Rajshekhar (Raj) G. Javalgi
CLEVELANDSTATEUNIVERSITYD. Steven White
UNIVERSITY OFMASSACHUSETTSDARTMOUTH
Oscar Lee
CLEVELANDSTATEUNIVERSITY
This study examines 20,204 manufacturers in a midwestern state to of imports of goods and services, and the trade balance of goods and services for the period 1960 through 1994.
determine whether firm characteristics significantly influence the
propen-sity to export. By examining census data, the researchers seek to address Historically, from 1891 through 1970, the United States enjoyed an unbroken string of trade surpluses (USDC, 1994).
the speculation that the lack of representativeness of previous studies has
skewed the interpretation of the influence of firm characteristics on export Interestingly, the first year of surplus for the service sector (1971) coincides with the first year of an over-all trade deficit
propensity. The variables examined are number of employees, total sales,
years in business, international trade activity (exporter/nonexporter), and a deficit in goods exports. Both sectors have retained their respective balance of trade orientations since 1971, with two
primary industrial classification, and firm ownership. The results of the
study indicate that the value of using firm characteristics to predict export exceptions: a $900 million goods surplus in 1973 and a $8.9 billion goods surplus in 1975.
behavior varies by industry. J BUSN RES2000. 47.217–228. 1999
Elsevier Science Inc. A closer examination of the data contained in Table 1
provides interesting insight. Since 1983, the average annual rate of growth in service exports is 10.75%. During the same period, the average annual growth rate in the exports of goods
I
n recent years, overseas business has become a matter of registers 8.87%. When comparing the percentage increase per necessity for U.S. firms of all sizes. Such factors as market year, the growth in services surpasses the growth in goods saturation and the trade deficit have fueled the need to exported every period with two exceptions: 1987 to 1988 and consider exporting as a desirable alternative for growth. Al- 1993 to 1994. Conversely, the average annual increase in the though the United States is still the world’s largest economy growth of goods imports equals 8.81% for the period 1983 and market (USDC, 1994), its trade deficit has grown dramati- through 1994, and the average increase in the import of ser-cally for the last two decades. In 1975, the U.S. share of world vices during the same period is 8.70%. On average, service trade was 15.4%. This declined to 12.3% in 1988 and 12.2% exports grew at an average annual rate in excess of three in 1991 (Jain, 1993). In 1992, the trade deficit exceeded $40 percentage points over the increase in imports; whereas, goods billion, and in 1993, it reached $75.7 billion. This change exports grew on average .06% faster than the increase in goods from market leader to market follower has “occurred more imports. The United States’ declining position in balance of quickly and completely than most Americans thought possi- trade indicates the need for its manufacturing firms to compete ble” (Serey, Lindsay, and Myers, 1989, p. 6). more effectively in the global market (Dertouzos, Thurow, U.S. exports account for relatively little of the country’s and Solow, 1989; Hill, Hitt, and Hoskisson, 1988; Kedia, Gross National Product (GNP) (12%). However, the relative 1993; Young, 1985). To address the trade deficit, the U.S. importance of foreign trade as a percentage of the GNP has government is renewing efforts to expand manufacturing ex-almost doubled in the past 20 years. Table 1 presents the ports (Kotabe and Czinkota, 1992).total volume of exports of goods and services, total volume The U.S. potential for export growth is colossal: over 85% of all U.S. manufacturers do not export. Therefore, a great opportunity exists to expand the international trade efforts of Address correspondence to D. Steven White, University of Massachusetts
Dartmouth, 285 Old Westport Rd., North Dartmouth, MA 02747-2300. the nations’s domestic manufacturing firms. In addition, the
Journal of Business Research 47, 217–228 (2000)
1999 Elsevier Science Inc. All rights reserved. ISSN 0148-2963/00/$–see front matter
Table 1. U.S. International Trade in Goods and Services Balance of Payments (BOP) Basis (Billions of U.S. Dollars)
Exports Imports Trade Balance
Year Total Goods Services Total Goods Services Total Goods Services
1960 25.9 19.7 6.3 22.4 14.8 7.7 3.5 4.9 (1.4)
1961 26.4 20.1 6.3 22.2 14.5 7.7 4.2 5.6 (1.4)
