Globalization, Inequality,
and Development:
The Big Picture
William R. Easterly

Models of trade and factor flows based on differences in factor endowments
give clear predictions as to how globalization affects inequality and development. Models in which productivity differences between countries drive
trade and factor flows gave more ambiguous predictions. Unfortunately,
productivity differences seem necessary to understand many, though not all,“big picture” globalization, inequality, and development outcomes. The
factor endowment predictions help give us insight into how the North
Atlantic economy achieved decreasing inequality between countries in thelast five decades. They also give us insight into the great migration ofEuropeans from the land-scarce Old World to the land-abundant New
World in the late 19th and early 20th centuries, accompanied by thepredicted movements in land rental/wage ratios. However, productivity
differences appear to be an important facet of many globalization,
inequality, and development episodes. In the Old Globalization era, they
seem to be crucial to understand the lack of convergence between North
Atlantic economies, the great divergence between rich and poor countries inthat same era, and the bias of capital flows toward rich countries. In the
New Globalization era, productivity differences are important to capture
the very different performance of poor country regions in recent decades, theflow of all factors of production toward the rich countries, the low returns
to physical and human capital in many poor countries, and the “perverse”
behavior of within-country inequality in reaction to trade and capital flows.
Keywords: Globalization; Trade; Capital flows; Migration; Education;
Development
JEL Classification: F11, F21, F22, I2, N10, O1, O4

New York University (E-mail: william.easterly@nyu.edu)
I am grateful for the comments of my discussants, Etsuro Shioji and Stefan Gerlach, and forcomments by participants at the conference.
MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004
DO NOT REPRINT OR REPRODUCE WITHOUT PERMISSION.

57

I. Introduction
Globalization and inequality is on the minds of many. To anti-globalization protesters,“transnational corporations . . . expand, invest and grow, concentrating ever more
wealth in a limited number of hands.”1 Agents such as the International Monetary
Fund (IMF) and World Bank are said to be aiming at an outcome “in which all
productive assets are owned by foreign corporations producing for export.”2 Recently,“globalization from above” has shifted “towards a more destructive phase, marked
by increased militarization, worldwide recession, and increased economic inequality.”3
The protesters usually claim that globalization is a disaster for the workers, throwing
them into “downward wage spirals in both the North and the South.” They pointout that the total income of the poorest half of humanity is less than the worth of just475 billionaires.4
Apart from such extreme rhetoric, what are the facts on globalization andinequality? Through what channels does globalization affect inequality between
and within countries? Globalization is the movement across international bordersof goods and factors of production. The conventional analysis of the effects of globalization on inequality looks at the effect of trade and factor flows on returns to factors,
on factor accumulation, and on national income. I examine how the predictions ofglobalization’s effect on inequality differ if income differences are due to productivity
differences rather than different factor ratios.
I do not try to answer the large question of whether globalization raises or lowers
inequality. Instead, I follow many previous authors in setting out textbook alternatives and then discussing whether factor endowments or productivity channels are
consistent with particular outcomes. I thus examine the actual behavior of inequality
and trade, trends in trade and factor flows, factor returns, and relative incomes toassess which model is more relevant in particular cases.
I conclude that the clear theoretical channels between globalization and inequality
featured by factor endowment models help us to understand some important
globalization and inequality episodes. Unfortunately, many other episodes seem torequire productivity channels to accommodate the facts. Even more unfortunately,
we know much less about how productivity channels work than we know about
factor endowments.

II. How Globalization Affects Inequality and Development
in Standard Models
A. Factor Movements
In the neoclassical model of factor movements, free movement of factors tends toreduce inequality between nations, while having different effects on inequality in rich
1. International Forum on Globalization (2002, p. 140).
2. International Forum on Globalization (2002, p. 52).
3. Aronowitz and Gautney (2003, p. xxv).
4. International Forum on Globalization (2002, p. 30).

58 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004

Globalization, Inequality, and Development: The Big Picture

and poor nations. In the neoclassical model, international inequality—income differences between countries—is due to different capital-labor ratios. Rich nations havemore capital per worker than poor nations. Rates of return to capital will be higher
in poor nations than in rich ones, while wages will be higher in rich nations thanin poor ones.
The equations are as follows. Let Yi , A i , K i , and L i stand for output, laboraugmenting productivity, capital, and labor in country i (where i can either be rich
[R ] or poor [P ]).


Yi = K i (A i L i )1−.
Let ki = K i /L i and y =Yi /L i . The rate of return to capital r and wage w in country i is

Yi = k −1A 1−,ri = –––ii
K i
Yi = (1 −  )k A 1−.
wi = –––ii
L i
If AR = AP = A, then the per capita income ratio between the two countries when
A is the same isy  kR ––R = ––.
y P  kP 
If there is free mobility of factors, then capital will want to migrate from rich topoor nations (“outsourcing”), while workers will want to migrate from poor to richnations. This will decrease the capital-labor ratio in rich countries, while increasing
it in poor countries. These flows will continue until capital-labor ratios are equal
across nations and factor prices are equal, steadily decreasing income gaps between
nations (reducing international inequality). Compared to the no-factor-mobility
state, returns to capital will rise in rich countries and fall in poor ones. With factor
mobility, wages will fall in rich countries and rise in poor ones. If everyone has raw
labor but less than 100 percent of the population owns capital, then the capitalrental/wage ratio is positively related to inequality. Hence, factor flows (globalization)
will reduce inequality in poor countries and increase it in rich countries.
The predicted capital flows are very large. Denoting ki * as the capital-labor ratio incountry i (i = P or R ) in the final equilibrium, with the unstarred values of ki and y i
as the initial values, we have the following:
1kP* − kP
 y P ––
–––––
–
=
1
−
–– ,
 yR
kR*
kP* = kR*,59

1

kP* − kP y P*
 y P  ––
–––––
–
––
=
1
−
––
,
 yR
y P* kP*y * r*––P* = –– ,kP 
kP* − kP    y P  –––––––
– = ––* 1 − ––  .
r   yR 
y P*
1In the neoclassical model, even small differences in initial income trigger massive
factor flows. If we assume a capital share of one-third, a ratio of poor to rich country
income of 0.8, and a marginal product of capital (r *) of .15, then the cumulative
capital inflows into the poor country will be 108 percent of the terminal equilibrium
GDP in the poor country !
Things are very different if income differences between nations are due to productivity differences rather than differences in capital per worker. Suppose first of allthat relative productivity is the same in the two sectors in both nations, but the richcountry has an absolute productivity advantage in both sectors. Now both capital andlabor will want to move to the rich country, unlike the prediction of opposite flows in
the neoclassical model case. Unlike the neoclassical model case, the final outcome in africtionless world would be a corner solution in which all capital and labor moves tothe rich country to take advantage of the superior productivity. Obviously, there must
be some frictions such as incomplete capital markets, preference for one’s homeland,rich country immigration barriers, costs of relocating to a new culture, etc., to avoid
this extreme prediction. Pritchett (2003) argues that there may in fact be countries that
could become “ghost countries” if factor mobility were unimpeded, just like the ruralcounties currently emptying out on the Great Plains in the United States.
Of course, there is one factor that does not move—land and natural resources.
Even if productivity is higher elsewhere, land prices could adjust to retain some
capital and labor in the home country. This was an important factor in the 19th
century, but seems less so now in today’s urbanized world. If land and capitalare perfect substitutes, then an economy could substitute away from land and not
drive up the return to the other factors to make them want to stay. However, inmany countries agriculture is sufficiently important that land and natural resource
availability is a potentially relevant sticky factor preventing flight of all factors tohigh-productivity places.
Land acts much like productivity in its effect on the marginal products of capitaland labor. Hence, a land-rich place could attract both capital and labor, just like ahigh-productivity place does. This was a very important factor in the 19th century
wave of globalization. It still seems relevant today in that natural resources may
attract capital and labor into areas that otherwise have low productivity.
The relevant equations including land (T ) are the following. Let the production
function including land be

60 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004

Globalization, Inequality, and Development: The Big PictureYi = Ti K i (A i L i )1−−.
Now let capital and labor move freely to equate rates of return to capital andwages. Let t i = Ti /L i and ki = K i /L i . The rate of return to capital and wage will beYi = t k  −1A 1−− ,
–––
i i
i
K i
Yi = (1 −  − )t k A 1−−.
–––
i i
i
Li
Obviously, both capital and labor will be attracted to the land-abundant places aswell as the places with higher productivity. Since both capital and labor can move, we
can show that capital-labor ratios in the two places will be equated. Labor will moveaccording to equate wages, which reflect both land abundance and productivity.
If there were no productivity differences between places, land-labor ratios would also
be equated. With differences in productivity, population density will be greater in thehigher-productivity places:1−−LR /T–R =  ––AR ––––––
––––
.
 
