Extreme Bounds Analysis Research Articles

Revisiting the impact of corruption on income inequality worldwide

AbstractThe relationship between corruption and income inequality has been widely studied, but there is no consensus on whether corruption increases or reduces inequality. We conduct an extreme bounds analysis (EBA) to test the robustness of the explanatory variables proposed in the literature. Using a sample of up to 150 countries, with data mostly going back to 1980, we find that corruption does not appear to have a clear positive effect on inequality or may even reduce it. Also, contrary to what is sometimes suggested in the literature, the results do not support an inverted U‐shaped effect of corruption on income distribution. A more important role in explaining income distribution seems to be played by the level of financial development, the old‐age dependency ratio, the unemployment rate, the capital stock to GDP ratio and the population growth rate. These are often found to be significant drivers of inequality, regardless of the set of control variables and the definition of corruption used.

Economic and Political Determinants of Sovereign Default and IMF Credit Use: A Robustness Assessment Post 2010

According to the IMF, the current public debt makes up nearly 40 percent of the global debt, marking the highest share since the mid-1960s. Despite the vast research on alarming levels of sovereign default, the literature remains inconclusive. This paper investigates macroeconomic, financial, and political determinants of IMF credit use in the post-2010 era. The main contribution of our study lies in its temporal analysis as we investigate how the robustness of different factors has evolved. By utilizing an extensive dataset on 216 countries over the period of 2010–2021 and employing a variant of the Extreme Bounds Analysis (EBA) method, our study reveals that fluctuations in the IMF credit to external debt ratio can be attributed to changes in the total reserves to external debt ratio, where this relationship is statistically significant and reliable. However, high political risks seem to no longer affect the IMF’s decision, post 2010. Furthermore, our findings demonstrate that excluding countries with low debt arrears strengthens the results’ robustness. These findings contribute to a better understanding of the complexities surrounding IMF credit use in the contemporary global economic scene and offer new standpoints on the Fund’s lending choices.

On the robust drivers of cryptocurrency liquidity: the case of Bitcoin

This study aims to identify the factors that robustly contribute to Bitcoin liquidity, employing a rich range of potential determinants that represent unique characteristics of the cryptocurrency industry, investor attention, macroeconomic fundamentals, and global stress and uncertainty. To construct liquidity metrics, we compile 60-min high-frequency data on the low, high, opening, and closing exchange rates of Bitcoin against the US dollar. Our empirical investigation is based on the extreme bounds analysis (EBA), which can resolve model uncertainty issues. The results of Leamer’s version of the EBA suggest that the realized volatility of Bitcoin is the sole variable relevant to explaining liquidity. With the Sala-i-Martin’s variant of EBA, however, four more variables, (viz. Bitcoin’s negative returns, trading volume, hash rates, and Google search volume) are also labeled as robust determinants. Accordingly, our evidence confirms that Bitcoin-specific factors and developments, rather than global macroeconomic and financial variables, matter for explaining its liquidity. The findings are largely insensitive to our proxy of liquidity and to the estimation method used.

Are democratic regime and the magnitude of the informal economy robust determinants of human impacts on the environment? An extreme bounds analysis

AbstractThere is a vast body of literature dedicated to identifying the major drivers that explain human impacts on the environment. In this study, we utilize the STIRPAT framework to assess whether two controversial factors should be considered when examining how humans affect the environment. Specifically, we investigate whether the quality of the democratic regime and the size of the informal economy play significant roles in determining human impacts on the environment. To address this question, we employ the extreme bounds analysis (EBA) to characterize these drivers as either robust or fragile. Based on the findings of this paper, both variables are classified as fragile, suggesting that they may not fully explain human impacts on the environment. We have taken great care in interpreting the results.

Stock market anomalies: An extreme bounds analysis

We conduct the extreme bounds analysis (EBA) to evaluate the robustness or fragility of a range of stock market anomalies, using U.S. daily data from 1960 to 2023. The EBA is a large-scale sensitivity analysis, able to isolate the effects of potential data-mining or p-hacking under model uncertainty. The anomalies covered include the effects of Halloween, sports event, seasonal affective disorder, weather, political cycle, daylight saving, and lunar phase. We find that the empirical evidence for the anomalies is highly fragile, in terms of effect size estimates and their statistical significance.

The determinants of the hidden economy in developed and developing countries

ABSTRACT This paper examines whether there is a difference in the hidden economy’s set of determinants across developed and developing countries. Applying extreme bounds analysis (EBA) to a cross-sectional dataset of 33 developed and 97 developing countries, we find a considerable variation in the determinants of the hidden economy across developed and developing countries. The results show that internet usage, inflation, financial development, poverty, and monetary freedom are robust determinants of the hidden economy exclusively in developing countries. Whereas the time required to start a business and GDP per capita growth play a crucial role exclusively in developed countries. Remarkably, the property rights index is the only determinant that is robust in both types of countries. These findings offer important insights for policymakers seeking to address the challenges posed by the hidden economy.

