William H. Branson

English

Research Papers in Economics

WILLIAM  H.  BRANSON 
Princeton University 
U.S.  Comparative 
Advantage:  Somne 
Further  Results 
IN  THE LAST  ISSUE  OF this journal, Helen Junz and I reported the pre- 
liminary results of an analysis of the sources of U.S. comparative advan- 
tage in trade in manufactured goods. The basic answer was that the U.S. 
advantage is in commodities whose production uses human capital inten- 
sively.' We looked only at data for the mid-1960s, which were developed 
separately by Keesing and Hufbauer.2 To study the data, we performed 
multiple regressions relating net exports by standard international trade 
classification (SITC) commodity groups to six production characteristics: 
human capital per man (H), physical capital per man (K), a measure of the 
presence of economies of scale in production (S),  the date at which the 
commodity first appeared in the U.S.  export schedule (P), the ratio  of 
expenditures on research and development to value added (RD), and the 
1. See William  H. Branson  and Helen B. Junz, "Trends  in U.S. Trade  and Compara- 
tive Advantage,"  Brookings  Papers  on Economic  Activity  (2:1971), pp. 285-338. 
2. Donald B. Keesing, "The Impact  of Research  and Development  on United States 
Trade,"  Journal  of Political Economy,  Vol. 75 (February  1967), pp. 38-48; G. C. Huf- 
bauer, "The Impact of National Characteristics  and Technology on the Commodity 
Composition  of Trade  in Manufactured  Goods," in Raymond Vernon  (ed.), Thle  Tech- 
nology  Factor  in International  Trade,  A Conference  of the Universities-National  Bureau 
Committee  for Economic  Research  (Columbia  University  Press  for the National Bureau 
of Economic  Research,  1970). 
754 William H. Branson  755 
fraction of  employees in the professional, scientific, and technical cate- 
gories  (T).3 
While the earlier results showed fairly clearly the positive relation of net 
exports to human capital per man and their negative relation to physical 
capital per man, several discussants suggested that the analysis would be 
improved if the dependent variable in the regressions were scaled to reflect 
size of industry. The point is that large industries, all other things equal, 
are likely to have larger trade surpluses or deficits than small industries. 
For example, consider a large industry producing commodity A and a small 
industry producing commodity B, both with exactly the same set of pro- 
duction characteristics. The United  States might export $12 billion and 
import $10 billion of good A for a $2 billion surplus, while if industry B is 
one-tenth the size of A, the United States would export $1.2 billion and 
import $1.0 billion of B, for a $200 million surplus. The earlier estimates 
would  be  inefficient in  that  they  include no  variable that  reflects this 
difference in scale. 
Several ways to remedy this deficiency have been suggested. One that 
could be applied with the data already in hand is to use as the dependent 
variable the  ratio  of  exports to  gross trade (exports plus  imports) by 
industry. In the example above, this would give a ratio of exports to gross 
trade of 0.545 (=  12/22  =  1.2/2.2)  for both commodities. This measure 
is thus scale free, varying between zero and unity, and should improve the 
estimates. 
Human-capital  Intensiveness  of U.S. Exports 
The new estimates are shown in Table 1. Parts A, B, and C of that table 
correspond to  Tables 9,  10, and  11 in the earlier paper. In each case I 
have reproduced the first equation of the earlier table, then reestimated the 
equation replacing net exports (X) with the ratio of exports to gross trade 
(XR), and then provided another equation or two to sharpen the results. 
3. All of these measures  are taken directly  from Hufbauer  and Keesing except one, 
the human  capital  measure.  This is calculated  by subtracting  the median  income in 1964 
for a worker  25 years  old and over  with  less than  an eighth-grade  education,  $1,717,  from 
Hufbauer's  average  wage per industry,  and then capitalizing  this difference  at a 10 per- 
cent rate of return.  The median income is the weighted  average  for males and females 
from U.S. Bureau  of the Census, Currenit  Population  Reports,  Series  P-60, No. 47, "In- 
come in 1964 of Families  and Persons  in the United States"  (1965), p. 39. 756  Brookings Papers on Economic Activity, 3:1971 
Table 1.  Regressions  Explaining  Net Exports of Manufactured  Goods, 
by SITC Commodity  Groups, 1964a 
Summary 
Coefficients  of independent  variables  statistics 
Equa-  Dependenit 
tionl  variable  K  H  S  P  RD  T  R2  F 
A. Regressionzs  on 101 three-digit  commodity  groups 
(1-1)  X(64)  -8.63  8.18  3.44  9.22  ...  ...  0.21  6.65 
(2.7)  (3.1)  (1.3)  (2.3) 
(1-2)  XR(64)  -1.08  1.18  0.25  0.90  ...  ...  0.26  8.43 
(3.2)  (4.3)  (0.9)  (2.1) 
(1-3)  XR(64)  -1.20  1.37  ...  ...  ...  ...  0.22  13.96 
(3.8)  (5.2) 
B. Regressionis  onz  61 three-digit  commodity  groups 
