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Three-sector model

The three-sector model is an economic framework that classifies productive activities into primary (extraction of natural resources such as agriculture and mining), secondary (processing and manufacturing of goods), and tertiary (provision of services) sectors, positing a sequential shift in employment and output shares from primary to secondary and then tertiary dominance as economies advance through stages of development driven by productivity gains and income growth.^[1]^[2] Independently articulated by economists Allan G. B. Fisher in the 1930s, Colin Clark in his 1940 analysis of global economic conditions, and Jean Fourastié in postwar French economic theory, the model empirically correlates rising per capita income with declining agricultural employment and expanding service activities across industrialized nations.^[3]^[4] While instrumental in explaining mid-20th-century structural transformations and informing development policies, it has drawn critique for oversimplifying modern economies by neglecting quaternary knowledge-intensive sectors and failing to fully capture variations in developing contexts or post-industrial shifts toward informal and digital economies.^[5]^[6]

Origins and Historical Development

Early Formulations by Fisher and Clark

Allan G. B. Fisher introduced the foundational distinction among primary, secondary, and tertiary economic sectors in his 1935 book The Clash of Progress and Security. He categorized primary activities as those involving direct extraction from nature, such as agriculture and mining; secondary activities as transformation of raw materials into goods, including manufacturing and construction; and tertiary activities as services not directly tied to material production, encompassing distribution, finance, and personal services. Fisher posited that technological advancements and productivity gains in the primary and secondary sectors would generate labor surpluses, necessitating reallocation to the expanding tertiary sector to maintain employment equilibrium, a process driven by relative cost reductions in goods production rather than uniform progress across all activities.^[7]^[8] Colin Clark extended Fisher's conceptual framework with empirical analysis in his 1940 work The Conditions of Economic Progress, drawing on statistical data from the United Kingdom and United States to quantify sectoral employment shifts. Clark documented a long-term decline in primary sector employment shares—from over 40% in the UK during the early 19th century to around 5% by the 1930s—and a parallel rise in tertiary employment, which had surpassed secondary shares in advanced economies by the interwar period. He attributed these patterns to differential income elasticities of demand: necessities from primary and secondary sectors exhibit inelastic demand that plateaus with income growth, while services display higher elasticity, absorbing released labor without implying automatic or universal economic advancement.^[9]^[10] Both Fisher and Clark emphasized causal mechanisms rooted in uneven productivity growth, particularly mechanization in agriculture and industry, which displaced workers while elevating output per capita in those sectors; this reallocation to services reflected pragmatic adaptation to technological realities rather than ideological inevitability. Their formulations, grounded in interwar observations of industrialized nations, highlighted potential frictions such as skill mismatches and regional disparities in labor mobility, underscoring that sectoral transitions depend on institutional and policy responses to productivity differentials.^[4]^[11]

Fourastié's Theoretical Framework

Jean Fourastié formalized the three-sector model in his 1949 book Le Grand Espoir du XXe siècle, presenting it as a predictive framework for economic maturation driven by differential technological progress across sectors.^[12] He posited that economies evolve through distinct phases: a traditional phase dominated by the primary sector, where agriculture and raw material extraction employ the majority of the workforce; a transitional phase marked by secondary sector expansion through industrialization; and a tertiary phase characterized by service-led affluence, as labor shifts to meet rising non-material demands.^[13] This progression stems from causal mechanisms rooted in uneven productivity growth, with technical innovations advancing more rapidly in goods-producing primary and secondary sectors than in labor-intensive services.^[14] Central to Fourastié's reasoning is the observation that productivity in services remains relatively stagnant due to their inherent reliance on human labor, contrasting with mechanization and efficiency gains in material production.^[12] He argued that this disparity compels economies to reallocate surplus labor from high-productivity goods sectors to low-productivity services, ensuring full employment while sustaining overall growth.^[13] Innovations in the primary and secondary sectors generate economic surplus, enabling societies to prioritize services that fulfill emerging wants beyond basic subsistence, such as education, healthcare, and leisure, rather than viewing the tertiary sector as a mere residual absorber of displaced workers.^[14] Fourastié's framework emphasized first-principles causality, linking sectoral shifts directly to technological determinism: faster progress in tangible output creation liberates resources and labor for intangible, demand-driven activities, forecasting a tertiary-dominant economy by the late 20th century.^[12] This countered passive interpretations of service growth, instead attributing it to active income effects where productivity-driven affluence expands the scope of human needs satisfied through personalized, non-standardized services.^[15] His predictions drew initial support from mid-20th-century European trends, where post-war reconstruction highlighted accelerating shifts toward services amid industrial productivity surges.^[14]

