CRISTHIAN SEMINARIO-AMEZ
Working Papers
Working Papers
This paper studies a labor market where heterogeneous workers climb a job ladder with informal and formal rungs. In this environment, the incidence of informal jobs in a worker's career is a function of her skill level and the economy's history of aggregate states. I estimate the model in Brazilian labor-force survey data, and show it successfully reproduces the observed heterogeneity and dynamics around informality. In equilibrium, informal jobs are less productive and are subject to higher layoff risk than their formal counterparts. However, workers rely on informal contracts not only to smooth transitions between employment and non-employment, but also to advance their careers through moves within and between jobs. According to the model, stronger enforcement of penalties against informal matches (i) increases unemployment and self-employment, (ii) dampens job-to-job transitions, (iii) reduces total output, and (iv) disproportionately hurts the low skilled.
This paper studies a labor market where heterogeneous workers climb a job ladder with informal and formal rungs. In this environment, the incidence of informal jobs in a worker's career is a function of her skill level and the economy's history of aggregate states. I estimate the model in Brazilian labor-force survey data, and show it successfully reproduces the observed heterogeneity and dynamics around informality. In equilibrium, informal jobs are less productive and are subject to higher layoff risk than their formal counterparts. However, workers rely on informal contracts not only to smooth transitions between employment and non-employment, but also to advance their careers through moves within and between jobs. According to the model, stronger enforcement of penalties against informal matches (i) increases unemployment and self-employment, (ii) dampens job-to-job transitions, (iii) reduces total output, and (iv) disproportionately hurts the low skilled.
Firm-level Risk Exposures and Stock Returns in the Wake of COVID-19, with Steven Davis and Stephen Hansen, NBER Working Paper 27867 and CEPR Discussion Paper 15314 [Link, VoxEU column]
Firm-level Risk Exposures and Stock Returns in the Wake of COVID-19, with Steven Davis and Stephen Hansen, NBER Working Paper 27867 and CEPR Discussion Paper 15314 [Link, VoxEU column]
Firm-level stock returns differ enormously in reaction to COVID-19 news. We characterize these reactions using the Risk Factors discussions in pre-pandemic 10-K filings and two text-analytic approaches: expert-curated dictionaries and supervised machine learning (ML). Bad COVID-19 news lowers returns for firms with high exposures to travel, traditional retail, aircraft production and energy supply -- directly and via downstream demand linkages -- and raises them for firms with high exposures to healthcare policy, e-commerce, web services, drug trials and materials that feed into supply chains for semiconductors, cloud computing and telecommunications. Monetary and fiscal policy responses to the pandemic strongly impact firm-level returns as well, but differently than pandemic news. Despite methodological differences, dictionary and ML approaches yield remarkably congruent return predictions. Importantly though, ML operates on a vastly larger feature space, yielding richer characterizations of risk exposures and outperforming the dictionary approach in goodness-of-fit. By integrating elements of both approaches, we uncover new risk factors and sharpen our explanations for firm-level returns. To illustrate the broader utility of our methods, we also apply them to explain firm-level returns in reaction to the March 2020 Super Tuesday election results.
Firm-level stock returns differ enormously in reaction to COVID-19 news. We characterize these reactions using the Risk Factors discussions in pre-pandemic 10-K filings and two text-analytic approaches: expert-curated dictionaries and supervised machine learning (ML). Bad COVID-19 news lowers returns for firms with high exposures to travel, traditional retail, aircraft production and energy supply -- directly and via downstream demand linkages -- and raises them for firms with high exposures to healthcare policy, e-commerce, web services, drug trials and materials that feed into supply chains for semiconductors, cloud computing and telecommunications. Monetary and fiscal policy responses to the pandemic strongly impact firm-level returns as well, but differently than pandemic news. Despite methodological differences, dictionary and ML approaches yield remarkably congruent return predictions. Importantly though, ML operates on a vastly larger feature space, yielding richer characterizations of risk exposures and outperforming the dictionary approach in goodness-of-fit. By integrating elements of both approaches, we uncover new risk factors and sharpen our explanations for firm-level returns. To illustrate the broader utility of our methods, we also apply them to explain firm-level returns in reaction to the March 2020 Super Tuesday election results.
