telecommunications in rural europe: economic implications

This article outlines a study undertaken on behalf of the Commission of the European Communities by Analysys Ltd using advanced econometric modelling techniques. The main findings are that substantial aggregate employment gains are likely to result from investment in telecommunications and IT in Europe’s rural economies. The cost of creating this new employment compares very favourably with other means. However, telecommunications operators may suffer considerable net cash outflows in the initial years of investment, so that it may be necessary to deploy some form of public assistance to get the investment off the ground.

The authors are with Analysys Ltd, 8-9 Jesus Lane, Cambridge, CB5 8BA, UK (Tel: 0223 460600).

We wish to acknowledge the considerable assistance provided during the course of this study by Dr Vincent Murphy (IT Centre in Letterkenny, County Donegal); Mrs R.L. Langslow and other staff of North Norfolk Council: the Staff of the Consorzio per la Bonifica della Capitanata in Foggia, Italy; Dr Maria Sasso and Mr Aldo Circella of Tecnopolis, Bari, Italy, and Dr Peter John- ston, Mr Jacques Agniel and Mr Ross Cooper of the CEC. The authors of the paper also wish to acknowledge the substantial contributions to this work made by Allison Miller, Bram Moerman and other members of staff of Analysys Ltd. We are also grateful to Dr Peter Johnston of the CEC for permission to publish the results of our study. We stress, however, that the

Continued on p 208

Telecommunications in rural Europe

Economic implications

Suella Hansen, David Cleevely, Simon Wadsworth, Hilary Bailey and Oliver Bakewell

This article outlines the scope and results of a study undertaken on behalf of the Commission of the European Communities (CEC) by telecommunications economists at Analysys Ltd, a consultancy spe- cializing in strategy evaluation using advanced econometric modelling techniques, based in Cambridge, UK. The study - which concerned economic implications of stimulatin g applications of telecommunications and information technology (IT) in rural areas across Europe - was one of three to be conducted in support of the planning phase for a possible Community action known as ORA, whose broad objectives are to stimulate the provision of technologies, services and infrastructure suited to business activities and public services in rural areas.

The main findings of this study are that substantial aggregate employment gains are likely to result from investment in telecommunications and IT in the rural economies. In quantitative terms we estimate that if all the ‘very rural’ and ‘rural” areas of the EC invested 1% of their GDP in telecommunications and IT, between 700 000 and 900 000 new jobs would be created. The cost of creating this new employment is approximately ECU 11 600 per job, which compares very favourably with the costs of other means of job creation.

We find that the benefits (in terms of increased income and employment) of telecommunications and IT investment are likely to be lower for regions which are extremely rural compared to less rural regions. In fact our analysis suggests that as we move along the spectrum of rurality from very rural to intermediate rural, the ability of the regions to benefit from telecoms and IT investment increases to a maximum point and then declines. This could be interpreted as support for the hypothesis that a certain minimum level of general infrastructure or development is an essential prerequisite for a sustainable economic take-off. In other words, for extremely rural economies with a lack of general infrastructure an investment in telecoms and IT alone may not be enough to stem economic decline or fuel economic progress. Furthermore, at the other end of the spectrum some rural economies

0308-5961190/030207-16 @ 1990 Butterworth-Heinemann Ltd 207

Trlecommunications in rurul Europe

Continued from p 207 views expressed in this article are our own, and not necessarily those of the Commis- sion of the European Communities nor of any of the people named above.

‘Defined according to the Analysys Index of Rurality (see next section). *It should .be noted that .although the REGIO database forms a solid statistical base, the Index would be improved significantly if the data could be disaggregated further. Because of the size of the areas covered by Level III, many problem areas (as well as potentially strong centres of telecoms and IT) may be ‘masked’; for example the Algarve region of Portugal scored as a very rural area, yet significant pockets within the region are concentrated centres of advanced telecoms and IT.

may have alrsady reaped the gains of new investment in this fitid in the past, reducing the potential for any future gains. This leaves us with a ‘middle ground’ of rural economies vvhich are most suitable for this type of investment.

In these ‘middle ground’ areas vve find that the economic benefits arise both directly from the initial investment itself, and also - perhaps more importantly - as the cumulative result of an ensuing ‘chain reaction’. In other words, the increase in economic activity will have direct and indirect knock-on effects in other areas of the economy. and these effects may cause a further increase in activity. hloreover. as the industrial structure of a region changes with increased investment in telecoms and IT, it is likely that the region will appear increasingly attractive to new firms considering starting up business or relocating. Firms which might otherwise have shifted may therefore be encouraged to stay within the region.

