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Transaction services work proceeds from a map of where capital is willing to move and where it already sits. The Anderson & van Wincoop gravity framework predicts that bilateral investment scales with the product of origin and destination economic mass, damped by distance; Erel, Liao & Weisbach (2012, Journal of Finance67(3): 1045-1082) and Rossi & Volpin (2004, Journal of Financial Economics74(2): 277-304) overlay the deal-level mechanics: cross-border M&A tracks relative valuations, corporate-governance quality, and legal protection. Applied to IMF balance-of-payments Direct-investment flows in 2024 and IMF International Investment Position stocks in 2024, nine figures trace outward and inward FDI mass, the stock of foreign-owned productive capital, a PROXY attractiveness index, the five-year change in corridor intensity, the five-year change in host-economy inward DI stock, FDI geographic concentration, a greenfield-vs-M&A stock-flow proxy by region, sectoral composition of the top-12 destinations, and the long-run outward DI trajectory of the top-5 source economies.
Rows are the twenty economies with the largest BOP Net acquisition of Direct-investment assets in 2024 (outward flow); columns are the twenty with the largest Net incurrence of Direct-investment liabilities (inward flow). Cell intensity is sqrt(assets_origin × liab_dest) / (1 + km / 1000), the product-of-masses form with a log-linear distance decay. This is a gravity-structured proxy for bilateral corridor mass, not observed bilateral FDI: the IMF BOP is a per-country aggregate, and bilateral decomposition would require the IMF Coordinated Direct Investment Survey (CDIS), which is not in this parquet set.
Stock is the balance-sheet counterpart to flows. The Lane & Milesi-Ferretti 'external wealth of nations' framework (2018, IMF Economic Review) maps these cross-border claims for macro-prudential work; for transaction services the same map identifies which economies already host the largest stock of foreign-owned productive capital, i.e. the thickest pool of existing targets, carve-out candidates, and buyer franchises.
The workbench has no deal-level M&A database. In its absence, this figure presents a transparent PROXYindex combining inward FDI stock scaled by GDP (capital already sited), the Harvard Growth Lab Economic Complexity Index (breadth of the productive base, per Hidalgo & Hausmann 2009), and five-year real exports CAGR (demand-side growth pull). Each component is z-scored across the cross-section, then averaged. The result is an attractiveness rank, not a forecast of deal volume: Head & Ries (2008) document that FDI gravity loadings on market size, capability depth, and demand growth are positive and significant, but the mapping from those three to realised M&A flow is not one-to-one and sector composition matters.
Caveat: this index is a proxy, not a count of announced or closed deals. It should be read as a screening shortlist, not as evidence of current transaction activity.
Using the same gravity-structured proxy as Figure 1, we recompute corridor intensity in 2019 and 2024 for every candidate corridor among the top-30 outward and top-30 inward economies, then rank the fifteen largest-intensity corridors in 2024by percentage change over the window. Rising corridors are the practical early signal for transaction services: an accelerating bilateral FDI position has historically been followed by elevated cross-border M&A activity along the same pairing, even though the proxy itself contains no deal information.
A stock-side complement to the BOP-flow proxy in Figure 4. Taking the fifteen largest inward DI stocks in 2024and sorting by five-year growth, we see which host economies have actually accumulated foreign-owned productive capital over the window, not just which corridors look gravity-dense. Erel, Liao & Weisbach (2012) find that cross-border acquirer-target matching is predicted by relative valuation, cultural similarity, and source-side shareholder protection; Rossi & Volpin (2004) link higher target-country governance quality to greater inbound M&A volume. A rising stock series is consistent with these mechanisms but does not identify them. Commercial deal databases (Thomson Reuters SDC Platinum, Bloomberg, Dealogic) provide the deal-level observables needed for that identification and are not in this workbench.
A time-varying Herfindahl index across destinations captures whether world FDI inflows are spreading across more host economies or crowding into fewer. HHIt= 10,000 · ∑d sd,t² over Net incurrence of DI liabilities by reporting country in year t, keeping only ISO3 rows with positive inflow. Under DOJ/FTC horizontal-merger conventions 2,500+ is 'highly concentrated', 1,500-2,500 is 'moderately concentrated', and below 1,500 is unconcentrated. Damgaard, Elkjaer & Johannesen (2024, Journal of International Money and Finance 140: 102971) note that offshore-hub phantom FDI inflates destination shares, so post-COVID moves should be read jointly with phantom-FDI adjustments.
UNCTAD's annual World Investment Reportsplits cross-border FDI into greenfield project announcements and M&A deal values from commercial feeds. Neither is in the workbench parquet set, so Figure 7 constructs a transparent stock-flow proxy by region: cumulative inward BOP Direct-investment liabilities between 2019 and 2024(a greenfield-like new-money channel) stacked against the residual between IIP stock change and cumulative flow over the same window. Per balance-of-payments accounting (IMF BPM6 §6.5), the residual captures valuation effects on existing positions, retained earnings, and cross-border M&A premia above book value, three channels that all scale with M&A-driven accumulation. A positive residual is consistent with but does not identifyM&A-led accumulation: Lane & Milesi-Ferretti (2018) and Damgaard, Elkjaer & Johannesen (2024) document that valuation effects alone can move the residual by double digits of GDP in offshore-finance hubs. Countries are aggregated to World Bank regions via a hand-coded top-40 lookup; regions are ranked by the sum of positive components.