(1-4)  X(64)  -9.92  8.47  2.24  17.21  ...  ...  0.29  5.72 
(2.5)  (1.7)  (2.6)  (1.9) 
(1-5)  XR(64)  -1.22  1.08  0.60  0.52  ...  ...  0.24  4.50 
(3.1)  (2.2)  (1.6)  (0.8) 
(1-6)  XR(64)  -1.14  0.92  0.44  0.43  1.34  ...  0.27  4.15 
(2.9)  (1.9)  (1. 1)  (0.7)  (1.6) 
(1-7)  XR(64)  -1.25  1.01  ...  ...  1.61  ...  0.25  6.45 
(3.2)  (2.2)  (2.0) 
(1-8)  X(64)  -10.47  9.28  ...  ...  25.15  ...  0.28  7.39 
(2.6)  (1.9)  (3.1) 
C. Regressions  on 28 two-digit  commodity  groups 
(1-9)  X(64)  -55.71  39.01  27.52  28.96  ...  ...  0.33  3.00 
(2.4)  (2.1)  (1. 0)  (1. 1) 
(1-10)  XR(64)  -0.60  1.50  0.49  0.79  ...  ...  0.56  7.64 
(1.1)  (3.4)  (0.8)  (1.2) 
(1-11)  XR(64)  -0.71  1.77  ...  ...  ...  ...  0.52  14.1 
(1.3)  (4.5) 
(1-12)  XR(64)  -0.84  1.08  ...  ...  ...  2.37  0.71  22.0 
(1.9)  (3.0)  (4.0) 
(1-13)  X(64)  -61.74  42.85  ...  ...  ...  25.74  0.30  3.57 
(2.6)  (2.2)  (0.8) 
a.  See Table la below for definitions, units, means, and sources. 
Note:  The numbers in parentheses are t-ratios. 
In Part A of Table 1, the coefficients  of the regressions  on the full 
sample  of 101 three-digit  SITC  commodities  are reported.  Replacing  net 
exports by the scaled version improves  the estimate  and raises the sig- 
nificance  of both the physical  and human  capital  variables. 
The earlier  finding  that U.S. exports  are human-capital  intensive,  which William H. Branson  757 
Table la.  Definitions, Units, and Means of Variables for Table 1 
Mean 
101  61  28 
ob-  ob-  ob- 
serva-  serva-  serva- 
Variable  Definition  Unit  tions  tionsa  tions 
X(64)  Net exports  in 1964  Millions of dollars  74.8  120.7  270.2 
(XR64)  Ratio of exports  to exports 
plus imports  in 1964  Percent  56.7  60.9  57.7 
K  Physical  capital  per man  Thousands  of dollars  12.2  15.7  12.2 
H  Human capital  per man  Thousands  of dollars  42.7  47.6  41.3 
S  Scale economies measure  Percent  3.1  3.6  4.2 
P  First trade date  Year  1945.2  1945.5  1944.5 
RD  Research  and development  Percent  ...  3.4  ... 
expenditures  as fraction 
of value added 
T  Professional,  scientific,  and  Percent  ...  ...  8.98 
technical  workers  as 
fraction  of all employees 
Sources: Data  on U.S.  trade by SITC commodity groups are from Organisation for Economic  Co- 
operation and Development, Trade  by Coinmodities,  Detailed Analysis by Pr  oducts, Imports, 1964, Series C 
(OECD), and OECD, Trade  by Com,nodities,  Detailed Analysis  by Products,  Exports, 1964, Series C (OECD). 
Data on research  and development expenditures  are from Donald B. Keesing, "The Impact of Research and 
Development on United States Trade," Journial  of Political Ecotionoy,  Vol. 75 (February 1967), p. 47. All 
other data are from G. C. Hufbauer, "The Impact of National Characteristics  and Technology on the Com- 
modity Composition of Trade in Manufactured Goods," in Raymond Vernon (ed.), The Technology  Factor 
in Inzternzational  Trade,  A Conference of the Universities-National Bureau Committee for Economic Research 
(Columbia University Press for the National Bureau of Economic Research, 1970), Table A-2. 
a.  In Tables 9, 10, and 11 of William H. Branson and Helen B. Junz, "Trends  in U.S,Trade and Compara- 
tive Advantage," Brookings  Papers on Econoonic  Activity (2:1971), the mean of the human capital variable 
was reported low by a factor of 10: The mean is 43, not 4.3, for example. The human capital coefficients in 
Tables 9 and 10 were high by a factor of 10: In equation (1-1), for example, the coefficient was reported as 
81.82 instead of the correct 8.182. Table 11 avoided this error. In addition, the means for the 101-observation 
sample reported in Table 9 were also reported in Table 10 as the means for the 61-observation sample. Both 
of these errors  are corrected in Table 1 here. 
is  consistent  with  numerous findings since  Leontief's  initial  work,4 is 
strengthened by the revision. On the other hand, the significance of both 
"technological" variables-scale  economies  and the  first trade date-is 
reduced. Thus the new estimates offer even stronger support for the validity 
of a basic three-factor view of trade in manufactured goods as opposed to 
a  more  complicated model  with  additional technological  variables. In 
equation (1-3) the technological variables are dropped, and the physical 
4.  Wassily  Leontief,  "Domestic  Production  and Foreign  Trade;  the American  Capital 
Position Re-examined,"  in Richard  E. Caves  and Harry  G. Johnson,  Selection  Commit- 
tee, Readings  in International  Economics,  Vol. 11 (Richard  D. Irwin for the American 
Economic  Association, 1968). 758  Brookings Papers on Economic Activity, 3:1971 
and human capital variables become more significant, with an increase in 
the significance of the entire regression, as measured by the F-statistic. 
The Role of Research and Development  Expenditures 
Part  B  of  Table  1  explores research and  development expenditures 
