A long-running question in international finance: how much of the cross-section of global equity returns is driven by which country a stock belongs to versus which industry it belongs to? The answer determines whether geographic or sector allocation is the primary lever for global portfolio construction.
The Heston-Rouwenhorst framework
Heston and Rouwenhorst (1994) introduced the standard decomposition. Stock returns are modeled as:
where is the pure country effect and is the pure industry effect, identified by constraining both sets of effects to sum to zero (value-weighted). The variance of equally weighted country and industry portfolios then measures the relative importance of each.
Using 829 stocks across 12 European countries (1978-1992), they found country effects roughly 3x larger than industry effects. Industry composition explained very little of cross-country return differences.
Global evidence and the convergence debate
Industry effects rise (late 1990s)
Cavaglia, Brightman, and Aked (2000) updated the Heston-Rouwenhorst framework with broader global data and found that industry effects had grown substantially, overtaking country effects in developed markets for the first time. They attributed this to globalization, sector convergence (especially technology), and cross-border M&A integrating firms along industry lines rather than national lines.
Brooks and Del Negro (2004) confirmed the shift using a latent factor approach: the “global industry” component of returns rose while the “pure country” component declined.
Reversal after the tech bust (2000s)
The dominance of industry effects proved partly cyclical. After the technology bubble burst, country effects regained prominence. Several studies (e.g., Ferreira and Ferreira 2006) showed that the late-1990s surge in industry effects was concentrated in technology and telecom sectors and did not generalize.
Typical variance decomposition
Across the full literature, the rough consensus for individual stock returns:
| Component | Share of variance |
|---|---|
| Global market | 15-20% |
| Country | 10-15% |
| Industry | 5-10% |
| Firm-specific | 60-70% |
Industry composition alone explains roughly one-third to one-half of country-level return variation in developed markets, and substantially less in emerging markets.
The European case
Europe provides the sharpest natural experiment because of the EU/EMU integration process, which progressively harmonized monetary policy, trade, and regulation while leaving fiscal policy and banking supervision national.
Pre-euro (before 1999)
Country effects dominated. Each country had its own monetary policy, currency, and fiscal regime, creating strong country-level return comovement. Heston and Rouwenhorst’s original sample was entirely European and reflected this regime.
Euro adoption and convergence (1999-2007)
The introduction of the euro eliminated intra-eurozone currency risk and harmonized monetary policy. This produced a structural shift:
- Moerman (2008) found that industry effects overtook country effects for eurozone members, while country effects remained dominant for non-euro European countries (UK, Switzerland, Nordics).
- Ferreira and Ferreira (2006) documented the reversal within the eurozone specifically: industry factors explained more variance than country factors by the early 2000s.
- Convergence was uneven. Core eurozone (Germany, France, Netherlands) converged faster than periphery (Greece, Portugal, Ireland).
Bekaert, Harvey, Lundblad, and Siegel (2013) studied equity market integration and found that eurozone equity markets became significantly more integrated after EMU, with country-specific variance declining relative to global and industry components.
Sovereign debt crisis and re-divergence (2010-2015)
The European debt crisis sharply reversed the convergence:
- Country effects spiked as markets priced sovereign risk, redenomination risk, and divergent fiscal positions.
- Greek, Irish, Portuguese, Spanish, and Italian equities moved as country blocks regardless of industry.
- The crisis demonstrated that monetary union alone does not eliminate country risk. Fiscal policy, banking system health, and political risk remained country-specific.
Current state (2020s)
| Region | Dominant effect | Notes |
|---|---|---|
| Eurozone core (DE, FR, NL, BE) | Industry >= country | Sufficient integration that sector allocation matters as much as geography |
| Eurozone periphery (IT, ES, GR) | Country > industry | Sovereign risk and domestic banking exposure keep country comovement elevated |
| Non-euro Europe (UK, CH, Nordics) | Country > industry | Independent monetary policy and currency maintain divergence; UK post-Brexit has re-diverged |
| CEE / emerging Europe (PL, HU, CZ) | Country >> industry | Similar to broader emerging markets |
Global vs. local industries
The country-industry debate is not uniform across sectors. Griffin and Karolyi (1998) introduced the key insight: industries differ dramatically in how “global” their return drivers are, and this maps to the tradeable vs. non-tradeable goods distinction.
The tradeable/non-tradeable framework
Industries producing tradeable goods (commodities, manufactured exports) have prices set in world markets, creating strong cross-country comovement. Industries producing non-tradeable goods and services (utilities, retail, construction) are dominated by local demand, regulation, and pricing. Griffin and Karolyi found the global component explained 4-12x more variance for tradeable-goods industries than for non-tradeable ones.
