP of trading partners to a country’s own economic performance.
We look at changes in per capita GDP and relate them to economic conditions abroad.
2.1.
Past Literature
Generations of economists have studied the question whether openness to trade causes
domestic economic growth.6 If there is an effect of openness on growth, the literature suggests
that it is likely positive. Frankel and Romer (1999) construct an instrument for openness using
geographic variables and conclude that trade raises income. Openness in their case is defined as
the trade share of GDP, the same measure used throughout our analysis. Rodríguez and Rodrik
(2001) review this and several other cross-country studies relating openness to growth, and
provide a potent voice of dissent. Their conclusion is that most studies overstate the case for
trade. We do not argue for or against openness per se. We also do not ask whether trade played a
role in bringing countries to their present status, but what it does given their current status. Our
analysis does not question where bilateral trade flows come from and whether they themselves
are good or bad for growth. Instead, we focus on the effect of trading partners on countries once
they have opened their economies and allowed for bilateral trade flows. Holding fixed your level
of trade, does it matter who you trade with? Few others have looked at this effect.
6
See Baldwin (2004) for a historical survey of the openness and growth literature, followed by a Comment by
Simon Commander in the same volume, who summarizes the critique of cross-country analyses of trade and
openness in general.
8
Arora and Vamvakidis (2004), to our knowledge, conducted the only analysis of the
effect of trading partners on domestic economic growth. The authors construct export weights,
equivalent to ActX in equation (4). They find that growth in ActX has a large and significant effect
on domestic growth. Making use of both imports and exports, we are able to weight trading
partners’ income levels by imports, ActM , as well as all bilateral trade flows for each country, Act .
ActM and ActX
This Act is our primary measure, though we also look at the different effects of
independently. This will allow us to pinpoint the effects of overall trade versus imports and
exports alone. There is no reason to assume that exports matter more for how trade affects
performance than do imports, or vice versa. We consider both, separately and in combination.
2.2.
Linked Economic Fortunes
We relate a country’s own economic growth to that of its trading partners using three
measures: (1) Act , (2) growth in Act , defined as
(A
ct
Ac,t 1 ) Ac,t 1 , and (3) what we will later
call the “change in ActX ’s Ydt .”
Arora and Vamvakidis (2004) focus on growth in ActX , defined as
(A
X
ct
X
Ac,t
1
)
X
Ac,t
1 .
This is equivalent to our second measure, but excludes imports. (They also look at levels of ActX ,
but only in conjunction with their primary measure.) Column (1) of Table II attempts to duplicate
Arora and Vamvakidis’s (2004) benchmark result from their Table 3, column (1). They find a
significant coefficient on their measure of growth in ActX . Our coefficient is positive, but barely
misses significance at the ten-percent significance level. We do find significant results if we
replace ActX with ActM and Act in columns (2) and (3), respectively. We expect, but can not be
confident, that they would take these results as supportive of their work. The difference between
effects of growth in ActX and ActM is also significant, as column (4) shows. We include both the
growth in ActX and in ActM in the same regression. The coefficient on the growth on ActM is greater
than that on the growth on ActX . We conduct an F-test of whether the two coefficients differ
significantly, and barely reject a difference on a two-sided test at the ten-percent significance
level (F(1, 629) = 2.45, P > F = 0.1184). However, regardless of whether we use growth in ActX ,
9
ActM , or Act , the effect disappears once we control for other covariates of growth such as the
investment share in GDP and human capital. Column (5) reports the results for growth in Act .7
The measures of growth in ActX , ActM , and Act all conflate two effects. First, changes in
each variable could be caused by movements in the respective trade weights, for example
Xcdt
X
cdt
in the case of
ActX . Shifting trade from a poor to a rich trade partner positively
d
affects domestic growth. The same effect, however, could also be caused by increases in Ydt .
Trade weights do not change – country c still trades with the very same trading partners – but
one of several of these trading partners show increases in real Ydt . That again could lead to
increased growth at home. To separate the “switching partners” and “partners’ growing” effects,
we construct a new measure,
(5)
(Yct Yc,t 1 ) Yc,t 1 Xcdt ,
d
Xcdt
d
which we refer to as “Change in ActX ’s Ydt .” The coefficient on that variable is one order of
magnitude higher than anything we have previously found and is highly significant. A one
percentage change in GDP of one’s trading partners is associated with a 0.5 percent increase in
domestic growth. The coefficient on growth in ActX itself loses all its significance. Repeating the
regression in column (6) for changes in ActM ’s Ydt and in Act ’s Ydt results in the same conclusion
with very similar magnitudes. The same goes for a measure for openness and the interaction term
between openness and change in ActX ’s Ydt . Adding further covariates, GDP investment share and
human capital, decreases the sample size because of limited human capital data, but further
solidifies the conclusion. Column (7) of Table II reports the coefficients. A one percent change
in ActX ’s Ydt is again linked to a 0.5 percent increase in domestic growth. It is important to note
that this regression does not establish causality. It simply measures the relationship of changes in
7
This finding goes counter to Arora and Vamvakidis’s (2004) conclusion, who find significant effects even after
controlling for several covariates.
10
economic conditions abroad to domestic growth. Much as domestic growth is partially
influenced by growth abroad, so is growth abroad influenced by growth at home.
This analysis shows that domestic growth is, in fact, not achieved by changes in who we
trade with, but by domestic economic growth in one’s trading partners. Interpreted differently, it
shows that the fortunes of trading economies are intimately linked. Exogenous shocks to one
country’s economy likely affect others as well. Hence, it is good to have one’s trading partners
prosper. While interesting in and of itself, this does not tell us whether trading up or down,
holding growth of partners fixed, is good for economic growth. To determine that we need to
turn to levels of Act , rather than its changes.
2.3.
