A job-based assessment of economic complexity: from hidden to revealed

Andrea Zaccaria; Antonio Russo; Pasquale Scaramozzino

REVIEW 4 major objections 5 minor 40 references

A job-based assessment of economic complexity: from hidden to revealed

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A job-based complexity measure built from occupational skills predicts wages and productivity growth where export-based complexity does not.

desk verdict A useful new job-based complexity measure, but the headline claim that it beats export-based revealed complexity rests on unmatched sample comparisons and needs a joint test before I'd trust it. read the letter →

arxiv 2507.05846 v1 pith:BXC4BBDV submitted 2025-07-08 econ.GN physics.soc-phq-fin.EC

Antonio Russo , Pasquale Scaramozzino , Andrea Zaccaria This is my paper · ORCID

classification econ.GNphysics.soc-phq-fin.EC

keywords hiddencomplexityjob-basedeconomicoccupationalskillsfitnessalgorithmlaborproductivityregionalgrowthhumancapital

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that the capabilities behind economic complexity can be measured directly from the skills that jobs require, rather than inferred from what a country exports. It builds a job-based 'hidden complexity' score for 220 US industries from occupational skill data, then aggregates it to US counties. The central finding is that this hidden score is positively associated with industry wages and labour productivity growth, and with county GDP per capita growth, while the traditional export-based 'revealed' measure shows no significant association. If right, complexity analysis could extend to services and to regions with little manufacturing, where export-based measures fail.

What carries the argument

The central object is a four-layer network—skills, jobs, industries, and counties—connected by three bipartite matrices $M^{(1)}$, $M^{(2)}$, and $M^{(3)}$. On the skill-job layer, the paper runs the Economic Fitness and Complexity algorithm: job fitness is a complexity-weighted sum of required skills, and skill complexity is a nonlinear function of the fitness of the jobs that require it, iterated to a fixed point. High-fitness jobs require many rare, complex skills. Industry complexity $Q^{\mathrm{JB}}_i$ is the employment-weighted average of job fitnesses over the jobs in that industry, using an Industry Wage Quotient threshold to define the job-industry links; county fitness $F^{\mathrm{JB}}_c$ is the sum of the complexities of the industries in which the county has a Wage Location Quotient above one. The machinery does the work of replacing an unobservable capability layer with an observable human-capital layer, then lets the same algorithmic logic used for exports operate on skills instead of outputs.

What would settle it

Run one regression of log 2017 average compensation and one of 2017–2022 labour productivity growth on the 72 industries where both $Q^{\mathrm{Hidden}}$ and $Q^{\mathrm{Revealed}}$ are available, including both measures and the same controls; the paper's comparative claim survives only if the revealed coefficient stays statistically insignificant while the hidden coefficient stays significant. A county-level analogue would regress GDP per capita growth on both hidden and export-based fitness for the subset of counties where both exist.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that capabilities—treated in the literature as an unobservable layer between territories and activities—can be approximated by the skill content of occupations. The authors compute a fitness for each occupation by applying the Economic Fitness and Complexity algorithm to the skill-occupation network, so that a job is complex if it requires many rare, complex skills. The Job-Based Complexity of an industry is the employment-weighted average fitness of its jobs, and the Job-Based Fitness of a county is the sum of the complexities of the industries in which it has a wage-location-quotient advantage. Across 220 industries, this hidden complexity is positively and significantly related to 2017 average compensation and to 2017–2022 labour productivity growth; the revealed, export-based complexity, computable for only 72 goods-producing industries, is not significant in either regression. At the county level, the job-based fitness is positively and significantly related to real GDP per capita growth over 2017–2022, with diversification separately controlled.

Load-bearing premise

The paper's comparative claim that revealed complexity is not associated with wages or productivity growth rests on comparing a regression on 72 goods-producing industries with regressions on 74 to 220 industries, without ever including both complexity measures in the same regression or matching the sample.

