Transportation Research Part D 14 (2009) 453–460

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Transportation Research Part D

journal homepage: www.elsevier .com/ locate/ t rd

Telecommuting and urban sprawl

Hyok-Joo RheeDepartment of Public Administration, Seoul National University of Technology, 172 Gongneung-2-Dong, Nowon-Gu, Seoul 139-743, South Korea

a r t i c l e i n f o

Keywords:TelecommutingUrban sprawlCity sizeAgglomerative forcesSpatial equilibrium modeling

1361-9209/$ - see front matter � 2009 Elsevier Ltddoi:10.1016/j.trd.2009.05.004

E-mail address: rheehj@snut.ac.kr1 Rhee (2008) compares the city with and without

a b s t r a c t

This study examines what happens to city size when telecommuting occurs. It assumesthat more telecommuting occurs when telecommuters’ labor cost share increases and/orworkers adopt a more favorable attitude toward working from home. The study shows thattelecommuting produces opposing forces that regulate the city size, one centralizing andthe other decentralizing urban activities. These forces are examined in a city where work-ers and firms are given the option to freely mix working at the office and at home, and thecity’s land use is endogenously determined. A rise in the productivity of an economy due totelecommunications technology could work to centralize urban activities, while urban con-traction can occur with a fixed city population.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

A popular image is that telecommuting causes a city to expand or even to vanish at the extreme. As the invention of carsenabled people to break the traditional size of cities, the networked society would liberate people from the traditional work-places of the industrial age. As workers can now work on fancy telecommunication networks, they do not have to commuteto the workplaces. Instead, they can work in an ‘‘electronic cottage” (Toffler, 1980), in the ‘‘extended urban regions” oraround the intelligent telecommunication network anywhere, anytime in the digital economy of the information age (Gra-ham, 1997), while making commuting a phenomenon of the pre-information age. Telework is regarded as a pillar of the neweconomy, and ‘‘new work paradigms” and ‘‘economic restructuring” claim to necessitate the increased adoption (ElectronicCommerce and Telework Trends, 2000). One extension of this line of reasoning is that people will live on bigger housing lotscausing further expansion of cities. Although some reservations are expressed (Nijkamp and Salomon, 1989), planners tendto take it for granted that more telecommuting, or increased reliance on telecommunications technology, will spur theexpansion of urban areas and invite leapfrog developments (Mokhtarian, 1998).

Various analyses have tended to support this position: both empirical and theoretical. One serious problem of the empir-ical work is that the time span adopted is possibly too short for household relocations, and definitely too short for adjust-ments in land use. Because the long-term impact on activity distributions is not fully considered, the impact on city sizeremains unclear. The same view is seen in the formal theory. Lund and Mokhtarian (1994) and Kim (1997) use a monocentricmodel to examine the impacts of home-based telecommuting on the city size and travel distance. All the jobs are located atthe city center, and the rest of the city is residential. Consequently, more telecommuting indicates the relocation of peoplefrom inner to outer parts of the city causing a physical expansion. Because people now live in bigger cities, they consumemore land on average.1

. All rights reserved.

telecommuting in the dispersed employment setting, and finds the similar results.

Fig. 1. Shape of the city.

454 H.-J. Rhee / Transportation Research Part D 14 (2009) 453–460

2. The model

2.1. Households

Telecommuting is often viewed as a social construct with its adoption being subject to personal and social factors thatmay override any considerations of its technical advantages (Risman and Tomaskovic-Devey, 1989). These factors even in-clude labor relations at work and tax considerations of both employers and employees (General Accounting Office, 2001),and among the personal factors are considerations given to family, community, work relations, and career development (Le-wis, 1997). Here, we parameterize the personal (attitude) factor by looking at the propensity to work at site, g. A housewifegiving more weight to childcare will exhibit a smaller g; the same housewife giving more weight to career and work rela-tions on-site will have a larger g. A worker feeling isolated working remotely will exhibit a smaller g than those who are not(McGregor, 2006).

A linear city (Fig. 1) with 11 discrete zones is adopted. Without loss of generality, circumferential movement in the city isassumed to cost nothing. Let gi denote the travel time required to cross one kilometer of zone i. Specifically, set gi = a[1 + b(Fi/(fRi))c], where a, b, c, and f are all positive coefficients, and Fi and Ri are daily traffic volume and road area of zone i, respec-tively. Because b > 0, there is traffic congestion. We denote city population by N, and the proportion of people whose home-work pair is (i, j) by Wij. Daily one-way commute trips between (i, j) are Fij = NWij.