Trading up and higher growth
If you wish to grow, does it matter with whom you trade? Table III regresses growth in
per capita income on openness, the log of Act , the interaction between the two terms and other
covariates. The sample ranges from 1960 through 2000 and covers 136 countries for regressions
including only openness as well as Act . (As throughout our analysis so far, we define openness
as the trade share in GDP.) It also studies 93 countries when including human capital and
investment share in GDP as covariates, where the sample-limiting covariate is human capital.8
Columns (1) present results for the country and time fixed effects regression of growth on
initial GDP per capita, openness, log of Act and the interaction term between the latter two. Both
openness and the interaction term exhibit high economic and statistical significance. Once we
include the GDP investment share and human capital in column (2), openness remains positive,
although it loses its significance. Now log of Act becomes significant at the 10% level and the
interaction term between openness and Act decreases in absolute magnitude but still remains
large and significant. More open economies and countries that trade with higher income
countries conditional on openness grow faster.
8
The coefficient for human capital negative throughout our analysis, albeit often insignificant. This is unexpected
and largely unexplained. Using lagged human capital does not change the sign.
11
We have shown in Table II that growth in Act and changes in Act ’s Ydt are linked to
growth. Adding both, as well as their interaction terms with openness to the fixed effects
regression, does not change the conclusions about absolute levels in Act . It does, however,
increase the magnitude and significance of log Act and decreases the significance of its
interaction term with openness. The two significant terms are openness interacted with growth in
Act and the change in Act ’s Ydt by itself. Trading with faster growing economies, holding our
mix of trading partners constant, fosters domestic growth regardless of how open one’s own
economy is. The residual growth in Act , due to shifts in trade-weights, also increases domestic
growth. In this case, however, openness matters. Growth in Act is only significant once we
interact it with openness.
2.4.
Combating Endogeneity of Openness and Act
Openness and higher Act are associated with more growth. It would be nice if we could
show that higher openness and higher Act lead to growth. However, the causal link in our
analysis so far is not clear. Policies that may be associated with higher growth may also lead to
higher openness and vice versa. Similarly, common factors leading to increased Act may also
cause increased domestic growth. We have already shown that global economic fortunes are
closely linked. Exogenous demand shocks may be common across countries, trading partners
may institute similar economic policies, and growth at home may directly influence growth
abroad through the same channels that produce the reverse effect. We use two methods to
combat the potential for endogenous relationships. The first employs the commonly used gravity
trade equations as controls for openness. The second uses the equally widespread annual lags for
Act .
Frankel and Romer (1999) instrument for openness using only geographic characteristics,
such as distance between two trading partners, whether or not either or both of them is
landlocked, their respective populations and geographic size, and whether or not they share a
common border. Because of the importance of the common-border dummy, they also interact it
with all other regressors. Table IV attempts to duplicate their results. Column (1) does so exactly,
12
using 1985, the year of Frankel and Romer’s analysis, with the only exception that we have data
for 121 countries compared to their 63. Our sample size is 9942, more than three times as large
as theirs. Reassuringly given the sample size difference, we are able to duplicate their benchmark
result fairly accurately. All single variable effects remain the same. The two interaction variables
that point in a different direction are log area of country c interacted with the common-border
dummy and log population of country d interacted with the same dummy. Both are insignificant.
All other variables are significant in their study and ours. The correlation between predicted and
actual trade shares is 0.54, compared to Frankel and Romer’s 0.62.
We repeat the analysis for the entire Frankel-Romer-Rose dataset from 1970 through
1999 for 134 countries, using year fixed effects. Most coefficients prove robust to that change.
Lastly, we repeat the analysis using the same geographic data, but employ instead Gleditsch’s
(2002) trade data. This produced no significant changes to the coefficients. Following Frankel
and Romer (1999), we predict trade shares for all years in the sample. The correlation between
predicted and actual trade shares now increases to 0.59, compared to 0.54 from using 1985 as the
single year for the instrumental variable regression.
Once we have predicted trade shares for all years, we include them in the growth
regression from Table III, and thereby mitigate endogeneity concerns linked to the openness
measure. These results, presented in column (4), mimic those of column (3) very closely, except
for the coefficients on two interaction variables: openness interacted with growth in Act and
openness interacted with the change in Act ’s Ydt . The first reverses signs and becomes highly
negative and significant. The second reverses signs to become highly positive, although not
statistically significant.
Instrumenting for openness using gravity trade equations removes one kind of
endogeneity. Since we cannot instrument for trade itself, but only for trade shares, we cannot use
predicted values in calculating Act to counter its potential endogeneity. We use lagged values
instead, going back both one and two years. Lags preempt direct feedback loops from domestic
growth to growth abroad in the same year. They do not completely remove potential effects of
exogenous demand shocks that may be common across countries, or similar economic policies
13
instituted by trading partners, since such phenomena may have ramifications for several years.
They do, however, remove any potential short-term effects up to one or two years (if using twoyear lags).
Columns (5) and (6) present the results of using instrumented openness and one or twoyear lags for Act , respectively. Adding lags, removes the significance of growth in Act interacted
with openness. We can no longer rely on overall growth in Act to explain domestic growth.
However, our two other variables of interest, log Act and change in Act ’s Ydt , remain large and
significant. Adding one or two-year lags only marginally decreases their magnitude or their
significance. Using three-year lags and above keeps their signs intact but renders them
insignificant. This is not surprising, since we would expect the effects of past Act s on current
growth to peter out.
Endogeneity is more pronounced, the more closely related are Act and Yct . As shown
above, there is a positive relationship between the two. However, the correlation between the two
is relatively low. Measured every ten years between 1960 and 2000, it is 0.31 on average. The
year 2000 is an outlier with a correlation of 0.41. 1970 has the second largest correlation with
0.32.
Finally, we can test endogeneity by removing Act and openness interacted with Act from
regression (1) in Table III. If the two are closely related, this change would cause the coefficient
on log initial GDP per capita to become more negative. The opposite is the case, and the
difference is not statistically significance. (The coefficient changes from –3.22 to –3.07.)
Regardless of whether we use one or two-year lags, the effects of trade on performance
are sizeable. Starting from mean levels of Act , a one-percent increase in Act causes growth to
increase by between 2.7 and 3 percent. Similarly, a change in Act ’s Ydt of one percent, leads to
0.4 percent domestic growth. The magnitude of the coefficient on Act seems extremely large.
