u.s. export performance in manufacturing industries: an empirical investigation

U.S. Export Performance in Manufactur- ing Industries: An Empirical Investigation

By

Bernard Goodman and Fikret Ceyhun

C o n t e n t s : I. Introduction. -- II. The Model and Its Specification. -- I I I . Empirical Analysis: I. Principal Components Regression Method ; 2. Statistical Results. -- IV. Conclusion. - - V. Appendix.

I. Introduction

S ince Leontief's pioneering work testing the Heckscher-Ohlin s t rong

factor intensity hypothesis 1, theoretical and empirical studies of trade structure have taken two directions. On the one hand, there

have been substantial theoretical efforts to understand the critical as- sumptions of the basic two-factor static model and to extend it in various ways, by more careful definition of factors, by specifically taking human capital and natural resources into account, and by using different types of production functions; and, to some extent, further to test such models. They may be characterized as "factor proport ion" models, since they all stress in one way or another factor endowment as fundamental to an explanation of the structure of foreign trade 9. On the other hand, there is a second direction in which inquiries into trade structure have gone. Studies in this group commonly emphasize technological and other forms of innovation, and have been described by Hufbaner as "neo- technological" explanations 8. Characteristically the underlying theories

1 See Wassily Leontief, "Domestic Production and Foreign Trade: The American Capital Position Re-Examined", Proceedings o/ the American Philosophical Sociely, Vol. 97, New York, x953, PP. 68 sqq. - - ld~m, "Factor Proportions and the Structure of American Trade: Further Theoretical and Empirical Analysis", The Review o! Economies and St~i~ics, Vol 38, Cambridge, Mass., x956, pp. 386 sqq.

t Most of the studies are well-known, and do not require specific citations. Recent con- tributions include William H. Branson and Helen B. Junz, "Trends in U.S. Trade and Com- parative Advantage", Brookings Papers on Economic Aaivity, Washington, D. C., i97x , pp. 285 sqq. ; Daniel J. B. Mitchell, "Recent Changes in the Labor Content of U. S. International Trade", Industrial and Labor Relations Review, Vol. 28, Ithaca, N. Y., x975, pp. 355 sqq.; Jon Harkness and John F. Kyle, "Factors Influencing United States Comparative Advan- tage", Journal o/ International Economics, Vol. 5, Amsterdam, x975, PP. x53 sqq.

* G. C. Hut'bauer, "The Impact of National Characteristics and Technology on the Com- modity Composition of Trade in Manufactured Goods", in: The Technology Factor in Inter-

526 B e r n a r d G o o d m a n and F i k r e t C e y h u n

have been dynamic, in their emphasis upon the ongoing process of relative growth and decay of products and industries. There are, of course, different ingredients in the scenarios of the different authors. We refer to the work of Linder, Posner, Vernon and associates, Hufbauer, Hirsch, Wells and Teubal 1. More recently there have been empirical efforts to incorporate both factor proportions and technological factors in a syn- thesis, as in the work of Hufbauer, Morrall, Leamer and Hirsch z.

This study is in the spirit of the "neo-technological" explanations of trade structure. It is an empirical exploration into the determinants of U. S. export performance in manufacturing industries, for trade in whose products, it appears reasonable to surmise, the newer approaches seem particularly suited. We attempt to formulate a fairly general model, incorporating what we believe to be the most important explanatory variables, and to test it by the use of principal components analysis as applied to both cross section and time series (covering the period I956--r968 ) of U. S. two-digit and three-digit SIC data on U. S. exports of manufactures. We stress that we do not intend our investigation as a formal test by denial of the simple Heckscher-Ohlin model, although it has, indeed, certain implications concerning the validity of the strong

natioru~l Trade, Universities-National Bureau Conference Series, 22, New York, London, x97o, pp. 145 sqq.

t S. B. Linder, Essays on Trade and Trans]ormation, Stockholm, x96x. - - M. V. Posner, "International Trade and Technical Change", OxIord Economic Papers, N. S.,Vol. I3, x96I, pp. 323 sqq. - - Raymond Vernon, "International Investment and International Trade in the Product Cycle", The Quarterly Journal o] Economics, Vol. 80, Cambridge, Mass., r966, pp. xxo sqq. - - The Technology Factor in International Trade, op. cir. - - G. C. Hufbauer, Synthetic Materials and the Theory o/ International Trade, London, x966. - - Seer Hirsch, Location o] Industry/or International Competitiveness, Oxford, x967. - - Louis T. Wells, Jr., "A Product Life Cycle and International Trade", Journal o] Marketing, Vol. 32, Chicago, Ill., x968, pp. z sqq. - - Idem, "Test of a Product Cycle Model of International Trade: U. S. Exports of Consumer Durables", The Quarterly Journal o/Economics, Vol. 83, x969, pp. x52 sqq. - - Morris Teubal, "Toward a Neo-Teehnology Theory of Comparative Costs", ibid., Vol. 89, x975, PP- 4x4 sqq.

t Hufbauer, "The Impact of National Characteristics", op. cir. - - John F. Morrall I I I . , Human Capital, Technology, and the Role o/the United States in International Trade, University of Florida, Social Sciences Monograph, No. 46, Gainesville, x972. - - Edward Learner, "The Commodity Composition of International Trade in Manufactures: An Empirical Analysis", Ox/ord Economic Papers, N. S.,Vol. 26, x974, PP. 350 sqq. - - Seer Hirseh, "Capital or Tech- nology ? Confronting the Neo-Factor Proportions and Neo-Technology Accounts of Inter- national Trade", Weltwirtscka]lliches Archly, Bd. xxo, x974, pp. 535 sqq.

For excellent evaluative surveys see Jagdish Bhagwati, "The Pure Theory of Internation- al Trade, A Survey", The Economic Journal, Vol. 74, London, x964, pp. r sqq., repr. in: idem, Trade, Tariffs and Growth, Essays in International Economies, London, z969. - - See also Robert M. Stern, "Testing Trade Theories", in: International Trade and Finance: Fron- tiers o] Research, Ed. by Peter E. Kenen, New York, x975, pp. 3 sqq.

u.s. Export Performance 527

factor intensity hypothesis as a general explanation of trade structure. We shall compare and contrast our results with those of some other recent investigations.

II. The Model and Its Specification

Our starting point is the assumption (which we view as not at all unrealistic) that exporting in manufacturing is done primarily by large firms in oligopolistic industries, operating in product markets characterized by uncertainty, differentiated products (including new products) and subject to important internal economies of scale in production, research and development activities and marketing and promotional efforts, all fundamental to the innovative process. Successful exporting firms are viewed as operating from a base of a large and flourishing domestic U. S. market for their products, as Linder has suggested. New products are developed and promoted at home and abroad, displacing in the product mix older, more standardized products, in the manner outlined in the Vernon product cycle model. These oligopolistic firms are characteristically "large," not only in the domestic U.S . market but also in world markets. They are also the leading U. S. foreign direct investors - - the so-called multinationals. We view these firms as making interrelated decisions to develop, manufacture, promote and distribute a product line, and to invest at home and abroad in capital facilities, in a domestic and international environment in which they are subject to actual and potential competition from existing or new firms, and upon which commercial tax and other policies of governments may intrude. The emphasis is on non-price rather than price-cost factors in the export success of firms, particularly in "new" products. We do not assume that a single factor (such as labor skills, or R & D activity,) explains export performance, but rather conjecture that a variety of factors, centered in the large oligopolistic firm, determines export success. We include some variables that may be considered pertinent to a factor proportion explanation, although they can be given a "neo-technological" interpretation. We now turn to the specifics of the model and its estimation.

Symbolically, the full model may be written as follows:

Y = F (Xx ..... Xs)

where

Y = export performance

X x = technological innovations

X, = industry concentration

528 Bernard Goodman and Fikret Ceyhun

X a ----- industry growth

X 4 ~ scale economies X 6 ----- sales promotion

X 6 ----- labor skills ratio

X~ ----- unit labor costs

X 8 ----- capital-labor ratio

Export Per]ormance (Y) - - There are quite obviously several ways of measuring export performance: among others, U.S. share of world exports, by industries; industry share of total U.S. exports; and the ratio of U. S. exports (or net exports) to total U. S. sales, by industry. The first measure is, in many ways, a "best measure," but it entails serious and, quite possibly, insurmountable data problems for the level of disaggregation we believe desirable. The other measures at least allow an ordinal ranking of U. S. industries according to their export "success," a major objective of this study. We would have preferred to use net exports throughout, but data in SIC form are not readily available before I96O. We have chosen as our measure:

(I) E .P . = {(Yt/Yt_l) (Rt/Rt-x)}i ~/2

Y, where Yt = exports in the i-th industry in period t ; and 1~ = -~-, the

ratio of exports to sales in the i-th industry in the period t. This index is, in effect, the geometric average of the export growth rate and export share in total sales, a compromise between two indices, each of which has some serious deficiencies. Considerations of data availability also ruled out other possible (and, perhaps, theoretically more defensible) measures of export performance.

