Empirical Economic Research Research Articles

The path of economics research production: Insights into the seesaw between theory and empirics

AbstractThis paper provides insights into the apparent seesaw between the generation of theoretical and empirical economics research over time. A dynamic model considers the incentives of researchers to focus on empirical versus theoretical papers. It yields the main characteristics of the path‐changing of economics research, from theoretical‐intensive to empirical‐focused. The model has two equilibria, one with a higher proportion of theoretical papers and another with a higher proportion of empirical papers. Curiously, the equilibrium with greater theoretical papers is stable, while the one with more empirical papers is a saddle point. This suggests that the current trend of increasing empirical research is unlikely to last.

Dependence matters: Statistical models to identify the drivers of tie formation in economic networks

Networks are ubiquitous in economic research on organizations, trade, and many other areas. However, while economic theory extensively considers networks, no general framework for their empirical modeling has yet emerged. We thus introduce two different statistical models for this purpose – the Exponential Random Graph Model (ERGM) and the Additive and Multiplicative Effects network model (AME). Both model classes can account for network interdependencies between observations, but differ in how they do so. The ERGM allows one to explicitly specify and test the influence of particular network structures, making it a natural choice if one is substantively interested in estimating endogenous network effects. In contrast, AME captures these effects by introducing actor-specific latent variables affecting their propensity to form ties. This makes the latter a good choice if the researcher is interested in capturing the effect of exogenous covariates on tie formation without having a specific theory on the endogenous dependence structures at play. After introducing the two model classes, we showcase them through real-world applications to networks stemming from international arms trade and foreign exchange activity. We further provide full replication materials to facilitate the adoption of these methods in empirical economic research.

Teaching Reproducible Methods in Economics at Liberal Arts Colleges: A Survey

Economics has become increasingly empirical and, alongside this shift, has come more demand for improved transparency and reproducibility in empirical economic research. In this article, we distribute a survey to almost 1500 economics faculty from the top 161 liberal arts colleges with an economics major (according to U.S. News & World Report) in the United States to determine the prevalence of teaching reproducible methods in undergraduate economics, summarize the most-common methods of instruction, and determine the intended student learning objectives. We find that of the economics faculty at liberal arts colleges who teach these reproducible methods, most do so in advanced upper-level (42%) and basic econometrics (31%) courses. Those faculty report teaching reproducibility using the following methods: transparent coding (85%), organizational skills (78%), and producing replication documentation (47%) through individual research projects (82%), homework assignments (55%), and/or workshops (33%). We conclude with some qualitative text analysis to shed light on the intended learning objectives and find that research skills (59%) and the importance of reproducibility (37%) are the most common reasons cited for teaching these methods.

Tobacco Prevalence and Economic Growth: Evidence from Low and Lower-Middle-Income Countries

ABSTRACT: The economic costs of tobacco use are substantial particularly in developing countries. While much has been written on healthcare costs from tobacco use related diseases, little is known on the macro-economic impacts of tobacco-related productivity loss. The objective of the current research is to understand the macroeconomic burden of tobacco use in developing countries. Using World Bank's development indicators database for 44 low-and lower-middle-income countries, most of which are African, this paper assesses the relationship between tobacco consumption and economic growth. Specifically, using the fixed effect regression approach, we examined whether tobacco prevalence affects long-run growth rate in the real gross domestic product (GDP) per capita. We adopted an empirical framework from the growth literature by including the national tobacco prevalence rate as one of the determinants of long-run growth in the real GDP per capita. The tobacco prevalence rate varies across countries ranging from 5% in Ethiopia to almost 50% in Myanmar. The prevalence rate is much higher among males than females in almost all countries, with Myanmar males having above 70%. The regression results indicate the existence of a statistically significant negative relationship between the prevalence of tobacco and economic growth after controlling for other determinants of growth, including the initial level of GDP per capita, gross fixed capital formation, trade, resource revenues, institutional quality, a human capital indicator, and infrastructure capacity. A 1% increase in the total tobacco prevalence rate reduces the economic growth rate by almost 2%, all else equal. These findings are robust even after running separate models for male and female tobacco prevalence rates. The results on the other determinants of growth are consistent with the previous empirical studies on economic growth in the context of developing countries. The findings of this study provide additional support to the existing body of empirical economic research on the role of better health, as a form of human capital, in increasing economic output. Thus, actively targeting tobacco control interventions, both in and outside of the workplace, can likely improve workers' productivity and hence economic growth.