Part of the explanation no doubt is the fact we discovered at the outset, namely that Act itself is
14
relatively stable across countries (even though it is increasing). In other words, it does take a
large coefficient on log Act to account for large growth differences across countries.
2.5.
Robustness checks
Our results hold, in part, by construction. Conditional convergence suggests that poor
countries grow faster than rich countries, holding initial conditions such as the investment share
in GDP or years of education constant. Poor countries also tend to trade up, while rich ones tend
to trade down. Hence, trading up may be associated with higher growth rates based on
conditional convergence alone.
To check this phenomenon, we repeat regressions (5) and (6) from Table III after
dropping the thirty richest countries in 2000. Columns (1) and (2) in Table V report the results.
Neither the coefficients on log Act nor on the “Change in Act ’s Ydt ” change appreciably. In work
not shown, we repeat the exercise for all but OECD countries, again without any appreciable
differences.
We then limit ourselves to middle income countries alone. We choose countries ranked
31st through 60th in 2000.9 This leaves us with only 17 countries for the entire sample period.
Column (3) in Table V reports the results for 1-year lags; column (4) reports those for 2-year
lags. As expected for such a small sample, the significance on the coefficient for log Act
decreases. For 1-year lags, the coefficient is only positive at the 10% significance level; for 2year lags, the coefficient becomes insignificant. However, its positive magnitude is maintained
throughout, and the coefficient on the “Change in Act ’s Ydt ” maintains its sign, magnitude and
significance, despite this drastic reduction in sample size.
Lastly, we repeat columns (5) and (6) from Table III using 10-year instead of 5-year
averages and report the results in columns (5) and (6) of Table V. The investment share of GDP
9
See Table I for the list of countries.
15
loses its significance. However, the coefficients of interest on log Act and the “Change in Act ’s
Ydt ” remain stable and maintain their significance.
We also perform a series of unit root tests following Im, Pearson, and Shin (1997). We
may face the danger of a “pseudo” panel, where all variables follow strong time trends, rendering
the actual panel analysis meaningless. We combat this, in part, by using 5 and 10-year averages.
The unit room test confirms this suspicion. With a p-value of 0.067, the annual panel is borderline stationary. Averaged over 5 and 10-years, however, the p-values decrease to zero in both
cases. Having a stationary panel is not a problem.
2.6.
Why Trading up may be good for growth
It seems intuitive that trading with fast growing economies is good for domestic growth.
Growth abroad increases demand for one’s own goods. The path from high levels of Act to
higher growth is less direct. One explanation rests on the observation that trade is but a proxy for
all interactions among countries. With trade comes the exchange of people and ideas. It is these
positive externalities that could make up a large part of the benefits accrued by poor countries
who trade with richer ones.
Hausmann, Hwang and Rodrik (2005) construct a measure of the income level of a
country’s exports and show that what you export matters. Because of local knowledge spillovers,
the mix of goods a country produces has important implications for economic growth. We apply
the same reasoning to country-level strategies and show that who you trade with matters.
International knowledge spillovers associated with increased trade relations between relatively
poor and rich countries, enables the poorer country to grow faster. This analysis may lead us to
believe that spillovers associated with imports and exports may be different. The differences,
however, are not significant.
16
3. Conclusion
This paper offers three sets of conclusions, two descriptive and one with some normative
content. First, countries tend to trade up. With some notable exceptions, most countries trade
with relatively richer economies. This phenomenon leads us to reclassify some economies as
absolutely rich yet relatively poor.
Second, as one’s trading partners’ income rise, our own income rises as well. Hong Kong
and Singapore have done well in part because China has done so well. This is not a phenomenon
driven by conscious choices of one’s trading partners, but rather by luck of the draw. If a
country’s existing trading partners grow fast, it will benefit as well.
Third, trading up, holding other factors equal, is an advantage. Countries, which trade
with relatively rich countries, grow faster. This conclusion has some normative content. It may
not always be possible to deliberately choose one’s trading partner, but economic policy may be
able to foster trade relationships with richer countries. It may also, in part, explain the anecdotal
evidence why many countries want to forge closer trade relationships with the European Union
or the United States.
We might have been able to begin this paper by stating that “we take Ricardo seriously.”
Since David Ricardo’s times, it has been well understood in the trade literature that, for the most
part, relatives – not absolutes – matter. Yet, most analyses of countries’ fortunes rely on absolute
measures.
One reason why the average income of a country’s trading partner has not received much
attention within the trade literature may lie in standard trade theory. David Ricardo’s famous
theory of comparative advantage postulates that countries with technological advantages in
producing a particular good will become exporters of that good. Heckscher-Ohlin’s model relies
on factor endowments as the driving factor of world trade. Countries with high capital/laborratios export capital-intensive goods, and vice versa.
17
These classical trade models, as well as more recent models of increasing returns-toscale, however, are incapable of explaining bilateral Greek-EU trade. These phenomena fall into
the realm of gravity models. Greece’s geographic and cultural location causes it to trade more
with the EU than with other countries, despite capital/labor-ratios in other parts of the world that
may potentially be more conducive to trade.
Theoretical analyses based on gravity models of trade provide one explanation for such
empirical observations. Theory based on our analysis of trade-weighted income, which as of yet
has to be written, may provide another.
18
References
Arora, Vivek, and Athanasios Vamvakidis. “How Much Do Trading Partners Matter for Economic
Growth?” IMF Working Paper 04/26, February 2004.
Baldwin, Robert E. “Openness and Growth: What’s the Empirical Relationship.” In: Baldwin, Robert E.
and L. Alan Winters (Eds.). Challenges to Globalization: Analyzing the Economics. NBER Conference
Report, Chicago University Press, 2004: 499–521.
Barro, Robert J. Determinants of Economic Growth: A Cross-Country Empirical Study. MIT Press, 1997.
Barro, Robert J. and Jong-Wha Lee. “International Data on Educational Attainment: Updates and
Implications.” CID Working Paper No. 42, April 2000.
