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Special Purpose Entities and Pass-Through

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Financial Globalisation and FDI Statistics

In the modern global economy, substantial flows of cash are transferred daily between many different countries, a lot of which have different taxation systems and accounting practices. This reality necessitates a separation of funds going into economies, from funds that are intended as actual investment, and those which are simply passing through. For a globalised economy like Ireland, these complex capital flows can sometimes lead to a difficult interpretation of macroeconomic statistics.

Therefore, it is becoming increasingly important to adopt new methods in FDI statistics to reduce globalisation effects. In line with operational guidance from the IMF and Eurostat as well as through the implementation of international best practices (Finnish pass through methodology), the following two statistical procedures aim to establish FDI figures that are more in tune with real economic phenomena.

SPE Method

A Special Purpose Entity (SPE) is a company set up for reasons that are beyond the production of goods and services; often SPEs are established for financing purposes or to hold certain assets or liabilities. The present IMF guidance defines them as businesses which: have no more than five employees, very little production within their resident economy, have a foreign ultimate controlling parent, and have a high ratio of foreign assets relative to domestic. A question for many countries such as Ireland, the Netherlands and Luxembourg (countries which exhibit a large disparity in FDI figures with their European counterparts), is to what extent these types of firms may influence investment figures, making them more difficult to interpret.

“The use of SPE structures has rocketed in a context of multifaceted and flexible multinational enterprise (MNE) structures, which have become increasingly global and seek to obtain benefits from different legal and tax regimes” (IMF SPE Task Force, 2018). In light of these factors, the EU and the OECD is encouraging member states to report more information on them.

Figure 4.1 shows that SPEs account for the majority of FDI for some countries such as Luxembourg and the Netherlands. For most EU countries however, the percentage of SPE based FDI is far lower. Some countries such as France and Germany report that none of their FDI can be attributed to SPEs. 6% of Ireland’s FDI in 2019 can be attributed to SPEs based on the IMF definition.

% of FDI Attributed to SPEs
Luxembourg95.416838619186
Hungary60.7453804247715
Belgium15.2580494590086
Denmark10.0017422511459
Sweden7.34808174154275
Ireland5.80099806100475
Spain5.01864919206427
Iceland 4.3864028791945
Estonia2.61030653503516
Slovak Republic0
Slovenia0
France0
Germany0
Austria-0.924225765583894

Get the data: OECD Statistics

As seen in Figure 4.2, SPEs operating in Ireland contribute a relatively small amount of FDI when compared to the rest of the international businesses established here. Overall SPE presence in FDI figures increased significantly in 2015 coinciding with the onshoring of many intellectual assets which were funded with FDI liabilities. The figure maintains a relative consistency following this with the exception of the €30 billion decrease in 2018 which corresponds, largely, to a reduction in FDI equity liabilities for several firms which have been classified as SPE.

The consistency of SPE figures over the period 2015 to 2017 relates to minimal changes in several large FDI positions held by a select group of firms. This is consistent with the idea that many of these types of firms are established with a view to holding specific financial assets over a length of time.

These figures have increased markedly from the reporting on SPEs from previous editions of this publication. The reason for this has been further clarification and guidance from the IMF, and international statistical agencies more broadly, on the compilation of these firms. It is however, in the pass through and intellectual property figures where the main components of Ireland’s FDI liabilities can currently be distributed as seen in Figure 4.9.

SPE FDITotal Inward FDI
2013-6.628300.733
201410.484354.045
201591.167817.58
201691.151797.521
201791.641882.171
201855.981915.849
201959.5061025.789

Figure 4.3 shows FDI attributed to SPEs as a percentage of total Irish inward FDI. Following the dissolution of various debt liabilities in 2013 and 2014, the effects of these firms in the overall FDI inward data increased significantly. Following aforementioned declines in 2018, the figures have levelled off at approximately 6% of total Irish inward FDI.

Percentage of FDI Attributed to SPE Firms
2013-2.20394835285785
20142.96120549647644
201511.1508353922552
201611.4292915170886
201710.3881220307627
20186.1124705055091
20195.80099806100475

As illustrated in Figure 4.4, the 25 firms that receive the most amount of FDI in Ireland are responsible for the majority (70%) of Ireland’s FDI inward positions. In relation to the other stratified compilations of firms, the SPEs have a relatively low amount of FDI that is attributed to them.

Top 2526-5051-7576-100101+SPEs
201357.803320630681320.064404790591811.54437777733037.70938180881432.87851499258228-2.20394835285785
201460.561459884297517.738727097547510.14146407065167.151633466973994.406715480529542.96120549647644
201571.94929405570111.8394673037865.489367993124473.722988698594776.9988819487937811.1508353922552
201675.91902989724811.91512553207595.952320146550084.002396238362152.2111281857638711.4292915170886
201777.784099063930310.75331411788645.650382419936383.459933408200152.3522709900468410.3881220307627
201869.156077485441713.27781874663866.826150156229184.195973017623286.543980594067216.1124705055091
201968.352916308422114.3354454080816.827543309491524.044173301136986.159241788087035.80099806100475

 

Pass-Through Method

The second method used to mitigate globalisation effects in FDI statistics involves estimating Pass-Through which can be defined as foreign multinational investment in Irish affiliates which is then subsequently invested into another economy. It has been shown globally that there exists a strong correlation between inward and outward FDI flows (Blanchard & Acalin, 2016). This suggests that FDI statistics are being inflated as investments flow through their economies instead of entering them. The result of this is that a highly globalised economy’s inward and outward FDI figures may be inflated.