Technological Innovations (Xx) - - The driving force behind technological innovations is expected large profits stemming from at least tem- porary monopolistic market control x. Although R & D is generally a high-risk activity, this risk is reduced in the United States by substantial governmental support, in part because there is a large military market which demands highly sophisticated products. The firms which heavily emphasize R & D attain a high level of technological sophistication which, when embodied in their products, gives them a competitive advantage over foreign firms, who generally do not have either the same technological sophistication or level of governmental support. The civilian

* See Jesse J. Friedman and Murray N. Friedman, "Relat ive Profitability and Monopoly Power", Gon/erence Board Record, Vol. 9, New York, x972, pp. 49 sqq.

U.S. Export Performance 529

sector of the American economy is certainly the largest, most affluent in the world. I t exhibits a high propensity to demand "new" goods, which gives great impetus to technological innovations. Moreover, the United States has proportionately more college graduates than other countries and, in addition, is also geared to produce more utilizable skilled manpower with informal training, which facilitates the commercial- ization of new technical discoveries. Hence both the supply of skilled manpower and R & D activity, within the framework of technically oriented demand, have created conditions in the United States that are conducive to a high rate of technological innovation.

Trade in newly developed products appears to be growing faster than trade in older, more standardized products. One explanation that has been offered is that the income elasticity of demand for the former is much larger than the latter 1, among the reasons for which is that new ideas, which are needed in economic growth for increasing the sophistication of production and distribution, are transferred with the new goods which embody them. As the OECD has put it2: any "leading commercial position in the United States will almost necessarily mean a leading position on the world markets." American production in many fields reaches as high as fifty percent of world production and these are characteristically new products.

Since the technological innovations variable cannot be directly measured it must be represented by a proxy. There are three proxies which are often employed: R & D expenditures, employment of scientists and engineers (S&E), and patent statistics. The first two measure inventive activities from the " input" side while the last measures it from the "ou tpu t" side. In this s tudy R & D expenditures anp S & E are chosen for a technological innovations index, defined as follows:

(2)

and

(3)

R & D = (R & Di/Si )

where Si and TE #re total sMes and total employment in i-th industry. Technical innovative intensity of the industry is shown in terms of the proportion of revenue spent and the proportion of scientific personnel employed.

t OECD, Gaps in Technology: Analytical Report, OECD-Publications, Paris, x97o. Tables 2, 32 and 34 provide casual empirical suppor t of the claim that new products are the fastest growing segment of international trade.

t Ga#s in Technology: Electronic Components, OECD-Publieations, Paris, x968, p. 47.

Weltwirtschaitliches Archly Bd. CXII. 35

S & E ---- (S & E~/TE~)


Market Concentration (X2) - - Market concentration plays a significant role in export performance through the creation of an environment which can be controlled by the firms to a high degree. In other words, the firms can generate a market condition which is stable for their opelations and output. Their cash-flows are larger and their risks are lower than firms with less concentration; they are often integrated vertically a.

Giant American firms are both financially powerful and technologically sophisticated enough to regulate to a significant degree the market and life cycles of their producis both at home and abroad according to their best long-run interests. In order to maintain their control of the market or further increase their influence in the market, these firms may undertake very costly measures. For example, they can generate a high rate of obsolescence by rapid innovation of the technology embodied in their products, thus creating an unbearable cost burden on smaller firms. This procedure, of course, is profitable in the long-run for large firms because it reduces competition through creating cost barriers to entry.

The size of firms and the implied market concentration is also important for reasons of economies of scale in production, research, marketing and distribution. They give large firms a competitive advantage, including export advantage, in that they can:

I. maintain a large scale research center and provide full-time employment for scientists and engineers to generate a constant flow of innovations 2;

2. establish an export marketing research center and constantly gather information on the changes of foreign economic conditions;

3. develop efficient distribution channels;

4- be financially able to up-date their sales and service outlets;

5. afford to finance exports on credit.

a S tephen H y m e r , "The Eff ic iency (Contradic t ions) of Mul t i na t i ona l Corpora t ions" , The American Economic Review, Vol. 60, Menasha, Wise., i97o , pp. 44x sqq.

t There is subs t an t i a l agreement in empir ica l s tudies of size and technological i n n o v a t i o n t h a t there are " th reshho ld effects" in the inven t ion and innova t ions s tages of p roduc t develop- m e n t (i. e., t h a t there are increasing r e tu rns in these ac t iv i t i es up to a point) b u t t h a t there are clear con t inu ing advan tages of size in the app l i ca t ions of the new ideas or t echn iques to new products . - - For excel lent su rveys see. F. M. Scherer, Industrial Market Structure and Economic Perlormance, R a n d McNal ly Economic Series, Chicago, x97o; idem, " E c o n o m i e s of Scale and Indus t r i a l Concent ra t ion" , in : Industrial Concentration: The New Learning, Ed. b y H. J. Goldschmid et al., Boston, New York, I974, PP. x6 sqq ; J . W. M a r k h a m , "Con- cen t ra t ion : A S t i m u l a n t or R e t a r d a n t to I n n o v a t i o n ?" in: ibid., pp. 247 sqq. ; J . E. S. Parker , The Economwso] Innovation, London, x974; Morton I. Kamien and N a n c y L. Schwar tz , "Marke t S t r u e t m e and Innova t ion : A Survey" , Journal o/ Economic Literature, Vol. I3 , Kingspor t , Tenn. , x975, pp. r sqq.


Because of da ta limitations, i t was necessary to develop rather un- conventional indices for time-series and cross-section measurement of this variable. For time-series analysis, the firms were grouped into two categories by asset size. The first category represented firms with assets of $ 50 million or more and the second represented firms with assets of $ IOO million or more. Revenues of firms in each category were then related to total industry revenues, as follows:

(41 IC = R x / E m Rj j= l

and

(5) IC = R 2/E mRj j= l

where R 1 = revenues of firms with assets of $ 50 million or more; R 2 -~ revenues of firms with assets of $ IOO million or more; ~mRj = total

j=t industry revenues of m firms. (It was necessary to use these overlapping groupings because of gaps in the Internal Revenue Service data.)

For cross-section analysis, firms were divided into three categories according to number of employees. Category I included those plants in the industry with 500--999 employees, category 2 included those plants with lOOO--2499 employees and category 3 included plants with 2500 and more employees. The concentration ratios were:

(6) IC = y.3 Si / Em St i=l i=1

where E 8 S i = sum of the value of shipments by plants in each category j=l

(i, 2, 3), and ~m Sj = total value of shipments by all plants in the industry. j=l

I t should be noted that the categories were broken down by plants instead of firms. Since, quite obviously, a firm m a y include more than one plant, this is an important limitation of the indices.

Industry Growth (X3) - - Expor t performance is affected by the firm's ability to meet growing foreign demand which m a y have been fostered through sales promotion abroad or generated by exogenous factors such as cyclical or secular trends. Time-lags in meeting demand m a y be quite important because they might create unfavorable marke t conditions which the firm will have to overcome in the long run. Foreign orders

*35


must be filled by the firm with a minimum time-lag in order to gain a competitive edge over other nations' firms. This is measured by the industry growth variable which may be defined as

( 7 ) IG = ( S , ) / ( S t _ ~ ) - - 1

where S t is sales in period t. This variable also captures the product 's life cycle in terms of a proxy for income elasticity (used by both Wells and Morrall). Relation (7) may be a better proxy for supply elasticity than demand elasticity; they are difficult to separate empirically because we observe only an equilibrium, thus creating identification problems.

Sales Promotion (Xs) - - The absence of perfectly competitive market conditions stimulates product promotion activities not only in the passive sense of informing potential customers about the products and services but also in order to influence the customers' taste pattern in favor of the firm's products. Firms use promotional techniques to overcome static market barriers that exist in foreign markets, and to adjust to dynamic changes in demand and market shares.

Although we believe that export promotional activity is quite significant, it is difficult to assess correctly since direct measures or proxies are not available. Our index accounts only for total advertising outlays, defined as the ratio of advertising expenditures to total sales, both domestic and foreign.

(8) AD = (total advertising outlays) / (total current sales)

The implied assumption is that the level of export promotion is directly proportional to total promotion. We have no way of knowing whether this is a valid procedure, but no alternative is available.