Methodology of the narrative analysis in economics: The case of the entrepreneurial networks

The purpose of the article is to provide theoretical and empirical evidence that narrative analysis in economics is not an integral part of qualitative research and is quite compatible with the methodology of quantitative research. The development of methods for collecting and processing data is one of the significant directions in the development of both empirical and theoretical research in economics. Of particular importance to this direction is the consistent expansion of the consideration of social factors in the study of decision-making processes, both at the micro and macro levels. Narrative economics, which has emerged and developed in the last decade, is an integral part of this trend. The article proposes and substantiates a methodology for empirical analysis of narratives considered as sources of quantitative information used in decision making. This distinguishes it from the methods of narrative analysis used in qualitative research in the social sciences, although they are not always distinguished in the literature. The technique combines the search for the frequency of occurrence of various phrases on the Internet with interview analysis, which significantly reduces the time and effort required to search and analyze the necessary information about social factors. The effectiveness of the proposed methodology is tested on the example of the analysis of narratives that characterize personal entrepreneurial networks, no statistical information about which is collected, although the networks themselves play an important role in the formation of new small businesses. The presented results of the analysis show that the technique makes it possible to obtain useful quantitative data on such objects of economic research.

The First 50 Contributions to the Data Observer Series – An Overview

Abstract Since 2016 the Journal of Economics and Statistics has the Data Observer section with descriptions of data that can be used in empirical research in economics and in the social sciences in general. This note gives a short overview of the first 50 contributions to the series published until 2022.

When Should You Adjust Standard Errors for Clustering?

Abstract Clustered standard errors, with clusters defined by factors such as geography, are widespread in empirical research in economics and many other disciplines. Formally, clustered standard errors adjust for the correlations induced by sampling the outcome variable from a data-generating process with unobserved cluster-level components. However, the standard econometric framework for clustering leaves important questions unanswered: (i) Why do we adjust standard errors for clustering in some ways but not others, for example, by state but not by gender, and in observational studies but not in completely randomized experiments? (ii) Is the clustered variance estimator valid if we observe a large fraction of the clusters in the population? (iii) In what settings does the choice of whether and how to cluster make a difference? We address these and other questions using a novel framework for clustered inference on average treatment effects. In addition to the common sampling component, the new framework incorporates a design component that accounts for the variability induced on the estimator by the treatment assignment mechanism. We show that, when the number of clusters in the sample is a nonnegligible fraction of the number of clusters in the population, conventional clustered standard errors can be severely inflated, and propose new variance estimators that correct for this bias.

Credibility crisis in agricultural economics

AbstractWe provide empirical evidence of research practices that can undermine the credibility of empirical research in agricultural economics. We find that, in four popular journals, most studies are underpowered, a characteristic that, when coupled with publication bias, yields unreliable and exaggerated effect sizes. This problem is exacerbated by selective reporting of statistically significant results and multiple hypothesis testing without any adjustments to statistical inferences. Survey respondents self‐report engaging in practices that are consistent with these empirical findings. Addressing these problems in agricultural economics will require that editors, reviewers, and donors change the norms and incentives for authors.

Analysis of China’s Regional Economic Competitiveness, Regionalization, and Spatial Aggregation Characteristics Based on Density Clustering Algorithm

Regional development disparities, especially in developing countries, have traditionally been one of the central issues of empirical research in regional economics. However, this rapid change is accompanied by profound changes in the spatial distribution of economic activities in China, the formation of regional economic “blocks,” the widening of regional disparities, and the geographical concentration of economic growth efficiency are important issues highlighted in this change. Therefore, it is important to explore the spatial clustering characteristics and patterns of regional economic growth to provide a scientific basis for relevant government departments to formulate reasonable regional development strategies and promote the balanced and stable development of economic growth. Clustering analysis is an important research topic in the field of data mining, which is used to discover unknown object classes in large-scale data sets. This paper proposes a density-clustering algorithm based on the regional economic competitiveness of China and analyzes its spatial aggregation characteristics. From the perspective of spatial structure theory, economic development is a dynamic process, and to optimize the spatial pattern of China’s regional economic development and improve the efficiency of economic interaction between regions, it is necessary to fully exploit the diffusion and trickle-down effects of important growth poles in the region to the surrounding areas. The experimental results show that the error rate of KSNN is very small, and the error rate of K-means and PSO has increased to a certain extent. Therefore, it can be obtained that the density-clustering algorithm based on the regional economic competitiveness zoning method in China can find out the correct clustering results without the given clustering individual cases. Thus, it is important to grasp the current situation of regional economic agglomeration and reveal the driving factors of agglomeration formation to promote the coordinated development of regional economy and guide the spatial layout of economic development.

