The Micro and Macro Dynamics of Capital Flows
What this paper finds — and why it matters
Using the 2001 Hungarian capital account liberalization as a quasi-natural experiment and census-level firm data covering the entire economy (1992–2008), the paper identifies two channels through which capital inflows affect resource allocation: an input-cost channel (lower cost of capital benefits capital-intensive sectors) and a consumption channel (higher household incomes benefit high-expenditure-elasticity sectors, chiefly services). The paper finds the consumption channel dominates: one standard deviation increase in expenditure elasticity is associated with 8.4% greater real value-added growth, versus 4.2% for one standard deviation in capital elasticity. Along the extensive margin, high-expenditure-elasticity sectors experience 15% higher net entry and 19% higher gross entry. A calibrated multi-sector heterogeneous-firm model with non-homothetic preferences (à la Comin–Lashkari–Mestieri 2021) replicates 12 non-targeted moments and reproduces 70% of the reallocation toward services observed in Hungary. Counterfactual exercises show that a neoclassical homothetic model underpredicts reallocation by a factor of ten and generates counterfactual real exchange rate depreciation. Despite reallocation toward less productive service firms (a negative composition effect), aggregate TFP increased 11.4% in Hungary — driven by a love-of-variety effect from entry (mass-of-firms effect of +3.5% versus composition effect of −1.9%). Non-homothetic preferences amplify this mechanism: capital-scarce economies experience 21.9% larger TFP gains than homothetic models predict.
Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.
Q1. Why is Hungary’s 2001 capital account liberalization a clean quasi-natural experiment?
Hungary deregulated only cross-border financial flows, without simultaneous trade or FDI liberalization, and the reform was predetermined by the Copenhagen Criteria of 1993 as a condition for EU accession. The content and timing of the reform were not driven by Hungarian firm-level fundamentals: by March 2001, financial liberalization was the sole remaining EU accession requirement, and neither trade nor FDI changed around the reform (Figures C.4–C.5). Exports to the EU already accounted for 80% of total exports before 2001. The nine other EU accession candidates at the time did not experience comparable patterns of capital inflows, consumption booms, or sectoral reallocation (Tables C.2–C.3), ruling out EU accession itself as the driver.
Q2. How does the paper identify the input-cost and consumption channels separately?
The identification strategy exploits three sources of variation: pre- versus post-reform timing, heterogeneous capital elasticities across four-digit industries (input-cost channel), and heterogeneous expenditure elasticities across two-digit industries (consumption channel), derived from model-implied structural relationships. Using equation (4), the DiD regression estimates γ₁ (capital elasticity × reform dummy) and γ₂ (expenditure elasticity × reform dummy). These two structural parameters are nearly orthogonal (correlation 2.1% between USDA capital and expenditure elasticities), allowing separate identification. The capital elasticities are estimated using the Petrin–Levinsohn–Wooldridge method on pre-reform data; expenditure elasticities come from USDA Seale–Regmi–Bernstein (2003) estimated for Hungary in 1996. Parallel trends hold: firms across elasticity levels shared similar pre-reform growth trajectories (Table C.9).
Q3. What do the baseline regression results show about which channel dominates?
In the preferred specification with both channels and all controls (column 4, Panel A of Table 1), capital elasticity raises value added by 4.2% per standard deviation (0.045 SD), while expenditure elasticity raises it by 8.4% per standard deviation (0.223 SD USDA); standardized beta coefficients confirm the consumption channel is larger. For capital accumulation (Panel B), only the capital elasticity coefficient is significant: a one standard deviation increase in capital elasticity is associated with 4.4% more firm-level capital, while expenditure elasticity has no significant effect — firms in high-expenditure-elasticity sectors do not accumulate more capital, they hire more workers. Employment (Panel C) shows 9.3% higher employment per standard deviation in expenditure elasticity (5.9% using Bils–Klenow–Malin elasticities). These patterns survive controls for non-tradability, financial frictions (Rajan–Zingales, Raddatz inventories-to-sales, cash conversion cycle), and firm-level debt obligations.
Q4. How does the model fit the non-targeted moments for Hungary?
Calibrated to 13 internally targeted moments (including the 3.5 percentage point decline in the domestic real interest rate and sectoral firm-size distributions), the model matches 12 non-targeted moments spanning consumption, capital accumulation, cross-sector reallocation, and within-sector selection (Table 6). Key matches: household consumption +5.8% (data), +7.2% (model); within-firm capital accumulation +22.5% vs +24.9%; value-added share of services +3.9pp vs +2.7pp (70% match); relative operational cutoff of services vs manufacturing −2.3% vs −1.7% (74% match); relative export cutoff +4.6% vs +4.5% (98% match). The model accounts for roughly 60% of the 2.9% relative price appreciation (real exchange rate). The model also reproduces the differential increase in entry rates: services +10.8pp (data) vs +18.4pp (model), manufacturing +5.7pp vs +8.6pp.