However. providers of telecoms and IT facilities in rural areas face specific problems - relating to distance costs, the uncertainty of demand and the length of user take-up periods - which may be insurmountable without either public assistance or the formation of some private cooperative arrangement. Our modelling illustrates, for example, that telecommunications operators may suffer considerable net cash outflows in the initial years of investment.

These results are based on questionnaire data gathered from three different case study areas, together with a theoretical investigation into the economic and technical issues involved. Both microeconomic and macroeconomic modelling were undertaken, and Analysys STEMT” -

a technoeconomic tool - was used to provide input regarding the cost of the investment. Information from the microeconomic analysis and the STEM study was fed into the macroeconomic model to produce estimates of the effect of the investment in the case study areas; these results, together with regional economic indicators and the Analysys Index of Rurality, were then used to arrive at estimates of the EC-wide effect.

The estimates of the economic effects of telecoms and IT which we derive by this approach are inherently conservative, since at every stage we have made the most pessimistic assumptions. In particular, the estimates do not include any allowance for indirect external benefits (or ‘externalities’), which - because of the well-established theoretical difficulties in quantifying them meaningfully - are typically assessed in subjective and qualitative terms.

In this article we describe both the methodology which vve designed for the study, and the study results based on the three case studies. The results of three further case studies are not available at the time of writing.

Rural areas of Europe

We found no satisfactory procedure in the literature for classifying areas according to their rurality, and accordingly we devised an original Index of Rurality based on discriminant analysis, using NUTS Level III data from the CEC‘s REGIO database.’ We found the main explanatory variables to be population density and income.

The Analysys Index of Rurality classifies the regions of Europe (in 1986) according to six broad categories: very rural, intermediate rural, rural, intermediate urban, urban and very urban. This classification

208 TELECOMMUNICATIONS POLICY June 1990

Ttkommunications in rural Europe

(illustrated, at a mixture of Level II and Level III, in Figure 1) was used to examine the economics and demographics of rural areas at an aggregate level, and to select representative case study areas for the gathering of detailed microeconomic data.’

Macroecorlomics and demographics of rural areas

Analysis of the aggregate demographic and economic data for the 12 EC

member countries, in total and by rurality category, emphasizes the importance of the regions covered by the three rural categories - not only in term of land mass. but also in the large numbers of people who are affected by rural or regional policies (see Table 1). The economic importance of these regions must not be underestimated, since the

3Nole that the areas left blank on the map majority of economic wealth generated in Europe originates from these

represent missing observations in the RE- areas. This wealth is spread thinly across rural Europe, but the potential GIO database. economic gains from targsting these areas for structured investment

1

Figure 1. Map of Europe applying the Analysys Index of Rurality (NUTS Levels II and III).

TELECOMMUNICATIONS POLICY June 1990 209

Telecommunicarionr in rural Europe

Table 1. Aggregate economic and demographic data for all regions included in the REGIO database.

Very rural Rural Intermediate rural intermediate urban Urban Very urban Total

Total population (‘000) 80 654 73 169 52 882 11 946 32 637 21 383 272 692 No of regions 177 170 123 ta 47 27 562 Surface (km*) 1 275 651 495 256 209 266 28 954 44 303 19 188 2072 168 Population density (per km*) 64 153 253 413 737 1114 132 Average GDP/head (ECU) 9 468 11 074 12 236 12801 13 363 20 489 11 522 Low GDPihead (ECU) 2 406 4 002 6 a40 9717 3 908 9 471 2 406 High GDP/head (ECU) 16 148 15792 19 919 18 159 19 a98 43 405 43 405 Total GDP (MEW) 593 301 742 117 626 290 159 174 415 958 453 168 2 990 009 Average unemployment 10.4 8.7 7.9 8.1 a3 95 90 Low unemployment 2.8 2.1 2.4 3.7 2.3 3.7 2.1 High unemployment 30.5 21.7 24.8 15.8 19.5 15.7 30.5

As a % of tot.&

Population 30 27 19 12 a No of regions 31 30 22

: a 5

Surface (km’) 62 24 10 1 2 1 Total GDP (MEW) 20 25 21 5 14 15

could be hioh 3 , given the slack already evident in these areas (high unemployment), and the combined economic weight of the regions.

Case study areas and microeconomic data guthering

Since time and resources limited us to three case studies. we attempted to select regions representative of different aspects of the spectrum of rurality. Using the Analysys Index, we chose two ‘very rural’ regions and one ‘rural’ region:

a

a

0

As

County Donegal (Republic of Ireland): the most rural of the three areas (according to the Analysys Index), with some serious economic problems and a poor physical infrastructure, particularly with respect to transportation. Over the past decade, hoivever, the local telecommunications system has been upgraded dramatically from a manual exchange system to an automatic digital system, providing an excellent opportunity to investigate the impact of such an investment in terms of economic gains for the area. Foggia (Mezzogiorno, Italy): the most rural province in the region of Puglia, with an underdeveloped telecoms and IT infrastructure. North Norfolk (UK): a rural area with a very Icw population density, designated a ‘problem area’ by the UK Rural Development

Commission; chosen as a readily accessible area for the first (pilot) case study.