A worker (i, j) residing in zone i and working in zone j consumes the composite good xij, land qij, and leisure lij. He is en-dowed with H hours per month that are to be allocated between working, leisure, and commuting. Since commuting requirestwo-way trips, and he commutes dij days a month, the monthly commuting time is 2dijgij for a worker (i, j). The householdspends $ pXixij on the composite good and $ riqij for land, where pXi is the mill price of the composite good in zone i, and ri isthe unit land rent in zone i. Once he commutes to the office, he works for eight hours. In addition, he chooses monthly workhours from home, hij. In this way, he freely mixes types of work mode. At the optimum, the utility gain from working onemore hour of either type of work will be equal to the utility loss from one less hour of leisure.

The utility function of a household living in zone i and working in zone j is

2 Wo

uij ¼ a ln xij þ b ln qij þ c ln lij þ g ln dij þ eij ð1Þ

with time and income constraints

H ¼ hij þ ð8þ 2gijÞdij þ lij;hij P 0; ð2Þwhjhij þ 8wjdij þ D ¼ pXixij þ riqij þ 2tijdij; ð3Þ

where a, b, c, and g are positive constants less than unity, and D is the rent dividend. The g ln dij term in Eq. (1) reflects thesubjective value given to the on-site work involving psychological, social, and job considerations, and it increases in propor-tion to dij.2 When a worker (i, j) chooses to work only at the office, the hours worked from home, hij, is zero.

Assuming that citizens own the urban land equally and enjoy an equal land rent dividend, D, net of the cost of providingthe roads for transportation, the error term, eij, reflects inter-household variations in utility for locations of residences andjobs. Accordingly, workers view the 11 zones as imperfectly substitutable districts for residence and work. In contrast, lotswithin each zone are treated as homogenous and perfect substitutes. On the basis of the distinct characteristics that eachzone has, workers sort themselves among a discrete set of zones.

rk at home may adds cost for more space (Graaff and Rietveld, 2007) but this is not included.

H.-J. Rhee / Transportation Research Part D 14 (2009) 453–460 455

Although daily on-site work is assumed to be fixed at eight hours per commute, a worker can indirectly control on-sitework hours by choosing the number of commute days per month, dij. For simplicity, dij is treated as continuous, and weignore discretionary trips such as for shopping trips.

After solving the household’s problem (Eqs. (1)–(3)), the solutions are put back into the utility function to give the indirectutility function u�ij ¼ Uij þ eij, where Uij is the deterministic part of the random utility function. Given the optimized utilitiesfor (i, j), each household compares commuting arrangements and chooses the most preferred, taking into account its idio-syncratic tastes. Since these are distributed among the households for each (i, j), choices are described probabilistically inthe form of a discrete choice model:

3 Assthe cho

W�ij ¼ Pr½uij > u�nv8ðn;vÞ– ði; jÞ� ¼ Pr½Uij þ eij > Unv þ env8ðn; vÞ – ði; jÞ�;

where Wij is the probability that a randomly selected consumer most prefers the commuting arrangement (i, j).3

2.2. Firms

An increase in telecommuting could be driven by the production sector. Partly for calibration reasons and partly for easeof presentation, we introduce two types of industry in each zone i: a composite good industry Xi and a structural servicesindustry Si. The former employs labor (Mi man-hours of on-site work and Mhi man-hours of telecommuters), structures(Si) and intermediate inputs (CXi) other than the structures. Following Brueckner (2000b), land (Qi) and intermediate inputs(CSi) are assumed to be the only inputs for the production of structural services.

Xi ¼ EXMi

Mi þMhi

� �m

Ml1i Ml2

hi C1�l1�l2Xi ð4Þ

Si ¼ ESQui C1�u

Si ð5Þ

where m P 0;0 < l;u < 1, and calibrators EX, ES > 0.No firms can produce without inputs, reflected by the presence of the intermediate goods, CXi, CSi, in Eqs. (4) and (5). These

inputs are frequently treated as non-land, non-labor capital. To avoid excessive complications, we assume that one unit ofcapital is readily convertible from one unit of Xi at zero cost. In fact, whenever non-labor capital is used in production, it hasto take a physical form and to come from the composite good industry, Xi.