Definition and Sectoral Composition

Primary Sector Characteristics

The primary sector comprises economic activities involving the direct extraction, harvesting, or production of raw natural resources, primarily through agriculture (including crop cultivation and livestock rearing), forestry, fishing, mining, and quarrying. These processes rely on natural endowments such as land, water, minerals, and biological resources, with minimal initial processing to yield commodities like grains, timber, fish, coal, and ores essential for downstream economic stages.^[16]^[17] The sector's operations are inherently tied to geographic and climatic conditions, rendering output susceptible to weather variability, soil quality, and resource depletion, which historically necessitated diversified subsistence practices in agrarian societies.^[18] In pre-industrial economies, the primary sector absorbed the bulk of labor due to its labor-intensive requirements for manual tilling, planting, herding, and extraction, often employing 50-70% or more of the workforce in Europe before widespread industrialization. For example, in England around 1700, agricultural employment (the dominant primary activity) accounted for approximately 47% of the labor force, with higher proportions in earlier centuries and less developed regions where non-agricultural alternatives were scarce.^[19] This dominance stemmed from the sector's role in meeting basic food and material needs, limiting surplus labor for other pursuits until productivity enhancements emerged.^[20] Productivity within the primary sector underwent transformative increases from the late 18th century onward, driven by mechanization, improved crop rotation, selective breeding, and fertilizers, which sharply reduced per-unit labor demands and freed workers for secondary and tertiary activities. In 19th-century Britain, these innovations elevated agricultural yields by up to 80% above continental European averages, exemplifying the structural shifts observed across Europe as mechanized tools like seed drills and reapers supplanted hand labor.^[21]^[22] Such gains, accelerating post-1800, initiated the empirical decline in the sector's relative economic weight despite sustained absolute contributions of raw materials to global supply chains. Historical data illustrate this contraction: while agriculture alone comprised roughly 40% of global GDP estimates in the early 19th century, reflecting primary sector predominance in less industrialized economies, its share in OECD countries dwindled to under 2% by 2000, with mining and related extraction adding only marginally more, for a combined primary contribution below 5%.^[23]^[24] This pattern underscores the sector's foundational provision of inputs—vital for food security and industrial bases—amid disproportionate output growth elsewhere, without implying obsolescence but rather reallocation efficiency.^[24]

Secondary Sector Role

The secondary sector comprises manufacturing, construction, and utilities, activities that process raw materials extracted from the primary sector into finished goods, thereby generating higher value added through industrial transformation.^[17]^[25] This processing involves mechanical, chemical, and assembly operations that enable the creation of durable products such as machinery, vehicles, and infrastructure, distinct from mere extraction.^[26] Central to industrialization, the secondary sector drives wealth creation by amplifying productivity and innovation, as capital-intensive techniques in manufacturing allow for scalable output and technological spillovers that enhance overall economic efficiency.^[27] In mid-20th-century developed economies, secondary sector employment typically peaked at 30-40% of the total workforce, reflecting its dominant role during phases of rapid structural shift from agriculture; for example, in the United States, manufacturing alone accounted for nearly 34% of nonfarm employment in 1950.^[28]^[29] Causal evidence from East Asian economies in the 1960s-1990s illustrates the sector's indispensable function in sustaining per capita income growth, where export-oriented manufacturing expansions—averaging over 10% annual growth in key countries like South Korea and Taiwan—propelled real GDP per person increases of 4-6% yearly, prerequisites for escaping middle-income traps without reliance on services alone.^[30]^[31] This pathway contrasted with regions attempting service-led transitions prematurely, highlighting manufacturing's role in building human capital, infrastructure, and foreign exchange reserves essential for long-term prosperity.^[32]

Tertiary Sector Dynamics

The tertiary sector comprises service-based activities such as retail trade, financial services, education, healthcare, and transportation, which provide intangible outputs to consumers and businesses rather than physical goods.^[33] ^[34] This sector expands primarily as an absorber of surplus labor displaced from agriculture and manufacturing, accommodating workforce shifts in maturing economies through its capacity to employ large numbers in non-material production roles.^[35] A defining dynamic of the tertiary sector is its persistently lower average productivity growth compared to goods-producing sectors, attributed to the labor-intensive nature of many services where technological substitution is constrained by requirements for personal interaction. William Baumol's 1967 model of unbalanced growth highlights how stagnant productivity in services, coupled with inelastic demand, drives relative cost increases without corresponding output gains, a phenomenon termed Baumol's cost disease.^[36] ^[37] In advanced economies, the tertiary sector dominates employment, comprising over 70% of total jobs on average across OECD countries and reaching about 80% in the United States by 2020, reflecting its role in structural transformation.^[38] However, productivity exhibits significant heterogeneity among sub-components: knowledge-intensive areas like information technology demonstrate robust gains through digital scalability, while personal services such as hospitality and routine care lag due to inherent difficulties in standardization and automation.^[39] The sector's flexibility allows for rapid adaptation to demand variations and customization, enabling innovation in knowledge-driven subfields that enhance economic resilience.^[40] Conversely, lags in adopting automation technologies heighten vulnerability to labor market disruptions, exacerbating wage polarization by favoring high-skill roles in progressive services over low-skill, routine ones.^[41] ^[42]