(R&D) as an explanation of variations in U.S. trade. Since the R&D data 
published by Keesing cover only sixty-one of our observations, I first show 
the reestimated basic equation from Part A. Introducing the scaled version 
of the trade variable (equation 1-5) reduces the significance of the entire 
regression in the sixty-one-observation sample. On the other hand, scaling 
increases the significance of the K and H variables. Furthermore, a com- 
parison of  equation (1-5) with (1-2) makes apparent that reducing the 
sample size reduces the importance of the human capital variable sub- 
stantially. 
When the research  and development variable (RD)  is added to the analy- 
sis in equations (1-6) and (1-7), its coefficient has the expected sign and is 
marginally significant. Thus  R&D  expenditures may  affect trade inde- 
pendently from their effect on human capital inputs into production. 
Scaling the export variable reduces the significance of R&D  expendi- 
tures, as can be seen by comparing equations (1-7) and (1-8). It reduces the 
t-ratio of  RD  from 3.1 to  2.0,  a major drop in the  significance of the 
variable.5 
Human Capital and Skill Ratios 
In Part C of Table 1, the ratio of professional, scientific, and technical 
workers to total employees-the  "skill ratio," T-is  added to the analysis 
to  see  if  it  has  explanatory power  independent of  its  contribution to 
human capital. These data are available for only twenty-eight two-digit 
SITC commodities. 
First, the results are blurred all around when the sample is reduced from 
101 observations (equation 1-1) to 28 observations (equation 1-9). Aver- 
5. The potential  bias in not scaling  the net export  variables  was not noted by Baldwin, 
who apparently  used as his dependent  variable  (Xj/2X,)  -  (M/l2Mi), where X is ex- 
ports and M imports.  If trade is roughly balanced,  this boils down to (Xi -Mj)12X, 
so that no scaling  is involved;  each industry's  net exports  are divided  by the same num- 
ber,  total exports.  See Robert  E. Baldwin,  "Determinants  of the Commodity  Structure  of 
US.  Trade," American Economic Review, Vol.  61 (March  1971), p.  133, note  22. William H. Branson  759 
aging the three-digit data into two-digit categories eliminates much of the 
independent variation observed at the disaggregated  level. Introduction of 
the scaled version of the trade variable, in equation (1-10), raises the sig- 
nificance of the entire equation, as well as that  of  human capital, and 
lowers that of physical capital. This relation is observed also in equation 
(1-1 1), which eliminates the scale economies and first-trade-date  variables. 
In equation (1-12) the skill ratio is added to the analysis. It takes on a 
significant coefficient and reduces the importance of the human capital 
measure, which nevertheless remains quite significant. Thus it appears, at 
least  in  this  twenty-eight-observation sample, that  the  fraction  of  em- 
ployees in the professional, scientific, and technical categories contributes 
to the explanation of trade beyond its contribution to human capital. A 
comparison of equations (1-12) and (1-13) demonstrates that scaling the 
trade variable produced this new result. At this level of aggregation human 
capital and the skill ratio are quite significant and physical capital mar- 
ginally so in explaining trade. 
Conclusion 
The introduction of a scaled version of the trade variable has generally 
improved the significance of the results and also  strengthened the con- 
clusion that exports by the United  States are intensive in human capital 
while its imports are intensive in physical capital. The earlier  impressions of 
the marginal importance of scale economies and the first trade date, as a 
proxy for the product cycle, are also confirmed. 
The importance of research and development expenditures  was probably 
overstated in the earlier results; here they contribute positively to the U.S. 
trade advantage but with less statistical significance. 
The only real change from the earlier results is in the role of the skill 
ratio in explaining trade independently from its contribution to  human 
capital. When the trade variable is scaled, the skill ratio becomes quite 
significant, while the human capital measure is also significant.6  But as we 
noted in the earlier paper, this result comes from a small sample of highly 
aggregated data; its importance remains to be confirmed by a much more 
careful study than this set of preliminary estimates. 
6. This observation  is in agreement  with the intuition  of the co-author  of the earlier 
paper, Helen Junz, who felt all along that there was something  wrong with the results 
concerning  the skill ratio. 

U.S. Comparative Advantage: Some Further Results

http://www.brookings.edu/~/media/Files/Programs/ES/BPEA/1971_3_bpea_papers/1971c_bpea_branson.pdf

U.S. Comparative Advantage: Some Further Results

Abstract

Similar works

Full text

Available Versions

Research Papers in Economics