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Matching to suppliers in the production network: an empirical framework
Alfaro-Ureña, A. ; Zacchia, Paolo
This paper develops a framework for the empirical analysis of the determinants of input supplier choice on the extensive margin using firm-to-firm transaction data. Building on a theoretical model of production network formation, we characterize the assumptions that enable a transformation of the multinomial logit likelihood function from which the seller fixed effects, which encode the seller marginal costs, vanish. This transformation conditions, for each subnetwork restricted to one supplier industry, on the out-degree of sellers (a sufficient statistic for the seller fixed effect) and the in-degree of buyers (which is pinned down by technology and by “make-or-buy” decisions). This approach delivers a consistent estimator for the effect of dyadic explanatory variables, which in our model are interpreted as matching frictions, on the supplier choice probability. The estimator is easy to implement and in Monte Carlo simulations it outperforms alternatives based on group fixed effects. In an empirical application about the effect of a major Costa Rican infrastructural project on firm-to-firm connections, our approach yields estimates typically much smaller in magnitude than those from naive multinomial logit.
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Collusion in Public Procurement Auctions: Evidence from Russia
Polishchuk, Valentina ; Korovkin, Vasily (advisor) ; Zacchia, Paolo (referee)
This paper documents collusion between firms using micro-level data on 4.4 million first-price sealed-bid procurement auctions conducted in Russia in 2011−2017. The data contains unique information on the timestamps of all bids and the bidding data itself. This study is one of the first to use bid timing to design a method for detecting collusion between firms based on a simultaneous bidding pattern: bidders place bids simultaneously or within a small time interval. The method performs well and identifies at least 7−25% of winner - runner-up bid pairs as collusive in validation subsamples: the pharmaceutical industry, known for its propensity to collusion in Russia, and three cartels formed by pharmaceutical firms. In the main data, the share of collusive winner - runner-up bid pairs varies between 8% and 23%. For a more general case that considers the pairs of each bidder with four other auction participants closest in the rank price, the share of collusive bid pairs is around 13%. In both cases, the share of collusive bid pairs is the highest in two-bidder auctions and gradually declines as the number of bidders increases. Collusive firms tend to place bids simultaneously more frequently when a few bidders participate in auctions, because of higher chances of manipulating auction outcomes. I also document...
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Exogenous Crises and Technology Adoption: Evidence from the Effect of COVID-19 on FinTech Adoption
He, Tao ; Ochsner, Christian (advisor) ; Zacchia, Paolo (referee)
i Abstract Can exogenous crises affect technology adoption, and if so, how? In this thesis, I study whether a public health crisis, the COVID-19 pandemic, could affect individuals' adoption of financial technology. I combine the health shock of the pandemic and governments' policy responses to measure a country's intensity of exposure to COVID-19. I employ an instrumental variable strategy, using the number of airports and the time of the first confirmed COVID case, to instrument the pandemic exposure intensity in a country. Additionally, I use the difference-in-difference approach to identify the causal effect of the pandemic exposure and I combine the IV and DiD approaches for further identification. The results reveal that a higher intensity of exposure to the pandemic has positive effects on fintech adoption. These effects on fintech adoption can be attributed to increased concerns and distress among individuals about the pandemic situation, which motivate them to adopt financial technologies. The findings of this thesis provide valuable insights into the impact of COVID-19 on society and shed light on the technology adoption process within the context of a public health crisis. Keywords: Technology Adoption, COVID-19 Pandemic, Financial Technology JEL Codes: I18, O14, O16, O33
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Revisiting Treatment Effects with Causal Forests
Bakirov, Aslan ; Zacchia, Paolo (advisor) ; Menzel, Andreas (referee)
Revisiting Treatment Effects with Causal Forests Aslan Bakirov Abstract This thesis focuses on the application of Causal Forests, a prominent causal machine learning algorithm, to estimate heterogeneous treatment effects in complex socio-economic phenomenon. Causal Forests leverage the capabilities of random forests to partition the high-dimensional covariate space and identify subgroups where the effect of an intervention remains constant. This approach is particularly valuable when dealing with heterogeneous causal effects, where a uniform measure of gains for all is an unrealistic assumption. Unlike traditional manual methods that are susceptible to p-hacking, the algorithm objectively uncovers nuanced treatment effect variations through data-driven analysis. The thesis demonstrates the algorithm's potential in exploring causal effects and providing valuable policy insights. An empirical illustration showcases the modeling of a complex socio-economic phenomenon, such as the gender wage gap, and leverages Causal Forests to extract policy learning from the identified heterogeneity. The study highlights the algorithm's contribution to credible and robust causal inference, bridging the gap between traditional decomposition methods and data-informed heterogeneity analysis. Keywords: Causal machine learning,...
