Impact of Tax Incentives on Industry Performance and Growth

Abstract:

In this paper we develop a theoretical framework to analyze the impact of tax incentives on productivity growth of firms. The effect of subsidies on agricultural performance is one of the most debatable topics in economics academic literature and in agricultural economics in particular. Subsidies, however, can take many forms. The effect of explicit budgetary outlays on sector efficiency received a great deal of attentions in the literature. The effect, however, of implicit forms of subsidization on efficiency and productivity is not well explored in the empirical agricultural economics literature. And this is despite the fact that implicit subsidies in the form of tax incentives, for example, are very popular for developing countries. The empirical analysis is carried out using Ukraine-wide farm-level accounting data for an unbalanced panel of agricultural enterprises over the period 1995-2014

Please not that the lecture will take place in lecture hall 104 in Garystraße 21.

Philipp Bach: Causal Machine Learning - New Estimation Approaches for Empirical Economics

Abstract: In this welcome event, Philipp Bach will present his research on causal machine learning and its applications in empirical economics.High-performance machine learning algorithms have been a significant innovation that has transformed research across various fields over recent decades. However, applying machine learning techniques for empirical economic analysis, such as policy evaluation, presents specific statistical challenges. Philipp Bach will provide an overview of his current research projects, outline his future research plans, and offer an outlook on upcoming topics for teaching econometrics at FU Berlin.

Shushanik Margaryan: An Applied Micro Perspective on Causal Machine Learning 

Abstract: Recent development in econometrics offer new ways for causal evaluation. This talk presents an applied perspective on the role of machine-learning based modeling in microeconometrics with a focus on education, labour economics, and health economics. We'll discuss the huge potentials and open challenges of ML-based causal evaluation as compared to established estimation approaches. 

Combining portfolio rules to improve prediction of global minimum variance portfolio

Abstract: 

We consider the prediction of the global minimum variance portfolio (GMVP) weights based on realized covariance matrices computed from high-frequency intraday returns of risky assets. As the multivariate high-dimensional time series process of covariance matrices is rather complex and hard to estimate without substantial simplifications of the model structure, there exist various competing approaches for predicting the GMVP weights. Our major contribution is the development of a novel approach for combining several given GMVP prediction rules in order to determine a low dimensional time-varying vector of these rules‘ proportions for the GMVP. We provide statistical results on realized rule proportions and suggest a feasible low-dimensional approach to forecast the proportions based on a set of pre-determined GMVP prediction rules. Our findings are illustrated in an empirical study where we forecast the GMVP weights based on 265 risky assets by combining various popular portfolio rules.

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The Inflation-Uncertainty Amplifier

Abstract: 

We study how uncertainty shocks affect the macroeconomy across the inflation cycle using a nonlinear stochastic volatility-in-mean VAR. When inflation is high, uncertainty shocks raise inflation and depress real activity more sharply. A nonlinear New Keynesian model with second-moment shocks and trend inflation explains this via an ”inflation-uncertainty amplifier”: the interaction between high trend inflation and firms’ upward price bias magnifies the effects of uncertainty by increasing price dispersion. An aggressive policy response can replicate the allocation achieved under standard policy when trend inflation is low.

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Harsh Rhetoric and Cultural Identity: Backlash Effects of Denmark‘s Ghetto List

Abstract:

We employ a regression discontinuity design to examine the effects of Denmark‘s Ghetto List on the cultural identity of non-Western residents in neighborhoods publicly designated as ``Ghettos‘‘. Despite its harsh rhetoric, the policy involved only modest measures aimed at altering neighborhood composition. We find that non-Western individuals residing in listed neighborhoods became more likely to give their children foreign-sounding names and less likely to enroll them in early childcare. This shift was accompanied by more traditional gender attitudes, stronger self-identification as an immigrant or member of a religious group, and lower propensity to follow Danish news and think immigrants get recognition in Denmark. The cultural responses do not appear to result from changes in labor market integration or residential peer composition, suggesting that the stigmatizing nature of the policy and its surrounding public discourse catalyzed the cultural backlash we observe.

Switching to English to Attract Global Talent

Abstract:          

When organizations’ demand for talent exceeds domestic supply, they can recruit from abroad, but language barriers impede matching. Switching operations to the lingua franca can, in principle, relax this constraint. We show for a model of assortative matching that the abolition of a language constraint increases the average ability of new hires. But an increase in capacity lowers it. We test these predictions in a laboratory of European universities’ economics and business academe. Leveraging a staggered difference-in-differences design, we show that when universities introduce English-taught degree programs, the number of new hires and their average ability—measured by top-quartile publications—rises substantially. After the switch European universities increase their size to that of the US control group. These gains must be weighed against potential costs related to cultural preservation and native-language labor supply.