Mechanisms Driving Structural Transformation

Uneven Productivity Growth Across Sectors

The uneven productivity growth across sectors forms a primary supply-side mechanism in the three-sector model, where technological advancements disproportionately enhance output per worker in the primary and secondary sectors compared to the tertiary sector. Labor-saving innovations, such as mechanized tractors in agriculture and assembly-line production in manufacturing, enable capital-intensive processes that reduce the labor required for a given level of output, thereby elevating real wages in those sectors.^[36] This displacement of workers, unable to find equivalent productivity gains elsewhere, drives labor reallocation toward the tertiary sector, where activities like personal care, education, and retail resist similar automation due to their inherent reliance on human interaction and customization.^[36] The resulting wage equalization across sectors—stemming from competitive labor markets—occurs not through productivity convergence but via rising relative costs in low-productivity services, a dynamic formalized in models of unbalanced growth.^[36] Empirical data from the United States illustrates this disparity: Bureau of Labor Statistics records indicate that manufacturing sector labor productivity grew at an average annual rate of approximately 2.5% from 1947 to 2020, driven by continuous process improvements and capital deepening.^[43] In contrast, productivity in service-providing industries, such as trade and professional services, advanced at roughly 1.0-1.5% annually over the same period, reflecting limited scope for scalable technological substitution.^[43] Agricultural productivity exhibited even steeper gains, with output per farm worker increasing over 20-fold between 1948 and 2020 due to hybrid seeds, fertilizers, and machinery, sharply contracting primary sector employment from 12% to under 2% of the workforce.^[43] These differentials underscore a causal chain: productivity surges in goods-producing sectors release labor surplus, compelling structural shifts to services absent offsetting innovations.^[44] Without spillovers from secondary sector innovations—such as information technology adapting to service delivery—this mechanism risks expanding low-productivity tertiary employment, potentially diluting aggregate economic efficiency through resource misallocation toward labor-intensive activities.^[45] Historical patterns confirm that sustained transformation hinges on the persistence of these productivity asymmetries, as observed in post-World War II industrialized economies where secondary sector advances outpaced tertiary by factors of 2:1 or greater in annual growth rates.^[36] This supply-driven reallocation thus enforces a real wage convergence grounded in marginal productivity, though it exposes vulnerabilities if tertiary stagnation impedes broader innovation diffusion.^[43]

Demand-Side Factors and Engel's Law

In the three-sector model, demand-side factors contribute to structural transformation by altering the composition of consumer expenditure as per capita incomes rise. Engel's Law, formulated by statistician Ernst Engel in 1857 based on analysis of European household budgets, posits that the share of income allocated to food—a primary sector output—declines as household income increases, even as absolute food spending may rise.^[46] This shift initially redirects demand toward manufactured goods from the secondary sector, satisfying material needs unmet by subsistence agriculture, before further income growth elevates spending on tertiary sector services such as education, healthcare, and leisure.^[47] Empirical evidence supports this progression through income elasticities of demand, where food exhibits elasticity below 1 (necessity good), while many services display elasticity exceeding 1, indicating luxury characteristics that expand disproportionately with income.^[48] Household expenditure surveys across developed economies confirm that rising affluence correlates with services capturing a larger budget share, often 60-70% in high-income contexts, as non-material wants like personal services supplant basic goods consumption.^[49] This demand reallocation incentivizes labor and capital movement across sectors, aligning with observed patterns where secondary sector expansion follows primary contraction, paving the way for tertiary dominance. While Engel's Law elucidates these demand-induced reallocations, it primarily describes compositional changes rather than causal drivers of overall growth, overlooking supply-side bottlenecks such as infrastructure or skill mismatches that can constrain sectoral responses.^[50] Moreover, expansions in non-tradable luxury services do not inherently replicate the productivity spillovers or export dynamism of an industrial base, potentially limiting sustainable development in economies skipping robust secondary phases, as evidenced in cases of unbalanced service-led transitions.^[49] Thus, demand factors provide a necessary but insufficient explanation for structural shifts, requiring integration with supply dynamics for comprehensive causal analysis.

Empirical Evidence from Economic History

Patterns in Developed Economies

In the United States, the primary sector's employment share declined from approximately 40% in 1900 to less than 5% by 2000, coinciding with the secondary sector's expansion to a peak of around 30% in the mid-20th century before stabilizing near 20%, and the tertiary sector's rise from 31% to 78% of total employment by 1999.^[51] Similar patterns emerged in the United Kingdom, where agricultural employment fell from 11% in 1900 to 2% by 2000, manufacturing decreased from 28% to 14%, and services correspondingly increased to over 80%.^[52] Across Western Europe, primary sector employment shares dropped sharply post-1900, with industry peaking around 1970 at 30-40% in many regions before a gradual decline, reflecting mechanization and urbanization trends.^[53] Post-World War II Japan demonstrated accelerated structural transformation, with agricultural employment contracting from about 48% in 1950 to 39.3% by 1961 and further to 12.7% by 1980, as workers shifted to manufacturing, which expanded to roughly 35% of employment during the 1960s-1970s high-growth period, before services overtook as the dominant sector.^[54] This shift correlated with Japan's GDP per capita surging from $1,921 in 1950 to $23,425 by 1990 (in 2011 international dollars), underscoring the model's linkage between sectoral reallocation and overall economic advancement.^[55] Employment reallocations outpaced changes in output shares due to uneven productivity gains, particularly in primary and secondary sectors; for example, in high-income economies, agriculture's GDP contribution typically remains under 2% while employing less than 5%, and industry maintains 20-25% of GDP despite employment around 20%, as reported in World Bank aggregates for developed nations.^[56] These patterns validate the three-sector model's predictions of sequential decline in primary, peak in secondary, and tertiary dominance in mature economies, driven by technological progress and rising incomes.^[57]

