Quota Formula - Data Update and Further Considerations
Author: International Monetary Fund
Publication Date:
23 Jul 2013
eISBN:
9781498341431
Language:
English
Keywords:
PP; openness share; GDP blend; PPP GDP; blend share; PPP-GDP data; aggregate share; ratio of Openness share; share to GDP blend share; advanced economy; ratio of share
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The IMF staff has updated individual member country data for the variables used in the quota formula for the period 1999-2011; the tables also include the comparable value of each variable for the previous quota dataset, which was based on data covering the period 1998-2010. The information is presented in millions of SDRs (Table A1) and in percent of their respective global totals (Tables A2 and A3). A table showing calculated quota shares based on the quota formula is also included (Table A4). The current quota formula includes a GDP variable, which is a blend of GDP at market rates and GDP at purchasing power parity (PPP), openness, variability, and international reserves (see Box 1 in Reform of Quota and Voice in the International Monetary Fund-Draft Report of the Executive Board to the Board of Governors). Data sources and a description of the quota variables are discussed in Quota Formula – Data Update and Further Considerations - Statistical Appendix; IMF Policy Paper; June 2013. Download Quota Data: Updated IMF Quota Formula Variables - July 2013

Abstract

The IMF staff has updated individual member country data for the variables used in the quota formula for the period 1999-2011; the tables also include the comparable value of each variable for the previous quota dataset, which was based on data covering the period 1998-2010. The information is presented in millions of SDRs (Table A1) and in percent of their respective global totals (Tables A2 and A3). A table showing calculated quota shares based on the quota formula is also included (Table A4). The current quota formula includes a GDP variable, which is a blend of GDP at market rates and GDP at purchasing power parity (PPP), openness, variability, and international reserves (see Box 1 in Reform of Quota and Voice in the International Monetary Fund-Draft Report of the Executive Board to the Board of Governors). Data sources and a description of the quota variables are discussed in Quota Formula – Data Update and Further Considerations - Statistical Appendix; IMF Policy Paper; June 2013. Download Quota Data: Updated IMF Quota Formula Variables - July 2013

Introduction1

1. In January 2013, the Executive Board reported to the Board of Governors on the outcome of the quota formula review (QFR).2 The report identified areas of common ground as well as areas where views differed among Board members and further discussions were needed. It was recognized that views on the key elements of the quota formula had evolved during the review and would continue to evolve as further work is undertaken and new data become available. The report noted that important progress has been made in identifying key elements that could form the basis for a final agreement on a new quota formula. It was agreed that achieving broad consensus on a new quota formula would best be done in the context of the 15th General Review of Quotas rather than on a standalone basis. This approach was subsequently endorsed by the IMFC and the G-20 Ministers and Governors at their April meetings.3

2. This paper seeks to provide the basis for a further discussion of issues relating to the quota formula. It first presents the results of updating the quota database by one year. After briefly reviewing the outcome of the QFR, it then discusses the results of further staff work on two existing quota variables: openness and variability. The following section presents some illustrative simulations of possible reforms of the quota formula using the new data. The paper then concludes and presents some issues for discussion. Individual country data and simulation results, as well as some additional technical material, are presented in the Statistical Appendix and Annexes (circulated separately).

Updated Quota Database

3. Staff has updated the quota database through 2011. This advances by one year the data presented last June.4 The update uses the same sources as in past updates (see Box 1, Annex I, and the Statistical Appendix). The results for country groups and individual members are shown in Tables 1 and A1. This update, like the previous ones, continues to be affected by the impact on quota variables of the global financial crisis.

Data Sources and Methodology

The data sources and methodology remain broadly in line with past practice. The primary data source is the Fund’s International Financial Statistics (IFS). Missing data were supplemented in the first instance by the World Economic Outlook (WEO) database. Remaining missing data were computed based on staff reports and, in very few instances, country desk data. As is customary, a cutoff date of January 31, 2013 for incorporating new data in the quota database was employed for IFS; consistent with this cutoff, the Fall 2012 publication was used for WEO data.

