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This study investigates the association between corporate sustainability disclosures and firm performance and value.

This study collected data from 694 manufacturing companies operating in 34 countries between 2007 and 2019, yielding 6,181 firm-year observations. This study employs a dual-model framework to analyze the influence of environmental, social, and governance (ESG) performance on return on assets (ROA), return on equity (ROE), and Tobin's Q ratio. Two sets of control variables, firm- and country-specific, were incorporated to account for potential confounding factors. To validate the robustness of the findings, we utilized a battery of econometric techniques, including traditional ordinary least squares (OLS), firm-fixed effects, quantile regression, and instrumental variables-generalized method of moments (IV-GMM), applied to both the pooled and firm-fixed effects models.

The findings are contradictory: there is a negative relationship between sustainability disclosure and operating performance and return on equity, but a positive relationship between sustainability disclosure and firm value. The negative correlation is consistent with agency theory and the positive correlation is consistent with the legitimacy and shareholder theories. These results are robust to performance measures and estimation methods.

Short-term profit shouldn't deter sustainability. It boosts legitimacy, reputation, efficiency, and long-term market value. Investors must look beyond profitability ratios, embracing ESG metrics. Firms should see sustainability as strategic investment, not cost. Patience pays off: long-term gains await. Regulation can guide balanced growth, prioritizing both shareholders and societal well-being.

This study is the first to adopt a firm’s fixed-effect quantile regression, which provides deep insights into the role of sustainability disclosure in meeting stakeholders’ expectations.

This chapter demonstrates that fixed-effects and first-differences models often understate the effect of interest because of the variation used to identify the model. In particular, the within-unit time-series variation often reflects transitory fluctuations that have little effect on behavioral outcomes. The data in effect suffer from measurement error, as a portion of the variation in the independent variable has no effect on the dependent variable. Two empirical examples are presented: one on the relationship between AFDC and fertility and the other on the relationship between local economic conditions and AFDC expenditures.

This study aims to measure the impact of information and communication technology (ICT) infrastructure on divorce for a sample of developed countries for the period (2012-2021).

This paper uses fixed effect and generalized method of moments (GMM) estimation to delve into the impact of eight ICT infrastructure factors on divorce. The analyses are based on data collected from the World Bank Development Indicators.

The fixed effect analysis revealed that an increase in both fixed-telephone subscriptions and population covered by at least a 4G led to an increase in divorce. However, an increase in mobile cellular subscriptions and active mobile broadband subscriptions lead to a decrease in divorce. The results of the GMM estimator showed that fixed broadband internet prices, mobile broadband internet prices, the percentage of the population covered by the 4 G mobile network and fixed-telephone subscriptions had a substantial effect on divorce. While fixed broadband subscriptions, active mobile broadband subscriptions and mobile cellular basket were found to hinder divorce.

The findings of this study contribute to the existing literature by highlighting the impact of ICT on divorce in a sample of developed countries. Furthermore, it helps decision-makers and families to understand the causes of divorce related to ICT infrastructure in these societies, so they can understand this phenomenon and provide proactive policies.

This study used multiple factors to measure ICT infrastructure as well as the GMM estimator to delve into the impact of ICT on divorce.

This paper aims to test the significance of unobservable firm-specific effects on a capital structure model.

The paper employs the restricted least squares method to test the significance of unobservable firm-specific effects in a fixed effects model that includes unobservable effects against a pooled ordinary least squares model that excludes unobservable effects.

The empirical findings indicate that models that include unobservable firm-specific effects are correctly specified.

The limitation of this study comes from lack of data to measure unobservable effects such as managerial ability or managerial skills. Future research can develop index measures of managerial ability or managerial skills and borrow from management theory to explain the connection between managerial ability or managerial skills and firms' capital structure.

The findings imply that a capital structure model that excludes firm-specific effects could be mis-specified because such a model does not control for unobservable firm-specific factors such as managerial ability or managerial skills which have significant effects on firms' capital structure decisions.

The findings are important because the paper applies the restricted least squares method to test the significance of unobservable firm-specific effects. This technique has not been applied previously. The paper contributes to capital structure research in the fast growing South Africa.

The purpose of this paper is to estimate benefits to debt reduction by using the natural experiment provided by the debt relief programs: the Heavily Indebted Poor Countries Initiative launched by the International Monetary Fund and World Bank in 1996 and the Multilateral Debt Relief Initiative extension in 2005.

The authors apply a time-shifted difference-in-differences strategy to evaluate the effects of this intervention. The date of each country’s decision to participate in the program is used as one treatment point while the date of the completion of the debt relief program is used as another treatment point. The exercise compares different economic outcomes such as domestic and foreign investment, schooling, and employment of the treated observations to the counterfactual of untreated country-years. The period between the decision and completion points is a short run while the period after the completion point is considered a long run.

The authors found that debt relief increased capital investment as much as 1.63 percent in the short run and 5.79 percent in the long run. However, there was no effect on foreign direct investment suggesting that debt overhang does not affect incentives of foreign investors. Output and schooling enrollment increased both in the short and long run.

This paper exploits a natural experiment of debt relief in a number of developing countries to shed light on the possible benefits to debt reduction. The authors are able to separate the short- and long-run effects of debt reduction. The finding that domestic but not foreign investment responds to debt reduction is suggestive of the differences in incentives across these two sources of investment.