No bilateral sector-FDI feed is in this parquet set, so Figure 8 presents a transparent PROXYfor the sectoral anchor of each destination's inward-FDI stock: the host's own export composition. Primary is BACI HS sections 1-5 (animal, vegetable, fats & oils, food, minerals); manufacturing is HS sections 6-20 excluding section 14 (precious metals); services is the WDI commercial-services exports share (BX.GSR.NFSV.CD over BX.GSR.GNFS.CD). Under Dunning's (1993, Multinational Enterprises and the Global Economy) OLI framework, FDI flows to where the host holds locational advantage, so the sectoral split of exports is a first-order proxy for where inbound capital anchors. This does not identify the sector of any specific inward FDI position; it shows the host's revealed productive structure.
Figures 1, 4, and 6 take cross-sections or single-window changes; this figure plots the longer 2010-2024 trajectory of the BOP outward Direct-investment flow for each of the five largest origins in 2024. UNCTAD's annual World Investment Reportdocuments that originator-side outward FDI capacity has historically driven cross-border M&A volume; Lane & Milesi-Ferretti (2018, IMF Economic Review 66(1): 189-222) trace the post-2008 balance-sheet retrenchment, and the US Tax Cuts and Jobs Act of 2017 (P.L. 115-97, Section 965 deemed repatriation) registers as a sharp 2018 dip in US outward DI assets. The Chinese outbound profile under SAFE controls (State Administration of Foreign Exchange tightening, 2017 onward) shows a second visible kink at the 2024 pull-back, after outward DI assets peaked in 2023. Read the slope, not the level.
Four observations line up across the figures. First, FDI mass concentrates sharply: the top-5 outward and top-5 inward economies dominate both BOP flow and IIP stock (Figures 1 and 2), in line with UNCTAD's annual World Investment Reportfinding that a handful of advanced economies and offshore-finance hubs account for most cross-border DI. Second, the gravity proxy in Figure 1 aligns with Head & Ries (2008) loadings: short-distance, high-mass corridors dominate. Third, the attractiveness PROXY (Figure 3) ranks economies where capability depth (ECI), existing MNE presence (stock/GDP), and demand pull (export CAGR) coincide, consistent with the Erel-Liao-Weisbach acquirer-target matching mechanism. Fourth, corridor momentum and host-stock momentum (Figures 4 and 5) can move in opposite directions when BOP flows are reclassified away from actual change in productive capital (tax-driven phantom FDI, documented by Damgaard, Elkjaer & Johannesen 2024, Journal of International Money and Finance140: 102971), a reminder that stock and flow disagree in offshore-finance hubs.
Method note (M&A announcement effects and data sources).A commercial deal feed (Thomson Reuters SDC Platinum is the canonical academic source; Bloomberg and Dealogic are commercial competitors) records announcement date, deal value, acquirer, target, and payment form. Classical event studies (Andrade, Mitchell & Stafford 2001, JEP15(2): 103-120) compute cumulative abnormal returns in a [-1, +1] or [-2, +2] trading-day window around announcement, estimated off a market-model first stage. Typical stylised facts: target CARs average +20% to +30%, acquirer CARs hover near zero with wide variance, and cross-border deals show larger target CARs but smaller or negative acquirer CARs (Moeller, Schlingemann & Stulz 2005, JF 60(2)). None of this can be reproduced here because the workbench parquet set contains no deal database; the nine figures proxy corridor activity via BOP flows, IIP stocks, and gravity structure only. Labelling is conservative throughout: PROXY, not deal count.
WITH flows AS (
SELECT year, iso3, SUM(value * CASE scale WHEN 'Millions' THEN 1e6 WHEN 'Billions' THEN 1e9 ELSE 1 END) AS v
FROM 'data/parquet/imf/bop.parquet'
WHERE indicator = 'Direct investment, Net incurrence of liabilities, Liabilities'
AND TYPE_OF_TRANSFORMATION = 'US dollar' AND frequency = 'Annual'
AND value IS NOT NULL AND regexp_matches(iso3, '^[A-Z0-9]{3}$')
AND year BETWEEN 2005 AND 2024
GROUP BY year, iso3 HAVING v > 0
),
tot AS (SELECT year, SUM(v) AS tot FROM flows GROUP BY year),
shr AS (SELECT f.year, f.v/t.tot AS s FROM flows f JOIN tot t USING (year))
SELECT year, SUM(s*s)*10000 AS hhi FROM shr GROUP BY year ORDER BY year;SELECT iso3, CAST(year AS INTEGER) AS year,
SUM(value * CASE scale WHEN 'Millions' THEN 1e6 WHEN 'Billions' THEN 1e9 ELSE 1 END) AS v
FROM 'data/parquet/imf/bop.parquet'
WHERE indicator = 'Direct investment, Net acquisition of financial assets, Assets'
AND TYPE_OF_TRANSFORMATION = 'US dollar' AND frequency = 'Annual'
AND value IS NOT NULL
AND iso3 IN ('USA','JPN','LUX','CHN','CAN')
AND year BETWEEN 2010 AND 2024
GROUP BY iso3, year ORDER BY iso3, year;