scholarly journals Does social capital affect wages? A panel data analysis of causal mechanisms

2020 ◽  
Author(s):  
Gerhard Krug ◽  
Paul Schmelzer ◽  
Mark Trappmann
Keyword(s):  
Social Capital ◽  
Panel Data ◽  
Job Search ◽  
Fixed Effects ◽  
Panel Data Analysis ◽  
Positive Association ◽  
Job Interviews ◽  
Cross Sectional ◽  
Causal Mechanisms ◽  
Panel Data Regression

Many studies document the positive association between accessed social capital and wages. It is widely accepted that the underlying relationship is causal. However, most studies use cross-sectional data, and only a few test causal mechanisms. In our analysis, we first test a broad range of social capital indicators by applying fixed-effects panel data regression to a sample of currently employed and a sample of newly employed individuals. Second, we test reservation wages, network search, being offered a job without prior job search, and the number of job interviews as some of the theoretical mechanisms put forward to explain positive social capital effects. Overall, we find no empirical evidence for wage effects of the social capital measure and no evidence that any of the proposed mechanisms are empirically relevant.

scholarly journals Country innovative development: impact of the shadow economy

2020 ◽  
pp. 41-49
Author(s):  
Alina Vysochyna ◽  
Olena Kryklii ◽  
Mariia Minchenko ◽  
Aygun Akbar Aliyeva ◽  
Kateryna Demchuk
Keyword(s):  
Regression Analysis ◽  
Panel Data ◽  
Fixed Effects ◽  
Shadow Economy ◽  
Slovak Republic ◽  
Panel Data Analysis ◽  
Innovative Development ◽  
State Management ◽  
Panel Data Regression ◽  
Data Regression

This article generalizes arguments and counterarguments within the scientific discussion regarding the determination of the influence of illegal economic activity and expansion of the shadow economy on innovative country development. The systematization of the scientific works on the above problems proves that there is no one no complexity and unity in the above-mentioned scientific findings, which, in turn, demonstrates the necessity of further theoretical and empirical search in this sphere. Thus, it was developed a scientific hypothesis about the negative influence of the shadow economy on innovative country development. In order to test this hypothesis it was developed a scientific and methodological approach that consists of several stages: 1) correlation analysis in order to eliminate multicollinearity problem between control variables; 2) analysis of dataset descriptive statistics; 3) running Hausman test in order to clarify specification of the regression model (fixed or random effects model); 4) realization of the panel data regression analysis for the whole country sample and separately for Ukraine, characteristics of its results. Technically all stages of the research are realized with the help of Stata 12/S.E. software. The country sample consists of 9 countries (Azerbaijan, Estonia, Hungary, Latvia, Lithuania, Poland, Slovak Republic, Slovenia, and Ukraine). Time horizon – 2008-2018. Running of the panel data regression analysis (model specification – with fixed effects) allow confirming research hypothesis for the whole country sample (an increase of shadow economy negatively affected innovative country development: an increase of shadow economy to GDP ratio in 1 % leads to the decrease of the Global Innovation Index in 0.5 points). However, it was not proved for Ukraine separately. It leads to the conclusion that innovative development in Ukraine does not highly dependent on the shadow economy scale because of more significant obstacles on the way to innovation adoption (institutional inefficiency, regulatory drawbacks, etc.). Keywords: innovative economic growth, innovative state management, panel data analysis, shadow economy.

scholarly journals In whom do you trust? Analysing the Role of Social Capital in Reducing Income Inequality-A Panel Data Approach

2017 ◽  
pp. 134-160
Author(s):  
Moina Rauf Et al.,
Keyword(s):  
Social Capital ◽  
Panel Data ◽  
Income Inequality ◽  
Fixed Effects ◽  
Good Governance ◽  
Cooperative Behavior ◽  
Panel Data Analysis ◽  
Critical Role ◽  
Social Spending ◽  
Trust Networks

In this study, we examine if social capital, in the form of trust, networks, and institutions, affects income inequality across countries. We aim to build a narrative, supported by empirics that institutions embedded deeply in the fabric of the society play a critical role in motivating policies towards inclusive growth and distributive initiatives by the state. Policies aimed at distribution are a conscious choice that is driven by the society’s level of trust in each other. Higher levels of trust among citizens leads to cooperative behavior and solves social dilemmas thus reducing free riding problems. Also, high trusting and cooperative citizens are more likely to push governments for reforms and policies that aim at the provision of public goods and increased social spending. Voters’ limited acceptance of morally questionable behavior among politicians restrains rent-seeking problems in politics and encourages good governance. The study uses panel data analysis utilizing data from World Values Surveys. The basic model used for testing the relationship between social capital and income inequality is through the fixed effects models. For developing a meaningful analysis, we distinguish between civil social capital and government social capital. We establish that higher levels of generalized trust (civil social capital) among citizens leads to a reduction in income inequalities. The significance of social capital is reiterated when the variables for government social capital are introduced in the model. Our study establishes that indicators of government social capital, particularly lower corruption levels have a significant impact on reducing income inequality.

