Just as doctors monitor in real time the vital signs of their hospitalised patients to determine the best course of treatment, economists are turning towards real-time tracking of economic conditions to inform policy decisions – for example, to proxy for GDP in Woloszko (2020) or inflation in Cavallo and Rigobon (2016).
In a new paper (Rigobon et al. 2022), we introduce a new quasi-real-time estimation of business opening and closure rates using data from Google Places – the dataset behind the Google Maps service. We find that the lifting of COVID-19 restrictions in Canada coincides with a wave of re-entry of temporarily closed businesses, suggesting that government support may have facilitated the survival of hibernating businesses.
How can policymakers track business health in quasi-real time to assess the inherent trade-off between health and socioeconomic restrictions during the pandemic (Cont et al. 2021), as well as to help calibrate government support to avoid closures of viable businesses? Unfortunately, existing methodologies often rely on low-frequency data proxies available with a time lag – such as tax disclosures, business registry, or surveys. In the absence of timely data on business health, it is hard to strike the right balance between ‘too little, too late’ government support that causes a persistent loss of productive firms, thus hurting long-run productivity (Aghion et al. 2019) and employment (Sedlacek 2020), and ‘too much, too broad’ government support that enables the survival of non-productive ‘zombie’ firms (e.g. Gourinchas et al. 2021, Cros et al. 2021).
The fast-paced nature of the COVID-19 pandemic accelerated the search for timely measures of business dynamism (Agresti et al. 2022). Crane et al. (2020) investigate the value of using Google searches, paycheque issuance, and phone-tracking data. Yelp (2020) uses its US platform to compute temporary closures during the early phase of the pandemic. Kurmann et al. (2021) find that part of the rebound in small business employment in the US service sector is due to business reopenings, identified from SafeGraph, Facebook, and Google.
A new method to track businesses using Google Places
To aid policymakers in the timely tracking of business dynamics, in Rigobon et al. (2022) we introduce a new estimation of opening and closure rates using non-traditional quasi-real-time data from Google Places. We track the appearance and disappearance of ‘pins’ on Google Maps that represent unique businesses using a bisection algorithm (Figure 1).
Figure 1 Illustration of the scraping algorithm to collect all downtown Ottawa restaurants (the ‘pins’') as of May 2021 in Google Places
Note: The higher the density of businesses along the main streets (dots), the finer the algorithm search grid needs to be (squares).
To form a picture of how business conditions are changing, we need only collect the identifiers, number, and status of businesses in each geographic area or sector. Since the Google Places API returns only the most recent information on individual business establishments, the algorithm is repeatedly run to collect data for the same area and thus to build a time series. Entries and exits are identified by unique business identifiers that appear and disappear from the dataset. Temporary closures and reopenings are informed by changes in the business status that is either operational or temporarily closed.
Application to the food and retail businesses
In Duprey et al. (2022), we apply our methodology to a set of Canadian cities for the food service (‘cafe’, ‘bar’, ‘restaurant’, ‘night club’) and retail (‘store’) sectors, precisely those most impacted by the pandemic. We find that the lifting of COVID-19 restrictions in the summer of 2021 (Figure 2a) led to a large wave of business entries, which was largely driven by the reopening of temporarily closed businesses (Figure 2b). This suggests that government support may have facilitated the survival of hibernating businesses and contributed to a faster recovery once restrictions were lifted. We further observe that the timing of the reopening of businesses largely coincides with the timing of the lifting of the restrictions. For instance, restrictions were lifted one month earlier in Vancouver, and this is associated with an earlier rise of new entries and reopenings. Similarly, restrictions were lifted one month later for night clubs, and the peak of opening rate is delayed accordingly.
Figure 2 The lifting of COVID-19 restrictions and entry/exit rates for retail businesses in 2021 in Ontario, Canada
a) COVID-19 cases and restrictions
b) Entry/exit rates for the retail sector
Notes: Panel (a) displays the COVID-19 case count from the Public Health Agency of Canada for the Province of Ontario. The vertical bars are the three phases of the reopening of the economy and the shaded area represents the lockdown and stay-at-home order. Panel (b) displays the end of month opening and closure rates for the retail and food sectors estimated from Google Places data for the city centres of Toronto and Ottawa.
During the early 2021 restrictions, about 92% of businesses in the retail sector were operational (Figure 3a). Among those businesses in the retail sector that were temporarily closed at the start of the April 2021 lockdown, 40% had reopened and 30% had permanently closed by the end of the summer of 2021 (Figure 3b). For the food sector, about 87% of businesses were open during the lockdown, and upon lifting of the restrictions, about half of those temporarily closed reopened. Most reopenings took place for bars (62% of those temporarily closed reopened), while most permanent closures took place for cafes.