1962 27.7 20.8 6.9 24.4 16.3 8.1 3.4 4.5 (1.2)
1963 29.6 22.3 7.3 25.4 17.0 8.4 4.2 5.2 (1.0)
1964 33.3 25.5 7.8 27.3 18.7 8.6 6.0 6.8 (0.8)
1965 35.3 26.5 8.8 30.6 21.5 9.1 4.7 5.0 (0.3)
1966 38.9 29.3 9.6 36.0 25.5 10.5 2.9 3.8 (0.9) 1967 41.3 30.7 10.7 38.7 26.9 11.9 2.6 3.8 (1.2) 1968 45.5 33.6 11.9 45.3 33.0 12.3 0.3 0.6 (0.4) 1969 49.2 36.4 12.8 49.1 35.8 13.3 0.1 0.6 (0.5) 1970 56.6 42.5 14.2 54.4 39.9 14.5 2.3 2.6 (0.3) 1971 59.7 43.3 16.4 61.0 45.6 15.4 (1.3) (2.3) 1.0 1972 67.2 49.4 17.8 72.7 55.8 16.9 (5.4) (6.4) 1.0 1973 91.2 71.4 19.8 89.3 70.5 18.8 1.9 0.9 1.0 1974 120.9 98.3 22.6 125.2 103.8 21.4 (4.3) (5.5) 1.2 1975 132.6 107.1 25.5 120.2 98.2 22.0 12.4 8.9 3.5 1976 142.7 114.7 28.0 148.8 124.2 24.6 (6.1) (9.5) 3.4 1977 152.3 120.8 31.5 179.5 151.9 27.6 (27.2) (31.1) 3.8 1978 178.4 142.1 36.4 208.2 176.0 32.2 (29.8) (33.9) 4.2 1979 224.1 184.4 39.7 248.7 212.0 36.7 (24.6) (27.6) 3.0 1980 271.8 224.3 47.6 291.22 249.8 41.5 (19.4) (25.5) 6.1 1981 294.4 237.0 57.4 310.6 265.1 45.5 (16.2) (28.0) 11.9 1982 275.2 211.2 64.1 299.4 247.6 51.7 (24.2) (36.5) 12.3 1983 266.0 201.8 64.2 323.8 268.9 54.9 (57.8) (67.1) 9.3 1984 290.9 219.9 71.0 400.1 332.4 67.7 (109.2) (112.5) 3.3 1985 288.8 215.9 72.9 410.9 338.1 72.8 (122.1) (122.2) 0.1 1986 309.5 223.3 86.1 448.3 368.4 79.8 (138.8) (145.1) 6.3 1987 348.0 250.2 97.8 500.0 409.8 90.2 (152.0) (159.6) 7.6 1988 430.2 320.2 110.0 545.0 447.2 97.9 (114.8) (127.0) 12.1 1989 489.0 362.1 126.8 579.3 477.4 101.9 (90.3) (115.2) 24.9 1990 537.6 389.3 148.3 616.0 498.3 117.7 (78.4) (109.0) 30.7 1991 581.2 416.9 164.3 609.1 490.7 118.4 (27.9) (73.8) 45.9 1992 616.9 440.4 176.6 657.3 536.5 120.9 (40.4) (96.1) 55.7 1993 641.7 456.9 184.8 717.4 589.4 128.0 (75.7) (132.6) 56.9 1994 698.0 503.0 195.0 804.0 669.0 135.0 (107.0) (167.0) 60.0
Note:1. Compiled from official statistics of the U.S. Department of Commerce, Bureau of Economic Analysis. Data reflect all revisions through June 1995.
2. Balance of Payments (BOP) basis for goods reflects adjustments for timing, coverage, and valuation to the data compiled by the Census Bureau. The major adjustments concern: military trade of U.S. defense agencies, additional nonmonetary gold transactions, and inland freight in Canada and Mexico.
3. Goods valuation are F.A.S. for exports and Customs value for imports.
4. Source:National Trade Data Bank.
global market for manufactured goods is projected to grow have focused on a variety of possible antecedents—including firm characteristics—of export marketing. Much research on as more nations develop their manufacturing sectors
(Mittel-hauser, 1994). Approximately 51,000 U.S. firms export regu- firm-specific characteristics has been generated (c.f., Bonac-corsi, 1992), and at least three observations can be drawn larly, and about 87% of those employ fewer than 500 workers
(Jeannet and Hennessey, 1995), more indication that only a from these studies. meager percentage of U.S. export potential is used.
Addition-1. Little has been done to document the impact of firm ally, “eight out of ten new jobs created between 1985 and
characteristics on export propensity across industries. 1990 were in export-related industries. A $10 billion increase
2. Research designs, such as sampling procedures and in exports generates about 193,000 American jobs both
di-sample sizes employed, have restricted the generaliz-rectly and indigeneraliz-rectly” (Jain, 1993). Exports are not only a
ability of the findings. significant aspect of international business activity for the
3. The need for empirical studies focusing on a larger data nation, but also a major economic issue to be dealt with
bases (e.g., census data) is evident (c.f., Bonaccorsi, at the state level as well (Kotabe and Czinkota, 1992). For
1992; Calof, 1994). manufacturers in each state, global orientation has become a
matter of necessity. This study differs from previous efforts to examine the
the data used comprise a census of manufacturing firms. Fur- and Naor, 1987; Cavusgil and Nevin, 1981; Christensen, de Rocha, and Gertner, 1987; Gottko and McMahon, 1988; thermore, the study examines the difference in influence firm
characteristics have upon export propensity across 16 indus- Hirsch and Adar, 1974; Malleksadeh and Nahavandi, 1985; Terpstra and Yu, 1988); and a positive relationship between tries. The underlying motivation for undertaking this research
is to contribute to the export marketing literature by overcom- firm size and percentage of total export sales (Cavusgil, 1984b; Madsen, 1987; Reid, 1982). Culpan (1989) concluded that ing the limitations stated above.