Observations in Developing and Emerging Economies

In developing and emerging economies, structural transformations often deviate from the model's predicted progression, with many nations exhibiting premature deindustrialization—where the secondary sector's share of employment or output peaks at lower per capita income levels than in historical advanced economies, followed by a premature shift toward services without commensurate productivity gains.^[58] This pattern, documented across a broad sample of countries since the 1990s, reflects stalled or incomplete transitions, limiting broad-based income convergence.^[59] Latin American countries provide stark examples of such deviations. In Brazil, manufacturing's value-added share of GDP reached a peak of about 27% in 1985 before declining to 11.4% by 2023, coinciding with specialization in commodities and resource-based activities rather than diversified industrial expansion.^[60] Similar trends appear in Argentina and Chile, where secondary sector shares plateaued or fell amid rising primary exports, constraining overall productivity growth.^[61] India mirrors this, with manufacturing's GDP share stagnating around 14-17% from the 1990s onward, failing to surpass historical peaks despite liberalization efforts, and remaining overshadowed by informal services and agriculture.^[62] China represents an outlier, sustaining a manufacturing value-added share above 27% of GDP from 2010 to 2023—higher than most peers at comparable income levels—through heavy state-led infrastructure investment and export-oriented policies, which supported average annual GDP growth exceeding 6% over the decade.^[63] This persistence in secondary sector dominance contrasts with service-led paths in other emerging markets, yielding faster accumulation of physical and human capital. Cross-regional data underscores a causal link between sustained secondary sector expansion and income convergence: the Asian Tigers (South Korea, Taiwan, Hong Kong, Singapore) achieved manufacturing shares of 25-35% during their 1960s-1990s growth surges, enabling per capita income rises from under $1,000 to over $20,000 (in 1990 dollars) via export-led industrialization.^[64] In contrast, Sub-Saharan Africa's manufacturing share has hovered below 10% since 2000, correlating with per capita growth rates averaging under 2% annually and persistent reliance on primary commodities.^[65] These patterns suggest that weak secondary sector dynamics hinder the productivity spillovers central to the three-sector model's transformative logic in lower-income contexts.^[58]

Criticisms and Theoretical Limitations

Challenges to Predicted Linear Progression

The three-sector model's prediction of a uniform, linear shift from primary to secondary and then tertiary dominance has been challenged by evidence of non-monotonic sectoral trajectories, where manufacturing employment or output shares fail to follow the expected rise-and-fall pattern. For instance, in post-communist economies following the Soviet Union's dissolution in 1991, industrial output plummeted by up to 50% in Russia by 1998, reversing prior manufacturing prominence without a subsequent stabilization or predictable tertiary ascent, as supply chain disruptions and privatization inefficiencies disrupted linear development paths.^[66] ^[67] This non-linearity is further evidenced by inverted U-shaped relationships between GDP per capita and manufacturing shares, where high-income economies exhibit premature deindustrialization without the model's anticipated secondary sector peak.^[68] Critics argue that the model's portrayal of tertiary expansion as unambiguous progress overlooks Baumol's cost disease, wherein service sectors exhibit persistently lower productivity growth—often 1-2% annually versus 3-4% in manufacturing—leading to rising relative costs and aggregate economic strains in service-dominant economies.^[37] ^[36] Empirical data from advanced economies confirm this, with total factor productivity (TFP) growth, akin to Solow residuals, concentrated in tradable manufacturing activities that embody technological innovations, while non-tradable services lag, undermining the notion of inherent superiority in sectoral succession.^[69] These deviations highlight the model's oversight of institutional and exogenous shocks, such as policy reversals or global trade dynamics, which can halt or invert progression, rendering the linear framework descriptively inadequate for diverse historical contexts beyond Western industrialization patterns.^[70]

Empirical Mismatches and Productivity Concerns

Empirical data from the OECD indicate that labor productivity growth in service sectors has consistently lagged behind manufacturing across member countries, with services exhibiting weaker overall rates from the mid-1990s onward due to inherent difficulties in scaling output without proportional input reductions.^[71] This disparity aligns with Baumol's cost disease, where stagnant productivity in labor-intensive services drives wage pressures upward in line with more productive sectors, elevating relative costs and straining public finances, particularly in government-provided services like education and healthcare that constitute a large tertiary share.^[36] The three-sector model posits a smooth transition mitigating such imbalances through overall growth, yet it underemphasizes how persistent tertiary productivity shortfalls—evident in OECD aggregates where services contributed to broader slowdowns post-2000—can exacerbate fiscal burdens without offsetting innovations.^[72] In developing economies, the model's expectation of primary sector decline gives way to empirical persistence driven by the resource curse, where abundant natural endowments hinder diversification into secondary manufacturing via mechanisms like Dutch disease, which appreciates currencies and erodes competitiveness in tradable goods.^[73] Resource-rich nations in sub-Saharan Africa and Latin America, for instance, have seen primary exports dominate GDP shares—often exceeding 20% in cases like Nigeria and Venezuela—without the anticipated shift to tertiary expansion, as weak secondary bases fail to generate the spillovers needed for broad service booms.^[74] This mismatch underscores that tertiary growth does not universally follow without a robust industrial precondition, with data showing stalled structural transformation in resource-dependent economies where primary reliance correlates with lower overall productivity and human development outcomes.^[75] Deindustrialization in advanced economies like the United States post-2000 reveals further empirical tensions, as manufacturing employment plummeted by nearly six million jobs between 2000 and 2010 amid widening trade deficits in goods, which surged from under $400 billion in 2000 to over $700 billion by 2006, correlating with heightened income inequality as measured by Gini coefficients rising from 0.40 to 0.42.^[76] ^[77] Critics, including analyses linking these shifts to offshoring and import competition, argue this reflects not mere maturity but causal risks of hollowed-out secondary sectors, fostering dependency on low-productivity services and persistent deficits that undermine wage growth for non-college-educated workers.^[78] ^[79] The model's linear progression overlooks how such imbalances—evident in the U.S. manufacturing trade deficit's 59% attribution to employment losses since 1998—can perpetuate economic vulnerabilities rather than resolve them through tertiary dominance.^[78]

