new developments and applications in survey to survey imputation |
|
| Coordinator 1 | Dr Federico Crescenzi (University of Tuscia) |
| Coordinator 2 | Professor Gianni Betti (University of Siena) |
| Coordinator 3 | Professor Tiziana Laureti (University of Tuscia) |
| Coordinator 4 | Dr Lorenzo Mori (University of Bologna ) |
Gathering data to produce accurate and timely welfare estimates re-
quires complex and costly surveys. Only a handful of countries can conduct
these surveys annually to estimate welfare and inequality. Most National
Statistical Offices (NSOs) worldwide carry out these surveys every four to
five years, making it challenging to generate reliable annual welfare indices.
Furthermore, even more common surveys do not follow a standard process
from one year to the next, with changing definitions and questions making
them incomparable. To address these issues, researchers have developed
methods to compare welfare indicators over time using surveys that may
not be directly comparable. These methods, known as Survey-to-Survey Im-
putation Techniques (SSITs), have been effective in predicting comparable
welfare indicators but have struggled with predicting comparable inequality
indicators.
SSITs are a derivative of the poverty map literature (Elbers et al.,
2003; Tarozzi and Deaton, 2009). Poverty maps, which involve imputing
income data onto censuses, have been used to generate geographically de-
tailed poverty estimates in many developing countries. Alongside survey-
to-census imputation, there has been a recent trend toward SSIT, which
involves mapping data from a surveys with certain information to those
with other outcomes of interest but lacking standard welfare aggregates.
The aim of this session is to further explore this argument by examin-
ing the pros and cons of SSITs. The global trend of conducting smaller or
non-probabilistic surveys, along with the increase in independent NSOs and
organizations, brings several benefits to global statistics. However, these
trends also risk reducing the comparability of welfare estimates. This re-
duction in comparability can be influenced by factors such as the definitions
used. SSITs can help by standardizing methods, which aids in obtaining
cross-nationally comparable estimates that transcend cultural boundaries.