smaller firms demonstrate less success in exporting than do medium or large firms. Other studies indicate that the size
Literature Review
correlation only exists to a certain level, beyond which therelationship fails (Czinkota and Johnston, 1983). Finally, some For the past two decades, research on the subject of exporting
studies find that firm size is not significantly related to propen-has been increasing. Topics studied vary widely; however,
sity to export (Diamantopoulos and Inglis, 1988; Hester, some common areas of focus include: obstacles or barriers to
1985). Czinkota and Johnston (1981) question the direction exporting (Alexandrides, 1971; Cavusgil and Nevin, 1981;
of causality with regard to firm size: Do exporting activities Rabino, 1980); factors influencing export performance (Aaby
increase size or does size lead to exporting? and Slater, 1989; Axinn, 1985; Cooper and Kleinschmidt,
Calof (1994), in his thorough investigation of the associa-1985; Dominquez and Sequeria, 1993; Koh, 1991);
organiza-tion of firm size to export behavior, brought to light several tional structuring and exporter profiles (Brasch, 1991; Burton
critical issues in the status of the research to date. One concern and Schlegelmilch, 1987; Cavusgil, Bilkey, and Tesar, 1979;
raised and addressed by his study is the generalizability of Cavusgil and Nevin, 1979; Diamantopoulos and Inglis, 1988);
the results of previous studies because of their small sample pre-identification criteria for potential exporters (Cavusgil,
size. Building on the earlier work of Bonaccorsi (1992), who Bilkey, and Tesar, 1979; Czinkota and Johnston, 1983); the
used an Italian national database of 8,810 companies, Calof development of a series of stages of export involvement based
investigated 14,072 Canadian firms and concluded that size on Rogers’ diffusion of innovation theory (Bilkey and Tesar,
may offer limited insight into a firm’s propensity to export. He 1977; Reid, 1981); and marketing mix issues of exporting
ends by questioning the generalizability of his study, however, firms (Hill and Still, 1984; Peebles, Ryans, and Vernon, 1977;
because of the bias of the database used, which contained Seifert and Ford, 1989). These studies no doubt contribute
information on Canada’s largest firms. Others have suggested to our understanding of export marketing both in the areas
that the conflicting empirical evidence regarding firm size may of industrial goods and consumer goods.
be attributable to variance in contextual factors, such as the The influence of firm characteristics on export potential
firm’s industry and market environments (Samiee and Walters, has also received much attention in the past (c.f., Bonaccorsi,
1990, p. 236). 1992; Calof, 1994; Delacroix, 1984; Dichtl, Liebold,
Kogel-Given the previous research efforts, our goals are threefold: mayr, and Muller, 1984; Kaynak and Kothari, 1984; O’Rourke,
(1) to examine the influence of firm characteristics on propen-1985; Reid, 1982). Mostly, researchers have investigated the
sity to export; (2) to address the concerns of Samiee and differences between exporters and nonexporters with regard
Walters (1990) by examining the relationship between the to readily identifiable firm characteristics. The objective of
variables investigated and industry type; and (3) to end the studies using the exporter/nonexporter dichotomy is to
de-debate over the contribution of firm characteristics to export velop a profile of characteristics that differentiate the categories
propensity by using data collected as part of a 1994 investiga-(Burton and Schlegelmilch, 1987; Cavusgil and Nevin, 1981;
tion of 20,204 Ohio manufacturing firms. Christensen, de Rocha, and Gertner, 1987; Yaprak, 1985).
The prevalent belief is that by understanding key differences
between exporters and nonexporters, a concentrated effort to
Hypotheses and Rationale
motivate and assist nonexporters into entering the globalmar-ket may undertaken. Consistent with Calof (1994), this study uses two dimensions
of firm size rather than one: number of employees and total Czinkota and Ursic (1991) report that the variables of firm
size and age have been the most closely scrutinized of the sales. Those seeking a logical argument as to why one should investigate multiple measures of firm size will find an excellent characteristics investigated. Internationalization requires
ap-propriate resources; therefore, firm size is an important pre- explanation in Calof ’s comprehensive examination of the theo-retical foundation of the export literature. The additional firm dictor of export propensity (Calof, 1994; Tookey, 1964).
Larger firms have a greater ability to expand resources and characteristics included in the analysis are years in business, international trade activity (export/nonexport), primary in-absorb risks than smaller ones and may have greater
bar-gaining power (Erramilli and Rao, 1993); and larger firms dustrial classification (by two-digit Standard Industrial Classi-fication (SIC) code), and firm ownership (private vs. public). have specialized managerial resources and can make use of
economies of scale (Samiee and Walters, 1991). The aggregate and industry-level hypotheses developed in the ensuing section, allow the researchers to investigate the impact Studies have identified a positive relationship between
pensity exists. Finally, Cooper and Kleinschmidt (1985)
con-Number of Employees
tend that the age of an exporting firm varies by its strategy. Numerous studies have measured firm size in terms of the
Firms identified by them as “world marketers” were signifi-number of employees (Bilkey and Tesar, 1987; Bonaccorsi,
cantly younger than firms guided by other strategies. Given 1992; Burton and Schlegelmilch, 1987; Cavusgil and Naor,
the mixed results of previous studies, the following hypotheses 1987; Hirsch, 1971; Holzmuller and Kasper, 1991; Kaynak
emerge. and Kothari, 1984; Lee and Yang, 1990; Madsen, 1989;
Malek-sadeh and Nahavandi, 1985; Mugler and Miesenbock, 1986; H3a: The average age of exporting firms will significantly Yang, Leone, and Alden, 1992). Overall, the results indicate differ from the average age of nonexporting firms. that exporting firms are larger in terms of number of
employ-H3b: Within each industry, as the age of the firm increases, ees than nonexporting firms (Keng and Jiuan, 1989). Kedia
the propensity to export increases. and Chhokar (1986) go so far as to proclaim that most
small-and medium-sized firms do not export. Differences in
em-Firm Ownership
ployee size within size groups (small, medium, and large)Few studies investigating propensity to export have included investigated are also documented. Kaynak and Kothari (1984)
a firm ownership variable, and those that do measure owner-found that small and medium-sized businesses participating
ship in terms of foreign versus domestic (Keng and Jiuan, in international trade have more employees than businesses
1989). The explanation offered is that foreign-owned firms within the same size categories who do not participate. The
are more likely to send goods out of the country, perhaps mixed evidence suggest the following.
