I am a Postdoctoral Researcher at the Department of Economics of the Universidad Carlos III de Madrid.
I obtained my PhD degree in Economics from CERGE-EI in September 2024. My supervisor at CERGE-EI is Professor Marek Kapicka. During my PhD studies, I spent 1.5 academic years at Yale Department of Economics as a visiting scholar. My supervisor at Yale is Professor Aleh Tsyvinski.
My research interests and expertise include macroeconomics of labor markets, life-cycle modelling, human capital, routine-biased technological change, automation, and environmental economics.
Embedded in a rich heritage of four generations of Biologists and Ecologists, I am the first in my family to channel this legacy into the field of Economics. My current research introduces the state-of-the-art adaptation concepts from Biology and Ecology into the models of workers' adaptation to changing labor market environment.
CV: Open CV
Email: daniil.kashkarov@uc3m.es; daniil.kashkarov@cerge-ei.cz
References:
Prof. Marek Kapicka (CERGE-EI, Chair)
Prof. Aleh Tsyvinski (Yale)
Prof. Ctirad Slavik (CERGE-EI)
Job Market Paper
Ability to Adapt: From Biology to Labor Markets
Abstract: How do workers adapt to employment in different occupations, industries, and labor markets for different education groups? What common features do labor market disruptions have that make it hard for workers to adapt? What forges the adaptive capacity of workers? To shed light on these questions, I consider a state-of-the-art economic model of workers' decision making and use it to quantitatively evaluate the predictions of adaptation theory from modern biology and ecology — sciences that study the adaptation of the most diverse entities in the universe. The universality of the results delivered by many decades of adaptation research in biology and ecology allows me to analyze the adaptive responses of workers across different contexts within a single framework, to predict the consequences of major labor market disruptions, such as automation, the introduction of AI, and climate change, and, ultimately, to make a step towards the development of a general theory of worker adaptation.
RBTC and Human Capital: Accounting for Individual-Level Responses
Abstract: What is the contribution of individual human capital responses to earnings inequality arising in the process of the routine-biased technological change (RBTC)? To answer this question, I develop a life-cycle model of human capital and occupational choice, calibrate it to the NLSY79 data, using the price series for human capital in abstract and routine occupations estimated from the cross-sectional CPS data. I find that an increase in the price for human capital in abstract occupations and a fall in its price in routine occupations associated with RBTC has a modest contribution to the evolution of variance of log-earnings — up to 10.8 per cent by the end of the working life cycle. However, the contribution of RBTC to an increase in the abstract wage premium over the lifetime of the NLSY79 cohorts is up to 28.6 per cent. The growth of the abstract wage premium is significantly dampened by the human capital responses of workers switching from routine occupations.
Disappearing Stepping Stones: Technological Change and Career Paths with Valentin Artemev (CERGE-EI)
Abstract: Which career paths lead workers towards high-skilled non-routine cognitive occupations? Using PSID data, we show that, for a significant share of workers, a career path towards non-routine cognitive occupations goes through middle-skilled routine occupations, with the majority going through a subset of routine cognitive occupations. We then argue that the decline in employment in routine cognitive occupations due to routine-biased technological change can negatively affect the chances of younger cohorts joining high-skilled occupations. To test this hypothesis, we develop a structural occupational choice model that endogenously generates realistic career paths and estimate it using PSID data and job ad data from three major US outlets covering the period from 1940 to 2000. Our estimations suggest that, on average, 6% of workers ending up in non-routine cognitive occupations use routine cognitive occupations as stepping stones that allow them to maintain and accumulate human capital and experience relevant for later employment in high-skilled occupations. A fall in employment opportunities in routine cognitive occupations over the period of the most intensive routine-biased technological change led to at least 1.37 million lost high-skilled workers who got stuck in less skilled occupations.