Feenstra, Robert C., Robert E. Lipsey, Haiyang Deng, Alyson C. Ma, and Hengyong Mo. “World Trade
Flows: 1962-2000.” NBER Working Paper No. 11040, January 2005.
Frankel, Jeffrey A. and David Romer. “Does Trade Cause Growth?” The American Economic Review
89(3), June 1999: 379–99.
Gleditsch, Kristian Skrede. “Expanded Trade and GDP Data.” Journal of Conflict Resolution 46(5),
October 2002: 712–724.
Hausmann, Ricardo, Jason Hwang, and Dani Rodrik. “What You Export Matters.” Mimeo, Harvard
University, December 2005.
Helpman, Elhanan, Marc Melitz and Yona Rubinstein. “Trading Partners and Trading Volumes.” Mimeo,
Harvard University, March 31, 2005.
Heston, Alan, Robert Summers and Bettina Aten. Penn World Table Version 6.1, Center for International
Comparisons at the University of Pennsylvania (CICUP), October 2002.
Rodríguez Francisco, and Dani Rodrik. “Trade Policy and Economic Growth: A Skeptic’s Guide to the
Cross-National Evidence.” In: Bernanke, Ben S. and Kenneth Rogoff (Eds.). NBER Macroeconomics
Annual 2000, Volume 15, MIT Press, 2001: 262–325.
Rose, Andrew K. “Do We Really Know That the WTO Increases Trade?” The American Economic
Review 94(1), March 2004: 98–114.
19
Appendix A: Data Sources
We have compiled the necessary trade statistics by value from two distinct databases, the
Feenstra et al. (2005) world trade flows dataset and the IMF’s Directions of Trade Statistics, as
compiled by Rose (2004) as part of the “Frankel-Romer-Rose” dataset and available on his
webpage, http://faculty.haas.berkeley.edu/arose, and in much more detail by Gleditsch (2002),
available at http://weber.ucsd.edu/~kgledits. Feenstra et al. (2005) includes bilateral trade data
for the years 1962 to 2000 covering more than 180 countries and other trading entities. Rose
(2004) covers bilateral trade from 1948 to 1999 for 178 countries and trading entities. Rose
deflated his trade data by the American CPI for all urban consumers. We inflate them to current
values using the same index and subsequently convert both Feenstra et al. and Rose’s data to
2000 U.S. dollars using GDP deflators generated by the U.S. Department of Commerce’s Bureau
of Economic Analysis (BEA).10 We also use Rose (2004)’s dataset for geographic variables as
instruments for bilateral trade and supplement this dataset with area data from the World
Reference Atlas, also available on Rose’s website.
GDP, population, inflation, and investment share data come from the Penn World Table
Version 6.1 (Heston et al. 2002). We again convert all data to 2000 U.S. dollars using BEA’s
GDP deflators. Education data come from Barro and Lee (2000). We use data for years of
schooling for the total population aged 25 and over.
Once we join the IMF’s trade statistics and the Penn World Table’s economic data, we
are left with 129 countries. Removing all countries with less than or equal to five years of
observations results in a panel data set spanning 112 countries from 1963 to 1999. The panel data
set is unbalanced, with an average coverage of 35 years per country out of a maximum of 37.
The dataset covers all major industrial economies and other significant world trading partners.
Two important omissions are Germany and Taiwan. The Penn World Tables contain aggregated
data for East and West Germany combined beginning in 1970. Rose et al.’s dataset also covers
the entire period. Feenstra et al.’s bilateral dataset does not include German data prior to 1989,
10
Bureau of Economic Analysis Table 1.1.4. Price Indexes for Gross Domestic Product.
20
and data for 1989 and 1990 are unexplainably low compared to data after the official
reunification. Taiwan is entirely omitted.
Some holes in Feenstra et al.’s aggregate bilateral dataset used in this analysis may be
filled by relying on disaggregated data by industry. That dataset covers Germany beginning in
1970. It also includes trade data by weight for 1984 to 2000. These data are recorded in several
different quantities, including area, weight, volume, and energy, depending on the commodities.
We restrict our sample to quantities by weight, which account for over 90 percent of the data.
From 1988 to 2000, we use adjusted trade flows for China and Hong Kong to avoid doublecounting of Chinese re-exports through Hong Kong. We use data by weight and adjusted trade
flows for China and Hong Kong as further robustness checks of our results.
Trade flows in Feenstra et al. and Rose’s datasets match well, but not perfectly. The
correlations are 0.999 for trade by countries and 0.998 for total annual trade flows. These high
correlations, however, mask a persistent discrepancy in absolute levels. Annual aggregate trade
flows estimated by Rose are, on average, 5.5 times larger than Feenstra et al.’s, with a minimum
of 4.6 and a maximum of 6.5. For 1999, Rose’s dataset estimates aggregate world trade to equal
48 trillion in 2000 U.S. dollars; Feenstra et al.’s data sum to a bit under 10 trillion. The WTO’s
online statistical database, available at http://stat.wto.org/, estimates total merchandise trade for
1999 to be 12 trillion U.S. dollars. Feenstra et al.’s data seem to estimate absolute trade levels
more accurately. Our best explanation for this discrepancy is the use of divergent deflators in
calculating current values. Since we have not been able to pinpoint the exact cause, though, we
eschew ad hoc adjustments to either dataset.
Gleditsch (2002) provides an alternative source for bilateral trade and economic data. We
use version 4.1 of his Expanded GDP and Trade Data. Gleditsch bases his derivations primarily
on the IMF’s Directions of Trade Statistics and the Penn World Tables, and supplements them
with data from various sources. With these alternative data sources and other imputations,
Gleditsch is able to expand the coverage of trade and economic data from a maximum 129
countries in the joint dataset of IMF’s trade statistics and the Penn World Table’s economic data
to 157 countries with 102 countries spanning the entire period from 1960 to 2000 and an average
21
coverage of 36 years. The most notable improvements over standard data sources are Gleditsch’s
imputations for Taiwan, Germany before reunification, and several Eastern European and
formerly socialist economies. One drawback is the classification of Hong Kong as part of China.
