C ONCLUSIONES

This approach, which seeks to examine actual outcomes against those which would have occurred without the given policy instrument, most closely replicates the conditions of a

82 The gender ratio MDG target for primary education is reversed to reflect greater female participation.

83 Maternal health was identified as a priority and received specific attention in the PRS (GOM, 2003)

controlled experiment and is, therefore, the more favoured option within the policy analysis literature. However, such comparisons are often the most difficult to operationalize since there is no clear analogue for a specific country’s performance over a specific period of time.

In the following, Mongolia’s economic and poverty reduction outcomes are tracked against: a set of comparator country-group averages; its two neighbours and major trading partners, Russia and China; and that of Kazakhstan, the only proximate country with a similar economic structure. It is accepted that this selection is necessarily problematic, due to the difficulties of providing a comparison against Mongolia’s highly specific characteristics.

Moreover, it is important to emphasize the role played by specific non-programme factors on individual country performances, most notably in Mongolia’s case, the rapid expansion of the mineral sector from 2003/04 onward. This timing also presents problems, since given a lag of two to three years following adoption, any PRS impacts would emerge concurrently.

Figure 4.12 charts Mongolia’s annual per capita economic growth rate since 2001 (the year the I-PRS was adopted). Although outcomes have been consistently better than the low income country (LIC) average, performance has been varied (with Mongolia’s annual growth ranging from 2 to 9 per cent). This is less impressive when compared against others, and especially, China and the East Asian regional average. However, in 2004, per capita output outstrips all of the comparators (at 9.1 per cent) except for China (at 9.4 per cent), and in 2007, Mongolia’s annual growth rate (at 9.2 per cent is above those of the other two minerals dependent economies within the group, Russia (8.4 per cent) and Kazakhstan (7.7 per cent).

Figure 4.12: Annual per capita growth performance versus comparators (2001 to 2007)

Source: Author’s calculations based on WDI data accessed via ESDS (www.esds.ac.uk) 0.0

3.0 6.0 9.0 12.0 15.0

2001 2002 2003 2004 2005 2006 2007

Annual % Change

Low income East Asia & Pacific LMICs

China Kazakhstan

Mongolia Russian Federation

As the cumulative presentation in Figure 4.13 shows, Mongolia has the weakest overall performance except for the LIC average - rising by only 48 per cent on the base year versus:

China (up by 77 per cent) , Kazakhstan (up by 67 per cent), the East Asian average (up by 63 per cent) and Russia (up by 53 per cent). It is worth emphasizing that this occurs in spite of the very rapid expansion of the minerals sector after 2003, and peak global prices for Mongolia’s primary exports in the latter years. There is, nevertheless, a suggestion of better relative performance at the end of the period. Yet, clearly, this cannot convincingly be attributed to PRS adoption.

Figure 4.13: Cumulative growth performance versus comparators (2001 to 2007)

Source: Author’s calculations based on WDI data accessed via ESDS (www.esds.ac.uk)

An examination of the comparative poverty and inequality data shows a more positive picture.

Drawing on the specially constructed panel dataset of national poverty data developed for the analysis reported in Chapter Three, Table 4.16 provides annualized percentage changes in comparator averages for PRS and Non-PRS countries, alongside the respective Growth Elasticity of Poverty (GEP) statistics. To recap, this is a pooled dataset of 83 country episodes of varying lengths during an interval of 12 years. The PRS group (of 26 countries) is indentified where a PRS has been in place for at least two years. These are compared against Mongolia’s post adoption performance, taking the 2002/ 03 LSMS data as the baseline.

Encouragingly, Mongolia’s poverty record, on the basis of the re-estimated data (the preferred option given in 4.4.2), is very favourable. Referring to column 2, poverty reduction in Mongolia (an annualized reduction in the headcount of -9.6 per cent) ranks above the global sample average (-3.34 per cent); well above that for the non-PRS country group (-2.89 per

90.0 110.0 130.0 150.0 170.0 190.0

2001 2002 2003 2004 2005 2006 2007

Index of GDP per capita (2001=100)

Low income East Asia & Pacific LMICs China

Kazakhstan Mongolia Russian Federation

cent); and somewhat above that of the adopting group (-4.33 per cent). It is worth noting, however, that these figures rely on the preferred re-estimated scenarios, and if performance is assessed on the basis of the published data (an annualized reduction of -0.5 per cent) the comparative picture is very different.

Table 4.16: Comparative poverty and inequality data for Mongolia (post PRS adoption)

1 2 3 4 5

Source: Author’s calculations based on re-estimated data

The growth data (given in column 4) are also very positive for Mongolia, with the increase in per capita consumption (based on the re-estimated scenario) running at 6.81 per cent per annum post-2002. This is well above the comparative rates given (with the sample average of 2.77 per cent per annum). However, it must be underlined that these are not fully comparable as the group averages rely on SNA as opposed to survey data.

In contrast, the distributional outcomes are poor (see column 3). Mongolia experienced a significantly higher percentage annual deterioration in the Gini coefficient compared with each of the group averages (1.82 per cent versus a worsening of a mere 0.26 per cent for the global sample). This somewhat explains the mismatch between the relatively high levels of growth and the more moderate reductions in poverty. This poor comparative performance is replicated in the data for the Growth Elasticity of Poverty (GEP), which offers a measure of

the responsiveness of the poverty rate to growth⁸⁴. The figures given in column 5, based on the re-estimated poverty level, show Mongolia’s GEP to be only -1.02 versus a global average of -1.41. However, this figure is still close to the average GEP for the PRS adopting group (at -1.00). Overall, the data confirm the findings of Section 4.4 that the recent growth, although poverty reducing, would have been more pro-poor had the distribution not deteriorated. It is worth noting that GEP for Mongolia’s published poverty data is strikingly low at -0.16.

Some qualification of the above findings is however necessary, as two comparability problems arise in matching the Mongolian data record with the pooled dataset. First, there is a timing issue. The pooled averages represent annualized changes for poverty episodes of varying lengths over a 12 year interval from 1996. In contrast, the Mongolian series runs from 2002/3 to 2007/08. As global economic growth was higher in the later years, and given the majority of the poverty reduction episodes included within the pooled dataset come from the earlier years, the comparators’ performance may be underestimated. Second, there is a sample selection issue. The averages rely on 57 poverty reduction episodes for non-PRS countries and 26 episodes for PRS countries. The groups also vary widely, including low and middle income countries from different regions of the world. Closer examination of the data reveals substantial variation within each of the measures and each of the comparator groups, and clearly, the quality of the comparisons suffers as a result.