LP /TP APPer capita incomes will move toward equality as well, since labor moves in response
to both relative land abundance and productivity. Hence, there will be convergence ofper capita incomes if both labor and capital can move freely, in either the neoclassicalmodel or the productivity differences view. The only remaining sign of higher productivity in the rich countries in equilibrium is that they will have attracted capitaland labor away from the lower-productivity poor countries. Similarly, the only effectremaining in equilibrium of higher land abundance will be that land-abundant
countries will wind up with more labor and capital.
Obviously, these are extreme predictions that only apply under special circumstances. Free capital mobility seems more likely than free labor mobility, so rates ofreturn across countries are more likely to be equalized than wages. An interesting
intermediate case that may be more realistic is that labor cannot move freely, but
capital can.
Let us revert again to the model without land. Equating rates of return to capitalacross countries implies that the ratio of kR to kP is the same as the ratio of AR to AP .
This will also be the ratio of relative per capita incomes and the ratio of relative wages
under free capital mobility:

YR = k  –1A 1− = –––YP = k  −1A 1− ,
–––
R
R
P
P
KR
KP

61

kR = ––,
AR
––
kP AP
wR kR   AR  1− AR yR–– = –– –– = –– = ––.
AP yP
wP kP  AP 
If there are capital inflows into the poor country because of factor imbalances, they
can be of much smaller size compared to the strict neoclassical model prediction,
because the differences in capital-labor ratios between rich and poor countries are nearly
offset by the differences in productivity. It follows also that the (transitional) growtheffects of capital inflows must be small.
The poor country will thus have lower wages and per capita incomes both becauseof lower productivity and lower capital-labor ratios. Unlike the predictions of theneoclassical model, globalization (in the form of capital flows) does not eliminate
large degrees of international inequality. Inequality is a function of productivity
differences rather than factor intensity differences.
To assess the impact of this particular kind of globalization (free capital mobility)
on inequality, we need to know the counterfactual. What would have been the ratioof kR to kP if capital had not been free to move across borders? This is equivalent
to asking when capital controls exist in poor countries, are they binding on inward
capital movements or on outward capital movements? It is also equivalent to asking
whether the rate of return to capital in poor countries with capital controls is lower
than the rate of return to capital in rich countries. Probably the answer to thesequestions varies for different poor countries.
If capital controls are binding on outward capital movements, then removing
them would result in capital movements from poor to rich countries (reverse outsourcing !). This would lower capital-labor ratios in the poor countries and raisethem in rich countries. This initial situation means free capital mobility increases the
per capita income ratio between rich and poor countries, increasing international
inequality. Free capital mobility would lower the rate of return to capital in richcountries and increase it in poor countries; it would increase wages in rich countries
and lower them in poor countries. Therefore, it would lower domestic inequality inrich countries and increase domestic inequality in poor countries. Capital flight frompoor countries increases both international inequality and domestic inequality in thepoor countries.
B. Trade Flows and Inequality
In the textbook neoclassical model, goods mobility will have the same effect as factor
mobility even if factors cannot move. The capital-abundant rich nation will export
capital-intensive goods, while the labor-abundant poor nation will export laborintensive goods. The expansion of demand for labor and fall in demand for capital in
the poor country (compared to autarky) will raise wages and lower capital rentals.
The reverse will happen in the rich country. If the equilibrium is for less thancomplete specialization, factor prices will move toward equality in the two countries
just like in the factor mobility case. Trade will reduce inequality between nations,

62 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004

Globalization, Inequality, and Development: The Big Picturesince the ratio of per capita incomes is proportional to the ratio of wages. Again, if
the capital rental/wage ratio is positively related to inequality within the nation, trade
will increase inequality in the rich country and decrease it in the poor country.
We can substitute “land” for “capital” in all of the above statements and derive thesame conclusions. A land-abundant nation opening to international trade will see
rising land rental to wage ratios, which probably implies increasing inequality. A
land-scarce nation opening up will see falling land rent/wage ratios and decreasing
inequality. The effects are as if labor is migrating from the land-scarce country to theland-abundant country.
What if the absolute level of labor-augmenting productivity differs between thetwo countries? With productivity differences, the factor price equalization theorem
still applies, but now applies to effective labor A i L i . The wage per unit of effective
labor will be equalized between the two countries under free trade, as will the rate of
return to capital in the two countries. This means that the wage per unit of physicallabor in the two countries will differ. The ratio of the wage per unit of physical laborin the higher-productivity (rich) country to the lower-productivity (poor) country
will be AR /AP .
The analysis of which country is more labor abundant will also differ from the equal
productivity case. If the relative scarcity of labor in the rich country is sufficiently
offset by higher relative productivity, then the rich country will be “labor abundant”
and will export “labor-intensive” goods (the Leontief-Trefler paradox).5 In this case,trade will reduce inequality in the rich country and increase it in the poor country.
If productivity differences are not so stark as to offset relative factor scarcity, the richcountry will be capital abundant, and we will return to the usual prediction that trade
increases inequality in the rich country and lowers it in the poor country.
Now suppose that we allow relative productivity across the two sectors (capitalintensive and labor intensive) to differ between countries, as well as allow absolute
productivity to differ. This will give us another way in which the simple principle ofcapital-abundant countries producing capital-intensive exports need no longer apply.
If the capital-abundant country has a sufficiently strong relative productivity advantage
in the labor-intensive sector, it could wind up exporting labor-intensive goods. This
would raise the price of labor in the rich country and depress the rental price ofcapital, decreasing inequality in the rich country. Similarly, if the capital-scarce poor
country has a relative productivity advantage in the capital-intensive sector, thenit could wind up exporting capital-intensive products, raising further the rate of
return to capital and increasing inequality in the poor nations. When we allow for
productivity differences, the effect of trade on domestic inequality could go either way.
The pattern of trade driven by relative differences in productivity seems to fit thereal world in which countries hyper-specialize in particular products in which they
have gained enough expertise to produce efficiently (such as surgical instruments
in Pakistan). Hausmann and Rodrik (2002) point out how general is the phenomenon of hyper-specialization, which seems inconsistent with factor endowment
stories of trade.
5. See Trefler (1993).

63

As noted by many previous authors, there are interesting interactions between
trade and factor flows arising from the unconventional productivity differences viewof comparative advantage. Whereas in the neoclassical model, trade and factor flows
do the same things to factor prices and are effectively substitutes, trade and factor
flows can be complements in the productivity differences view. For example, if therich country is perversely “labor abundant” because of productivity advantages in thelabor-intensive sector, then trade will raise the wage in the rich country (relative tothe poor country) and lead to more labor migration from poor to rich countries. This
makes the rich country even more “labor abundant,” strengthening its comparativeadvantage in labor-intensive products. Analogously, trade could lead to capital
inflows into the “capital-abundant” poor country, if relative productivity differences
lie in that direction. This is the opposite of what happens in the neoclassical model,
in which exports from the poor country of labor-intensive goods lower the rate of
return to capital, eliminating the capital inflows that would have otherwise responded
to the high returns to scarce capital.
The bottom line is that the effect of trade on inequality in the poor and richcountries depends on relative productivity levels as well as factor endowments. Whichway the effect goes is an empirical matter. What all these simple models predict,however, is that trade usually has opposite effects on rich and poor countries.
The effect of trade is to clearly reduce international inequality in the neoclassicalmodel, but it is ambiguous with regard to productivity differences. Trade in which
the rich country is exporting (effective) labor-intensive goods and the poor country
capital-intensive ones, as is possible with different productivity levels, could wind up
raising rich country wages relative to poor country wages.
C. Domestic Factor Accumulation and Globalization
How do trade and factor movements affect domestic savings and factor accumulation?
In the neoclassical model, differences in income reflect the rich country being
further along than the poor country in the transition to the (same) steady state.
Capital inflows tend to crowd out domestic saving, while capital outflows crowd indomestic saving. Labor inflows crowd in domestic saving, while labor outflows crowdout domestic saving.
In the transition to the steady state, the domestic accumulation of capital per worker
depends monotonically on the rate of return to capital. The rate of return to capital isin turn an inverse function of the capital-labor ratio. An inflow of foreign capitalincreases the capital-labor ratio (speeding the transition to the steady state, in which
the rate of return to capital will be fixed by intertemporal preference parameters). In
the transition in the poor country, the foreign capital inflow (holding labor migration
constant) substitutes for domestic saving, in that it lowers the rate of return to capitaland leads to less domestic accumulation of capital per worker. Conversely, an outflow
of labor migration from the poor country raises the capital-labor ratio and lowers therate of return to capital, which will decrease domestic capital accumulation (holding
foreign capital inflows constant). Decreased domestic capital accumulation tends toincrease capital rentals and lower wages, offsetting the fall in capital rentals and therise in wages induced by capital inflows and labor outflows. The decreased inequality

64 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004

Globalization, Inequality, and Development: The Big Pictureassociated with capital inflows and labor outflows is thus offset by the domestic capital
accumulation effects.
The opposite predictions apply to the rich country if it has capital outflows andlabor inflows. In a mirror image to capital accumulation in the poor country, notethat the negative effects of “outsourcing” and “cheap migrant labor” on inequality inthe rich country are offset by increased domestic capital accumulation, which lowers
the rate of return to capital back down and drives wages back up from where they
were driven by these factor movements.
In the productivity differences model, countries are already at their steady states
given by their different productivity levels. Growth of capital per worker is given by
the need to maintain K /AL constant, so growth of capital per worker is simply given
by productivity growth. There is no tendency for capital inflows in this steady state,since rich and poor countries will have the same K /AL (with differences in A offset bydifferences in K /L ), and thus the same rate of return to capital (assuming the sameintertemporal preferences in the rich and poor countries).
There will be the same wage per unit of effective labor, but a higher wage per unitof physical labor in the rich country. Whether workers migrate from the poor country
depends on whether they immediately gain access to the higher productivity in therich country. If they are stuck with their home-country productivity level, there is no
incentive to migrate. However, the evidence seems to point to immigrants almost
immediately getting a wage increase compared to their home country and to getting
a comparable wage to the unskilled workers in the destination country. This suggeststhat workers do get access to the higher productivity in the destination country. In thiscase, labor migration induces both capital inflows to the rich country and increased
domestic investment by rich country agents until KR /A RLR regains its equilibrium level.
We again get the phenomenon of all factors of production flowing to the richcountry, with the added prediction that domestic investment will also increase within-migration of labor. The poor country with the outmigration of labor will have an
incipient increase in KP /A PLP , which will be met by a combination of capital outflows
and decreased domestic investment. There is no effect on relative per capita incomesin the rich and poor countries, but note that global inequality and poverty havedecreased in that the migrant workers are getting higher wages without any otherworkers getting lower wages.
D. Trade and Growth
What if trade has an effect on productivity growth? The theory here is not very clear,but some argue that trade carries with it access to technology. In this case, wewould expect the poor countries to gain access to the superior technologies in therich countries by trading with them, and hence trade could be a vehicle that reduces
international inequality through convergence in productivity levels.
There is a huge empirical literature on trade and growth investigating thispossibility, which has failed to establish a consensus for growth effects of trade.
Previous literature covered the correlation between export growth and GDP growth.
That literature eventually failed to make the case for growth effects of trade becauseof the difficulty of establishing causality from export growth to GDP—after all, they65both will grow at the equilibrium productivity growth rate plus population growth ina steady state. If productivity growth differs across countries, for whatever reason,
there will be a spurious cross-section correlation.
The cross-country literature has revived the trade-growth debate with regressionsof per capita growth on trade shares (usually insignificant), or some broad measure of
trade policy (highly significant in Sachs and Warner [1995]). However, the latter has
been criticized as a trade argument for really being a general measure of bad policies
and institutions (Rodríguez and Rodrik [2001]). Frankel and Romer (1999) did aregression of levels of per capita income on trade shares, using geographically determined “natural openness” as an instrument. As with all income level regressions, thesolution to the identification problem is not very convincing. Recently Dollar andKraay (2004) have proposed the testing of a relationship between per capita growth
and the change in the trade share. This takes us back almost to where we started—
they regress GDP growth implicitly on trade growth (the latter interacted with tradeshare), and again causality is unconvincing. It is hard to have much confidence basedon the existing literature that trade has strong growth effects.