Editorial extensions

If this is right

Hidden complexity can be computed for service industries and for counties with little or no manufacturing, extending complexity analysis beyond goods trade.
Job-based complexity is a statistically significant predictor of 2017 wage levels across goods-producing industries, services, and all industries combined, with wages first rising and then plateauing at higher complexity.
Job-based complexity predicts 2017–2022 labour productivity growth out of sample, for both goods and services, while export-based complexity does not.
County job-based fitness is positively associated with 2017–2022 real GDP per capita growth, while diversification alone is negatively or insignificantly related to growth.
The hidden measure produces a smoother, better-behaved distribution of county fitness values than the endogenous fitness algorithm, avoiding the multimodal and zero-clustered values that complicate regression analysis.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the hidden measure is the truer capability signal, then industries where the two measures disagree—such as Pharmaceuticals and Aerospace, scored high by hidden and low by revealed complexity—deserve a re-examination of how complexity rankings are used in policy.
The paper does not test international portability, but because the job-based measure needs only employment and occupational data, it could in principle be computed for subnational units and service economies that lack export data.
The wage plateau at high hidden complexity suggests diminishing returns to skill complexity; a direct extension would split industries by complexity quartile and test whether the wage elasticity falls at the top.
The paper does not include both complexity measures in a single regression, so a natural next step is to test whether hidden complexity retains its predictive power once revealed complexity is held constant.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

What you should know: this paper builds a plausible new measure of industry and county "hidden complexity" from O*NET job-skill data, and it shows that measure tracks wages and productivity growth. The part I do not yet believe is the headline claim that it beats export-based revealed complexity, because that claim comes from comparing regressions on different samples rather than from a head-to-head test.

The construction is genuinely useful. The authors compute a job fitness from the skill-occupation network, average it over BLS employment weights to get industry complexity, and sum over counties. That gives a complexity measure that covers services and counties with little manufacturing, and it avoids the degenerate, multimodal distributions that plague the endogenous fitness algorithm. The regressions are run on public data with sensible controls, and the examples where hidden and revealed diverge (Pharma, Aerospace, Oil and Gas) are informative. This is a real extension of Lo Turco and Maggioni and Buyukyazici et al., not a repackaging.

The soft spot is exactly where the stress-test note points. Tables 1 and 2 compare model [1] with QRevealed on 72 goods industries against models [2]-[4] with QHidden on 74, 146, and 220 industries. The revealed measure is insignificant in its small goods-only sample; the hidden measure is significant on the goods-only sample too. But no specification includes both measures, and no common-sample regression is shown. So the sentence "whereas the classic revealed measure is not" is stronger than the evidence. The revealed measure could be insignificant for other reasons, and hidden could be adding nothing once revealed is controlled for. That is fixable: run the 72-industry sample with both measures, and maybe a horserace on the goods sample. The county-level Table 3 has the same issue in milder form: the exogenous fitness model loses about a thousand counties, described as less developed, so the comparison across models is not clean. Also, thresholds and sample exclusions get no robustness checks, and no code or data are posted.

This is a paper worth engaging with. The measure itself is a contribution, and the comparison can be repaired. I would send it to peer review and ask for a matched-sample joint test before the headline claim can stand.

Referee Report

4 major / 5 minor

Summary. This paper constructs a 'hidden' job-based economic complexity measure from a four-layer network: O*NET skills to occupations, BLS occupations to industries, and BLS industries to US counties, with UN COMTRADE exports used for the standard 'revealed' measure. The hidden industry complexity is the employment-weighted average of job fitness scores from the economic fitness algorithm, and county fitness is the sum of hidden complexities over industries with wage location quotient above one. The authors regress industry wage levels and labor productivity growth (2017–2022) on the hidden and revealed complexity measures, and county GDP per capita growth on three fitness measures. They report that the hidden measure is significantly associated with wages and productivity growth while the export-based revealed measure is not, and that all county fitness measures are positively associated with growth.

