the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Jul 2026
Medium-term urban business recovery after COVID-19: cross-country evidence after the outbreak
Abstract. To identify factors associated with medium-term business recovery, this study analyzes large-scale survey data on businesses located in diverse urban contexts around the world. While most studies of business recovery focus on the short term, the factors influencing short-term recovery may differ from those important in the medium term. This study provides cross-country evidence four to five years after the COVID-19 outbreak, based on a survey of 3,454 firms across seven global regions, including major cities in Canada, the United States, the European Union, New Zealand, South Africa, Thailand, and Japan, with 35 % of firms located in downtown districts.
Across 26 variables, including policy measures, business characteristics, and strategies, several consistent patterns emerge. Non-governmental mentoring and training show the strongest positive association with sales recovery, exceeding the effects of loans and subsidies, highlighting the importance of non-financial support. By contrast, smaller firms (1–19 employees) exhibit persistently lower recovery levels and rely more heavily on personal funds, indicating structural vulnerability. While some conventional comparisons align with prior expectations, substantial heterogeneity is observed across regions and sectors. In particular, regional context and sectoral composition significantly influence the effectiveness of adaptation and policy measures. These findings provide new cross-country and urban-level evidence on medium-term SME recovery and highlight the importance of tailored, region- and sector-specific policy design to strengthen business resilience against future systemic shocks.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Natural Hazards and Earth System Sciences.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: open (until 07 Sep 2026)
- RC1: 'Comment on egusphere-2026-3993', Milad Basirifard, 30 Jul 2026 reply
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1. The causal claims are not convincing. This is a cross-sectional survey collected four to five years after the outbreak. IPTW cannot establish the timing of business actions and recovery. Most reported effects should be called associations.
2. The covariate strategy is inconsistent. Revenue loss and employment change may occur after loans, subsidies, or adaptation decisions. Controlling for them could remove part of the treatment effect or introduce collider bias. The authors need treatment-specific causal diagrams and clearly justified covariate sets.
3.The propensity-score diagnostics are inadequate. Where are the propensity-score distributions, common-support checks, extreme weights, weight truncation, and effective sample sizes? Showing only selected pooled balance results is not sufficient for 26 treatments and numerous subgroup analyses.
4. Equation 4 appears incorrect. The denominator seems to use the difference between the two variances rather than their pooled sum. The authors must verify the equation and confirm what was implemented in the code.
5.The manuscript performs a very large number of statistical tests. There are 26 treatments, followed by pooled, regional, and sectoral analyses. No correction for multiple comparisons is reported. How many “significant” findings would remain after controlling the false discovery rate?
6.The uncertainty estimation is unclear. How were the 95% confidence intervals calculated? Were propensity scores re-estimated during bootstrapping? Were standard errors clustered by country, city, or sector?
7.The threshold of 30 treated and control observations is arbitrary. Raw sample size does not guarantee reliable IPTW estimation. A subgroup with 30 observations and poor overlap may provide a meaningless estimate.
8.The survey methodology is seriously underreported. Sampling frames, recruitment procedures, response rates, nonresponse adjustments, country-specific survey dates, and harmonization procedures are missing. Referring readers to a website that is still “under preparation” is unacceptable.
9.How were permanently closed businesses handled? Surveying only surviving firms creates major survivor bias. A study of business recovery cannot ignore firms that failed completely.
10. The outcome is a retrospective, self-reported sales percentage. The authors provide no assessment of recall error, outliers, skewness, or cross-country measurement comparability. A simple linear outcome model requires much stronger diagnostics and sensitivity analyses.