Instead of laboratory: Analyzing data from natural experiments (Nobel Memorial Prize in Economic Sciences 2021)

The 2021 Nobel Prize in Economic Sciences was awarded to David Card, Joshua Angrist, and Guido Imbens for advancing methodology to establish casual relationships in economics. Their approach brought the notion of the natural experiment, situations in which heterogeneous reactions of different groups of people to chance shocks or policy changes allows to elicit causal effects, to the forefront of empirical analysis, and spearheaded a revolution in development of statistical methods needed to analyze the data. After the initial contributions in labor economics and economics of education, the new approach has become a new standard in economic sciences.

The Analytical Review of Foreign Studies on the Impact of Taxation on Corporate Investment

The article provides an overview of foreign studies, mainly American ones, on the impact of tax policy on corporate investment. The research revealed the positive impact of maneuvering tax rates and tax incentives on investments in the corporate sector. At the macro level, special attention is paid to the Laffer curve and its modern applications. The possible use of the King - Fullerton model, as well as the active use of regression analysis in its various variations, are analyzed at the micro level. Besides, the taxation of income from investors' capital, including taxes on dividends, capital gains, and income from bonds are the ussies under analysis. The article also pointed out the complexity of tax systems in most countries, the importance of tax competition between different countries, the impact of tax policy on the structure as well as the cost of capital of companies. The authors identify the areas for further research at the macro and micro levels, emphasizing the lack of such research for developing countries and the need for the study of «natural experiments».

Does the Construction of National Eco-Industrial Demonstration Parks Improve Green Total Factor Productivity? Evidence from Prefecture-Level Cities in China

This study conducted quasi-natural experiments based on the panel data of 239 prefecture-level cities in China from 2005 to 2017. The difference-in-difference (DID) and mediation effect model are used to test the impact and mechanism of the construction of national eco-industrial demonstration parks (NEDP) on green total factor productivity (GTFP). The results show that: (1) The construction of NEDP has significantly improved the urban GTFP, and the conclusion is still valid after running the robustness test. (2) Mechanism analysis shows that the construction of NEDP has improved GTFP through technological innovation and industrial structure upgrading. (3) The heterogeneity results reveal that NEDP has a significant positive effect on GTFP in the central and western regions, while the effect was insignificant in the eastern region. Moreover, NEDP significantly contributes to GTFP in resource-based and non-resource-based cities, while the contribution of resource-based cities is greater than that of non-resource-based cities. This study provides a reference for China to further promote the construction quality of NEDP and green development.

Aftershock: CEO Great Famine Experience and Stock Price Crash Risk

This study examines the effect of CEOs’ early-life traumatic experience on firm-specific stock price crash risk. Drawing on the idea of natural experiments, we take the Great Famine in China as an external traumatic event which cannot be selected or controlled by human. The analysis points out that compensation psychology and irrational defense psychology after the trauma of Great Famine are important factors that cause CEOs to hoard bad news. Based on a large sample of Chinese companies from 2007 to 2017, we find evidence that CEOs who experienced the Great Famine during early-life tend to hoard bad news, which result in higher stock price crash risk. The more severe and prolonged the Great Famine that the CEOs experienced, the greater the effect of this traumatic experience. CEOs decision-making power enhances the adverse effect of CEOs’ early-life traumatic experiences on crash risk. Findings of this study contributes to the literature by providing a new explanation for the stock price crash risk, which is of great significance for the sustained and healthy development of capital markets.

Induction, Experimentation and Causation in the Social Sciences

Inductive thinking is a universal human habit; we generalise from our experiences the best we can. The induction problem is to identify which observed regularities provide reasonable justification for inductive conclusions. In the natural sciences, we can often use strict laws in making successful inferences about unobserved states of affairs. In the social sciences, by contrast, we have no strict laws, only regularities which most often are conditioned on ceteris paribus clauses. This makes it much more difficult to make reliable inferences in the social sciences. In particular, we want knowledge about general causal relations in order to be able to determine what to do in order to achieve a certain state of affairs. Knowledge about causal relations that are also valid in the future requires experiments or so called ‘natural experiments’. Only knowledge derived from such experiences enable us to draw reasonably reliable inferences about how to act in order to achieve our goals.