Work in Progress
Work in Progress
The Stock Market Reaction to Trump's Surprise Election Win, with Steven Davis and Stephen Hansen [Slides]
The Stock Market Reaction to Trump's Surprise Election Win, with Steven Davis and Stephen Hansen [Slides]
The 2016 presidential election surprise triggered a large, positive stock market response on 9 November, strongly contradicting pre-election assessments of how the market would react if Trump won. Moreover, equity returns varied enormously across firms one, two, and three days after this event. We show that policy risk exposures calculated from the text in Part 1A of 10-K filings explain much of the cross-firm differences in Nov 9 equity returns. Firms with high exposures to generic regulation, labor regulation, food and drug regulation, financial regulation, intellectual property policy, government purchases, and brown-energy regulations had relatively high equity returns. On the other hand, firms with high exposures to healthcare policy risks, business tax credits, taxation of foreign profits, sales and excise taxes, subsidies, and green-energy regulation performed relatively poorly. The stock market did not fully digest the implications of the election outcome by market close on 9 November. Instead, (conditional) firm-level returns over Days 2 and 3 after the election strongly reinforced the initial market response to the election surprise on Day 1. These results suggest that equity prices do not immediately and fully adjust to surprise events that (i) involve unusual shifts in the structure of price-relevant risks and (ii) require large information processing resources to fully assess. This is at odds with the Efficient Markets Hypothesis, which says that stock prices quickly adjust to publicly available information.
The 2016 presidential election surprise triggered a large, positive stock market response on 9 November, strongly contradicting pre-election assessments of how the market would react if Trump won. Moreover, equity returns varied enormously across firms one, two, and three days after this event. We show that policy risk exposures calculated from the text in Part 1A of 10-K filings explain much of the cross-firm differences in Nov 9 equity returns. Firms with high exposures to generic regulation, labor regulation, food and drug regulation, financial regulation, intellectual property policy, government purchases, and brown-energy regulations had relatively high equity returns. On the other hand, firms with high exposures to healthcare policy risks, business tax credits, taxation of foreign profits, sales and excise taxes, subsidies, and green-energy regulation performed relatively poorly. The stock market did not fully digest the implications of the election outcome by market close on 9 November. Instead, (conditional) firm-level returns over Days 2 and 3 after the election strongly reinforced the initial market response to the election surprise on Day 1. These results suggest that equity prices do not immediately and fully adjust to surprise events that (i) involve unusual shifts in the structure of price-relevant risks and (ii) require large information processing resources to fully assess. This is at odds with the Efficient Markets Hypothesis, which says that stock prices quickly adjust to publicly available information.
Recent empirical work suggests the issue of transitional dynamics is central to the study of informal employment relationships in developing economies. Dix-Carneiro and Kovak (2019), for example, study the regional labor market effects of a unilateral trade liberalization (UTL) episode in Brazil. They document strong medium-run increases in non-employment among regions that were highly exposed to the implemented tariff cuts. Most importantly, they find the longer-run employment recovery in these markets took place entirely through informal jobs. Similarly, Ulyssea and Ponczek (2018) show negative labor-demand shocks induced by UTL caused significant increases in informality among regions with weak enforcement, but significant increases in non-employment among regions with strong enforcement. These results are qualitatively consistent with the implications of the model I develop in Seminario-Amez (2020). The availability of informal jobs allows workers that fall off the job ladder to rejoin employment more easily. Moreover, this channel is particularly important under bad states of the economy – as is the case with regions negatively affected by UTL in this application. The model, however, provides stronger predictions. If informal jobs are indeed part of a job ladder, and many informal workers transit eventually into formal jobs, stronger levels of enforcement can worsen the effects of UTL on outcomes specific to the formal sector. To test this prediction, I implement a similar research design as in Ulyssea and Ponczek (2018) but using log-earnings of formal workers at the municipality level as the outcome variable as in Dix-Carneiro and Kovak (2017). Preliminary results support the model prediction and are robust to several checks.