Skilled Labor (Xe). - - This factor, suggested by Leontief and explicitly introduced by Keesing 1, reflects human capital inputs. Clearly this is an important extension of the simple two-factor Heckscher-Ohlin model. But skilled labor inputs can also serve as a measure of capability to innovate in sophisticated manufactured products. Cross-sectional census data usually lend themselves to detailed classification of skilled labor employ-

Donald B. Keesing, "Labor Skills and International Trade: Evaluating Many Trade Flows with a Single Measuring Device", TIw Review o/ Economics and St~iaics, Vol. 47, x965, pp. 287 sqq. - - Idera, "Different Countries' Labor Skill Coefficients and the Skill Intensity of International Trade Flows", Jour~tal o/l~v~erna3ional Economics, Vol. x, Amster- dam, x97x, pp. 443 sqq.


ment in various industries but the same richness is absent in t ime series data. For this reason, we employed two indices for the labor skills variables: one for time-series estimation and the other for cross-section. This index is defined as the ratio of skilled labor to unskilled labor. I t can also be defined as the ratio of non-production personnel to production personnel. Production workers represent unskilled labor in assembly lines while non-production workers, who are not directly involved in actual production, represent a broad category of skilled laborers. The lat ter of these definitions m a y be written as:

(9) SL = (non-production workers) / (production workers)

The former definition may be symbolically writ ten as:

(IO) SL = ( 3 I + 2 I I + V ) / ( V I + VII I )

o r

VIII

( I I ) S L = ( 3 1 + 2 I I + V ) ! 1~ Xi i = l

where

I I =

I I I =

I V =

V =

VI =

VI I =

V I I I =

professional, technical and kindred workers,

managers, officials, proprietors, except farm,

clerical and kindred workers,"

sales workers,

craftsman, foreman and kindred workers,

operatives and kindred workers,

service workers,

laborers, except farm and mine.

Cost Factors (X 4, X 7 , XR) - - Cost becomes an especially impor tan t factor in a firm's export performance when its technological, marketing, and market domination over its competitors gradually erode as the product travels along the life cycle and as other firms enter the product line. I t is in the "s tandard product" industries where the competi t ion among firms is most price oriented because product differentiation is at a minimum and the number of firms is at a maximum. This is a tempo- rary stage where the American firm can maintain its competitiveness in world markets perhaps through the realization of scale economies by mass production. However, the success of this process is limited because there is a limit to the number of cost-reducing devices tha t c a!~. be used


in mass production. Sooner or later foreign firms can acquire similar cost-reducing devices to imitate the production methods of their American counterparts. A competitive advantage over firms in the United States may thus accrue to foreign firms to the extent they are located in low- wage countries.

We at tempt below to identify three separate cost factors: economies of scale, unit wage costs, and the capital-labor ratio. We would expect indices of the first two of these cost factors to move in opposite directions: the larger the scale economies, the lower the wage cost per unit of output.

The index for scale economies is derived in a manner similar to tha t in measuring industry concentration for both time-series and cross-section analysis. For time-series analysis, firms were grouped by size of assets. Category i included those firms with assets of $ 5o million or more and category 2, those with assets of $ IOO million or more. They were designat- ed by A 1 and A v respectively. The index for scale economies of firms in the industry may then be writ ten as

(12) E S = A 1 / ~-]m Aj j = l

and

(13) ES = A z / y m A j j= l

where A] (j = I , 2) is the value of assets of firms in the j-th category, and ]~m Aj is total assets in the industry. ]=1

For cross-section analysis, the categorization was on the basis of employment. If we let Nj be the number of employees of establishments in the j-th category, and ]g=Nj be total employment in the industry,

j = l then the index for scale economies can be expressed by

(14) E S = Z 8Nj/Z =Nj j~ l j--1

The unit wage-cost variable has a dual role in the analysis because of its association with labor skills. Since we expect successful export industries to have highly skilled labor as opposed to import-competing industries, wage rates are expected to be higher in those industries which perform better in world markets 1. On the other hand, the wage-cost index

1 See Morrall, op. cir.


is a proxy for the price index indicating that it adversely affects export performance. However, empirical evidence for the relationship between wages and prices is tenuous.

Because the wage-cost factor is the cost of the labor input in the value added per unit of output, its proxy index may be written as

(I5) WC = w/APL

that is, wages per unit of value added output, where w = W / L , W = wage bill for production workers and L is total employment of production workers in man-hours, and APL is output per man-hour.

The third cost factor is the capital-labor ratio, defined as the ratio of capital expenditures on plant and equipment to the number of production worker man-hours. That is

(16) KL = KE/L

where L includes both production and non-production workers. If data were available separately, non-production workers could be included in this index with an appropriate assignment of weights. Using only production worker man-hours avoids the introduction of arbitrary weighting while causing relatively little degradation of the quality of the index.

Other Factors ~ In addition, there are several other factors which influence export performance, such as international investment, export financing, and government policy (both U.S. and foreign). Many of these factors cannot be measured because of lack of data at the industry level, although dummy variables could be used to capture some of the effects of, for example, changes in government policy. But they seem clearly important in an explanation of trade performance. For instance, in the early phases of a product's life the firm may provide fast and smooth delivery to foreign customers through establishing affiliates by direct investment abroad. These affiliates function as the parent firm's sales and distribution outlets. In later periods these foreign affiliates could become branch plants which assume the role of assembling the products abroad as well as continue to function as sales and service outlets. The branch plant function could be further transformed into a production unit if cost factors become more significant in the determination of the firm's export performance. The role of direct investment is thus a continuous transformation from the point where initially domestic production was promoted through the addition of foreign demand to domestic demand to a point where foreign production is substituted for home production.


This, of course, changes the patterns of trade in the conventional or narrow sense of physical commodities. Taking this literally, one may be led to conclude that the direct foreign investment would weaken the firm's export performance. However, multinational firms export "know-how" in terms of technology, managerial skills, marketing, etc., when they transfer production abroad. Further, they receive returns from the package of "know-how" that they transferred abroad. Hence one ought to redefine export performance of the firm to include not only earnings from the export of physical commodities but also from the export of package of "know-how" through investment abroad. We here ignore these correlated "trade" effects.

Another factor important in export performance is "export financing." We earlier suggested that large firms are in a superior position to capture export markets. We may then be picking up some of the effects of export financing in our X 2 and X 4 variables. The role of export financing mecha- nisms resembles the firms' sales on credit in domestic markets. Some domestic manufacturing firms establish their own financing corporations to increase sales. Similarly, exporting on credit is becoming important not only to promote the volume of trade but also to determine its direction. According to a U. S. Treasury Department Survey 1, export sales-on-credit, as a percentage of total exports, vary from 47% (in foods and kindred industries) to 91% (in chemicals and allied products). The same survey also shows that the greater proportion (85%) of export sales-on-credit is found in the larger firms. These large firms also accounted for the bulk (77%) of total manufactured exports.

The last but not least important factor in determining export performance is the role of the government. One may use the size of GNP or military spending as a proxy for the degree of governmental influence on trade relations when cross-country data are used. However, the same proxy could not be employed in time series analysis. We have not included variables to measure the influence of government policy, but it is clearly important. Leamer 2 specifically addresses himself to this influence.

Expected Per/ormance o/ the Variables. - - It seems useful to group activity into "old" and "new." Industry technical innovative capacity, proxied by R & D expenditures and by employment of scientists and engineers, is the classification criterion which we employ, as both of

1 U. S. Treasury Department, Department of Commerce, Bureau of the Census, Survey o/Export Financing, Washington, D. C., x966.

* Learner, op. cir.


these factors are highly correlated with innovative output 1. That is, manufacturing industries are ranked according to amounts spent for inventive inputs from their revenues. The ranking using R & D generally agrees with that of scientists and engineers (S & E). Those industries which ranked above the national average of manufacturing industries are called "new" and those below the national average are called "old" or "standard" industries. The ranks are shown in Table A.3. The industries are listed in Table A.I with corresponding conversion codes in Table A.2.

The following industries place above the average: electrical equipment and communication (# I2), transportation equipment (# x3}, professional and scientific instruments (#I4), chemicals and allied products (#5), and nonelectrical machinery (# IX). Those below the average are: rubber and plastics products industry (# 7), fabricated metal products (# to), primary metal products (# 9), paper and allied products (# 4), food and kindred products (# x), textiles and apparels (# 2), and other manufacturing products, n.e.c. (# I5). The industries found on the list in Table A.I which are excluded because of their obvious natural resource orientation are: petroleum (# 6), stone, clay, and glass products (# 8), and lumber, wood products, and furniture (# 3).

