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This study demonstrates a simple and reliable method for calculating ex post power. We first conduct a series of Monte Carlo experiments to assess its performance. The experiments are designed to produce artificial datasets that resemble actual data from 23 studies funded by the International Initiative for Impact Evaluation (3ie). After determining that the method performs adequately, we then apply it to the 23 studies and compare their ex post power with the ex‐ante power claimed on their funding applications. We find the average ex post power of the 3ie studies is close to 80%. However, there are more estimates of low power than would be expected if all studies had 80% true power. Most of the differences between ex post and ex ante power can be explained by differences between planned and actual total observations, number of clusters, and the degree of intracluster correlation. This demonstrates how ex post power can be used by funders to evaluate previously funded research and identify areas for improved power estimation in future research. We further show how ex post power can aid in the interpretation of both insignificant and significant estimates. [ABSTRACT FROM AUTHOR]

We analyse a quasi-experiment where a much sought-after state secondary school with no close substitutes unexpectedly reduced its enrolment zone twice over a three-year period. We use difference-in-differences to estimate the impact of the two downsizings on housing sales prices. We use controls for housing characteristics for pooled cross section or housing fixed effects for repeat sales, test for parallel trends, and conduct numerous robustness checks. In our main analysis, we find the first downsizing may decrease housing prices between 3.2% and 12.9%, with most estimates statistically significant, while the second downsizing may decrease prices between 2.1% and 7.2%, with most estimates insignificant. Tests in the pre-treatment period suggest parallel trends cannot be rejected, though some cross section interactions are significant when the two downsizings are analysed separately. We conclude the school zone's first downsizing likely had a negative effect on housing prices of a small to moderate magnitude. [ABSTRACT FROM AUTHOR]

In many transition countries, including Ukraine, decreases in population and fertility have led to substantial falls in the number of school-aged children. As a consequence, these countries now have school networks that consist of many small schools, leading many countries to consider reorganizing their networks by closing smaller schools and reallocating students. Using a rich data-set of almost the entire population of Ukrainian secondary schools, we estimate the effect of school size and class size on the performance of secondary schools on Ukraine's External Independent Test. We find that larger schools tend to have somewhat better performance, both in terms of test scores and in terms of test participation. The size of this effect is relatively small, however, especially in rural areas for which the estimates are likely to be less biased. Class size is found to be insignificant in most specifications and, if significant, of negligible size. The importance of this finding for the "rationalization" of the large school networks in Eastern Europe is discussed.

There is a substantial literature that suggests that search behavior data from Google Trends can be used for both private and public sector decision-making. In this paper, we use search behavior data from Baidu, the internet search engine most popular in China, to analyze whether these can improve nowcasts and forecasts of the Chinese economy. Using a wide variety of estimation and variable selection procedures, we find that Baidu's search data can improve nowcast and forecast performance of the sales of automobiles and mobile phones reducing forecast errors by more than 10%, as well as reducing forecast errors of total retail sales of consumptions goods in China by more than 40%. Google Trends data, in contrast, do not improve performance. [ABSTRACT FROM AUTHOR]

Using quasi‐experimental data from a survey that was conducted immediately before and after the November 2016 presidential election, we analyze how the election of Donald Trump affected the willingness of Europeans to sign a trade and investment agreement with the United States. We find that the election outcome lead to an immediate and sizable negative effect on Europeans' image of the United States. But we do not find that, at the same time, there was a negative reaction in the willingness of Europeans to sign an agreement with the United States. [ABSTRACT FROM AUTHOR]