Often, the high cost of space can be a prime motivator for firms favoring telecommuting (Apgar, 1998; Blodgett, 1996).Thus, our production activity uses labor as its primary input factors and requires a certain amount of office space andservices for the workers on-site. To be specific, Mi man-hours of office work is assumed to require s0Mi units of structuralservices. Assume that the structures are purchased from local markets at local prices, then, Mi man-hours of office work cost$ pSis0Mi for those services. Thus, a firm in industry Xi chooses the optimal input mix by minimizing production cost (wi +pSis0)Mi + whiMhi subject to the production technology (Eq. (4)). More telecommuting means less interaction between work-ers on-site, which works against the productivity of firms (Kemerling, 2002). This aspect is captured by the interaction termMi/(Mi + Mhi) and the associated coefficient m P 0.

In equilibrium, the following should hold in product and factor markets at each zone i in a city of N workers:

Composite good : Xi ¼X

j

NWijxij þ CXi þ CSi ð6Þ

Structures : Si ¼ soMi ð7ÞLabor on-site :

Xj

8NWjidji ¼ Mi ð8Þ

Labor off-site :X

j

NWjihji ¼ Mhi ð9Þ

Land : Ai ¼X

j

NWijqij þ Q i þ Ri ð10Þ

The composite good industry in zone i produces Xi, which is consumed by workers and used as intermediate inputs forother production activities. The structure industry in zone i produces Si, which is used in the composite good industry. Ai isthe land stock available in zone i, and Ri is the area reserved for roads in zone i. We treat Ri as exogenous. Because there arelabor markets for all types of work, the two types of wage rates are endogenously given.

We initially examine the rent bid by the firms of the structural services industry. In equilibrium, a firm earns zero profit,and the market rent is what remains after paying cost of capital.

ri ¼ maxSi ;CSi ;Qi

pSiSi � pXiCSi

Q i: ð11Þ

uming that the independent and identically-distributed eij s are Gumbel distributed with Eeij ¼ 0, variance r2, and dispersion parameter k ¼ 6�1=2p=r,ice probabilities are given by the standard multinomial logit model: Wij ¼

expðkUijÞPnv

expðkUnv Þ;P

i;jWij ¼ 1.

Fig. 2. Conceptual framework of the numerical analysis.

456 H.-J. Rhee / Transportation Research Part D 14 (2009) 453–460

The increasing reliance of the firm on telecommuting, in reduced form, is given by telecommuters’ greater labor cost share,l2. For an individual firm operating in competitive markets, the prices of structures and capital are taken as fixed; largershares, l2, could either reduce or increase the productivity of the composite good industry through the general equilibriumeffect, and the prices of Si and Xi will change. If we embrace this general equilibrium effect, the envelope theorem applied toEq. (11) returns

4 A p

@ri

@l2¼ 1

Q iSi@pSi

@l2� CSi

@pXi

@l2

� �: ð12Þ

While we do not examine the general equilibrium impacts, we can make an informed guess about their form. When com-plementarity of telecommuting and other economic activities works, the economy produces more, and the price of the goods,pXi, will fall (@pXi/@l2 < 0); firms needs more space, and the price of structural services increases (@pXi/@l2 > 0). This couldlead to an increase in the rent bid (Eq. (12)) and stimulate an enlargement of the city’s radius.4

In contrast, suppose that the complementarity effect is weak and that the interaction effect coefficient between workers,m, is large. Then even a modest increase in the share of telecommuters out of labor input could result in a large decrease inthe value of the worker’s interaction term and, eventually, reduce production. In the end, more telecommuting could hurt theproductivity of the composite goods industry, and Xi is produced less. In turn, the supply curve shifts left with higher prices(@pXi/@l2 > 0). At the same time, the Xi industry demands fewer structures, and their prices go down (@pSi/@l2 < 0). In sum,the sign in Eq. (12) could be negative even when the economy relies on more telecommuting. This is not directly translatedinto the contraction of a city, because its size also depends on how people react to the perturbation in land markets. But theexercise shows that a city could get smaller as its economy relies more on telecommuting.

3. Numerical illustration

Viewed narrowly, information and communications technology (ICT) involves the automation of production processes; itcollects, processes, and distributes information over telecommunication networks. In this way, the ICT substitutes for otherproductive factors such as workers and non-IT production factors. The precipitous decrease in the prices of computer hard-ware, software, and communication equipment has enabled such substitutions and is often seen as a driver of the increase inthe output and productivity of the US economy in the 1990s (Jorgenson, 2001). It has prompted a fundamental change inhow business is conducted (Dedrick et al, 2003). The direct and indirect impact on firms, workers, and the industry in generalare formed, and we can draw the first part of Fig. 2. The EX term captures the overall impact on productivity of the comple-mentarity. Microagents such as firms and workers not only mediate that impact through the economy, but also react to thechanging landscape of the economy. A similar approach is found in the empirics of Choo and Mokhtarian (2007) who explainthe complementarity between telecommunications and travel, using these indirect and long-term effects of telecommuni-cations technology.