The variables influence export performance differently depending upon whether the industry is new or standard. A new industry's export performance depends primarily on the firms' capacity to innovate and their ability to market their products in world markets. The need for a strong emphasis on R & D activities in these industries is obvious. This emphasis is reflected in the high ratio of R & D investments to sales revenue, and/or in the relatively high rate of employment of scientists and engineers. Capacity to differentiate the products is reflected in market concentration. The innovating firms produce a substantial portion of the total industry output enabling a monopolistic command of the market. Capacities to innovate and differentiate are also reflected in the employment characteristics of the firms where, in new industries, the ratio of employed skilled labor in relation to the firms' total employment is very high. These capacities are also evident where there is a low capital- labor ratio because the specifications and modifications on innovated products are not asstable as those for old products. Probably changes in new products usually limit production processes to skill intensive (or less capital-intensive) methods. Because of monopolistic controls in new industries, the products are tess sensitive to price competition than old

a See, for instance, Daniel Hamberg, R ~ D: Essays on the Econondcs ol Research and Development, New York, x966; Edwin Mansfield, The Economies o/ Technological Change, London, x968; Seherer, Industrial Market Structure and Economic Perlormance, op. cir.


products. As a corollary to this labor costs are expected to be higher. New products require advertisement emphasis as well as other product promotional activities. Those activities are reflected in the high ratio of product promotion expenditures in relation to sales. Hence, we expect the following characteristics to be found in "new" industries:

i. high R & D sales ratio, and/or high ratio of scientists and engineers in total employment,

2. high degree of market (or industry) concentration,

3. high rate of increase in industry output,

4. low scale economies 1,

5. high ratio of advertising to sales,

6. high ratio of employment of skilled labor to unskilled labor,

7. high unit labor cost, and

8. low capital-labor ratio.

On the other hand, in U. S. export-competitiveness in the standard product "old" industries cost factors become more important. The products' successful competitiveness in the world market is more responsive to marketing approaches, wage-costs, scale-economies, and other factors such as export-credit, aid, preferential tariffs, distance, etc. Since diffusion of the technology of new products is taking place, monopoly advantages of the early starter are diminishing and more competition exists. Price factors exert their influence more conspicuously. Therefore we conjecture that the following major characteristics may be found in "standard" industries:

I. low level of research activity; i. e., low ratio of R & D expenditures to sales, and/or of scientists and engineers to total employment,

2. low degree of market concentration,

3. low rate of increase of industry output,

4. high utilization of scale economies,

5. high ratio of advertising to sales, 6. low ratio of employment of skilled labor to unskilled labor,

7. low wage-costs per unit of output, and

8. high ratio of capital to labor.

1 That is, under ceteris paribus conditions, one would expect to find new industries (or new products) realizing lower scale economies than the standard industries. But, with respect to export performance, the new U. S. industries are expected to have larger scale economies than their counterparts abroad.


All but the advertising to sales ratio differ as between "new" and "old" industries. We conjecture that it is high in both, although for different reasons.

III. Empirical Analysis

To examine the significance of the model as a whole and the variables, individually, in the export performance of American manufacturing industries, the statistical findings from time-series and cross-section data were analyzed by the use of principal components analysis. The results were compared with those of other recent studies.

I. P r i n c i p a l C o m p o n e n t s R e g r e s s i o n M e t h o d (PCR)

The explanatory variables appear to be highly collinear because the phenomena they at tempt to explain require tha t their occurrence be jointly dependent, as we argued above. Therefore, the problem which we face in estimating the parameters of the structural model is not a sample phenomenon; it is inherent in the underlying population.

Given this problem, a classical least-squares (CLS) estimation method seems inappropriate. I t is well known that the CLS method with a significant degree of multicollinearity produces large standard errors for the estimators: the regression estimates fail to pass "t"-tests . Under these circumstances, the researcher is usually led to conclude that the variables under study do not have an important influence on the dependent variable 1. On the other hand, rank correlation methods tend to overestimate the importance of the explanatory variables. The PCR method provides one means of overcoming - - or at least reducing - - this difficulty. PCR analysis as a multivariate statistical method is not limited to collinear explanatory variables 2. However, the overriding consideration for its use in this study is the problem of multicollinearity 3. As has been demon-

x A. S. C. Ehrenberg, "Bivariate Regression Analysis Is Useless", Applied Statistics, Vol. x2, London, Edinburgh, x963, pp. x6z sqq.

' For possible uses see J. N. R. Jeffers, "Two Case Studies in the Application of Principal Component Analysis", Applied StaHstics, Vol. x6, *967, No. 3, PP. 225 sqq., in addition to errors in variables.

' We also ran least-squares regressions corresponding to principal components regressions. The results confirmed our suspicions. High multicollinearity generated large standard errors for the coefficients of regressors in the full model. However, the difference in standard errors between PC and CLS regressions became minimal when the regressors were reduced by excluding the multicollinear variables.

Learner, op. cir., proposes the use of Bayesian methods as an alternative solution to the multicollinearity problem. I t must be mentioned, however, that this approach is not free from criticism.


strated by several writers in exploratory studies ~, estimates of regression coefficients and their standard errors can be obtained more reliably through principal components "intermediaries" which are orthogonal to each other.

2. S t a t i s t i c a l R e s u l t s

As a statistical procedure, the number of explanatory variables was reduced from eight to some smaller number when some improvement was observed. In other instances, where the removal process did not produce a significant change in the regression, the industry regressions are presented in the full eight variable model. The same procedure is applied to the cross-sectional analysis. We are well aware of the statistical and analytical pitfalls of dropping (or adding) variables. The procedure ideally would call for justification in each and every case.

The basic model may be expressed in terms of the time-lags of the regressors for time-series and cross-section, respectively, as follows:

For time-series:

(17) Yt = b0 + bl Xlt-* + b, X2t_ s + b a Xst_ 1 + b 4 X4t_ 2

+ bs Xst-~ + bs Xet-1 + b~ X7t_ 1 -{- b s X8t__ 1 -~- e t

For cross section:

(IS) Wt = b0 + bl Xl t - s + b~ X2t__ , + b 8 X3t_ 4 + b. X4t_ 3

+ b~ Xst..._. 6 -~- b e X6t_ s + b~ XTt._. 4 + b s X8t_ 4 AVet

x See, for instance, William F. Massy, "Principal Components Regression in Exploratory Statistical Research", Journal o] American Statistical Association, Vol. 60, 1Vashington, D. C., I965 , pp. 234 sqq.; J. T. Scott, Jr., "Factor Analysis and Regression", Econometrica, Vol. 34, New Haven, Conn., z966, pp. 552 sqq.; R. J. Nicholson and N. Topham, "Step- Wise Regression and Principal Components Analysis in Estimating a Relationship in an Econometric Model", The Manchester School o] Economic and Social Studies, Vol. 41, x973, pp. 187 sqq. - - More theoretical work on PCR has been going on recently: see Yoel Haitovsky, "A Note on Regression on Principal Components", The American Statistician, Vol. 2o, Washington, D. C., i966, No. 4, PP. 28 sq. ; T. Amemiya, "On the Use of Principal Compo- nents of Independent Variables in Two-Stage Least-Sqares Estimation", International Eco- nomic Review, September z966; B. T. McCallum, "Artificial Orthogonalization in Regression Analysis", The Review o! Economics and Statistics, Vol. 52, I97o, pp. Izo sqq. ; Bridget M. Mitchell, "Estimation of Large Econometric Models by Principal Component and Instrumental Variable Methods", ibid., Vol. 53, x97x, PP. x4 o sqq.; R. W. Farebrother, "Principal Component Estimators and Minimum Mean Square Error Criteria in Regression Analysis", ibid., Vol. 54, 1972, pp. 332 sqq. - - For derivation of estimators and further discussion see Fikret Ceyhun, Export Perlormance o/ U. S. Manu/acturing Industries: A n Econometric Study, Wayne State University, Detroit, I972, unpubl, diss.

U.S. Export Performance 54I

The lags in the time-series equation (17) are selected not because they are the best theoretically but because of data constraints. Further lags are desirable, especially for variables X 1 and X 5 (technological innovations and sales promotion) but they significantly reduce the degrees of freedom available. Since we are free from the degrees of freedom constraint in the cross-section equation (i8), more lags are provided. The particular lags which were formulated in equation (18) appeared to be superior in terms of "goodness of fit," i. e., Rz, than the other that were tried 1.

Time-series and cross-section PCR results are presented in Table I. In total, there are thirteen observations for the time-series (1956--68 in terms of Yt) and twenty-six for the cross-section analysis. In the time- series analysis the industries are grouped into four categories. These groupings are shown in Table A.3. From these industries, aircraft and parts (# I3B ), petroleum refining and extraction (#6) , stone, clay and glass products (# 8), and lumber, wood products and furniture (# 3) are excluded from Table I. Two industries (# I3B and # 6) are rather special in their characteristics. Both are highly capital-intensive and the government has considerable control over production and trade for political and other reasons. In the aircraft industry, it is also difficult to separate the civilian component from the military components of the explanatory variables. The influence of the government in this industry may be inferred from the small size of Rz (.35), indicating that much is left unexplained by the regression. The petroleum industry is different from other industries in that it is not "foot-loose" (oriented toward neither resources nor demand) as the others appear to be, but it too operates under government restrictions.

