Numerous public policies have been promulgated on the assumption that telecommunications will be a useful trip reduction instrument. However, many scholars have suggested that the predominant effect of telecommunications may be complementarity – increasing travel. Although short-term, disaggregate studies of single applications such as telecommuting have tended to find a substitution effect, more comprehensive studies, on the aggregate scale, are needed. One of the few such studies used input-output analysis to examine relationships between transportation and communication input intensities across 44 industry classes in Europe for 1980, and found strong evidence of complementarity. The present study has applied a similar methodology to the input-output accounts for the US across multiple points in time.

Specifically, this study applied the input-output analysis technique developed by Leontief in 1936 to analyze the relationship between transportation and communications as industrial inputs in the U.S. Generally, input-output analysis offers a static view of the structural relationships, expressed purely in monetary terms, among the different sectors of an economy for a certain period of time. We analyzed correlations between transportation and communications using the input coefficients of transportation and communications in the input-output table (direct coefficients matrix). Positive correlation coefficients indicate complementarity: industries that require a lot of transportation inputs also tend to require a lot of communications inputs, and conversely. Negative correlation coefficients imply substitution.

Ten benchmark I-O accounts (between 1947 and 1997, inclusive) were collected, which are prepared using the most detailed data sources available, generally the economic censuses. Trying to find the best balance between highly disaggregated industry classifications (which may exhibit a lot of random noise that would obscure the pattern of interest) and highly aggregated ones (which contain such a small number of cases that it may also be hard to identify underlying relationships), we created four scenarios reflecting different levels of aggregation across sub-industries. Scenario 1 is the most disaggregate level (containing 79-131 categories, depending on year), while Scenario 4 is the most aggregate categorization, containing just the nine top-level industries. We analyzed correlations for five selected pairs of transportation and communications industry categories: the manufacturing pair (i.e., transportation manufacturing correlated with communications manufacturing), the utilities pair, the two manufacturing-utilities pairs, and the overall pair (all transportation manufacturing and utilities correlated with all communications manufacturing and utilities).

In this study the Spearman correlation is used, which is a nonparametric correlation measure. Since the input-output coefficients are not normally distributed, the Pearson correlation coefficients are not strictly appropriate. Using Spearman correlations, we conducted a cross-sectional analysis for each time period, and compared results across time based on the five sets of correlations between transportation and communications. Thus, 200 correlation coefficients in all are computed in this study (five sets for each of four scenarios for each of 10 benchmark years).

Numerous public policies have been promulgated on the assumption that telecommunications will be a useful trip reduction instrument. However, many scholars have suggested that the predominant effect of telecommunications may be complementarity — increasing travel. Although short-term, disaggregate studies of single applications such as telecommuting have tended to find a substitution effect, more comprehensive studies, on the aggregate scale, are needed. One of the few such studies used input-output analysis to examine relationships between transportation and communication input intensities across 44 industry classes in Europe for 1980, and found strong evidence of complementarity. The present study has applied a similar methodology to the inputoutput accounts for the US across multiple points in time.

Specifically, this study applied the input-output analysis technique developed by Leontief in 1936 to analyze the relationship between transportation and communications as industrial inputs in the U.S. Generally, input-output analysis offers a static view of the structural relationships, expressed purely in monetary terms, among the different sectors of an economy for a certain period of time. We analyzed correlations between transportation and communications using the input coefficients of transportation and communications in the input-output table (direct coefficients matrix). Positive correlation coefficients indicate complementarity: industries that require a lot of transportation inputs also tend to require a lot of communications inputs, and conversely. Negative correlation coefficients imply substitution.

Ten benchmark I-O accounts (between 1947 and 1997, inclusive) were collected, which are prepared using the most detailed data sources available, generally the economic censuses. Trying to find the best balance between highly disaggregated industry classifications (which may exhibit a lot of random noise that would obscure the pattern of interest) and highly aggregated ones (which contain such a small number of cases that it may also be hard to identify underlying relationships), we created four scenarios reflecting different levels of aggregation across subindustries. Scenario 1 is the most disaggregate level (containing 79-131 categories, depending on year), while Scenario 4 is the most aggregate categorization, containing just the nine top-level industries. We analyzed correlations for five selected pairs of transportation and communications industry categories: the manufacturing pair (i.e., transportation manufacturing correlated with communications manufacturing), the utilities pair, the two manufacturing-utilities pairs, and the overall pair (all transportation manufacturing and utilities correlated with all communications manufacturing and utilities).

In this study the Spearman correlation is used, which is a nonparametric correlation measure. Since the input-output coefficients are not normally distributed, the Pearson correlation coefficients are not strictly appropriate. Using Spearman correlations, we conducted a cross-sectional analysis for each time period, and compared results across time based on the five sets of correlations between transportation and communications. Thus, 200 correlation coefficients in all are computed in this study (five sets for each of four scenarios for each of 10 benchmark years).

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 1-0 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 1-0 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 modem 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.