Recent empirical work suggests the issue of transitional dynamics is central to the study of informal employment relationships in developing economies. Dix-Carneiro and Kovak (2019), for example, study the regional labor market effects of a unilateral trade liberalization (UTL) episode in Brazil. They document strong medium-run increases in non-employment among regions that were highly exposed to the implemented tariff cuts. Most importantly, they find the longer-run employment recovery in these markets took place entirely through informal jobs. Similarly, Ulyssea and Ponczek (2018) show negative labor-demand shocks induced by UTL caused significant increases in informality among regions with weak enforcement, but significant increases in non-employment among regions with strong enforcement. These results are qualitatively consistent with the implications of the model I develop in Seminario-Amez (2020). The availability of informal jobs allows workers that fall off the job ladder to rejoin employment more easily. Moreover, this channel is particularly important under bad states of the economy – as is the case with regions negatively affected by UTL in this application. The model, however, provides stronger predictions. If informal jobs are indeed part of a job ladder, and many informal workers transit eventually into formal jobs, stronger levels of enforcement can worsen the effects of UTL on outcomes specific to the formal sector. To test this prediction, I implement a similar research design as in Ulyssea and Ponczek (2018) but using log-earnings of formal workers at the municipality level as the outcome variable as in Dix-Carneiro and Kovak (2017). Preliminary results support the model prediction and are robust to several checks.
Firm-Level Stock Returns around FOMC Announcements and the Channels of Monetary Policy Transmission, with Steven Davis and Stephen Hansen
Firm-Level Stock Returns around FOMC Announcements and the Channels of Monetary Policy Transmission, with Steven Davis and Stephen Hansen
Recent research suggests that FOMC announcements affect beliefs not only about monetary policy but also about the economic outlook – e.g., Nakamura and Steinsson (2018), Cieslak and Schimpf (2019). We use firm-level variation in (i) risk-factor exposures calculated from the text in Part 1A of 10-K filings and (ii) high-frequency stock return responses to FOMC announcements to gain insight into the transmission channels of monetary policy. Initial results show that monetary policy tightening reduces stock prices, particularly among firms with many mentions of terms associated to floating-rate debt risk and investment and R&D tax credits. Companies with high exposure to oil markets also underperform, probably through the effect of monetary policy on exchange rates. Conversely, firms highly exposed to financial regulation matters (e.g., banks) perform relatively better during these episodes. The effects differ depending on what tool the monetary authority uses: forward guidance or news related to large-scale asset purchases.
Recent research suggests that FOMC announcements affect beliefs not only about monetary policy but also about the economic outlook – e.g., Nakamura and Steinsson (2018), Cieslak and Schimpf (2019). We use firm-level variation in (i) risk-factor exposures calculated from the text in Part 1A of 10-K filings and (ii) high-frequency stock return responses to FOMC announcements to gain insight into the transmission channels of monetary policy. Initial results show that monetary policy tightening reduces stock prices, particularly among firms with many mentions of terms associated to floating-rate debt risk and investment and R&D tax credits. Companies with high exposure to oil markets also underperform, probably through the effect of monetary policy on exchange rates. Conversely, firms highly exposed to financial regulation matters (e.g., banks) perform relatively better during these episodes. The effects differ depending on what tool the monetary authority uses: forward guidance or news related to large-scale asset purchases.
Stock Market Jumps and Dispersion in Firm-Level Returns, with Steven Davis and Stephen Hansen
Stock Market Jumps and Dispersion in Firm-Level Returns, with Steven Davis and Stephen Hansen
Davis, Hansen, and Seminario-Amez (2020) document that larger market-level moves in the wake of COVID-19 correspond to greater variation in firm-level returns. In this project, we explore whether this pattern extends to other periods and how that depends on the type of news inducing stock market jumps (e.g., tax policy, monetary policy, trade policy). This exercise can shed light on the characterization of aggregate shocks in macroeconomic models: higher dispersion in firm-level outcomes can result from heterogeneous exposures to first-moment shocks or from uncertainty shocks as in Bloom (2009).
Davis, Hansen, and Seminario-Amez (2020) document that larger market-level moves in the wake of COVID-19 correspond to greater variation in firm-level returns. In this project, we explore whether this pattern extends to other periods and how that depends on the type of news inducing stock market jumps (e.g., tax policy, monetary policy, trade policy). This exercise can shed light on the characterization of aggregate shocks in macroeconomic models: higher dispersion in firm-level outcomes can result from heterogeneous exposures to first-moment shocks or from uncertainty shocks as in Bloom (2009).
Pre-Doctoral Research
Pre-Doctoral Research