Telecommunications infrastructure investment and economic development

Telecommunications infrastructure investment and economic development

Francis J. Cronin, Edwin B. Parker, Elisabeth K. Colleran and Mark A. Gold

A two-way causal relationship between tebcommunications infrastructure investment and economic development, eetabiished for the US economy in prevkus anaiysis, Is tested at the more iocaiized state and sub-state level and for two specific sub-categories of teiecommunications infrastructure investment- central office equipment, and cable and wire. For time series of these two sub-categoriee of teiecommunicatfons investment complied for the Commonwealth of Pennsylvania and two countries within Pennsylvania this analysis tests two causal hypotheses consistent with the US national-level analysis: First, the level of economic actlvity at any point in time is a reliable pmdictor (‘cause’) of the amount of telecommunications investment at a iatef point In time. Second, the amount of t&communfcations investment at any poht in time is a reliable predictor (‘cm~se’) of the level of economic activity at a later point in time.

One of the most difficult questions in exploring the relationship between telecommunications and economic development is determining a causal connection: While we observe that telecommunications development and general economic development often proceed together, is it telecommunications investment that promotes economic development, or economic development that creates demand for more telecommunications services?’

Researchers have investigated the relationship between telecommunications investment and economic development for almost 30 years2 Studies have found that highly developed national economies are correlated with highly developed telecommunications infrastructure. However, it was only recently that statistical techniques sufficiently rigorous to disentangle the relationships began to be applied to these issues. The evolution of this research, employing state-of-the-art statistical techniques, has now confirmed at the national economic level the existence of a feedback process in which telecommunications investment enhances economic activity and growth, while economic activity and growth stimulate demands for telecommunications infrastructure investment.3 What has not been answered, prior to the present study, is whether or not this interdependent relationship between economic activity and Francis J. Cronin is Managing Director of telecommunications infrastructure investment at the national level also DRiIMcGraw-Hill, 24 Hartwell Avenue, holds at the state and sub-state levels - and if so, how it occurs. Does it Lexington, MA 02173, USA (Tel: + 1 617 660 6424; fax: +1 617 660 6463). Edwin result from more highly developed economies needing, and being able B. Parker is Presidentof Parker Telecomto afford, more communications? Is telecommunications investment a munications, PO Box 402, Gleneden productive stimulus contributing to economic growth, or merely a Beach, OR 97366, USA (Tel: +l 503 764 3056; fax: +l 503 764 3059). ElisahethK. consequence of that growth? Or is it both? Does the relationship Colieran and Mark A. Gold are Senior between telecommunications investment and the economy vary beAssociateswith DRI. tween the national and state level, or between the state and sub-state This study was originally performed by level? Does the relationship vary across sub-state areas (urban versus DRYMcGraw-Hillin coniunctionwith ParAnd finally, does the relaker Telecommunicationsfor a consortium rural, high-income versus low-income)? tionship between investment and telecommunications infrastructure of Pennsylvaniatelephone companies. 0306-5961/93/060415-l 6 0 1993 Butterworth-HeinemannLtd

415

Telecommunications infrastructure investment and economic development

‘Department of Commerce, ‘Comprehensive study of domestic telecommunications infrastructure: Notice of Inquiry’, Federal Register, 9 January 1990, p 601. *A. Jipp, ‘Wealth of nations and telephone Journal, density’, Tel&wmmunications July 1963, pp 199-201; International Consultative Committee on Telephone and Telegraph (CCllT), Economic Studies at the National Level in the Field of Telecommunications, International Telecommunication Union. Geneva, 1966, 1976, 1976; E.L. Bebee and E.T.W. .Gilling, ‘Telecommunications and economic development: a model for planning and policy making’, T&communications Journal, August 1976, pp 537443; P.D. Shapiro, ‘Telecommunication and industrial development’, IEEE Transactions on Communications, March 1976; R.J. Saunders, J.J. Warford and B. Wellenius, Telecommunications and Economic Development, Johns Hopkins University Press, Baltimore, MD, for the World Bank, 1963; ITLfOECD, Telecommunications for Development, Joint Report 2, 1963; ITU, Telecommunications and the National Economy, 1966. 3F. Cronin, E. Parker, E. Colleran and M. Gold, ‘Telecommunications infrastructure and economic growth: an analysis of causality’, Tekommunications Policy, Vol 15, No 6, December 1991, pp 52%535. 4A. Hardy, ‘The role of the telephone in economic development’, Tekommunications Policy, Vol5, No 4, December 1960, pp 27%266.

416

vary by type of investment (eg central office equipment versus outside plant)? Statistical tests can empirically evaluate such questions. Often termed ‘causality’ tests, these analyses evaluate to what extent changes in one variable relate to corresponding changes in a second variable. It is important to note that statistical measures of causality can only be used to accept (ie fail to reject) or reject the stated hypotheses. They provide no measures of the magnitude of the causal relationship. For example, causality tests themselves will not provide answers to such questions as: How much additional economic activity can be generated by investing an additional dollar in telecommunications? This study goes beyond the previous research in this area in four ways. First, we use information for the State of Pennsylvania to investigate the causal relationship between economic activity and investment in telecommunications infrastructure at the state and sub-state levels. Second, to capture the impacts of evolving technology and the continuing restructuring of the economy, we use data through 1991, the last year for which data are available. Third, to better understand the potential for non-contemporaneous relationships, we examine how the relationships between telecommunications investment and economic activity vary over time - do changes in one factor immediately translate into changes in the other, or do such changes take place over a longer time horizon? Fourth, to determine whether different types of investment in telecommunications infrastructure result in varying causal relationships, we conduct the analysis separately on investment data categorized by central office equipment (COE) and cable and wire (outside plant). With this framework one can trace causal relationships to specific segments of network equipment.