III. Empirical Evidence on Globalization, Inequality,
and Development
In this section, I review the evidence on globalization and both international anddomestic inequality. I look first at the overall patterns of trade and factor flows,then at the behavior of relative international incomes and factor prices, and finally atthe effect of globalization on domestic inequality. I then adduce evidence from twoother sources: the experience with the Old Globalization era from the 19th century,and the evidence on factor movements within countries. The overall pattern tends
to support the productivity differences view versus the neoclassical model, withoccasional exceptions.
A. Empirical Evidence on Trade and Factor Flows across Countries
Supporting the conventional wisdom that recent decades have shown increasing
globalization, we do see steadily rising trade/GDP ratios over 1950–2001 in
Figure 1. The era of globalization has coincided with movements of millions ofpeople from poor countries to rich ones (Figure 2).
Figure 2 shows the flows of migrants into the rich countries in absolute numbers.
The migration of labor is overwhelmingly directed toward the richest countries. The
three richest countries alone (the United States, Canada, and Switzerland) receive halfof the net immigration of all countries reporting net immigration. Countries in therichest quintile are all net recipients of migrants. Only eight among the 90 countries
in the bottom four-fifths of the sample are net recipients of migrants (Easterly andLevine [2001]).
Embodied in this flow of labor are flows of human capital toward the rich countries,
the famous “brain drain.” In terms of the simple models above, human capital
movements are governed by the same predictions as physical capital movements.

66 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004

Globalization, Inequality, and Development: The Big Picture

Figure 1 Fifty Years of Openness: Median Trade to GDP Ratio for All Countries,
1950–2001
Share of exports plus imports in GDP, percent
80
Current prices
Constant prices
70

60

50

40

30

20
1950 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 2000
Source: Summers and Heston (1993) and online updates.

Figure 2 Migrants into Rich Countries, 1984–2001
Millions
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
1984 85

86

87

88

89

90

91

92

93

94

95

96

97

98

99 2000 01

Source: World Bank, World Development Indicators.

67

We used Grubel and Scott’s (1977) data to calculate that in the poorest fifth ofnations, the probability that an educated person will immigrate to the United Statesis 3.4 times higher than that for an uneducated person. Since we know that education and income are strongly and positively correlated, human capital is flowing towhere it is already abundant—the rich countries.
A more recent study by Carrington and Detragiache (1998) found that those withtertiary education were more likely to migrate to the United States than those with asecondary education in 51 out of the 61 developing countries in their sample.
Migration rates for those with a primary-level eduction or below to the United States
were less than migration rates for those with either a secondary- or tertiary-level
education in all 61 countries. Lower-bound estimates for the highest rates of migrationby those with a tertiary education from their data range as high as 77 percent (Guyana).
Other exceptionally high rates of migration among the tertiary-level educated are
Gambia (59 percent), Jamaica (67 percent), and Trinidad and Tobago (57 percent).6
None of the migration rates for those with a primary-level education or below exceed
2 percent. The disproportionate weight of the skilled population in U.S. immigration
may reflect U.S. policy. However, Borjas (1999) notes that U.S. immigration policy has
tended to favor unskilled labor with family connections in the United States rather thanskilled labor. In the richest fifth of nations, moreover, the probability is roughly thesame that the educated and uneducated will emigrate to the United States. Borjas,
Bronars, and Trejo (1992) also find that the more highly educated are more likely to
migrate within the United States than the less educated.7
Capital also flows mainly to areas that are already rich, as famously pointed out
by Lucas (1990). In 1990, the richest 20 percent of the world’s population received
92 percent of portfolio capital gross inflows; the poorest 20 percent received
0.1 percent. The richest 20 percent of the world’s population received 79 percent offoreign direct investment (FDI); the poorest 20 percent received 0.7 percent.
Altogether, the richest 20 percent of the world’s population received 88 percent ofprivate capital gross inflows; the poorest 20 percent received 1 percent.
The developing countries do receive net inflows of private capital, as shown in
Figure 3. However, the amounts of net capital flow are small relative to their GDP,
not at all the huge numbers predicted by the neoclassical model. Moreover, theimportance of capital inflows rises with the per capita income of the developing
country, counter to the prediction of the neoclassical model (Figure 4).
Capital inflows to the poorest countries are primarily made up of FDI, as shown
above. Even so, private FDI into the poorest region, Africa, is low and is directed
mostly to natural resource exploitation (such as oil, gold, diamonds, copper, cobalt,
manganese, bauxite, chromium, and platinum). The correlation coefficient between
FDI and natural resource endowment across African countries is .94 (Morriset [1999]).
6. Note these are all small countries. Carrington and Detragiache (1998) point out that U.S. immigration quotas areless binding for small countries, since with some exceptions the legal immigration quota is 20,000 per country
regardless of a country’s population size.
7. Casual observation suggests “brain drain” within countries. The best lawyers and doctors congregate within a few
metropolitan areas such as New York City, where skilled doctors and lawyers are abundant, while poorer areas
where skilled doctors and lawyers are scarce have difficulty attracting the best professionals.

68 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004

Globalization, Inequality, and Development: The Big Picture

Figure 3 All Developing Countries’ Private Capital Inflows as a Percentage of GDP,
1970–2000

5.0

Percentage of GDP
All net private capital flows/GDP
Median foreign direct investment/GDP

4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
1970 72

74

76

78

80

82

84

86

88

90

92

94

96

98 2000

Source: World Bank, World Development Indicators.

Figure 4 Private Capital Inflows to Developing Countries and Per Capita Income,1990–2001 (Moving Median of 20 Observations)

4.5

Private capital inflows as percentage of GDP, 1990–2000

4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
6.5

7.0

7.5

8.0

8.5
9.0
Per capita income, 1990

69

This tends to confirm the prediction for capital flows of the model including land andnatural resources.
Moreover, these numbers do not reflect the movements of private capital out ofdeveloping countries outside of official channels, i.e., capital flight. Fragmentary
evidence suggests that capital flight is very important for poor regions. Collier, Hoeffler,and Pattillo (1999) estimate that capital flight accounts for 39 percent of private wealthin both Sub-Saharan Africa and the Middle East (Table 1). It is also important in Latin
America (10 percent of wealth), but less so in South and East Asia.
One measure often used to estimate capital flight is to cumulate the net errors andomissions data in the balance of payments accounts. There one finds evidence oflarge-scale outmigration of capital in absolute terms in East Asia, Russia, and Latin
America (Table 2). As a percentage of GDP, the outflow of capital is very significant in
the African countries. This tends to confirm the findings of Collier, Hoeffler, andPattillo (1999) for Latin America and Africa. The availability of more recent data since
the East Asian crisis in my findings suggests that recent capital outflows out of East
Asia are more dramatic than what Collier, Hoeffler, and Pattillo (1999) found earlier.
What does this picture of factor flows between rich and poor countries tell us?
Although there are some poor country exceptions that attract capital inflows, in most

Table 1 Wealth and Capital Flight by Region
US$
RegionSub-Saharan Africa
Latin America
South Asia
East Asia
Middle East

Public capitalper worker
1,271
6,653
2,135
3,878
8,693

Private wealth Private capitalper worker
per worker
1,752
1,069
19,361
17,4242,500
2,425
10,331
9,711
6,030
3,678

Capital flight
per worker
683
1,936
75
620
2,352

Capital flight
ratio
0.39
0.10
0.03
0.06
0.39

Source: Collier, Hoeffler, and Pattillo (1999).

Table 2 “Top 10” Countries in Cumulative Negative Errors and Omissions
Absolute amounts,
sum 1970–2002(US$ billions)
China
Russian Federation
MexicoVenezuela
Korea, Rep. of
Philippines
Argentina
BrazilIndonesia
Malaysia

–142
–68
–27
–17
–16
–16
–14
–11
–8
–8

Liberia
Mozambique
Guinea-Bissau
EritreaGambia, The
Ethiopia
ZambiaBolivia
BurundiAngolaSource: World Bank, World Development Indicators.

