Persistent effects of mega-events on local economies: an application of administrative big data

ABSTRACT

Do the effects of a mega-event on a local economy persist even after the event has ended? Using a novel administrative big data set of buildings in the host regions, we analyze the long-term effects of the 2018 PyeongChang Olympics. For data collection, we converted building-level GIS data into a township-level data set. We used the synthetic control method which enables causal analysis even with a small number of treated units. Our analysis reveals that, despite a short-term increase in the number of local buildings due to the Olympics, effects did not persist 1 year after the event.

KEYWORDS:

• Mega-events
• regional economy
• administrative big data
• synthetic control model
• Pyeong Chang

JEL CODES:

• Z32
• O12
• O47

Acknowledgement

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2018S1A5A8027680).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 They constructed a synthetic Basque Country using optimal weights of two regions, Catalonia and Madrid, with weight values of 0.85 and 0.15, respectively.

2 The data can be accessed at www.juso.go.kr.

3 We used QGIS 3.4.4 to process the digital maps.

4 Appendix Table 1 shows the complete list of 22 two-digit building types that appeared in the data.

5 We use synth, a module for STATA software package, for implementing the synthetic control method we use for the empirical analysis. To find $\mathit{V}$ of the norm $\parallel {\mathit{X}}_1-{\mathit{X}}_0\mathit{W}\parallel =\sqrt{\left({\mathit{X}}_1-{\mathit{X}}_0\mathit{W}{)}^{\mathrm{\prime }}\mathit{V}\right({\mathit{X}}_1-{\mathit{X}}_0\mathit{W})}$, we chose a nested optimization procedure using the optional “nested” option in the synth module for better results. This option further minimizes the mean squared prediction error of the outcome variable of interest for the pre-intervention periods compared with the default algorithm in the module.

Additional information

Funding

This work was supported by National Research Foundation of Korea: [grant number NRF-2018S1A5A8027680].