We propose a simple to implement panel data method to evaluate the impacts of social policy. The basic idea is to exploit the dependence among cross-sectional units to construct the counterfactuals. The cross-sectional correlations are attributed to the presence of some (unobserved) common factors. However, instead of trying to estimate the unobserved factors, we propose to use observed data. We use a panel of 24 countries to evaluate the impact of political and economic integration of Hong Kong (HK) with Mainland China. We find that the political integration hardly had any impact on the growth of the Hong Kong economy. However, the economic integration has raised HK's annual real GDP by about 4%.

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A panel data method is used to evaluate the impact of China's accession to the WTO. Time-series data for China,

Hosting mega-events is often perceived as a way to stimulate economic growth through tourism. However, the cost of infrastructure investments and promotion may outweigh the benefits generated by the mega-events. Measuring the impact of such events on a hosting country’s economy is not easy, especially as mega-events generally involve many sectors of a destination’s economy. In this study, we adopt a panel data approach to evaluate the impact of the London Olympic Games, Brazil World Cup, and Rio Olympic Games on the economic growth of the respective destinations. Using cross-sectional correlations between countries, we construct scenarios in which the hosting countries did not hold the mega-events and then estimate the time-varying impact of the events on the economy. The developed and developing countries exhibit different results.