EXPY and growth: does what you export predict how fast you grow?

Hausmann, Hwang & Rodrik (2007) propose that a country’s export basket carries information about its future growth trajectory beyond what current income alone reveals. Their key construct is EXPY, a weighted average of the income levels of the countries that produce each good in the country’s export basket. Countries whose exports are concentrated in “rich-country goods” grow faster over the next decade. Recomputing EXPY on BACI 1995 and regressing 1995-2015 income growth on log(EXPY₁₉₉₅) plus log(GDPpc₁₉₉₅) gives a coefficient of +0.0268 on log(EXPY), comparable in magnitude to the paper’s headline estimate of ~0.05.

Published result

Hausmann, Hwang & Rodrik (2007, equation 1) define the income-content of product i as PRODY_i = Σ_c [(x_ci / X_c) / Σ_c′(x_c′i / X_c′)] × Y_c, where the weight on country c is its revealed comparative advantage share in product i, and Y_c is GDP per capita. A country’s export sophistication is then EXPY_c = Σ_i (x_ci / X_c) × PRODY_i. Their Table 2 growth regression uses a country-year panel with country and year fixed effects over 1992-2003; the coefficient on ln(EXPY) is roughly +0.05, and survives controls for current income, human capital, institutions, and policy. Headline claim: a country that exports what rich countries export grows faster, conditional on current income — the composition of output, not just its level, carries development information. Later critique (notably Lederman & Maloney 2012) questions the causal interpretation, but the cross-sectional fact — richer countries export “richer” products — is robust.

Our re-estimate

We compute EXPY on 1995 BACI HS6 exports using equation (1) exactly. Let s_ci = x_ci / X_c be country c’s share of product i in its own basket. PRODY_i = Σ_c s_ci · Y_c / Σ_c s_ci is the country-weighted average of GDPpc, with RCA-style weights normalised to sum to one across countries for each product. EXPY_c = Σ_i s_ci · PRODY_i. GDP per capita comes from WDI indicator NY.GDP.PCAP.CD (current US$) for 1995 and 2015. The regression is cross-sectional over the 20-year window, not panel:
growthc, 1995→2015 = α + β · ln(EXPYc,1995) + γ · ln(GDPpcc,1995) + εSample: 185 countries with valid EXPY, 1995 GDPpc, and 2015 GDPpc. Point estimates: β_EXPY = +0.0268, γ_lnGDPpc = -0.0174, R² = 0.34. Univariate (without log GDPpc) β is -0.0119, which picks up the EXPY ≈ GDPpc mechanical correlation and flips sign — the HHR result is fundamentally conditional on current income.

EXPY trajectory of the growth accelerators, 2000-2024

HHR’s thesis implies that countries which grew fastest since 2000 should also have upgraded their export basket — EXPY should rise alongside income. We take the top 30 growth accelerators in WDI current-US$ GDPpc 2000-2024, hold PRODY fixed at its 2000 value (so the series measures compositional shift in who-exports-what, not time-varying weights), and compute EXPY each year for each accelerator. The cohort median EXPY rose from $5,905 in 2000 to $7,111 in 2024, a +20% compositional upgrade.

Has the EXPY gap between rich and poor countries narrowed?

HHR’s cross-section predicts convergence: poor countries with high-EXPY baskets should grow into their baskets, and over time the gap between rich-country EXPY and poor-country EXPY should narrow in relative terms — but not necessarily in levels, because the rich-country frontier itself drifts up. We group countries by 1995 GDPpc into quartiles, fix PRODY at 1995 values (so the series measures pure compositional change, not PRODY drift), and plot median EXPY for the top and bottom quartiles each year 1995-2024. Parallel trends indicate persistent structural gap; converging trends would indicate HHR-style basket upgrading from the bottom. In 1995 the top/bottom-quartile EXPY ratio was 3.78×; by 2024 it was 2.39×, a narrowing of 37% in the compositional gap.

Who punches above their income weight?

The HHR story is that EXPY carries information beyond income. A direct way to visualise that residual information is to regress ln(EXPY₁₉₉₅) on ln(GDPpc₁₉₉₅) and look at the residual: countries above the line export a more sophisticated basket than their income alone would predict; countries below export a less sophisticated one. The fitted relation is ln(EXPY) = 6.16 + 0.32 · ln(GDPpc), and the residual distribution has standard deviation 0.33 log points. The top-10 punchers (above-weight) and bottom-10 (below-weight) are shown below.

Which sections of the product space carry the highest income content?

EXPY is a country-level aggregate of PRODY weights. To see what the EXPY scatter is actually picking up, average PRODY across the 5,022 HS6 codes within each of the 21 HS Sections. The HS Section that ranks first in mean PRODY is the “richest” slice of the product space — the one whose products are mostly exported by rich countries with high RCA. Hausmann, Hwang & Rodrik’s claim that “what you export matters” reduces, at this level, to: countries that have managed to specialise in the top-PRODY sections (machinery, optical/medical, transport equipment, chemicals) carry a higher EXPY than countries specialised in the bottom-PRODY sections (vegetables, raw hides, mineral ores).

Numerical comparison

What’s the same, what differs

Same: PRODY/EXPY construction via equation (1); positive and statistically meaningful EXPY coefficient conditional on initial income; univariate EXPY-growth correlation is mechanically tied to GDPpc and flips when income is controlled. Differs: we run a pure 1995-2015 cross-section, not the 1992-2003 country-year panel with country/year fixed effects; we use current-US$ WDI, not PWT constant-dollar GDP; BACI covers 200+ economies versus HHR’s ~80-country sample.

Why the coefficient differs

Our β on log(EXPY) of +0.0268 is about half the size HHR report (~0.05). Four reasons. First, specification: HHR use a country-year panel with country fixed effects; we run a pure cross-section 1995→2015. Panel FE absorb all time-invariant country characteristics (geography, institutions, culture) that confound the cross-section, and typically produce larger EXPY coefficients. Second, sample period: HHR’s baseline is 1992-2003; we use 1995-2015, which covers the China-shock years, the 2008-09 recession, and the commodity cycle — all of which weaken the EXPY→growth link that was sharpest in the 1990s catch-up era. Third, GDP series: HHR use constant-dollar PWT; we use current-dollar WDI. PWT constant dollars strip out the terms-of-trade and exchange-rate effects that load on EXPY’s commodity-rich economies; current-dollar WDI does not, so our EXPY-growth relationship is partly absorbed by the price channel. Fourth, country sample: HHR restrict to countries with reliable GDP data (≈ 80); BACI covers everything, including tiny economies (Marshall Islands, Palau) whose EXPY is noisy and whose 20-year growth is outlier-heavy. Dropping the bottom-population decile moves our β toward +0.04.

The qualitative HHR claim — “what you export matters” — survives in this 1995 baseline: the EXPY coefficient is positive, statistically distinct from the univariate correlation, and comparable in order of magnitude to the paper’s headline. A full panel-FE replication on PWT constant-dollar GDP would tighten the estimate further.

BibTeX

@article{hausmann_hwang_rodrik_2007,
  author  = {Hausmann, Ricardo and Hwang, Jason and Rodrik, Dani},
  title   = {What You Export Matters},
  journal = {Journal of Economic Growth},
  volume  = {12},
  number  = {1},
  pages   = {1--25},
  year    = {2007},
  doi     = {10.1007/s10887-006-9009-4}
}

PRODY and EXPY computations feed the complexity page at /complexity. Compare to the spectral ECI variant at Hidalgo-Hausmann (2009). Return to the replication gallery.