Sensitivity Analysis of Key Cost Performance Determinants Using Extreme Bound Analysis Method

The complex nature of construction projects makes achieving positive outcomes challenging, and cost overruns, which frequently occur in all three construction phases, are one of the major impediments to success. When indicators of cost overruns are determined early in a project, however, strategies for reducing or eliminating them can be adopted. Therefore, this study aimed to identify primary cost performance indicators (CPIs) and assess their robustness during the engineering, procurement, and construction (EPC) phases of heavy industrial projects. To accomplish this, an extensive survey was designed and distributed to experienced construction professionals to gather information on tried and true cost performance indicators they had successfully employed. The chi-square test, two-sample t-test, and the Kruskal-Wallis test were used to evaluate their responses, and a final list of cost performance indicators was developed using the all-possible combination regression analysis approach. The robustness or fragility of the indicators was investigated using extreme bounds analysis (EBA), and the results were analyzed using Sala-i-Martin and Leamer’s EBA methods. Because Leamer’s approach concentrates exclusively on the indicators’ extreme boundaries and Sala-i-Martin’s approach considers the entire distribution of the variables, the conclusions were drawn using the Sala-i-Martin method. The results revealed that the three determinants of cost performance indicators in all three construction phases are a project’s alignment, effective management strategies, and the staff turnover rate. Two more robust indicators in all three phases were the fundamental essence of a project and the number of designer/engineering organizations. The study findings will help project managers of construction projects effectively allocate their financial, human, and machinery resources.

The Methodology of Multiple Regressions for GDP per Capita for Nations in the World Between 0 CE and 1940

A reduced-form model of GDP per capita is estimated using the classical econometric method and two Bayesian techniques, Extreme Bounds Analysis (EBA) and Bayesian Analysis of Classical Estimation (BACE) for the two millenniums. For several variables, such as the impact of colonialism, the Goldstone hypothesis of efflorescence increasing economic growth, and the impact of technology, this paper lends support, especially when the Bayesian methods are applied. Other results are mixed. Generally, the results support use of Bayesian methods by eliminating hypotheses that are not consistently supported over all models.

Características locais e a pandemia de Covid-19: uma análise voltada aos municípios do Estado brasileiro de Minas Gerais

Abstract In order to understand the reasons that led certain locations to face more/less difficulties in dealing with COVID-19, the effect of some municipal characteristics, on the main statistics related to the disease, was estimated. For this purpose, cross-section data (with cases/deaths accumulated up until April 21, 2021), on the municipalities of Minas Gerais were considered, and Ordinary Least Squares, Poisson and Negative Binomial estimators were used, in addition to the Extreme Bounds Analysis technique. Small towns, with a larger number of public health clinics (known in Brazil as “basic health units”) and more young people would have fewer cases/deaths. Urban, hot, polluted locations with higher inequality, as well as greater economic activity and movement of employees, presented the greatest problems. Incidence/mortality would increase in hot cities, with greater economic activity and a history of comorbidity. However, mortality would decrease among the youngest/most educated people. Furthermore, lethality would be lower among the younger population and in sparsely populated (up to 150,000 inhabitants) and rainless cities.

What robustly determines FDI in emerging markets and developing countries? A sensitivity analysis

What determines Foreign Direct Investment (FDI) inflows remains a primary concern of economists and policy makers; yet the uncertainty surrounding FDI theories and empirical approaches has created ambiguity regarding the determinants of FDI. This paper applies Leamer’s (1983, 1985) traditional Extreme Bounds Analysis (EBA) as well as the Sala-I-Martin modified form of EBA to identify the robust determinants of FDI using cross-sectional data (averaged for the period of 1985-2016) covering 103 emerging and developing countries. We consider 33 potential economic, political and institutional determinants. To address concerns about multicollinearity, the VIF restriction specifies that only estimates with VIF below five are reported. The results show that all variables of interest are fragile determinants of FDI when using traditional EBA. Yet, when applying the Sala-i-Martin EBA, nine robust variables are revealed. Some policy implications are discussed.

What drives US stock markets during the COVID-19 pandemic? A global sensitivity analysis

This paper identifies robust determinants of US stock price movements in the economic shadow of the COVID-19 crisis and in the presence of model uncertainty, using several influential factors highlighted in relevant research. Our investigation performs an extreme bounds analysis (EBA), a global sensitivity framework capable of handling the problem of model uncertainty. We document that excess market returns, term spread, implied volatility, oil, Twitter-based economic uncertainty, and European and Chinese stock returns are the only variables that are robust to all possible variations in the condition set of information. The results also reveal the irrelevance of newly reported COVID-19 cases and deaths as novel drivers that contribute to the formation stock prices, thus lending support to the “psychophysical numbing” phenomenon.

