Individuals derive benefits from their connections, but these may, at the same time, transmit external threats. Individuals therefore invest in security to protect themselves. However, the incentives to invest in security depend on their network exposures. We study the problem of designing a network that provides the right individual incentives.Motivated by cybersecurity, we first study the situation where the threat to the network comes from an intelligent adversary. We show that, by choosing the right topology, the designer can bound the welfare costs of decentralized protection. Both over-investment as well as under-investment can occur depending on the costs of security. At low costs, over-protection is important: this is addressed by disconnecting the network into two unequal components and sacrificing some nodes. At high costs, under-protection becomes salient: it is addressed by disconnecting the network into equal components. Motivated by epidemiology, we then turn to the study of random attacks. The over-protection problem is no longer present, whereas under-protection problems is mitigated in a diametrically opposite way: namely, by creating dense networks that expose the individuals to the risk of contagion.JEL classification: D82, D85
In the cross‐section of countries, there is a strong positive correlation between trade and income, and a negative relationship between trade and inequality. Does this reflect a causal relationship? We revisit the Frankel and Romer (1999) identification strategy, and exploit countries’ exogenous geographic characteristics to estimate the causal effect of trade on income and inequality. Our cross‐country estimates for trade’s impact on real income are consistently positive and significant over time. In addition, we do not find any statistical evidence that more trade increases aggregate measures of income inequality in the long run. A decile‐level decomposition reveals that the positive effect of trade on income decreases monotonically along the income distribution, suggesting that trade integration should be an integral part of long‐run development agendas under any reasonable social welfare function. Addressing previous concerns in the literature, we carefully analyze the validity of our geography‐based instrument, and confirm that the IV estimates for the impact of trade are not driven by other direct or indirect effects of geography through non‐trade channels.
Individuals derive benefits from their connections, but these may expose them to external threats. Agents therefore invest in security to protect themselves. What are the network architectures that maximize collective welfare? We propose a model to explore the tension between connectivity and exposure to an external threat when security choices are decentralized. We find that both over-investment and underinvestment in security are possible, and that optimal network architectures depend on the prevailing source of inefficiencies. Social welfare may be maximized in sparse connected networks when under-investment pressures are present, and fragmented networks when over-investment pressures prevail.
This paper proposes channels through which technological decoupling can affect global growth, and embeds these different layers in a global dynamic macroeconomic model. Multiple scenarios are considered that differ along two dimensions: (i) the coalition of countries (hubs) that initiate the decoupling, and (ii) whether non-hub countries are also forced to decouple via 'preferential attachment' -i.e. by aligning themselves with the hub they trade most with. All global technology hubs lose across scenarios, and losses are largest under preferential attachment. Smaller countries with relations that straddle multiple hubs generally lose, whereas those whose trade is heavily concentrated with one hub may gain due to reduced competition under some scenarios. Technological fragmentation can lead to losses in the order of 5 percent of GDP for many economies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.