Background studies Summaries of three recent important studies investigating the relationship between telecommunications and economic growth are presented below. These studies represent the latest research in this area and cover international, national and sub-state applications. Hardy’s research

Andrew Hardy conducted an analysis of data from 45 countries over the interval 1960-73. Hardy considered the sequencing of relationships between gross domestic product (GDP) and the number of telephones; he concluded that the evidence supported bi-directional causality - a change in the number of telephones caused economic growth and a change in economic growth caused a change in the number of telephones.4 Using one-year time lags, Hardy found that telephones per capita in one time period were significantly related to GDP in later time periods and vice versa. According to Hardy, his analysis demonstrates a potential diminishing returns problem. The size of the effect of telecommunications investment (measured by the number of telephones per capita) was inversely related to the prior level of telecommunications development. In other words, the largest effect of telecommunications investment on GDP was found in the least-developed economies, and the smallest effect was found in the most-developed economies. This finding would suggest that, in the current analysis, we might expect the relationship

TELECOMMUNICATIONS

POLICY August 1993

Telecommunications infrastructure investment and economic development

between telecommunications investment and economic activity to vary depending on the overall level of development found in the local economy. Previous

DRI research

Previous DRI research applied sophisticated statistical techniques to empirically determine causal relationships between economic development and investment in telecommunications infrastructure at the national level.5 The analysis revealed not only that increases in output or GNP lead to increases in telecommunications investment, but the converse is also true: increases in telecommunications investment stimulate overall economic growth. This ‘causality’ was determined to be significant in both directions. A review of the DRI national results may be found in Cronin et aL6 The Parker

and Hudson

analysis

More recently, Parker and Hudson concluded a county-level analysis of the relationship between telecommunications infrastructure and economic development for rural areas in Oregon and Washington states.7 They concluded that a correlation’ exists between telecommunications infrastructure and rural economic performance. They had data for only a single time period and hence were able to demonstrate simply an association and not the causal direction of the relationship. Specifically, more advanced telecommunications infrastructure such as single-party service (relative to multiparty service) and modern digital switches (relative to electromechanical switches) were positively correlated with levels of economic performance in rural counties. These findings were confirmed after statistically controlling for the effects of population density. Less densely populated counties had both poorer telecommunications infrastructure and poorer economic performance. This statistical control ruled out the possibility that the relationship was merely an artefact of the relationship between each variable and population density. Parker and Hudson recognize that these results are strictly descriptive of conditions holding in the two specific states covered by their study. They do, however, see in the DRI causality analysis an explanation at the national level for the results they have found at the county level in these specific rural areas.’

Objective of this study %ronin et al, op tit, Ref 3. %rU, p 533. ‘E. Parker and H. Hudson, Electronic Byweys: State Policies for Rural Development through Telecommunications, Westview Press, Boulder, CO, 1992. *Correlation analysis establishes the degree of association between two events. Causality tests determine whether one event causes or explains another event. ‘Parker and Hudson.-.on cit. Ref 7...,DD164181. ‘?he statistical procedure for confirmation of such research hypotheses is one of rejecting, as being statistically highly improbable, the opposite or so-called null hypotheses which states that there is no such relationship.

These previous studies all point to the economic effects of investment in telecommunications infrastructure. DRI’s study and Hardy’s analysis also suggest that the impetus to telecommunications investment is economic growth. The purpose of this current analysis is to determine whether historical investment in the network at the state and sub-state level for Pennsylvania has led to a statistically identifiable impact on the State of Pennsylvania and its local economies. Furthermore, the analysis seeks to determine whether economic development at the state and sub-state level has caused the historical level of investment in the telecommunications network, or if these decisions have been made independently of the local economic environment. The objective of this study is therefore to determine whether there is statistical evidence to support either or both of the following hypotheses: lo

TELECOMMUNICATIONS POLICY August 1993

417

Telecommunications

infrastructure investment and economic development l

l

Hypothesis 1: Changes in the level of economic activity in any time period predict (‘cause’) changes in the amount of telecommunications investment in a later time period. Hypothesis 2: Changes in the amount of telecommunications investment in any time period predict (‘cause’) changes in the level of economic activity in a later time period.

Data

“State-level personal income data were obtained from the US Department of Commerce, Bureau of Economic Analysis (BEA). The employment series for Somerset and York Counties were developed from data obtained from the Pennsylvania Department of Labor and Industry and from the BEA.

418

The establishment of causal relationships is a form of time series analysis and, as such, several decades of historical data are required. In order to conduct the analysis, measures of economic activity and telecommunications investment in both the state and sub-state (county) level are required. At the national level DRI’s analysis was conducted using investment data developed by the US Department of Commerce, Bureau of Economic Analysis. The BEA adjusts its series for factors that may affect reported industry investment data. Such factors may include changes in the tax law and regulatory rules. The use of unadjusted accounting data in Pennsylvania introduces potential errors in the data that may not have any underlying economic factors. Such errors reduce the likelihood of observing a significant relationship. In addition, any observed economic impacts at the state and sub-state levels introduced by telecommunications infrastructure investment will be significantly mitigated compared with national-level findings since the former economies are significantly more open than the latter. That is, the fact that the national economy is significantly less open (ie has fewer linkages) allows it to capture a higher share of induced effects. Therefore it would not be surprising for the robustness of the state results to suffer compared to DRI’s national-level results, nor for sub-state results to be less robust than state results. For the state-level analysis we used an annual time series of Pennsylvania total personal income as a measure of the performance of the state economy. ‘i While gross state product would have been more comparable with the prior research by Hardy and DRI, the three-year lag in compiling the data by the BEA would have meant excluding from the analysis post-1989 data. We believe this would have seriously diminished the information content and statistical validity of our analysis. Furthermore, gross state product and personal income in Pennsylvania exhibit a 94% correlation over the 1965-89 period. To control for the effects of inflation, values are adjusted to 1977 constant dollars. As a result of data limitations at the county level, total employment is used as the best available indicator of economic activity.” On the positive side, this will make our comparison with the Parker and Hudson findings easier since their analysis also uses local employment as the measure of economic performance. The data cover a 27-year time period from 1965 to 1991. This analysis employs data on telecommunications investment activity collected from the common carriers in Pennsylvania, both local exchange carriers (LECs) and interexchange carriers (IXCs). Although data were requested from all common carriers in the state, only 12 local exchange carriers and two interexchange responded with plant investment data. Of the 12 LECs that responded, only seven provided data with the required level of detail over the necessary time interval. Still, these seven LECs comprise over 96% of the state’s access lines. Specific time series have been constructed for net plant investment in central