back to company headquarters for distribution or inclusion
H1a: The average number of employees working for
ex-in other products. Because the goal of delex-ineatex-ing exporter/ porting firms will significantly differ from the average nonexporter characteristics is to identify, encourage, and assist number of employees working for nonexporting potential manufacturers who are not yet exporting to do so,
firms. we believe that measuring firm ownership in terms of private
versus public makes more intuitive sense. Yang, Leone, and
H1b: Within each industry, as the number of employees
Alden (1992) included private versus public ownership in increases, the propensity to export will increase.
their analysis, but found the difference not to be significant. It is generally believed, however, that the pressure of publicly
Total Sales
held corporations to maximize shareholder wealth will lead Studies measuring firm size as the sales level of firm (Calof,
these businesses to explore new markets more readily than 1994; Cavusgil, 1984a; Cavusgil and Nevin, 1981;
Chris-their privately held counterparts. Thus, the mixed evidence tensen, de Rocha, and Gertner, 1987; Czinkota and Johnston,
leads us to hypothesize (H4ain null form) the following. 1983; Hester, 1985; Holden, 1986; Kaynak and Kothari, 1984;
Keng and Jiuan, 1989; Lall and Kumar, 1981; Lee and Yang, H4a: The ownership structure of exporting firms will not 1990; Madsen, 1989; Maleksadeh and Nahavandi, 1985; significantly differ from the ownership structure of Yang, Leone, and Alden, 1992) indicate that firms with higher nonexporting firms.
sales are more likely to engage in exporting activity. Thus, it
H4b: Within each industry, being publicly held will posi-is prudent to hypothesize the following.
tively influence a firm’s propensity to export.
H2a: The sales level of exporting firms will significantly
differ form the sales level of nonexporting firms.
Industry Type
Samiee and Walters (1991) investigated the differences
be-H2b: Within each industry, as the level of sales increases,
tween regular and sporadic exporters based upon 2-digit SIC the propensity to export will increase.
codes and found no difference in distribution between the two groups. Bonaccorsi (1992) posits that industry type is an
Age of Firm
intervening mediator in the relationship between firm size Czinkota and Ursic (1991) report that much research exists
and export propensity. However, the authors found no re-illustrating the contribution of firm age to export propensity.
search that empirically investigated exporter/nonexporter dif-Previous studies indicate that younger firms exhibit more
ferences based on industry type. Because the sampling frame interest in foreign markets than older, established firms
(Kay-for the current study is a census of Ohio manufacturers, the nak and Kothari, 1984; Kirpalani and MacIntosh, 1980; Lee
opportunity to identify differences between industries presents and Brasch, 1978; Ursic and Czinkota, 1981). Conversely,
itself. Based upon the lack of research in this area, it is prudent evidence also suggests that older firms are more likely to
to hypothesize the following. export than younger firms (Lee and Yang, 1990; Welch and
Wiedersheim-Paul, 1978). Diamantopoulos and Inglis (1988) H5a: The number of exporting and nonexporting firms will differ by industry type.
pro-H5b: The importance of the variables in determining ex- gorically by Harris Publishing. Classification follows a port propensity will differ by industry type. topology similar to that of Cavusgil and Kirpalani (1993): small (less than $4.9 million), medium ($5– 49.9 million) and large ($50 million and over).
Population Under Study
3. Export—measured dichotomously: yes or no The midwestern state of Ohio has become increasingly export 4. Age—The age of the firm is a continuous variable. oriented in recent years. In fact, it is the United States’ third 5. Ownership—measured categorically: private or public largest exporter of manufactured goods, exceeded only by 6. Industry Type—This variable is categorical and the clas-California and Texas. The state’s manufacturing exports were sification by the first 2-digits of the manufacturer’s pri-worth $21.6 billion in 1994, an increase of 182% since 1987. mary SIC code as proposed by Samiee and Walters A total of 784,435 manufacturing jobs are accounted for by (1991).
firms in the state who export. Over 50% of the firms that export employ 36 people or less, and over 74% of the state’s
exporters have fewer than 100 employees. Major trade part-
Methodology
ners for the state, in descending order of dollar volume, areTo investigate the aggregate and industry-level differences in Canada, France, Japan, the United Kingdom, Mexico, and
the variables, multiple statistical methodologies are enlisted. Germany. The state proactively supports the export of
manu-Differences in continuous variables are tested with an initial factured goods.