Currently, there is no benchmark definition of pass-through FDI since it can come in different forms and largely depends on the level of operational activity within MNEs in an economy. For instance, although the SPE method is able to explain the large majority of FDI for Luxembourg and the Netherlands, it fails to explain a similar portion of Ireland’s FDI figures. This is because MNE activity in Ireland is quite different to that of Luxembourg’s or the Netherland’s. Many multinationals here are non-financial enterprises, often with large employment i.e. firms that fall outside the definition of an SPE. Therefore, pass-through activity in these regular operational enterprises may not be captured using the SPE method.

The method used in this report to estimate pass-through FDI occurring in Ireland involves comparing a firm’s FDI assets and liabilities. The lower of these is then chosen and used as the estimate of pass-through FDI for that particular enterprise. The results are then aggregated to give an overall estimate of pass-through FDI in Ireland. The premise for this method is that it captures non-conventional FDI activity among all firms, not just SPEs. The method is formally shown at the end of this chapter and is based on pass-through analysis first introduced by Leino and Ali-Yrkko (2014).

Figure 4.5 shows this method applied to Irish FDI data. In 2019, pass-through FDI was estimated to be €786.9 billion. For a better conceptual understanding of this figure, it can be viewed in terms of a % of FDI liabilities. In 2019, 51% of FDI liabilities are estimated to be pass-through.

FDI AssetsFDI LiabilitiesPass-ThroughPass-Through as a % of FDI Liabilities
2012613.491591.626316.4837241653.4938836629898
2013711.919624.894364.7675700758.3727112230234
2014912.372756.712441.1011886858.2918189060039
20151325.7981307.848672.7709165951.4410632267664
20161413.8021398.684766.0218003954.7673241697195
20171363.3441422.683786.707477855.2974540217322
20181479.1061549.619900.4794244558.1097304853645
20191495.8471554.995786.859302250.602047093399

The estimate for pass-through rises to 54% of FDI liabilities when firm-level data is aggregated to group-level where possible to capture pass-through that may occur in chains of subsidiaries. FDI is presented on an asset and liability basis in Figure 4.5 whereas in the SPE part of this publication it was done on a directional basis. For a breakdown of the difference between these two presentations see this information note. The difference between FDI inward (directional) and FDI Liabilities (asset and liabilities) essentially comes down to the removal of reverse investment i.e. investment from the subsidiary or child firm back to the parent of the organisation.

Figure 4.6 shows Pass Through relative to FDI Inward. When we analyse Pass Though with respect to the directional presentation of FDI or FDI Inward, it’s percentage of the total drops to 31% in 2019. The reason for this, is that there exists overlap between the funds excluded though the filter of removing reverse investment and those removed from filtering out pass through.

FDI Inward RemainderPass Through
2013225.57575951775.157240483
2014285.38796437468.657035626
2015554.439476133263.140523867
2016531.7612983265.7597017
2017583.18879253298.98220747
2018594.65540751321.19359249
2019711.54301632314.24598368
Table 4.1 Pass-Through Presence in Inward FDI€ billion
 2013201420152016201720182019
Total FDI Inward300.7354.0817.6797.5882.2915.81025.79
Pass Through75.268.7263.1265.8299.0321.2314.25
Remaining FDI Inward225.6285.4554.4531.8583.2594.7711.54
Pass Through as % of FDI Inward25%19%32%33%34%35%31%

Figure 4.7 shows Pass-through FDI by NACE classification. In 2019, manufacturing enterprises engaged in the most pass-through activity with €216 billion. Finance and Insurance Activities enterprises followed second with €182 billion. Finance and Insurance Activities in 2019 were approximately €100 bn lower year on year, the reasoning for this was a large decrease in the amount of pass through for a small number of captive financial institutions.

Financial & Insurance ActivitiesManufacturingProfessional, Scientific, and TechnicalInformation & CommunicationAdministrative
2012170.4134527557.31012589320.22785421913.0066090828.87570028
2013168.0698545881.69163834531.00751072115.26652377738.185910623
2014189.5008463585.8743143872.75537826720.2948003938.156758192
2015219.37499985143.30393499175.0180088428.09992430283.423889415
2016242.87614919147.22539151220.4165781134.53908133796.283649675
2017170.71344879139.18967256177.27045003178.817249190.147388842
2018279.10779663227.44391817170.52797057136.9658495260.08552846
2019181.8747589215.97308428190.60555569118.382143953.160447358

Figure 4.8 shows that firms with an ultimate controlling parent in the US have engaged in the most pass-through activity, totalling €504.8 billion in 2019. The estimated pass-through occurring in firms with an Irish ultimate controlling parent is mostly coming from redomiciled PLCs. These PLCs can have FDI liabilities coming from debt instruments such as loans and trade credits. Given that these firms have large outward FDI positions, the applied method will label their FDI liabilities as pass-through.

