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Correlations between industrial demands (direct and total) for communications and transportation in the U.S. economy 1947–1997

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Abstract

Using input–output (I–O) accounts provided by the U.S. Department of Commerce, this study investigates the aggregate relationships between the transportation and communications inputs demanded (directly and in total) by all industries in the U.S., and compares the results across time. We analyzed five pairs of Spearman correlations of transportation and communications demands (utilities, manufacturing, and overall) using the direct and total coefficient tables from the ten benchmark input–output years spanning 1947 to 1997. To correctly represent the overall economy-wide relationship, each industry (direct table) or commodity (total table) in the correlation was weighted proportionately to the monetary value of its contribution to the U.S. economy. In the analysis using direct I–O coefficients, we found a pattern of predominant complementarity between transportation and communications manufacturing, and substitution between transportation and communications utilities. There are intriguing indications, however, of a shift from substitution to complementarity in the latter case, beginning around 1987. In the analysis using total I–O coefficients, we found a pattern of complementarity for all years between transportation and communications manufacturing, and a pattern changing from substitution to complementarity for the remaining four pairs (transportation manufacturing and communications utilities; transportation utilities and communications manufacturing; the utilities pair; and the overall pair). Thus, from the industrial perspective (which constitutes a sizable proportion of the total demand for communications and transportation), it is not realistic in modern times to expect telecommunications to substitute for travel. Nevertheless, further research is needed into the specific causes of the observed shift from substitution to complementarity, and current trends should continue to be monitored for any changes.

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Notes

  1. Input–output analysis is an analytical framework and an economic tool to describe the interdependence of various industries in an economy. In the decades since Wassily Leontief (1936) introduced I–O analysis and conducted path-breaking research with it (Leontief 1951), I–O analysis has become widely used as a quantitative model not only in planning processes (Sand 1988; Szymer 1986), but also in policy design (Baumol and Wolff 1994).

  2. Plaut notes that although this is not the classical economic definition of complementarity, it is similar in concept.

  3. The five tables are (1) make, (2) use, (3) commodity-by-industry direct requirements (the input coefficient matrix), (4) commodity-by-commodity total requirements, and (5) industry-by-commodity total requirements. Industries are viewed as making and using commodities, which can be purchased by consumers.

  4. “Noncomparable imports include imported services that are not commercially produced in the United States, and goods and services that are produced abroad and used abroad by U.S. residents” (Planting and Kuhbach 2001, p. 51).

  5. “Scrap is a secondary product of many industries, and used goods are sales and purchases typically between final users. Industry output is zero because there is no primary producing industry” (Planting and Kuhbach 2001, p. 51).

  6. “The commodity entries include adjustments among personal consumption expenditures (PCE) and government expenditures to eliminate counting the expenditures by foreign residents in both exports and PCE or government expenditures” (Planting and Kuhbach 2001, p. 51).

  7. In 1997, the classification of industries was changed to a format based on the North American Industry Classification System (NAICS). Prior to that year, the Standard Industrial Classification (SIC) system was used as the basis. For this study, we modified the NAICS to make it as consistent as possible with the SIC system of previous benchmark years.

  8. We considered including the industry category titled “Computer and Data Processing Services” (SIC 737) as one of our communications-related categories in the analysis. But we chose not to do so, for three reasons. First, that category lies in an entirely different top-level industry group (#8, services) than do the others we analyze (#4, manufacturing and #5, utilities). Second, the SIC 737 category is not uniform across the 10 benchmark tables (only available as a sub-category under “Business and Professional Services, except Medical” (industry #73) in 1972 1977, and 1982). Third, correlation analyses (on the most disaggregate case) defining “Communications Utilities” (#14, see Table 2) to include industry #73A do not show very different results compared to the original ones.

    Table 2 Numbers of significant (p = 0.2 and p = 0.1) positive and negative spearman correlations
    Full size table

  9. This category consists of for-hire transportation services that are offered by transportation companies (e.g., railroads, trucking companies, and air carriers) to industries. However, transportation services are also rendered in-house (own-account), and in the benchmark I–O accounts these are included under the own-industry inputs demanded by either the sending or the receiving industry, not individually classified (US DOT 1999). To provide a more comprehensive measure of the requirement for transportation services (both for-hire and in-house), the Bureau of Transportation Statistics (BTS) of the U.S. Department of Transportation and the Bureau of Economic Analysis (BEA) of the U.S. Department of Commerce jointly developed the Transportation Satellite Accounts (TSAs). Thus, TSAs include a separate in-house transportation services sector, category #65G (data available at http://www.bea.gov/industry/index.htm#satellite, accessed on June 4, 2007). Unfortunately, however, the TSAs were published only for two years (1992, a benchmark year, and 1996, a non-benchmark year), and discontinued thereafter. We conducted the 1992 analysis for both the benchmark accounts and the TSAs (in which we included category #65G among the components of category #13, Transportation Utilities); the TSA results are described in Note 11 below.

