Appendix A: Timeline of the Industry Evolution
Appendix B: Model Solution
This appendix provides additional details and the procedure of numerically solving the model. Recall in our model, the exogenous parameters are (β, d0, d1, c0, c1, πϕ, K, ωa, ωd, I0, Ig, g0, g1, λ, γpre, γ). The endogenous variables are the number of new entrants Nϕ at time T, the final time of debit adoption T′, the starting time of voluntary exit T″, and the sequences of prices
As we have characterized in the paper, the dynamics of the prices, outputs per network, profits, network numbers, value functions, and voluntary exits will be determined by the number of new entrants Nϕ and the timing of endogenous final adoption and voluntary exit T′ and T″. Among them, T″ (> T′) will be determined by the outside option value πΦ. So we can use the following algorithm to solve for the model solution with two-dimensional grid search over control space of NΦ and T′, and in the meantime we derive the dynamics of all other endogenous variables.
Step 1: Define the grid points by discretizing the control space of the numbers of entrants NΦ and the endogenous time T′. Make an initial guess of the numbers of entrants NΦ.
Step 2: Take NΦ as given, and make a guess of the final adoption time T′. We can characterize the dynamics of the solution for three time ranges—from T to T′, from T′ to T″, and from T″ and onward. Given the initial numbers of entrants and the final adoption time, we first obtain the sequences of prices, outputs per network, profits, network numbers, voluntary exits till T′. As T″ > T′ we then derive the voluntary exit time T″ with the condition that the profits of ATM-only networks equate the outside option value πΦ. With the known T″, we then solve the full paths of all other endogenous variables
. Applying the backward induction based on 400 periods, we also derive the sequences of value functions from equations (8)—(10) given NΦ and T′.
Step 3: Given NΦ, we now verify whether the guess of time T′ satisfies the condition ψt ≥ 0 for all t ≤ T′ and ψt < 0 for t > T′ shown in equation (12). If the condition is not satisfied, we then make another guess of T′ and repeat Step 2 until we derive the consistent final adoption time T′ and other variable values for the given NΦ.
Step 4: We then verify whether the guess of the number of entrants NΦ satisfies the condition shown in equation (11). Check the discrepancy of equation (11) given NΦ and the derived T′ from Step 3. If it is above the desired tolerance (set to 1e—5), go back and repeat Step 2 and 3 until both conditions in equations (11) and (12) are satisfied within the desired tolerance level. Thus, we have solved for the dynamics of all endogenous variables.
Appendix C: Anticipated Shocks
This appendix provides details for solving the pre-shock steady-state equilibrium with anticipated shocks.
Under the free entry condition, we can rewrite Eq (23) as
This implies that
Because of the sunk cost paid, an incumbent network would strictly prefer staying in the industry. Hence, we can rewrite Eq (24) as
In addition, at the steady state, we have
and the network profit πa is determined by Pa* (Na). Under our parameterization, this means that
The pre-shock steady-state equilibrium is then pinned down by Eqs (28), (30), (32), and (33). Note that Va is the value function of being an ATM-only network in the period when the shocks indeed arrive and the number of existing networks is Na, and Va(Na) can be numerically solved using the algorithm described in Appendix B above.
Agarwal, Rajshree and Michael Gort, (1996). “The Evolution of Markets and Entry, Exit and Survival of Firms,” Review of Economics and Statistics, Aug., 489–498.
Agarwal, Rajshree, MB Sarkar and Raj Echambadi, (2002). “The Conditioning Effect of Time on Firm Survival: A Life Cycle Approach,” Academy of Management Journal, 45(8), 971–994.
Cabral, Luis, (2012). “Technology Uncertainty, Sunk Costs, and Industry Shakeout,” Industrial and Corporate Change, 21, 539–552.
Demers, Elizabeth and Baruch Lev, (2001). “A Rude Awakening: Internet Shakeout in 2000,” Review of Accounting Studies, 6, 331–359.
Fein, Adam, (1998). “Understanding Evolutionary Process in Non-manufacturing Industries: Empirical Insights from the Shakeout in Pharmaceutical Wholesaling,” Journal of Evolutionary Economics, 8, 231–270.
Felgran, Steven D, (1985). “From ATM to POS Networks: Branching, Access, and Pricing,” New England Economic Review, May-June, 44–61.
Filson, Darren, (2001). “The Nature and Effects of Technological Change over the Industry Life Cycle,” Review of Economic Dynamics, 4(2), 460–494.