While politically correct, the economies of Hong Kong and Mainland China are significantly
different from each other to often warrant separate discussions. For consistency, we do not make
any adjustments for Hong Kong but maintain its position within China assigned by Gleditsch.
The dataset reports four trade flows between each country. The value of exports from 1 to 2 as
reported by country 1 and the same flow as reported by country 2. In theory, the two should be
the same. In practice, reporting is not always completely accurate. Moreover, exports are valued
“free on board.” Imports are, for the most part, reported with customs and freight charge
included. Following Feenstra et al. (2005), we focus on reported imports. This allows us to
account for shipping costs as well as product values themselves. Our main results are invariant to
which reported standard we use.
Using both Gleditsch (2002) and Rose’s (2004) versions of the IMF’s Directions of Trade
Statistics as well as Feenstra et al.’s (2005) bilateral trade flows enables us to conduct robustness
checks across datasets with differing country coverage. We focus on Gleditsch (2002) for our
main results.
22
Table I
All 157 countries in sample ranked by absolute income per capita, Yct , the
average per capita GDP of a country’s trading partners, Act , and trade-weighted
relative income per capita, Wct , in the year 2000.
Yct
Luxembourg
USA
Singapore
Norway
Canada
Denmark
Switzerland
Ireland
Australia
UAE
Iceland
Japan
Netherlands
Finland
Belgium
Austria
Sweden
Kuwait
Germany
France
UK
Italy
Qatar
New Zealand
Spain
Taiwan
Israel
Oman
Bahamas
Barbados
Portugal
absolute relative
46,868
6.07
35,471
4.59
28,965
3.75
28,831
3.73
28,665
3.71
28,349
3.67
28,142
3.64
28,107
3.64
27,231
3.52
26,469
3.43
26,398
3.42
26,290
3.40
25,904
3.35
25,349
3.28
25,337
3.28
25,226
3.27
25,182
3.26
24,916
3.23
24,351
3.15
23,821
3.08
23,642
3.06
23,205
3.00
21,142
2.74
20,047
2.59
19,228
2.49
18,172
2.35
18,063
2.34
17,758
2.30
17,609
2.28
17,489
2.26
16,965
2.20
Act
rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
absolute relative
23,275
1.11
18,504
0.88
18,130
0.87
24,105
1.15
30,886
1.48
23,068
1.10
22,652
1.08
24,998
1.19
20,362
0.97
18,255
0.87
24,008
1.15
20,261
0.97
22,317
1.07
21,273
1.02
22,623
1.08
22,184
1.06
23,220
1.11
19,425
0.93
21,989
1.05
22,197
1.06
23,326
1.11
20,978
1.00
22,192
1.06
22,749
1.09
20,955
1.00
20,886
1.00
24,700
1.18
17,298
0.83
22,750
1.09
21,907
1.05
21,521
1.03
23
Wct
rank
23
100
107
13
2
27
34
9
78
106
14
81
38
60
35
41
24
88
43
39
21
68
40
33
70
71
11
115
32
44
54
relative
2.01
1.92
1.60
1.20
0.93
1.23
1.24
1.12
1.34
1.45
1.10
1.30
1.16
1.19
1.12
1.14
1.08
1.28
1.11
1.07
1.01
1.11
0.95
0.88
0.92
0.87
0.73
1.03
0.77
0.80
0.79
rank
1
2
3
10
24
9
8
14
5
4
18
6
12
11
15
13
19
7
16
20
22
17
23
26
25
27
37
21
35
32
33
Yct
South Korea
Cyprus
Slovenia
Greece
Malta
Mauritius
Czech Republic
Bahrain
Saudi Arabia
Slovakia
Trinidad
Argentina
Hungary
Seychelles
Chile
Malaysia
Uruguay
Estonia
Poland
Mexico
Croatia
Gabon
Russia
Latvia
Botswana
South Africa
Kazakhstan
Lithuania
Brazil
Saint Vincent
Thailand
Turkey
Tunisia
Belize
Venezuela
absolute relative
16,915
2.19
16,848
2.18
16,775
2.17
15,570
2.02
15,544
2.01
14,843
1.92
14,560
1.88
14,129
1.83
13,047
1.69
12,156
1.57
11,906
1.54
11,727
1.52
11,122
1.44
10,911
1.41
10,575
1.37
10,568
1.37
10,251
1.33
10,216
1.32
9,820
1.27
9,336
1.21
9,084
1.18
8,952
1.16
8,533
1.10
8,153
1.06
8,044
1.04
8,035
1.04
7,875
1.02
7,728
1.00
7,660
0.99
7,616
0.99
7,306
0.95
7,279
0.94
7,220
0.93
7,022
0.91
6,840
0.89
Act
rank
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
absolute relative
20,428
0.98
19,954
0.95
20,957
1.00
18,889
0.90
24,116
1.15
18,919
0.90
21,161
1.01
18,418
0.88
21,633
1.03
19,104
0.91
22,856
1.09
16,115
0.77
21,803
1.04
22,049
1.05
19,197
0.92
23,147
1.11
14,933
0.71
21,808
1.04
21,178
1.01
32,014
1.53
19,606
0.94
25,029
1.20
17,835
0.85
20,124
0.96
21,754
1.04
20,737
0.99
13,839
0.66
18,416
0.88
21,147
1.01
23,555
1.13
21,576
1.03
20,417
0.98
21,188
1.01
22,989
1.10
25,841
1.23
24
Wct
rank
75
85
69
95
12
94
65
102
51
91
31
126
49
42
90
26
134
48
64
1
87
8
109
83
50
72
141
103
66
19
52
76
62
28
5
relative
0.83
0.84
0.80
0.82
0.64
0.78
0.69
0.77
0.60
0.64
0.52
0.73
0.51
0.49
0.55
0.46
0.69
0.47
0.46
0.29
0.46
0.36
0.48
0.41
0.37
0.39
0.57
0.42
0.36
0.32
0.34
0.36
0.34
0.31
0.26
rank
29
28
31
30
41
34
39
36
43
42
46
38
47
48
45
54
40
50
52
75
53
63
49
56
58
57
44
55
62
72
67
64
66
74
77
Yct
absolute relative
Saint Lucia
6,745
0.87
Grenada
6,583
0.85
Serbia
6,575
0.85
Panama
6,463
0.84
Iran
6,387
0.83
Costa Rica
6,254
0.81
Lebanon
6,164
0.80
Bulgaria
6,163
0.80
Fiji
5,798
0.75
Colombia
5,736
0.74
Dominican Rep.