An Examination of Audit Report Lag for Banks: A Panel Data Approach

2000 ◽  
Vol 19 (2) ◽  
pp. 159-174 ◽  
Author(s):  
B. Charlene Henderson ◽  
Steven E. Kaplan
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Explanatory Power ◽  
Omitted Variables ◽  
Audit Report ◽  
Cross Sectional ◽  
Cross Sectional Analysis ◽  
The Cross ◽  
Sectional Analysis

This study investigates the determinants of audit report lag (ARL) for a sample of banks. Researchers have been interested in the determinants of ARL, in part, because it impacts the timeliness of public disclosures. However, prior ARL research has relied exclusively on regression analysis of cross-sectional samples of companies from many industries. In addition to focusing exclusively on banks, panel data analysis is introduced and compared with cross-sectional analysis to demonstrate its power in dynamic settings and its potential to improve estimation. Results reveal important differences between cross-sectional analysis and panel data analysis. First, bank size is negatively related to ARL in cross-section but positively related to ARL using panel data analysis. The cross-sectional size estimate is subject to omitted variables bias, and furthermore, cross-sectional analysis fails to capture variation in size over time in relation to ARL. Panel data analysis both accounts for omitted variables and captures the dynamics of the relationship between size and ARL. As well, the panel data model's explanatory power far exceeds that of the cross-sectional model. This is primarily due to the panel model's use of firm-specific intercepts that both capture the role of reporting tradition and eliminate heterogeneity bias. Thus, panel data analysis proves to be a powerful tool in the analysis of ARL.

Using information from singletons in fixed-effects estimation: xtfesing

2020 ◽  
Vol 20 (4) ◽  
pp. 965-975
Author(s):  
Laura Magazzini ◽  
Randolph Luca Bruno ◽  
Marco Stampini
Keyword(s):  
Panel Data ◽  
Method Of Moments ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Panel Data Regression ◽  
Data Regression ◽  
Method Of Moments Estimator ◽  
Fixed Effects Estimation

In this article, we describe the xtfesing command. The command implements a generalized method of moments estimator that allows exploiting singleton information in fixed-effects panel-data regression as in Bruno, Magazzini, and Stampini (2020, Economics Letters 186: Article 108519).

scholarly journals The Importance of Maritime Transport for Economic Growth in the European Union: A Panel Data Analysis

2021 ◽  
Vol 13 (14) ◽  
pp. 7961
Author(s):  
Alexandra Fratila (Adam) ◽  
Ioana Andrada Gavril (Moldovan) ◽  
Sorin Cristian Nita ◽  
Andrei Hrebenciuc
Keyword(s):  
Economic Growth ◽  
European Union ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Maritime Transport ◽  
The European Union ◽  
Panel Data Regression ◽  
Green Investment ◽  
Port Infrastructure ◽  
The Impact

Maritime transport is one of the main activities of the blue economy, which plays an important role in the EU. In this paper, we aim to assess the impact of maritime transport, related investment, and air pollution on economic growth within 20 countries of the European Union, using eight panel data regression models from 2007 to 2018. Our results confirm that maritime transport, air pollutants (NOx and SO2) from maritime transport, and investment in maritime port infrastructure are indeed positively correlated with economic growth. In other words, an increase of 10% in these factors has generated an associated increase in economic growth rate of around 1.6%, 0.4%, 0.8%, and 0.7% respectively. Alongside the intensity of economic maritime activities, pollution is positively correlated with economic growth, and thus it is recommended that policymakers and other involved stakeholders act to diminish environmental impacts in this sector using green investment in port infrastructure and ecological ships, in accordance with the current European trends and concerns.

Determinants of Corporate Cash Holdings

2017 ◽  
Vol 18 (2) ◽  
pp. 416-427 ◽  
Author(s):  
Yogesh Maheshwari ◽  
K.T. Vigneswara Rao
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Capital Expenditure ◽  
Cash Holdings ◽  
Working Capital ◽  
Financial Policies ◽  
Panel Data Regression ◽  
Strong Positive Relationship ◽  
Development Expenditure ◽  
Corporate Cash Holdings

This article aims at examining the financial determinants of corporate cash holdings. The study employs panel data regression method. It uses the fixed-effects method based on Hausman test results for the estimation of panel data model. This study has implications that are beneficial for the business managers to have a better understanding and appreciation of the role and importance of the determinants of corporate cash holdings in formulating and evaluating the corporate financial policies. The results of the study indicate a strong positive relationship between cash holdings and cash flow, dividend payment, market-to-book ratio, net debt issuance and net equity issuance of the sample firms. It is also found that the cash holdings of these firms are negatively affected by net working capital, leverage, research and development expenditure as well as capital expenditure of the firm. The article will help researchers as well as managers to understand as to what motivates the firms to hold cash, given the fact that despite being often termed as a non-earning asset, firms generally hold more cash than their normal working capital requirement.