70 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004

Percentage of GDP,sum 1970–2002/
GDP 2002(percent)
–129
–82
–66
–63
–45
–41
–41
–35
–31
–29

Globalization, Inequality, and Development: The Big Picture

poor countries all factors of production tend to move toward the rich countries. This
supports the productivity differences view of globalization instead of the neoclassicalview. The attractive force of higher productivity in the rich countries overturns theneoclassical model’s predictions of convergence through capital flows and trade. The
productivity differentials among sectors could actually lead to divergence.
However, the flows of migrants are still relatively small out of the entire poor
country population (three million out of five billion), so we should not jump to theconclusion that the poor countries are virtually emptying out, or that there is freelabor mobility. The flows involved are actually too small to make much difference
to either rich country or poor country incomes, hence the fact we will examine next:
the relative stability of the poor country/rich country relative income ratio in the eraof globalization.
B. Behavior of Cross-Country Per Capita Income Ratios
The overall record of international inequality during recent globalization is controversial. Figure 5 shows why different authors reach different conclusions. If we take theunweighted average of developing countries’ income ratios to the rich countries, there
has been increasing inequality between countries. This is the right number if we takeeach poor country, no matter how small or large, as an independent experiment forincreased globalization and all the other factors affecting relative country growth.
Other authors stress the population-weighted average of poor countries’ income
ratios to rich countries. This shows decreasing international inequality between
countries. The different result represents the catching up over the last two decades ofthe large populations in India and China. Of course, the more striking aspect of
Figure 5 is how high international inequality is—the average poor country by either
Figure 5 Ratio of Poor Countries’ Income to Rich Countries’ Income, 1960–2002
0.3

0.2

0.1

Developing countries (unweighted average)
Developing countries (population-weighted average)
0.0
1960 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 2000 02

71

measure has a per capita income that is only one-fifth the average for the member
countries of the Organisation for Economic Co-operation and Development (OECD).
Even the population-weighted average slows excruciatingly slow convergence. Figure 6
breaks this out explicitly by developing country region, as well as treating India andChina separately.
The regions that have the worst trends are Latin America, the Middle East, and
Sub-Saharan Africa, all of which are diverging from the United States. Recall thatthese are the same regions with significant capital flight, and they also account forlarge shares of the population migration to rich countries. In these cases, the relative
productivity advantage of the rich countries is apparently increasing, attracting all
factors of production toward the rich countries. In this same category would be theformer Soviet Union, for which there is only a decade of data.
The counter-examples are China, East Asia (excluding China), and India (although
Figure 6 makes clear that the recent catch-up in India is still a blip). The very different
performance of developing country regions does not have any obvious neoclassicalexplanation. For the rapid growers of East Asia, the consensus now seems to be that
their growth cannot be largely explained by factor accumulation without generatingsome counterfactual predictions for returns to physical and human capital (Klenow andRodriguez-Clare [1997], Hsieh [2003], and Bils and Klenow [2000]). Hence, there
seem to be large differences in productivity growth across developing countries, forwhich we have no clear theoretical story. The large cross-country empirical literatureon growth suggests the importance of such factors as macroeconomic stability andinstitutions, but no clear theory underlies these correlations.
Figure 6 Ratio of Regions to U.S. Per Capita Income 1960–2000
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
1960

63

66

69

China
Latin America

72

75

78

81

84

87

East Asia (excluding China)
Middle East

72 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004

90

93

96

99

India
Sub-Saharan Africa

Globalization, Inequality, and Development: The Big Picture

C. Western Europe and North America as a Globalization Experiment
Another interesting experiment is to examine the trends in countries within Western
Europe and North America, where we have already seen that most capital flows (andmost trade) are concentrated. Also, the case of free labor mobility could be somewhat
closer to reality in this region than for the world as a whole. The North Atlantic
economy has seen decreasing inequality between countries over the last five decades.
Figure 7 shows the convergence of these economies from 1950 to 2001. A measure ofinequality among these countries is the standard deviation of log per capita incomes.
This has declined at a nearly constant rate over the last five decades (Figure 8).
This seems to suggest convergence among one highly globalized group of countries.
If there was no free labor mobility between these countries, then the predictions ofcapital movements and trade in the neoclassical model are borne out by the data forthis group. Several caveats apply. One must always be careful that one is not selecting
countries by their income at the end of the period, which would create a spurious
finding of convergence (the De Long effect). I have tried to deal with this by choosing
geographic regions (North America and Western Europe) that have recorded intensive
capital and trade flows. Second, part of the dispersion in 1950 is artificially induced
Figure 7 Per Capita Income in Europe and North America, 1950–2001

10.5

Per capita income (log)

10.0

9.5

9.0

8.5

8.0
1950 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 2000
Austria
Germany
Switzerland

Belgium
Italy
United Kingdom

Denmark
Netherlands
IrelandFinland
NorwayCanadaFranceSwedenUnited States73

Figure 8 Standard Deviation of Log Per Capita Income in Western Europe andNorth America

0.35Standard deviation of log per capita income

0.30

0.25

0.20

0.15

0.10
1950 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 2000

by wartime destruction, and rapid growth after that is mainly reconstruction for the
initial period. However, it is notable that among this group of countries, the rate ofincome convergence did not slow down, even after we would have expected wartime
reconstruction to be complete. Also, if wartime destruction eliminated more capitalthan labor, then the pattern above is exactly what the neoclassical model would predict.
Third, the convergence could have come from technological dissemination rather
than neoclassical effects. This is hard to test, although one would think that the core
countries in this group (the United States, the United Kingdom, France, and Germany)
would have had fairly similar technologies, since they had all industrialized by about
the same extent as of the early 20th century.
D. Evidence on Factor Returns within Countries
We have some evidence on the behavior of returns to skill and returns to physicalcapital within countries. Easterly and Levine (2001) noted that skilled workers
earn less, rather than more, in poor countries. This seems inconsistent with the
open-economy version of the factor accumulation model by Barro, Mankiw, andSala-i-Martin (BMS) (1995). In the BMS model, capital flows equalize the rate of
return to physical capital across countries, while human capital is immobile. Immobile
human capital explains the difference in per worker income across nations in BMS.
As pointed out by Romer (1995), this implies that both the skilled wage and the skill
premium should be much higher in poor countries than in rich ones. To illustrate this,we specify a standard production function for country i as
Yi = AK iL iHi 1−−.

74 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004

Globalization, Inequality, and Development: The Big Picture

Assuming technology (A) is the same across countries and that rates of return tophysical capital are equated across countries, we can solve for the ratio of the skilled
wage in country i to that in country j , as a function of their per capita incomes,
as follows:−––––––

–––––
Yi /HYi /L i  1−− .
––i = ––––Yj /Hj  Yj /L j 
Using the physical and human capital shares (.3 and .5, respectively) suggested by
Mankiw (1995), we calculate that skilled wages should be five times greater in India
than in the United States (to correspond to a 14-fold difference in per capita income).
In general, the equation above shows that skilled wage differences across countries
should be inversely related to per capita income if human capital abundance explains
income differences across countries, à la BMS.
The skill premium should be 70 times higher in India than in the United States.
If the ratio of skilled to unskilled wages is about two in the United States, then theskilled to unskilled wage ratio in India should be 140. This would imply a fantastic
rate of return to education in India, 70 times larger than the return to education in
the United States.
The facts do not support these predictions: skilled workers earn more in richcountries. Fragmentary data from wage surveys indicate that engineers earn an averageof US$55,000 in New York City compared to US$2,300 in Bombay (Union Bank of
Switzerland [1994]). Instead of skilled wages being five times higher in India thanin the United States, skilled wages are 24 times higher in the latter than in the former.
The higher wages across all occupational groups is consistent with a higher A in the
United States than in India. The skilled wage (proxied by salaries of engineers, adjusted
for purchasing power) is positively associated with per capita income across countries,
as a productivity differences explanation of income differences would imply, and not
negatively correlated, as a BMS human capital explanation of income differences would
imply. The correlation between skilled wages and per capita income across 44 countries
is .81.
Within India, the wage of engineers is only about three times the wage of building laborers. Rates of return to education are also only about twice as high in poor
countries—about 11 percent versus 6 percent from low-income to high-income
ones (Psacharopoulos [1994, p. 1332])—not 42 times higher. Consistent with thisevidence, we have also seen that the incipient flow of human capital, despite barriers to
immigration, is toward the rich countries.
Returns to physical capital are much more difficult to observe across countries.
Devarajan, Easterly, and Pack (2003) show some indirect evidence that private
investment does not have high returns in Africa. They find that there is no robust
correlation within Africa between private investment rates and per capita GDP
growth. There is no correlation between growth of output per worker and growth ofcapital per worker. They also find with micro evidence for Tanzanian industry that75

private capital accumulation did not lead to the predicted growth response (as shownby strongly negative total factor productivity [TFP] residuals).
E. Empirical Evidence on Trade, Capital Flows, and Domestic Inequality
To test the effects of trade on inequality, I regress Gini coefficients on trade shares in
GDP for a pooled cross-country, cross-time sample of decade averages for the 1960s,
1970s, 1980s, and 1990s, for all countries (developed and developing) with available
data. The source of my data for inequality is the Deininger and Squire (1998)
inequality database, updated with World Development Indicators (WDI) data fromthe World Bank. The source of the data on trade shares is the WDI. Since the theory
predicts different signs on the inequality and trade relationship in rich and poor
countries, I use an interaction term that allows the slope to differ for developing
countries (Table 3).
The results suggest that trade reduces inequality in rich countries. The slope
dummy on trade for developing countries is highly significant and of the predicted
opposite sign. However, the net effect of trade in poor countries (the sum of the twocoefficients) is to leave inequality unchanged. I checked whether the developing
country effect reflected commodity exporting, which is often associated with higher
inequality, and also reflects the role of “land” in the neoclassical model. However, thedeveloping country slope dummy is robust to this control. I also checked robustness
to a time trend for the Gini coefficient; although it is significant and negative, it does
not change the results.
The pattern of results for rich countries suggests that some of the productivitydriven models of trade may be relevant. If we interpret the falling inequality as a dropas the capital rental/wage ratio (or as a decline in the skilled wage/unskilled ratio forhuman capital), then more trade is actually good for the workers in rich countries.
We could have the paradox that labor-augmenting productivity is so much higher inrich countries than in poor countries that rich countries are actually (effective) laborabundant. Trade then decreases the capital rental/wage ratio. If this is true, then wemight expect trade to increase inequality in the poor countries. While there is a