TELECOMMUNICATIONS

POLICY August 1993

Telecommunications infrastructure investment and economic development

office equipment and in cable and wire. To control for inflation, values have been adjusted to 1977 constant dollars. At the county level, our original intent was to conduct the analyses on data provided by LECs for a diverse set of many counties. This approach would have allowed us to test whether the relationship between economic activity and telecommunications investment varies, based on a community’s characteristics (industrial composition, population density, socioeconomic characteristics, etc). However, data limitations stemming from the perpetual property records accounting system maintained by most of the LECs prevented us from compiling sufficiently long historical records of investment activity by local area. As a result our analysis is limited to the information provided by GTE in York and Somerset counties, for which decades of historical investment data for the individual local exchanges are available. To control for inflation, values have been adjusted to 1977 constant dollars. Additionally, DRI’s sub-state analysis could not extend below the county level as a result of the statistical requirements that a substantial amount of history be available for each input to the tests. Data on economic activity below the county level have not been compiled in a consistent time series prior to 1988. For this reason, analysis at the exchange level was not possible unless the exchange borders were closely aligned with a county. A limited set of counties, including Somerset and York, exhibited this characteristic. DRI’s sub-state analysis focused on these counties.

Statistical background 13J. Geweke, ‘Inference and causality in economic time series models’, in Z. Griliches and M.D. Intriligator, eds, Handbook of Economics, Vol- 2, North HollandElsevier. Amsterdam. 1964, Ch 19. “See, & A. Zellner; ‘Causality and causal law in economics’, Journal of Econometrics. Vol 39. No l/2, Seotember/ October 1966, pp 7-21; and-C.W:J. Granger, ‘Some recent developments in a concept of causality’, Journalof Econometrics, Vol 39, No l/2, September/October 1966, pp 19*212. I%. Granger, ‘Economic processes involving feedback’, information and Contrd, Vol 6, 1963, pp 2646; C. Granger, ‘Investigating causal relations by econometric models and cross spectral methods’, Econometrica, July 1969, pp 424436. “Geweke, op cif, Ref 13. “See. for examole. E. Berndt. The fractice of Econothethcs, Addison-Wesley, Reading, MA, 1991, Ch 6. ‘*For a detailed discussion of the properties of causality tests when used with small samples, as in the present study, see Guilkey and Salemi. They conclude that both the Granger and Modified Sims procedures provide accurate estimates with smell samples. 0. Guilkey and M. Salemi, ‘Small sample properties-of three tests for Granger-causal ordering in a bivariate stochastic system’, Review of Economics and Statistics, November 1962, pp 666 660.

TELECOMMUNlCATlONS

Causality literature

nature and estimation of causal relationships is covered extensively in economic literature.r3 A survey of broad philosophical issues and perspectives may be found in the Journal of Econometrics. l4 A subset of this literature, pioneered by Granger,” concerns the application of certain forms of time series analysis to establish evidence of predictability that would be consistent with causation. This avenue of thought has developed through time and is effectively surveyed by Geweke.16 The statistical procedures involved in applying ‘Grange? causality or similar tests now constitute a subject that appears regularly in applied econometrics textbooks.i7 Specific mathematical specifications are required to depict the assumed relationships between the phenomena being examined. Two standard statistical tests have been judged appropriate for testing the direction of causality in small samples. These are the Granger test and the Modified Sims test.18 Granger defines causality as: x causes y if, and only if, y is better predicted by using the past history of x than by not doing so, with the past of y being used in either case. The Modified Sims test is very similar to Granger’s. The Modified Sims test finds x causes y if, and only if, a specification including earlier values of x and earlier, current and future values of y is a better predictor of the time series of x than a specification including earlier values of x and earlier and current values of y (see the Appendix for a complete discussion of these causality tests). Of course, we would prefer to specify the ‘true’ relationship. Since, in general, we do not have such knowledge, we employ standard alternative specifications (ie Granger and Modified Sims). DRI employes the The

POLICY August 1993

419

Telecommunications

infrastructure investment and economic development

Granger test and the Modified Sims test because they are the standard tests used in causality analysis and both possess acceptable small-sample properties. It has been noted by researchers that these two tests, while designed to examine the same phenomena, have slightly different representations of the time sequencing of the relationships and, therefore, may generate similar or different results. In fact, when examining a relationship between two variables, one test may indicate a causal relationship during a given time frame or for a specific geographic area while the other test does not; for a different time frame or geographic area the latter test might indicate a causal relationship while the first test does not. This does not invalidate the tests; rather it indicates that the ‘true’ relationship is more closely approximated by one test for a given time interval or geographic area and by the second test for other time intervals or areas. We report results for both tests in order to increase the robustness of our research. It is not necessary that both tests nor all lag structures indicate a causal relationship - only that one of the tests and one of the lag structures give test results greater than the critical value chosen. Estimation issues

‘% determine the appropriate lag structure, LaGrangian Multiplier tests can be run on the residuals of estimated autoregressive processes for different variable series. For a discussion of the LaGrangian Multiplier (LM) test, see T.C. Mills, Time Series Techniques for Economists, Cambridge University Press, Cambridge, UK, 1990, pp 147-l 50. *% identify the presence of trends (unit roots), the Dickey-Fuller test can be applied. The Dickey-Fuller test applies data to a specified autoregressive process to ascertain whether a trend exists in the data; if a trend exists, the estimation of parameters using ordinary least squares will probably be biased. For more information on the Dickey-Fuller test, see D. Dickey and W. Fuller, ‘Distribution of the estimator for autoregressive time series with a unit root’, S&tis&a~ Association, 1979, pp 427431.