Extensions and Evolving Interpretations

Introduction of Quaternary and Quinary Sectors

The quaternary sector represents a post-hoc extension to the three-sector model, introduced in the 1970s by geographer Jean Gottmann to delineate knowledge-intensive activities—such as research and development (R&D), information technology (IT), software engineering, and scientific consulting—that generate economic value through intellectual innovation rather than physical production or routine services. This classification emerged amid the shift toward an information-driven economy in the late 1970s and 1980s, driven by technological advancements like microelectronics and computing, which positioned these activities as high-productivity offshoots of the tertiary sector capable of spillover effects into primary and secondary industries via patents and process improvements.^[80] Empirical data from developed economies, including the United States, show the quaternary sector's contributions approximating 10-12% of GDP by 2020, primarily through professional, scientific, and technical services that exhibited labor productivity growth rates often surpassing 3-5% annually in the postwar period, outpacing stagnant sectors like government administration.^[81]^[82] The quinary sector, a more contested and less standardized addition, typically encompasses non-market or human-oriented services including high-level executive decision-making, nonprofit organizations, cultural institutions, and certain government functions focused on policy formulation and social welfare provision, intended to account for activities absorbing low-skill labor in areas resistant to automation.^[83]^[84] Proposed as a fifth category to address gaps in earlier models, it lacks a consensus definition, with some classifications subsuming it under quaternary elites while others treat it as a repository for inefficient, demand-driven services like healthcare administration and domestic care that expanded post-1980s due to demographic aging and welfare state growth.^[85] Data reveal quinary activities often correlate with subdued productivity, as evidenced by near-zero or negative total factor productivity growth in public and nonprofit subsectors, which ballooned to over 15% of employment in OECD countries by the 2010s without commensurate output gains, attributing drag to regulatory rigidities and Baumol's cost disease dynamics rather than innovation.^[86] While these extensions offer partial analytical utility by highlighting structural shifts toward intangible assets and service specialization, they risk blurring distinctions with the tertiary sector, as knowledge activities frequently overlap with traditional information handling and quinary elements embed low-efficiency traps that empirical studies link to fiscal burdens rather than growth drivers; causal evidence from productivity decompositions underscores quaternary contributions to aggregate expansion via scalable tech diffusion, whereas quinary expansions primarily reflect labor reallocation without proportional value added, challenging the model's linear progression assumptions in knowledge economies.^[87]^[88]

Integration with Modern Multi-Sector Models

Contemporary multi-sector dynamic stochastic general equilibrium (DSGE) models extend the three-sector framework by incorporating heterogeneous productivity growth, capital accumulation, and trade frictions to better capture deviations from uniform sectoral shifts observed in historical data. These models often approximate larger economies with three core sectors—agriculture, manufacturing, and services—for computational tractability while allowing for sector-specific shocks and input-output linkages. For example, approximations of multisector New Keynesian models reduce dimensionality to three sectors, preserving key propagation mechanisms like price stickiness and monetary policy transmission across production chains.^[89] This refinement addresses limitations in the original model's assumption of frictionless reallocation by endogenizing labor mobility costs and intermediate goods trade, which empirical evidence shows delay or alter predicted transformations in open economies.^[89] Daron Acemoglu and Pascual Restrepo's task-based frameworks in the 2010s integrate automation and offshoring into multi-sector settings, explaining why manufacturing employment may decline before productivity peaks due to task displacement rather than pure Baumol-Balassa-Samuelson effects. Their models posit that robots and imported intermediates substitute middle-skill tasks in manufacturing, accelerating service-sector absorption and contributing to "premature deindustrialization" in emerging markets, where trade openness amplifies these shifts beyond domestic TFP differentials.^[90] Empirical calibration of these models to U.S. data from 1980–2010 reveals that automation accounts for up to 50% of manufacturing job losses, with reinstatement via new service tasks partially offsetting declines, thus refining the three-sector model's causal emphasis on productivity to include technological complementarity.^[91] Berthold Herrendorf and coauthors' work in the 2010s advances TFP estimation within three-sector models using cross-country panel data on employment shares and GDP per capita from 1950–2005, deriving time-varying sectoral TFPs consistent with observed reallocations. Their approach decomposes structural transformation into engine-of-growth (productivity-led) and push-out (agricultural surplus) components, finding that non-homothetic preferences and relative price changes explain 60–80% of shifts in developed economies, while TFP gaps drive the rest.^[92] These estimations highlight how lumping diverse services masks urbanization-driven subsector booms, such as in construction and retail, prompting extensions that treat urban informal activities as an implicit fourth layer within the tertiary sector to enhance causal accuracy in forecasting.^[92] Such integrations validate the three-sector model's core progression while quantifying modern confounders like globalization, yielding more precise policy-irrelevant benchmarks for transformation drivers.^[93]