analysis of variance. Similarly, differences in categorical vari-The data used are part of Harris Publishing’s 1994 Ohio
ables are tested by means of a Chi-square analysis. In addition
Industrial Directory. Harris collects information annually, with
to descriptive measures, the multivariate technique of logit is support from the state’s Department of Development,
regard-used to test the hypothesized relationships. Logistic analysis is ing each firm’s products, size, location, ownership, etc. The
one of the most widely used statistical techniques for analyzing 1994 directory provided information on more than 20,000
binary dependent variables, such as export (y51) and don’t manufacturers. In 1994, the total number of manufacturing
export (y50). A brief discussion ensues in which the cumula-firms in the United States was 378,000, according to the
tive logistic probability function is briefly explained. Industrial Technology Institute. Ohio, therefore, accounts for
Logit is known to be robust. The logit model used in this just over 5% of the manufacturing firms in the country. The
study is based on the cumulative logistic probability function: manufacturing data of a state so actively involved in exporting
allows one to develop an accurate portrayal of the impact
Pi5 e
ojbjXij
11eojbjXij each of the variables investigated has upon the propensity to
export. By doing so, a clearer picture of the relationship be-tween firm characteristics and exporting should emerge.
Maximum likelihood procedures may be applied to the logit model written directly as an equation of the form:
Industries Studied
Pi5F(
o
jbjXij) Sixteen different industrial classifications were examined in
this study: Food Products; Apparel; Lumber; Paper; Printing;
Chemical; Rubber; Stone; Primary Metals; Fabricated Metals; where F(.) is specified as the cumulative logistic function. Machinery; Electrical; Transportation; Measuring Devices; This definition has been frequently used in cases where the Miscellaneous Manufacturing; and a catchall category labeled dependent variable is binary.
Other. The basis for categorizing the industries in this manner, In the present study, the logit model shown above is used that is, consistent with their 2-digit SIC codes, is that each for two purposes:
corresponding category contains over 400 businesses. The 12
1. to determine the over-all contribution of independent or more SIC codes subsumed by the category Other did not
variables (firm characteristics) to export propensity, contain enough observations individually to allow for
mean-which is defined in this study as the likelihood of ex-ingful statistical investigation.
porting as a change in predictor variables (firm charac-teristics) used in the logit model represented in the
Component Measures
mathematical form above; and2. to statistically test the pattern of logit coefficients by To operationalize the hypotheses, variable measurement
con-industry type. To accomplish this, the following test sisted of the following.
statistic is used: 1. Number of Employees—the number of employees per
firm, a continuous variable 22{L(*P)2[
o
kj51 L(*j)]}
cate-Table 2. Aggregate Level Analysis Exporters versus Nonexporters
Export Nonexport
Variables n520,204 n56,460(31.97%) n513,744(68.03%) Significance
Employee: (mean) 121.43 33.86 t522.22,p50.000 Sales:
Low ($4.9 million or less) 3564 (23.2%) 11791 (76.8%) Medium ($5 to 49.9 million) 2418 (57.9%) 1757 (42.1%)
High ($50 million1) 478 (70.9%) 196 (29.1%) x252303.97,p50.000
Age of the Firm: (mean) 38.46 30.86 t518.59,p50.000 Ownership:
Private 5679 (30.0%) 13279 (70.0%)
Public 781 (62.7%) 465 (37.3%) x25575.65,p50.000
Industry:
Food products (SIC 2000) n51010 146 (14.5%) 864 (85.5%) Apparel (SIC2300) n5400 88 (22.0%) 312 (78.0%) Lumber (SIC 2400) n5897 130 (14.5%) 767 (85.5%) Paper (SIC 2600) n5441 150 (34.0%) 291 (66.0%) Printing (SIC 2700) n52437 193 ( 7.9%) 2244 (92.1%) Chemical (SIC 2800) n5872 432 (49.5%) 440 (50.5%) Rubber (SIC 3000) n51115 593 (53.2%) 522 (46.8%) Stone, clay and glass (SIC 3200) n5975 260 (26.7%) 715 (73.3%) Primary metals (SIC 3300) n5737 320 (43.4%) 417 (56.6%) Fabricated metals (SIC 3400) n52786 1020 (36.6%) 1766 (63.4%) Machinery (SIC 3500) n54205 1557 (37.0%) 2648 (63.0%) Electrical (SIC 3600) n5755 418 (55.4%) 337 (44.6%) Transportation (SIC 3700) n5532 258 (48.5%) 274 (51.5%) Measure/control dev. (SIC 3800) n5547 311 (56.9%) 236 (43.1%) Misc. manufacturing (SIC 3900) n5854 216 (25.3%) 638 (74.7%)
Other n51641 368 (22.4%) 1273 (77.6%) x251922.93,p50.000 where L(*P) represents the likelihood function of the
Number of Employees
pooled sample of subsamples of all of the industries As hypothesized in H1a, the average number of employees studied (k5 16), and L(*j) represents the likelihood
working for firms who export (121.43) differs significantly function for thejthindustry.