These estimates for pass-through activity are derived using FDI asset/liability data. However, when seeking to analyse the effect that FDI has on the economy, it is more precise to look at FDI figures presented using the directional principle which involves a netting process to remove reverse investment (see: Two Methods of Measuring Foreign Direct Investment). Since it is likely that some reverse investment is already captured in the pass-through estimate, applying the two methods separately is necessary so as to eliminate the possibility of double-counting.

United States Ireland United KingdomJapanFranceBermuda
2012231.1713248433.67893629616.9704692052.483535849.139569087.692366171
2013267.8515125436.58785131920.8752226477.55093563610.3795397778.020658466
2014305.7453533475.2542369227.7305548588.40472897511.50100903611.048864389
2015428.57303501170.8745445616.24997054912.51241543411.23351484111.326261701
2016477.46004746215.3815405615.65535395913.1902387712.8424830297.909829073
2017528.01591523177.0438779414.75238770212.7609573112.4142882849.830451625
2018630.43820777182.4425901214.90464331213.3235401812.05794517811.315872405
2019504.83631913173.735276968.20603404243.266907618.20830634513.64410372

 

FDI Liabilities Breakdown

One procedure that can be applied to Ireland's FDI liabilities figure is to remove reverse investment (directional presentation) as well as pass-through investment. The on shoring of intangible assets, intellectual property and tangible fixed assets in the aircraft leasing sector has additionally led to increased difficulty in the interpretation of headline FDI figures in Ireland. Therefore, we will strip the FDI Liabilities figure of these too.

These estimates for pass-through activity are derived using FDI asset/liability data. Since some reverse investment is already captured in the pass-through estimate, applying the two methods separately is necessary so as to eliminate the possibility of double-counting.

Remaining Inward FDIAircraft (alc classification issue removed)Intangible AssetsPass-Through (net of reverse investment)Directional
2013195.489940519.18417603410.90142243675.157240483324.161320547
2014223.38115918829.14288599532.86434551768.657035626402.666273674
2015250.80606489933.275270.358771811263.140523867490.267739423
2016228.94501470925.059277.757083591265.7597017601.1636
2017253.0656258627.794302.32936667298.98220747540.512
2018215.89749131830.645869772348.11204642321.19359249633.77
2019261.37014224626.463012784423.70986129314.24598368529.206

Figure 4.9 displays Inward FDI with the removal of these aforementioned inflationary features. The pass-through component here represents the amount over and above reverse investment to avoid double counting. Positions of intangible assets, IP, and aircraft were used as a proxy for the associated FDI financing of these assets. In 2019, after removing these elements from our FDI liabilities figure, inward FDI was €261.4bn. This figure can be interpreted as an estimate for inward FDI removed of globalisation effects and helps explain the investment in Ireland's capital stock (see: Estimates of the Capital Stock of Fixed Assets)

By removing these elements from our FDI figures it implies that they provide zero contribution to real economic activities in Ireland. However, it’s conceivable that pass-through funding could in some way create economic spill-overs that result in positive effects on the economy. This applies to the on-shoring of intangible and tangible fixed assets in the aircraft leasing sector where employment and earnings have increased substantially along with the increases in those assets. This is noteworthy if seeking to use the remaining inward FDI figure derived above to estimate the effect FDI has on the Irish economy.

Further Notes

Due to how the CSO classifies firms in the aircraft leasing sector, not all their financing investments are defined as FDI. Therefore, only tangible fixed assets financed by reported FDI in this sector are included in the 'Aircraft, Intangible Assets, & IP' component in Figure 10.9. This meant excluding a considerable amount of this sector’s fixed assets from the analysis. The excluded fixed assets in these firms are financed by other investments in the context of CSO internal classifications and therefore are not relevant to this experimental FDI analysis.

Pass-Through Estimation Method

Pass-through formula

where Ii,t denotes inward FDI position of an enterprise and 0i,t is the outward FDI position.


References

Final Report of the Task Force on Special Purpose Entities, Washington, D.C., 2018 (PDF)

Blanchard, Olivier & Acalin, Julien (2016) “What does Measured FDI Actually Measure?”, Peterson Institute for International Economics

Leino, Topias & Ali-Yrkko, Jyrki (2014) “How Well Does Foreign Direct Investment Measure Real Investment by Foreign-Owned Companies? – Firm-Level Analysis”, Bank of Finland Research Discussion Papers, vol. 12

Statistics Netherlands, CBS (2018)

 

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