Policy Relevance and Contemporary Debates

Applications in National Accounting and GDP Measurement

In the System of National Accounts (SNA) 2008, promulgated by the United Nations, gross domestic product (GDP) is decomposed by economic activity into primary, secondary, and tertiary sectors using gross value added (GVA), defined as a sector's total output minus intermediate consumption. This metric isolates each sector's net contribution to final output, distinguishing the primary sector's extraction of raw materials (e.g., agriculture yielding unprocessed commodities with limited intermediates) from the secondary sector's transformation processes (e.g., manufacturing adding value through fabrication and assembly) and the tertiary sector's intangible outputs (e.g., services with high knowledge-based components but variable intermediate inputs). Such decomposition tracks structural shifts, as primary GVA typically declines in share with industrialization, while secondary and tertiary rise, enabling precise measurement of value creation rather than gross aggregates.^[94]^[95] Sectoral GVA facilitates standardized cross-country comparisons, revealing divergences in economic composition despite similar aggregate GDP levels. In the United States, for 2022, the tertiary sector comprised 80.6% of GDP via GVA, the secondary sector 18.4%, and the primary sector 1.0%, underscoring dominance of non-tradable services in value terms. The European Union exhibited a comparable tertiary skew at around 73-75% but with secondary shares elevated to 20-25% in nations like Germany due to manufacturing exports, highlighting tertiary overrepresentation in total GDP relative to tradable goods production. These metrics, drawn from harmonized national accounts, support empirical assessments of competitiveness, as secondary GVA correlates more closely with exportable outputs than tertiary, which often inflates domestic GDP through non-tradables like retail and administration.^[96]^[97] Reliance on employment shares for sectoral analysis introduces distortions, as productivity variances—higher in secondary manufacturing (e.g., via capital-intensive automation) versus heterogeneous tertiary subsectors—decouple labor inputs from GVA outputs. In advanced economies, the tertiary sector absorbs 75-85% of employment yet matches its GDP share only through aggregated productivity, masking underperformance in low-skill services; overemphasizing employment thus understates secondary sector efficiency and risks misguiding policy on structural imbalances. National accountants prioritize GVA for its alignment with market prices and avoidance of such biases, ensuring verifiable contributions over labor-centric proxies.^[98]^[99]

Implications for Industrial Policy and Deindustrialization Critiques

The three-sector model posits a sequential shift from primary to secondary and then tertiary activities, yet policy debates emphasize interventions to prevent premature deindustrialization, where manufacturing declines before services achieve comparable productivity gains. In lagging economies, targeted industrial policies—including subsidies, tariffs, and infrastructure investments—aim to extend the secondary sector's expansion, fostering structural transformation amid global competition. Empirical analyses indicate that such measures can mitigate risks of stalled growth, as unchecked shifts to low-skill services correlate with persistent poverty traps in regions like sub-Saharan Africa and parts of Latin America. Dani Rodrik's 2016 study documents this phenomenon, showing manufacturing's share of employment peaking at around 15-20% of GDP in emerging markets—far below the 25-30% threshold observed in historical developers like South Korea—potentially capping per capita income at $10,000-15,000 levels.^[100] Critiques of deindustrialization reject portrayals of it as mere "fiction" or inevitable progress, highlighting empirical vulnerabilities exposed by events like the COVID-19 pandemic, which disrupted global supply chains and amplified shortages in critical goods due to over-dependence on offshore manufacturing. In advanced economies, post-2010 reshoring trends in the United States demonstrate manufacturing output rebounding to record highs—surpassing 2007 peaks by 2014 and growing 20% cumulatively through automation-driven efficiency—despite employment stabilizing at lower levels around 12.8 million jobs by 2022. This resilience underscores the secondary sector's role in buffering economic shocks, contrasting narratives of unchallenged service-sector dominance that overlook causal links between industrial erosion and heightened fragility. Policies like the CHIPS and Science Act of 2022 have since accelerated domestic semiconductor production to address such gaps, with initial investments yielding $200 billion in private commitments by 2024.^[101]^[102] A sustained manufacturing base proves essential for defense readiness and innovation spillovers, with data revealing its disproportionate contributions to high-wage jobs, patents, and R&D intensity compared to services. For instance, U.S. defense production constraints during 2022-2023—such as 155mm artillery shell shortages—stemmed partly from atrophied industrial capacity, prompting $45 billion in supplemental funding to revitalize output. While tertiary sectors enable labor absorption and adaptability, econometric evidence prioritizes secondary activities for causal drivers of technological diffusion and national security, as manufacturing clusters generate productivity premiums of 10-20% over service equivalents through knowledge externalities.^[103]^[104]^[105]