(p,0.000) when contrasted with the average number work-ing for nonexporters (33.86). Across all manufacturwork-ing indus-Because the logit model provides the likelihood function
tries, exporters average more than 3.5 times as many employ-separately for all industries, it is easy to compute the pooled
ees as nonexporters. Thus, the results support the earlier
L(*P). The test statistic shown above is asymptotically
distrib-findings of Keng and Jiuan (1989). At the aggregate level, uted as chi-square withk-degrees of freedom, wherekis the
based on the logit model, as the number of employees in-number of parameters in the logit model (see Chapman and
creases, the propensity to export also increases, as evidenced Staelin, 1982).
in Table 3. However, the importance of the number of employ-As a refresher, in order to interpret the individual beta
ees in predicting export propensity within each industry var-coefficients for each variable in each industry, one must think
ies.H1bis true with two exceptions: an increase in the number of its impact on the odds ratio (exports propensity). A positive
of employees does not increase a firm’s export propensity in parameter indicates an increase in the odds and negative
indi-the apparel industry (SIC 23) and in indi-the lumber industry cator shows a decrease. Therefore, beta coefficients represent
(SIC 24). the odds of increasing or decreasing a firm’s propensity to
export depending on the magnitude and sign of the predictor.
Total Sales
As evidenced in Table 2, the level of a firm’s sales is
signifi-Discussion
cantly related to the export versus nonexport variable (p,0.000). To testH2asales categories of exporters and nonex-Table 2 presents the aggregate level analysis of exporters versus
nonexporters. Almost one-third (31.97%) of all manufacturers porters were examined using a chi-square analysis. Manufac-turers with sales of $4.9 million or less are more likely not in the state participate in export activity. To understand better
the contribution of each variable to a firm’s export propensity, to export (76.8 versus 23.2%). This changes when total sales exceed $5 million, where the number of firms within this aggregate level results are compared and contrasted with
Table 3. Propensity to Export as Indicated by Logistic Regression
Food
Products Apparel Lumber Paper Printing Chemical Rubber
Variable Total SIC 20 SIC 23 SIC 24 SIC 26 SIC 27 SIC 28 SIC 30
Employee
SE 0.0006* 0.0012 20.0007 20.007 0.0019 0.0005 0.00005 0.0017 0.0001 0.0007 0.0013 0.0011 0.0014 0.0008 0.0005 0.0009 Sales ($)
,4.9 mm
b 21.3872* 21.5681* 23.4256 23.2594* 21.186 22.2693* 21.9328* 20.5521 SE 0.1192 0.3914 1.7969 1.1246 0.6409 0.8593 0.3908 0.5236 5–49.9 mm
b 20.0232 20.2917 21.7372 21.9349 20.3883 20.5526 20.8479* 0.0638 SE 0.1134 0.3454 1.6672 1.0791 0.5621 0.7900 0.3782 0.4683
.50 mm
b 0.0711 20.9855* 1.5742 1.0707 20.3363 20.5631 1.0712* 20.1537 SE 0.1219 0.3841 1.8133 1.1429 0.6484 0.8661 0.3947 0.5445 Age
b 0.0041* 20.0012 0.0098* 0.0063 0.0055 20.0002 0.0063* 0.0183* SE 0.0006 0.0031 0.0043 0.0039 0.0037 0.0022 0.0027 0.0039 Owner
b 0.4631* 0.2852 20.2897 0.2424 0.3664 1.0398* 0.3758 0.1253 SE 0.0713 0.2841 1.5531 0.8036 0.3143 0.3363 0.2374 0.2628
x2 2293.80 83.42 36.26 41.78 49.67 130.94 111.72 103.18
Sig. 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Hit ratio 72.53 85.27 77.40 85.78 67.29 92.05 66.03 62.73
Stone Measure/
Clay and Primary Fabricate Control Misc
Glass Metals Metals Machinery Electrical Transport Devices Manufac
Variable SIC 32 SIC 33 SIC 34 SIC 35 SIC 36 SIC 37 SIC 38 SIC 39 Other
Employee
SE 0.0058* 0.0002 0.0004 0.0039* 0.0005 0.00009 0.0061* 0.0355* 0.0004 0.0018 0.0003 0.0004 0.0012 0.0007 0.0001 0.0025 0.0057 0.0003 Sales ($)
,4.9 mm
b 20.5247 21.3010* 20.5968 20.8511 20.7884 20.7677* 0.9494 10.1492* 21.1953* SE 0.8322 0.3918 0.3852 0.5839 0.6192 0.3729 1.2413 3.4434 0.3902 5–49.9 mm
b 0.4394 20.2770 0.6565 0.9200 0.4659 0.2047 1.6229 9.3099* 20.0275 SE 0.7551 0.3774 0.3684 0.5450 0.5755 0.3549 1.1013 3.2666 0.3961
.50 mm
b 21.2505 0.1810 20.7913* 20.5761 0.2383 20.1764 21.1816 211.9497* 20.2650 SE 0.8474 0.4062 0.3941 0.5943 0.6339 0.3728 1.2840 3.4771 0.4026 Age
b 0.0049 0.0081* 0.0141* 0.0173* 0.0129* 0.104* 0.0040 0.0012 20.0006 SE 0.0028 0.0029 0.0018 0.0017 0.0038 0.0044 0.0047 0.0034 0.0023 Owner
b 0.6406 0.3694 0.8592* 0.1967 0.3354 0.8184* 0.6413 0.4727 20.0883 SE 0.3718 0.3150 0.2297 0.2364 0.3401 0.3221 0.4643 0.7111 0.2352
x2 153.73 78.59 346.52 790.09 100.62 66.06 65.01 124.75 72.14
Sig. 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Hit ratio 78.51 65.87 69.70 72.70 64.57 66.28 65.67 77.90 76.90
* Denotes significance beyond the .05 level.
