Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may D I S C U S S I O N P A P E R S E R I E ABSTRACTAppraising Cross-National Income Inequality Databases: An Introduction *In response to a growing interest in comparing inequality levels and trends across countries, a number of cross-national inequality databases are now available. These databases differ considerably in purpose, coverage, data sources, inclusion and exclusion criteria, and quality of documentation. A special issue of the Journal of Economic Inequality, which this paper introduces, is devoted to an assessment of the merits and shortcomings of eight such databases. Five of these sets are microdata-based: CEPALSTAT, Income Distribution Database (IDD), LIS, PovcalNet, and Socio-Economic Database for Latin America and the Caribbean (SEDLAC). Two are based on secondary sources: "All the Ginis" (ATG) and the World Income Inequality Database (WIID); and one is generated entirely through multipleimputation methods: the Standardized World Income Inequality Database (SWIID). Although there is much agreement across these databases, there is also a non-trivial share of country/year cells for which substantial discrepancies exist. In some cases, different databases would lead users to radically different conclusions about inequality dynamics in certain countries and periods. The methodological differences that lead to these discrepancies often appear to be driven by a fundamental trade-off between a wish for broader coverage on the one hand, and for greater comparability on the other. These differences across databases place considerable responsibility on both producers and users: on the former, to better document and explain their assumptions and procedures, and on the latter, to understand the data they are using, rather than merely taking them as true because available. 2A number of databases containing summary inequality statistics for multiple countries over many years are now publicly available. These cross-national inequality databases are being used by researchers, with increasing frequency, to document global or regional trends (e.g. Atkinson and Bourguignon, 2014;Atkinson, 2015;and Piketty, 2014), as well as by scholars interested in including inequality measures in cross-country regression analyses, either as dependent or independent variables (e.g. Acemoglu et al., 2013;and Ostry et al., 2014).Yet, these different databases are often designed for different purposes, and are constructed in very diffe...

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may D I S C U S S I O N P A P E R S E R I E ABSTRACTAppraising Cross-National Income Inequality Databases: An Introduction *In response to a growing interest in comparing inequality levels and trends across countries, a number of cross-national inequality databases are now available. These databases differ considerably in purpose, coverage, data sources, inclusion and exclusion criteria, and quality of documentation. A special issue of the Journal of Economic Inequality, which this paper introduces, is devoted to an assessment of the merits and shortcomings of eight such databases. Five of these sets are microdata-based: CEPALSTAT, Income Distribution Database (IDD), LIS, PovcalNet, and Socio-Economic Database for Latin America and the Caribbean (SEDLAC). Two are based on secondary sources: "All the Ginis" (ATG) and the World Income Inequality Database (WIID); and one is generated entirely through multipleimputation methods: the Standardized World Income Inequality Database (SWIID). Although there is much agreement across these databases, there is also a non-trivial share of country/year cells for which substantial discrepancies exist. In some cases, different databases would lead users to radically different conclusions about inequality dynamics in certain countries and periods. The methodological differences that lead to these discrepancies often appear to be driven by a fundamental trade-off between a wish for broader coverage on the one hand, and for greater comparability on the other. These differences across databases place considerable responsibility on both producers and users: on the former, to better document and explain their assumptions and procedures, and on the latter, to understand the data they are using, rather than merely taking them as true because available. 2A number of databases containing summary inequality statistics for multiple countries over many years are now publicly available. These cross-national inequality databases are being used by researchers, with increasing frequency, to document global or regional trends (e.g. Atkinson and Bourguignon, 2014;Atkinson, 2015;and Piketty, 2014), as well as by scholars interested in including inequality measures in cross-country regression analyses, either as dependent or independent variables (e.g. Acemoglu et al., 2013;and Ostry et al., 2014).Yet, these different databases are often designed for different purposes, and are constructed in very diffe...

Many families live on the financial edge, but a natural disaster can throw even better-situated families into financial turmoil. Comparing the financial outcomes of residents in areas hit by natural disasters with otherwise similar people in unaffected communities, this study finds that natural disasters lead to declines in credit scores and mortgage performance, increases in debt in collection, and impacts on credit card access and debt-effects that persist or even worsen over time. We also find that people who are more likely to be struggling financially before disasters strike are often the hardest hit by the disaster. Specifically, for people with low pre-disaster credit scores, as well as those who live in a community of color, the estimated declines in credit scores are particularly substantial. We find a similar pattern for mortgage delinquency and foreclosure. This pattern of results suggests that disasters may be not only harmful for affected residents on average, but may also have the effect of widening already existing inequalities. Our results also suggest that medium-sized disasters, which are less likely to receive long-term public recovery funding, lead to larger negative declines on credit scores than large disasters.

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Financial coaching, a hands-on financial wellness approach, has emerged as a go-to strategy to help clients establish and reach their personal financial goals. We analyzed the borrowing and repayment behavior of 1,790 clients who received financial coaching through a program sponsored by the state of Delaware. Relative to a matched comparison group, financial coaching clients cure 0.24 more delinquent accounts, reduce credit card utilization by 5 percentage points, reduce the number of debts in collections by an additional 0.37 accounts, and have $422 less in credit card debt. Findings also show a 7 percentage point increase in the share of clients with a credit card and a 6 percentage point increase in the share of clients with a student loan. We do not see consistent differences in personal installment loans or mortgage holding. These estimates provide evidence that financial coaching can provide benefits for clients while being provided on a state-wide scale, illustrating the potential of public–private programs to provide services.