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The three main sectors are: primary (extraction of raw resources), secondary (manufacturing and construction), and tertiary (service industry).Missing: proponents | Show results with:proponents
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Primary sector | Research Starters - EBSCO
The primary sector is that portion of an economy that extracts or cultivates resources as primary goods. Examples of these are iron ore, petroleum, grains, ...
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How important was agriculture before and during the Industrial ...
Dec 12, 2024 · Figure 3 (below) shows the labour force shares in the primary sector (excluding mining, so this is overwhelmingly agriculture) for a number of ...Missing: pre- | Show results with:pre-
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Economic growth before the Industrial Revolution: Rural production ...
We show how England became a high-income economy before 1750. Rural manufacturing and the weakening of guilds mattered for England's success.
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https://historyguild.org/the-agricultural-revolution/
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[PDF] The Agricultural Revolution and the Industrial Revolution: England ...
In the first, 1500-1860, the primary driver of productivity growth in agriculture is gains in yields. In the second phase, in the late nineteenth century, labor ...<|separator|>
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What drove economic growth in pre-industrial Europe, 1300–1800?
Agriculture was the most important input in the process of economic development before the 19th century, as it produced by far the largest share of GDP.
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Agriculture, forestry, and fishing, value added (% of GDP) | Data
Agriculture, forestry, and fishing, value added (% of GDP) from The World Bank: Data.Sub-Saharan Africa · Colombia · South Africa · India
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What Is an Economic Sector and How Do the 4 Main Types Work?
The secondary sector consists of processing, manufacturing, and construction companies. The secondary sector produces goods from the natural products within the ...
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Secondary Sector of the Economy: Definition and Examples (2025)
Nov 6, 2022 · The secondary sector of the economy involves the transformation of raw materials produced by the primary sector into finished goods.
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Secondary Industry Definition, Examples, Types, Benefits, and ...
A secondary industry refers to the sector of the economy that transforms raw materials into finished products or goods through manufacturing and industrial ...<|separator|>
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[PDF] Historical trends, 1950-92, and current uncertainties
centage points, from nearly 34 percent in 1950 to slightly more than 22 percent in 1980. The level of manufacturing employment peaked in 1979, de- clined ...
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Is U.S. Manufacturing Disappearing?
Aug 19, 2010 · In 1950, nearly 31% of nonfarm workers were employed in manufacturing. Since then, the share has been dropping three or four percentage points ...
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[PDF] East Asian Growth Before and After the Crisis - WP/98/137
... east Asia enjoyed average growth of real GDP per person at 4.6 per year from 1960 through the mid-1990s. (Collins and Bosworth, 1996, p. 136). Details of the ...
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[PDF] East Asian miracle - World Bank Documents & Reports
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[PDF] The East Asian Miracle: Four Lessons for Development Policy
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What Are Tertiary Sectors? Industry Defined, With Examples
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17 Examples of the Tertiary Sector - Simplicable
Feb 7, 2024 · 17 Examples of the Tertiary Sector · Education · Utilities · Transportation · Hospitality · Finance · Insurance · Media · Entertainment.
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Tertiary sector of the economy | Research Starters - EBSCO
This sector includes a wide array of activities such as banking, retail, healthcare, education, and entertainment. In developed economies, the tertiary sector ...Missing: components transport
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[PDF] Baumol's Cost Disease: A Macroeconomic Perspective
William Baumol and his co-authors have analyzed the impact of differential productivity growth on the health of different sectors and on the overall economy.
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Revisiting Baumol's Disease: Structural Change, Productivity ...
Based on this empirically verifiable fact, William Baumol formulated his renowned model of the cost disease of services in 1967. This model elucidates the ...
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Employment by major industry sector - Bureau of Labor Statistics
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Structural Change at a Disaggregated Level: Sectoral Heterogeneity ...
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Tertiary Sectors of the Economy: Definition with Examples
Flexibility: Services can be easily customised to meet customer needs. These factors make the tertiary sector a cornerstone of sustainable economic development.
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Robots and wage polarisation - CEPR
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Innovation, automation, and inequality: Policy challenges in the race ...
The model predicts that automation leads to an increasing share of college graduates, increasing income and wealth inequality, and a declining labor share.
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Productivity Home Page : U.S. Bureau of Labor Statistics
Output increased in 21 industries while hours worked rose in 13 industries. Unit labor costs went up in 25 of the 31 industries.Productivity Publications... · Productivity glossary · Productivity Methods Overview...
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The U.S. productivity slowdown: an economy-wide and industry ...
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Productivity in the Services Sector - Brookings Institution
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[PDF] Engel's Law Around the World 150 Years Later - PERI UMASS
In 1857, Ernst Engel argued that there is a relationship between food expenditure and income using data for 36 European households provided by Le Play and 199 ...
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[PDF] Engel's Law in the Global Economy: Demand-Induced Patterns of ...
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ARE SERVICES INCOME‐ELASTIC? SOME NEW EVIDENCE - 1996
The hypothesis that the demand for services is income-elastic tended to find support in early empirical work. Recent studies however, adopting improved ...
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(PDF) How Much Can Engel's Law and Baumol's Disease Explain ...
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Deindustrialization -- Its Causes and Implications
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[PDF] American Labor in the 20th Century
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A century of labour market change: 1900 to 2000 - ResearchGate
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[PDF] Regional Economic Development In Europe, 1900-2010 - LSE