  10. In actuality, although ∑ J j=1 IECBW j  = J,  ∑ J j=1  ECBW j may not equal J exactly, due to round-off differences. For simplicity of exposition, however, we use the same notation for both.

  11. As mentioned in Note 9 describing the transportation satellite accounts (TSAs), we also conducted the correlation analyses using the direct and total TSAs of 1992. Comparing the direct results using the TSAs to our results using the benchmark accounts, we see stronger complementarity for the TSAs, with more significant values for the “all” pair (30 × 31). The transportation utilities and communications manufacturing pair (13 × 11) shows a sign change from negative to positive (though small in magnitude in both cases), and the utilities pair (13 × 14) exhibits an increased magnitude of correlation and significance level (p = .114)—showing stronger complementarity. For the remaining two pairs (10 × 11 and 10 × 14), the results from the benchmark I–O accounts and the TSAs show similar complementarity (though slightly weaker for the TSAs). With respect to the total results using the TSAs, the relationship is uniformly complementarity across all pairs, with correlations that are quite similar to those of the benchmark I–O accounts for the three pairs 10 × 11,  10 × 14, and 30 × 31; a bit lower for the 13 × 11 pair; and a bit higher for the 13 × 14 pair. The specific values are listed here, where the first number of each pair is the correlation and the second is the significance value: i) Direct: (10 × 11: 0.115, 0.283) (10 × 14: 0.129, 0.227) (13 × 11: 0.082, 0.442) (13 × 14: 0.169, 0.114) (30 × 31: 0.111, 0.300); ii) Total: (10 × 11: 0.259, 0.014) (10 × 14: 0.062, 0.564) (13 × 11: 0.190, 0.075) (13 × 14: 0.179, 0.093) (30 × 31: 0.158, 0.139). A reviewer suggested that the benchmark results might underestimate a complementarity effect, due to the omission of in-house transportation expenditures from the transportation utilities category. That is clearly true for the direct results, where the two correlations involving transportation utilities (category #13, the one most directly affected by the separate identification of in-house transportation) increase the most for the TSAs compared to the benchmark accounts. The total results are more mixed, but in any case the qualitative finding of the predominance of complementarity in 1992 is similar for both sets of accounts.

  12. The results are substantively different for the unweighted correlation analysis, more strongly supporting a substitution relationship. Specifically, there are a few more significant negative correlations (18) in the unweighted analysis than in the weighted analysis (15), and considerably fewer significant positive correlations (6) in the unweighted analysis than in the weighted analysis (15). In the unweighted analysis, the correlations for the 10 × 14  13 × 14, and 30 × 31 pairs are all negative or essentially zero, and for the 13 × 11 pair, only the final-year (1997) correlation is positive (and significant). For the 10 × 11 pair, however, five of the 10 correlations are significantly positive (the final year and the four benchmark years within 1958–1972), and only one (1982) is significantly negative. So the dominant complementarity between transportation and communications manufacturing is a robust result, occurring for both weighted and unweighted analyses of our data as well as in Plaut’s analysis of similar categories.

    For the rest, we believe that the weighted results are more credible, since they more appropriately reflect the contribution of each commodity to overall economic output. The reversal in dominant direction of correlation between the two results suggests that smaller industries (which receive weight equal to larger industries in the unweighted results) have a greater tendency to use transportation and communications as substitutes than do larger industries. To the extent that smaller industry sectors tend to have smaller firms, one explanation might be that smaller firms are more resource-constrained and also more institutionally nimble than larger ones, so that they are both more motivated to seek, and better able to adopt, the substitution of communications for travel as a cost-saving measure. The correlation between industry size (measured by economic contribution) and firm size within industry is in the expected direction (larger industry sectors tend to have larger firms), though not statistically significant.

  13. The ideal approach in analyzing industry-specific correlations across time would be to apply an industry-specific price index to each I–O coefficient. Since the PPI (data available in Bureau of Labor Statistics in U.S. Department of Labor, http://www.bls.gov/ppi/home.htm, last accessed on June 3, 2007) provides indices that can be applied for various industry categories (i.e., for both i and j), it could potentially be used to convert current dollars into constant ones.

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Acknowledgements

This study was partly funded by the University of California Transportation Center. Comments by Arpad Horvath and two anonymous reviewers helped improve this paper.

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  1. Department of Civil and Environmental Engineering and Institute of Transportation Studies, University of California, One Shields Avenue, Davis, CA, 95616, USA

    Taihyeong Lee & Patricia L. Mokhtarian

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Lee, T., Mokhtarian, P.L. Correlations between industrial demands (direct and total) for communications and transportation in the U.S. economy 1947–1997. Transportation 35, 1–22 (2008). https://doi.org/10.1007/s11116-007-9141-9

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