Significance. If the central comparison were established, the paper would offer a capability-based complexity measure that covers services and non-exporting counties and avoids some numerical pathologies of the standard fitness algorithm. The paper is transparent about data construction and provides falsifiable predictions, and the use of O*NET/BLS data is a constructive step. However, the headline finding of hidden-measure superiority is currently supported only by unmatched-sample comparisons, and the county-level results actually show significant coefficients for all three measures; these issues must be resolved before the paper's claims can be accepted.

major comments (4)

[Section 4.2, Tables 1 and 2] The central claim that the hidden measure is associated with wages and productivity growth while the revealed measure is not rests on a comparison of non-nested regressions with different samples: model [1] uses 72 goods-producing industries for QRevealed, whereas models [2]–[4] use 74 goods-producing, 146 service, and 220 total industries for QHidden. This design does not isolate the predictive power of the two measures, because the revealed coefficient could be insignificant in the smaller goods-only sample even if the measures were equally predictive on a common sample. Please report a common-sample regression on the 72 industries for which both measures are available, a specification including QHidden and QRevealed jointly, and a goods-only hidden-complexity model so that samples are matched.
[Section 4.4 and Table 3] The conclusion states that 'the revealed complexity shows no statistical significance,' but Table 3 model [3] reports a positive and strongly significant coefficient on the exogenous export-based fitness (2.691, s.e. 0.544). The sentence is therefore internally inconsistent with the reported county-level results unless it is explicitly restricted to the industry wage and productivity regressions of Tables 1 and 2. In addition, model [3] drops about 1,000 counties relative to models [1] and [2], and the text acknowledges that the excluded counties are on average less developed; this sample selection further weakens any comparison of the three county-level measures. Please clarify the scope of the claim and provide comparable-sample estimates.
[Sections 2.1–2.3 and 4.2] The binarization thresholds (skill importance above the skill average, IWQ>1, WLQ>1, RCA>1), the choice of the 2017–2022 window, and the exclusion of 30 industries are not subjected to sensitivity analysis. Because the headline result is a comparison of significance across measures, it would be important to show that the conclusion is robust to reasonable variations in these choices, at least for the main specifications in Tables 1 and 2.
[Section 3.2 and Table 1] Because QJB_i is a weighted average of job fitness scores that in [31] are explicitly designed to predict wages, the strong association in Table 1 may partly reflect a mechanical link rather than a distinct complexity channel. Please compare the hidden measure against simpler occupational skill or wage aggregates (e.g., average occupational wage or average skill level) in the same regressions, to demonstrate that the complexity aggregation adds predictive content beyond its inputs.

minor comments (5)

[Section 4.3] There is a typo: 'Morever' should be 'Moreover'.
[Tables 1 and 2] The AIC and BIC values are not comparable across models with different numbers of observations; please state this explicitly or restrict the information-criterion comparison to models estimated on the same sample.
[Figure 2] The figure excludes four outliers without specifying the selection rule; please state the rule or show the outliers in an inset.
[Tables 1 and 2] The notation '*** significant at 1‰ level' is nonstandard; consider using '0.1%' for clarity.
[Section 2.2] The text reports 435 occupations in OEWS versus 439 in O*NET; the matching procedure between these classifications should be described.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the job-based complexity measure is not fitted to the outcome variables; the main weaknesses are sample incomparability and reliance on a shared-author prior, neither of which makes the derivation equivalent to its inputs.

full rationale

The paper's hidden-complexity measures are not fitted to the outcomes they are asked to predict. Job fitness is computed by iterating Eq. (1) on the O*NET skill-occupation matrix, with the standard normalization from [8]; industry complexity is the employment-weighted average in Eq. (2); county fitness is the sum in Eq. (3). None of these equations uses average compensation, labor-productivity growth, or GDP-per-capita growth as an input. The shared-author citation [31] supplies the skill-network binarization and the job-fitness construction, but it is an externally published, falsifiable prior whose assumptions do not include the present regression targets; it therefore does not make the wage or productivity associations true by construction. The main weakness is the unmatched-sample comparison: Table 1 and Table 2 compare QRevealed on 72 goods-producing industries with QHidden on 74, 146, and 220 industries, and Table 3 loses about 1,000 counties for the exogenous export-based fitness, a limitation the paper itself acknowledges. This is a statistical comparability problem, not a circularity. No equation in the paper reduces a predicted variable to the explanatory measure by definition.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