With aggregate data from the U.S. Consumer Expenditure Survey for 19 years, 1984 through 2002, this study analyzes relationships between expenditures on transportation and communications. Several classification schemes for expenditure categories were used, from the most aggregate [two categories (transportation and communications)] to the most disaggregate [nine transportation categories (new vehicle purchases, used vehicle purchases, vehicle finance charges, gasoline and motor oil, vehicle maintenance and repairs, vehicle insurance, public transportation, out-of-town lodging, and other entertainment including bikes and recreational vehicles) and five communications categories (telephone service; miscellaneous household equipment including phones and computers; television, radio, and sound equipment; postage and stationery; and reading)]. Aggregate demand system modeling (in particular, the linear approximate almost ideal demand system) was then used to determine the relationships between expenditures on transportation and those on communications, again for several different classifications. The model results indicate that transportation and communications categories have substitution and complementarity relationships, often not symmetric. However, a dominant effect of complementarity can be found in the influence of communications on transportation.

This study analyzes aggregate consumer expenditure data from the US between 1984 and 2002, to determine relationships between expenditures on transportation and communications. We first identified 15 categories of goods - nine for transportation, five for communications, and one for all others - and obtained prices for each category across time. Then, we applied the linear approximate almost ideal demand system (AIDS) method for estimating consumer demand functions, aggregating the categories to six (non-personal vehicle (PV), PV capital, PV operation, electronic communications media, print communications media, and all others) due to the small sample size. The results indicate that transportation and communications categories have both substitution and complementarity relationships. The existence of effects in both directions (substitution and complementarity) is testimony to the complexity of the relationships involved, with both generation and replacement possible and happening simultaneously. In addition, expenditures in the transportation categories are generally more income-elastic and price-elastic than those in communications, indicating that communications expenditures are more essential than those for travel. The transportation categories have both substitution and complementarity relationships with each other, while the two communications categories have a substitution relationship.

With aggregate data from the U.S. Consumer Expenditure Survey for 19 years, 1984 through 2002, this study analyzes relationships between expenditures on transportation and communications. Several classification schemes for expenditure categories were used, from the most aggregate [two categories (transportation and communications)] to the most disaggregate [nine transportation categories (new vehicle purchases, used vehicle purchases, vehicle finance charges, gasoline and motor oil, vehicle maintenance and repairs, vehicle insurance, public transportation, out-of-town lodging, and other entertainment including bikes and recreational vehicles) and five communications categories (telephone service; miscellaneous household equipment including phones and computers; television, radio, and sound equipment; postage and stationery; and reading)]. Aggregate demand system modeling (in particular, the linear approximate almost ideal demand system) was then used to determine the relationships between expenditures on transportation and those on communications, again for several different classifications. The model results indicate that transportation and communications categories have substitution and complementarity relationships, often not symmetric. However, a dominant effect of complementarity can be found in the influence of communications on transportation.

The relationship between telecommunications and travel has been a fertile area of research for several decades. Early speculation (e.g., Owen, 1962) focused on the potential of telecommunications to replace travel. That hope eventually led to the establishment of several telecommuting programs, and empirical evaluations of those programs (e.g., Hamer, et al., 1991; Mokhtarian, et al., 1995) seemed to support the substitution expectation. While empirical evidence for other telecommunications applications was far more scarce, it was similarly expected that teleconferencing, teleshopping, distance learning, and other such services would also replace travel. In the meantime, however, some scholars (e.g. Albertson, 1977; Salomon, 1985; Mokhtarian, 1990, 2003; Niles, 1994) began to point out that substitution was not the only possible impact of telecommunications on transportation. In particular, it was argued that a very likely impact would be the generation of more travel, or complementarity. In the short term, this effect could arise in two kinds of ways, which the literature (Salomon, 1986) labels enhancement and efficiency.

* Enhancement refers to a direct impact of one mode of communications (e.g. telecommunications) on the demand for another mode (e.g. travel). For example, the increasing ease of electronically obtaining information about interesting locations, activities, and people could stimulate the demand for travel to visit those locations or people and engage in those activities (Gottman, 1983; Couclelis, 1999). * Efficiency refers to the use of one mode (e.g. telecommunications) to improve the operation of another mode (e.g. the transportation network). The effect on demand is indirect in this case, by increasing the effective supply of transportation and hence, by lowering its (generalized) cost, making travel more attractive and thus increasing the demand for it.

With aggregate data from the U.S. Consumer Expenditure Survey for 19 years, 1984 through 2002, this study analyzes relationships between expenditures on transportation and communications. Several classification schemes for expenditure categories were used, from the most aggregate [two categories (transportation and communications)] to the most disaggregate [nine transportation categories (new vehicle purchases, used vehicle purchases, vehicle finance charges, gasoline and motor oil, vehicle maintenance and repairs, vehicle insurance, public transportation, out-of-town lodging, and other entertainment including bikes and recreational vehicles) and five communications categories (telephone service; miscellaneous household equipment including phones and computers; television, radio, and sound equipment; postage and stationery; and reading)]. Aggregate demand system modeling (in particular, the linear approximate almost ideal demand system) was then used to determine the relationships between expenditures on transportation and those on communications, again for several different classifications. The model results indicate that transportation and communications categories have substitution and complementarity relationships, often not symmetric. However, a dominant effect of complementarity can be found in the influence of communications on transportation.