The analysis embraces decentralizing and centralizing forces. One oft-used decentralizing force is the relocation of urbanactivities toward outer locations of the city as the need to commute declines. Another is the impact of more income on thecity size that generally results in urban sprawl (Brueckner, 2000a) but has received limited treatment in telecommutingstudies. The general equilibrium treatment of the urban space allows evaluation of the particular effects of the ICT andtelecommuting.

The factors causing centralization are space savings and agglomerative forces due to the complementarity, while thoseprompting decentralization are relocation and the output effect of the complementarity. Fig. 2 shows that the two underly-ing forces have impacts through filters or ‘‘moderators”; namely spatial structure, worker interactions on-site, work spacerequirement, city types, and the flexibility of the land use system.

ositive effects continues to prevail even when the price of capital is fixed @pXi=@l2 ¼ 0).

H.-J. Rhee / Transportation Research Part D 14 (2009) 453–460 457

All prices are determined endogenously, and the model system is homogenous of degree zero in these prices. Thus, onlythe relative prices matter, and these are arbitrarily standardized on an average household income of $20,000 per year. We setthe edge zone rents equal to the exogenous agricultural rent $0.08/m2/month ($324/acre/month) and adjust the radius of thecity to attain this rent on the periphery. In this way, we can evaluate the impact of parameter change on the city radius,which is the central concern of the paper. The wedge angle in Fig. 1 is arbitrary and has no effect on the simulation at all.The numerical solutions are obtained by iteration. The convergence of the solutions is checked twice. First, the endogenousvariables are compared with values from the previous iteration. Next, the market equilibrium conditions (Eqs. (6)–(10)) arechecked for convergence. The tolerance is set at 10�5%. The uniqueness is checked by seeding sufficiently different initialvalues into the model system.

Parameters of the utility function are set to conform to the real world as much as possible. Among these are land use,wage rates, car speeds, household budget allocations, and the time allocation between work, leisure, and commutes. It isassumed that roads are financed using head tax. Half of the land is allocated to roads in zone 6, and the ratio is graduallydecreased to 10% in the zones on the edge. Because the areas of edge zones are set such that the equilibrium unit rents thereare equal to the exogenous agricultural rent, the widths of edge zones are allowed to vary. For this reason, road shares arefixed in the edge zones, but the road areas vary.

The land cost share of the structural services industry is set to u = 0.3 in line with previous work. The coefficient of theworker interaction term is set equal to either 0 or 1. This parameter affects the output level. Labor cost share is set at 0.7 anddivided into l1 = 0.5 for on-site work and l2 = 0.2 for telecommuting. The image of most cities is that their economies arelargely composed of office activities where worker interactions matter and telecommuting is seen as an alternative workmode.

We assume that the city under review has 5000 workers (equivalent to 0.6 million in the entire circular city). When thecity is ‘‘open” to population movement between it and the outside world, the impact of telecommuting on its size could bediluted by the presence of residents, hence we fix population size. All the parameters used in this study are listed in Table 1.

Initially, we examine the impacts of the change in labor cost shares, l1, l2, with l1 + l2 fixed at 0.7 and worker attitude, g,on city size. Table 2 shows the simulation results where complex chain reactions cause the city to either shrink or expand.

In the Base case, the city expands as the worker interaction coefficient, m, increases. Bigger m invites two opposing effects.In the first place, bigger m means a greater decrease in firm productivity for a given increase in the telecommuter’s share ofwork hours. Wage earners earn less, and people bid less for land. This all finally results in a smaller city. The other effect,which is similarly of the general equilibrium nature, is that a drop in firm productivity instigates firms to substitute landfor labor. This of course works for a bigger city. Cases 1–3 show that the latter effect overwhelms the former effect whenthe land market works free from any institutional restriction.

In Cases 2 and 3, either g or l2 is changed one at a time from the Base case. Smaller g means less attachment to officework as opposed to the work from home. Surprisingly, however, Case 2 shows that a decreased propensity to work on-sitehas resulted in a smaller city. One notable reason for this is that decreased propensity to work on-site itself could translateinto less labor supply anyway, and this income effect again could make people consume smaller housing lots, while decreas-ing the city size.