No new physical or theoretical entities are introduced; the 'hidden complexity' is a composite index, not a postulated entity. The main free parameters are binarization thresholds and model choices inherited from the economic complexity literature.

free parameters (7)

Skill importance threshold = average importance per skill
Section 2.1: links in O*NET are kept only if weight exceeds the average importance of that skill; this binarization choice affects the skill-job network and hence job fitness.
Industry Wage Quotient threshold = 1
Section 2.2: job-industry links are binarized by IWQ>1; alternative thresholds would change Q^JB.
Wage Location Quotient threshold = 1
Section 2.2: industry-county links are binarized by WLQ>1; this affects county hidden fitness.
RCA threshold = 1
Section 2.3: the country-product export network is binarized by RCA>1 before computing revealed complexity.
HS-NAICS concordance weights = US worldwide export values
Section 3.3: product complexities are mapped to NAICS industries by weighting with US export values.
Convergence criteria for Fitness algorithm = criteria from [36]
Section 3.1: iteration until convergence; results may depend on the stopping rule.
Sample year window = 2017 and 2022
The growth window is chosen and no robustness to other windows is reported.

assumptions (5)

domain assumption Capabilities are well proxied by the skills required by occupations (human capital).
Section 1 states 'we focus on human capital as the essential element of the capability structure of production'.
domain assumption The Fitness-Complexity fixed point yields a meaningful ordering of job sophistication.
Section 3.1 relies on [8] and [35] for fixed-point properties; this is an algorithmic method, not a proven physical law.
domain assumption Binarizing networks with fixed thresholds preserves the capability structure relevant for complexity.
Sections 2.1 to 2.3 apply thresholds without sensitivity analysis.
domain assumption The regression models are correctly specified and free of omitted variable bias.
Section 4.2 and 4.4 regressions include limited controls (revenues, employment, CR4, diversification, initial GDP) but not other local characteristics.
ad hoc to paper The comparison between Q^Hidden (220 industries) and Q^Revealed (72 industries) is valid despite different samples.
Tables 1 and 2 compare significance across different sample sizes; no joint model includes both measures.

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Cite this review

Pith. "Pith review of A job-based assessment of economic complexity: from hidden to revealed." pith.science (2026). https://pith.science/paper/BXC4BBDV

@misc{pith2026250705846,
  author       = {Pith},
  title        = {Pith review of: A job-based assessment of economic complexity: from hidden to revealed},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BXC4BBDV}},
  note         = {Machine review of arXiv:2507.05846}
}

read the original abstract

Economic complexity measures aim to quantify the capability content or endowment of industries and territories; however, capabilities are not observable, and therefore cannot be directly used in the computations. We estimate such endowments by quantifying the quality and diversity of the skills in the occupations required in specific industries. We refer to this job-based assessment as the hidden complexity, in contrast with the usual revealed complexity, which is computed from economic outputs such as exports or production. We show that our job-based measure of complexity is positively associated to wage levels and labor productivity growth, whereas the classic revealed measure is not. Finally, we discuss the application of these methods at the territorial level, showing their connection with economic growth.

Figures

Figures reproduced from arXiv: 2507.05846 by the authors.

**Figure 2.** Comparison between the revealed complexity, based on the country export data, and our measure based on [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Positive association between job-based complexity and compensation at the sectoral level. Wage levels first [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Top panel: job-based assessment of the economic complexity of US counties. Middle panel: exogenous [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

40 extracted references · 39 canonical work pages

[1]

Reprint of the new paradigm of economic complexity

Pierre-Alexandre Balland, Tom Broekel, Dario Diodato, Elisa Giuliani, Ricardo Hausmann, Neave o’Clery, and David Rigby. Reprint of the new paradigm of economic complexity. Research Policy, 51(8):104568, 2022

work page 2022
[2]

The atlas of economic complexity: Mapping paths to prosperity

Ricardo Hausmann, César A Hidalgo, Sebastián Bustos, Michele Coscia, and Alexander Simoes. The atlas of economic complexity: Mapping paths to prosperity. Mit Press, 2014

work page 2014
[4]