Q5. What do counterfactual exercises reveal about the role of non-homothetic preferences?
A neoclassical representative-firm model with homothetic preferences generates only 0.4 percentage points of reallocation toward services — ten times less than the 3.9pp observed in Hungary — and produces a counterfactual real exchange rate depreciation. In Table 7, four counterfactuals are compared: (1) baseline model (εS ≠ εM, αS ≠ αM): consumption ratio CS/CM +6.9pp, service value-added share +2.7pp, relative price appreciation +1.7%; (2) consumption channel only (εS ≠ εM, αS = αM): similar service reallocation but no RER appreciation; (3) input-cost channel only (εS = εM, αS ≠ αM): modest reallocation (~1.1pp) but correct RER appreciation; (4) homothetic heterogeneous-firm model (εS = εM, αS = αM): ~0.7pp reallocation, wrong RER; (5) neoclassical model: ~0.4pp, wrong RER. Non-homothetic preferences account for about two-thirds of the service reallocation; differential capital elasticities are necessary to replicate exchange rate dynamics.
Q6. How can aggregate TFP increase when resources move toward less productive services?
Financial liberalization induces firm entry — especially in high-expenditure-elasticity services — generating a love-of-variety effect that increases aggregate output more than proportionally with the number of varieties (since σ > 1), overwhelming the negative composition effect from reallocation to lower-productivity service firms. The TFP decomposition (Table 9) shows: composition effect −1.9%, mass-of-firms effect +3.5%, interaction +0.7%, sum +2.3% model (data: +11.4%). The composition effect is consistently negative across all capital-scarcity levels because service firms are less productive. But the mass-of-firms effect is consistently larger and positive. Non-homothetic preferences amplify entry in services (the high-expenditure-elasticity sector), strengthening the love-of-variety channel.
Q7. How do non-homothetic preferences affect TFP gains in capital-scarce economies, and what are the policy implications?
Capital-scarce economies experience larger consumption booms upon financial liberalization (given lower initial capital levels and higher intertemporal borrowing gains), inducing stronger entry in high-expenditure-elasticity services and larger mass-of-firms TFP effects; non-homothetic preferences amplify this gradient by 21.9% relative to homothetic preferences (Table 10). Specifically, an economy liberalizing at 25% of its open-economy steady-state capital stock gains 5.5× more TFP than one liberalizing at 70%; under homothetic preferences the ratio is 4.5×, yielding a 21.9% amplification from non-homotheticity. This helps explain the empirical puzzle documented by Bekaert–Harvey–Lundblad (2011) and Bonfiglioli (2008) that financial liberalization episodes associate with productivity gains in capital-scarce economies, which neoclassical models predict incorrectly as productivity declines. The policy implication is that the gains from financial openness are largest — and most driven by consumption-driven entry — when economies are capital-scarce, but these gains also carry macro-financial risks (as in Gyongyosi–Rariga–Verner 2023 on the 2008 Hungarian forint depreciation).
Key concepts
input-cost channel : the mechanism through which capital inflows reduce firms’ cost of capital (borrowing rate), benefiting sectors with higher capital elasticity; identified in Hungary through the differential expansion of firms in high-capital-elasticity industries after the 2001 deregulation.
consumption channel : the mechanism through which capital inflows increase household consumption, benefiting sectors with higher expenditure elasticity; found to dominate the input-cost channel in Hungary, explaining the reallocation toward services.
non-homothetic preferences : demand preferences (modeled following Comin–Lashkari–Mestieri 2021) in which sectoral expenditure shares change with income levels — goods with expenditure elasticity above one gain share as income rises; these preferences are quantitatively necessary to explain the 3.9pp reallocation toward services in Hungary (versus 0.4pp under homothetic preferences).
mass-of-firms effect : the aggregate productivity gain from an increase in the number of active firm varieties under CES demand (σ > 1), whereby output grows more than proportionally with the number of varieties; this love-of-variety mechanism explains why aggregate TFP increases in Hungary despite resource reallocation toward less productive service firms.
expenditure elasticity : the sector-level responsiveness of consumption to a proportional increase in aggregate income; used in the paper’s DiD identification to separate the consumption channel from the input-cost channel, measured using USDA (Seale–Regmi–Bernstein 2003) estimates for Hungary, with services having higher elasticity (1.18 in model calibration) than manufacturing (0.75).