PPP GDP data were taken from the WEO database and were calculated by dividing a country's nominal GDP in its own currency by the PPP price level index. The WEO PPP price indexes are based on the data from the 2005 International Comparison Program (ICP) that were published in December 2007 and are extended through 2011 using WEO methodology. The results of the next 2011 ICP round are scheduled to be released in December 2013.

The only change in methodology reflects the introduction of BPM6, which affects the data for openness and variability. In August 2012, the IFS began publishing balance of payments and IIP data under BPM6 (Balance of Payments and International Investment Position Manual, sixth edition). Under this new methodology, the full value of goods for processing is no longer counted under the reported (gross) exports and imports (these are goods processed under contract for an explicit fee by a non-resident processing entity, where the goods being processed do not change ownership); rather only the fees from processing are recorded under services. As discussed in Annex I, the overall impact of this change is relatively modest.

Table 1.

Distribution of Quotas and Calculated Quotas

(In percent)

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Source: Finance Department.

Based on the current formula: CQS = (0.50*GDP + 0.30*Openness +0.15*Variability + 0.05*Reserves)^K. GDP blended using 60 percent market and 40 percent PPP exchange rates. K is a compression factor of 0.95. Years in parentheses indicate end period for the IFS data used in the calculations.

The “post second round” reflects the ad hoc quota increases for 54 members under the 2008 reform, which became effective in March 2011. Includes South Sudan which became a member on April 18, 2012. For the two countries that have not yet consented to and paid for their quota increases, 11th Review proposed quotas are used.

Includes South Sudan which became a member on April 18, 2012; reflects the proposed doubling of its quota after the 14th Review becomes effective.

Reflects the impact of adjustments to current receipts and payments for re-exports, international banking interest, and non-monetary gold.

Including Czech Republic, Estonia, Korea, Malta, Singapore, Slovak Republic, and Slovenia.

Including China, P.R., Hong Kong SAR, and Macao SAR.

PRGT-eligible countries.

4. The data update reflects several broad trends observed in previous updates. Based on the current formula, the calculated quota share (CQS) of emerging market and developing countries (EMDCs) as a group increases by a further 1.3 pps to 45.3 percent (Table 1).5 Within the EMDCs, the largest gains continue to be recorded by Asia while most other regions register modest increases. Among the advanced economies (AEs), over 80 percent of the reduction is accounted for by the largest economies—all countries in this group record a decline. The share of other advanced economies as a group falls modestly by 0.2 pps—compared to a decline of 0.5 pps in the previous update.

5. Reflecting these trends, the aggregate CQS of EMDCs has risen by 3.5 pps since the 14th Review and by 9.1 pps since the 2008 Reform. More than half of the overall increase for EMDCs is accounted for by Asia, particularly China, while most other regions registered more modest increases. The share of major advanced economies has declined by 8.1 pps since the 2008 reform, while the share of other advanced economies has fallen by 1.0 pps (Figure 1).

Figure 1.
Figure 1.

Evolution of CQS 2005 - 2011 1/

(In percent)

Citation: Policy Papers 2013, 068; 10.5089/9781498341431.007.A001

Source: Finance Department.1/ Figures adjacent to each line denote the change in percentage points between the current CQS based on data through 2011, relative to the CQS based on data through 2005.

6. The aggregate share of EMDCs increased across all quota variables (Table 2 and Table 2.1). The largest increase is in shares of the GDP blend variable, reflecting a continued marked divergence in growth trends between AEs and EMDCs—see Figure 2. EMDCs now account for 43.4 percent of total shares based on the current GDP blend measure. EMDCs also gained share of global openness and, to a lesser extent, variability, associated with the rebound in external flows in the wake of the financial crisis (Figure 3). The share of EMDCs in global reserves also increased to almost 77 percent, reversing the decline observed in the previous data update.