Handbook of economic complexity for policy

Dario Diodato. Handbook of economic complexity for policy. Technical report, Joint Research Centre, 2024

work page 2024
[3]

Economic complexity theory and applications

César A Hidalgo. Economic complexity theory and applications. Nature Reviews Physics, 3(2):92–113, 2021

work page 2021
[31]

Mapping job fitness and skill coherence into wages: an economic complexity analysis

Sabrina Aufiero, Giordano De Marzo, Angelica Sbardella, and Andrea Zaccaria. Mapping job fitness and skill coherence into wages: an economic complexity analysis. Scientific Reports, 14(1):11752, 2024

work page 2024
[5]

Equivalence between the fitness-complexity and the sinkhorn-knopp algorithms

Dario Mazzilli, Manuel Sebastian Mariani, Flaviano Morone, and Aurelio Patelli. Equivalence between the fitness-complexity and the sinkhorn-knopp algorithms. Journal of Physics: Complexity, 5(1):015010, 2024

work page 2024
[6]

Ranking species in complex ecosystems through nestedness maximization

Manuel Sebastian Mariani, Dario Mazzilli, Aurelio Patelli, Dries Sels, and Flaviano Morone. Ranking species in complex ecosystems through nestedness maximization. Communications Physics, 7(1):102, 2024

work page 2024
[7]

The building blocks of economic complexity

César A Hidalgo and Ricardo Hausmann. The building blocks of economic complexity. Proceedings of the national academy of sciences, 106(26):10570–10575, 2009

work page 2009

Show all 40 references

[8]

A new metrics for countries’ fitness and products’ complexity

Andrea Tacchella, Matthieu Cristelli, Guido Caldarelli, Andrea Gabrielli, and Luciano Pietronero. A new metrics for countries’ fitness and products’ complexity. Scientific reports, 2(1):1–7, 2012

2012
[9]

The nature and dynamics of organizational capabilities

Giovanni Dosi, Richard R Nelson, and Sidney G Winter. The nature and dynamics of organizational capabilities. Oxford university press, 2000

2000
[10]

From organizational capabilities to corporate performances: at the roots of productivity slowdown

Stefano Costa, Stefano De Santis, Giovanni Dosi, Roberto Monducci, Angelica Sbardella, and Maria Enrica Virgillito. From organizational capabilities to corporate performances: at the roots of productivity slowdown. Industrial and Corporate Change, 32(6):1217–1244, 2023

2023
[11]

How the taxonomy of products drives the economic development of countries

Andrea Zaccaria, Matthieu Cristelli, Andrea Tacchella, and Luciano Pietronero. How the taxonomy of products drives the economic development of countries. PloS one, 9(12):e113770, 2014

2014
[12]

Innovation and economic development

Jan Fagerberg, Martin Srholec, and Bart Verspagen. Innovation and economic development. In Handbook of the Economics of Innovation, volume 2, pages 833–872. Elsevier, 2010

2010
[13]

Using structural diversity to measure the complexity of technologies

Tom Broekel. Using structural diversity to measure the complexity of technologies. PloS one, 14(5):e0216856, 2019

2019
[14]

Bayesian inference of minimally complex models with interactions of arbitrary order

Clélia de Mulatier and Matteo Marsili. Bayesian inference of minimally complex models with interactions of arbitrary order. Physical Review E, 111(5):054307, 2025

2025
[15]

Matrix-weighted networks for modeling multidimensional dynamics: Theoretical foundations and applications to network coherence

Yu Tian, Sadamori Kojaku, Hiroki Sayama, and Renaud Lambiotte. Matrix-weighted networks for modeling multidimensional dynamics: Theoretical foundations and applications to network coherence. Physical Review Letters, 134(23):237401, 2025

2025
[16]

What you export matters

Ricardo Hausmann, Jason Hwang, and Dani Rodrik. What you export matters. Journal of economic growth, 12:1–25, 2007

2007
[17]

Unfolding the innovation system for the development of countries: coevolution of science, technology and production