Figure 3 Share of temporarily closed businesses around the June 2021 reopening of the Canadian economy
a) Share of temporarily closed businesses as the economy reopens
b) Status of businesses that were initially flagged as temporarily closed in April
Notes: End of month estimates for April to September 2021 using Google Places data for the city centre of Toronto and Ottawa. In panel (a), the vertical bars are the three phases of the reopening of the economy and the shaded area represents the lockdown and stay-at-home order. In panel (b), we track only the subset of businesses that were identified as temporarily closed at the beginning of the lockdown in April 2021 and assess the recovery rate of those businesses.
The latest Omicron wave at the end of 2021 was not accompanied by business restrictions as severe as those during wave of early 2021. Consequently, this wave is associated with a closure rate only slightly higher than the entry rate by the end of December 2021, with a reversal by the end of January (Figure 2b).
Moving forward, quasi-real-time business opening and closure rates could be used as an input for indices that track the overall health of the business sector. For example, Statistics Canada (2021) constructs a Real-Time Local Business Conditions Index by combining openings and closures with real-time traffic data around businesses to proxy for both the extensive and intensive margin of business activity. Information on individual business opening and closure could also be combined with real-time job vacancies to investigate the impact on labour dynamics. Eventually, high-frequency business health data could also help document the effect of natural disasters that are localized in space and time.
More broadly, the data collected at a micro level could provide a finer understanding of small business dynamics. For instance, the flexibility of the data collection process could allow for the investigation of the rise of online retailers operating from the owner’s residence, or the relative dynamism of downtown businesses compared to commercial areas outside the city centres.
There are a few limitations of our method to note. First, the Google Places data are continuously updated but cannot be collected back in time, limiting the ability to benchmark results to pre-pandemic levels. Second, business closures are harder to assess because a business no longer exists to confirm the timing of its closure (for business openings, see the survey in Duprey et al. 2022). Third, the quality of estimation is dependent on the quality of the data, which is controlled by Google. Nonetheless, entry and exit estimates seem to correlate with data from Statistics Canada (2020), despite differences in definitions (Duprey et al. 2022).
Eventually, as the digitalisation of economies continues, the coverage and reliability of real-time measures of business openings and closures will continue to improve. As such, it will become increasingly important to policymakers and researchers. Thus, data providers such as Google Places, SafeGraph, and others may want to consider compiling (and possibly monetising) business health statistics themselves.
Authors’ note: The views expressed in this column are those of the author(s), and do not represent those of the Bank of Canada nor of the Bank of England and its committees.
Aghion, P, A Bergeaud, T Boppart, P J Klenow and H Li (2019), “Missing growth from creative destruction”, American Economic Review 109(8): 2795-2822.
Agresti, S, F Calvino, C Criscuolo, F Manaresi and R Verlhac (2022), “Tracking business dynamism during the COVID-19 pandemic: New cross-country evidence and visualisation tool”, VoxEU.org, 17 January.
Cavallo A and R Rigobon (2016), “The billion prices project: Using online prices for measurement and research”, Journal of Economic Perspectives 30(2): 151-178.
Cont, R, A Kotlicki and R Xu (2021), “Modelling COVID-19 contagion: risk assessment and targeted mitigation policies”, Royal Society Open Science 8, 201535.
Crane, L D, R A Decker, A Flaaen, A Hamins-Puertolas, and C Kurz (2020), “Business exit during the COVID-19 pandemic: Non-traditional measures in historical context”, Board of Governors of the Federal Reserve System.
Cros, M, A Epaulard and P Martin (2021), “Will Schumpeter Catch Covid-19? Evidence from France”, VoxEU.org, 4 March.
Duprey, T, D E Rigobon, P Schnattinger, A Kotlicki, S Baharian, T R Hurd (2022), “Business Closures and (Re)Openings in Real Time Using Google Places”, Bank of Canada Staff Working Paper 2022-1.
Gourinchas, P-O, S Kalemli-Ozcan, V Penciakova and N Sander (2021), “COVID-19 and small and medium-sized enterprises: A 2021 ‘time bomb’?”, AEA Papers and Proceedings 111: 282-286.
Kurmann, A, E Lale and L Ta (2021), “The Impact of COVID-19 on small business dynamics and employment: Real-time estimates with homebase data”, CIRANO.
Rigobon, D E, T Duprey, A Kotlicki, P Schnattinger, S Baharian, T R Hurd (2022), “Business closures and (re)openings in real-time using Google Places: proof of concept”, Journal of Risk and Financial Management 15(4): 183.
Sedlacek, P (2020), “Lost generations of firms and aggregate labor market dynamics”, Journal of Monetary Economics 111: 16-31.
Statistics Canada (2020), “Experimental estimates for business openings and closures for Canada, provinces and territories, census metropolitan areas, seasonally adjusted”.
Statistics Canada (2021), “Real-time Local Business Conditions Index: Concepts, data sources, and methodology”.
Woloszko, N (2020), Tracking activity in real time with Google Trends, OECD Publishing.
Yelp (2020), Local Economic Impact Report.