Official google trends api6/30/2023 ![]() ![]() al (2019) documents the progress made toward the goal-and the challenges to be overcome to realize the full potential-of using big data in the production of statistics.Įxploiting online platforms for tracking economic developments gained traction as the data observations harvested became longer, more accessible, and stable. Within the last decade, scanner data on purchases, credit card transaction records, and prices of various goods and services scraped from the websites of online sellers have been increasingly mainstreamed in the compilation programs of statistical agencies in advanced and emerging economies. Since then, the rapid growth of new sources of big data-enabled by internet-based technologies-has expanded the toolkit for tapping real-time information at a more scalable and granular level. (1997) identified a correlation between illuminated areas, electric power consumption, and GDP at the country level. Interest in the use of real time, non-traditional data sources 1 to measure economic activities is not new. Data available from the Google Places and Google Trends Platforms may provide part of the answer. The challenges facing many statistical organizations are:(l) acquiringthe source data (2) processing these data and (3) integrating these data with high quality official measures of economic activity to improve their timeliness and frequency. Statisticians need to quickly figure out how to bridge the gap between “big data” and official measures of economic activity. As has been widely acknowledged, “big data” and the vast amount of data collected by an increasing number of digital platforms can offer part of the solution. Economic statisticians need to examine new data sources and develop new methods to provide users with the type of ‘statistical tickers’ they are becoming accustomed to. When constrained by traditional data sources and approaches used to compile economic indicators, this is certainly the case. Economic statisticians often refer to this as the timeliness versus quality tradeoff in which policy makers are told they need to accept lower quality data if they want improved timeliness. Improving the timeliness and frequency of economic statistics while maintaining their quality is a longstanding challenge in the realm of economic measurement. This means that in many countries, statisticians will not have a final tally of the effect of the start of the pandemic until sometime in late 2021 and those estimates will say very little about the path of the economy since its onset. The rest produce annual measures of GDP and most are released 9 to 12 months following the reference period. Just over two-thirds of the 190 IMF member countries produce quarterly estimates of gross domestic product (GDP). As we have seen with the pandemic, those 45 to 60 days can mean the difference between staying in business or losing your business. ![]() Even among those countries with the most advanced statistical systems it often takes at least 45 to 60 days following the reference period to get a reading on what is happening. Traditional economic data collection and processing methods to produce indicators of economic activity do not meet the timeliness and frequency demands of policymakers during a pandemic (or any other crisis for that matter). Tasked with identifying the path out of the pandemic-represented by letter shapes whether that be V, W, U, K (choose your letter of choice)-data users and policy makers require more frequent, timely and granular economic statistics. Users of economic data are now starting to demand a similar service from economic statisticians. Case counts, moving averages and trends, cycles, peaks, and troughs are now a common part of our vocabulary and daily conversations. Data consumers have become accustomed to seeing daily charts of health-related data. We have gone from a world of short-term predictability to one where policymakers need to take a daily pulse of economic activity and adjust course often. To say the needs of users of economic statistics have changed since the start of the pandemic would be an understatement. ![]()
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