The last two industries (# 8 and # 3 ) , are excluded from this s tudy because there was not a regressor that explained the variance of the regressand. The R2s for the last two industries are .35 and zero, respectively. One explanation for these small R2s is that in these industries, export

i Better fit in equation (x8) may partly be explained as follows. A particular commodity which is commercially exploited first in the domestic market and then in foreign markets shares the industry characteristics in which it is innovated. However, the products experience growth in sales first at home and then abroad after successful adjustment in their specifications. Hence, according to industry studies, there is, on the average a 5--xo years lag between the R & D commitments and commercial exploitation. Similarly there is considerable lag between the market promotional efforts and the market 's response. This is more so in international markets. As far as lags in other variables are concerned, the product's production characteristics are first established for the home market, and they do not change over a period of time when the product is promoted in foreign markets until new characteristics are discovered and prove to be more suitable. I t must also be noticed that the obove argument is a pos ter ior i justification rather t h a n a p~'iori reasoning since there is no theoretical guide to the optimum length of these lags. The view adopted here is empirical rather than theoretical.

542 B e r n a r d G o o d m a n a n d F i k r e t C e y h u n

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performance is greatly dependent upon trade resistance factors, such as tariffs and transport costs, and compared to other industries they are large volume and low price products; we believe that trade resistance factors are crucial in determining the patterns in both industries, especially #31 .

N e w I n d u s t r i e s . - - In Table i the first five industries are considered to be new because they rank high in terms of technological innovations (see Table A.3). The export performance indices of these industries show a gradual improvement in the I96OS over the I95OS. A close examination reveals that these industries are also net exporters ~.

An examination of new industries reveals tha t two variables, the capital-labor ratio and the growth of industrial output, are statistically non-significant, except in the non-electrical machinery industry. This industry is not only highly capital intensive but it is also a growth industry. The ranking association of these factors with export performance is very high. It appears that the technological innovations and industry concentration variables in this group of industries behave exactly as expected, i. e., they are not only statistically significant but their influence

i Learner's overall results suggest this explanation; although not by particular reference to these industries. See Learner, op. cir.

t It must be pointed out that examination of available data indicates that there was a general deterioration in net exports of U. S. manufacturing industries in the x96os through x972. Among factors that have been suggested as explaining this net export decline are the formation of the Common Market and its advancement in science-based industries, Japanese economic export-led growth, and the auto pact between the United States and Canada. European progress in technologically advanced fields may be partly attributed to the diffusion of technology through direct investment of the U.S. multinational corporations in the Common Market countries. American direct investment in Europe increased from $ L7 billion in I95o to $ 24.5 billion in x97o (annual average increase is 67% per year). In Canada it rose from $ 3.6 billion to $ 22.8 billion (average annual increase of 27%). In the fields of manufacturing alone, U.S. direct investment abroad increased from $ 3.8 billion in x95o to $ 32.2 billion in I97o (an average annual increase of 37%). The deterioration of U. S. net exports to Canada may also be attributed to the x965 auto agreement which contributed to the deterioration of trade balance by $ x.x billion from x966 to x97o. The deterioration of U. S. net exports may also be accounted for by Japanese export-led industrial growth, especially in the fields of new industries. This is partly due to exportation of new technology to Japan by U. S. firms through licensing agreements. See U.S. Tariff Commission, Implicatione ol Multinational Firms/or World Trade and Investment and/or U. S. Trade and Labor, Report to the Committee on Finance o/the United States Senate and Its Subcommittee on International Trade, Washington, D.C., x973. - - Also Betty L. Barker, "U. S. Foreign Trade Associated with U.S. Multinational Companies", Survey o! Current Business, Vol. 52, Washington, D. C., z972, pp. 2o sqq. - - Terutomo Ozawa, "Imitation, Innovation, and Japanese Exports" , in: The Open Economy, Essays on International Trade and Finance, Columbia Studies in Economics, x, Ed. by Peter B. Kenen and Roger Lawrence, New York, London, x968,

pp. x9 o sqq.

544 B e r n h a r d G o o d m a n and F i k r e t C e y h u n

on export performance is positive because the innovating firms have substantial market power. The other remaining variables - - labor skills, wage-cost, sales promotion and economies of scale - - also turn out to be significant factors in export performance. However, their relationship differ from industry to industry. This relationship, while generally negative in the case of labor skills, is positive in the case of sales promotion and is almost evenly divided for the wage-cost and scale economies variables.

These six variables, being consistently significant when combined as in Table A. 4, seem to verify our hypothesis; however, as far as individual industries are concerned, support for the hypothesis is not as strong. The most disappointing is the chemical industry whose export performance is negatively related to technological innovation. This may be due to the spill-over effects of innovations from this industry to other industries. Some innovations may find their commercial exploitations in other industries, such as man-made fiber, paints, plastics, etc. 1. Although the hypothesis has general support as far as these six variables are concerned, the skills theory (especially Keesing's version) is suspect because of the frequency with which negative signs on the coefficients occur s . The conclusion reached above also applied to the rubber and plastics industry which, in terms of R & D intensity, ranks in the middle.

In addition, there are other factors which might be par t ly responsible for the negat ive sign. One of these factors is foreign direct investment in chemical industries. According to Behrman, U. S. direct investment in Europe during the I96os increased rapidly, especially in chemicals, which were not growing in the United States. "The value of U. S. direct investments in chemicals throughout the world rose from $ 1.5 billion in x957 to $ 3.I billion in i964 - - an increase of nearly xo7 percent or about I5 percent per year - - and sales from overseas facilities rose from $ 2.4 billion to $ 5.9 billion over the same period, for an increase of I45 percent or nearly I8 percent per year - - a t a t ime when output in the U. S. chemical and allied products sector was growing only 8.3 percent per year." Jack N. Behrman, Some Patterns in the Rise of Multinational Enterprise, Universi ty of North Carolina, Research Paper, x8, Chapel Hill, x965, p. 4. This growth abroad, especially in Europe, occurred because of market penetration of U. S. mult inat ionals into Europe's t radi t ionally strong markets, despite declining profit rates in Europe vis-~t-vis the United States.

This suggests tha t U.S. export performance in new industries is bet ter in unskilled labor categories. I t must be pointed out tha t this outcome is the result of one sample pattern. Other regressions which were run (reported in Table A.4 in terms of the four groups of industries which are classified in Table A.3), using different definitions of proxies for the regressand and the regressors, indicate the opposite pattern, i. e., in favor of Keesing's hypothesis. The results of time-series regression which are summarized in Table A.4 in terms of s ta t is t ical ly significant regression coefficients for four industry groupings indicate a trend which is more in agreement with our contentions than the results in Table I as far as new and old industries are concerned. Examinat ion of Table A.4 indicates tha t the influence of technological innovations on export performance gradual ly changes from positive to negative, except for group IV industries as the ratio of positive to negative declines from newer to older industries. We also find more support for our hypothesis as far as other variables are concerned.

U.S. Expor t Performance 545

O l d I n d u s t r i e s . - - The regression results for "old" or "s tandard" industries, summarized in Table I, indicate that the capital-labor ratio and industry growth variables are statistically non-significant in terms of their influence on export performance, except for the industry growth variable in the fabricated metal product industry. On the other hand, the technological innovations variable is statistically significant and its coefficient is positive for all industries, except in the primary metal industries. This is also contradictory to our hypothesis because the theory formulated predicts that innovations in old industries play, at best, a minor role in determining export performance. In other words, the influence of non-price factors on export performance is minimal and price-implied variables become the major determinants of export performance. The coefficients of labor skills and wage cost variables assume their expected roles in determining export performance for the standard industries. The fact that the sales promotion variable in this group of standard industries is generally negative and semi-positive in new industries is consistent with our hypothesis. To some extent the predicted pattern for the industry concentration and scale economies factors is also present.

The overall regression equations are statistically significant, except in the primary metal and food industries. The R2s for the new industries are generally higher than the old industries and they are consistently higher than those found in comparable studies, with the exception of the two industries mentioned above and three not reported, as we shall see later. We suspect that the reason for the low value of ~,~ for the primary metal industries is government export controls for the primary metal industries for "strategic" reasons. In the food and kindred products industry import restrictions imposed by foreign governments, designed to protect their own agriculture, affect the value of R~. The reader must be cautioned here not to attach to o much importance to the size and statistical significance of the R2s as they are subject to the small number of degrees of freedom 1.