Table 2.

Distribution of Quotas and Updated Quota Variables

(in percent)

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Source: Finance Department.

Includes South Sudan which became a member on April 18, 2012; reflects the proposed doubling of its quota after the 14th Review becomes effective.

Based on IFS data through 2011.

Based on IFS data through 2010.

GDP blend using 60 percent market and 40 percent PPP exchange rates.

Variability of current receipts minus (instead of plus) net capital flows due to change in sign convention in BPM6.

Including Czech Republic, Estonia, Korea, Malta, Singapore, Slovak Republic, and Slovenia.

Including China, P.R., Hong Kong SAR, and Macao SAR.

PRGT- eligible countries.

Table 2.1.

Updated GDP Blend Variable

(In percent)

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Source: Finance Department.

Includes South Sudan which became a member on April 18, 2012; reflects the proposed doubling of its quota after the 14th Review becomes effective.

Based on the following formula: CQS = (0.50*GDP + 0.30*Openness +0.15*Variability + 0.05*Reserves)^K. GDP blended using 60 percent market and 40 percent PPP exchange rates. K is a compression factor of 0.95.

Based on IFS data through 2011.

Based on IFS data through 2010.

Current PPP-GDP data were retrieved from the WEO database for 183 countries. For five countries with no WEO data PPP-GDP was estimated. PPP-GDP data reflect parity rates published by the International Comparison Program in December 2007.

GDP blended using 60 percent market and 40 percent purchasing power (PPP).

Including Czech Republic, Estonia, Korea, Malta, Singapore, Slovak Republic, and Slovenia.

Including China, P.R., Hong Kong SAR, and Macao SAR.

PRGT-eligible countries.

Figure 2.
Figure 2.

Average GDP Growth Rates

Citation: Policy Papers 2013, 068; 10.5089/9781498341431.007.A001

Source: World Economic Outlook
Figure 3.
Figure 3.

Developments in External Flows

Citation: Policy Papers 2013, 068; 10.5089/9781498341431.007.A001

Source: Finance Department.

7. There were significant changes for some individual countries. China again recorded the largest individual increase in CQS by a wide margin, registering increases in the share of all quota variables, particularly GDP and openness. India and Brazil also recorded significant increases. All of the 10 largest declines in CQS were recorded by AEs. Germany saw the largest decline, followed by the United States, United Kingdom, and Italy, with the declines mainly driven by losses in GDP and openness shares (Table 3).

Table 3.

Top 10 Positive and Negative Changes in Calculated Quota Shares

(In percentage points)

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Source: Finance Department.

Current and previous calculations are based on data through 2011 and 2010 respectively, using the existing formula.

The difference between the current dataset through 2011 and the previous dataset through 2010, multiplied by the variable weight in the quota formula. The change in CQS also reflects the effect of compression.

GDP blended using 60 percent market and 40 percent PPP exchange rates.

Includes China, P.R., Hong Kong SAR and Macao SAR.

8. Out-of-lineness based on the current formula has increased compared to the last update. Comparing CQS with 14th Review quota shares, at the aggregate level AEs are over-represented and EMDCs under-represented by 2.9 pps, compared with 1.6 pps in the previous update (Table 4). Total over- and under-representation also increased since the last update. The number of underrepresented members increased to 68 compared with 66 in the previous update, and these members are under-represented by 7.5 pps of total quota shares. Almost half of this shortfall is accounted for by China. Relative to the situation prior to the 14th Review, EMDCs are now slightly more under-represented as a group (2.9 versus 2.2 pps), but out-of-lineness at the individual country level is still significantly lower (7.5 versus 10.7 pps).

Table 4.

Under- and Overrepresented Countries by Major Country Groups 1/

(In percentage points)

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Source: Finance Department.

Under- and over-represented countries for the two datasets, respectively.