Filson, Darren, (2002). “Product and Process Innovations in the Life Cycle of an Industry,” Journal of Economic Behavior & Organization, 49 (1), 97–112.
Gort, Michael and Steven Klepper, (1982). “Time Paths in the Diffusion of Product Innovations,” The Economic Journal, 92, Sept., 630–653.
Hayashi, Fumiko, Richard Sullivan, and Stuart E. Weiner, (2006). A Guide to the ATM and Debit Card Industry: 2006 Update. Federal Reserve Bank of Kansas City.
Hayashi, Fumiko, Richard Sullivan, and Stuart E. Weiner, (2003). A Guide to the ATM and Debit Card Industry. Federal Reserve Bank of Kansas City.
Jayaratne, Jith, and Philip Strahan, (1997). “The Benefits of Branching Deregulation,” Federal Reserve Bank of New York Economic Policy Review, Dec., 13–29.
Jovanovic, Boyan and Saul Lach, (1989). “Entry, Exit, and Diffusion with Learning by Doing,” American Economic Review, 79 (4), 690–699.
Jovanovic, Boyan and Glenn M. MacDonald, (1994). “The Life Cycle of a Competitive Industry,” Journal of Political Economy, 102(2), 322–347.
Klepper, Steven, (1996). “Entry, Exit, Growth, and Innovation over the Product Life Cycle,” American Economic Review, 86, June, 562–583.
Klepper, Steven and Elizabeth Graddy, (1990). “The Evolution of New Industries and the Determinants of Market Structure,” Rand Journal of Economics, 21, 27–44.
Klepper, Steven and Kenneth Simons, (2000). “The Making of an Oligopoly: Firm Survival and Technological Change in the Evolution of the U.S. Tire Industry,” Journal of Political Economy, 108(4), 728–758.
Klepper, Steven and Kenneth Simons, (2005). “Industry Shakeouts and Technological Change,” International Journal of Industrial Organization, 23, 23–43.
McAndrews, James J., (2003). “Automated Teller Machine Network Pricing - A Review of the Literature,” Review of Network Economics, 2(2), 146–158.
Mussa, Michael, (1977). “External and Internal Adjustment Costs and the Theory of Aggregate and Firm Investment,” Economica, New Series, 44 (174), 163–178.
Tibbals, Elizabeth, (1985). “NOTE: ATM Networks Under the McFadden Act: Independent Bankers Association of New York v. Marine Midland Bank, N.A.” American University Law Review, 35, 271–300.
Wang, Zhu, (2008). “Income Distribution, Market Size and the Evolution of Industry,” Review of Economic Dynamics, 11(3), 542–565.
We thank Ernie Berndt, Andreas Hornstein, Boyan Jovanovic, Sam Kortum, Yoonsoo Lee, Timothy Simcoe, Victor Stango, and participants at various seminars and conferences for helpful comments, and Joseph Johnson for excellent research assistance. The views expressed herein are solely those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Kansas City, the Federal Reserve Bank of Richmond, and the International Monetary Fund.
For example, shakeouts have been documented in the wholesale drug industry (Fein, 1998), the internet industry (Demers and Lev, 2001), and the telecommunication industry (Barbarino and Jovanovic, 2007).
Data source: Hayashi, Sullivan, and Weiner (2006). Note that the ATM transaction volumes reported after the 2000s no longer include certain subcategories used in the pre-2000 data, so the seeming decline of the ATM transactions in the 2000s is an artifact of changing data definition.
The debit innovation can be traced back to the early 1980s, when the point of sale debit function was first tested in a large scale at some gas station chains (Hayashi, Sullivan, and Weiner, 2003). Based on our data source, 1984 was the first year that debit networks were reported.
Data source: the FDIC.
Bank branching restrictions date back to the Banking Act of 1933. In the mid-1970s, no state allowed out-of-state bank holding companies to buy in-state banks, and most states had intrastate branching restrictions. Starting in the late 1970s and early 1980s, most states began gradually relaxing restrictions on both statewide and interstate branching (Jayaratne and Strahan, 1997).
In 1967, England’s Barclays Bank installed the first cash dispenser. In 1968, Don Wetzel developed the first ATM in the United States using modern magnetic stripe access cards.
In addition to the shared networks, some exclusive networks serving a single financial institution also existed in the early times. While our theory can equally apply to them, the data of exclusive networks are not available for analysis.