The findings from cross-section analysis (last column of Table I) are somewhat different from time-series analysis. Using equation (I8), several regressions were run with eight or less regressors 2. The results indicate

1 R* is also subject to other influences. See James P. Bar re t , " The Coefficient of Deter- mination, Some Limitat ions", The American Stagistician, Vol. 28, x974, pp. x9 sq. The Durbin-Watson statistics are excluded due to insufficient degrees of freedom.

t This choice was dictated by the consideration tha t correlation coefficients for some variables were as high as 99% (such as industry concentration and scale economies), hence, they appear to be the same rather than different variables. On the other hand, the stat is t ical significance of some variables with eight and less regressors did not change. Therefore, those variables contributing nothing to export variance were dropped from the regressions. In

Weltwirtschaftlicht~ Atr.~v Bd. c x n . 36


that the export performance of American manufacturing industries in the I96OS was determined by technology, marketing, and industry growth factors. The size of the R~ indicates that these factors only capture 45% of the variation in export performance. The removal of the wage-cost variable (not reported in the table), improved the regression in terms of R~ slightly without altering the relationships of the included variables.

The results of both time-series and cross-section analysis seem consistently to exclude the capital-labor ratio as an important factor in the determination of export performance in American manufacturing industries. These results do not lend support to the factor proportions theory. However, when the dependent variable is regressed on the full model (eight regressors), the coefficients of the capital-labor ratio are preponder- antly negative, though not statistically significant, which would be expected following Leontief's analysis. That is, Leontief's interpretation of the factor proportions theory receives some support.

Substantially the same conclusion was reached by Baldwin and by Branson and Junz 1. Baldwin notes that the capital-labor ratio is statistically significant (with a negative sign) when natural resource industries are included. However, eliminating natural resource industries from the regression data also eliminates the capital-labor ratio as a statistically significant variable S .

As a further check, the ratio of the capital intensity of import-competing to export industries was calculated a. This ratio was 1.4o and 1.29 for I958 and I966 data, respectively, compared to Baldwin's calculation of 1.27. As the ratio exceeds unity in both cases, it can be concluded that American imports (import-competing products) are more capital intensive than American exports. However, this ratio indicates that, over the

orde~ to get better information concerning these variables, we have run r2 regressions, each using different definitions of proxies for the variabIes. In none of these I2 regressions was the coefficient of capital-labor ratio significant; only once (with positive sign) was industry concentration statistically significant, twice (with negative signs) scale economies, and twice (with positive signs) labor skills. As for the other variables, technological innovations were statistically significant nine times (with all positive signs), wage cost eight times (once positive and seven negative signs), and sales promotion i i times (all positives). These results generally are in accord with Table 1. (For these, see last three rows of Table A.4.)

1 See Robert E. Baldwin, "Determinants of the Commodity Structure of U. S. Trade", The American Economic Review, Vol. 6I, 197I, pp. I26 sqq. - - Branson and Junz, op. cil.

Baldwin, op. eit., p. 142.

' { X (mi/M) ( K / L ) i ) [ ( X (xi/X) (K/L)i} is used to compute the coefficient, where mi and xi stand for imports and exports in the i-th industry, and M and X are total imports and exports, respectively. (K/L)i is the capital-labor ratio for the i-th industry. This methodology assumes that technology is universally identical for a given industry, whereas in our model we implicitly assume that technology is not the same internationally.


years, the capital-intensity of export industries is increasing relative to import-competing industries. An interpretation, if any can be made, is that in terms of the industry's relative "newness," which is determined by allocations to inventive activities, the gap between American export and import-competing industries is narrowing. This also might mean, in terms of the product life cycle theory, that American industries are becoming mature, and are thus traveling toward the "standard" products phase in their life cycles.

If we can group the variables into two broad categories, some labeled as factor proportions theory variables, such as skilled labor, wage-cost, and capital-labor ratio; and others as various proxies of the neo-technology theory, such as R & D, industry growth, industry concentration, and scale economies, a tentative comparison can be made between the results of this study and those made by Baldwin, Hufbaner, Leamer, and Morrall. These various studies differ in terms of data (industry and commodity, time-series and cross-section, one country's industry coefficients for other countries), statistical methodology (family of correlations, classical least- squares regression, Bayesian regression, and principal components regression), and definition and number of variables. Nonetheless, some general statements are still possible.

Combining the results from time-series and cross-section analysis of this study, the variables describing different facets of the technology phenomena are singularly the most important variables, which suggests the importance of the neo-technology hypothesis in the explanation of international trade in manufactures. The combination of these variables with the sales promotion variable explains most of the variations in export performance. The results of time-series analysis in this respect are the most striking. The patterns of these variables' influence are preponder- antly positive in "new" industries and negative in "old" industries, as predicted (see Table A.4).

Using the same methodology which was employed in the capital- labor ratio variable, we calculated the factor intensity of imports and exports for technical innovations inputs 1. The coefficients are .51 and �9 70 for 1958 and 1966, respectively. The same conclusion was reached by Baldwin. He calculated a similar coefficient which he found as .66 when R & D was used as inventive input and .74 when scientists and

1 Extending the methodology in footnote 3, P, 546, we formed {~ (mi/M) (R & D/S)i} / {~ (xi/X) (R & D/S)i}, the ratio of research intensity of imports to exports, where (R & D/S)i stands for the ratio of research and development expenditures to sales in the i-th industry.

36"

548 Bernard Goodman and F ikre t Ceyhun

engineers was employed. Ba ldwin found a signif icant re la t ionship be tween expor t growth and the number of sc ient i s t s and engineers (S&E) em- p loyed and no re la t ionship between impor t and S & E. Ba ldwin also repor ts t ha t " research and deve lopmen t act iv i t ies also show up as being much more i m p o r t a n t in expor t o u t p u t t han in i m p o r t - c o m p e t i n g p roduc t ion" 1.

Other i n d u s t r y character is t ics of the neo- technology h y p o t h e s i s t es ted were i n d u s t r y concent ra t ion , economies of scale, and i n d u s t r y growth - - all associa ted wi th ol igopolis t ic m a r k e t s t ruc tures . New indus- t r ies are more concen t r a t ed than the old ones th rough the g e n e r a t i o n of new produc t s which give monopol is t ic m a r k e t control , on the one hand, and the r ising d e m a n d for these p roduc t s because of h igh i ncome elast ic i ty , on the other. Scale economies increase the c o m p a r a t i v e a d v a n - tage of these industr ies because t hey no t on ly reduce the u l t i m a t e cost of selling abroad, bu t also inh ib i t e n t r y of o ther firms. As seen in Tab le A.4, the t ime-series analysis , bo th i n d u s t r y concen t r a t i on a n d scale economies, are s t a t i s t i ca l ly s ignif icant in the m a j o r i t y of regressions, and thei r sign of influence changes f rom pos i t ive to nega t ive mov ing f rom new indus t r ies to old, as predic ted . Whi le the i n d u s t r y g rowth v a r i a b l e is not s t a t i s t i ca l ly s ignif icant in the m a j o r i t y of t ime-ser ies regressions, i t behaves, sign-wise, s imilar to those above. However , i t s inf luence on Amer ican t r ade in cross-sect ion ana lys i s is p r e p o n d e r a n t l y pos i t ive (eleven ou t of twelve cases).

Ba ldwin ' s regressions s ingled ou t b o t h i n d u s t r y concen t ra t ion a n d economies of scale as s t a t i s t i ca l ly s ignif icant . This is no t surpr i s ing as he employed cross-section analysis , and ours revea led the same in cross- sect ion da ta . But , according to Ba ldwin , b o t h the concen t r a t i on a n d economies of scale indices are s t a t i s t i c a l l y s ignif icant in t e r m s of the i l cor re la t ions 2. Similar resul ts for these var iables , in t e r m s of r a n k corre la- t ions, have been found b y Hufbauer . Morra l l ' s s t u d y found on ly the i n d u s t r y g rowth va r i ab le (as ours ind ica tes as well) as be ing s t a t i s t i c a l l y

Baldwin, op. dr., p. x36. Baldwin rests many of his conclusions on correlation methodology. We have contended

that this is not a sound methodology because the influence of other factors on the dependent variable (whose variance in question is to be explained) is not removed. It resembles aregression analysis with one explanatory variable. It is likely that the coefficient of the explanatory variable would turn out to be significant because, due to misspecification of the model, the variable is "loaded ;" the estimator suffers from specification bias. When the model, however, is correctly specified, the same coefficient tends to be lower while its standard error rises (with more than one explanatory variable which are intercorrelated). This would greatly diminish the chances of the variable passing the test of statistical significance, as Baldwin discovered.


significant while the economies of scale (tested in terms of only rank correlation) is not 1.

Leamer's evaluation is quite different. Taking the import side of trade, commodity composition is best predicted by development (such as GNP) and resistance factors (such as tariffs), while what he calls the resource group (factor proportions and R & D) is insignificant. Learner concluded that in the resource group "the neo-technological theory performs well when supported by either the factor-proportions or the neo-factor proportions theories," when the dependent variable is the import to GNP ratio 2. This conclusion changed when he defined his dependent variable as "net imports." In the latter case, he found that "the resource group is almost uniformly superior to the resistance and development groups. Among the resource variables, research and development is most often the best performer ''8. Hence, his s tudy indicates, too, the significance of a set of variables whose interpretation is to be found in the neo-technology theory.