Includes South Sudan which became a member on April 18, 2012; reflects the proposed doubling of its quota after the 14th Review becomes effective.

Difference between calculated quota shares and 14th General Review quota shares.

Based on IFS data through 2011.

Based on IFS data through 2010.

The “post second round” reflects the ad hoc quota increases for 54 members under the 2008 reform, which became effective in March 2011. For the two countries that have not yet consented to and paid for their quota increase 11th Review proposed quotas are used.

Difference between calculated quota shares based on IFS data through 2008 and post second round quota shares.

PRGT-eligible countries.

Quota Formula Variables: Further Reflections

A. Taking Stock

9. The Board’s deliberations under the QFR provided important building blocks for agreement on a new quota formula that better reflects members’ relative positions in the world economy.6 It was agreed that the principles that underpinned the 2008 reform remained valid. Thus, the formula should be simple and transparent, consistent with the multiple roles of quotas,7 produce results that are broadly acceptable to the membership, and be feasible to implement statistically based on timely, high quality and widely available data. Other key results of the review were:

  • Agreement that GDP should remain the most important variable, with the largest weight in the formula and scope to further increase its weight.

  • Agreement that openness should continue to play an important role in the formula, and concerns regarding this variable need to be thoroughly examined and addressed.

  • Considerable support for dropping variability from the formula, with some conditioning their support on other elements of the reform package, including how its weight is reallocated and the adequacy of measures to protect the poorest members.

  • Considerable support for retaining reserves with its current weight.

  • Consideration will be given to whether or not (i) the weight of PPP GDP in the GDP blend variable and (ii) the current level of compression should be adjusted.

  • Consideration will be given to whether and how to take account of very significant voluntary financial contributions through ad hoc adjustments as part of the 15th Review.

  • Agreement that measures should be taken to protect the voice and representation of the poorest members, with considerable support for addressing this issue as part of the 15th Review.

10. Openness and variability were both discussed extensively in the QFR and during the 2008 Reform:

  • Views on openness have continued to diverge. Some stress that economic and financial openness is central to the Fund’s mandate and as such should play a key role in the quota formula. They consider that openness captures inter-connectedness and external engagement through trade and capital flows, and that more open economies have a strong stake in international economic cooperation. In this regard, they also argue that openness is relevant to the multiple roles of quotas, including as a measure of potential vulnerabilities and countries’ ability and willingness to contribute to the Fund’s finances.8 Others question the relevance of openness for a member’s stake in global economic and financial stability, noting that it is more a reflection of economic size (smaller economies tend to be more open). They also argue that the current gross measure leads to double counting of cross-border flows which can exaggerate the importance of openness, and that intra-currency union flows should be excluded as they take place in a common domestic currency and may exaggerate a member’s broader integration into the global economy.

  • The rationale for including variability in the formula relates to the role of quotas in determining members’ potential access to Fund resources. As such, it is more amenable to empirical testing than openness, where issues such as stake and engagement in promoting global economic and financial stability are more difficult to capture. Extensive staff work has so far failed to identify any clear linkages between variability and demand for Fund resources or a superior alternative measure, and has also highlighted some technical difficulties with the current variability measure. As noted, in light of this work, there is considerable support for dropping variability from the formula, though some see it having a continued role and a few Directors asked staff to explore the links between variability and balance of payments difficulties that do not necessarily lead to use of Fund resources.

11. The remainder of this section focuses on these two variables. It begins with a discussion of openness in light of the outcome of the QFR, and then reports on the results of further staff work on variability responding to the above request.

B. Openness

Characteristics of the Current Openness Variable

12. Several characteristics of the openness variable are worth highlighting:

  • Openness benefits many smaller economies. Relative to shares of the GDP blend, 130 members (about 70 percent of the membership) have larger shares in openness, while 140 members have larger shares in variability (Table 5). The number of countries that benefit from openness is inversely related to size (28 out of 47 members in the top quartile benefit from openness, compared with 37 out of 47 for the bottom quartile). Thus, openness tends to boost the CQS of smaller economies and reduce the role of size in the formula.