Table 2 shows, there are significant differences between the economies of the three areas.

Microeconomic data on these three regions were gathered by means of interviews with representative rural businesses. The questionnaire which formed the basis of these interviews included questions on the number of employees, value of sales in the last financial year. location of customers and suppliers, usage of and expenditure on telecoms and IT equipment and services, level of importance attached to them, nature and level of benefits derived from them, effects on the level of employment, projected future usage and expenditure. and desired improvements.

Microeconomic cost-benefit analysis

Using the case study data, we conducted a microeconomic cost-benefit


Tekcommunicarions in rural Europe

a 1985 data.

Table 2. Economic indicators by case study region Y EC averages (1986).

Norfolk Foggia Donegal EC

GDP per head (ECU) GDP in PPS per head (ECU) Unemployment (%) % change in unemployment (1985-86) Net migration Population density per km2 % of population aged 15-64 % participation rate:

Males Females

9 186 7 793 9 381 10 971 13267 10 301 11 126 13 696

10.3 14.7 26 10.6 0.4 5.0 0.5 0

9 347 -1 674 -230 273 660 136 97 27 143 64 61’ 56 69

7 64a 70 69 - 26= 28 40

analysis to investigate the effect of telecoms and IT at the level of the individual organization. The model developed for this purpose allowed us to calculate the net effect, in monetary terms, of firms investing in telecoms and IT, and enabled a comparison with the general return on turnover of all those companies. The results of this analysis were used as input to the regional macroeconomic cost-benefit analysis (which is described further below), in the form of increased company and employee expenditure.

Methodology

The methodology for the microeconomic analysis was designed to provide estimates of the net benefits and costs accruing from the usage of telecoms and IT in the different case study regions. At all points in the analysis the ‘worst case’ or most conservative estimate was used, to ensure that the results indicate the minimum level of benefits.

Costs of using tefecoms and IT: Businesses were asked how much they had spent or were planning to spend on computers and communications in various time periods; we used their responses for the 1988-89 financial year.

Benefits of using telecoms and IT: The questionnaire data provided three key indicators for the benefits which firms accrue from the use of telecoms and IT:

0 the increase in output; 0 the increase in efficiency; 0 the increase in service quality.

Of course the levels of benefit to be gained might not be the same for all three of these; ia order to capture this variation we weighted the values obtained for each of these indicators to derive a Net Weighted Benefit. The weightings used depended upon two factors:

0 the importance to the business of each variable; 0 change in employment due to telecoms and IT.

We must account for the way in which increases in efficiency will lead to 4This worst-case assumption makes an changes in output. From the questionnaire we knew whether the use of allowance for the possibility that telecoms and IT applications may be labour-saving

telecoms and IT had led to an increase or decrease in employment in the

at the microeconomic level. The employ businesses (or had had no effect). Given that we assumed (on the ment implications are quite different at the ‘worst-case’ principle) that no extra jobs would be created directly macroeconomic level, as the increased turnover of the businesses indirectly cre-

within the business due to the use of telecoms and IT,“ then if the

ates more job opportunities in the eco- change in employment was zero, all efficiency gains must have been nomy; see below. translated into increased output and service quality. If the change in



< employment was positive. it was impossible to decide whether the L 4.5

; 3 3.:

~-‘bGI!

business was more or less efficient. and so the weightings remained

- 3 unchanged. If the change in employment was negative. then not all

G 2.5 efficiency gains were being translated into increased output. ‘; 2 $ I .5

Having calculated and applied the weightings to the three indicators

^ 1 dependent upon the change in employment, we arrived at a weighted 3 0.5

-. 0 percentage benefit. To convert this percentage benefit into monetary

- s z

horfolk DOIXg;ll Fogg!z terms we applied it to the turnover of the company. The resulting value represented the monetary benefits accrued from the usage of telecoms

Figure 2. Comparison of average and IT. benefit-cost ratios for case study areas. Results

Figure 2, which compares the benefit-cost ratio of the three case study regions, shows that a region such as Foggia, with a poor telecoms and IT infrastructure, is much more likely to reap higher benefits (compared to costs) from a new investment in telecoms and IT than a rural area such as Donegal that has already invested in new technology.