420

In conducting causality tests, it is important to address several estimation issues that can have important implications for the results of the analysis. First, it is important to determine the appropriate relationship between the variables across time. For example, does a change in investment in COE lead to a change in economic activity in the following year, or does the effect show up two, three or four years later? This time dimension is referred to as the lag structure of the variable. (Should the test determine that COE is related to economic activity two years later, economic activity is said to have a two-year lag structure.) Statistical tests exist to determine the lag structure that should not be considered in the analysis, based on possible biases in the behaviour of the variables. I9 In the current study these tests indicate that at least a two-year lag structure would be appropriate to remove any biases. Our analysis uses two, three and four-year lag structures. While it is not necessary to test more than one lag structure, the current analysis includes these longer time frames to capture any latent impacts between two variables that may take time to materialize. A second issue to be addressed in the estimation process is whether the existence of trends in the variables will cloud the findings of the analysis. In other words, as both the level of economic activity and the level of telecommunications investment have increased over time it is possible that this upward trend could mask the true relationship between the change in one variable and the related change in the other variable. DRI conducted statistical tests to determine whether trends in the variables exist, and the appropriate solution.*’ These tests uncovered evidence of trends in certain variables which were removed by using, for each year, the change in the variable from the previous year (referred to as the first difference of the variable) rather than the actual levels. The technical specifications and tests used in the analyses are described more fully in the Appendix. Another important issue is the selection of the level of statistical significance at which the hypothesis that telecommunications investment causes economic growth can be satisfied. The causality tests are actually constructed to test the reverse hypothesis, ie that there is no

TELECOMMUNICATIONS

POLICY August 1993

Telecommunications infrastructure investment and economic development Tabb 1. Themlatlonship behveencentral offke telecommunicationsplant investment and total personal income in Pennsylvanta.

Two-yearlag Income causes investment Investment causes income ‘99% confidence level; b95% confidence level; ‘90% confidence level. ‘W stands for ‘degrees of freedom’, a measure of the number of unconstrained cases used in testing statistical significance. The ‘F value’ is the statistic used to

measurethe significanceof the relationship.‘NS’ stands for not significant.

Three-yearlag Income causes investment Investment causes income

Four-yearlag Income causes investment Investment causes inwme

Granger test

Modified Sims test

F value (df = 2.22) NS 9.288

F value (df = 1.20) NS 7.49b

F value (df = 3,20) NS 6.4@

F value (df = 1.19) NS 6~35~

F value (df = 4,18) NS 4.18’

F value (df = 1 ,I 8) 3.41= 4.15c

causal relationship between telecommunications investment and economic activity. This hypothesis is maintained unless the statistics reveal a very high probability that it is not correct. We have chosen to report results significant at levels ranging from the 0.01 to the 0.15 level. A 0.05 level indicates that the probability of getting a significant result from a random sample, in the absence of a real effect in the population, is less than 5%. In other words, the probability of accepting a finding that ‘causality’ exists when, in fact, it does not is less than 5%. Therefore the tests that confirm the existence of a causal relationship can be accepted as reliable evidence of the importance of telecommunications.21 Finally, statistical tests assume that the measured observations are a random sample from an infinitely large population. They measure the probability of observing a relationship that is significant in a randomly selected sample of the population when no such relationship exists in the population. In this case the tests were conservative, because the measures are not truly a sample of a larger population, but are the ‘population’ of observations for the Pennsylvania economy for the period 1965-91. The data describe the actual relationship in the population, subject to measurement errors. Therefore the tests are conservative and, when statistically significant, confirm a real effect in the measured economy.

Findings State level

“Since numerous factors (eg data reporting errors, definitional problems and specification errors) can affect the statistical results - ie obscuring a relationship that in fact exists - we cannot ‘technically’ conclude that no relationship exists, only that we fail to reject the null hypothesis of no relationship.

TELECOMMUNICATIONS

This section discusses the relationships between two significant components of telecommunications investment and economic activity at the state level. The two investment variables are net investment in central office equipment and net investment in cable and wire. Table 1 presents the results of the analyses of investment in central office equipment. As the table shows, for the two- and three-year lag periods we find no significant effect of changes in total personal income in Pennsylvania on central office equipment investment. One of the two tests (the Modified Sims) shows a significant effect with a four-year lag period. This result is consistent with the long investment planning cycles used in the telecommunications industry for new central office equipment investment. Installation and replacement of telephone switching equipment have been more typically determined by multiyear investment budgeting cycles than by quick response to market demands. Furthermore, the longer time lag between economic activity and investment may reflect

POLICY August 1993

Telecommurzications infrastructure investment and economic development Table 2. The relationship between cable and wire telecommunicetionr plant investment and total personal income In Pennsylvania.

Two-year lag Income causes investment investment causes income

Three-yearlag Income causes investment Investment causes income

Four-year/ag b95% confidence

level.