Table 3 Regression of Log Gini Coefficient on Trade/GDP Shares and Interaction
Terms and Time Trend, Decade Averages, 1960s through 1990s: Fixed
Effects (Within) Regression
Log of trade share
Log of trade share interactedwith developing country dummy
Log of trade share interacted with
commodity-exporting dummyTime trend
Constant
Number of observations
Number of groups
R-squared

Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic–0.407–4.90–0.407–4.93–0.256
–2.77
0.400

4.103
312
112
0.2142

4.47

31.85

0.364

3.99

0.137

1.82

4.069
312
112
0.250931.42

76 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004

0.324

3.59

–0.030
3.966
312
112
0.2261

–3.36
30.04

Globalization, Inequality, and Development: The Big Picture

significant positive shift in the effect of trade on inequality in poor countries, the net
effect turns out to be close to zero. There is a marginally significant slope dummy forcommodity-exporting poor countries, in which more trade does increase inequality.
These countries may reflect the effect of earnings from natural resources (what I
called “land” in the models above), in which a land-abundant country sees an increasein the land rental/wage ratio from opening up to trade. Thus, we could understandthe increase in inequality with trade in commodity exporters, if inequality is driven
by the land rental/wage ratio.
I next test the effect of international capital flows on within-country inequality.
I do fixed-effect regressions for the change in the log of the Gini coefficient regressedon capital inflows as a percentage of GDP. Data on FDI and total net private capital
flows are from the WDI over 1970–2002. Inequality data are from the same sources
as before, but are only available through 1999, so the effective sample is 1970–99.
FDI has a positive effect on inequality in the rich countries, with a significantly
less positive effect on inequality in the poor countries (Table 4). The net effect oninequality in the poor countries is not significantly different from zero. This result isrobust to including a slope dummy for commodity exporting, which is not significant.
The paradox of capital inflows increasing inequality does not fit the simple factor
endowment predictions. The unequalizing inflow of FDI capital in rich countries could
be complementary to an expansion of capital-intensive exports, which would be
associated with an increased capital rental relative to wages.
I next test the effect of capital flows on domestic saving. The results are notvery strong, but we see an interesting hint that FDI tends to crowd in domestic
saving in countries that are not commodity exporters, while there is modest crowdingout of domestic saving in commodity exporters (Table 5). There is no significant
relationship of domestic saving to total private capital flows. The positive correlationof domestic saving with FDI is inconsistent with the transitional dynamics of theTable 4 Fixed Effects (Within) Regressions for Change in Log(Gini) as Functionof Capital Flows
All private
FDI •All private net capital
FDI •LDC commodity- net capital inflows • Number of Number of R-squared
withindummy
exportinginflows/ commodity- observations countries
dummy
GDP
exporting
dummy

Regression Constant

FDI/GDP

Coefficient–0.0650.027

t -statistic

–3.65

2.40

Coefficient–0.0690.090–0.081t -statistic

–4.03

4.02

–3.21

Coefficient–0.0690.087–0.0920.032

t -statistic

–4.00

3.89

–3.49

1.38

Coefficient–0.0360.716

t -statistic

–1.30

0.73

Coefficient–0.037

0.521

0.684

t -statistic

–1.31

0.44

0.31

195

88

0.0516195

880.1365195

88

0.1520130630.0079130630.009477

Table 5 Fixed Effects (Within) Regressions of Gross Domestic Saving/GDP on Private Capital Flows/GDP
PrivateFDI •FDI •net capital
Privatedeveloping commodityinflows• Number of Number of R-squared
net capital
withincountry
exportingdeveloping observations countries
inflows
dummy
dummy
countrydummy

Constant

FDI

Coefficient16.8270.294

t -statistic

39.41

1.31

Coefficient

16.8180.353–0.065t -statistic

38.13

0.48

–0.08

Coefficient16.6120.836–1.068t -statistic

38.56

2.65

–2.41

Coefficient16.664

0.428

0.496

–1.150t -statistic

37.93

0.59

0.63

–2.49

Coefficient

15.05934.497t -statistic

25.44

1.59

Coefficient

14.98450.272–29.788t -statistic

24.86

1.59

–0.68

297

111

0.0093297

111

0.0093297

111

0.0397297

111

0.0417246

850.0156246

85

0.0185

neoclassical model. A productivity increase could induce both higher domestic saving
and higher FDI. Commodity exporters may be more subject to factor endowment
effects of capital inflows.
F. Evidence from Historical Globalization
The first wave of globalization during the late 19th and early 20th centuries (the Old
Globalization era) is another important historical experiment to inform our priors
about the relationship between inequality and globalization. This has been wellcovered by economic historians (see Bordo, Taylor, and Williamson [2001]), but I
look at it from the viewpoint of the productivity differences versus neoclassicalworldviews.
The most obvious event during this globalization was the movement of 60 million
Europeans from the Old World to the New (Figure 9). As pointed out by many authors,
this migration supports a neoclassical prediction. Labor was moving from theland-scarce Old World to the land-abundant New World.

78 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004

Globalization, Inequality, and Development: The Big Picture

Figure 9 Gross Intercontinental Emigration from Europe, 1846–1939(Annual Averages)

1,600

Thousands
Total gross emigration
Eastern and Southern Europe

1,400
1,200
1,000
800
600
400
200

36–39

31–35

26–30

21–25

16–20

11–15

06–10

1901–05

1896–1900

91–95

86–90

81–85

76–80

71–75

66–70

61–65

56–60

51–55

1846–500Source: Chiswick and Hatton (2001).

O’Rourke and Williamson (1999, pp. 60–63) and Lindert and Williamson(2001) present evidence that wage/land rental ratios fell in the migrant-recipient
countries of the New World and rose in the migrant-sending countries of the Old
World, as predicted by the neoclassical model. The evidence on wage convergence isless clear. For all countries in the North Atlantic, there is no overall tendency toward
convergence of wages (Figure 10). However, if we pick out those countries that were
the heaviest senders of migrants (Norway, Sweden, and Italy) and compare them towages in the main destination (the United States), there is more evidence that wages
were converging (Figure 11).
O’Rourke and Williamson (1999, p. 179) and Lindert and Williamson (2001) alsopresent some interesting evidence on inequality trends within countries. Inequality fell
between 1870 and 1913 in the countries that were the heaviest senders of migrants,
while it rose among the highest recipients of migrants (relative to the respective
labor forces). If the land rental/wage ratio is one of the main determinants ofinequality in the 19th century, then this outcome would nicely follow the neoclassicalmodel prediction.

79

Figure 10 Real Wages in the Atlantic Economy, 1870–1913

5.5

Log of real wages

5.0

4.5

4.0

3.5

3.0

2.5
1870
Denmark
Spain
Great Britain
Canada1913
IrelandSwedenNetherlands
United States

Italy
Belgium
Argentina

NorwayFranceAustralia

Portugal
Germany
BrazilSource: O’Rourke and Williamson (1999).

Figure 11 Real Wages in Important Source Countries for Immigrants to the UnitedStates Compared to Real Wage in the United States, 1870–1913

5.2

Log of real wages

5.0
4.8
4.6
4.4
4.2
4.0

Italy
NorwaySwedenUnited States

3.8
3.6
3.4
3.2
1870

80 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004

1913

Globalization, Inequality, and Development: The Big Picture

Capital was also flowing from the Old World to the New. We can think of this asalso supporting the neoclassical model (augmented by land) thesis. Both capital andlabor were flowing to the countries rich in land and natural resources (Table 6).
However, capital was not flowing everywhere according to the predictions of theneoclassical model. The labor-abundant low-wage economies in Eastern and Southern
Europe and Africa and Asia did not attract much British capital (Figure 12). The
failure of Eastern and Southern Europe to attract capital despite their much lower
wages compared to the New World may suggest that productivity was lower in that
region. This would provide another reason for the huge outflow of migrants from
Eastern and Southern Europe to the Americas (see Figure 9).
Africa and Asia were left out like Eastern and Southern Europe, as they failed to
attract capital, and for them it was even worse because migration was not an option.
The majority of British capital went to the land-rich and plausibly higher-productivity
countries of Canada, the United States, Australia, New Zealand, and Argentina.
Clemens and Williamson (2004) point out that capital inflows were correlated with per
Table 6 Capital Flows from Europe to North America
Current US$ billions
1900
17.8
–2.5

Europe, net foreign capital assets
North America, net foreign capital assets

14
23.1
–10.9

38
22.0
–9.0

Source: Obstfeld and Taylor (2001).

Figure 12 Cumulative British Capital Outflows, 1865–1914
US$ billions
7
6
5
4
3
2
1
0
Latin America(excluding
Argentina)

Eastern andSouthern Europe

Asia and
Egypt

North
America

Argentina/Australia/
New Zealand

Source: Stone (1999).