While the relationship between military expenditure and economic growth during the Cold War period is well-researched, relatively less is known on the issue for the post-Cold War era. Equally how the relationship varies with respect to exposure to conflict is also not fully examined. Therefore, the purpose of this paper is to investigate the causal impact of military expenditure on growth in the presence of internal and external threats for the period 1990-2013 using data from 70 developing countries.

The main estimates are based on the generalized method of moments (GMM) regression model. But for comparison purposes, the authors also report estimates using fixed and random effects as well as pooled cross-section regressions. The regression specification accounts for non-linear effect of military expenditure allowing for interaction with conflict variable (where distinction is made between external and internal conflict).

The analysis indicates that methods as well as model specification matter in studying the effect of military spending on growth. Full sample estimates based on GMM, fixed, and random effects models suggest a negative and statistically significant effect of military expenditure. However, fixed effects estimate becomes insignificant for low-income countries. The effect of military spending is also insignificant in the cross-sectional OLS model if conflict is not considered. When the regression model additionally controls for conflict, the effect of military spending conditional upon (internal) conflict exposure is significant and positive. No such effect is present conditional upon external threat.

One important limitation of the analysis is the small sample size – the authors had to restrict analysis to 70 low and middle-income countries for which the authors could construct post-Cold War panel data on military expenditure along with information on armed conflict exposure (the later from the Uppsala Conflict Data Program, 2015).

To the best of the author’s knowledge, this is the first paper to examine the joint impact of military expenditure and conflict on economic growth in post-Cold War period in a sample of developing countries. Moreover, an attempt is made to review and revisit the large Cold War literature where studies vary considerably in terms findings. A key reason for this is the somewhat ad hoc choice of econometric methods – most rely on cross-section data and rarely conduct sensitivity analysis. The authors instead rely on panel data estimates but also report results based on naïve models for comparison purposes.

This chapter develops a set of two-step identification methods for social interactions models with unknown networks, and discusses how the proposed methods are connected to the identification methods for models with known networks. The first step uses linear regression to identify the reduced forms. The second step decomposes the reduced forms to identify the primitive parameters. The proposed methods use panel data to identify networks. Two cases are considered: the sample exogenous vectors span Rn (long panels), and the sample exogenous vectors span a proper subspace of Rⁿ (short panels). For the short panel case, in order to solve the sample covariance matrices’ non-invertibility problem, this chapter proposes to represent the sample vectors with respect to a basis of a lower-dimensional space so that we have fewer regression coefficients in the first step. This allows us to identify some reduced form submatrices, which provide equations for identifying the primitive parameters.

The purpose of this study is to examine the impact of crime on apartment prices for Hamburg, Germany, for the years 2012 to 2017.

The authors use a panel data setting with fixed effects estimators and temporal lags to moderate the endogeneity concerns related to crime. The authors consider the effect of total crime, violent and property crime and some sub-categories of crime.

The estimates show that it takes two to three years for prices to react, with the longer run elasticity reaching −0.12 for total crime, −0.15 for property crime and −0.06 for violent crime. The elasticities are much larger in high-crime areas (−0.22 for total crime, −0.28 and −0.09 for property and violent crime) and elevated also in low-income areas.

The finding that property crime matters more in terms of quantitative impact for housing values than violent crime provides reasonable grounds for rethinking the resource allocation of public spending on crime clearance and prevention in Germany. Far more emphasis on preventing property crime appears in order and especially so in the lower income or higher crime areas, which are significantly more affected by crime and in particular property crime than those in high income or low crime areas.

The estimates for Hamburg provide the first detailed results of the impact of crime on real estate prices in Germany. It is also the first study for Continental Europe using panel data.

This paper aims to analyse factors that influence child schooling outcomes in India, specifically the role of gender.

This paper uses data from the nationally representative Indian National Family Health Surveys 1995-1996 and 2005-2006 and estimates Heckman sample selection, cluster fixed-effects and household fixed-effects econometric models. The dependent variables are the child’s enrolment status and conditional on enrolment child’s years of schooling.

This analysis finds statistically significant evidence of male advantage both in schooling enrolment as well as years of schooling. However, using a cluster fixed-effects model, our analysis finds that within a village, conditional on being enrolled, girls spend more years in school relative to boys. Other results show that parental schooling has a positive and statistically significant impact on child schooling. There is statistically significant wealth effect, community effect and regional disparities between states in India.

The large sample size and the range of questions available in this data set, allows us to explore the influence of individual, household and village level social, economic and cultural factors on child schooling. The role of gender on child schooling within a village, intrahousehold resource allocation for schooling and regional gender differences in schooling are important issues in India, where education outcomes remain poor for large segments of the population.

Previous research provides evidence of a negative effect of body mass on women's economic outcomes. We extend this research by using a much older sample of individuals from the Panel Study of Income Dynamics and by using a body mass measure that is lagged by 15 years instead of the traditional 7 years. One of the main contributions of this paper is a replication of previous research findings given our differing samples and measures. We compare OLS estimates with sibling fixed effects estimates and find that obesity is associated with an 18% reduction in women's wages, a 25% reduction in women's family income, and a 16% reduction in women's probability of marriage. These effects are robust – they persist much longer than previously understood and they persist across the life course, affecting older women as well as younger women.