The hypothesis that American exports are more skill-intensive than their foreign counterparts (or import-competing industries) does not receive support from our analysis, either in terms of skilled employment (Keesing) or in terms of the earnings of skilled labor (Kravis, Waehrer, Yudin). The direction of the influence of the labor skills and wage-cost variables on export performance is generally negative, and this direction does not change according to new or old industries. However, using the methodology similar to the ratio of capital-intensity of imports to exports, the computation of the skills intensity for imports to exports is .76 for i96o 4. If anything, this indicates that the skill content of export industries is higher than import-competing industries.

Another important variable of this group is the capital-labor ratio. Its coefficient is not statistically significant in multivariate analysis. However, as we have also observed in Hufbauer and Morrall, the bivariate analysis of this variable indicates that, both in new and old industries, its correlation coefficient is significant and positive in most cases.

1 Following the analogy of footnote 3, P. 546, we calculated the coefficient for scale economies. The test may be considered as being inconclusive because the ratio is approximately unity: .96 and i.o2 for x958 and x966, respectively.

2 Learner, op. cir., p. ":'5.

s 1bid . , p. 26.

, The adaptation of it to labor skills takes the following form: ( ~ (mi/l~) (SKi)} / ( ~ (xi/X) tSKi)~, where SKi is skilled index in the i-th industry.


I t seems that the factor endowments group does not perform well in predicting international trade. Within this group, however, human capital (both skilled labor and wage-cost versions) is distinctly superior over physical capital (see Table A.4).

IV. Conclusion

I t is clear that beginning with Hufbauer x a new course was embarked upon in the empirical examination of the bases for trade. One result has been a proliferation of explanatory variables. This, no doubt, increases the measured statistical significance of models but raises serious questions concerning the nature of the underlying theoretical model. As a practical mat ter not only theories but also variables must be consoli- dated into a few basic groups. In this consolidation of variables the statistical methodology plays an important role. I t is apparent from both Hufbauer 's and Morrall's experience that correlation analysis is a questionable tool in grouping these variables, since it often fails to identify relevant factors. This is particularly the case with time-series analysis since the correlation coefficients include spurious relationships. On the other hand, least- squares solutions to regression estimates are frustrating because of the existence of multicollinearity. I t seems that both the principal components and Bayesian methods may prove to be more fruitful in these investigations 2.

We feel that both the capital-labor ratio and the wage-cost variables may be dropped from the neo-factor proportions theory without really weakening the theoretical model: the capital-labor ratio since the gap in capital-intensity of production between export and import industries is narrowing both internally and externally due to the high mobility of capital; and the wage-cost variable since it measures the same phenomenon as labor skills.

We can do the same screening of other variables in the neo-technology theory as well. The technology phenomenon in industries is reflected in their R & D spending, growth, concentration, profits, and, perhaps, scale economies. All these variables measure different facets of the technology factor, some of which may be left out without losing much information. Our data correlation matrices suggest that industry concentration and economies of scale variables measure the same things. Hence, by dropping one of them it may be found that the other may very well capture the

Hufbauer, "The Impact of National Characteristics", op. cir.

t Stern, op. cir., believes that Bayesian methods may prove particularly useful. We performed neither of these tests because they themselves constitute a different subject matter which is considered beyond the scope of this paper.

U.S. Export Performance 55I

explanation contained in both. (Correlation coefficients both in cross- section and time-series are not less than .95 in most cases, and in some instances they are as high as .99.) We think that the scale variable is the logical choice for exclusion because it correlates highly with other variables and it is only loosely connected to this group in terms of theoretical justification. Another variable, industry growth, may also be excluded because growth phenomenon of industries is related to their innovativeness. Empirical evidence overwhelmingly supports this point. Thus, each group, containing only a few variables, may better lend themselves to theoretical scrutiny.

For almost fifty years the factor-proportions theory has dominated as an explanation of trade structure. However, it faces a challenge from the neo-technology theory. There appears to be a new consensus emerging concerning the power of the neo-technology theory over the neo-factor proportions theory. But at this point, the evidence can only be regarded as suggestive. Morrall's conclusions concerning the complementarity and substitutability of the two theories which he derives by analysis of variance methods cannot be viewed with much confidence because of the inherent limitations of the methods used.

With proper regard for the dangers inherent in drawing inferences on the basis of a single study, which can only speak for itself, there are two general conclusions which can be made with relative safety. First, it would appear that trade theory to date has been characterized by a fairly general and unwarranted underemphasis of non-price factors. At an earlier point in time, perhaps this could be justified. But with the contemporary revolutions in communications, managerial techniques, marketing institutions, and industry organization, and the rapid transformation of the world economy into a cohesive, tightly interwoven whole, concentration exclusively or even primarily on cost-price factors becomes virtually untenable. Although the results of this s tudy are far from conclusive, they do indicate that there is much to be gained by transferring a large amount of attention to non-price factors.

Secondly, given the influences mentioned above and the rapid development of multivariate statistical techniques, it becomes difficult to ratio- nalize an approach in terms of a single factor, which is at best incomplete, and at worst may be seriously misleading. A multi-factor approach seems clearly indicated. Future studies should also address themselves systematically to the incorporation of direct investment, trade financing, and government policy into the models.

Finally, there are many obvious serious data gaps for disaggregated analysis of trade flows. As many of them as possible should be filled as quickly as funds permit.

552

Industry code no.

4 5 5A 5 B 5C 6

7 8 9 9A 9B

IO

Berna rd Goodman and F i k r e t Ceyhun

V. A p p e n d i x

T a b l e A . I - - Classification o/Industries

Industry name

Food and kindred products Textiles and apparels Lumber, wood products, and

furniture Paper and allied products Chemicals and allied product., Industrial chemicals Drugs and medicines Other chemicals Petroleum refining and extrac-

tion Rubber and plastics products Stone, clay, and glass products Primary metal industries Primary ferrous products Nonferrous and other metal

products Fabricated metal products

Industry c o d e n o .

I I

12

I2A

I2B 13 I3A

I3B 14

~4 A

I4B

I5

Indust ry name

Machinery, except electrical Electrical equipment and communication

Communication equipment and electronic components

Other electrical equipment Transportat ion equipment Motor vehicles and other

t ransporta t ion equipment Aircraft and parts Professional and scientific

ins t ruments Scientific and mechanical

measuring instruments Optical, surgical, photograph- ic, and other ins t ruments

Other manufacturing industries

T a b l e A.2 - - Conversions among the Codes

s i c code no. SITC code no.

20

Our code no.

2

3 4 5 5A

5B 5C 6

7 8

9 9A 9B

22 + 23 2 4 + 2 5 26 28 28x + 2 8 2

283

O (O4I + 0 4 3 + 044 + 045 + O5I ) + xxi + i i2

65 + 8 4 - - 6 5 L 6 243 + 63 + 82 64 + 25I 5 + 23 + 6 5 1 . 6 512 + 5x3 + 514 + 521 + 531 + 58

+ 23 + 6 5 1 . 6 54

2 8 - - ( 2 8 I + 2 8 2 +283) 29 30 32 33 331 +,332 333 + . . . + 3 3 9

515 + 53 z + 533 + 55 + 56 + 5 7 + 59 332 62 + 893 6 6 - - 6 6 7 67 + 68 67 68

c o n t i n u e d

U.S. Export Performance

Tab le A.2 c o n t i n u e d

Our code SIC code no. SITC code no. n o .

553

IO

II

12

12A

x2B

13 13A

13B

14 I4A

I4B

15

34 35 36 3 6 6 + 3 6 7 3 6 - - ( 3 6 6 + 367) 37 3 7 - - 3 7 2 372 38 38I + 382 3 8 - - ( 3 8 1 + 382) 21 + 27 + 31 + 39

69 7 I 72 + 89I.x + 891.2 724 7 2 - - 7 2 4

73 7 3 - - 7 3 4 734 86 + 8 9 1 - - ( 8 9 1 . 1 + 891.2 ) 861 8 6 - - 8 6 1 122 + 6I + 83 + 85 + 89

Table A.3 - - Rankings o/the Industries

Industry code no.

~.~ 1 2

~3 ~ ~4

5

~ 6

~ 7

~ 8 ~ I O

~ i5 ~ 9

~" 4

~ 2 ~ 3

Average

Industries ranked according to R & D intensity: average of

(R & D/S) in 1956---66

.09865

.09647

.05275

.0364 ~

.o3x83

.01794

.01686

.00847

.00697

.oo544

.00481

�9 oo367 .00175 .ooio5 .00088

.02550

Industry code no.