81

capita income in the Old Globalization era, just as Lucas (1990) pointed out they werein the New Globalization era. This suggests a productivity differences view of global
capital flow rather than a factor endowments view.
The Old Globalization era is also associated with high trade flows between the Old
World and the New. Canada, the United States, Australia, and Argentina became
exporters of land-intensive agricultural products to the land-scarce Old World, which
presumably helped the convergence of land prices described earlier.
Looking at relative per capita incomes in the migrant-sending regions relative to
the United States may suggest an additional role for productivity differences. All of
the sending regions saw a fall in their relative per capita incomes (Figure 13). The
United States industrialized much faster than the sending regions. If we associate
rising total productivity with industrialization, then the combination of migrantflows, capital flows, and relative per capita incomes suggests that differential productivity stories play an important role even in those Old Globalization episodes that
support some neoclassical model predictions.
During the first wave of globalization, there was no strong movement toward
convergence in the North Atlantic economy (Figure 14), in contrast to the convergence we have seen among these countries in the New Globalization era. The UnitedStates rose from the middle of the pack to be the world leader. The neoclassicalmodel prediction of convergence among countries heavily integrated by trade andFigure 13 Ratio to U.S. Per Capita Income of Migrant-Sending Countries, 1820, 1870,
and 1913
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
1820
Denmark
Sweden70
Finland
Ireland

Italy
Russia1913
NorwaySeven East European countries

82 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004

Globalization, Inequality, and Development: The Big Picture

Figure 14 Per Capita Income in Europe and North America, 1820–1914

9.0

Log of per capita income

8.5

8.0

7.5

7.0

6.5

6.0
1820 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 1900 04 08 12
Austria
Germany
Switzerland

Belgium
Italy
United Kingdom

Denmark
Netherlands
IrelandFinland
NorwayCanadaFranceSwedenUnited States

capital flows fails. Thus, while some the land/labor and land/capital predictions of
the neoclassical model are consistent with the direction of flows and factor prices, theneoclassical view of the Old Globalization era among rich countries ultimately fails totell the whole story.
This conclusion is even stronger if we include the poor countries in the analysis of
the Old Globalization era. The behavior of income differences between rich and poor
countries in that era was even worse than in the New Globalization era (Figure 15).
As Pritchett (1997) says, there was “divergence, big time.” It seems obvious that the
big story was that there was an industrial revolution in Europe and its offshoots, andnone in the rest of the world. In other words, technological productivity took off inGreater Europe, while little happened technologically in the poor countries. Thus, we
can understand the lack of capital flows to the rest of the world as a consequence of
their low and falling relative productivity levels. (Latin America is an interesting
intermediate case, where perhaps land and natural resources attracted enough capitalin the Golden Age of 1870–1913 to prevent further divergence.) Although I havenoted some interesting exceptions, the big story in the Old Globalization era is morein line with productivity differences than the neoclassical model.

83Figure 15 Ratio of Developing Country Regions to U.S. Per Capita Income, 1820–1950
0.6
Latin America
China
India
East Asia (excluding China)
Sub-Saharan Africa
Middle East

0.5

0.4

0.3

0.2

0.1

0.0
1820

70

1913

50

IV. Conclusions
I sum up the stylized facts on globalization and inequality in Table 7. The purpose of
the table is not so much to anoint the neoclassical model or productivity differences as
the correct view of the channels from globalization to inequality. Rather, it is to showthat productivity differences are more relevant than differences in factor endowments
in some circumstances, while factor endowments dominate in others.
These mixed results are not a surprise. The neoclassical model and productivity
differences are not mutually exclusive, because different situations will involve varying
mixtures of factor endowment differences and productivity differences. The factor
endowment predictions help give us insight into how the North Atlantic economy
achieved decreasing inequality among countries in the last five decades. They also give
us insight into the great migration of Europeans from the land-scarce Old World tothe land-abundant New World in the late 19th and early 20th centuries, accompaniedby the predicted movements in land rental/wage ratios.

84 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004

Globalization, Inequality, and Development: The Big Picture

Table 7 A Scorecard
Stylized fact or episode
Recent decades
All factors of production flow to richest countries
Unweighted between country inequality increasing
Population-weighted between country inequality decreasing
Latin America, Middle East, Africa, and former Soviet Union
falling behind
China, India, and East Asia catching up
Between-country inequality in Western Europe andNorth America falling
Higher skilled wages in rich countries compared to poor countries
Low returns to investment in Africa
Trade reduces within-country inequality in rich countries
FDI inflows increase inequality in rich countries
FDI crowds in domestic saving in non-commodity exporters
Historical experience, 1870–1913
Great migration from Old World to New World
Fall in wage ratio/land rental in land-abundant countries,
rise in land-scarce countries
Inequality falling within land-scarce countries,
rising in land-abundant countries
Capital flows to land-abundant countries
Divergence between United States and migrant-sending countries
Lack of capital flows to cheap-labor countries in Eastern andSouthern Europe, Africa, and Asia
Failure of between-country inequality in North Atlantic economy
to decline“Divergence, big time” between rich and poor countries

Supports theneoclassicalmodel

Supports
productivity
differences
X
X

X?
X
X?

X?

X
X
X
X
X
X
X
X
X
X
X
X
X
X

However, productivity differences appear to be an important facet of many globalization and inequality episodes. In the Old Globalization era, they seem to be crucial
to understand the lack of convergence among North Atlantic economies, the great
divergence between rich and poor countries in that same era, and the bias of capital
flows toward rich countries. In the New Globalization era, productivity differences areimportant to capture the very different performance of poor country regions in recent
decades, the flow of all factors of production toward the rich countries, the low returns
to physical and human capital in many poor countries, and the “perverse” behavior ofwithin-country inequality in reaction to trade and capital flows.
Productivity differences to explain patterns of globalization and inequality area nuisance ! The factor endowment model specifies very clear channels by which
globalization would affect inequality within and between countries (usually to reduceit). We have no such off-the-shelf models of productivity differences that would allow
us to identify the channels by which globalization affects inequality. We need newmodels to understand the productivity channels that seem to be so important for somany globalization and inequality outcomes (often disappointing ones).

85

References

Aronowitz, Stanley, and Heather Gautney, “The Debate about Globalization, An Introduction,” inStanley Aronowitz and Heather Gautney, eds. Implicating Empire: Globalization and Resistancein the 21st Century, New York: Basic Books, 2003.
Barro, Robert J., N. Gregory Mankiw, and Xavier Sala-i-Martin, “Capital Mobility in NeoclassicalModels of Economic Growth,” American Economic Review, 85 (1), 1995, pp. 103–115.
Bils, Mark, and Peter J. Klenow, “Does Schooling Cause Growth?” American Economic Review, 90 (5),2000, pp. 1160–1183.
Bordo, Michael D., Alan M. Taylor, and Jeffrey G. Williamson, eds. Globalization in Historical
Perspective, Chicago: The University of Chicago Press, 2001.
Borjas, George J., Heaven’s Door: Immigration Policy and the American Economy, Princeton: Princeton
University Press, 1999.
———, Stephen G. Bronars, and Stephen J. Trejo, “Self Selection and Internal Migration in the
United States,” Journal of Urban Economics, 32, 1992, pp. 159–185.
Carrington, William J., and Enrica Detragiache, “How Big Is the Brain Drain?” IMF Working Paper
No. 98/102, International Monetary Fund, 1998.
Chiswick, Barry R., and Timothy J. Hatton, “International Migration and the Integration of Labor
Markets,” in Michael D. Bordo, Alan M. Taylor, and Jeffrey G. Williamson, eds. Globalizationin Historical Perspective, Chicago: The University of Chicago Press, 2001.
Clemens, Michael A., and Jeffrey G. Williamson, “Wealth Bias in the First Global Capital Market
Boom, 1870–1913,” Economic Journal, 114 (4), 2004, pp. 304–337.
Collier, Paul, Anke Hoeffler, and Catherine Pattillo, “Flight Capital as a Portfolio Choice,” Policy
Research Working Paper No. 2066, World Bank, 1999.
Deininger, Klaus, and Lyn Squire, “New Ways of Looking at Old Issues: Inequality and Growth,”
Journal of Development Economics, 57 (2), 1998, pp. 259–287.
Devarajan Shanta, William R. Easterly, and Howard Pack, “Low Investment Is Not the Constraint
on African Development,” Economic Development and Cultural Change, 51 (3), 2003.
Dollar, David, and Aart Kraay, “Trade, Growth, and Poverty,” Economic Journal, 114 (2), 2004,
pp. F22–F49.
Easterly, William R., and Ross Levine, “It’s Not Factor Accumulation: Stylized Facts and Growth
Models,” World Bank Economic Review, 15 (2), 2001.
Frankel, Jeffrey A., and David Romer, “Does Trade Cause Growth?” American Economic Review, 89 (3),1999, pp. 379–399.
Grubel, Herbert G., and Anthony Scott, The Brain Drain: Determinants, Measurement and Welfare
Effects, Ontario: Wilfrid Laurier University Press, 1977.
Hausmann, Ricardo, and Dani Rodrik, “Economic Development as Self-Discovery,” CEPR Discussion
Paper No. 3356, Centre for Economic Policy Research, 2002.
Hsieh, Chang-Tai, “What Explains the Industrial Revolution in East Asia? Evidence from the Factor
Markets,” American Economic Review, 92 (3), 2003.
International Forum on Globalization, Alternatives to Economic Globalization: A Better World Is
Possible, San Francisco: Berrett-Koehler Publishers, Inc., 2002.
Klenow, Peter J., and Andres Rodriguez-Clare, “The Neoclassical Revival in Growth Economics: Has It
Gone Too Far?” in Ben S. Bernanke and Julio J. Rotemberg, eds. NBER Macroeconomics Annual
1997, Cambridge, Massachusetts: MIT Press for the National Bureau of Economic Research,
1997.
Lindert, Peter H., and Jeffrey G. Williamson, “Does Globalization Make the World More Unequal?” inMichael D. Bordo, Alan M. Taylor, and Jeffrey G. Williamson, eds. Globalization in Historical
Perspective, Chicago: The University of Chicago Press, 2001.
Lucas, Robert E., Jr., “Why Doesn’t Capital Flow from Rich to Poor Countries?” American Economic
Review Papers and Proceedings, 80, 1990, pp. 92–96.