Emanuele Pugliese, Giulio Cimini, Aurelio Patelli, Andrea Zaccaria, Luciano Pietronero, and Andrea Gabrielli. Unfolding the innovation system for the development of countries: coevolution of science, technology and production. Scientific reports, 9(1):1–12, 2019

2019
[18]

The virtuous interplay of infrastructure development and the complexity of nations

Matthieu Cristelli, Andrea Tacchella, and Masud Cader. The virtuous interplay of infrastructure development and the complexity of nations. Entropy, 20(10):761, 2018

2018
[19]

Does institutional quality foster economic complexity? the fundamental drivers of productive capabilities

Trung V Vu. Does institutional quality foster economic complexity? the fundamental drivers of productive capabilities. Empirical Economics, 63(3):1571–1604, 2022

2022
[20]

Economic complexity, human capital and economic growth: empirical research based on cross-country panel data

Shujin Zhu and Renyu Li. Economic complexity, human capital and economic growth: empirical research based on cross-country panel data. Applied Economics, 49(38):3815–3828, 2017

2017
[21]

Economic complexity, human capital, and fdi attraction: A cross country analysis

Pegah Sadeghi, Hamid Shahrestani, Kambiz Hojabr Kiani, and Taghi Torabi. Economic complexity, human capital, and fdi attraction: A cross country analysis. International Economics, 164:168–182, 2020

2020
[22]

Economic complexity and employment in emerging countries: a comparative analysis

Tolulope Osinubi, Munacinga Simatele, and Olajide O Oyadeyi. Economic complexity and employment in emerging countries: a comparative analysis. Policy Studies, pages 1–24, 2025

2025
[23]

The role of complex analysis in modelling economic growth

Angelica Sbardella, Emanuele Pugliese, Andrea Zaccaria, and Pasquale Scaramozzino. The role of complex analysis in modelling economic growth. Entropy, 20(11):883, 2018

2018
[24]

Skill dependencies uncover nested human capital

Moh Hosseinioun, Frank Neffke, Letian Zhang, and Hyejin Youn. Skill dependencies uncover nested human capital. Nature Human Behaviour, pages 1–15, 2025. 13

2025
[25]

From products to capabilities: Constructing a genotypic product space

Ulrich Schetter, Dario Diodato, Eric Porter, Frank Neffke, and Ricardo Hausmann. From products to capabilities: Constructing a genotypic product space. 2024

2024
[26]

Skill relatedness and firm diversification

Frank Neffke and Martin Henning. Skill relatedness and firm diversification. Strategic Management Journal, 34(3):297–316, 2013

2013
[27]

C. A. Hidalgo, B. Klinger, A.-L. Barabási, and R. Hausmann. The product space conditions the development of nations. Science, 317(5837):482–487, 2007

2007
[28]

Machine learning to assess relatedness: the advantage of using firm-level data

Giambattista Albora and Andrea Zaccaria. Machine learning to assess relatedness: the advantage of using firm-level data. Complexity, 2022, 2022

2022
[29]

The knowledge and skill content of production complexity

Alessia Lo Turco and Daniela Maggioni. The knowledge and skill content of production complexity. Research Policy, 51(8):104059, 2022

2022
[30]

Workplace skills as regional capabilities: Relatedness, complexity and industrial diversification of regions

Duygu Buyukyazici, Leonardo Mazzoni, Massimo Riccaboni, and Francesco Serti. Workplace skills as regional capabilities: Relatedness, complexity and industrial diversification of regions. Regional Studies, 58(3):469–489, 2024

2024
[32]

The location quotient approach to estimating regional economic impacts

Andrew M Isserman. The location quotient approach to estimating regional economic impacts. Journal of the American Institute of Planners, 43(1):33–41, 1977

1977
[33]

Tariff protection in industrial countries: an evaluation

Bela Balassa. Tariff protection in industrial countries: an evaluation. Journal of Political Economy, 73(6):573–594, 1965

1965
[34]

Economic development and wage inequality: A complex system analysis

Angelica Sbardella, Emanuele Pugliese, and Luciano Pietronero. Economic development and wage inequality: A complex system analysis. PloS one, 12(9):e0182774, 2017

