TFP Improvements in Economic Efficiency

• Total Factor Productivity improvements are defined as gains in output efficiency beyond measured input growth, driven by innovation and better resource allocation.
• Empirical methods like the Solow residual and Malmquist Productivity Index decompose TFP to isolate impacts of technology, human capital, and policy reforms.
• TFP gains are crucial for long-run economic growth, inter-country convergence, and sectoral competitiveness, highlighting the need for precise measurement and targeted policies.

Total Factor Productivity (TFP) improvements refer to increases in the efficiency with which all inputs—typically capital and labor—are transformed into output, as measured by the residual in an aggregate production function. TFP growth is thus credited to technological progress, organizational innovation, human capital improvements, and other factors not captured by measured input volumes. TFP improvements are central to long-run economic growth, inter-country income convergence, and sectoral competitiveness, and their determinants, measurement, and propagation are the subject of a large literature across both theoretical and applied research.

1. Theoretical Foundations and Decomposition of TFP Improvements

TFP is defined in the context of production functions such as Cobb–Douglas or trans-log. In its simplest form: $Y = A \cdot F(K, L)$ where $Y$ is output, $K$ is capital, $L$ is labor, and $A$ is TFP. TFP improvements are captured by growth in $A$, holding $K$ and $L$ fixed. The growth accounting decomposition expresses output growth ($\Delta \ln Y$) as the sum of growth in TFP ($\Delta \ln A$) and input contributions: $$\Delta \ln Y = \Delta \ln A + \alpha \Delta \ln K + \beta \Delta \ln L$$ with $\alpha$ and $\beta$ as output elasticities. Improvements in $A$ reflect technology adoption, better resource allocation, scale and allocative efficiency, and innovation, and they are distinguished from pure input accumulation (capital deepening, labor growth).

Production function variations for sectoral and cross-country analysis allow for richer decompositions:

• Flexible trans-log forms capture sector-specific capital (e.g., ICT vs. non-ICT) and allow identification of technology spillovers (Mitrevski et al., 2013).
• In growth accounting, the Solow residual method isolates TFP as the portion of output growth not attributable to observed input growth (Mahmoudzadeh et al., 2016, Tahamipour et al., 2018).
• Aggregation across industries can utilize Hulten’s Theorem, weighting sectoral TFP shocks by Domar weights (Bombarde et al., 2023).

2. Drivers of TFP Improvements: Sectoral and Cross-Country Evidence

TFP improvements originate from multiple, often interrelated sources:

• Technological Change and Innovation: Direct R&D investment, adoption of new ICTs, or generative AI systems can significantly augment TFP by enhancing process efficiency or product quality. For example, ICT investment was a dominant driver of TFP in the EU, particularly where local industries were both ICT producers and adopters (Mitrevski et al., 2013). In online retail, GenAI-based workflows have yielded immediate, causally identified TFP gains via higher sales and conversion rates with constant inputs, formalized as $d(\ln A) = d(\ln Y)$ under Cobb–Douglas with $K$ and $L$ held fixed (Fang et al., 14 Oct 2025).
• Human Capital and Worker Quality: Higher employee education and health substantially raise firm-level TFP. In China, each 1% increase in life expectancy (proxy for health) drives a 13.9% rise in TFP, and a 1% increase in the fraction of college-educated workers raises TFP by 0.203% (He, 28 Feb 2025). However, combined health and education effects display diminishing returns, emphasizing nonlinearities in human capital's impact.
• Organizational, Policy, and Institutional Factors: Regulatory quality, labor market flexibility, and effective sectoral allocation policies influence the translation of technological and human capital improvements into TFP. In the EU, labor market rigidities can dampen potential gains from ICT investment (Mitrevski et al., 2013).
• Network Effects and Spillovers: Inter-industry input-output linkages amplify local productivity shocks, with upstream industries able to propagate their improvements economy-wide via (I – βA)$^{-1}$ multipliers (Bombarde et al., 2023). Conversely, bottleneck sectors can stifle aggregate TFP growth.
• Public R&D and Knowledge Accumulation: Long-run agricultural TFP, for instance, is sensitive to accumulated public R&D spending with decades-long lags. In the U.S., offsetting projected climate-induced TFP declines by 2050 would require sustained public R&D growth rates of 5.2–7.8%/yr, vastly exceeding historical norms (Ortiz-Bobea et al., 13 May 2024).