86 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004

Comment

Mankiw, N. Gregory, “The Growth of Nations,” Brookings Papers on Economic Activity, 1, 1995,
pp. 275–326.
Morriset, Jacques, “Foreign Direct Investment in Africa: Policies Also Matter,” Policy Research Working
Paper No. 2481, World Bank, 1999.
Obstfeld, Maurice, and Alan M. Taylor, “Globalization and Capital Markets” in Michael D. Bordo,
Alan M. Taylor, and Jeffrey G. Williamson, eds. Globalization in Historical Perspective,
Chicago: The University of Chicago Press, 2001.
O’Rourke, Kevin, and Jeffrey G. Williamson, Globalization and History: The Evolution of a 19th
Century Atlantic Economy, MIT Press, 1999.
Pritchett, Lant, “Divergence, Big Time,” Journal of Economic Perspectives, 11 (3), 1997, pp. 3–17.
———, “Boomtowns and Ghost Countries: Geography, Agglomeration and Population Mobility,”
mimeo, Harvard University, Kennedy School of Government, 2003.
Psacharopoulos, George, “Returns to Investment in Education: A Global Update,” World Development,
22, 1994, pp. 1325–1343.
Rodríguez, Francisco, and Dani Rodrik, “Trade Policy and Economic Growth: A Skeptic’s Guide to the
Cross-National Evidence,” in Ben S. Bernanke and Kenneth S. Rogoff, eds. NBER Macroeconomics
Annual 2000, Cambridge, Massachusetts: MIT Press for the National Bureau of Economic
Research, 2001.
Romer, Paul M., “Comment on N. Gregory Mankiw, ‘The Growth of Nations,’” Brookings Papers on
Economic Activity, 1, 1995, pp. 313–320.
Sachs, Jeffrey D., and Andrew M. Warner, “Economic Reform and the Process of Global Integration,”
Brookings Papers on Economic Activity, 1, 1995, pp. 1–118.
Stone, Irving, The Global Export of Capital from Great Britain, 1865–1914: A Statistical Survey, New
York: St. Martin’s Press, 1999.
Summers, Robert, and Alan Heston, Penn World Table, Version 5.5, 1993.
Trefler, Daniel, “International Factor Price Differentials: Leontief Was Right!,” Journal of Political Economy,
101 (6), 1993.
Union Bank of Switzerland, Prices and Earnings around the Globe, Zurich, 1994.

Comment
ETSURO SHIOJI
Yokohama National University
This paper explores the implications of observed patterns of factor flows and trade on
the effects of globalization on inequality. It considers two candidates for the major
determinant of inequality: relative factor endowments and productivity differences.
Using data from the Old Globalization and the New Globalization eras, it concludes
that productivity differences seem to be the more important of the two.
I think it is a very good idea to look at factor flows and trade patterns to investigate
this difficult issue, as any attempt to test those two competing hypotheses directly
is bound to be marred by endogeneity biases and measurement error problems.
I especially like the sections where the author uses data from the Old Globalization era.
I also believe that the conclusion makes intuitive sense. Below, let me express some of
my concerns about the paper, regarding (1) the need to distinguish the “technology
level” and “labor quality,” (2) the potentially complex role of human capital, (3) theliterature on migration, (4) inequality due to “returns” versus “distortion,” and (5) theneed to check robustness of empirical results; at the end, I will summarize (6) somerelatively minor comments on the author’s regression analysis.

87

I. “Technology Level” versus “Labor Quality”
In the paper, the author frequently makes statements like the following: “If the relative
scarcity of labor in the rich country is sufficiently offset by higher relative productivity,
then the rich country will be ‘labor abundant’ and will export ‘labor-intensive’ goods(the Leontief-Trefler paradox).”8 That is to say, when productivity, A, enters into the
production function in a labor-augmenting way, an increase in this A would induce a
country to export more labor-intensive goods (or export fewer capital-intensive goods)(let me call this “statement X”). This is incorrect. It would be correct if A in
the production function were to be interpreted as the quality of workers: that is
how trade economists have tried to reconcile the apparently perverse observed tradepatterns with theory.9 However, in this paper, A is not labor quality but is interpreted
as the level of technology. In such a case, a higher A would not necessarily mean thatthe country would be exporting more labor-intensive goods. To see why, let me use
the simple example of the Cobb-Douglas production function, Y = AK L1−, toillustrate my point. It is well known that, with that type of production function, an
increase in A is both labor augmenting (Harrod-neutral) and capital augmenting(Solow-neutral) at the same time. Suppose for the moment that the above statement X
is correct. Then, an increase in A should induce this country to export more laborintensive goods, because this is a labor-augmenting technological improvement (callthis “conclusion X-1”). However, by the same logic as statement X, an increase in
A should induce this country to export more capital-intensive goods, because thisis also a capital-augmenting technological improvement (call this “conclusion X-2”).
But conclusion X-1 and conclusion X-2 are incompatible. It therefore follows thatstatement X cannot be right.

II. The Role of Human Capital Might Be More Complex
The author reports a very interesting finding that skilled workers tend to flow frompoor countries to rich ones. He argues that this is due to productivity differences.
That might well be the case, but before coming to this conclusion let me offer twoalternative explanations. The first is capital-skill complementarity (Griliches [1969]).
If capital and skilled labor are sufficiently complementary in production, then themarginal product of skilled labor might actually be higher in advanced countries
with more capital stock than in developing countries, even if the level of technology
is the same. In such a case, it would be natural to observe skilled workers flowing
from poor countries to rich ones. The second possible explanation is agglomeration
economies. It seems likely that skilled workers employed in knowledge-intensive
sectors would benefit more from the presence of other skilled workers in geographicalproximity, due to knowledge spillovers. In such a case, again, we would observeskilled workers flowing into rich countries where there are already more of them.
8. Easterly (2004, p. 63).
9. I would like to thank Akira Otani of the Bank of Japan and Morihiro Yomogida of Hitotsubashi University for
their discussion and information on the trade literature.

88 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004

Comment

III. Literature on Skilled Migration
Continuing my discussion on the pattern of skilled migration, the underlying
assumption of the author’s argument is that the observed pattern of factor flows
largely reflects return differentials to factors of production. However, in the literatureon migration, there are other arguments. It is often said that more educated workers
tend to move more frequently because they can gather more information about otherparts of the world and recognize new opportunities. Others have argued that richer
workers are more likely to move because they are more capable of paying the fixed
costs of migration.

IV. Inequality Due to Returns or Distortions?
An important underlying assumption of the author’s entire analysis is that inequality
within countries largely reflects differential returns to production factors, as would
be the case in a perfectly competitive world. It is not clear to me how realistic
this assumption is. Could it not be the case that, in the real world, especially indeveloping countries, inequality is due more to the ability of some powerful people
to distort the market mechanism in their own favor and extract rents? If that isthe case, political and institutional determinants of inequality would have to be thepriority of our study. At least, it would be nice to get an approximate sense of what
proportion of inequality within countries around the world is due to the economic
reasons on which the author focuses.

V. Robustness of the Regression Analysis
Toward the end of the paper, the author performs regression analysis on the effects oftrade on inequality. In the literature on growth regressions, it is known that many
explanatory variables are not “robust”: that is, a variable whose coefficient turns out
to be significant under certain specifications may turn out to be insignificant underan alternative set of regressors. Sala-i-Martin, Doppelhofer, and Miller (2000) used
an approach called the Bayesian model averaging approach to check the robustness oftypical explanatory variables that appear in growth regressions. Kawai and Shioji(2001) applied this methodology to regressions of the type considered in this paper,
where inequality is the dependent variable, and found that in cross-sectional studies,after controlling for the effects of per capita income, almost nothing is robustly
related to inequality, with the exception of some dummies. The result was worse for
fixed-effect regressions. I redid the robustness study with specification close to theone in this paper, and found that there is some evidence (though not so strong) that(DC dummy)(log trade share) is robustly positively correlated to inequality. On theother hand, (log trade share) itself is not robust. This result is preliminary but doessuggest the importance of performing robustness studies in this kind of analysis.

89VI. Other Minor Comments and Questions on the Regression
Analysis
My final comments are as follows.
(1) Restricted by data availability, the author’s sample over-represents developed
countries. What kind of bias would this introduce to the result?
(2) How were ex-communist countries treated in the regression? As they haveexperienced dramatic rises in inequality recently, their treatment might have afirst-order effect on the results.
(3) Inequality data come from various types of surveys (such as individual surveys
and household surveys) and it is known that the measure of inequality issensitive to the type of survey. How were such differences controlled in
the analysis?
(4) Various measures of openness have been proposed in the literature. Why not
try measures other than the trade-to-GDP ratio?
(5) From the author’s regression results, the biggest lesson I have learned is that, atleast, trade does not seem to be bad for equality. That is consistent with my priorbelief on this subject, but would the author agree with such an assessment?