2017
[35]

Measuring the intangibles: A metrics for the economic complexity of countries and products

Matthieu Cristelli, Andrea Gabrielli, Andrea Tacchella, Guido Caldarelli, and Luciano Pietronero. Measuring the intangibles: A metrics for the economic complexity of countries and products. PloS one, 8(8):e70726, 2013

2013
[36]

On the convergence of the fitness-complexity algorithm

Emanuele Pugliese, Andrea Zaccaria, and Luciano Pietronero. On the convergence of the fitness-complexity algorithm. The European Physical Journal Special Topics, 225:1893–1911, 2016

1911
[37]

Dynamics in the fitness-income plane: Brazilian states vs world countries

Felipe G Operti, Emanuele Pugliese, José S Andrade Jr, Luciano Pietronero, and Andrea Gabrielli. Dynamics in the fitness-income plane: Brazilian states vs world countries. PloS one, 13(6):e0197616, 2018

2018
[38]

A concordance between ten-digit us harmonized system codes and sic/naics product classes and industries

Justin R Pierce and Peter K Schott. A concordance between ten-digit us harmonized system codes and sic/naics product classes and industries. Journal of Economic and Social Measurement, 37(1-2):61–96, 2012

2012
[39]

buttarla in caciara

Luciano Pietronero, Matthieu Cristelli, Andrea Gabrielli, Dario Mazzilli, Emanuele Pugliese, Andrea Tacchella, and Andrea Zaccaria. Economic complexity:" buttarla in caciara" vs a constructive approach. arXiv preprint arXiv:1709.05272, 2017

2017 arXiv
[40]

Integrated database for economic complexity

Aurelio Patelli, Luciano Pietronero, and Andrea Zaccaria. Integrated database for economic complexity. Scientific Data, 9(1):628, 2022. 14

2022

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

Reprint of the new paradigm of economic complexity

Pierre-Alexandre Balland, Tom Broekel, Dario Diodato, Elisa Giuliani, Ricardo Hausmann, Neave o’Clery, and David Rigby. Reprint of the new paradigm of economic complexity. Research Policy, 51(8):104568, 2022

work page 2022

[2] [2]

The atlas of economic complexity: Mapping paths to prosperity

Ricardo Hausmann, César A Hidalgo, Sebastián Bustos, Michele Coscia, and Alexander Simoes. The atlas of economic complexity: Mapping paths to prosperity. Mit Press, 2014

work page 2014

[3] [4]

Handbook of economic complexity for policy

Dario Diodato. Handbook of economic complexity for policy. Technical report, Joint Research Centre, 2024

work page 2024

[4] [3]

Economic complexity theory and applications

César A Hidalgo. Economic complexity theory and applications. Nature Reviews Physics, 3(2):92–113, 2021

work page 2021

[5] [31]

Mapping job fitness and skill coherence into wages: an economic complexity analysis

Sabrina Aufiero, Giordano De Marzo, Angelica Sbardella, and Andrea Zaccaria. Mapping job fitness and skill coherence into wages: an economic complexity analysis. Scientific Reports, 14(1):11752, 2024

work page 2024

[6] [5]

Equivalence between the fitness-complexity and the sinkhorn-knopp algorithms

Dario Mazzilli, Manuel Sebastian Mariani, Flaviano Morone, and Aurelio Patelli. Equivalence between the fitness-complexity and the sinkhorn-knopp algorithms. Journal of Physics: Complexity, 5(1):015010, 2024

work page 2024

[7] [6]

Ranking species in complex ecosystems through nestedness maximization

Manuel Sebastian Mariani, Dario Mazzilli, Aurelio Patelli, Dries Sels, and Flaviano Morone. Ranking species in complex ecosystems through nestedness maximization. Communications Physics, 7(1):102, 2024

work page 2024

[8] [7]

The building blocks of economic complexity

César A Hidalgo and Ricardo Hausmann. The building blocks of economic complexity. Proceedings of the national academy of sciences, 106(26):10570–10575, 2009

work page 2009