Income causes investment Investment causes income

Granger test

Modtfled Sims test

F value (df = 2,22) NS NS

F value (df = 120) 4.93b NS

F value (df = 3,20) NS NS

F value (df = 1 ,I 9) 4.54b NS

F value (df = 4,18) NS NS

F value (df = 1,19) E1.32~ NS

both wider network considerations and the ‘lumpy’ nature of investment activity.*’ Table 1 also shows that for all six tests the effects of central office equipment investment on the Pennsylvania economy are statistically significant at least at the 90% confidence level, and that four of these six tests are significant at the 95% confidence level or better. The significance of the relationship for each of the three-year lag structures is not surprising. Theoretically, it takes time for telecommunications investment to influence the economy. The availability of newer, high-quality telecommunication services through new switching equipment makes productivity gains possible by businesses utilizing the new or improved services. 23More productive businesses in other sectors of the Pennsylvania economy in turn produce gains in real personal income. These effects are strong: they appear in the shortest (two-year) lag structure as well as in the longer-lag periods. At the state level, statistical tests fail to determine that investment in cable and wire causes economic growth. It is not clear why this is the case, yet numerous factors (eg data reporting errors, definitional problems and specification errors) can affect statistical results, obscuring a relationship that may exist. As a result, it cannot be concluded that no relationship exists simply because the test fails to find such a relationship. However, with respect to the converse hypotheses, the Modified Sims test indicates that changes in economic activity cause changes in cable and wire activity (see Table 2). These results are significant at the 95% confidence level for lags of two, three and four years. The strongest results are recorded in year four, which would support the expectation that it takes time for investment plans to be developed in response to changes in the economic environment. County level “8. Egan and L.D. Taylor, Capital Budgeting for Technology Adoption in Telecommunications: The Case of Fiber, Center for Telecommunications and Information Studies, Research Working Paper Series, Columbia Universitv, New York, April 1989; comments by Lee M. Bauman,’ in ‘Pacific Telesis - surviving and striving’, Telephony, 9 September 1991, p 28. =Ff. Cohen, The Economic impact of Broadband Communications on the US Economy and on US Competitiveness, Economic Strategy Institute, Washington, DC, 1992.

The intent of the county-level analysis is to determine whether the statistically significant causal relationship between telecommunications investment and economic growth identified in previous studies at the national level, and in this study at the state level, can be observed at more localized geographic locations. Additionally, if such relationships can be determined at the county level, it would be useful to explore potential differences in the impact of telecommunications development on counties varying in size and industrial composition. However, the scope of the analyses is limited to the two counties - Somerset and York - for which data could be collected for the appropriate time frame. Somerset and York are the state’s seventh and ninth largest counties (respectively), based on a square mile measure. These counties differ in

TELECOMMUNICATIONS

POLICY August 1993

Telecommunications infrastructure investment and economic development Tabla 3. Economic Eharact~Wka

of York County.

Total non-agricultural jobs (‘000)

1992 figures are DRI estimates. Source: US Department of Commerce, Bureau of Economic Analysis, and DRIIMcGraw-Hill.

Share of total (96): Construction Mining Non-durable manufacturing Durable manufacturing Finance, insurance and real estate Transport and utilities Services Trade Government Resident population (‘000) Per capita income ($900)

1980

1985

lggo

1992

138.4

139.6

155.8

153.2

4.3 0.3 13.1 29.2 2.4 5.8 12.7 21 .o 11.1

4.9 0.3 12.4 24.2 2.9 4.9 18.5 23.7 10.2

5.5 0.2 11.1 20.4 3.0 4.9 19.0 26.1 9.6

4.7 0.2 11.1 19.9 3.2 5.1 19.4 26.9 9.6

313.4 10.6

313.4 15.0

315.4 20.9

319.5 22.5

characteristics across several dimensions. York is a relatively more urban economy, with several large companies (including Caterpillar and Harley Davidson); it is within commuting distance of Baltimore. Somerset is a small, rural community in the Allegheny Mountains, relying on tourism (skiing) and agriculture. York’s population is approximately four times the size of Somerset’s. Tables 3-5 compare the industrial composition of York and Somerset and the State of Pennsylvania. With respect to telecommunications characteristics, York County has approximately 173 000 telephone access lines, 78% of which are residential. Nearly 84 000 (49%) of these residential lines have touch-tone service; more than 28 000 (16%) have custom calling features. Of the county’s more than 38 000 business access lines, nearly 89% are equipped with touch-tone service, while only 3900 (10%) have custom calling features installed. The county’s access lines are 98% digital. Somerset has almost 38 000 access lines, 82% residential. Only 13 000 (34%) of these residential lines have touch-tone service and only 4000 (11%) have custom calling features. Approximately 72% of the county’s 6800 business lines are equipped with touch-tone services, while 12% have custom calling features installed. One hundred per cent of the county’s access lines are digital. It is important to note that it is impossible to draw conclusive cross-county comparisons on such a limited data set. Yet the results of the county-level analysis are valuable because they are consistent with the state-level findings and demonstrate that relationships between telecommunications investment and economic development can be

Table 4. Economic charactwiatlcs of Somerset County. IS60

1985

1990

l!BZ

Total non-agricultural jobs (‘000)

22.6

20.7

25.0

25.5

1992 figures are DRI estimates.

Share of total (%): Construction Mining Nondurable manufacturing Durable manufacturing Finance, insurance and real estate Transport and utilities Services Trade Government

4.6 11.5 10.3 9.8 3.4 5.7 17.7 19.1 18.0

2.7 4.7 12.1 11.1 4.6 4.3 21.9 19.9 18.3

4.6 5.6 9.9 11.9 3.4 5.3 22.8 21.5 15.0

4.5 4.3 10.2 12.2 3.4 5.3 23.3 21.8 15.0

Source: US Department of Commerce, Bureau of Economic Analysis, and DRVMcGraw-Hill.

Resident population (‘GJO) Per capita income ($‘OOO)

81.2 8.5

80.3 10.4

79.4 14.0

82.3 14.4

TELECOMMUNICATIONS POLICY August 1993

Telecommunications

infrastructure investment and economic development Table 5. Economic characteristks ot the Commonwealth ot Pennsylvania. 1999 Total non-agricultural jobs (‘000)

1992 figures are DRI estimates.

Share of total (%): Construction Mining Non-durable manufacturing Durable manufacturing Finance, insurance and real estate Transportation and utilities Services Trade Government

Source: US Department of Commerce, Bureau of Economic Analysis, and DRINcGraw-Hill.