5,615
0.73
Cuba
5,604
0.73
Macedonia
5,478
0.71
Algeria
5,216
0.68
Paraguay
4,990
0.65
Ukraine
4,925
0.64
Peru
4,889
0.63
Namibia
4,751
0.61
El Salvador
4,725
0.61
Iraq
4,634
0.60
Romania
4,566
0.59
Egypt
4,458
0.58
Syria
4,362
0.56
Cape Verde
4,291
0.56
Guatemala
4,170
0.54
Jordan
4,150
0.54
China
3,992
0.52
Morocco
3,960
0.51
Jamaica
3,934
0.51
Indonesia
3,881
0.50
Guyana
3,850
0.50
Equ. Guinea
3,840
0.50
Ecuador
3,695
0.48
Philippines
3,649
0.47
Albania
3,565
0.46
Act
rank
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
absolute relative
23,634
1.13
19,875
0.95
14,148
0.68
20,291
0.97
18,544
0.89
25,318
1.21
19,046
0.91
16,669
0.80
23,602
1.13
23,645
1.13
28,546
1.36
15,390
0.74
16,363
0.78
21,903
1.05
14,954
0.71
13,412
0.64
21,323
1.02
21,360
1.02
21,877
1.05
21,476
1.03
19,305
0.92
21,185
1.01
18,389
0.88
17,020
0.81
22,868
1.09
16,311
0.78
23,632
1.13
20,621
0.99
25,328
1.21
21,564
1.03
22,523
1.08
17,164
0.82
21,209
1.01
23,286
1.11
18,384
0.88
25
Wct
rank
16
86
139
80
99
7
92
119
18
15
3
128
121
45
133
146
59
56
47
55
89
63
104
117
30
122
17
73
6
53
36
116
61
22
105
relative
0.29
0.33
0.46
0.32
0.34
0.25
0.32
0.37
0.25
0.24
0.20
0.36
0.33
0.24
0.33
0.37
0.23
0.22
0.22
0.22
0.24
0.21
0.24
0.25
0.18
0.25
0.17
0.19
0.16
0.18
0.17
0.22
0.17
0.16
0.19
rank
76
70
51
73
65
80
71
59
81
82
93
61
68
83
69
60
86
88
89
90
85
92
84
79
96
78
105
95
108
97
101
87
99
107
94
Yct
absolute relative
Suriname
3,564
0.46
Sri Lanka
3,516
0.46
Kyrgyzstan
3,212
0.42
Papua N. Guinea 3,113
0.40
Azerbaijan
2,981
0.39
Armenia
2,975
0.39
Bolivia
2,902
0.38
Zimbabwe
2,649
0.34
India
2,641
0.34
Haiti
2,503
0.32
Moldova
2,222
0.29
Honduras
2,184
0.28
Cameroon
2,175
0.28
Pakistan
2,139
0.28
Bhutan
2,097
0.27
Cote d’Ivoire
1,991
0.26
Viet Nam
1,931
0.25
Congo
1,926
0.25
Nicaragua
1,883
0.24
Bangladesh
1,794
0.23
Senegal
1,728
0.22
Lesotho
1,696
0.22
Nepal
1,555
0.20
Laos
1,457
0.19
Angola
1,451
0.19
Ghana
1,439
0.19
Tajikistan
1,407
0.18
Mauritania
1,401
0.18
Cambodia
1,356
0.18
Mongolia
1,351
0.17
Kenya
1,326
0.17
Gambia
1,297
0.17
Benin
1,293
0.17
Sudan
1,235
0.16
Liberia
1,139
0.15
Act
rank
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
absolute relative
23,439
1.12
21,081
1.01
14,963
0.71
22,912
1.09
17,629
0.84
17,532
0.84
16,224
0.78
15,251
0.73
20,104
0.96
24,914
1.19
16,161
0.77
28,182
1.35
20,192
0.96
20,320
0.97
12,359
0.59
15,271
0.73
18,941
0.90
18,558
0.89
17,768
0.85
21,335
1.02
14,308
0.68
23,150
1.11
14,888
0.71
10,725
0.51
21,333
1.02
16,897
0.81
11,699
0.56
17,589
0.84
18,776
0.90
14,086
0.67
16,493
0.79
13,010
0.62
13,319
0.64
13,780
0.66
20,453
0.98
26
Wct
rank
20
67
132
29
111
114
123
130
84
10
125
4
82
79
154
129
93
98
110
57
137
25
135
157
58
118
155
112
96
140
120
152
147
142
74
relative
0.15
0.17
0.21
0.14
0.17
0.17
0.18
0.17
0.13
0.10
0.14
0.08
0.11
0.11
0.17
0.13
0.10
0.10
0.11
0.08
0.12
0.07
0.10
0.14
0.07
0.09
0.12
0.08
0.07
0.10
0.08
0.10
0.10
0.09
0.06
rank
109
106
91
111
104
103
98
100
113
123
110
135
117
119
102
114
122
121
118
131
115
138
120
112
143
130
116
133
139
126
132
124
125
128
149
Yct
Somalia
Mozambique
C.A.R.