In some cases, a regional network might establish sharing agreements allowing its cardholders to access another network’s ATMs under certain conditions and payments, but the network would maintain its separate identity and revenue.
In reality, a bank either charges its customers explicit fees for card transactions (e.g., per-transaction fees or annual fees) or bundles the fees with other banking services.
ATM&Debit News (formerly, Bank Network News) publishes the EFT Data Book annually (EFT stands for “Electronic Funds Transfer”). The dataset does not include national networks, such as Cirrus and Plus, because national networks used to play a different role than regional networks. They offered a “bridge” between regional networks. See Hayashi, Sullivan, and Weiner (2003) for details.
Because our analysis considers the impact of commercial banking deregulation on ATM networks, it is necessary to exclude networks serving exclusively credit unions and/or savings and loan banks. Credit unions and savings and loan banks serve special groups of customers and were subject to different regulatory regimes, so the networks they used could have behaved differently than those serving commercial banks.
In the data, some banks belong to multiple networks. This raises a concern of double counting when we measure networks’ sizes based on their numbers of cards in circulation. To address this issue, we collect data on each network’s ATM transactions. As shown in Figures 4D-4E, the two network size measures (cards in circulation vs. ATM transactions) deliver largely consistent patterns.
Note that assuming networks charge per transaction fees instead of per card fees would not affect our analysis since the number of card transactions is closely related to the number of cards.
Note that bank size does not play a role in our analysis so this is an innocuous assumption.
We can interpret πϕ as the foregone income of the network owner/manager for participating in the industry. For instance, it may equal the salary he or she could have earned in the banking or other comparable financial service sectors.
This assumption follows the convention of the literature (e.g., Jovanovic and MacDonald, 1994), which was motivated by the empirical evidence of “time-to-build” found in many industries (Koeva, 2000).
The “failure” captures the uncertainties involved in adopting the debit function. Industry evidence has shown that it was not easy for networks to recruit merchants to accept debit cards due to the conflicts between merchants and banks over payment of transaction fees and the cost of POS terminals, and by the existence of multiple technical standards (Hayashi, Sullivan, and Weiner, 2003).
The data show that almost all the new entrants entered as ATM-only networks after the debit innovation arrived.
Note that if a low-quality card charges a higher price, it would have no demand.
There are several sources of the increasing cost efficiency of ATM-debit networks. First, the synergies of providing ATM and debit services improve over time. For instance, providing debit services allows networks to learn about their customers’ shopping patterns so that they can better allocate the ATM machines and services. Second, providing debit services allows networks to bring another user group, the merchants, on board. Over time, the increasing merchant sponsorship for debit services (e.g., merchant fees) helps offset the network costs. Third, the debit service itself has experienced rapid technological progress. Particularly, the operational cost and fraud rate has declined tremendously over time.
The increasing adoption cost It helps explain why ATM-only networks eventually stopped adopting the debit innovation. It also reflects the increasing difficulties for a new debit network to recruit merchants and compete with the established networks in the debit arena.
For example, paths with no entry are possible (e.g., when the technological progress associated with the debit innovation is too slow or the investment costs are too high). In the counterfactual analyses in Section V, we show how the number of entrants is affected by the exit risk and by the rate of technological progress.
In our numerical exercises, we assume that gt and It will reach constant levels after 150 periods.
While we do not have direct observations, we derive the network numbers in 1983 using the network numbers and new entrants in 1984 together with the network exit rate in 1983 (according to our assumption, the banking deregulation started in 1983 so the exit rate γ = 0.08). Also, we estimate the number of cards per network in 1983 based on the average size of the ATM-only network in 1984.
Our baseline calibration yields T″ = T +115; and all the remaining ATM-only networks exit at T″:
For instance, we may extend our baseline model by assuming in the pre-debit era, potential entrants can pay either a high fixed cost Kl to set up a large ATM-only network or a low fixed cost Ks to set up a small ATM-only network. At equilibrium, entrants are indifferent with either option, and large networks charge a higher fee than small networks because they provide a better ATM service. Banks then choose to participate in different networks based on their customers’ heterogenous taste for network services θ. The supply equals the demand, which pins down the network numbers by type. After the debit innovation and banking deregulation arrive, as suggested by Figures 10 A-D, we could then allow large ATM-only networks to adopt the debit innovations with a higher success rate than small networks, though both networks are subject to the same higher exogenous exit rate γ caused by the deregulation.