3. Measurement Methodologies, Biases, and Causal Inference

Rigorous measurement is crucial for accurately attributing improvements in aggregate or sectoral productivity to TFP:

• Solow Residual and Growth Accounting: Widely used for sectoral and national decompositions by subtracting input growth from observed output growth under estimated elasticities (Mahmoudzadeh et al., 2016, Tahamipour et al., 2018).
• Malmquist Productivity Index (MPI): Employs Data Envelopment Analysis to measure TFP changes over time, interpretable as productivity "catch-up" to a shifting efficiency frontier (Jahan, 2020).
• Production Function Estimators: Contemporary methods utilize subjective firm expectations as controls to relax strict monotonicity assumptions on input choices, yielding more robust TFP estimates, especially in the presence of material mis-optimization (Keiller et al., 10 Jul 2024).
• Technical Efficiency Indices (TEIs): Single-step Tobit–translog models provide absolute TEIs, robust to truncation and useful for characterizing temporal and cross-firm technical efficiency patterns in sectors like airlines (Larbi et al., 6 Apr 2024).
• Experimental and Field Evidence: Randomized control trials and large-scale field experiments directly identify TFP gains from technology adoption, with the causal pathway reinforced when all other inputs are held constant (Fang et al., 14 Oct 2025).
• Paradoxes and Bias in the Public Sector: Conventional cost-based measures in settings with distorted or unmeasured output prices frequently produce paradoxical TFP statistics (e.g., technical progress misreported as declining TFP). Non-market valuation methods are recommended for accurate TFP measurement in the public sector (Kuosmanen et al., 18 Sep 2025).
• Machine Learning and Predictive Models: Nonlinear regressions (e.g., XGBoost) coupled with SHAP value decomposition robustly predict TFP from variables like ESG rating disagreement, quantifying both causal and predictive contributions (Li et al., 25 Aug 2024).

4. TFP Improvements in Empirical Context: Sectoral and Macro Evidence

Comprehensive empirical studies illustrate both the scale and heterogeneity of TFP improvements:

Sector/Context	TFP Growth Rate or Impact	Main Driver(s)
Iran mining (Mahmoudzadeh et al., 2016)	Avg. +2.94%/yr (1976–2006), 56% of growth	Technical change, resource allocation
Iran agriculture (Tahamipour et al., 2018)	Avg. –0.72%/yr (1991–2010), –19.6% of growth	Weak capital productivity
EU ICT sectors (Mitrevski et al., 2013)	Substantial acceleration in TFP	ICT investment, sectoral spillovers
US agriculture (Ortiz-Bobea et al., 13 May 2024)	–7–13% TFP projected by 2050 (climate)	Adverse weather, lagged R&D impact
Online retail (GenAI) (Fang et al., 14 Oct 2025)	Up to +16.3% sales, +21.7% conversion	GenAI integration, demand-side frictions
Commercial banks (Jahan, 2020)	MPI: regress <1.0, then improvement >1.0	Technology, operational efficiency
Airline industry (Larbi et al., 6 Apr 2024)	Slowdown (2013–19), sharp Covid-19 drop	Disembodied technical change, scale effects
Canadian firms (Cao et al., 30 Aug 2025)	Declining TFP (2003–15)	Drop in top-worker quality, not technology
Cereal farms, EU (Biagini et al., 2022)	CAP subsidies generally depress TFP	Subsidy allocation, farm productivity level

These studies demonstrate that TFP growth can be highly variable across sectors, time, and regions, subject to the interplay of technology, policy, human capital, and exogenous shocks (e.g., climate, pandemic).