Mali
Burkina-Faso
Uganda
Chad
Rwanda
Zambia
Niger
Togo
Madagascar
Yemen
Malawi
Eritrea
Nigeria
Sierra Leone
Ethiopia
Burundi
Tanzania
D. R. Congo
absolute relative
1,120
0.15
1,105
0.14
1,057
0.14
1,033
0.13
1,019
0.13
1,002
0.13
968
0.13
954
0.12
950
0.12
932
0.12
927
0.12
891
0.12
871
0.11
835
0.11
826
0.11
753
0.10
729
0.09
676
0.09
557
0.07
513
0.07
300
0.04
Act
rank
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
absolute relative
12,981
0.62
14,215
0.68
18,503
0.88
13,495
0.64
11,190
0.53
13,205
0.63
15,876
0.76
13,311
0.64
13,484
0.64
14,567
0.70
13,297
0.64
18,655
0.89
13,692
0.65
13,084
0.63
22,507
1.08
21,883
1.05
18,099
0.86
17,539
0.84
16,191
0.77
15,007
0.72
20,381
0.97
Wct
rank
153
138
101
144
156
150
127
148
145
136
149
97
143
151
37
46
108
113
124
131
77
relative
0.09
0.08
0.06
0.08
0.09
0.08
0.06
0.07
0.07
0.06
0.07
0.05
0.06
0.06
0.04
0.03
0.04
0.04
0.03
0.03
0.01
rank
129
134
148
136
127
137
147
140
141
144
142
150
146
145
153
155
151
152
154
156
157
Sources: Gleditsch’s (2002) Expanded GDP and Trade Data Version 4.1. See Appendix A for a detailed
description of the data.
27
Figure 1 Trading partners’ average GDP per capita for 1980 and 2000.
28
Figure 2 Ratio of GDP per capita to the world average, and trade-weighted
relative GDP per capita of trading partners for 1960 to 2000, by decade.
29
Figure 3 Trading partners’ average GDP per capita for 1980 and 2000, with
trading partners weighted by imports and exports.
30
Table II
(1)
(2)
-3.05**
(7.06)
(3)
-3.02**
(7.00)
(4)
768
136
0.10
768
136
0.10
-0.005
(0.22)
0.09**
(2.08)
-3.02**
(7.01)
0.84**
(2.59)
768
136
0.10
0.08**
(2.23)
-3.19**
(7.55)
0.027
(1.54)
768
136
0.09
(5)
600
93
0.10
0.031
(0.60)
-3.14**
(6.37)
0.044**
(2.00)
-0.016
(0.04)
2.18*
(1.89)
Regressions for per capita growth rate using country and time fixed effects.
log initial GDP/cap
GDP investment share
log human capital
Openness
Growth in ActX
Growth in A
M
ct
Growth in Act
dt
Change in A ’s Y
X
ct
(Openness)
(Change in ActX ’s Ydt )
Observations
Countries
R2 (within groups)
-2.33**
(5.15)
(6)
-2.51**
(4.93)
0.039*
(1.79)
0.45
(1.13)
2.62**
(2.26)
0.0044
(0.10)
(7)
0.51**
(4.76)
600
93
0.13
0.49**
(3.98)
-0.88
(0.92)
0.016
(0.93)
768
136
0.12
Fixed-effects regressions with country and year fixed effects, from 1960-2000 averaged over five years. The interaction term is calculated using
de-meaned variables. Openness is defined as Trade/GDP. Parentheses depict absolute values of t-statistics. Two stars (**) indicate significance at
the 5% level, one star (*) indicates significance at the 10% level.
Sources: IMF Directions of Trade Statistics and Heston et al. (2002) Penn World Table Version 6.1, as compiled by Gleditsch (2002). See
Appendix A for a detailed description of the data.
31
Table III Regressions for per capita growth rate using country and year fixed effects and
instrumental variables.
(1)
FE
log initial GDP/cap
-3.22**
(6.72)
GDP investment share
log human capital
Openness
log Act
(Openness)
(log Act )
3.19**
(3.05)
0.12
(0.21)
9.72**
(3.95)
(2)
FE
(3)
FE
-3.53**
(6.71)
0.064**
(3.10)
-0.59*
(1.36)
1.71
(1.59)
1.41*
(1.76)
6.52**
(2.79)
Growth in Act
(Openness)
(Growth in Act )
Change in Act ’s Ydt
(Openness)
(Change in Act ’s Ydt )
Observations
Countries
R2 (within groups)
841
136
0.11
659
93
0.13
(4)
IV
-3.36**
(5.81)
0.047**
(2.15)
-0.62
(1.24)
1.06
(0.87)
3.08**
(3.21)
11.10*
(1.78)
-3.48**
(6.06)
0.057**
(2.64)
-0.82*
(1.69)
5.24
(0.60)
3.11**
(3.25)
52.03
(1.47)
(5)
IV &
1-yr. lag
-3.58**
(6.11)
0.058**
(2.66)
-0.90*
(1.84)
10.15
(1.17)
3.02**
(3.19)
51.80
(1.39)
(6)
IV &
2-yr. lag
-3.56**
(5.91)
0.051**
(2.31)
-0.93*
(1.80)
6.80
(0.79)
2.66**
(2.82)
56.16
(1.47)
-0.063
(1.21)
0.99**
(2.06)
-0.048
(0.93)
-6.76**
(2.12)
-0.25
(0.56)
-2.54
(1.01)
-0.025
(0.58)
-2.54
(0.94)
0.61**
(4.68)
-1.69
(1.23)
0.55**
(4.10)
11.81
(1.10)
0.47**
(3.99)
10.00
(0.81)
0.40**
(2.96)
-4.66
(0.30)
600
93
0.17
600
93
0.17
598
93
0.16
596
93
0.14
Fixed-effects regressions with country and year fixed effects, from 1960-2000 averaged over five years.
All interaction terms are calculated using de-meaned variables. Openness is defined as Trade/GDP.
Parentheses depict absolute values of t-statistics. Two stars (**) indicate significance at the 5% level, one
star (*) indicates significance at the 10% level.
In Columns (4)-(6), openness is instrumented by gravity trade equations, following Frankel and Romer
(1999). Column (5) applies 1-year lags to all calculations of Act , growth in Act , and change in Act ’s Ydt .