Resident population (‘000) Per capita income ($900)

1996

1996

1992

4 753.0

4 729.9

5 170.4

5 033.6

4.0 1.0 10.6 17.1 5.0 5.5 20.5 20.6 15.2

4.0 0.6 9.7 13.3 5.6 5.1 24.7 22.6 14.4

4.4 0.5 8.5 11.2 5.8 5.1 27.9 22.8 13.7

3.9 0.5 8.5 10.5 6.0 5.2 29.0 22.7 13.8

11 862.2 9.9

11 777.0 13.6

11 905.5 18.7

12 050.1 19.6

statistically observed for smaller geographic areas despite the much greater dissipation of these impacts due to the more open local economies. We find statistical relationships in York County that are not identifiable in Somerset. Perhaps this is not surprising, given the difference in size, industrial composition and telecommunications infrastructure between the two counties. The results for York County are presented in Tables 6 and 7 below. Table 6 shows that the Modified Sims test, but not the Granger test, finds a statistically significant relationship between investment in COE in York County and economic growth using all three lag structures. The longer lag structures of three and four years reveal more robust results (at the 90% confidence level) since it takes time for the effects of telecommunications investment to feed through the economy. Consistent with the state-level results, we find statistical evidence to suggest that a significant causal relationship exists between economic activity and COE investment in York County. In fact, when using the Granger test to examine the relationship between a change in economic activity and investment in COE four years later a statistically significant relationship is found (at the 90% level). Once again, this result is consistent with the long investment planning cycles used in the telecommunications industry for new central office equipment investment and the probability that wider network considerations may affect carriers’ investment decisions for a specific county. The results of the analysis of the relationship between outside plant telecommunications investment and total employment in York County differ substantially from the state-level results (see Table 7). While we find no evidence of a relationship at the state level, the Modified Sims

Table 6. The relatlonshlp between central office te4ecommunkatlons plant lnveetment and total employment In York County Granger

Two-year lag

‘90% confidence level: ‘65%

424

test

Modlfled Sims test

Employment causes investment Investment causes employment

F value (df = 2,21) NS NS

F value (df = 1,19) NS 2.76’

Three-year lag Employment causes investment Investment causes employment

F value (df = 3,19) NS NS

F value (df = 1,18) NS 3.41c

Four-year lag Employment causes investment Investment causes employment

F value (df = 4,17) 2.34’ NS

F value (df = 1,17)

confidence level.

TELECOMMUNICATIONS

POLICY August 1993

Telecommunications infrmtructure investment and economic development Tabb 7. nw fowonship between out8lde plant teiecommunicntlons Investment and total employment in York County

Tweyear lag Employment causes investment investment causes employment

Three-yearlag Employment causes investment Investment causes employment

Four-year/ag =909/o confidence level.

Employment causes investment Investment causes employment

Oranger test

Modifbd Sims test

F value (df = 2.21) NS NS

F value (df = 1,19) NS 3.2P

F value (df = 3,19) NS NS

F value (df = 1,18) NS 3.94=

F value (df = 4.17) NS NS

F value (df = 1,17) NS 3.49c

test, but not the Granger test, indicates that the conclusion that investment in outside plant causes economic activity can be accepted at the 90% confidence level. The causal relationship of economic growth to investment in outside plant is not detected at the county level, even though the Modified Sims test demonstrates such a relationship at the state level. The statistical tests for Somerset County for both COE (see Table 8) and outside plant (see Table 9) fail to detect significant relationships in either direction for all lag structures of COE and for the two- and three-year lag structures for outside plant investment. However, for the four-year lag structure the Granger test finds that investment in outside plant causes economic activity at the 90% confidence level. This finding concerning the causal relationship of outside plant to economic growth in Somerset County provides some support for the results in York County. The positive impacts of central office equipment on employment in York County and outside plant investment on employment in both York and Somerset Counties are particularly important findings. These results are consistent with the significant negative correlation between unemployment and telecommunications infrastructure found by Parker and Hudson in rural counties in Oregon and Washington. The Parker and Hudson findings were comtemporaneous and therefore could not, by themselves, indicate causal direction. The York County data presented here show that investment in both central office equipment and outside plant causes changes in employment activity for the three lag structures tested, while the Somerset County analysis finds that investment in outside plant causes economic growth. The finding of a significant result for outside plant investment in both counties, but not in the state as a whole, is consistent with a finding of the 1980 Hardy study that the greatest economic benefits were obtained

Table 8. The relationship between central oftioe tekommunloationa employment in Somerset County.

ta8t

Grange test

Modtfted Sims

Two-year fag Employment causes investment Investment causes employment

F value (df = 2,21) NS NS

F value (df = 1,19) NS NS

Three-yearleg

F value (df = 3,19) NS NS

F value (df = 1,19) NS NS

F value (df = 4,17) NS NS

F value (df = 1,17) NS NS

Employment causes investment Investment causes employment

Four-yearkg Employment causes investment Investment causes employment

TELECOMMUNICATIONS

plant Investment and total

POLICY August 1993

425

Telecommunications infrastructure investment and economic development Tabk 9. Tha ralatkxnahlp bahvaan outside office talacommunlcationa invaatmant and total employment in %meraat County. Grangar teat

Modified Sims teat

Employment causes investment Investment causes employment

F value (df = 2,21) NS NS

F value (df = 1 ,19) NS NS

Three-year lag Employment causes investment Investment causes employment

F value (df = 3,19) NS NS

F value (df = 1,18) NS NS

Four-yearlag

F value (df = 4,17) NS 2.7T

F value (df = 1,17) NS NS

Two-yearlag

‘90% confidence level.

Employment causes investment Investment causes employment

in the locations with less-developed pre-existing infrastructure. The findings from the current analyses suggest employing telecommunications infrastructure investment as a means to stimulate local economic development. The county-level findings that investment in both COE (in York) and outside plant (in both York and Somerset) is related to economic activity are particularly important because they indicate that small geographic areas can be affected by such investment activity. Furthermore, the fact that county-level employment is the indicator of economic activity in this analysis provides additional insights concerning the extent of the impact of telecommunications investment. Previous analysis by DRI has demonstrated that investment in telecommunications infrastructure affects the productivity of other industries.24 The current analysis, however, was able to find evidence of a causal relationship between growth in telecommunications investment and growth in local employment, demonstrating that the efficiency gains generated by telecommunications investment do not outweigh the economic expansion resulting from this investment.