Industries ranked according to S & E

intensity: average of (S & E/TE) in x956---66

x3 .o6125 6 .o5562

x2 I .o5415 5 .04894

I4 .03285 II .02055

7 .01455

io ! .00732 8 .00640 9 .oo482

I5 .oo474

4 .oo398 I .00299 2 .00267 3 I .00084

Average .02145


T a b l e A. 4 - - Summary Results o/the Time-Series Regressions

Indus t ry groups

Signs of regression

coefficients

Number of s tat is t ical ly significant coefficients with thei r sign frequencies

"r~ =o 0 s

o

Group I industries, except # I3B (total number of regressions is 16)

Group II industr ies (total number of regressions is 12)

Group I l i industr ies (total number of regressions is 14)

Group IV industr ies (total number of regressions is 14)

Summ ary results of the cross- section regressions (total number of regressions is i2)

posit ive negat ive P/N ratio

positive negat ive P /N ratio

positive negat ive P /N ratio

positive negat ive P/N ratio

positive negat ive P/N ratio

8 6 6 4

1.33 1.5o

6 2 6 io

i.oo .20

4 I O I Z

�9 40 .oo

1o 3

4 3 2.50 i.oo

9 2

o o o o

9 6

I. 5

5 6

.83

4 9

.44

5 6

.83

I

- - ' - - 7

. I 4

I 0 �9 - -

5 2 , O O . - -

7 5

1.4 ~

9 5

1.8o

5 9

�9 56 , - -

4 ' - - '

7 -57 - -

c o

I 2

- - 2

o o I . O 0

- - I

- - . 0 0

- - 2

. 0 0

I I

c o

6

5 1 . 2 0

2

3 �9 67

2

7 .29

3 5

.5o

I

o o

g~

7 5

1.4 ~

4 6

.67

3 6

.5 o

2

5 �9 4 o

2

O

$

Z u s a m m e n f a s s u n g : Die Expor tentwicklung in der amerikanischen gewerblichen Wir tschaf t : Eine empirische Untersuchung. - - Die Unte r suchung wurde im Sinne des ~Neo-Teehnologie<*- Erkli~rungsansatzes der Hande l s s t ruk tu r durchgefBhrt. Sie iiberprtift empirisch die Dete rminan ten der Expor tentwicklung in der amerikanischen gewerblichen Wir tschaf t an einem allgemeinen Modell, das die wicht igs ten erkliirenden Variablen umfaBt. Dieses Modell wird mi t Hilfe der H a u p t k o m p o n e n t e n - Methode getestet, wobei sowohl Querschnit ts- als aueh Zei t re ihendaten (1956---1968) ftir zweistellige und dreistellige SIC-Nummern der Vereinigten Staa ten verwendet werden.

Zwei allgemeine Folgerungen k6nnen mi t relativer Sicherheit gezogen werden. Die ausschlieBliche oder auch hauptsiichliche Konzentra t ion der AuBenhandels- theorie auf Kosten-Preis-Faktoren, die in frt~heren Zeiten vielleicht gerechtfert igt werden konnte; wird in unserer Zeit, nach der Revolut ionierung des Verkehrs- und Nachrichtenwesens, des Managements , des Marketing und der industriei len Produk- tion, eigentlich unhaltbar . Obwohl die Ergebnisse dieser Studie noch keineswegs endgtiltig sind, zeigen sie doch, daB viel erreicht werden k6nnte, wenn ein groBer Tell


der A u f m e r k s a m k e i t N i c h t - P r e i s - F a k t o r e n g e w i d m e t wtirde. A u B e r d e m wird es schwierig, e inen A n s a t z m i t e i nem e inz igen B e s t i m m u n g s f a k t o r zu rech t fe r t igen , weil d ieser im g i ins t igs ten Fal l unvol ls t~indig ist , i m s c h l i m m s t e n Fal l a b e t e r n s t h a f t in die I r re f t ihren kann .

R d s u m d : L a p e r f o r m a n c e d ' e x p o r t a t i o n des E . U . d a n s les i n d u s t r i e s m a n u - fae tur i~res : U n e i n v e s t i g a t i o n empi r ique . - - L ' 6 t u d e s ' a p p r o c h e a u x exp l i ca t i ons ,neotdchnologiques* de la s t r uc t u r e du c o m m e r c e extdr ieur . I1 s ' ag i t d ' u n e exp lo r a t i on emp i r i que des d d t e r m i n a n t s de la p e r f o r m a n c e d ' e x p o r t a t i o n d a n s les i n d u s t r i e s m a n u f a c t u r i ~ r e s pa r u n mod~le assez gdndral i n c o r p o r a n t les va r i ab l e s exp l i ca t ives les p lus i m p o r t a n t s s et tes td en u t i l i s a n t l ' a n a l y s e des c o m p o s a n t e s p r inc ipa le s appl iqudes a u x sec t ions t r a n s v e r s a l e s auss i b i en q u ' a u x serids ch rono log iques (en c o u v r a n t la pdr iode de 1956---I968) des donndes de C U I en d e u x et t ro i s chiffres.

I1 y a d e u x conc lus ions gdndrales q u ' o n p e u t c o n s t a t e r avec u n e ce r t i tude re la t ive . P r e m i ~ r e m e n t la concen t r a t i on exc lus ive ou m ~ m e p r i m a i r e de la thdor ie du c o m m e r c e ex td r ieur su r les f ac teu r s de p r i x e t de coflt q u ' o n p o u r r a i t peu t -~ t re jus t i f ier a u p a r a - van t , d e v i e n t t o u t ~ fa i t i n t enab l e s u i v a n t no t r e t e m p s avec les rdvo lu t ions des c o m m u n i c a t i o n s , des t e c h n i q u e s de m a n a g e m e n t , des i n s t i t u t i o n s de commerc i a l i s a - t i on et de l ' o rgan i s a t i on industr ie l le . B ien que les r d su l t a t s de ce t t e d tude ne so ien t pa s conclusifs , ils i n d i q u e n t q u ' o n p e u t b i en gagne r en f a i s a n t b e a u c o u p de l ' a t t e n t i o n a u x fac teu r s non-pr ix . D e u x i ~ m e m e n t il d e v i e n t diffieile ~ r a t iona l i se r u n e a p p r o c h e de f ac t eu r u n i q u e qui es t i n c o m p l e t e d a n s le cas le p lu s f avo rab l e e t qu i p e u t ~tre dga ran te au pis aller.

R e s u m e n : E1 desempef lo de las e x p o r t a c i o n e s de i n d u s t r i a s m a n u f a c t u r e r a s e s t a d o u n i d e n s e s : U n a inves t i gac i6n empir ica . - - E1 e s tud io se real iz6 en el esp t r i tu de las exp l icac iones ~neo-tecnol6gicas~ de la e s t r u c t u r a del comercio . Es u n a exp lora - c i6n empfr ica de las d e t e r m i n a n t e s del desempef io de las expor t ac iones e s t a d o u n i d e n - ses en i n d u s t r i a s m a n u f a c t u r e r a s p e r m e d i o de u n mode lo b a s t a n t e general , que incorpora las va r iab les exp l i ca t ivas m ~ s i m p o r t a n t e s y que es s o m e t i d o a t e s t p e r med io del u s e del an~l is is de c o m p o n e n t e s p r inc ipa les con d a t e s de comerc io ex te r io r de E E U U (para el per iodo I 9 5 6 ~ I 9 6 8 ) a n ive l de dos d ig i tos de la clasif icaci6n i ndus t r i a l u n i f o r m e (SIC) en fo rma de cor te t r a n s v e r s a l y ser ies de t i empo . Se p u e d e n saca r dos conc lus iones genera les con r e l a t i va segur idad . P r imero , la exc lus iva o afin p r inc ipa l concen t r ac i6n de la teorfa del comerc io sobre fac tores de cos tos /precios , que ta l vez podr ia jus t i f icarse en u n per todo de t i e m p o an te r io r , se t o r n a v i r t u a l m e n t e insos ten ib le en nues t ro s t i e m p o s con revo luc iones en comun icac iones , t dcn icas gerenciales, i n s t i t uc iones de mercadeo y o rgan izac i6n indus t r i a l . A pe sa r de que los r e su l t ados de es te e s t ud i o no son c o m p l e t a m e n t e conc luyen te s , ellos i n d i c a n que se puede g a n a r m u c h o si se t rans f ie re u n a b u e n a p a r t e de a t e n c i 6 n a fac toros d i s t in tos det precio. Segundo, se t o r n a dificil r ac iona t iza r u n en foque en t ~ r m i n o s de u n solo factor , que en el me jo r de los cases es i n c o m p l e t e y en el pee r de los c a s e s puede ser s e r i a m e n t e engafioso.

u.s. export performance in manufacturing industries: an empirical investigation

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