Comparing Financial Transparency between For-profit and Nonprofit Suppliers of Public Goods: Evidence from Microfinance
John W. Goodell, Abhinav Goyal, Iftekhar Hasan
Journal of International Financial Markets, Institutions and Money,
January
2020
Abstract
Previous research finds market financing is favored over relationship financing in environments of better governance, since the transaction costs to investors of vetting asymmetric information are thereby reduced. For industries supplying public goods, for-profits rely on market financing, while nonprofits rely on relationships with donors. This suggests that for-profits will be more inclined than nonprofits to improve financial transparency. We examine the impact of for-profit versus nonprofit status on the financial transparency of firms engaged with supplying public goods. There are relatively few industries that have large number of both for-profit and nonprofit firms across countries. However, the microfinance industry provides the opportunity of a large number of both for-profit and nonprofit firms in relatively equal numbers, across a wide array of countries. Consistent with our prediction, we find that financial transparency is positively associated with a for-profit status. Results will be of broad interest both to scholars interested in the roles of transparency and transaction costs on market versus relational financing; as well as to policy makers interested in the impact of for-profit on the supply of public goods, and on the microfinance industry in particular.
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Nowcasting East German GDP Growth: a MIDAS Approach
João Carlos Claudio, Katja Heinisch, Oliver Holtemöller
Empirical Economics,
No. 1,
2020
Abstract
Economic forecasts are an important element of rational economic policy both on the federal and on the local or regional level. Solid budgetary plans for government expenditures and revenues rely on efficient macroeconomic projections. However, official data on quarterly regional GDP in Germany are not available, and hence, regional GDP forecasts do not play an important role in public budget planning. We provide a new quarterly time series for East German GDP and develop a forecasting approach for East German GDP that takes data availability in real time and regional economic indicators into account. Overall, we find that mixed-data sampling model forecasts for East German GDP in combination with model averaging outperform regional forecast models that only rely on aggregate national information.
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Drawing Conclusions from Structural Vector Autoregressions Identified on the Basis of Sign Restrictions
Christiane Baumeister, James D. Hamilton
Abstract
This paper discusses the problems associated with using information about the signs of certain magnitudes as a basis for drawing structural conclusions in vector autoregressions. We also review available tools to solve these problems. For illustration we use Dahlhaus and Vasishtha's (2019) study of the effects of a U.S. monetary contraction on capital flows to emerging markets. We explain why sign restrictions alone are not enough to allow us to answer the question and suggest alternative approaches that could be used.
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Nowcasting East German GDP Growth: a MIDAS Approach
João Carlos Claudio, Katja Heinisch, Oliver Holtemöller
Abstract
Economic forecasts are an important element of rational economic policy both on the federal and on the local or regional level. Solid budgetary plans for government expenditures and revenues rely on efficient macroeconomic projections. However, official data on quarterly regional GDP in Germany are not available, and hence, regional GDP forecasts do not play an important role in public budget planning. We provide a new quarterly time series for East German GDP and develop a forecasting approach for East German GDP that takes data availability in real time and regional economic indicators into account. Overall, we find that mixed-data sampling model forecasts for East German GDP in combination with model averaging outperform regional forecast models that only rely on aggregate national information.
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Does Machine Learning Help us Predict Banking Crises?
Johannes Beutel, Sophia List, Gregor von Schweinitz
Journal of Financial Stability,
December
2019
Abstract
This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance metric, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly efficiently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.
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Managerial Ability and Value Relevance of Earnings
Bill Francis, Iftekhar Hasan, Ibrahim Siraj, Qiang Wu
China Accounting and Finance Review,
No. 4,
2019
Abstract
We examine how management ability affects the extent to which capital markets rely on earnings to value equity. Using a measure of ability that captures a management team’s capacity for generating revenues with a given level of resources compared to other industry peers, we find a strong positive association between managerial ability and the value relevance of earnings. Additional tests show that our results are robust to controlling for earnings attributes and investment efficiency. We use propensity score matching and the 2SLS instrumental variable approach to deal with the issue of endogeneity. For further identification, we examine CEO turnover and find that newly hired CEOs with better managerial abilities than the replaced CEOs increase the value relevance of earnings. We identify weak corporate governance and product market power as the two important channels through which superior management practices play an important role in the corporate decision-making process that positively influence the value relevance of earnings. Overall, our findings suggest that better managers make accounting information significantly more relevant in the market valuation of equity.
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Foreign Ownership, Bank Information Environments, and the International Mobility of Corporate Governance
Yiwei Fang, Iftekhar Hasan, Woon Sau Leung, Qingwei Wang
Journal of International Business Studies,
No. 9,
2019
Abstract
This paper investigates how foreign ownership shapes bank information environments. Using a sample of listed banks from 60 countries over 1997–2012, we show that foreign ownership is significantly associated with greater (lower) informativeness (synchronicity) in bank stock prices. We also find that stock returns of foreign-owned banks reflect more information about future earnings. In addition, the positive association between price informativeness and foreign ownership is stronger for foreign-owned banks in countries with stronger governance, stronger banking supervision, and lower monitoring costs. Overall, our evidence suggests that foreign ownership reduces bank opacity by exporting governance, yielding important implications for regulators and governments.
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Should Forecasters Use Real‐time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence
Katja Heinisch, Rolf Scheufele
German Economic Review,
No. 4,
2019
Abstract
In this paper, we investigate whether differences exist among forecasts using real‐time or latest‐available data to predict gross domestic product (GDP). We employ mixed‐frequency models and real‐time data to reassess the role of surveys and financial data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real‐time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.
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Gender Stereotypes still in MIND: Information on Relative Performance and Competition Entry
Sabrina Jeworrek
Journal of Behavioral and Experimental Economics,
October
2019
Abstract
By conducting a laboratory experiment, I test whether the gender tournament gap diminishes in its size after providing information on the relative performance of the two genders. Indeed, the gap shrinks sizeably, it even becomes statistically insignificant. Hence, individuals’ entry decisions seem to be driven not only by incorrect self-assessments in general but also by incorrect stereotypical beliefs about the genders’ average abilities. Overconfident men opt less often for the tournament and, thereby, increase their expected payoff. Overall efficiency, however, is not affected by the intervention.
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Drivers of Effort: Evidence from Employee Absenteeism
Morten Bennedsen, Margarita Tsoutsoura, Daniel Wolfenzon
Journal of Financial Economics,
No. 3,
2019
Abstract
We use detailed information on individual absent spells of all employees in 4140 firms in Denmark to show large differences in average absenteeism across firms. Using employees who switch firms, we decompose days absent into an individual component (e.g., motivation, work ethic) and a firm component (e.g., incentives, corporate culture). We find the firm component explains 50%–60% of the difference in absenteeism across firms, with the individual component explaining the rest. We present suggestive evidence of the mechanisms behind the firm effect with family firm status and concentrated ownership strongly correlated with decreases in absenteeism. We also analyze the firm characteristics that correlate with the individual effect and find that firms with stronger career incentives attract lower-absenteeism employees.
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