Summary and conclusions

24F. Cronin, M. Gold and S. Lewitzky, ‘Telecommunications technology, sectoral prices and international competitiveness’, T&communications Policy, Vol 16, No 7, September/October 1992, pp 553-564.

Our findings at both the state and county level support the conclusion that telecommunications investment affects economic activity and that economic activity can affect telecommunications investment. These findings are consistent with national-level results. The county-level findings are particularly significant because they indicate that even relatively small geographic areas can be affected by investment in telecommunications infrastructure. These findings are summarized in Tables 10 and 11. Our analysis supports the conclusion that investment in COE by the common carriers at the state level is related to such measures of economic activity as personal income. This finding is confirmed using two statistical tests. Additionally, the results indicate that the impact on personal income from investment in COE is evident two, three and four years after the initial investment. The results of the analysis also provide support for the conclusion that a change in state economic activity leads Table 10. Doaa telacommunicationa invaatmant laad to economic actlvlty?

‘Yes’ indicates that a relationship exists.

426

Hypothesis

State level

County level York Somerset

Investment in central office equipment causes economic activity Investment in cable and wire causes economic activity

Yes _

Yes Yes

Yes

TELECOMMUNICATIONS POLICY August 1993

Telecommunication infrastructure investment and economic development Tabla 11. Doaa aconomlc advtty lead to telecommunicatlona investment?

‘Yes’ indicates that a relationship exists.

Hypothesis

state level

county level York Somerset

Economic activity causes investment in central office equipment Economic actMty causes investment in cable and wire

Yes Yes

Yes -

-

to a corresponding change in investment in COE. A statistically significant relationship, however, appears for one test only in the fourth year after the change in economic activity. These results would therefore indicate that changes in economic activity take longer to affect investment in COE than changes in COE take to affect economic activity. These findings are not unexpected, however, given the long investment cycle and wider network considerations affecting the capital expenditure decisions of the common carriers. One statistical test indicates that economic activity causes cable and wire investment at the state level. In fact, it can be concluded that a change in economic activity affects investment in cable and wire two, three and four years later. The findings reveal the strongest relationship between a change in economic activity and the change in investment in cable and wire four years later. This finding also supports the expectation that it takes time for infrastructure investment plans to be developed in response to changes in the economic environment, but that investment in cable and wire appears to respond more quickly to economic changes than do investments in COE. At the state level, the analysis fails to support the conclusion that investment in cable and wire causes economic growth. It is not clear why this is the case, so it cannot be concluded that no relationship exists simply because the tests fail to find such a relationship. Some support for the supposition that cable and wire is important for economic growth is found at the county level and is discussed below. The COE analysis for York County yields results almost identical to the state-level analysis. Results for one test indicate that a change in investment in COE in York County is related to changes in economic growth in the following two, three and four years. There is also less evidence to suggest that a significant causal relationship exists between economic activity and central office equipment investment in York County. When testing the relationship that a change in economic activity causes investment in COE four years later, a statistically significant relationship is found by one of the tests. The results of the county-level analysis concerning cable and wire and economic activity differ from the state-level findings. While there is no evidence of a relationship at the state level, one of the tests for York County indicates that a change in investment in cable and wire causes a change in economic activity in the following two, three and four years. Also, there is no evidence that economic growth in York County is related to investment in cable and wire, even though tests show such a relationship at the state level. Somerset County is significantly less industrialized than York, with a substantially smaller population. Still, the findings concerning investment in outside plant and economic growth in York County have some support in Somerset County. Although for the most part statistical tests for Somerset County fail to detect significant relationships in either direction in the COE analysis, one test indicates that a change in lELECOMMUN1CATIONS

POLICY August 1993

427

Telecommunications infrastructure investment and economic development

investment in outside plant leads to a related change in economic activity four years later. To summarize, our findings indicate the following: For both the State of Pennsylvania and York County, a change in investment in central office equipment causes a related change in economic activity in the second, third and fourth years; only in the fourth year after a change in economic activity do we observe a related change in COE investment. A change in economic activity in Pennsylvania leads to a resulting change in investment in cable and wire for all three time horizons tested; however, a change in investment in cable and wire at the state level does not cause a related change in economic activity. A change in investment in outside plant in York County leads to a change in county-level economic activity in the second, third and fourth years. Tests for Somerset County do not yield evidence of statistically significant relationships between investment in COE and the economy in either direction. However, in the fourth year after a change in outside plant investment, a related change in economic activity can be observed in the county. A change in county-level economic activity does not lead to a related change in outside plant investment.

Appendix Specifications and tests used in the analysis of causality Standard regression analysis is one technique economists use to determine the statistical relationship between two data series. Within this however, causal relastructure, tionships are presumed. In order to investigate, on a statistical basis, evidence of causal ordering, special methods are required. Therefore economists use other techniques to determine causality. These tests seek to determine the evidence of a causal relationship between two data series by assessing whether the inclusion of past information about one data series enhances the predictability of the second. Statistical tests of causality were proposed by Granger,25 building on earlier work by Weiner.26 A thorough review of theoretical and practical issues is presented by Geweke.” The analysis of causality performed in this study involves the application of a Granger test and a similar test proposed by Sims,‘* later modified by Geweke.