who do not (42.1%). Manufacturers with sales of $50 million export propensity, at the aggregate level, the category of low sales is significant. As evidenced by the beta coefficient gener-and over are much more likely to export than not. Therefore,
based upon the resultant chi-square statistic ofx252303.97, ated by the logit model, at the aggregate level, low sales reduces
a firm’s propensity to export. At the industry level, an increase
in sales level only increases export propensity (H2b) in five
Industry-Level Implications
of the 16 industries: Apparel (SIC 23), Lumber (SIC 24),Not only do firm characteristics differ in importance between Paper (SIC 26), Chemical (SIC 28), and Primary Metals (SIC
industries when delineating exporters from nonexporters, the 33). It should be noted, however, that the use of categorical
ability of researchers to predict correctly export behavior based data when determining the effect of total sales on export
upon these characteristics varies by industry. To address the propensity may not adequately reflect the true nature of the
concerns of Samiee and Walters (1991) with regard to the relationship between the two.
effect industry type has on exporting, the researchers compiled summary statistics per 2-digit SIC code. Table 4 presents the
Age of Firm
variable differences for each of the manufacturing industry The findings of the study provide support for H3a. At the
categories. The ensuing section is dedicated to describing aggregate level, exporting firms are significantly older than
within industry differences among exporting firms, all of nonexporting firms (38.46 vs. 30.86;t5 18.59;p,.000).
which are significant (p5 0.0000). Therefore, with respect to manufacturing, this study supports
the results of earlier researchers such as Lee and Yang (1990)
Food Products (SIC 20)
and Welch and Wiedersheim-Paul (1978). In examiningex-Exporting firms employ more than three times as many people port propensity at the aggregate level, as the age of the firm
than nonexporting firms in this industry. Firms with sales increases, its propensity to export also increases. Within each
levels under $4.9 million rarely export. Only 40% of firms with industry, an increase in firm age increases its propensity to
sales over $50 million export. The average age of exporting and export (H3b) in 14 of the 16 industries. The two exceptions
nonexporting firms in this industry does not differ signifi-are Food Products (SIC 20) and Printing (SIC 27).
cantly. Only 31.8% of the publicly held corporations export, one of the lowest percentages of all industries. The variable
Firm Ownership
with the greatest impact in predicting export propensity inExporting activity differs significantly by firm ownership cate- this industry is Sales. Firms with either low sales or high gory (H4a). Privately owned firms are less likely to export sales are significantly less likely to export than are firms with than publicly owned firms. More than 60% of the publicly medium sales.
owned manufacturers in this study reported some level of
export activity. At the aggregate level, public ownership of a
Apparel (SIC 23)
firm increases the propensity that the firm will export. This Three times as many people are employed in exporting firms relationship is also true, as inH4b, for 15 of the 16 industries in SIC 23 as are employed in nonexporting firms. More than investigated, the exception being the Apparel industry 83% of the manufacturers with sales over $50 million export.
(SIC 23). Exporting firms are, on average, 12 years older than
nonex-porting firms. One-third of the publicly owned manufacturers
Industry Type
export. The variable with the greatest impact in predictingexport propensity in SIC 23 is Age: an increase in firm age
H5ais also accepted. Exporters and nonexporters differ
signifi-leads to an increase in export propensity,ceteris paribus. cantly by industry type. Manufacturers producing Measuring,
Analyzing, and Controlling Instruments (SIC 3800) are the
Lumber (SIC 24)
most likely to export, followed by producers of Electrical andElectronic Machinery, Equipment and Supplies (SIC 3600), More than two times as many people are employed in ex-and Rubber ex-and Miscellaneous Plastic Products (SIC 3000). porting firms than in nonexporting firms in this industrial Industries least likely to export include Printing, Publishing classification. Firms with sales of $50 million or more are the and Allied Products (SIC 2700), Food and Kindred Products most likely to export. Exporting firms are, on average, 6 years (SIC 2000), and Lumber and Wood Products Excluding Furni- older than nonexporting firms. One-third of publicly owned
ture (SIC 2400). firms in SIC 24 export. The variable with the greatest
