Page 164 - Special Topic Session (STS) - Volume 3

STS538 Ibrahim S Y.
adjustment of salaries. These challenges are highlighted below, along with
strategies adopted by the PAS to address them.

2. Measurement challenges
The PAI is designed to reflect the international character of the UN staff
population and to be robust enough to be applicable to 200-odd locations
with widely varying levels of general economic development, stability of such
economic indicators as inflation and local currency exchange rates; availability
of goods and services; and differences in the number, composition,
expenditure patterns, and turnover of staff. The wide disparity in these
characteristics, across the covered locations, presents major measurement
challenges for data collection and processing.
i. Data collection
Four main types of data are collected in PAS cost-of-living surveys: (a) price
data on about 300 items in PAS’s market basket, from retail outlets at the
various locations; (b) expenditure data from eligible staff via a web-based
survey questionnaire; (c) consumer price indices (CPIs) obtained from national
statistics offices, and (d) currency exchange rates. Additionally, where
available, market rent data are available are obtained from external sources.
The issues with data collection range from the limitations of certain markets,
and problems with comparability of retail outlets and products across widely
different markets, which make it difficult to ensure like-to-like price
comparisons. In the absence of the requisite market research, the impact of
these problems is mitigated by more active engagement among stakeholders,
the development of detailed and tight item specifications, and an ex post
matching of outlets and items at the data processing stage. For electronic and
high-technology items, whose specifications change rapidly over time, the
real-time price-comparisons (RTPC) approach was developed, with broad
specifications to capture enough perfectly matching items at the comparison
duty station and the base in real time, thus rendering computationally
intensive quality-adjustment methods, such as hedonics, unnecessary.
Expenditure data are collected via self-administered online questionnaires,
developed and tested in-house, based on experience acquired in previous
survey rounds, but not subjected to rigorous cognitive testing with focus
groups. There is therefore the risk of misinterpretation of the survey questions
and instructions, leading to reporting errors. This problem is mitigated by
strategic engagement with stakeholders, including pre-survey consultations,
live demonstrations of survey instruments, and the provision of technical tools
to facilitate the administration of the surveys. Data validations and skip
patterns are embedded in the web questionnaire to facilitate the survey
experience, reduce respondent burden, and minimize reporting errors.

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