5. Policy, Institutional, and Organizational Implications

The identification of TFP improvement sources carries significant implications for policy design and institutional frameworks:

• Sector-Specific Policy Design: To boost TFP, policies must be tailored to sectoral drivers. In ICT-intensive economies, measures to remove labor market rigidities and accelerate skill development are critical (Mitrevski et al., 2013). In agriculture, sustained and forward-looking R&D funding is required to counteract climate-induced declines (Ortiz-Bobea et al., 13 May 2024). For high TFP sectors, reconfiguring subsidy structures may be necessary to reallocate resources toward more productive units (Biagini et al., 2022).
• Organizational Practice and Human Resource Management: At the firm level, maximizing TFP benefits from human capital investment requires fine-tuning for diminishing returns and implementing health and education programs that complement each other (He, 28 Feb 2025). Leadership demographic characteristics (age, technical background, gender) significantly modulate technology adoption rates and thus aggregate TFP outcomes (Kikuchi, 4 Aug 2025).
• Measurement Reform and Accountability: Especially in the public sector, measurement conventions must move beyond cost-based aggregation or distorted revenue proxies to economically sound, non-market-based methods for output valuation (Kuosmanen et al., 18 Sep 2025).
• Macrostructural Policy: Recognizing production network interdependencies implies that interventions in upstream or bottleneck sectors can have disproportionately large aggregate TFP effects, making network-informed industrial policy design imperative (Bombarde et al., 2023).

6. Dynamic and Network Aspects of TFP Propagation

Recent research formalizes how TFP improvements propagate through economic structures:

• Interdependent TFP Growth in Networks: The evolution of sectoral productivity is driven not just by own-sector research effort, but also by spillovers from technologically connected suppliers, captured by:

$$\dot{Z}_i = (E_i e^{\lambda_i t})^\alpha \prod_{j=1}^n Z_j^{\beta a_{ij}}$$

yielding steady-state growth rates:

$\gamma_0 = \alpha (I – \beta A)^{-1} \lambda$

where $A$ contains input-output technical coefficients (Bombarde et al., 2023).

• Heterogeneity in Propagation: Firm-level TFP shocks have aggregate implications that depend on the distribution of capital and labor across firms. FunVAR models demonstrate that TFP improvements shift the mass of the firm distribution toward higher input regimes, highlighting reallocation as a first-order channel in shock transmission (Marcellino et al., 8 Nov 2024).

7. Controversies, Limitations, and Evolving Methodologies

Several key controversies and measurement challenges remain:

• Interpretation of TFP in Intangible, AI-driven Economies: As AI and digital labor proliferate, standard TFP measures become less informative about true productivity sources. Explicitly modeling digital labor as a separate factor clarifies AI’s role and prevents underestimation of its contribution (Farach et al., 14 May 2025).
• Public Sector Paradoxes: Incomplete or distorted output pricing can invert TFP interpretations, making measured improvements misleading (Kuosmanen et al., 18 Sep 2025).
• Endogeneity and Optimization Errors: Traditional production function estimation is sensitive to assumptions about input choice optimization. Relaxing these via expectation data or robust instruments is an area of rapid methodological progress (Keiller et al., 10 Jul 2024, Kikuchi, 4 Aug 2025).
• Heterogeneity and Convergence: While convergence in physical and human capital remains key for income convergence across countries, TFP’s convergent role is often marginal outside direct technology transfer periods or when institutional constraints persist (Verma, 20 Dec 2024).

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TFP improvement thus remains a critical, yet conceptually and empirically challenging, metric of economic efficiency and competitiveness. Its accurate measurement and effective enhancement require integrated attention to measurement rigor, sectoral and network structure, policy levers, and organizational practice.