Building research capacity in an under‐represented group: The STAARS program experience

AbstractFor decades, the lack of high‐quality empirical economic research on the state of Africa's agriculture and rural economies has been an important factor impeding the formulation of evidence‐based policy on the continent. The Structural Transformation of African Agriculture and Rural Spaces program aims to build a critical mass of early career African scholars and policy research through an emphasis on mentorship and professional development to help remedy that deficiency. This paper explains the motivations, origins, and outcomes to date of the program, and offers lessons learned for others wishing to create research capacity development opportunities for under‐represented groups.

Does Ageist Language in Job Ads Predict Age Discrimination in Hiring?

We study the relationships between ageist stereotypes - as reflected in the language used in job ads - and age discrimination in hiring, exploiting the text of job ads and differences in callbacks to older and younger job applicants from a resume (correspondence study) field experiment (Neumark, Burn, and Button, 2019). Our analysis uses computational linguistics and machine learning methods to examine, in a field-experiment setting, ageist stereotypes that might underlie age discrimination in hiring. In so doing, we develop methods and a framework for analyzing textual data, highlighting the usefulness of various computer science techniques for empirical economics research. We find evidence that language related to stereotypes of older workers sometimes predicts discrimination against older workers. For men, we find evidence that age stereotypes about all three categories we consider - health, personality, and skill - predict age discrimination, and for women, age stereotypes about personality predict age discrimination. In general, the evidence that age stereotypes predict age discrimination is much stronger for men, and our results for men are quite consistent with the industrial psychology literature on age stereotypes.

Structural Vector Autoregressions with Imperfect Identifying Information

The problem of identification is often the core challenge of empirical economic research. The traditional approach to identification is to bring in additional information in the form of identifying assumptions, such as restrictions that certain magnitudes have to be zero. In this paper, we suggest that what are usually thought of as identifying assumptions should more generally be described as information that the analyst had about the economic structure before seeing the data. Such information is most naturally represented as a Bayesian prior distribution over certain features of the economic structure.

Structural Breaks in Interactive Effects Panels and the Stock Market Reaction to COVID-19

Dealing with structural breaks is an essential step in most empirical economic research. This is particularly true in panel data comprised of many cross-sectional units, which are all affected by major events. The COVID-19 pandemic has affected most sectors of the global economy; however, its impact on stock markets is still unclear. Most markets seem to have recovered while the pandemic is ongoing, suggesting that the relationship between stock returns and COVID-19 has been subject to structural break. It is therefore important to know if a structural break has occurred and, if it has, to infer the date of the break. Motivated by this last observation, the present article develops a new break detection toolbox that is applicable to different sized panels, easy to implement and robust to general forms of unobserved heterogeneity. The toolbox, which is the first of its kind, includes a structural change test, a break date estimator, and a break date confidence interval. Application to a panel covering 61 countries from January 3 to September 25, 2020, leads to the detection of a structural break that is dated to the first week of April. The effect of COVID-19 is negative before the break and zero thereafter, implying that while markets did react, the reaction was short-lived. A possible explanation is the quantitative easing programs announced by central banks all over the world in the second half of March.

Does an Empirical Economic Relation Have a Life?

Does an Empirical Economic Relation Have a Life?

Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects

Probably because of their interpretability and transparent nature, synthetic controls have become widely applied in empirical research in economics and the social sciences. This article aims to provide practical guidance to researchers employing synthetic control methods. The article starts with an overview and an introduction to synthetic control estimation. The main sections discuss the advantages of the synthetic control framework as a research design, and describe the settings where synthetic controls provide reliable estimates and those where they may fail. The article closes with a discussion of recent extensions, related methods, and avenues for future research. (JEL B41, C32, C54, E23, F15, O47)

Bias and discrimination: what do we know?

AbstractThe paper presents the economic literature on gender bias, illustrating the underpinnings in the psychology of bias and stereotyping; the incorporation of these insights into current theoretical and empirical research in economics; and the literature on methods to contrast bias, presenting evidence (where it exists) of their effectiveness. The second part of the paper presents results of an experiment in revealing unconscious bias.