Quantitative magnitudes
Using weekly data across 25 countries, Griffin and Karolyi (1998) decomposed industry-level variance into global and local components:
| Industry type | Global component share |
|---|---|
| Mining / Energy | ~35-50% |
| Manufacturing (tradeable) | ~15-25% |
| Utilities / Services (non-tradeable) | ~5-10% |
This means diversifying across countries within energy yields much less risk reduction than diversifying across countries within banking, because energy stocks already move together globally.
The spectrum
| More global | Mechanism | More local | Mechanism |
|---|---|---|---|
| Energy / Oil & Gas | Commodity priced in world markets | Utilities | Regulated, domestic customer base |
| Basic Materials / Mining | Global commodity prices | Real Estate | Local property markets, zoning |
| Technology | Global supply chains, common demand | Banking / Financials | Domestic regulation, local credit cycles |
| Luxury Goods | Global consumer base | Retail / Consumer Staples | Local consumer preferences |
| Semiconductors | Single global supply chain | Construction | Local demand, permits, labor |
| Pharmaceuticals | Global R&D and distribution | Telecom (historically) | National licensing and regulation |
Baca, Garbe, and Weiss (2000) confirmed that the late-1990s rise in industry effects was not uniform: energy and technology drove most of the increase, while utilities and financials remained stubbornly local. Brooks and Del Negro (2006) extended this, showing the apparent rise in global industry effects was heavily concentrated in technology and telecom during the bubble and did not generalize to most sectors.
Shifts over time
Some industries have moved along the global-local spectrum:
- Telecom: local to more global, as deregulation, privatization, and mobile technology created cross-border players
- Banking: moved slightly toward global during the 2000s (global banks, cross-border lending), then snapped back to local post-GFC as national regulators ring-fenced capital
- Technology: increasingly global as hardware manufacturing consolidated in Asia and software platforms became borderless
Implication
The country-vs-industry question has no single answer because different industries inhabit different points on the integration spectrum. A global portfolio tilted toward energy and materials is effectively making industry bets with minimal geographic diversification benefit. A portfolio tilted toward banks and utilities retains strong country diversification even with narrow sector concentration.
Implications for risk models
Global equity risk models like MSCI Barra GEM and Bloomberg MAC3 estimate country and industry factors jointly in the same cross-sectional regression. This is essential because:
- Industry composition varies by country (technology in the US, financials in the UK), confounding the two effects. Joint estimation disentangles them.
- The relative importance of country vs. industry shifts over time and across regimes. A model that drops one set of factors would be mis-specified in some periods.
- Even within the eurozone, maintaining separate country factors is justified. The sovereign debt crisis showed that dormant country risk can re-emerge rapidly, and a model that collapsed eurozone countries into a single factor would have badly underforecast risk in 2010-2012.
Implications for portfolio construction
- In a pan-eurozone core portfolio, sector allocation is at least as important as country allocation for managing tracking error.
- Once the portfolio extends to periphery, UK, or CEE, country allocation reasserts itself as the primary risk driver.
- During stress periods, country effects spike across all of Europe regardless of pre-crisis integration levels, making geographic diversification valuable precisely when it is most needed.
Key references
- Heston, S. L. and Rouwenhorst, K. G. (1994). “Does Industrial Structure Explain the Benefits of International Diversification?” Journal of Financial Economics, 36(1), 3-27.
- Griffin, J. M. and Karolyi, G. A. (1998). “Another Look at the Role of the Industrial Structure of Markets for International Diversification Strategies.” Journal of Financial Economics, 50(3), 351-373.
- Baca, S. P., Garbe, B. L., and Weiss, R. A. (2000). “The Rise of Sector Effects in Major Equity Markets.” Financial Analysts Journal, 56(5), 34-40.
- Cavaglia, S., Brightman, C., and Aked, M. (2000). “The Increasing Importance of Industry Factors.” Financial Analysts Journal, 56(5), 41-54.
- Brooks, R. and Del Negro, M. (2004). “The Rise in Comovement across National Stock Markets: Market Integration or IT Bubble?” Journal of Empirical Finance, 11(5), 659-680.
- Carrieri, F., Errunza, V., and Sarkissian, S. (2004). “Industry Risk and Market Integration.” Management Science, 50(2), 207-221.
- Ferreira, M. A. and Ferreira, M. A. (2006). “The Importance of Industry and Country Effects in the EMU Equity Markets.” European Financial Management, 12(3), 341-373.
- Moerman, G. A. (2008). “Diversification in Euro Area Stock Markets: Country Versus Industry.” Journal of International Money and Finance, 27(7), 1122-1134.
- Bekaert, G., Harvey, C. R., Lundblad, C. T., and Siegel, S. (2013). “The European Union, the Euro, and Equity Market Integration.” Journal of Financial Economics, 109(3), 583-603.