The Northern Ireland Longitudinal Study (NILS) as a resource for investigating neighbourhood peer effects: A case study using natural experiment(s) in fertility and labour market participation

Our neighbours can potentially influence our behaviour. For instance, poor health behaviours amongst neighbours may normalise and reinforce our poor health behaviours. This is an example of a peer effect. Imitative behaviour can cause small initial changes in individual behaviour to spread amongst their social networks and result in a social multiplier effect. Understanding the size and mechanisms behind the social multiplier effect allows for more effective health interventions. It also helps us understand why persistent health inequalities exist across different neighbourhoods and social groups.In an ideal experiment, we would randomly allocate people into treatment and control groups and change the behaviours of persons A in the treatment group (directly or through incentives). Then we would observe the effects of changes in person A's behaviour on their neighbour person B. This ideal experiment is practically and (possibly) ethically unfeasible. Furthermore, data on a large enough sample of people and their neighbours is very expensive to collect.Instead of a normal experiment, we can use natural experiments which change person A's behaviour. In this paper, we use a well-known natural experiment that affects women's fertility and, indirectly, their labour market participation. Furthermore, data on people and their neighbours are available from the Northern Ireland Longitudinal Study (NILS).The Northern Ireland Longitudinal Study (NILS) is a longitudinal study consisting of 28% of the NI census and roughly 50% of all households. The NILS is linked to a database of all addressable properties in NI which include the coordinates of residences. In theory, NILS contains a large sample of households in NI and their close neighbours that can be used for studying peer effects.This project is a proof of concept for studying peer effects using NILS. If any random intervention exists (e.g. natural, quasi- or actual randomised trial) then NILS can always be used to study peer effects (amongst neighbours). This is significant because peer effects are notoriously hard to study due data limitations and a large number of credible natural experiments exist in health research. This project's contribution is the discovery that NILS almost uniquely placed as resource for studying peer effects in the UK.

Influence of socioeconomic variables on physical activity and screen time of children and adolescents during the COVID‑19 lockdown in Germany: the MoMo study

The COVID‑19 (coronavirus disease 2019) pandemic created a multitude of natural experiments about the change of human behavior in a widely unfamiliar situation. Besides physical and mental health, physical activity (PA) and people’s movement behaviors were of particular interest to researchers all over the world. In a recent study, we found that among youth in Germany, sports activity declined, whereas recreational screen time and habitual activity increased during the first COVID‑19 lockdown. In the present study, we analyze the influence of the socioeconomic status and the housing situation on the changes in PA behavior and recreational screen-time before and during the first COVID‑19 lockdown among children and adolescents living in Germany. We found an alignment of PA behavior among youth from families with different socioeconomic backgrounds during the first lockdown and identified the housing situation to be a meaningful predictor of the increase in habitual activity. We conclude that restriction policies, communities, and in the last instance parents need to enable access to nonorganized PA to all children and adolescents every day and especially during potential future lockdowns.

Using Random Experiments to Measure the Impact of Computers, the Internet, and Other Forms of Technology on Educational Outcomes

This chapter examines empirical approaches to studying the question of whether computers, the Internet, and other computer-related technologies improve educational outcomes. Most studies in the literature examine the question using multivariate regression analysis controlling for detailed school, teacher, student, family, and/or parental characteristics. Because of concerns regarding selection bias a relatively new literature uses randomized field experiments to examine the educational impacts of computer-related technology. Additionally, studies use quasi-experimental approaches such as natural experiments and regression discontinuity designs to estimate educational impacts. This chapter reviews the literature using these approaches and discusses each of the methodologies along with their strengths and weaknesses. Understanding the impacts of technology on education is important, because it sheds light on whether technology is an important input in the educational production process and whether disparities in access to technology translate into educational inequality.