Essays on Political Economy of Technological Development

This dissertation explores the incentives that drive political leaders to invest in Research and Development (R&D) policies even though such investments are risky, less visible to the public than many other options, and typically bear fruit only after the incumbent has already left office. I provide several explanations and explore the economic consequences of political incentives that shape government R&D policy.I argue that the mere policy choice of investing in R&D improves the incumbent’s perceived competence among voters. Using a formal signaling model, I show that, under a set of conditions, the separating equilibrium is possible: a competent incumbent invests in (riskier) R&D policy, while a less competent incumbent invests in safe projects (infrastructure). I test the conclusions of this model by conducting survey experiments in the US and Russia and find that in both countries, respondents see pro-R&D politicians as more competent compared to control politicians.Turning to a setting with weak institutions, I show that government can use investment in R&D as a vehicle for rent seeking, as the risky nature of such invest- ment makes it hard to distinguish between policy failure and technology failure. Due to the inherent difficulty of accessing the value of patents produced with government funding, such funding encourages the growth of low-quality patents. The proliferation of low-quality patents in the technology market reduces the incentives to produce high-quality patents – a typical “lemon problem.” Such problems arise even if the government has a significant stake in technological development. In a cross-country setting, I document the wide discrepancies in the impact of government funding on the creation of patented technologies and show that countries with higher levels of corruption have a greater patenting efficiency, creating more patents on paper, but that does not translate into actual technological development. To achieve causal interpretation, I apply the difference-in-difference approach to data on Russia’s government policy to support nanotechnology to show how government support for innovation reduces the overall quality of patents in the supported field.Technological progress is an important factor in economic development, yet it can be a destabilizing force, upending the existing balance of power in the economy. Such changes can be unwelcome to a government that would like to preserve the status quo. Yet instead of stifling innovation, the incumbent can channel it into the hands of loyal supporters by directing government grants towards them and providing additional benefits that are contingent on the success of the R&D project. Using the trajectory balancing approach for all companies that applied for the Russian program of R&D support via government subsidy, I show that politically connected companies are more likely to obtain government R&D grants. Furthermore, they reap greater benefits from it in form of improved gross profits and return on assets, compared to unconnected companies. I also find that they receive greater volumes of government contracts during the phase of assessment of the progress of their R&D project

Measuring International Relations using Latent Network Approach

International political relations are hard to characterize, as they depend on the network of state relationships. Important political alliances between two countries are often made with the help of other countries, international conflicts often require mediation, efforts of countries to tackle complex issues often require coordination of many states. Hence, it is important to gauge the network structure of international relations when accessing relation between any pair of countries. Many current approaches rely on existing alliances or conflicts to characterize government-to-government interactions at a given point in time, but these are often the outcomes of ongoing negotiations or mounting conflicts, and thus are more likely to characterize past relations, rather than relations of the current period. In recent years, the availability of data capturing day-to-day interactions of countries has increased dramatically, greatly increasing the number of dimensions to be captured, and providing scholars with an opportunity to explore the network of smaller-scale country-to-country interactions. This thesis proposes a way to characterise state-to-state relations in a context of the whole network of international relations in a given year applying latent network approach proposed by Hoff, Raftery and Handcock (2002) to summarized Integrated Crisis Early Warning System (ICEWS) events dataset. Under the latent space framework the probability (magnitude) of a relation between countries depends on the positions of countries in an unobserved "social space." These positions are estimated within a Bayesian framework, using Markov chain Monte Carlo procedures to infer latent positions. I validate the resulting measure of government-to-government relations by demonstrating that they are strong predictors of international trade, outperforming the most commonly used measured of state relations, known as the S-Score