The results of the microeconomic cost-benefit analysis suggest, therefore, that across the spectrum of rurality and telecoms and IT infrastructure in Europe there are differing levels of benefits to the individual institutions from investment in telecoms and IT. Regions like Foggia are ripe for investment; others, like Donegal and Norfolk. may have reaped significant gains in the past from new telecoms and IT, so the current benefit-to-cost ratio is relatively less favourable.

Analysys STEM

The costs involved in upgrading the telecoms and IT infrastructure and service of a rural area were studied using Analysys STEM. a proprietary modelling system designed to allow an Analysis of the economic consequences of alternative investment decisions and implementation strategies. STEM was also used to examine the effect of various different pricing policies which might be adopted by the telecommunications operators.

Data supplied by Telecom Eireann (for Donegal) provided useful guidelines for the model building; it should be noted, however, that the outcome was not intended to be a model relating specifically to Donegal, but a general model illustrating the consequences of typical rural telecommunications features.

Scenario

One of the major problems facing the providers of telecommunications in rural areas is the high cost of installing the necessary infrastructure, and then the slow take-up of services by customers as they become available. A critical factor in the speed of take-up of new services will be the tariff and the customers’ perceived level of benefit they will derive by taking up the service - their utility.

To address these issues we developed a model of the evolution of service demand over time (based on Rohlfs’s uniform calling model of demand) ,j and used Analysys STEM to assess the costs of satisfying

‘J. Rohlfs, ‘A theory of interdependent demand based on data gathered in the Donegal case study. We were demand for a communications service’, Bell Journal of Economics and Manage-

then able to compare the cost of providing the improved telecom-

ment Science, Vol5, No 1, Spring 1974, pp munications with the total benefit to the local economy (measured by 1 e-37. the increase in the GDP - see below).


Telecommunicarions in rural Europe

q 2100 C

:$ *o - : x 2’ 60 8 D

; ,?

,,a 2 20

- 0 ;i

Demand and marginal urility. The scenario represented the provision of

various new services (such as facsimile. digital data, packet switching,

-- --_-_-__-_______ __ ED1 and EFTPOS) to a rural area where the take-up was slow. We -f 40 A

- .Llarglnai utlllty E assumed that some customers within the region would already have such ---- Price services, but that the penetration of the potential customer base was

0 IO 20 30 40 50 60 i0 30 90 100 quite low. Penetration % In order to assess the development of demand for the new service

Figure 3. Marginal utility plotted over time, we considered how the price being charged by the operator

against penetration. (the tariff) compared with the benefit which any potential new subscriber might gain by adopting the service (the marginal utility). We took the marginal utility to be dependent on two factors:

0 the nature of the potential customer (eg, at any given penetration the marginal utility for a financial consultant to gain access to a packet-switched service would be larger than that of a farmer):

0 the size of the network within which they can use the new services.

As an example, assuming that for a given user a communication link with any other user gives the same benefit (the uniform calling model), Rohlfs shows that the marginal utility as a function of service penetration can be represented by an inverted parabola with the maximum marginal utility when the penetration level is 50% (Figure 3). A user will only subscribe to a service if the marginal utility is greater than the price. If the telecommunications operator is providing the service at a price below the marginal utility, we would expect

penetration to increase to a point of stable equilibrium greater than 50%, where the marginal utility equals the price (point D).

We assumed that the rate of change of demand over time was a function of the difference between the marginal utility and the price, so we derived demand curves for the evolution of the network. starting at a given penetration and price. This analysis was developed further to allow the consideration of price changes in time.

An operator investing in the infrastructure to provide new services must choose its tariff levels carefully in order to stimulate take-up of the services. We considered three cases:

0 tariff starting at 88% of marginal utility and decreasing by 1% pa; 0 tariff starting at 95% of marginal utility and decreasing by 1% pa; l tariff starting at 100% of marginal utility and decreasing by 2% pa.

Equipment. The STEM model considered the need to install two sorts of equipment within the network:

0 infrastructure equipment (local exchanges, access links) installed in response to the network operator’s policy decision to upgrade the network to support new services;

0 ‘usage’ equipment (customer premises equipment [CPE], transmission capacity), installed as customers take up the upgraded services (ie in line with the demand profile calculated on the basis of marginal utility and price, as described above).

The main types of equipment modelled are shown in Table 3. We assume that the policy of the telecommunications operator is to

provide upgraded access links and the local exchange capacity to support the desired level of the new services; such equipment is therefore installed at a pace set by the infrastructure demand profile.


Telecommunicari0n.s m rum1 Europe

Source: Telecom Eireann

Figure 4. Demand, by different tariffing policies.

Table 3. Equipment in the model.

