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East Asia and the Pacific is a region of dynamic growth. Women have contributed significantly to this growth and have benefited from it through active participation in the labor market. However, women are still disproportionately represented in the informal sector and in low paid work. Efforts to identify barriers to women's business and entrepreneurial activities in the region are critical not only to facilitate inclusive growth in a national context but also to counter the increasing trend of female migratory flows in the region. This report highlights' both the challenges and the economic opportunities for businesswomen in the region offers some useful potential pointers for reform.

We examine the relationship between the widespread, long-lasting debt forbearance on household loans introduced in Hungary at the outbreak of the coronavirus pandemic and subsequent loan repayment difficulties. We estimate linear probability and logit models at the contract level. Although our method is not suitable for identifying causal effects, participation in the moratorium proves to be a strong predictor of subsequent defaults. This is true even if we take into account the wide range of relevant factors observed at the end of the general moratorium period (October 2021). Our main results show that contracts which left the general moratorium at the end of the moratorium and, within this, those that took full advantage of the programme, were on average 3.2 and 4.2 percentage points more likely to become non-performing in September 2022 than those that never participated in the moratorium. This relationship can explain almost half of the differences in default rates between the respective groups.

Whether bank credit is suitable to finance innovation is a key question. Using a sample of 6422 small firms across 22 emerging economies, we find that a lack of access to credit stifles innovation, especially of the technologically “hard” type. This detrimental impact is stronger in localities or sectors with more dependence on external financing, but only holds for firms that are limited in alternative financing sources. The negative impact is further mitigated by better institutions. Foreign or transactional banks, or banks in more diversified banking markets are better in promoting firm innovation.

This work was based on empirical studies of the credit register and loans issued, which have been compiled by all Russian banks since 2017, for the analysis of bank interest rates by a common factor and subject to the control of the characteristics of the loan. These dates identified bank-specific components in the management information system; also, the same data were used to measure the fragmentation of the accounting lending market in Russia. This paper illustrates the credit market in Russia during the COVID-19 pandemic by analyzing the management of loan rates with existing differences due to the heterogeneity of banks and borrowers in order to achieve a sustainable management system. The results show that the heterogeneity of banks’ decision-making when setting interest rates is high and increased at an early stage of the pandemic. Additionally, the results demonstrate that the banks tightened interest-free lending conditions during the pandemic for sustainable management goals.

Data science and machine-learning techniques help banks to optimize enterprise operations, enhance risk analyses and gain competitive advantage. There is a vast amount of research in credit risk, but to our knowledge, none of them uses credit registry as a data source to model the probability of default for individual clients. The goal of this paper is to evaluate different machine-learning models to create accurate model for credit risk assessment using the data from the real credit registry dataset of the Central Bank of Republic of North Macedonia. We strongly believe that the model developed in this research will be an additional source of valuable information to commercial banks, by leveraging historical data for all the population of the country in all the commercial banks. Thus, in this research, we compare five machine-learning models to classify credit risk data, i.e., logistic regression, decision tree, random forest, support vector machines (SVM) and neural network. We evaluate the five models using different machine-learning metrics, and we propose a model based on credit registry data from the central bank with detailed methodology that can predict the credit risk based on credit history of the population in the country. Our results show that the best accuracy is achieved by using decision tree performing on imbalanced data with and without scaling, followed by random forest and linear regression.

This article analyzes how mandatory accounting disclosure is grounded on different rationales for private and public companies. It also explores technological changes, such as computerised databases and the Internet, which have recently made disclosure of company accounts by small companies potentially less costly and more valuable, thanks to electronic filing and universal online access to credit information systems. These recent developments favour policies that would expand the scope of mandatory publication for small companies in countries where it is voluntary. They also encourage policies to reduce the costs and enhance the value of disclosure through administrative reforms of filing, archive and retrieval systems. Survey and registry evidence on how the information in the accounts is valued and used by companies is consistent with these claims about the evolution of the tradeoff of costs and benefits that should guide policy in this area.

The objective of this paper is to evaluate the impact on bank credit exposures to small- and medium-sized Spanish firms of the current proposal for reform of the 1988 Capital Accord using information from the Spanish Credit Register. Capital requirements for exposures to those firms, according to the various revisions of the proposed capital reform (from the January 2001 consultative document to the April 2003 one), are calculated to analyze whether the existing pattern of bank financing of small- and medium-sized firms might be altered. Finally, the incentives for individual banks to adopt the advanced internal ratings-based approach proposed by Basel II are evaluated.

Credit risk exposure evaluation is driven by the quality of the information available on the debtors and customers with multiple lending exposures, which could be evaluated differently by different lenders. The existence of an information asymmetry among lenders can be mitigated using private information sharing instruments, such as the credit registers. The paper analyses the effect of information disclosure through credit registers and evaluates the impact of revising the amount of credit offered to customers served also by other lenders. The results show that the information available for each lender is different and after the disclosure of past due or a default status declared by a financial intermediary, all the other lenders react to the new information available.

Doing Business 2007 focuses on reforms, identifies top reformers in business regulation, and best practices in how to reform. This volume is the fourth in a series of annual reports investigating global regulations that enhance business activity and those that constrain it. Co-sponsored by the World Bank and the International Finance Corporation - the private sector arm of the World Bank Group - this year's report measures quantitative indicators on business regulations and their enforcement compared across 175 countries - from Afghanistan to Zimbabwe - and over time. Doing Business 2007 updates indicators developed in the three preceding reports.The ten indicators are: starting a business, dealing with licenses, hiring and firing, registering property, getting credit, protecting investors, trading across borders, paying taxes, enforcing contracts, and closing a business. The indicators are used to analyze economic and social outcomes, such as informality, corruption, unemployment, and poverty. This annually published report gives policymakers the ability to measure regulatory performance in comparison to other countries, learn from best practices globally, and prioritize reforms. This year's report covers 20 additional countries.

This book's prime audience is government policy-makers. It provides a policy framework for governments to increase micro, small and medium enterprises' access to financial services?one which is based on empirical evidence from around the world. Financial sector policies in many developing countries often work against the ability of commercial financial institutions to serve this market segment, albeit, often unintentionally. The framework guides governments on how to best focus scarce resources on three things: ? developing an inclusive financial sector policy; ? building healthy financial institutions; and ? investing in information infrastructure such as credit bureaus and accounting standards. The book provides examples and case studies of how such a strategy has helped to build more inclusive financial institutions and systems in many countries.