Balsillie, Jim, 2018, “Why we need a Second Bretton Woods Gathering,” Keynote speech at the IMF’s Sixth Statistical Forum on Measuring Economic Welfare in the Digital Age: What and How? Washington, D. C., November 20.
Brander, J. A., and Spencer, B. J. (1985). “Export subsidies and international market share rivalry,” Journal of International Economics, 18(1–2), 83–100.
Brown, Michael and Pavneet Singh, 2019, China’s Technology Transfer Strategy: How Chinese Investments in Emerging Technology Enable A Strategic Competitor to Access the Crown Jewels of U.S. Innovation, U.S. Defense Innovation Unit Experimental.
Cherif, Reda and Fuad Hasanov, 2019, “The Return of the Policy That Shall Not Be Named: Principles of Industrial Policy,” IMF Working Paper 19/74.
Clarke, Richard A. and Robert K. Knake, 2019, The Fifth Domain: Defending Our Country, Our Companies, and Ourselves in the Age of Cyber Threats. Penguin Press.
Farrell, Henry and Abraham L. Newman, 2019, “Weaponized Interdependence: How Global Economic Networks Shape State Coercion,” International Security, Volume 44 (1), Summer.
Holmes, Thomas J., Ellen R. McGrattan, and Edward C. Prescott, 2015, “Quid Pro Quo: Technology Capital Transfers for Market Access in China,” The Review of Economic Studies, Volume 82 (3), July.
IP Commission, 2017, “The Theft of American Intellectual Property: Reassessments of the Challenge and United States Policy”, Update to the IP Commission Report.
International Monetary Fund, 2018, “Is Productivity Growth Shared in a Globalized Economy?” World Economic Outlook, Chapter 4, April.
International Monetary Fund, 2019a, “The Rise of Corporate Market Power and Its Macroeconomic Effects,” World Economic Outlook, Chapter 2, April.
Jones, Charles I. and Christopher Tonetti, 2020, “Nonrivalry and the Economics of Data,” Stanford University, mimeo (https://web.stanford.edu/~chadj/JT_Data.pdf).
Mandelman, F. S., and A. Waddle, 2020. “Intellectual property, tariffs, and international trade dynamics.” Journal of Monetary Economics, 109 (2020): 86–103.
Moore, Tyler, Richard Clayton, and Ross Anderson, 2009, “The Economics of Online Crime,” Journal of Economic Perspectives, Volume 23 (3), Summer.
O’Connor, Sean, 2019, “How Chinese Companies Facilitate Technology Transfer from the United States,” U.S.-China Economic and Security Review Commission, Staff Research Report.
World Intellectual Property Organization, 2019, World Intellectual Property Report 2019. The Geography of Innovation: Local Hotspots, Global Networks.
Appendix I: First-Best Allocation
The social planner chooses quantities to maximize the net present value of world utility, which is the sum of countries’ utilities:
subject to the resource constraints.
Appendix II: Model with a Rest of the World
This Appendix lays out the model extended with the rest of the world, denoted as country c.
Appendix III: Production Subsidies
Section II discussed a subsidy to the fixed cost, while this appendix focuses on a subsidy proportional to the quantity produced in a market.
This paper benefited from comments by Nathaniel Arnold, Vivek Arora, Philip Barrett, Sonja Davidovic, Andrew Giddings, Swarnali Hannan, Nan Li, Majid Malaika, Joannes Mongardini, Cian Ruane, Nadine Schwarz, Herve Tourpe, and Alejandro Werner, as well as review by the Chinese and U.S. authorities.
Communiqué of the Fifth Plenary Session of the 19th Central Committee of the Communist Party of China, October 2020.
In our model, exporters can switch to become importers (and vice versa), there is technological leapfrogging, and there are incentives for decoupling. These features differentiate the paper from Mandelman and Waddle (2020), who also study the interaction between technology diffusion and optimal trade policy but keep fixed the roles of importer and exporter.
While in practice cyberattacks can also originate domestically, this assumption reflects that opening up to foreign digital inputs increases the risk.
The annual cost of intellectual theft for the US is estimated at 1–3 percent of GDP (IP Commission, 2017).
The labor input could also be modeled with an exponent equal to one. The assumption here ensures that final good producers do not operate with negative profits.