429

Both the Granger and the Modified Sims tests seek to determine evidence of the causal role of A on B through an examination of comparative time series representations. In the case of the Granger test, an autoregressive representation of B is specified. The representation must include at least enough lagged variables to reduce the resulting series of residuals to ‘white noise’ (ie no serial correlation). Furthermore, B must be represented as a stationary autoregressive process. If the level B cannot be represented as a stationary process, an autoregressive representation of a first difference of B is considered, etc, until a stationary autoregressive representation may be made. A resulting autoregressive representation is then considered against another representation for the prediction of B. This representation includes all the autoregressive terms of the first but includes also lagged values of the variable A. If the inclusion of the prior history of A enhances the predictability of the variable B, as indicated by

any of a range of tests (eg an F test on the joint significance of the lagged A variables), then one concludes that A ‘Granger causes’ B. The Modified Sims test is a bit less considers Again one intuitive. whether A causes B by considering alternative time series representations. Here, however, one analyses a time series representation of the potentially causal variable A. The ‘base case’ representation involves an autoregressive representation of A combined with lagged and contemporaneous values of the variable B. As in the Granger test, this representation must be stationary and enough lagged variables must be included to render the residuals series as white noise. This base representation is compared to an alternative representation which includes not only the autoregressive terms of A and lagged and contemporaneous values of B but forward values of B as well. A is said to ‘cause’ B if the inclusion of the forward values of B increases the predictability (as mea-

TELECOMMUNICATIONS POLICY August 1993

Telecommunications infrnstructure investment and economic development

sured, eg, by an F test on the joint significance of the forward values of B) of the time series representation of A. A more formal mathematical presentation of these tests is given below. The Granger test of causality l%e Granger test asserts that n is caused by y if information about y helps to improve the forecast of x. It involves the consideration of two equations:

where = set of variables to predict = coefficient on x = set of variables against which to test predictability = coefficient on y = white noise = time = number of periods

x b Y C U t s

The Modified Sims test of causality The Modified Sims test is an extension of Granger’s test of causality. It involves the consideration of two alternative equations:

yr

=

i:

s=l

where

by-s

+

i:

h.~x,

s=o

+

“31

(3)

While the two tests are similar in concept, they are not mathematically linked. It is possible to reject the null hypothesis of no causality using one test while failing to reject the null hypothesis using the other. The structure of causality tests requires the specification of some sort of autoregressive process. In structuring the specification, two questions emerge. First, what is the appropriate lag structure, or order, for the process? Second, does the existence of a trend in any or all series bias the results? If, in fact, a trend does exist, a corollary question arises concerning the appropriate level of differencing to remove this trend.

by the appropriate degree of differencing. The presence of a trend will reflect itself in unit roots to a characteristic equation associated with the specified autoregressive process. To identify the presence of unit roots, the Dickey-Fuller test was applied. A second-order autoregressive process is represented as:

Determining appropriate lag structures

are required to satisfy 1z 1 > 1. If z1 = l/n and z2 = ‘/m, then the autoregressive process may be represented as:

The forms above are general with respect to lag structure. To apply the tests to a study of causal relationships between (constant-dollar) telecommunications investment and real personal income, a particular lag structure must be specified. To determine an appropriate lag structure, LaGrangian Multiplier (LM) tests must be run on the residuals of estimated autoregressive processes for each. If the order of the specification is inadequate, residuals will bear information in the form of autocorrelation. The appropriate lag structure will reduce residuals to white noise. The null hypothesis is that the residuals are serially uncorrelated. DRI performed LM tests on every general autoregressive specification considered in the causality study to determine the minimum lag structure sufficient to reduce the residuals of the autoregressive representations to white noise series. On this basis, it was established that at least second-order specifications should be used in the Granger and Modified Sims tests.

set of variables to predict Determining the presence of trends : coefficient on y = set of variables against which Given a determination of the lag structo test predictability ture, it was necessary to ascertain b = coefficient on x whether or not a trend existed in the = V white noise underlying relationships. For accurate = t time parameter estimates and tests it is = s number of periods necessary that any trend in the specific autoregressive process either be exThe variable y is said to cause x if the pressed explicitly or be removed. The future lags of x are jointly significant. removal of trends is typically achieved Y h n

TELECOMMUNICATIONS POLICY August 1993

(1- g@- g2B2) x, =

g0 +

e,

(5)

where B is the lag operator and e, is an error series. For stationarity of this process, the roots of the following quadratic: l-giz-g22=

0

(1 - a@(1 - mB)xl = go + e,

(6)

(7)

and then: Dx, = go + (a - l)(l - m)x,i + amDx,i + e,

(8)

where D is the difference operator. The existence of a unit root would cause the coefficient of xc-i to become zero. A test for unit roots can then be conducted by estimating the specification above and checking the statistical significance of the coefficient of xhl. The distribution of the test statistic, however, is not equal to the t distribution, not even asymptotically. Test values for particular sample sizes are given by Fuller. 2g More recently a comprehensive set of critical values for virtually any sample size was prepared by MacKinnonU) For these tests the critical value at the 0.05 level of significance is approximately -3.0; at the 0.01 level of significance the critical value is approximately -3.7. Dickey-Fuller test results Dickey-Fuller test results varied by communications equipment segment and, to some extent, geographic definition. When the analysis was applied to time series of cable and wire investment levels and every investment level series organized on a county-specific basis, the tests re429

Telecommunications infrastructure investment and economic development

evidence of time trends, and consequently causality analyses were conducted on level series for these equipment segments. For time series of state-wide central office equipment, however, Dickey-Fuller tests revealed evidence of time trends for two-year and three-year lag structures. Subsequent tests showed first-differencing to be sufficient to render such specifications stationary, eliminating the evidence of a trend. For this reason causality tests presented in this study were performed using variable first jetted

430

differences, ie the data used for each year were the changes from the previous year. Consequently causality analyses for central office equipment involving two- and three-year autoregressive specifications were conducted using first differences. Causality results obtained using first differences may also be imputed to level movements, since the levels are recovered through an integration of the first differences. Problems of specifications with over-differencing are presented effectively in Mills.3’

25Granger, op tit, Ref 15. “N. Weiner, ‘The theory of prediction’, in E.F. Beckenback, ed, Modern Mathematics for the Engineer, McGraw-Hill, New York, 1956. “Geweke, op tit, Fief 13. “% .A . Sims,. ‘Money, income and causality’, American Economic Review, Vol 62, 1972, pp 540-552. 29N. Fuller, lntruduction to Statistical Time Series, Wilev. Chichester. UK. 1976. 3oJ MacKi&on, Critical. Values for Coin&ration Tests, Working Paper, Department of Economics, University of California, 24 January 1990. 31MiIIs,op tit, Ref 19.

TELECOMMUNICATIONS

POLICY August 1993