I am an Assistant Professor of Economics at the University of British Columbia. I study how market frictions impact aggregate productivity by distorting input allocation across firms.
I am visiting the Economics Department at the University of Pennsylvania during Fall 2021.
Fields: Macroeconomics, Firm Dynamics, Labor Economics, and Economics of Information
bostanci [at] sas.upenn.edu
How quickly producers can adjust their workforce with changing demand is important for aggregate productivity. Labor outsourcing allows quick adjustments but potentially exposes sensitive information to outsiders, which may deter producers from outsourcing if the legal system does not adequately protect secret information. I quantify the impact of trade secret protection on labor outsourcing, and consequently, on aggregate productivity. First, using event studies and difference-in-differences around the staggered adoption of the Uniform Trade Secrets Act, I show that better trade secret protection leads to increased outsourcing. Second, to quantify the resulting gains in productivity, I build a structural model of outsourcing and multi-industry dynamics and estimate it with data from the U.S. manufacturing sector. I decompose the cross-state differences in labor outsourcing into differences in firing cost, industry composition, demand volatility, and trade secret protection. Strengthening trade secret protection for all states to match the state with the strictest protection would increase outsourcing employment by 29% and aggregate output by 0.8%.
Abstract: Recessions are characterized by slow input reallocation and increased measures of misallocation. A usual suspect is declining information quality about new investment opportunities. We study the role of information frictions by measuring how the informativeness of the stock prices changes with business cycles. We first build a stock market model in which both the information content and the noise in prices respond to changes in economic activity, affecting how well those prices reflect firm's performance. Then we incorporate this module in a dynamic model with heterogeneous firms to characterize how stock price informativeness and capital misallocation interact with one another. An increase in liquidity concerns of traders can simultaneously boost information production, decrease stock price informativeness, and increase capital misallocation in the economy.
Presented at 14th Macro Finance Society Workshop (2019-Poster Session), MFM Summer Session for Young Scholars (2018-Poster Session)
Abstract: Recent empirical work shows a strong positive correlation between job-to-job transition rates and nominal wage growth in the U.S. First, using time series regressions, structural monetary policy shocks, and survey data on search effort we provide evidence that inflationary shocks cause higher job-to-job transitions in the subsequent years. Second, to understand the aggregate implications, we build a structural model with aggregate shocks and competitive on-the-job search in which wages react sluggishly to inflation. In periods with high inflation, the decline in real wages incentivizes the employees to search on-the-job more actively, to negotiate a new contract, but also to be less selective in their search behavior. This creates a fundamental trade-off: increased search effort leads to more job-to-job transitions while being less selective reduces the expected efficiency gain in each transition. Therefore, the effect on output becomes ambiguous. Third, we calibrate the model to the U.S. economy and confirm that the output response to inflation shock is non-monotonic. Importantly, our paper highlights a novel role for inflation: the monetary authority can stimulate productivity with an inflationary shock through job-to-job transitions.
Presented at: Macro Lunch Talk at UPenn 2018, 2019
“Negative Advertising and Competitive Product Positioning ” (with Jerath, K. and Yildirim, P.) R&R Management Science
Abstract: Negative advertising provides information about the weaknesses of a competitor’s product. We study negative advertising with a focus on how it impacts product positioning for profit-maximizing firms. We build a model of informative advertising competition, where product positioning is endogenous and consumers have rational expectations. We show that despite the informational benefits of negative advertising, permitting it (as the Federal Trade Commission in the United States does) may lead to reduced product differentiation and lower consumer welfare, even in markets where firms do not utilize negative advertising in equilibrium. We then extend our model to political competition, where a candidate’s objective is to obtain a larger share of votes than the competitor. We show that political competition supports higher positional differentiation, along with more negative advertising than product competition, in line with observed high use of negative advertising in political races and their rarer use in product competition.
“How Connected is the Global Sovereign Credit Risk Network?” (with Yilmaz, K.) Journal of Banking and Finance (2020)
This paper estimates the global network structure of sovereign credit risk by applying the Diebold-Yilmaz connectedness methodology on sovereign credit default swaps (SCDSs). The level of credit risk connectedness among sovereigns, which is quite high, is comparable to the connectedness among stock markets and foreign exchange markets. In the aftermath of the recent financial crises that originated in developed countries, emerging market countries have played a crucial role in the transmission of sovereign credit risk, while developed countries and debt-ridden developing countries played marginal roles. Secondary regressions show that both trade and capital flows are important determinants of pairwise connectedness across countries. The capital flows became increasingly important after 2013, while the effect of trade flows decreased during the crisis and did not recover afterwards.
Presented at: Financial Globalization and Its Spillovers Workshop at TH Koln and Maastricht University (2017), the Third Economic Networks and Finance Conference at the London School of Economics (2015) and the Second Vienna Workshop on High Dimensional Time Series in Macroeconomics and Finance (2015)