Is premature deindustrialization still a thing, 2024 edition?

Rodrik (2016, Journal of Economic Growth) documented that developing countries that industrialised after 1990 peaked in manufacturing at lower income and lower employment shares than the older cohort of industrialisers. Eight years on, with BACI trade data through 2024, does the pattern still hold? We cannot replicate the GGDC value-added production panel here, so we use an export proxy: HS chapters 25-97 excluding 27 (mineral fuels) as a share of total merchandise exports, per CEPII BACI 202501. The story in the numbers below: yes, and by more than Rodrik saw.

Method and caveats

Rodrik (2016) uses GGDC 10-sector data on the value-added share of manufacturing in GDP and the employment share, cross-tabulated against log GDP per capita. The key finding is an inverted-U where the turning point has shifted left-and-down over successive cohorts: post-1990 industrialisers peak at GDP-pc around $3,000–$4,000 (2005 PPP) and at manufacturing shares roughly ten percentage points below the OECD pattern. Herrendorf, Rogerson & Valentinyi (2014, Handbook of Economic Growth, vol. 2, ch. 6) survey the structural-change literature that underwrites the inverted-U prior, and Felipe, Mehta & Rhee (2019, Cambridge Journal of Economics 43(1): 139–168) confirm the Rodrik pattern on a longer historical value-added panel from UNIDO INDSTAT.

We cannot reproduce that specification with trade data alone. Instead we track manufacturing exports as a share of total merchandise exports, a related but distinct object: it rises when a country specialises into manufactures and falls when it discovers resources or when services exports (not captured in BACI) expand. Current-USD GDP-pc from WDI (NY.GDP.PCAP.CD) is used rather than PPP income, so turning points here are not directly comparable to Rodrik’s published estimates. Read this page as a stylised-facts update on the trade margin, not as a value-added replication.

The cross-section in 2024

For every country with at least $500M of merchandise exports in 2024, manufacturing-export share against log GDP per capita. The Kuznets-style inverted-U predicted by three-sector growth models (Kongsamut, Rebelo & Xie 2001, Review of Economic Studies 68(4); Ngai & Pissarides 2007, AER 97(1)) should show a hump peaking somewhere in upper-middle income.

Three cohorts, three peaks

We classify each country by the decade in which its manufacturing-export share peaked over the 1995–2024 window. Three buckets: early (peak before 2005), middle (2005–2014), late (2015 or later). Restricting to interior peaks (not at the window endpoints) and to countries with at least $1B total exports in 2024 and GDP-pc recorded at the peak year, we compare the cohort-median GDP per capita at peak. The Rodrik (2016) hypothesis is that later cohorts peak at lower income.

Three paths: Korea, Vietnam, Ethiopia

Rodrik (2016) closes with country cases contrasting the OECD trajectory against East Asian latecomers and sub-Saharan Africa. Here we plot manufacturing-export share over 1995–2024 for Korea (the prototype late-industrialiser, already at about $11,800 GDP-pc in 1995 per World Bank WDI), Vietnam (the post-WTO-accession electronics boom, 2007 onward), and Ethiopia (Africa’s would-be manufacturing hub, with GTP-I and GTP-II industrial policy from 2010 onward).

The falling peak

The clean Rodrik (2016) visual is a scatter of year of manufacturing peak against GDP-pc at peak: a downward-sloping cloud is premature deindustrialization. We reproduce it on export data, one dot per country with an interior peak and a known GDP-pc at that year. The OLS line below fits log(GDP-pc at peak) on peak year.

World manufacturing-export share through the window

A single global time series anchors the panel: world manufacturing-export share (HS 25-97 ex-27) as a share of world merchandise exports, 1995–2024. If the world has been deindustrialising on the trade margin, this series should fall; if it has instead been re-composing who manufactures, the global share can be flat while country-level peaks shift earlier. The latter is the pattern Rodrik’s mechanism predicts.

Initial conditions: do poorer-in-2000 countries peak sooner?

Figure 4 correlates peak year with GDP-pc at peak, but peak-year GDP-pc is itself a function of the peak. A cleaner test of the premature-peak mechanism conditions on pre-determined initial income: GDP per capita in 2000, the start of the BACI panel window, against each country’s peak year for the manufacturing-export share. If the Rodrik (2016) mechanism operates via initial conditions — the automation/GVC headwind catches up with poor starters before they build capability — poorer-in-2000 countries should peak sooner, producing a positive slope of peak year on log(GDP-pc in 2000).

Discussion

Three readings of the above, compatible with Rodrik (2016) and with subsequent work. First, the cross-section in Figure 1 still shows an inverted-U but a visibly flatter one than in the Chenery & Syrquin (1975, Patterns of Development) cross-sections of the 1960s–70s: the peak is lower and occurs at a lower income, consistent with Herrendorf, Rogerson & Valentinyi’s (2014) survey of structural-change facts. Second, the cohort-median comparison in Figure 2 gives a quantitative anchor: the late cohort peaks at substantially less income than the early cohort, echoing Felipe et al. (2019) who find the same in UNIDO value-added data. Third, the Ethiopia vs Vietnam contrast in Figure 3 and the descending cloud in Figure 4 make visible the policy concern that motivated Rodrik’s paper: in an era of automation, fragmented global-value-chain supply, and rising services as a share of world GDP (Baldwin & Forslid 2020, WP), the industrialisation escalator is shorter than it was.

