conduct a two-sample Kolmogorov-Smirnov tests. We are not able to reject the null, i.e., the SIAT and DB1B distribution of fares are the same, in over 75% of the markets. The majority of those markets where we reject the null have Tokyo (NRT) as an endpoint. Summary Overall, we find that the distribution of in the SIAT matches that of the DB1B quite well despite the modest sample sizes. This gives us great confidence that the SIAT can serve as a detailed source of information on air travel. That is, since the DB1B provides no information on the reason for travel, time of purchase, passenger demographics, and many other items of interest to both academic and industry researchers, it is often of limited use. Further, we identify statistical relationships between the SIAT and DB1B that strongly suggest that even moderate increases in the sample size of the SIAT will make it a significantly more valuable tool for researchers. Precisely, we show the following: Match between SIAT and DB1B improves substantially with larger sample sizes. Specifically, correlation between market-year-quarter specific means (and other measures) in the two surveys is strongly decreasing in the number of observations in the SIAT. Alternative measures of central tendency that are less sensitive to outliers, e.g. median, perform very well in even very small and highly-disaggregated samples. The match between the SIAT and DB1B is worse for higher fare quantiles. Essentially, the tail of the distributions is difficult to match except with very large samples. However, Kolmogorov-Smirnov tests are not able to reject null that SIAT and DB1B are drawn from same distribution even when highly disaggregated. Rejections are largely due to Tokyo markets. Higher levels of aggregation, either over longer time horizons (rather than year-quarter) or across airports (rather than markets), substantially improves the between the SIAT and DB1B. Thus, even the currently moderate sample sizes are appropriate for analysis at this level of aggregation. Academic and industry researchers should have confidence in using the survey to supplement DB1B information to learn more about passenger populations even in relatively small and selected subsamples.
conduct a two-sample Kolmogorov-Smirnov tests. We are not able to reject the null, i.e., the SIAT and DB1B distribution of fares are the same, in over 75% of the markets. The majority of those markets where we reject the null have Tokyo (NRT) as an endpoint. Summary Overall, we find that the distribution of in the SIAT matches that of the DB1B quite well despite the modest sample sizes. This gives us great confidence that the SIAT can serve as a detailed source of information on air travel. That is, since the DB1B provides no information on the reason for travel, time of purchase, passenger demographics, and many other items of interest to both academic and industry researchers, it is often of limited use. Further, we identify statistical relationships between the SIAT and DB1B that strongly suggest that even moderate increases in the sample size of the SIAT will make it a significantly more valuable tool for researchers. Precisely, we show the following: Match between SIAT and DB1B improves substantially with larger sample sizes. Specifically, correlation between market-year-quarter specific means (and other measures) in the two surveys is strongly decreasing in the number of observations in the SIAT. Alternative measures of central tendency that are less sensitive to outliers, e.g. median, perform very well in even very small and highly-disaggregated samples. The match between the SIAT and DB1B is worse for higher fare quantiles. Essentially, the tail of the distributions is difficult to match except with very large samples. However, Kolmogorov-Smirnov tests are not able to reject null that SIAT and DB1B are drawn from same distribution even when highly disaggregated. Rejections are largely due to Tokyo markets. Higher levels of aggregation, either over longer time horizons (rather than year-quarter) or across airports (rather than markets), substantially improves the between the SIAT and DB1B. Thus, even the currently moderate sample sizes are appropriate for analysis at this level of aggregation. Academic and industry researchers should have confidence in using the survey to supplement DB1B information to learn more about passenger populations even in relatively small and selected subsamples.
DOC-brief1-final.docx