  • At the aggregate level, smaller advanced economies are the largest beneficiaries of openness. For this group, shares in openness are almost double their shares in the GDP blend (Figure 4). Smaller EMDCs also gain but to a much lesser extent (though some individual EMDCs are among the largest gainers), while LICs as a whole do not gain from openness. This pattern is also reflected in the distribution of gainers by per capita income (Table 5, middle panel), which shows that 44 out of 47 countries in the top income quartile gain from openness compared with only 21 out of 47 in the bottom quartile. Also, 23 out of 26 AEs gain from openness (compared with 107 out of 162 EMDCs), and they also gain to a larger extent on average relative to GDP.

  • The highly skewed distribution of shares in openness relative to GDP means that some countries receive a very large benefit from openness. The distribution of openness ratios across the membership is highly skewed, both the absolute ratio of openness to market GDP and the ratio of openness shares to GDP blend shares. While the median ratio of openness to market GDP for the membership as a whole is close to 1, 13 members have ratios greater than 2 (the highest being above 9) and 37 members have ratios greater than 1.5 (Figure 5a). Similarly, 29 members have openness shares that are more than double their shares in the GDP blend (the highest ratio is more than 15 times) (Figure 5b).

  • Openness is also highly correlated with variability. Excluding the 10 largest economies in terms of GDP, the correlation between openness and variability for the other 178 members is 92 percent, well above the correlations for all other variables (Table 6). The distribution of shares in openness and variability across major country groups is also very similar (Figure 6). AEs account for 61 percent of openness and 57 percent of total variability; within this group, other advanced economies account for 20 percent of openness and 19 percent of variability. Given the high correlation between openness and variability, many of the countries that benefit most from openness also benefit substantially from variability. For example, 12 of the 15 countries with the highest ratio of openness to GDP blend shares also have variability to GDP blend ratios that are at least 75 percent of the openness ratio (Figure 7).

Table 5.

Countries who benefit from Openness and Variability, by quartile of market GDP and GDP per capita

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Source: Finance Departmet

Each quartile includes 47 countries.

Average or median ratio among the countries which have ratios greater than 1.

Figure 4.
Figure 4.

Openness and Variability Shares Relative to GDP Share 1/

Citation: Policy Papers 2013, 068; 10.5089/9781498341431.007.A001

Source: Finance Department.1/ The ratio of the openness share of the relevant group to its GDP blend share and the ratio of the variability share of the relevant group to its GDP blend share.2/ Large EMDCs are those for which the GDP Blend share is greater than 1.0 percent.3/ Other EMDCs excluding LICs.
Figure 5a.
Figure 5a.

Ratio of Openness to Market GDP

Citation: Policy Papers 2013, 068; 10.5089/9781498341431.007.A001

Figure 5b.
Figure 5b.

Ratio of Openness Shares to GDP-Blend Shares

Citation: Policy Papers 2013, 068; 10.5089/9781498341431.007.A001

Source: Finance Department.
Table 6.

Correlations between Quota Variables

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Source: Finance Department.

Given the heterogeneity of data and differing distributions, it is possible for correlations for the full sample to fall outside of the range for the two sub samples.

Largest members in terms of share of GDP blend (60 percent market GDP and 40 percent PPP GDP).

Figure 6.
Figure 6.

Shares of Major Groups in Each Quota Variable

Citation: Policy Papers 2013, 068; 10.5089/9781498341431.007.A001

Source: Finance Department.
Figure 7.
Figure 7.

Top 15 Countries – Ratio of Openness Share to GDP Blend Share and Variability Share to GDP Blend Share 1/

Citation: Policy Papers 2013, 068; 10.5089/9781498341431.007.A001

1/ Countries ranked by openness to GDP blend share.Source: Finance Department.

