|Belgium|5.5 (base salaries), administrative stream|
|---|---|
|Canada|5.2 (estimate)|
|France|4.6 for base salaries, 5.7 (incl. allowances (estimates)|
|Germany|7.5 scales A and B|
|The Netherlands|8.2|
|United Kingdom|9.8 (estimate)|
|United States|7.3 for general white collar, 8.9 incl. Senior Executive Service|

Considerations similar to these also apply to ratios of average civil service earnings to per capita GDP. From country to country, average earnings statistics may represent different slices of earnings. If earnings are derived from wage bill data, they will reflect the types of classification problems noted in the above discussion of wage bill data. On the other side of the coin, calculation of GDP is more complicated in countries with sizable grey economies, again rendering cross-country comparison risky at best. One step better than a comparison of earnings to per capita GDP is to compare earnings to average household consumption levels (disaggregated by urban and rural areas), data that are usually available from national statistical offices. This approach was used in the Lao PDR and Armenia work, but remains relatively uncommon at present.

The limitations we have catalogued make it difficult to rely upon compression or other ratios for anything other than a cursory initial assessment.<sup>16</sup> Even when well executed with good data, they still leave unanswered key questions of whether earnings are appropriate to job requirements, and whether they are sufficient to attract and retain the required skills into the civil service and are reasonably competitive with alternatives (in conjunction with other factors such as benefits, working conditions, job security, etc.). The approaches we present below are, we believe, more rigorous and defensible ways to get at nuanced answers to these questions. The Lao case study presented in the second main section of this paper demonstrates how several analytical tools can be deployed together in achieving a comprehensive and balanced picture.

In any exploration of civil service pay, it is worth considering at the outset whether there is any empirical evidence pointing to difficulties in attracting and retaining staff. The presence of such evidence does not necessarily mean that inadequate pay is the cause, nor does the absence of recruitment and retention problems prove that pay levels are adequate, since other factors (including human resource management practices) might also be at play. Nevertheless, certain quantitative indicators can provide useful insights and identify areas for further exploration. Relevant indicators include a high turnover rate (staff resign after short periods to pursue better opportunities elsewhere); low numbers of applicants per advertised opening (lack of interest in civil service employment); and high rates of absence from work (possibly due to ‘moonlighting’ as staff work multiple jobs, or other factors such as poor management and low morale). In addition, the prevalence of secondary employment might suggest that civil servants wish to supplement their earnings or their professional skills.

For such statistics to be truly useful, they should be disaggregated by specific job types (e.g. senior manager, finance specialist, secretary) and, ideally, by location (urban, rural, remote), rather than more generally for the entire civil service. Such fine-tuned tracking makes it possible to identify the types of 16 Additional drawbacks include the fact that for some countries, underlying data on wage expenditures, public employment, and GDP are unavailable or unreliable. When available, comparator data may be several years old, covering different time periods and missing recent trends. The groups of employees covered are usually highly aggregated and rarely extend to individual jobs.

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