Subsidies could also be more broadly interpreted as including non-fiscal transfers of value, such as laws weakening consumers’ property rights over their personal data, that can support the production of digital goods.
The assumption of a single digital good is not crucial. It would be equivalent to assume that a continuum M of symmetric monopolistic competitors produce differentiated digital goods, which are aggregated as follows:
This could be micro-founded assuming a continuum of monopolistic network producers producing differentiated inputs, as shown in footnote 4.
Alternatively, ρ can be interpreted as a generic cost to trade in digital goods.
The assumption of lump-sum taxation is for simplicity. Funding the subsidy with distortionary taxation would increase its cost in terms of aggregate utility.
Final good producers, even if they are perfect competitors, have positive profits in equilibrium if B > 0.
Prices below marginal cost are a non-credible threat, as the global producer would increase its profits by not delivering to the foreign market.
This is an equilibrium selection mechanism. With ρ = 0, it is equivalent to selecting the producer that can make the lowest credible bid in a hypothetical price war. The case ρ > 0 is more complicated, as it implies producers from different countries can set different minimum prices in a given market, which could lead to equilibrium multiplicity. One way to interpret the proposed selection mechanism is assuming that the firm with the highest potential profits can buy out the other firm.
Krugman, Obstfeld and Melitz (2018) discuss how first-mover advantage could lead to goods being inefficiently imported at a higher price than the one that would prevail if those goods were produced locally. In our model, there is no first-mover advantage, as fixed costs are incurred every period. It is possible that fixed costs deter entry from a domestic supplier with a lower marginal cost than the foreign producer, which has already paid the fixed cost to produce in its own country. However, even in that case, domestic production would not be efficient as it would require duplicating the fixed cost. What makes it optimal for a country to ban imports here are monopoly rents.
If there is no global producer under free trade, import bans are irrelevant.
This “coordination game” case could be prevented with a tit-for-tat strategy where countries threatened each other to mirror the import ban if one is ever imposed, although making good on such threat would have a utility cost. If such threat was credible, country a would be the global producer in equilibrium.
Strategic interactions could depend on policies other than trade, such as requirements to transfer or share technology, but only if such transfer requirements can be used to penalize the technology leader after it has decided to ban exports. In our model, banning exports stops technology transfers; hence, this type of policies would have no teeth.
There are different reasons why banning exports might be easier than banning imports. First, the government in the producer’s country may have more control over the producer’s networks or data than the government of the destination country (see Farrell and Newman, 2019, for a discussion of this asymmetry in the context of the internet and the Swift payment system). Second, domestic political economy considerations may make it harder to ban imports, which benefit all consumers, than banning exports, which disproportionately benefit the shareholders of digital good producers.
Market size (proportional to population in the model) is approximately calibrated to relative PPP GDP levels. Initial technology rates and the fixed cost are selected to illustrate a complete cycle of technological leapfrogging. The value for g implies GDP per capita growth of 2 percent (α * (g — 1)) as time tends to infinity. The calibration of σ ensure realistic growth patterns in China and the EU. The digital goods share is calibrated to the capital share. The value of ρ = 0.02 implies a cost of cybercrime at 0.4 percent of US GDP, which is below recent estimates (McAfee, 2018). The intertemporal discount rate 1/β is relatively low in line with current risk-free rates close to zero.
This could be motivated for instance by the strong enforcement of intellectual property rights in the EU.
A report by the McKinsey Global Institute (2019) estimates that China already features the world’s largest consumer market in many technological goods.
Note though that, as the relative importance of fixed costs fades over time, the US may be able at a future point to once again become the supplier to the rest of the world owing to its technological advantage (assuming the growth rate of China and the US are similar; this is elaborated further below). This is a knife-edge result, however, and could go either way depending on how competition is modeled.
Note in Figure 5 the imposition of an export ban to China lowers the US long-run utility level, as slowing the time China leapfrogs permanently shifts down the technological frontier. This was not the case in Figure 4 because with gc = gu the frontier expands at the same rate no matter which is the leading country.
In practice, this may not be possible for all digital activities, as increasing returns to scale are not only caused by fixed costs but also by other drivers such as network externalities, which may not fade away with growth.
Entry cannot happen in the rest of the world only because a viable entrant in the rest of the world would also be more competitive as a domestic producer, as it would avoid the trade cost ρ in the domestic market.