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The Impact of Floods on Maternal and Newborn Healthcare in Pakistan
Kotarja, Angjelina ; Menzel, Andreas (advisor) ; Zacchia, Paolo (referee)
Floods can occur unexpectedly and affect certain groups more than others. I use a difference-in- difference method to identify the causal impact of the 2010 Pakistan flood on maternal and newborn health care utilization. To estimate the likelihood of health care utilization, I use Pakistan Demographic and Health Surveys data two years before and one year after the event, combined with the georeferenced data on the flood among studied years. Through logistic regressions, I determine whether flood-affected communities significantly predicted the differences in the utilization of health care services. Results show that the odds ratio of attending the required number of antenatal visits and postnatal checks was lower in flooded areas than in non-flooded areas. Similarly, the child's size at birth was reported as less than average in the exposed districts. Therefore, medical protection should be enhanced for vulnerable groups, and extra effort should be considered to ensure access to maternal health care services to protect pregnant women's livelihoods in similar disaster settings. Key Words: Difference-In-Difference, Flood, Maternal, Newborn, Pakistan
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Impact of Special Economic Zones on the Domestic Market: Evidence from Russia
Dubinina, Evgeniya ; Korovkin, Vasily (advisor) ; Zacchia, Paolo (referee)
Place-based policies can be an effective instrument for governments to encourage the economic development of a country. A Special Economic Zone (SEZ) is a place-based policy aimed at attracting FDI, employment growth, and supporting new economic reforms. In addition, an SEZ is a potential catalyst for development, particularly for emerging economies (Alder et al., 2016; Grant, 2017); foreign investors can have a drastic impact on the productivity of domestic firms, revenues, and market shares through the implementation of new technologies and the creation of new firms. However, the effects of SEZs on the domestic market at the firm level are largely understudied. In this thesis, I leverage the large-scale SEZ policy implemented by the Russian government in 2005 that aims to attract foreign investors to specific parts of the country by offering tax relief. The primary objective of this thesis is to quantify the effects of the Russian SEZ policy on local firms. To examine the effects, I use the generalized Difference-in-Difference methodology and apply it to a panel of firms in Russia for the 2006-2015 period. The data includes time-varying SEZ treatment on firms, firm characteristics, and accounting data. The primary outcome variables of interest are revenues, profits, and total factor productivity....
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Economics of Skill Formation
Supik, Lukáš ; Münich, Daniel (advisor) ; Zacchia, Paolo (referee)
vii Abstract People are by nature social beings. Most of us have a complex social network that connects us with other people in numerous aspects of our lives: neighbours, co-workers or peers in schools, and friends. Moreover, it is widely believed that people's behaviour is to some extent affected by others in their social networks, which is known as peer effects. Therefore, a precise understanding of the behaviour of an individual necessarily includes understanding her interactions with others within her social network. The first part of this thesis, literature review, summarizes contemporary research on peer effects, shows which aspects of human behaviour may be affected by social interactions, and highlights the importance of peer effects research. In the second part, the estimation of the linear- in-means peer effects model, we provide a detailed description of the model, derivations of its alternative formulations, and show the identification conditions. The main contribution of the second part is that we provide a step-by-step analysis of the linear-in-means peer effects model and detailed proofs of theorems in one place. The third part provides an empirical analysis of peer effects in education in the Czech Republic. In particular, we examine how the test scores of pupils are affected by their...
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