Column (6) applies 2-year lags to each.
Sources: IMF Directions of Trade Statistics and Heston et al. (2002) Penn World Table Version 6.1, as
compiled by Gleditsch (2002). See Appendix A for a detailed description of the data.
32
Variable
Interaction
-8.22**
(104.58)
-1.07**
(181.17)
-0.16**
(42.02)
-0.089**
(28.38)
0.91**
(236.14)
-0.19**
(59.54)
-0.85**
(85.21)
Variable
3.56**
(9.60)
0.35**
(5.28)
-0.12**
(5.03)
-0.082**
(2.92)
-0.12**
(5.06)
-0.035
(1.26)
0.29**
(8.47)
Interaction
(3)
Gleditsch, 1970-99
Interaction
5.02**
(13.82)
0.38**
(5.92)
-0.20**
(8.86)
-0.047*
(1.78)
-0.20**
(9.02)
0.0065
(0.25)
0.37**
(11.16)
(2)
Frankel-Romer-Rose, 1970-99
Variable
-7.32**
(89.55)
-1.14**
(180.02)
-0.10**
(25.97)
-0.11**
(31.83)
0.95**
(235.41)
-0.21**
(62.11)
-0.96**
(87.93)
275,796
134
0.35
5.19**
(3.00)
0.52
(1.48)
-0.29**
(2.66)
0.030
(0.21)
0.027
(1.54)
-0.28**
(2.57)
0.17
(0.98)
292,648
134
0.32
-6.94**
(15.32)
-1.21**
(31.93)
-0.89**
(3.92)
-0.13**
(6.82)
0.97**
(41.95)
-0.23**
(12.33)
-0.92**
(14.21)
9942
121
0.29
(1)
Frankel-Romer-Rose, 1985
Table IV Bilateral trade equations with openness, Trade/GDP, as the dependent variable.
Constant
Log distance
Log population
(country c)
Log area
(country c)
Log population
(country d)
Log area
(country d)
Landlocked
Observations
Countries
R2
OLS with robust standard errors. The first column for reach regression reports the coefficient on the variable itself, and the second column reports
the coefficient on the variable’s interaction with the common-border dummy. Parentheses depict absolute values of t-statistics. Two stars (**)
indicate significance at the 5% level, one star (*) indicates significance at the 10% level.
Columns (1) and (2) duplicate Frankel and Romer’s (1999) Table 1, using Frankel-Romer-Rose’s dataset (Rose 2004). Column (1) attempts to
duplicate it exactly, using only data for 1985; column (2) uses all data for 1970-99. Column (3) runs the same regression, using Gleditsch’s (2002)
trade data from 1970-99 combined with Rose’s (2004) geographic data. See Appendix A for a detailed description of the data.
33
Table V
Robustness checks for regressions for per capita growth rate using instrumental
variables and lags.
log initial GDP/cap
GDP investment share
log human capital
Openness
log Act
(Openness)
(log Act )
Growth in Act
(Openness)
(Growth in Act )
Change in Act ’s Ydt
(Openness)
(Change in Act ’s Ydt )
Observations
Countries
R2 (within groups)
(1)
(2)
IV &
IV &
1-yr. lag 2-yr. lag
5-yr. averages,
excludes richest 30
(3)
(4)
(5)
(6)
IV &
IV &
IV &
IV &
1-yr. lag 2-yr. lag 1-yr. lag 2-yr. lag
5-yr. averages,
10-yr. averages,
only second-richest 30
all countries
-3.88**
(5.43)
0.066**
(2.51)
-1.13*
(1.89)
24.90*
(1.83)
3.01**
(2.52)
81.03
(1.39)
-3.81**
(5.20)
0.06**
(2.31)
-1.16*
(1.83)
21.34
(1.58)
2.77**
(2.31)
43.51
(0.79)
-4.43**
(3.28)
0.14**
(2.62)
-0.74
(0.34)
20.18
(0.93)
5.32*
(1.85)
39.11
(0.40)
-4.37**
(3.25)
0.12**
(2.23)
-0.094
(0.04)
8.79
(0.40)
3.84
(1.38)
62.62
(0.60)
-4.73**
(6.73)
0.035
(1.45)
-0.59
(1.02)
-10.59
(0.42)
3.59**
(2.15)
116.10
(1.18)
-4.80**
(6.62)
0.034
(1.38)
-0.78
(1.30)
-11.19
(0.44)
3.48**
(2.07)
113.68
(1.10)
-0.029
(0.58)
-2.46
(0.86)
-0.036
(0.73)
-3.64
(1.18)
0.080
(0.53)
5.049
(0.60)
0.049
(0.40)
3.81
(0.54)
-0.0005
(0.00)
-3.46
(0.59)
0.015
(0.14)
-1.39
(0.23)
0.47**
(3.15)
26.44
(1.48)
0.39**
(2.34)
-6.64
(0.34)
0.72**
(2.70)
0.53
(0.02)
0.62**
(2.10)
-10.99
(0.27)
0.58**
(2.12)
46.88*
(1.83)
0.48**
(2.01)
22.66
(0.87)
436
69
0.15
434
69
0.14
112
17
0.28
111
17
0.25
258
93
0.34
257
93
0.32
Fixed-effects regressions with country and year fixed effects, from 1960-2000. Columns (1)-(4) are
averaged over five years, columns (5)-(6) over ten years. All interaction terms are calculated using demeaned variables. Openness is defined as Trade/GDP. Parentheses depict absolute values of t-statistics.
Two stars (**) indicate significance at the 5% level, one star (*) indicates significance at the 10% level.
Trade/GDP is instrumented by gravity trade equations, following Frankel and Romer (1999). Columns
(1), (3), and (5) apply 1-year lags to all calculations of Act , growth in Act , and change in Act ’s Ydt .
Columns (2), (4), and (6) apply 2-year lags to each.
Sources: IMF Directions of Trade Statistics and Heston et al. (2002) Penn World Table Version 6.1, as
compiled by Gleditsch (2002). See Appendix A for a detailed description of the data.
34