Policy read

• Don’t over-rely on garment-led industrialisation. Bangladesh’s high manufacturing-export share in Figure 1 is largely HS 61–62 readymade garments; Felipe et al. (2019) show this does not translate into the value-added or employment share that fuels sustained growth.
• Target high-complexity entry points early. The falling peak in Figure 4 means the window to build machinery/electronics capability is shorter than it was for Korea in the 1970s; Amirapu & Subramanian (2015, CGDev) make this case for India.
• Services tradability is the other leg. Baldwin & Forslid (2020) argue that tradable services can absorb some of the industrialisation slack; this page does not measure services exports, and the gap should be treated as a known-unknown in any policy reading.

Open questions

• Is the trade-margin peak shift a causal consequence of automation and robotisation (Rodrik 2022; Hallward-Driemeier & Nayyar 2017, Trouble in the Making?), or mostly a composition effect from services and GVC fragmentation?
• Which late-industrialising cohort members have durable value-added as well as export shares? Crossing this page with UNIDO INDSTAT value-added would pin that down but is out of scope for the BACI build.
• Does the pattern reverse in the 2024+ reshoring wave? Too early to tell in the BACI sample; a revisit in 2028 would have three more post-IRA/CHIPS years to fit.

The central caveat remains that export shares are not value-added shares. A country can show a rising manufacturing-export share while its manufacturing value-added share is falling if it absorbs more imported intermediates (a Grossman-Rossi-Hansberg 2008 trading-tasks effect) or if domestic services grow faster than manufacturing in GDP. Bangladesh in Figure 1 is the canonical example: the trade margin is dominated by apparel, but domestic manufacturing value-added is only about 22% of GDP per WDI NV.IND.MANF.ZS. Rodrik’s (2016) original finding is sharper on value-added; what we show here is that the trade margin is moving in the same direction.

References. Baldwin, R., & Forslid, R. (2020). “Globotics and development: When manufacturing is jobless, and services are tradable.” NBER WP 26731. Chenery, H. B., & Syrquin, M. (1975). Patterns of Development, 1950–1970. Oxford University Press for the World Bank. Felipe, J., Mehta, A., & Rhee, C. (2019). “Manufacturing matters… but it’s the jobs that count.” Cambridge Journal of Economics 43(1): 139–168. Grossman, G. M., & Rossi-Hansberg, E. (2008). “Trading tasks: A simple theory of offshoring.” American Economic Review 98(5): 1978–1997. Herrendorf, B., Rogerson, R., & Valentinyi, Á. (2014). “Growth and structural transformation.” In Handbook of Economic Growth, vol. 2, ch. 6. Elsevier. Kongsamut, P., Rebelo, S., & Xie, D. (2001). “Beyond balanced growth.” Review of Economic Studies 68(4): 869–882. Ngai, L. R., & Pissarides, C. A. (2007). “Structural change in a multisector model of growth.” American Economic Review 97(1): 429–443. Oqubay, A. (2015). Made in Africa: Industrial Policy in Ethiopia. Oxford University Press. Rodrik, D. (2016). “Premature deindustrialization.” Journal of Economic Growth 21(1): 1–33.

Peak manufacturing-export share across current income tiers

Figure 2 groups countries by the decade of their peak; this figure groups them by their current 2024 income tier under the World Bank FY26 threshold table (LIC: < $1,146; LMIC: $1,146–$4,515; UMIC: $4,516–$14,005; HIC: ≥ $14,006 in current USD GDP per capita). The Rodrik (2016) premature-peaking mechanism predicts that countries currently sitting in the low and lower-middle tiers never reached the peak manufacturing-export share that today’s high-income economies did on their way up. We report the median within-tier peak share and the median peak year.

When services exports overtook merchandise: the crossover year

Rodrik’s (2016) premature-peaking mechanism has a mirror on the services side: Baldwin & Forslid (2020, NBER WP 26731) argue that tradable services are absorbing the industrialisation slack. On the trade margin we can test this directly by tracking each country’s services-export share of goods-plus-services exports (WDI BX.GSR.NFSV.CD and BX.GSR.MRCH.CD, both current USD). We flag the earliest year in which services exports reached 50% of combined goods-plus-services exports and stayed there through 2024. Countries with a crossover in the early-1990s predate our trade-margin window; countries with a late crossover are post-industrial transitions visible within the BACI era.

The wave of peaks: how many economies are past their manufacturing-export peak?

Figure 4 plots the year-of-peak versus GDP-pc-at-peak cross section. A complementary read is the time profile itself: how many economies in the sample had already crossed their manufacturing-export-share peak by year t. Rodrik (2016, Journal of Economic Growth21(1): 1–33) anchors the premature-deindustrialisation argument on the claim that the wave of peaks accelerated after 1990. With BACI through 2024 we can read the wave directly. Each country contributes one observation at its peak year (interior peaks only: peaks pinned to 1995 or 2024 are excluded because their direction is undetermined).