Equipment type

CPE Usage Access links Infrastructure Urban LE Infrastructure Rural LE Infrastructure Transmission Usage

Lifetime (years)

203 10 10 15

Units

Subscnbing customer km Equipped customer Equipped customer Subscribing customer

The costs of exchange and access link equipment consist of only those portions of the investment which form the infrastructure. CPE and transmission equipment is installed at a rate determined by the number of customers subscribing to the upgraded services; these costs include any additional investment required to make a connection operational.

Within the class of access link equipment we made a further subdivision between long, medium and short access links, in order to create a very simple model of the geographical distribution of the potential customer base for new telecommunications services. The assumptions as to the proportion of demand mapped over each of these three types were based on the geographical distribution of the

population in Donegal and the average access link lengths for that area; we also assumed (again from Donegal data) that 35.9% of demand was carried by urban local exchanges. and 64.1% by rural local exchanges.

STEM model results

The STEM model calculated the costs of providing the additional equipment needed for the new services, and the revenue the network operator would receive as the services were taken up by customers. The model was run for the three example pricing policies described above. The demand curves shown in Figure -I show how usage demand lagged behind the supply of infrastructure (which was the same in each case), and how much greater that lag became as the charged tariff was raised.

We found that the effect of increased tariffs was to reduce demand to such an extent that total revenue was lower for the higher tariffs (see Figure 5). Not only was revenue reduced when higher tariffs were charged, but the profit margins were also cut quite dramatically (Figure 6) as a result of the reduced number of customers using the same amount of infrastructure, causing inefficiencies in the system.

Although service supply became profitable within a couple of years for all scenarios, the investment required to expand the network was

2000 - Infrastructure /. /

1800- - Tariff 33% h1.U.

2 1600 - - - -- Tariff 95% M.U.

z .......... g

Tariff 100% X1.U. 1400 -

;; 3 1200 - ”

; 1000- c z 800 -

z 600 - 3 z” 400 -

200 -

0 I 1989 1994 1999


Telecommunicnrions in rural Europe

3500000

2

1

L, 2 "

oooooo_ - Tarlff883 M.U.

----- Tariff 95% M.U.

......... 500000 - Tariff 1OOiTU.

000000 -

500000-

000000 -

500000 -

0 Figure 5. Revenue, by different tariffing policies. 1989 1994 1999

such that even in the best case - where tariff was pitched low to stimulate demand - cash flow remained negative for seven years (as shown in Figure 7). When the highest tariff was charged. cash flow remained negative throughout the ten years of the model run. The cumulative cash flow (Figure 8) illustrates the effect of such negative cash flows over a number of years.

Macroeconomic cost-benefit analysis

Clearly the pattern of investment shown in Figures 7 and 8 could easily dissuade network operators from extending the advanced network further into rural areas. However, our studies of the economies of rural areas suggest that as spending on telecoms and IT increases within an area, there will be an increase in GDP, the magnitude of which depends on the particular area. Using these wider criteria for the return on investment - ie taking into account not only financial gains or losses to the telecommunications operator, but also the economic benefits for the area - we reach a much more positive case for telecoms and IT investment.

In the macroeconomic cost-benefit analysis which we now go on to

describe, we attempted to assess in quantitative terms the economic benefits of telecoms and IT investment for each case study area, using the potential benefits of investment in telecoms and IT at the level of the individual firm derived from the microeconomic analysis, together with various multipliers which capture the knock-on effects in the economy of businesses’ increased turnover as a result of new telecoms and IT

investment.

7’ -Tariff 88% M.U.

6 Tariff 95% M.U. 'bl

g 5 Tariff 100% M.U. z 04

.c

2

3

Lu 2

rent tariffing policies. -1 I- 1989 1994 1999


Telecommrtnicafions in rural Europe

Figure 7. Network cash flow.

Methodology

The main focus of our macro cost-benefit analysis was to look at income and employment multipliers, which show the extent to which economic growth in a region can be self-reinforcing. Multipliers considered here are short-term, one-year multipliers.

The micro cost-benefit analysis showed that for any extra percentage of turnover spent on telecoms and IT, the individual firm would enjoy a y% increase in turnover due to increased efficiency, quality of product, etc. The additional turnover generated would have a knock-on effect in the whole economy, due to the operation of the multipliers. Our aim was to select the most appropriate multipliers for each region, apply them to the total new turnover figure Y for sector S. and thus specify a total GDP Benefit Ratio (GBR), made up of two stages, as shown in Figure 9.

Other benefits accruing to a region include improved employment and further increases in GDP due to new firms moving into the area. So, finally, a balance sheet was drawn up showing all the costs and benefits (to the whole region) of stimulating demand for telecoms and IT.