Table 1Parameters of the base case.

GeographyWedge: 1.5� in each direction (3� in total)Radius of the city = 25.7 km = 16 miles including agricultural landZone width = 3.2 km (zones 2–10 only) = 2 milesAgricultural rent = $0.08/m2/month = $324/acre/month

ProductionStructural services:

Land cost share u = 0.3, Calibrator ES = 0.9Composite good:

Structural services requirement per on-site work hours0 = 1/5 in the dispersed employment models0 = 1/15 in the central employment modelTelecommuter’s labor cost share l2 = 0.2l1 + l2 = 0.7 (sum of the two labor cost shares fixed at 0.7; l2 < l1 assumed)

ConsumersN = 5,000 worker-households (=0.6 million workers in the fully circular city)H = 0.6 � 365 � 24/12 = 438 h/month (time endowment)a = 0.37 (composite good budget share), b = 0.08 (land cost share),c = 0.55 (leisure share), g = 0.1 (propensity to work on-site),k ¼ 6 (dispersion parameter of the idiosyncratic taste term)Household income = $20,000/year

Transporta = 1/80 h/km ’ 1/50 h/mile, b = 10, c = 3 (coefficients of the congestion function), f = 3 (coefficient of the road capacity formula)

Table 2Impacts of the propensity to work on-site (g), telecommuters’ labor cost share (l2), and the workers’ interaction coefficient (m) on city size.

g l2 City radius (km)

Base case (Case 1) 0.10 0.20 22.29 (m = 0)22.62 (m = 0.13)24.11 (m = 1)

Land use at each zone Adjusted Case 2 0.09 0.20 22.26 (m = 0);24.09 (m = 1);

Case 3 0.10 0.22 22.22 (m = 0);24.26 (m = 1)"

Fixed at the Base case over zones 2–10 Case 4 0.09 0.20 22.30 (m = 0)"24.10 (m = 1);

Case 5 0.10 0.22 25.68 (m = 0.13)"28.56 (m = 1)"

Note: EX is fixed at 1 and l1 + l2 at 0.7 in all the simulations in this table.

Fig. 3. Flexibility of land use and city size.

458 H.-J. Rhee / Transportation Research Part D 14 (2009) 453–460

Some US cities control telecommuting using zoning ordinances (Handy and Mokhtarian 1995). To examine the impact ofzoning on city expansion in the telecommuting cities, let us fix the land shares of residences and production in zone 6 at thesame levels as the shares in zone 6 of the Base case and decrease g = 0.10–0.09 while keeping l2 at 0.20 as before. Case I inFig. 3 shows the result. Next, fix the land shares from zones 5 to 7 at the Base case levels and set g and l2 as in Case I. Theresult is illustrated by Case II. We may thus successively fix more zones starting from the central zone until only edge zonesare free to adjust. Case V in Fig. 3 (or equivalently, Case 4 in Table 2) is where only edge zones are left free to adjust, and thecity is shown to be the biggest. In the figure, the numbers in the y axis are expressed as multiples of the Base case numbers.

Fig. 3 suggests that the inflexibility of the land use planning system is conducive to the spatial expansion of a city. Fixingthe land use shares in a zone limits the office space saved due to more telecommuting from being converted into residentialuse, and activities are forced to settle down in outer zones. At the same time, more inflexible land use means lower welfare,although this inflexibility promotes higher production level. For this reason, Case V has the highest rate of production, but itswelfare level is the lowest. The welfare level is measured by the standard expected maximum utility, E[maxij(Uij + eij)]¼ k�1 ln

Pij exp kVij.

Would the complementarity effect of telecommuting make the city of dispersed employment more expansive? Let usexamine the question in the current dispersed employment city, while leaving the land use completely to free markets.The lower curve labeled sh = 0 in Fig. 4 is for this examination and shows surprisingly that the city shrinks along with biggerEX. This pattern is fundamentally different from the monocentric city (not reported here) where bigger EX always helped thecity expand (i.e., upward sloping curve with l2 fixed). We leave the solution of this puzzle unanswered for the time being andexamine the other factors that govern the city size.

The city radius depends on the worker interaction term, m, the flexibility of land use, the space needed for home offices,the closeness or openness of a city, and the fundamental forces working for the centralization or decentralization of urbanactivities. We already examined the first two in Table 2 and Fig. 3 and now examine the remainder.