13. In sum, many countries benefit from openness in the formula, but the gains for a narrower group of countries are very large. These gains arise from the very high shares of openness (relative to GDP) in some cases, and also from the combined effect of openness and variability, which are highly correlated and together have a 45 percent weight. Smaller high income economies are among some of the largest gainers. The resulting CQS for some countries under the current formula appear large in relation to other measures of their relative economic positions.

14. Different options have been considered to address the concerns about openness in the past. One approach has been to make ad hoc adjustments to the data. Prior to the 2008 reform, adjustments were made in an effort to exclude the impact of large entrepôt trade activities, international financial centers, or the processing of imports for re-export. However, this practice was discontinued as part of the 2008 reform because it was seen as arbitrary and lacking a strong conceptual basis. Suggestions have also been made to exclude intra-currency union flows. This approach poses both conceptual and practical issues. From a conceptual perspective, the potential distortions associated with cross-border flows arise whenever there is vertical integration in the production process and are not limited to currency unions, though the existence of a currency union may contribute to the growth of such flows. Also, members of currency unions can still face balance of payments difficulties that require Fund support. On the practical side, while data are generally available for merchandise trade within currency unions, they are not available on intra-currency union services flows.9

Data Availability

15. Improvements in official data availability are unlikely to fully address these issues, at least in the near term. The latest data update incorporates the implementation of BPM6, which removes one form of double-counting relating to goods that are processed for a fee by a nonresident (see Annex I). STA has developed BPM6 basis estimates for all of the countries still reporting on a BPM5 basis (of these, 58 reported data on goods for processing). However, gross trade flows are still included when there is a recorded change of ownership across economies, and the shift to BPM6 does not address some of the problems with large financial centers (investment income is still recorded in gross terms). Staff estimates suggest that the quantitative impact of the introduction of BPM6 appears to be small on average:

  • Countries reporting under BPM6: for 12 of the 20 countries reporting under BPM6 and also reporting goods for processing, the average change in nominal openness which can be attributed to the change in methodology is estimated at less than 1 percent; the largest individual change is estimated at 14 percent;10

  • Countries reporting under BPM5, including those reporting goods for processing: for the 58 countries in this group, STA has adjusted the reported data as a bridge to the BPM6 format. For this group, the estimated average change in openness attributable to this adjustment is less than 2 percent; and

  • Countries reporting under BPM5, but not reporting separately their goods for processing: for these 110 members, no adjustments to bridge to BPM6 are possible and their openness data are therefore not affected by the introduction of BPM6.

16. Efforts are also underway to improve estimates of trade data on a value added basis.11 The OECD and the WTO have launched a joint initiative to produce regular estimates of trade on a value-added basis that would complement the official statistics on gross trade (see Annex II for details).12 The database was recently extended to include estimates for value added trade for 54 members (for the years 2005, 2008, and 2009).13 Together, these countries cover 91 percent of current global openness shares. These estimates appear to support the view that the current openness variable may tend to over-state trade flows by larger amounts for countries that have relatively large recorded openness ratios. Specifically, for countries in the OECD-WTO sample, there is a relatively strong negative correlation (-0.71) between openness and the ratio of value-added to gross flows (see Table 7 and Figure 8).

Figure 8.
Figure 8.

Relationship between VAX Ratio and Ratio of Trade to GDP

Citation: Policy Papers 2013, 068; 10.5089/9781498341431.007.A001

Source: Finance Department
Table 7.

Openness Ratios and OECD-WTO Data, 2008-2009

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Source: Columns 1 and 2 are computed using data from the quota database; columns 3 and 4 are computed using OECD-WTO data. Data are from the average of 2008 and 2009

Ratio of value added exports to gross exports. Shading indicates VAX ratio below sample unweighted average of 0.71.

Ratio of value added imports to gross imports. Shading indicates VAM ratio below sample unweighted average of 0.75.

Weighted average for China, P.R. (Mainland) and Hong Kong SAR.