Effect on GDP (through GDP multipliers). Since multiplier effects differ between sectors, we needed firstly to split our analysis down to the sector level. Assuming our case study samples to be representative of the regions, the micro cost-benefit analysis results could be scaled up so that for any sector S, if every firm spent I ECU on telecoms and IT, total turnover would rise by ys.

Figure 8. flow.

Network cumulative cash

19a9 1994 :999

‘.., --_______--- Tat-iii 88% M.U. . . . . . .

----Tariff 95% 1h4.U. ‘....

. . . . . . . . . Tariff : 00% M.U. ‘......,,

‘.‘....................,,,,,,..,


Telecommunicarions in rural Europe

For every ECU spent on IT ant! telecommunicz:ians:

j 1 ECU,

Shcro analysis

Figure 9. Selection of inultipliers to

> ys iLsp;;;i. CD? k~enefl: ra:lo

specify GDP Benefit Ratio.

Thus, for any given sector, an expenditure of I gives:

LY, Y, = -

GDP,

(1)

where

Y, = total new turnover generated due to telecoms and IT investment;

GDP, = sectoral GDP;

Y, = percentage change in total turnover;

1, = total sectoral investment in telecoms and IT.

Since multipliers also differ according to whether money is spent by firms or employees, we then split the money generated in each sector into money paid to labour as wages, and money spent by the firm on other items. (The latter was estimated using figures representing labour intensity.)

6This has been derived from the simple macro model:

Y=G+I+C+X-M

C = a + cYd

M,, = b + mYd

Yd = (1-t)Y

where

Y = total income: G = government expenditure: I = investment: C = consumption; X = exports; M = imports; Yd = disposable income; a and b are constants; c = marginal propensity to consume; m = marginal propensity to import; t = marginal tax rate.

Y, = y”, + YO,

where

Y”‘, = expenditure on wages by firms in sector S; y”, = other expenditure by firms in sector S.

The basic multiplier used in our analysis is as follows:6

k = l/[ l-(c-m)( l-t)]

where

k = multiplier; C = marginal propensity to consume; m = marginal propensity to import from outside the region; t = marginal tax rate.

(3)

In order to explain the varying effects of the firms’ expenditure on non-wage items, we calculated c, m and t using data from Eurostat, our own questionnaires and the literature. We also calculated values of c, m and t for the effects of increased consumption by householders, thereby calculating a consumer multiplier. In general, additional funds passed onto consumers through wage increases are much more likely to be spent within the local economy. Using these multipliers we calculated the sectoral effect on GDP:

G, = Z,(Y,“J + Y,“k,) (4)

where G, is the increase in GDP due to I additional investment in telecoms and IT in sector S.

We then summed across sectors for the total increase in GDP, and calculated a GDP benefit ratio by taking the ratio of the amount of telecoms and IT expenditure to the change in total GDP.


7mmunications in rural Europe

First-round effect on employment. As explained above. a ‘worst case’ scenario was assumed, whereby no additional jobs are created as a direct result of investment in telecoms and IT. However, the total amount of GDP generated (GBR) will have an effect on the employment level of the region. and we attempted to quantify these employment effects.

The GBR derived above indicated how much money is made available for every percent of GDP spent on telecoms and IT, for the whole region. We assumed that this would be split between sectors in direct proportion to their size. However, only a proportion (equal to the labour intensity figure) is actually spent on labour in any sector. Thus, extra money available for expenditure on employment in a sector S will be:

GBR;Y”,/Y, (5)

Moreover, before new jobs are created, some of this money will be first absorbed by additional wage increases for existing employees, in return for increased productivity. Another diversion of funds will be into additional overtime payments made to existing employees. Furth- ermore, not all jobs created due to increased turnover will be full-time, permanent jobs. Our survey results showed that - particularly in agriculture and the services - there was a strong propensity to employ casual workers, especially if an increase in demand is deemed temporary. After allowing for these factors we were able to convert the total employment figure into ‘full-time equivalent jobs’.

Second-round effect on employment. When a new job is created there is a ‘knock-on’ effect in the whole economy, whereby further jobs are created to support the original new job. This concept is known as the employment multiplier. An allowance was made for this using information from the literature.

Using all of the above considerations, we were then able to relate telecoms and IT investment to employment, allowing us to predict a percentage increase in employment for a given percentage of GDP spent on telecoms and IT. We have called this the ‘ability to create jobs from telecoms and IT’ function. As well as the direct benefit to individuals finding employment, there will also be a total benefit to the economy equal to the amount of unemployment benefits that no longer have to be paid.

