Only by using longitudinal data one can understand the processes behind cross-sectional statistics: the events leading individuals into and out of poverty, and the associated impact on their living standards. Longitudinal poverty analysis can also identify ways in and out of poverty, which can help policymakers adopt better safety nets or other inclusion policies. Discrepancies between international and national databases often result from differences in the ways in which the associated indicators are defined and reported. Empty cells indicate that the corresponding figures do not appear in either database, for any of the years taken into consideration (from 1990 to 2009).