or individuals, all LSMS-ISA surveys use distant tracking to track and interview movers out of the original EA, as long as they still live within the country.

In the Malawi, Tanzania, and Uganda panel surveys, once the individual forms a new household, all household members residing with the individual enter the sample and become tracking targets. Integrating the joint household into the sample accounts for new populations who may be otherwise underrepresented and captures the evolving dynamics of the population at large. However, including the mover’s new household increases data collection costs. An alternative and less costly way to enhance representativeness in longitudinal studies are the adoption of rotating panels or sample refreshes, which have been used by some LSMS-ISA surveys. For instance, the Uganda UNPS 2013/14 has rotated out and replaced one-third of the EAs from its initial sample. The Tanzania NPS sample was refreshed in 2014/15, but the subsequent 2020/21 round of NPS data collection only follows up with this refresh sample, marking a discontinuity in the longitudinal representativeness of the population from initial rounds. In 2022, the Ethiopia ESS sample was fully refreshed and the Burkina Faso Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM) followed up with its 2018/19 households, complementing the sample with a set of new EAs selected from a more recent sampling frame. Finally, Nigeria also partially refreshed its survey sample in 2018/19.

# 6. EMPIRICAL EVALUATION OF THE UGANDA DATA

As a case study, we apply the estimator methods discussed above to the Uganda National Panel Survey (UNPS). The UNPS is a multi-purpose household panel survey implemented by the Uganda National Bureau of Statistics (UBOS) with the technical support of the World Bank LSMS-ISA project. Started in 2009/10 as a follow up of the Uganda National Household Survey 2005/6, its primary aim is to inform policy makers in budgetary decisions and policy interventions and to monitor major national policies and programs. Implemented on an annual basis, the UNPS also provides representative information on income dynamics at the household level and consumption expenditure estimates to monitor poverty during intervals between other national survey efforts. Moreover, the UNPS collects high-quality agricultural data integrated within a multi-topic framework, which allows for understanding the linkages between agriculture, welfare, and other sociodemographic characteristics.

Since its launch in 2009, the UNPS conducted 8 waves of data collection on a sample of approximately 3,000 households. The UNPS 2009/10 was followed by additional rounds in 2010/11, 2011/12, 2013/14, 2015/16, 2017/18, 2018/19, and 2019/20. Its sample is representative at the national, urban-rural, and regional levels. This study uses data from the UNPS 2009/10 (wave 1), the UNPS 2013/14 (wave 4), and the UNPS 2015/16 (wave 5). The UNPS 2009/10 comprises a sample of 2,975 households, the UNPS 2013/14 counts 3,118 households, and the UNPS 2015/16 interviewed 3,304 households. After data cleaning and data preparation for the analysis, our final dataset comprises 18,313 individuals from wave 1, 17,377 individuals from wave 4, and 15,905 individuals from wave 5.

Figure 6.1 shows the change in status of the surveyed individuals across the 3 panel waves of data collection used in this work. The 2009/10 level represents the 18,313 individuals interviewed in the first wave. Of these, 45% (8,211 individuals) were found and reinterviewed in 2013/14, 15% were not found, and 40% were rotated out. The sample refresher of 2013/14 brought in 5,387 new individuals, or around 30% of the original sample. New individuals in the non-mover and split-off households accounted for 3,779 new units, equal to the 21 % of the original sample. Between 2013/14 and 2015/16, t31% of the 2013/14 wave sample was lost, while 85% was found and reinterviewed. The figure shows the dynamics across years of the old and refresher samples as well. For instance, we can see that 87% of the refresher sample was 23