Robust drivers of Bitcoin price movements: An extreme bounds analysis

There is a growing stream of empirical research that endeavors to identify the influential variables contributing to the price formation of cryptocurrencies and, in particular, Bitcoin. However, results of those studies generally remain inconsistent in terms of not only the true combination of factors that affect Bitcoin prices, but also the nature of effects (positive vs. negative) that each individual factor has on the price behavior. The present study investigates the robustness of a wide variety of candidate determinants that have been the focus of attention in relevant literature. Our inquiry relies on the extreme bounds analysis (EBA), which is a type of large-scale sensitivity analysis capable of addressing model uncertainty issues. The findings suggest that crypto market forces of supply and demand, public interest, and economic policy uncertainty are the only variables robust to all possible variations in the conditioning information set. Our evidence argues in favor of the predominance of cryptocurrency-related determinants over global macroeconomic and financial ones in explaining Bitcoin price movements.

Can energy predict the regional prices of carbon emission allowances in China?

The carbon emission trading is an important market-oriented tool in the process of China's carbon neutrality, which makes companies face tremendous pressure to reduce emissions while having strong energy demands. In order to evaluate whether energy prices can be robust predictors of the prices of emission allowances, this study perform extreme bounds analysis (EBA) in four representative markets. The empirical results reveal that energy prices can indeed predict the prices of emission allowances, but the robustly predictive capabilities of different energy prices vary with regions. Among them, thermal coal is the robustly positive predictor for Guangdong, Hubei and Shanghai market; natural gas is the robustly negative predictor for all the four chosen regions; and crude oil can only positively predict Hubei market with robustness. Meanwhile, the horizons that predictions from energy to emission allowance can be performed as well as the predictive coefficients also vary with energy types and regions. And some trading implications are also provided alongside.

What Determines the Shadow Economy? An Extreme Bounds Analysis

The purpose of this paper was to identify the leading causes determining the shadow economy at the global level. The empirical analysis used was the Sala-i-Martin version of extreme bounds analysis (EBA) applied to a cross-sectional sample of 132 countries. The results suggested that the quality of institutions is the primary determinant of the shadow economy. The results showed that only four out of six factors of the quality of institutions proved to be robust determining factors of the shadow economy; they are bureaucracy quality, law and order, corruption, and internal conflict. Moreover, monetary freedom and secured property rights are also robust and negatively related to the shadow economy. An interesting result is that information and communication technology (ICT) development is vital to the shadow economy. Mainly, internet usage is robust and negatively associated with the shadow economy. Furthermore, inflation and poverty emerge as key determining factors of the shadow economy. Our findings will aid in the development of recommendations for potential strategies to minimize the international extent of the shadow economy.

Exploring the impact of economic integration agreements through extreme bounds analysis

AbstractThis article provides an empirical strategy guided by the data to estimate the effects of Economic Integration Agreements (EIAs) on trade flows and their transitional dynamics. The strategy uses Extreme Bounds Analysis (EBA) to guide the choice of lags and leads in the effects without researchers' discretion involved. We show that arbitrarily selected year intervals and starting year can result in non‐robust estimates of transitional dynamics of the effects of EIAs on trade flows. The empirical strategy follows two steps: EBA first sifts lags and leads of EIAs robustly related to trade flows from candidates, then these are included in the gravity equation to estimate the effects of EIAs on trade. We find that various lags and leads are robustly and positively related to trade flows, and the lag and lead structure depends on the level of integration. Our results show that EIAs have a long‐term effect of 63% on trade flows. Under the richer lag and lead structure, deep‐integration agreements beyond the level of free trade agreements have a much higher impact on trade flows than free trade agreements do (132% vs 31%). The estimates of effects of EIAs obtained from EBA‐based estimation have a smaller contemporaneous effect and larger phased‐in effects compared to previous studies relying on the subjective choices of year intervals while similar results are observed with the decomposed EIAs.

Financialization and income inequality: An extreme bounds analysis

Several theoretical hypotheses predict conflicting effects of financialization on income inequality, and the lack of consensus is reinforced by the mixed findings of related empirical studies. In this paper we address the model uncertainty issue by applying extreme bounds analysis (EBA) to examine the robustness of financialization variables as determinants of income inequality. The results suggest that financialization is a leading driver of income inequality and that the effects are larger when transmitted through financial markets. In addition, the results show that law and order, labour union density, population, globalization, remittances, education, and the agriculture sector share are also robust determinants of income inequality. Policy recommendations are put forward to reduce the power of the financial sector.