Nonparametric panel data regression with parametric cross-sectional dependence

2021 ◽  
Author(s):  
Alexandra Soberon ◽  
Juan M Rodriguez-Poo ◽  
Peter M Robinson
Keyword(s):  
Panel Data ◽  
Monte Carlo Study ◽  
Generalized Least Squares ◽  
Linear Estimator ◽  
Dependence Structure ◽  
Finite Sample ◽  
Cross Sectional ◽  
Panel Data Regression ◽  
Local Linear Estimator ◽  
Cross Sectional Dependence

Abstract In this paper, we consider efficiency improvement in a nonparametric panel data model with cross-sectional dependence. A Generalized Least Squares (GLS)-type estimator is proposed by taking into account this dependence structure. Parameterizing the cross-sectional dependence, a local linear estimator is shown to be dominated by this type of GLS estimator. Also, possible gains in terms of rate of convergence are studied. Asymptotically optimal bandwidth choice is justified. To assess the finite sample performance of the proposed estimators, a Monte Carlo study is carried out. Further, some empirical applications are conducted with the aim of analyzing the implications of the European Monetary Union for its member countries.

Limitations of Fixed-Effects Models for Panel Data

2019 ◽  
Vol 63 (3) ◽  
pp. 357-369 ◽  
Author(s):  
Terrence D. Hill ◽  
Andrew P. Davis ◽  
J. Micah Roos ◽  
Michael T. French
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Statistical Power ◽  
Unobserved Heterogeneity ◽  
Critical Discussion ◽  
Causal Inferences ◽  
Cross Sectional ◽  
Fixed Effects Models ◽  
Time Invariance ◽  
Type Ii Errors

Although fixed-effects models for panel data are now widely recognized as powerful tools for longitudinal data analysis, the limitations of these models are not well known. We provide a critical discussion of 12 limitations, including a culture of omission, low statistical power, limited external validity, restricted time periods, measurement error, time invariance, undefined variables, unobserved heterogeneity, erroneous causal inferences, imprecise interpretations of coefficients, imprudent comparisons with cross-sectional models, and questionable contributions vis-à-vis previous work. Instead of discouraging the use of fixed-effects models, we encourage more critical applications of this rigorous and promising methodology. The most important deficiencies—Type II errors, biased coefficients and imprecise standard errors, misleading p values, misguided causal claims, and various theoretical concerns—should be weighed against the likely presence of unobserved heterogeneity in other regression models. Ultimately, we must do a better job of communicating the pitfalls of fixed-effects models to our colleagues and students.

Globalization and health: An empirical investigation

2019 ◽  
Vol 19 (3) ◽  
pp. 207-224 ◽  
Author(s):  
Vishalkumar J Jani ◽  
Nisarg A Joshi ◽  
Dhyani J Mehta
Keyword(s):  
Health Status ◽  
Panel Data ◽  
Fixed Effects ◽  
Panel Data Analysis ◽  
Health Indicators ◽  
Developed Countries ◽  
Social Dimension ◽  
Three Dimensions ◽  
Infant Mortality Rate ◽  
The Impact

This article empirically examines the impact of globalization on the health status of countries by using panel data. Unlike previous studies, it has attempted to use three different dimensions of globalization and estimate their impact on health status measured by infant mortality rate and life expectancy. It also introduces an initial level of development status as an explanatory variable and found that it has an important role. The fixed effects panel data analysis shows that globalization has a positive impact on the health indicators. Out of the three dimensions of globalization, namely, economic, social and political, the first one has the highest influence on health for the less developed countries. However, as one moves up the ladder of development, social dimension becomes more important. Moreover, the pace of improvement in health indicators is faster in developed countries, indicating a divergence between the developed and the underdeveloped world.

scholarly journals Bias correction methods for dynamic panel data models with fixed effects

2017 ◽  
Vol 6 (2) ◽  
pp. 58
Author(s):  
Mohamed Abonazel
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Estimation Methods ◽  
Time Dimension ◽  
Dynamic Panel ◽  
Cross Sectional ◽  
Cross Sectional Dimension ◽  
Ls Estimator

This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally; we display new estimators that presented by Youssef and Abonazel [40] as more efficient estimators than the conventional estimators.

Sign in / Sign up

Export Citation Format

Share Document