and credit, containing a comprehensive summary of households ’ access to and use of credit. several of our research findings have influenced policy debates. for example, an important question regarding recent household deleveraging has been to what extent the decline in aggregate household debt was attributable to delinquent debt charge - offs as opposed to active debt paydowns by consumers. our research found that while charge - offs played an important role, a huge change in consumer behavior had taken place. while consumers extracted home equity and took on more debt during 2007, they reverted to actively paying down debt during 2009, creating a remarkable $ 480 billion reversal in cash flow available for consumption in just two years. in our most recent report we are seeing signs suggesting that the deleveraging period has reached its end. the availability of borrower - level data has been key to our ability to analyze consumer behavior. somewhat unexpectedly, it has also turned out that the ccp provides a comprehensive national picture of total outstanding student loan balances and delinquency rates that heretofore did not exist. it was in a quarterly report, for example, that we first reported that student loan debt exceeded credit card debt, in early 2010. our findings helped bring the surge in student debt and delinquencies to the attention of the public. our research also highlighted its potential implications for consumption growth, showing a strong association between the increase in student loan debt and the decline in mortgage and auto loan origination rates over the past few years. coming back to the broader issue of data innovation, it is interesting to note that dataset merges have been behind a growing number of important academic studies and research findings in recent years. while showing how the value of datasets can be greatly enhanced by linking them, these studies underscore the need for creative thinking to maintain anonymity and representativeness of the matched sample. we are currently pursuing various ways to enhance the value of the consumer credit panel further by linking to property deed records, employer payroll records, small business credit data, and student college records. the ccp shows how existing data – in this case credit reports – can be re - purposed to serve new public policy and research uses. but sometimes existing data sources just can ’ t provide the answers we need, and in those cases data innovation requires creating something completely new. survey of consumer expectations so a second important example of data innovation at the new york fed is the survey of consumer expectations, which we

james mcandrews : challenges and rewards associated with data in the new york fed remarks by mr james mcandrews, executive vice president and director of research of the federal reserve bank of new york, at the fifth data management strategies and technologies workshop, federal reserve bank of new york, new york city, 4 february 2014. * * * introduction welcome to the federal reserve bank of new york. we are fortunate to have many central bankers and international nongovernment organizations represented here today. because we are part of the federal reserve system, the central bank of the united states, we share many of the same data obstacles you are currently facing. as the head of research and statistics at the new york fed, i ’ d like to share with you the challenges and rewards associated with data in our organization, particularly the data that we use to analyze policy questions. the new york fed is uniquely positioned for several reasons. in addition to being the largest of the twelve federal reserve banks, it has the distinction of including a markets group that conducts open market operations for the federal reserve system. additionally, the new york fed supervises financial institutions, including foreign banks doing business in our district. these special responsibilities contribute to our tremendous reliance on data. data is crucial to our core objectives and all aspects of our work. data is the foundation for developing international standards in bank capital and liquidity requirements. data is an integral part of researching economic issues and the labor and financial markets. data is also central to our jobs of forecasting and identifying trends. our understanding of interest rates, reserves, and balance sheets is built on data. in our efforts to promote financial stability, data is fundamental to stress testing. in our financial services efforts, payment information and data flows figure critically in our payment systems. the scope of our data consumption and creation spans a wide range. it encompasses local, regional, national, and global coverage and extends from the micro to the most broadly aggregated levels. consequently, as an organization we are increasingly dealing with a growing number of data challenges. they include the multitude of legal topics associated with contractual terms and conditions of data use, data sharing, and privacy restrictions. we are constantly challenged to develop and maintain clear and comprehensive data dictionaries that enable us to repurpose data appropriately. moreover, as we continue to create more data, we are focusing on data governance. this entails oversight and measures to ensure that our own data are accurately posted ( with updated revisions and associated documentation