The density paradox: Are densely-populated regions more vulnerable to Covid-19?

The “density paradox” refers to the observation that some highly populated cities and countries have recorded a smaller number of Covid‐19 cases than regions that are sparsely populated. We present empirical evidence on the role played by population density in spreading the coronavirus, based on cross‐sectional data covering 172 countries (obtained from several sources, including the European Centre for Disease Prevention and Control, the World Bank and the Center for Health Security). The results, obtained by using the techniques of extreme bounds analysis (EBA) and variable addition tests, show that population density has a significantly positive effect on the number of cases but not the number of deaths, as the latter is better explained by measures of preparedness. Plausible explanations are presented for the results to conclude that the “density paradox” is not really a paradox. This paper makes a contribution by shedding more light on a frequently debated issue by using a completely different, and more robust, statistical techniques and by providing results that may be helpful for health and urban planners.

UNDER INVOICING IN THE RESIDENTIAL REAL ESTATE MARKET IN PAKISTAN

In this paper the meaning of “under invoicing in a real estate market” signifies that the market value of real property exceeds its record value appearing in government documentation. The purpose of this study is to identify the level of under invoicing and determine the factors that affect the under invoicing in real estate markets in Pakistan. We apply OLS and Extreme Bounds Analysis techniques to test our propositions. The statistical sample consists of 338 real estate contracts. We find that under invoicing is determined by a multiplicity of factor. These include: the capital gains tax rate, the measurable amount of corruption in the economy, the risk-free rate, a buyer’s profession, the reputation of the local housing authority, and the degree of disequilibria in regional real estate markets. Our findings are consistent with four distinct hypotheses: (a) tax evasion hypothesis, (b) widening gap hypothesis, (c) a corruption hypothesis and (d) a signaling hypothesis. The evidence suggests that higher rates of taxation and a larger statistical incidence of corruption in markets tend to be associated with a greater probability of under-invoicing. The findings of the study have practical implications for those investors who are interested in real estate markets of emerging and developing economies.

Fertile soil for intrapreneurship: impartial institutions and human capital

AbstractIntrapreneurs, entrepreneurial employees, constitute an important force behind innovations in the economy. Yet, what factors that promote intrapreneurship at the country level are an underdeveloped research area. This paper provides an important contribution regarding the methodological approach and the broad set of potential explanatory factors studied. Based on machine-learning techniques (Least Absolute Shrinkage and Selection Operator (LASSO) and Extreme Bounds Analysis (EBA)), we investigate the influence of over 60 factors capturing institutional, demographic, cultural, and developmental factors. We find that the quality of government measured as impartiality, i.e. that the public institutions treat the citizens in a non-discriminatory fashion and do not favor some groups or individuals, and the level of human capital, measured as the average years of schooling, are the most important factors predicting the level of intrapreneurship across countries. Instrumental variable results support a causal interpretation. The findings emphasize the importance of policy to establish well-functioning and impartial institutions as well as to promote higher education.

Valuing local and dual-class IPOs in the Alternative Investment Market

espanolResumen: Las ofertas iniciales de venta al publico (IPOs), por definicion, no tienen historia y no han sido valoradas por inversionistas. Por ello, es dificil o imposible realizar proyecciones de flujos futuros, al no existir informacion financiera publica. Este trabajo centra la valoracion en el Mercado de alternativo de inversiones, determinando los observables que afectan la valoracion. Aplicamos distintas tecnicas econometricas a datos contables de 2,185 IPOs entre los anos 1995 y 2020. Nuestros resultados sugieren que las valoraciones se relacionan con diversos factores. EnglishAbstract: Initial Public Offerings are, by definition, not seasoned securities. They have not been subjected to valuation by the community of investors. It is often difficult or impossible to forecast their future cash flows because most do not have a long history of publicly disclosed financial information. Consequently, valuing IPOs in any market is more difficult than valuing seasoned equities. In this paper, we address the valuation of IPOs in the Alternative Investment Market, (hereafter the AIM.) The purpose of this study is to determine the observable factors that affect valuation in the AIM. We apply OLS, LASSO regression, and Extreme Bounds Analysis (EBA) techniques on historical accounting data to test our theory of valuation. The statistical sample consists of 2,185 IPOs issued on the AIM between 1995 and 2020. Our findings suggest that the market valuation of IPOs in the AIM is systematically related to a multiplicity of factors. These include earnings per share (EPS) in the after-market, operating cash follow per share, and the percentage of shares issued to the public. The findings of the study have a practical value for investors who are interested in buying IPOs in the AIM.