; it may take time, and it should always be done with care. integration of surveys with non - traditional data sources to produce multivariate indicators, such as the ratio of debt to income, may be challenging, since the two sources are likely to suffer from different quality issues. for instance, britain ’ s labour force survey ( lfs ) response rate halved from 2011 to 2021. the share of households responding to the us current population survey ( cps ) has decreased by about 20 percentage points over the last decade. the bank of italy ’ s survey of household income and wealth suffered a similar reduction between 2010 and 2020. the rest of europe and canada have seen similar trends. the linkage may be subject to respondents ’ consent and thus introduce consent bias in lieu of nonresponse bias. difficult negotiations among institutions, and / or payment to for - profit data owners, will often be needed to access such data. privacy law or other confidentiality considerations will require adequate methods to protect the anonymity of respondents. and so on. there is little, however, that cannot be done with enough goodwill and ingenuity on all sides ( assuming, that is, that finance is no constraint, given the savings that the use of external data may entail ). other opportunities for the development of surveys stem from advances in data collection techniques that enable statisticians to run mixed - mode surveys in a better way. in fact, the form of the interview ( whether face - to - face, through the web, by telephone and so on ) influences participants ’ willingness to respond and the frankness or completeness of responses, and mixed approaches can improve participation rates if the mode is tailored to different groups ’ preferences. while more research is needed to evaluate its effects more accurately, mixed - mode data collection may indeed be useful. good distributional information remains essential for studying the transmission of shocks and underpins the decision - making of central banks and other institutions. scientifically designed sample surveys have long been a key tool for measuring the policy - relevant dimensions of heterogeneity across populations of households, individuals, firms and so on. they are still invaluable as a way to answer meaningful analytical questions, and to tailor the data to the constantly changing needs of research and policy debate. while surveys face increasing costs and other challenges, these can be tackled at least in part by, among other things, making clever use of newly available massive data. such usage can help enhance

rates drain budgets, as surveyors need to make more effort to enrol interviewees, and threaten the quality of the data, especially when the propensity to respond is correlated with the phenomena under investigation, like income and wealth. the usual statistical corrections are not always enough to do away with this kind of bias. this, moreover, is happening just as researchers and policymakers are looking for new, better and more comprehensive micro data. as societies become increasingly complex, and unprecedented shocks pose new questions for economic analysis, the need for timely and granular data grows ever more pressing. one is tempted to look for a response to both challenges in the proliferation of competitive alternative sources, such as administrative and unstructured ( ‘ big ’ ) data. the latter often have clear advantages in terms of timeliness, granularity and number of observations. efficiency is also a consideration, as the marginal social cost of putting existing data to new uses is usually negligible. ( the market price for accessing such data, of course, is not necessarily negligible in the case of proprietary data, especially if they possess monopoly value ). on the face of it, there are limits to what statisticians can do with data that are not collected primarily for statistical purposes. the sample design, when the concept is at all applicable to such data, is often obscure. coverage, accuracy, policy relevance and integrity are also typically unsatisfactory from the statistician ’ s point of view. however, given the phenomenal, ever - growing amount of data that is nowadays available somewhere in the global it ecosystem, finding ways to overcome these difficulties is a challenge that statisticians should be eager to take on. one promising avenue ( among many ) is integrating administrative and / or unstructured data into surveys. ideally, this would combine the richness and cheapness of the information contained in large databases with the methodological rigour of well - designed surveys. there are various possibilities. administrative data, when they more or less cover the universe, can be ( and are ) used to improve the sampling design and correct any nonresponse bias. unit - record linkage of surveys with administrative data ( or sometimes even big unstructured data ) can be used to populate certain survey variables, reducing the need for lengthy questionnaires and thus lessening the response burden. data integration may also facilitate matching between surveys. admittedly, exploiting such opportunities is not always an easy task