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ABSTRACT
The appropriate policy responses for COVID-19 are being vigorously debated, especially regarding whether there is a trade-off between containing the spread of the virus and reducing the economic recession. The aim of the paper is to examine the effect of various economic policies on the leisure and recreation industry under public health interventions during the pandemic. We collect data for 131 countries/regions from February to October 2020 and employ fixed-effects models to examine the impact of economic policies after controlling for public health interventions and country- and time-fixed effects. Results show that, with an impact lag, economic policies significantly promote current-date leisure and recreation activities under public health intervention; this effect peaks after around one month, and the two policies mutually reinforce each other in the medium term. The positive effect of economic policies ranges from 5 to 11%, depending on the magnitude of public health interventions. With regard to the different categories of measures, monetary policies have an immediate positive announcement effect while fiscal policies significantly promote leisure and recreation activities, though with a response lag. In addition, this study discusses the implications for the recovery of the leisure and recreation industry under pandemic crisis from a policy perspective.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 The data are downloaded from the Google mobility trend database; see detailed information in section 2.2.
2 The Google Mobility data ends on November 3, 2020, and the government policy data ends on October 1, 2020, so the merged data we used lasts until October 1, 2020.
3 For example, for Monday, February 17, 2020, this variable denotes the change of visits to leisure and recreation places relative to the median value of leisure and recreation participation over the five Mondays from January 3 to February 6, namely, January 6, January 13, January 20, January 27, and February.
4 See details for these six economic policy indices in section 2.2.
5 We choose one month as the benchmark and show the results for policy measures with different time lags in section 3.1.2.
6 We have also tried to add the linear and quadratic terms for the last variable, number of days after the COVID outbreak in a given country, to capture the potentially nonlinear effect of passage of time; the results are qualitatively the same as the one presented in the paper.
7 Detailed information on classification of economic policies are introduced in section 2.2.
8 The panel data covers 130 countries and 1 region (Hong Kong), which is the full sample that Google’s mobility trend database contains.
9 Google mobility data contains time trends of visits to six place categories: Supermarket and pharmacy, Parks, Public transport, Retail and recreation, Residential, and Workplaces. The data used in this research is from the ‘Retail and recreation’ category.
10 In Porcher (Citation2020), there is a seventh economic policy, ‘Support to importers and exporters’, which is not included in the study, as it is not directly relevant for leisure and recreation industry. We also applied regression analysis with an aggregate economic policy index that incorporates this seventh sub-policy, and the results are quite similar to those shown in the paper.
11 Measured this way, the policy index cannot capture cross-country heterogeneity in policy strength and implementations (e.g. differences in transfer residents received). Instead, it captures qualitatively if a country adopts a policy or not.
12 These ten public health policies are: bans on mass gatherings, bans on sports and recreational events, restaurant and bar closures, domestic lockdowns, travel restrictions, declarations of states of emergency, public testing, enhanced surveillance, school closures, and postponement of elections. An index for one policy takes a value of 1 if the country has strictly implemented that policy at the national level, a value of 0.5 if the implementation is partial or localized, or a value of 0 if the focal policy is not implemented.
13 Controlled variables include cumulative COVID-19 cases per million population, deaths from COVID-19 per million population, and number of days after the COVID-19 outbreak.
14 The effect of averaged-strength economic policy (E) when the public health policy index equals P is calculated as 10.87*E+20.48*E*P. The averaged-strength economic policy over the previous month is 0.46, and the 10th percentile public health policy index is 0. Substituting these values into the formula give us the value of 5.0002. For other percentile values of the public health policy index, the effect is calculated in the same way.
15 When the time lag is one week, the overall effect is calculated as . At 1 week, the average value of the economic policy index and public health policy index is 0.43 and 0.30 respectively. Substituting these values into the above formula gives the overall effect of 2.32. The numbers for other time lags are calculated in a similar way. We have also run a regression in which the time lag is seven weeks. At week 7, the overall effect of economic policies on leisure and recreation activities is 8.20, which is smaller than the value at week 6 and confirms the declining trend after five weeks.
16 More specifically, the coefficient in front of current-date ‘interest rate cuts’ is significantly positive, while the coefficient for current-date ‘credit schemes’ is not significant.