Article Review of The Critical and Empirical Research in Islamic Economics

Abstract This article depicts the nature scope and methodology of reconstruction of Islamic economics as well as it speaks about possibilities on theoretical and empirical research in this field. The article also intends to revel needy of reconstruction of Islamic economics, and its role in early stage and contemporary period and how it would be in future. Islamic economics through the shade of quran and sunnah clearly mandate about allocation of resources, goods and services as well distribution of output, input and wealth in an economy. It is also giving the solution for the problems such as financial crises, poverty, inflation, economic inequalities and unemployment. In addition, methodologically, description of the ground realities and prescription of solutions for economic issues are concern of Islamic economics, whereas, field of fiqh plays massive role in developing of Islamic economics. Theoretically, this article discusses about Islamic economics theory and its advantages, this includes Islamic economic system, Shari’ah based transaction matrix, institutional matters, etc. on the empirical side, there are some points affiliated with data and empirical works are discussed.

Lessons for climate policy from behavioral biases towards COVID-19 and climate change risks

COVID-19 and climate change share several striking similarities in terms of causes and consequences. For instance, COVID-19 and climate change affect deprived and vulnerable communities the most, which implies that effectively designed policies that mitigate these risks may also reduce the widening inequalities that they cause. Both problems can be characterized as low-probability-high consequence (LP-HC) risks, which are associated with various behavioral biases that imply that individual behavior deviates from rational risk assessments by experts and optimal preparedness strategies. One could view the COVID-19 pandemic as a rapid learning experiment about how to cope more effectively with climate change and develop actions for reducing its impacts before it is too late. However, the ensuing question relates to whether the COVID-19 crisis and its aftermath will speed up climate change mitigation and adaptation policies, which depends on how individuals perceive and take action to reduce LP-HC risks. Using insights into behavioral biases in individual decisions about LP-HC risks based on decades of empirical research in psychology and behavioral economics, we illustrate how parallels can be drawn between decision-making processes about COVID-19 and climate change. In particular, we discuss six important risk-related behavioral biases in the context of individual decision making about these two global challenges to derive lessons for climate policy. We contend that the impacts from climate change can be mitigated if we proactively draw lessons from the pandemic, and implement policies that work with, instead of against, an individual's risk perceptions and biases. We conclude with recommendations for communication policies that make people pay attention to climate change risks and for linking government responses to the COVID-19 crisis and its aftermath with environmental sustainability and climate action.

Identification and Formal Privacy Guarantees

Empirical economic research crucially relies on highly sensitive individual datasets. At the same time, increasing availability of public individual-level data that comes from social networks, public government records and directories makes it possible for adversaries to potentially de-identify anonymized records in sensitive research datasets. This increasing disclosure risk has incentivised large data curators, most notably the US Census bureau and several large companies including Apple, Facebook and Microsoft to look for algorithmic solutions to provide formal non-disclosure guarantees for their secure data. The most commonly accepted formal data security concept in the Computer Science community is referred to as differential privacy. Differential privacy restricts the interaction of the researcher with the data by allowing her to issue queries that evaluate the functions of the data. The differential privacy mechanism then replaces the actual outcome of the query with a randomised outcome with the amount of randomness determined by the sensitivity of the outcome to individual observations in the data. While differential privacy does provide formal data security guarantees, its impact on the identification of empirical economic models as well as on the performance of estimators in nonlinear empirical Econometric models has not been sufficiently studied. Since privacy protection mechanisms are inherently finite-sample procedures, we define the notion of identifiability of the parameter of interest as a property of the limit of experiments. It is naturally characterized by concepts from the random sets theory and is linked to the asymptotic behavior in measure of differentially private estimators. We demonstrate that particular instances of regression discontinuity design and average treatment effect may be problematic for inference with differential privacy. Under differential privacy their estimators can only be ensured to converge weakly with their asymptotic limit remaining random and, thus, may not be estimated consistently.This result is clearly supported by our simulation evidence. Our analysis suggests that many other estimators that rely on nuisance parameters may have similar properties with the requirement of differential privacy.

Robust multivariate analysis for mixed-type data: Novel algorithm and its practical application in socio-economic research

We propose a novel method and algorithm for the analysis and clustering of mixed-type data using a hierarchical approach based on Forward Search. In our procedure, the identification of groups is based on the identification of similar trajectories and then linked to very intuitive two-dimensional maps. The proposed algorithm can use different measures for the calculation of distance in the case of mixed-type data, such as Gower’s metric and Related metric scaling. A key feature of our algorithm is its ability to discard redundant information from a given set of variables. The practical usefulness of the algorithm is illustrated through two applications of high relevance for empirical economic research. The first one focuses on comparing different indicators of environmental policy stringency in different countries. The second one applies our procedure to identify clusters of countries based on information regarding their institutional characteristics.