References

Easterly, William R., “Globalization, Inequality, and Development: The Big Picture,” Monetary andEconomic Studies, 22 (S-1), Institute for Monetary and Economic Studies, Bank of Japan, 2004,
pp. 57–87 (this issue).
Griliches, Zvi, “Capital-Skill Complementarity,” Review of Economics and Statistics, 51 (4), 1969,
pp. 465–468.
Kawai, Masahiro, and Etsuro Shioji, “What Do We Really Know about Income Inequality?” mimeo,
World Bank, 2001.
Sala-i-Martin, Xavier, Gernot Doppelhofer, and Ronald Miller, “Determinants of Long-Run Growth:
A Bayesian Averaging of Classical Estimates (BACE) Approach,” NBER Working Paper
No. 7750, National Bureau of Economic Research, 2000.

Comment
STEFAN GERLACH10
Hong Kong Monetary Authority andHong Kong Institute for Monetary Research

I. Overview
I am very pleased to comment on William R. Easterly’s paper. While it provides arich overview from both a theoretical and empirical perspective of what we know
about the effects of globalization on the economy, the focus is on the question of
10. The views expressed are solely my own.

90 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004

Comment

how such effects depend on whether income differences between countries are due todifferences in productivity or in factor endowments. To understand the starting point
for the analysis, it useful to consider a standard production function of the form
Y = A f (K, L).
In words, firms employ capital, K, and labor, L, to produce aggregate output, Y,
using a technology described by the production function f ( • ) and a certain level ofproductivity, A. Other factors of production, in particular human capital and land,
are sometimes considered but are omitted from this specification for simplicity.
The benchmark in the analysis in the paper is the neoclassical view, according towhich countries have the same production technologies and enjoy the same levels of
productivity but may differ in terms of their endowments of capital and labor andtherefore in their factor ratios. Thus, some countries are relatively wealthy because
they have a high capital-labor ratio, which implies that the marginal product of labor,
and therefore the real wage, is high. Similarly, other countries are poor because they
have little capital and therefore low real wages and income.
The author argues persuasively that this view of the world does not fit the facts.
To understand this empirical failure, it is useful to consider two examples.11 First, since
the marginal product of labor is low in capital-scarce countries, the neoclassicalmodel implies that workers will wish to emigrate to capital-rich and wealthy countries.
This is indeed something that we observe in the data: movements of labor occur
typically from poor to rich countries. But firms’ incentives to invest also depend on theassumed capital-labor ratio. In particular, the marginal product of capital is muchhigher in capital-scarce economies and the model consequently predicts that capitalshould move to poorer countries. Indeed, as Lucas (1990) notes, given the large
differences in capital-labor ratios between rich and poor economies, the model predicts
massive capital flows toward poor economies. As Easterly argues convincingly in thispaper, this we do not observe.
Second, suppose that there are both skilled and unskilled labor and that the ratioof skilled to unskilled workers is relatively low in poor countries. What does this imply
for the international distribution of real wages? Given their scarcity, skilled workers
ought to earn higher real wages in poor than in rich countries. The neoclassical viewthus suggests that salaries of computer programmers ought to be higher in India or
Kenya than in Switzerland or the United States. This also is not the case.
Given the limited explanatory power of the neoclassical view, the paper concludes
that productivity differences between countries must be very important. Indeed, theneoclassical view appears relevant only in cases in which there are extreme differences
between countries in factor ratios, such as when the United States, Australia, Canada,
Argentina, and other economies opened up to globalization in the 19th century.
With a lot of land but with little labor and capital, these economies attracted large
amounts of investment and workers.
11. It should be noted that the exact implications of the neoclassical view depend on the assumed number of factorsof production and whether or not they are mobile internationally.

91II. Why Do We Care?
But why should we care about whether income differences are due to differences
between countries in factor ratios or in productivity? This is an issue that the author
unfortunately does not discuss at any great length. It seems to me that the main
reason why the answer to this question is important, beyond providing us with abetter understanding of how the global economy works, is that it may help policymakers design better policies. For instance, if we do not observe capital flows topoorer countries because there are obstacles to such flows, then policy should focus
squarely on removing these hindrances. If, by contrast, we do not observe capital
flows because capital is much less productive in poorer countries, then such policies
will be ineffective. Instead, policy should presumably focus on augmenting the
productivity of capital.
The finding that productivity is important to understand differences in economic
development leads me to ask what policies might help raise it. I suspect that economic
policies are crucial in that regard. Moreover, the legal and institutional framework,in particular property rights, may matter, as may political conditions. I would havebenefited from a discussion of these issues in the paper.

III. Globalization and Inequality
Turning to the relationship between globalization and inequality, both domestic andinternational, which is an important theme in the paper: while the neoclassical model
suggests that globalization tends to reduce inequality, it is not possible to draw any
firm conclusions in this regard in the presence of productivity differences. But are
inequality considerations important when policymakers face the question of whether
to embrace globalization or not? I suspect not.
The reasons for this are, I believe, as follows. The central issue for policymakers
would seem to be whether or not globalization improves resource allocation and raises
income. While it may or may not do so, suppose it does. If so, by assumption there will
be many that gain a lot, but potentially a few who lose a little, from globalization.
There is thus a potential trade-off between making the economy wealthier at thecost of a more uneven domestic income distribution. But if income redistribution ispossible through fiscal policy, the question of whether or not to embrace globalization
is independent from the question of the effects of globalization on domestic inequality. Policymakers should first consider whether it is beneficial or not to open up the
economy. If it is, in a second stage they should determine how much redistribution
is necessary to offset any increase in domestic inequality.
Finally, let me turn to the importance of international income inequality. Supposea country gains from embracing globalization. Would we then expect it not to accept
these gains because of the impact on international inequality? I would suspect not:most policymakers would probably focus on the effects on the domestic economy
in setting policy. Overall, then, if globalization raises income and if income redistribution is possible, the domestic and international inequality considerations will

92 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004

General Discussion

probably play little, if any, role in explaining countries’ policy choices. This raises
the question, then, of why much of the discussion on globalization, including thispaper, focuses so squarely on inequality. It would be interesting to hear the author’s
views on this issue.

Reference

Lucas, Robert E., Jr., “Why Doesn’t Capital Flow from Rich to Poor Countries?” American Economic
Review Papers and Proceedings, 80, 1990, pp. 92–96.

General Discussion
In response to the discussants’ comments, William R. Easterly emphasized that crosscountry inequality measured by per capita income was deemed more important than
within-country inequality in considering the effects of globalization. He maintainedthat implementation of redistribution was very difficult, thus people did care aboutcross-country inequality. He did, however, agree that some portion of within-country
inequality was attributable to political factors, as the experiences of Latin Americancountries showed.
Easterly added that his paper assumed labor-augmenting (Harrod-neutral) technological change, consistent with neoclassical growth theory. Etsuro Shioji reiterated hiscomment that labor-augmenting technological change need not lead a country to export
labor-intensive goods, using the example of the Cobb-Douglas production function.
Many participants agreed with Easterly’s main conclusion that productivity differences seemed more important than factor endowments in considering the effects ofglobalization on inequality, although they noted that further study of the former’ssources was needed. Vincent R. Reinhart (Board of Governors of the Federal Reserve
System) commented that workers’ resistance to the pressure to equalize wages in the
Old Globalization era could be consistent with the Heckscher-Ohlin-type factor
endowment model.
Shigenori Shiratsuka (Bank of Japan) pointed out that the factor endowment viewand the productivity differences view are very difficult to separate in practice becauseof dynamic interaction between the two mechanisms. Akira Otani (Bank of Japan)
suggested that the paper be extended to account for intra-industry trade in examining
the effects of globalization on inequality. Easterly generally agreed with the importanceof these lines of extension.
Maurice Obstfeld (University of California at Berkeley) remarked that, from ahistorical perspective, institutions had also played a significant role in determining
the effects of globalization on inequality. Hiroyuki Hino (International MonetaryFund) suggested that productivity differences might become smaller than anticipated
if the non-tradable nature of economic security, such as institutions and culture,
were considered. Easterly agreed that institutions were important in understanding
the history of globalization and trade structure, although he noted it was very hard toquantify the effects of institutions on inequality.

93

Obstfeld mentioned the importance of studying why globalization occurred andhow income distribution was decided, given that globalization itself would beendogenous. Hiroshi Fujiki (Bank of Japan) said that if a “superstar” developed a
new idea, then the superstar should earn a higher income to encourage his or her
innovation, and such inequality should be reconciled in the long term by appropriate
policy measures. Easterly agreed with Fujiki that wage gaps could provide incentives
to increase productivity. He noted, however, that whether such incentive effects
worked depended crucially on a society’s income mobility.
Hino remarked that rising inequality in both rich and poor countries seemed to beanother challenge for the factor endowment view. In response, Easterly remarked thatrising inequality in both rich and poor countries was another blow against the factor
endowment view and seemed to urge us to develop additional theoretical models.
Jean-Philippe Cotis (Organisation for Economic Co-operation and Development)
noted that an explanation was needed as to why the process of income convergenceamong European economies ceased at the end of the 1970s.
Regarding the importance of income inequality, Bennett T. McCallum (Carnegie
Mellon University) questioned whether people really cared about it based on theobservation that the United States was a popular country where workers wanted toimmigrate, despite inequality in its income distributions. He suggested that thisobservation might be interpreted as a revealed preference of the people, and further
empirical investigation would be interesting. Hino suggested that elections are anotherform of revealed preference. Assaf Razin (Tel Aviv University and Cornell University)commented that the lack of a common fiscal policy within enlarged European Union(EU) member states posed another challenge for redistribution policy in these member
states. He noted that this was in contrast to the United States, where redistribution was
carried out at the federal level. Easterly acknowledged McCallum’s counter-example of
the United States being a popular immigrant destination. Still, he was in favor of
Hino’s view that another form of revealed preference for inequality was elections,noting that people in Latin American countries today seemed to be voting in protest
against inequality.

94 MONETARY AND ECONOMIC STUDIES (SPECIAL EDITION)/DECEMBER 2004