Figure 2 shows the evolution of average employment shares across European regions, 1900-2010 for three broad sectors agriculture, industry (incl. mining) and ...<|control11|><|separator|>
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Economic Growth, Labour Markets and Gender in Japan - jstor
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[PDF] The Facts of Economic Growth
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Industry (including construction), value added (% of GDP) | Data
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[PDF] EUROPEAN GROWTH IN THE AGE OF REGIONAL ECONOMIC ...
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Premature Deindustrialization | NBER
Feb 5, 2015 · I document a significant deindustrialization trend in recent decades, that goes considerably beyond the advanced, post-industrial economies.
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[PDF] Premature Deindustrialization Dani Rodrik
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Premature deindustrialization in Latin America - CEPAL
We argue that Argentina, Brazil and Chile face premature deindustrialization, increasing their specialization in commodities, resource-based manufactures and ...
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Manufacturing, value added (% of GDP) - India | Data
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Manufacturing, value added (% of GDP) - China
Manufacturing, value added (% of GDP) - China. Country official statistics, National Statistical Organizations and/or Central Banks; National Accounts data ...
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Four Asian Tigers - Overview, Economic Growth, Financial Crisis
The Four Asian Tigers have steadily retained a high rate of economic growth since the 1960s, driven by exports and rapid industrialization. The primary reason ...
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Manufacturing, value added (% of GDP) - Sub-Saharan Africa | Data
This dataset shows manufacturing value added as a percentage of GDP for Sub-Saharan Africa from 1981 to 2024, using data from various sources.
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Apr 20, 2016 · 1) Breaking existing production ties. Splitting of USSR led to plants that previously worked together suddenly being separated by a newly formed border.
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Economic Collapse of the USSR: Key Events and Factors Behind It
Key turning points, including declining industrial output, rising consumer shortages, and political unrest, resulted in the formal end of the Soviet Union in ...
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[PDF] Benchmarking Structural Transformation Across the World
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Publication: Measuring Growth in Total Factor Productivity
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A new methodology to track the dynamics of structural ...
Rostow's (1960) model of economic development outlines a linear progression through five stages: traditional society, preconditions for take-off, take-off ...
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Can productivity still grow in service-based economies? - OECD
Services are very diverse, but overall tend to have weaker productivity levels and growth rates than manufacturing. As a result, the shift to services entails ...
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Chapter 2. The Natural Resource Curse: A Survey of Diagnoses and ...
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Natural resource curse: A literature survey and comparative ...
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Are natural resources a curse, a blessing, or a double-edged sword?
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Investigating the Decline: Who Killed US Manufacturing
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Deindustrialization of the U.S. Economy: The Roles of Trade ...
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Shifting blame for manufacturing job loss: Effect of rising trade deficit ...
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Trade union decline, deindustrialization, and rising income ...
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[PDF] The economic development and the emergence of the quaternary ...
Sep 12, 2024 · The quaternary sector is a knowledge-dependent service sector, distinct from the tertiary sector, emerging with new technologies and the Fourth ...
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GDP by Industry | U.S. Bureau of Economic Analysis (BEA)
Sep 25, 2025 · An industry-by-industry breakdown of gross domestic product. In addition to showing each industry's contribution to the US economy, known as its value added.
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Sectoral Determinants of Aggregate Productivity Growth
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The 5 Sectors of the Economy - ThoughtCo
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Quinary Sector of the Economy: Definition and Examples (2025)
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Economic Sectors Explained: The 4 Sectors of the Economy - 2025
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[PDF] New Evidence on Sectoral Labor Productivity
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[PDF] Approximating Multisector New Keynesian Models
In this paper we show how a large-dimension multisector economy can be approximated by a three-sector model that, aside from the number of sectors, features the ...
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Modeling Automation - American Economic Association
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[PDF] Automation and Rent Dissipation: Implications for Wages, Inequality ...
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[PDF] Growth and Structural Transformation
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[PDF] A Three-Sector Model of Structural Transformation and Economic ...
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Glossary of the 1993 SNA - Definition of Term - UN Statistics Division
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https://data.worldbank.org/indicator/NV.IND.TOTL.ZS?locations=EU
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GDP and Non-financial Accounts | OECD
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The importance of output choice: implications for productivity ...
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Premature deindustrialization | Journal of Economic Growth
Nov 27, 2015 · I document a significant deindustrialization trend in recent decades that goes considerably beyond the advanced, post-industrial economies.
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Global Supply Chains in a Post-Pandemic World
The economic turmoil caused by the pandemic has exposed many vulnerabilities in supply chains and raised doubts about globalization.
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Delivering the US manufacturing renaissance - McKinsey
Aug 29, 2022 · An effective transformation of the US manufacturing sector could boost GDP by $275 billion to $460 billion while adding up to 1.5 million jobs.
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Strengthening America's defense industrial base - Brookings Institution
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[PDF] Why Does Manufacturing Matter? Which Manufacturing Matters?
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America's Advanced Manufacturing Problem—and How to Fix It
Aug 20, 2023 · Manufacturing is treated just like any other sector, despite the evidence of the importance of manufacturing innovation to long-term growth.