Telecommunications operator’s net cash outgoings. Since improvements to telecoms and IT involve very heavy infrastructure expenditure for the telecommunications operator (TO), we also included the TO’s expected negative cash flow, based on the STEM model described above. This was a very general model, but allowed the definition of a ratio of the TO’s negative cash flow in a year to the initial expenditure on telecoms and IT, and this was used (scaled according to the expenditure considered) in our cost-benefit balance sheets (see below).

For this analysis it was assumed that 80% of new expenditure is spent on telecommunications; this was a deliberately high figure, in order to highlight the TO’s costs. However, it renders the results very conservative. We assume that the TO net cash outflow ratio is constant across regions.



Structural change. Our case studies showed that there were also ‘hidden’ benefits of telecoms and IT improvements in a region arising from changes in its industrial structure. In particular:

0 firms remaining that might have left if telecoms and IT had not been improved;

0 new firms being attracted to the area which might not have moved or started up in the absence of telecoms and IT improvements.

We assumed that if telecoms and IT were improved it was about 1.5 times more likely that firms would move into an area than that existing firms would move out (the figure varied between regions), and we also assigned a timescale to firms moving into the area. This enabled us to arrive at a figure for the value of new turnover that could be attracted into the region by improved telecoms and IT.

Results of macro cost-benefit analysis

The results of the cost-benefit analysis are presented in Table 4 in balance sheet format. They illustrate how many ECU are generated as a result of one additional ECU being spent on telecoms and IT in one year (80% of which is spent on telecommunications). Table 5 summarizes these results by region.

Given our pessimistic assumption that 80% of telecoms and IT expenditure would be on telecommunications, the negative cash flow of the TO becomes very significant. It has major implications for the likelihood of telecoms and IT improvements being implemented, since an operator will be unwilling to embark on a heavy investment programme if it will have seriously adverse effects on cash flow. However, it must be stressed that this effect is most significant in the first year, and gets progressively better, as installed infrastructure becomes more fully utilized.

Benefits and rurality - scaling to Europe

The ability to benefit from telecoms and IT investment tends to change with the degree of rurality of the region. If such a relationship can be defined, then, given its rurality score, the benefit/cost ratio can be read off for any region in Europe. It is this basic concept that lies behind our scaling up of our regional result to the rest of Europe.

For this methodology to be valid we have to be sure that most of the variables in the model are themselves related to rurality. Key variables in our analysis described above were:

Table 4. Balance sheet (costs and benefits of telecoms and IT investment, year 1).

Norfolk Donegal Foggia cost Benefit Cost Benefit Cost Benefit

Description (ECU) (ECU) (ECU) (ECU) (ECU) (ECU)

Additional expenditure by firms 1 .oo 1.00 1 .oo Additional GDP generated from higher

turnover, etc 2.23 4.74 5.17 Value from new iobs

~(unemployme;j benefit) 0.07 0.45 0.31 Telecoms operator’s net cash outflow 2.83 2.83 2.83 Turnover of firms remaining 0.01 0.01 0.01 Turnover of new firms 0.02 0.03 0.02

Total 3.83 2.33 3.83 5.23 3.83 5.52

Balance -1 so 1.41 1.69 Ratio of benefits to costs 0.61 1.37 1.44



Table 5. Summary of % change in GDP and unemployment resulting from an increase of 1% of GDP spent on telecoms and IT (by region).

Norfolk Donegal Foggia

Rurality score

-0.95 -2.48 -1.70

Value benefit (%) Employment benefit (%)

0.61 0.36 1.37 3.88 1.44 5.34

@ size of sectors, especially agriculture; 0 wage levels; 0 labour intensity of production techniques; 0 propensity to import by firms and households.

The work undertaken in defining the Analysys Index of Rurality suggested that many of these factors are related to rurality; indeed some have previously been used in the definition of rurality indices.

Figure 10 plots each region’s ‘ability to benefit’ against its rurality score and also shows how it changes over time, with the improvement of conditions for the TO. The graph contains points for all ten years; they suggest that an inverted parabolic curve (as shown) could express the relationship between rurality and the overall benefit-to-cost ratio, and that such a curve would drift upwards over time as the TO’s cash flow position improved. Obviously, further data points would be required in order to calibrate such an equation, but it seems reasonable to suggest that extremely rural areas (due to inertia) and the wealthier areas (due to ‘leakiness’) may benefit less, and that there is a central ground of regions that are ripe for telecoms and IT development.

It is interesting to consider how the ‘ability to benefit’ relationship changes over time. As soon as any region benefits from telecoms and IT, the components of the Rurality Index will change; as more firms move into the area, the population will become more dense and the region will also become richer. This suggests that, over time, all rural regions will ‘drift’ to the right along the x-axis. However, without investment in telecoms and IT, regions may drift to the left. even to the point where they lose the opportunity to benefit from telecoms and IT; this has obvious implications for the timing of investment.