We first see how the home office space requirement may affect the overall reasonableness of the simulation results. Sup-pose that one more hour of work at home requires sh more square meters at one’s residence, just as one less on-site man hoursaves the cost of office structures by s0. Further, assume that workers do not derive any other utility from this working spaceat home. Land market equilibrium condition is revised to be Ai �

Pjshhij =

PjNWijqij + Qi + Ri, and the worker’s budget con-

straint (Eq. (3)) is similarly revised. In Fig. 4, curve sh = s0 > 0 is located above sh = 0 as expected. However, irrespective of the

Fig. 4. Complementarity effect on city size in the closed city.

H.-J. Rhee / Transportation Research Part D 14 (2009) 453–460 459

value of sh, the curves slope downward along with larger EX. This pattern is, again, in sharp contrast with the monocentriccase where the city radius became bigger along with bigger EX.

The other possibility in Fig. 4 is that under sufficiently large values of EX the curves may bounce up. Closer examination ofthe curves, though, shows that they are mildly convex, suggesting that they may rise sharply if the values of EX are bigger.The other reason for a possible sharp rise is that as EX gets bigger and more land is used for production, it becomes increas-ingly difficult to obtain land for this purpose, and firms will have to look for it in the outer parts of a city. Hence, a city couldeventually grow.

To check the implication of our fixed population assumption, we allow free movement between cities and increase EX.Greater complementarity implies increased income for workers, and thus the welfare level of a city will go up. In the longrun, migrants will move in from other cities, and the city will get more congested. A new equilibrium is reached when thecity’s welfare level eventually returns to the previous level. Fig. 5 shows a common result: a city expands when its immigrantgate is opened.

It seems counterintuitive that a city expands in the monocentric case but shrinks in the dispersed employment case withpopulation fixed. It turns out that along with EX = 0.5 ? 1 in the dispersed employment cases (Figs. 4 and 5) the inner-mostzone (zone 6) produces over four times more than before, but the outer-most zones (zones 1 and 11) produce less than threetimes as much. This means that the economy gains when it focuses production more around the central zone. Accordingly,production activities have an incentive to be in a more compact location (i.e., the centralization of production). When theland use is left to the adjustment of the market, which facilitates the conversion of residential land at the city center toindustrial use, production indeed moved in, people followed jobs, and finally the city size shrank with the population fixed

Rad

ius

in m

eter

s,N

umbe

r of

wor

kers

Note: used for the base case;Agricultural rent = $0.1/sq meter/month

0.9XE = 20, 0.22h os sν μ= = = =New XE

0

5000

10000

15000

20000

25000

30000

0.9 0.92 0.94 0.96 0.98 1

Number of

workers

new XECity radius under

City radius of theBase DispersedCity ( )1XE =

Fig. 5. Complementarity effect on city size in the open city.

460 H.-J. Rhee / Transportation Research Part D 14 (2009) 453–460

in the dispersed employment case of Fig. 4. It is only when the city has net inward migration that it starts to expand outward(Fig. 5) overcoming two centripetal forces here: (1) higher productivity of the CBD land and (2) space-saving technology.

However, when the land supply for production is restricted by zoning (Fig. 3) or the excessive concentration of productionin the central area (the monocentric city), producers do not have much choice and settle down in outer zones where landmarket works with fewer or no legal restrictions. The development of these outer locations means a bigger city. We see thatcomplementarity has two opposing effects: (i) higher income drives people out and expands the city and (ii) higher produc-tivity at inner city locations invites more production and residences to the city center shrinking the city. These forces are,however, checked. Excessive decentralization is checked by the cost of long distance commuting. Excessive concentrationis checked by the mounting congestion in land and transportation markets. The monocentric city case demonstrates the lat-ter; the industrial land in the monocentric city already reaches over 98% of zone 6’s land, while effectively limiting furtherconcentration of production there.

4. Conclusion

The analysis shows that in the city of telecommuting just like any other cities two opposing forces are working, one cen-tralizing and the other decentralizing, and that depending upon which dominate the city could either shrink or expand. Fur-thermore, the two types of forces identified in this paper are nothing special in the sense that they are not at all uniquefeatures of the telecommuting city per se.

Acknowledgement

This work was supported by a Korea Research Foundation Grant funded by the Korean Government (MOEHRD, Basic Re-search Promotion Fund) (KRF-2007-327-B00454).

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