explana-Furthermore, to investigateH5band test for differences in tory power in this SIC is Low Sales. Firms with sales of less the pattern of logit coefficients across industries, the statistic than $US4.9 million are significantly less likely to export than
are firms in the other two sales categories. 22{L(*P)2 [
o
k
j51 L(*j)]}
Paper (SIC 26)
Table 4. Industry-Level Analysis Exporters versus Nonexporters
Stone,
Food Clay, and
Products Apparel Lumber Paper Printing Chemical Rubber Glass
SIC 20 SIC 23 SIC 24 SIC 26 SIC 27 SIC 28 SIC 30 SIC 32
Variable n51010 n5400 n5897 n5441 n52437 n5872 n51115 n5975
Employee 162.73 99.3 54.69 141.91 109.76 117.37 124.97 123.61 (46.73) (30.91) (24.54) (53.25) (21.96) (40.37) (53.11) (24.70) Sales ($)
,4.9 mm 40 60 92 46 122 154 275 136
(Low) 6.6% 17.1% 11.7% 20.7% 5.6% 34.2% 42.5% 18.0% (567) (290) (697) (176) (2078) (297) (372) (620) (93.4%) (82.9%) (88.3%) (79.3%) (94.4%) (65.8%) (57.5%) (82.0%)
5–49.9 mm 74 23 33 84 60 209 277 99
(Medium) 22.9% 52.3% 32.7% 44.0% 27.5% 62.2% 66.3% 52.1% (249) (21) (68) (107) (158) (127) (141) (91) (77.1%) (47.7%) (67.3%) (56.0%) (72.5%) (37.8%) (33.7%) (47.9%)
.50 mm 32 5 5 20 11 69 41 25
(High) 40.0% 83.3% 71.4% 71.4% 57.9% 81.2% 82.0% 86.2%
(48) (1) (2) (8) (8) (16) (9) (4)
(60.0%) (16.7%) (28.6%) (28.6%) (42.1%) (18.8%) (18.0%) (13.8%) Age of 45.47 42.77 34.03 43.38 47.53 43.06 31.13 46.70
the Firm (42.92) (30.52) (27.82) (31.76) (35.15) (33.36) (22.59) (38.01) Owner
Private 118 87 127 113 172 339 520 226
12.8% 21.9% 14.3% 30.1% 7.2% 45.8% 51.4% 24.5% (804) (310) (761) (263) (2204) (401) (492) (698) (87.2%) (78.1%) (85.7%) (69.9%) (92.8%) (54.2%) (48.6%) (75.5%)
Public 28 1 3 37 21 93 73 34
31.8% 33.3% 33.3% 56.9% 34.4% 70.5% 70.9% 66.7%
(60) (2) (6) (28) (40) (39) (30) (17)
(68.2%) (66.7%) (66.7%) (43.1%) (65.6%) (29.6%) (29.1%) (33.3%)
Measure/
Primary Fabricated Control Misc.
Metals Metals Machinery Electrical Transport Devices Manufac
SIC 33 SIC 34 SIC 35 SIC 36 SIC 37 SIC 38 SIC 39 Other
Variable n5737 n52786 n54205 n5755 n5532 n5547 n5854 n51641
Employee 232.19 91.19 93.99 140.69 367.81 104.48 59.57 88.85 (73.01) (34.92) (17.76) (50.82) (130.33) (24.98) (12.91) (49.30) Sales ($)
,4.9 mm 116 602 1022 218 80 195 163 243
(Low) 30.1% 28.3% 28.8% 43.8% 32.4% 48.2% 21.0% 18.01% (269) (1526) (2526) (280) (167) (210) (613) (1103) (69.9%) (71.7%) (71.2%) (56.2%) (67.6%) (51.8%) (79.0%) (81.9%)
5–49.9 mm 158 378 474 166 130 95 49 109
(Medium) 55.2% 63.3% 80.3% 77.9% 60.5% 79.8% 67.1% 41.8% (128) (219) (116) (47) (85) (24) (24) (152) (44.8%) (36.7%) (19.7%) (22.1%) (39.5%) (20.2%) (32.9%) (58.2%)
.50 mm 46 40 61 34 48 21 4 16
(High) 69.7% 65.6% 91.0% 77.3% 68.6% 91.3% 80.0% 47.1%
(20) (21) (6) (10) (22) (2) (1) (18)
(30.3%) (34.4%) (9.0%) (22.7%) (31.4%) (8.7%) (20.0%) (52.9%) Age of 43.01 41.04 37.39 35.15 34.13 30.92 39.32 36.19
the Firm (34.27) (29.75) (24.65) (23.76) (24.90) (25.19) (31.22) (33.01) Owner
Private 276 925 1422 351 194 270 205 334
41.2% 34.8% 35.3% 52.5% 43.3% 54.1% 24.5% 22.1% (394) (1731) (2607) (317) (254) (229) (633) (1181) (58.8%) (65.2%) (64.7%) (47.5%) (56.7%) (45.9%) (75.5%) (77.9%)
Public 44 95 135 67 64 41 11 34
65.7% 73.1% 76.7% 77.0% 76.2% 85.4% 68.8% 27.0%
(23) (35) (41) (20) (20) (7) (5) (92)
(34.3%) (26.9%) (23.3%) (23.0%) (23.8%) (14.6%) (31.2%) (73.0%)
export. The hit ratio for the logistic function in this SIC is data also exist. Would the results differ if the sales variable were collected as a continuous rather than as a categorical only 67.3%, and no one variable in the equation is significant.
This may indicate that variables other than the four firm variable? And, although the sampling frame consists of a cen-sus of manufacturing firms, one must question whether the characteristics examined for this study are better suited for
predicting export propensity in SIC 26. export behavior of firms in a midwestern state are generalizable to firms in other states or countries. These are both excellent The remaining 12 classifications may be interpreted in a
similar manner. Of the SIC codes not discussed, Printing questions and should serve as a call for future research to determine whether the results of this study are typical or (SIC 27) has the highest hit ratio (92%) when determining
export propensity and the lowest percentage of exporters in atypical. By thoroughly understanding the influence firm char-acteristics have upon the export propensity of divergent indus-any industry (7.9) as shown in Table 2. What becomes
appar-ent is the usefulness of firm characteristics in correctly pre- tries, future researchers can better focus their investigations. dicting nonexporters rather than in predicting exports for
The authors thank John K. Ryans Jr., Hans Muhlbacher, and three anonymous
certain industry types.
reviewers for their insightful and constructive comments on earlier versions of this manuscript.
Conclusions
This study contributes to the literature on exporting by testing
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