In a similar fashion, the relationship can be plotted between rurality

Donegalx

x

x

,Foggia

I I I I I I I

-4 -3.5 -3 -2.5 -2 -1.5 -i 0

Figure 10. Ability to benefit by re- Rurali:y

gion, plotted against rurality score. Time *


-4 -3.5 -3 -2.5 -2-1.5 -1 -0.5 0

Figure 11. Improvement in employment, plotted against rurality.

‘This implies that there would be no benefit from investments in telecoms and IT in urban areas. Since we have not carried out case studies in urban areas, it is really impossible to say how this curve will behave in the positive quadrant. *We have not included here the direct effects within the telecommunications in- dustry, since it is assumed that this will tend to have an effect on jobs outside the region. gReview of ihe Highlands and islands Development Board, HIDB Review Group Report to the Secretary of State for Scot- land, IDS, 1987.

Tekcommunicurions in rurui Europe

and the improvement in employment as a result of an increase in telecoms and IT spending. Figure 11 shows the three points from our case studies, and one possible curve that cuts through those points. We have superimposed a normal distribution through the three points. However, it should be noted that this will tend to underestimate values, because both tails are asymptotic to zero.’

The theoretical rationale for the shape of the above curve is found in the ‘threshold’ effects sometimes apparent in regions striving to take off into sustained economic advancement. In the absence of some minimum level of development it is difficult for regions to cross the threshold into a self-perpetuating economic spiral of growing income and employment. In this particular case we believe that the threshold constitutes a necessary minimum amount of general infrastructure.

Using this function, it is possible to estimate what the employment effects for any region will be, if its Rurality Score, GDP and employment figures are known.8 This function has been applied to each of the NUTS Level II and Level III regions, selecting only those areas that are classified as either ‘rural’ or ‘very rural’, and the results are set out in Table 6. The first two rows show the employment implications for the whole of Europe if every region in the category spent just 1 ECU on telecoms and IT. The last two rows show the effects of spending 1% of GDP on telecoms and IT.

We found the cost of creating each new job through telecoms and IT investment to be ECU 11 587. This is considerably lower than the minimum cost estimated by a report on the economic benefits of upgrading the telecommunications infrastructure in the Highlands and Islands of Scotland,’ which is ECU 15 246 per public sector job (maximum ECU 24 750).

It must be stressed that these results are entirely a function of the input assumptions, the quality of available data and the representative- ness of our case studies. The results were significantly sensitive to a number of the input assumptions, so it is essential that they are calibrated as carefully as possible.

Conclusions

The ability of regions to benefit from new investment in telecoms and IT varies across the spectrum of rurality, with higher benefits over cost estimated for more rural areas than for less rural areas. However, it appears that this benefit is reduced for extremely rural areas, possibly because of their lack of general infrastructure (other than telecoms and IT infrastructure). This lends support to ‘threshold’ theories which suggest that sustained economic take-off will not occur without some minimum level of development.

In quantitative terms, we estimate that between 700 000 and 900 000 new jobs would be created if all the very rural and rural areas of the EC

Table 6. Total employment effects for all rural and very rural regions of Europe.

NUTS level Telecoms and IT expenditure No regions Total jobs created

II ECU 1 79 0.0076 III ECU 1 329 0.0264

II 1% GDP 79 903 583 Ill 1% GDP 329 707 562



invested 1% of their GDP in new telecoms and IT (which is equivalent to approximately ECU 11 600 per job). This conclusion is reached by taking into account the effect at the level of the individual firm, and translating this into the effect on the whole rural area.

Quantitative modelling using Analysys STEM provided some guidelines for the cost involved in telecommunications investment in rural areas. The nature of these areas presents particular problems for the telecommunications operator - namely, a high cost of installation (primarily due to distance) and a slow take-up of the services (principally due to the type of customer groups involved). It is apparent that in the initial years of the investment the telecommunications operator would suffer from negative cash flows. The positive cash flows which appear in later years may not be of a sufficient magnitude (after discounting) to make the initial investment profitable to the operator.

These conclusions imply that it may be necessary to deploy some form of public assistance to get the investment off the ground, or alternatively for end-users (that is, whoever might benefit from the improvements in telecommunications in the short and long run) to form some type of cooperative for the same purpose. The same applies to information technology. The question of the appropriate mix of responsibilities between the public and private sectors must in the final analysis be left to policymakers. However, the scale of investment required, the multiplicity of end-users who might benefit - as well as the significant social benefits (not included in the scope of this study) which are likely to accrue - all point to substantial public involvement or indeed initiative in this field.


telecommunications in rural europe: economic implications

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