Diego Vallarino

If economic interactions are nonlinear and networks are heterogeneous, does equilibrium even matter, or are attractors the true objects of interest? #EconSky

#EconSky #SystemicRisk #FinancialStability #NetworkEconomics

#SystemicRisk #FinancialStability #NetworkEconomics

Results are stark: Spain would have suffered significant developmental decline under a Latin American configuration, while Uruguay would have achieved higher complexity and resilience within a European regime. Development emerges from structural position in institutional networks, not reforms alone.

To test this, it builds a generative counterfactual framework combining economic complexity, institutional path dependence, and a Wasserstein GAN trained on 1960–2020 data. It introduces the Expected Developmental Shift (EDS) to measure gains or losses from alternative institutional embeddings.

Spain integrated into dense European institutional architectures; Uruguay remained embedded in the Latin American governance regime, marked by weaker coordination and lower institutional coherence. These different institutional ecosystems shaped long-run development trajectories. (2/4)

Invited to Develop: Institutional Belonging and the Counterfactual Architecture of Development This paper examines how institutional belonging shapes long-term development by comparing Spain and Uruguay, two small democracies with similar historical endowments whose trajectories diverged sharpl...

Why did Spain and Uruguay—two small democracies with similar historical endowments—diverge so sharply after the 1960s? This paper argues the answer lies not only in domestic policy, but in institutional belonging. #Econsky 1/4
arxiv.org/abs/2511.21865

Update on the state of the job market courtesy of a nudge by @gottliebecon.bsky.social

It's bad!

paulgp.com/2025/11/24/j...

(5/5)
The takeaway: visibility is redistribution.
Credit data, when portable, interoperable, and fair, becomes an inclusion engine.
Policies should move beyond “open data” to data equity — aligning efficiency with justice.

#FinancialInclusion #DataEconomics #AI #Uruguay

(4/5)
Conceptually, we treat data as a non-rival public asset:
its reuse doesn’t deplete value — it multiplies it.
Like infrastructure, data can be a redistributive lever when governed ethically and shared equitably.

This reframes inclusion as an architectural problem, not a fiscal one.

(3/5)
The results:
- Average interest burden fell from 11.8% → 9.8% under Score+.
- Gini of financial burden dropped from 0.319 → 0.276.
- Poverty declined by nearly 1 percentage point.
These shifts occurred solely through improved data inclusion.

(2/5)
Using microdata from Uruguay’s 2021 Household Survey, we simulate three regimes:
• Negative-only data (status quo)
• Partial positive data (Score+)
• Full synthetic visibility (Open Finance)

Expanding visibility alone reduced poverty and interest burden — no transfers, no subsidies.

Data for Inclusion: The Redistributive Power of Data Economics This paper evaluates the redistributive and efficiency impacts of expanding access to positive credit information in a financially excluded economy. Using microdata from Uruguay's 2021 household surve...

(🧵 1/5)
What if data itself could reduce poverty?
Our new paper, “Data for Inclusion: The Redistributive Power of Data Economics”, shows how access to positive credit information can lower interest costs and inequality — even without income growth. #Econsky

🔗 arxiv.org/abs/2510.16009

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Featured on INOMICS: Scholarships and Tuition Fee Waivers for Master in Economics #Econsky

Fall in DC.

I appreciate any feedback, comments, or thoughts on this work.
If you find it relevant, feel free to share it so the discussion on fair and transparent AI in finance can reach a wider audience.
Thank you all for the support and engagement. 🙏 #Econsky

(4/4)
This work invites both academics and practitioners to rethink AI governance.
Moving beyond black-box models, it builds systems that not only predict—but also explain why.
I’d love to hear your views on how Causal AI can advance fairness and accountability in financial decision-making.

(3/4)
Results show that Causal-GNNs can reduce algorithmic bias without compromising predictive accuracy.
Validated on real datasets in fraud detection, credit scoring, and AML, the framework demonstrates how explainable AI can enhance trust and compliance in finance.

(2/4)
The model integrates a Structural Causal Model (SCM) with a Graph Neural Network (GNN) to separate causality from correlation.
It provides a transparent foundation for ethical AI, improving fairness, interpretability, and regulatory alignment (GDPR, ECOA, Fair Lending).

Causal-GNN for ethical AI in financial services: ensuring fairness, compliance, and transparency in automated decision-making - Artificial Intelligence and Law The increasing reliance on artificial intelligence (AI) in financial services has raised significant concerns regarding fairness, regulatory compliance, and ethical transparency. Machine learning (ML)...

(1/4) 🧵
New paper published in Artificial Intelligence and Law (Springer).
This study bridges causal inference and graph deep learning to mitigate bias in financial AI.
doi.org/10.1007/s105...

2/4 The proposed model integrates a Structural Causal Model (SCM) with a GNN architecture to disentangle causality from correlation — improving interpretability, fairness, and regulatory compliance (GDPR, ECOA, Fair Lending Laws).

4/4
Beyond prediction, this framework offers a policy tool: it helps governments identify unrelated but viable diversification opportunities.
It bridges AI and economic complexity — shifting industrial policy from “what we export” to “what we could sustainably build next.”
#EconAI #TradeComplexity

3/4
Results: the GNN achieves R² = 0.71, far outperforming traditional methods.
Simulated shocks reveal new diversification paths for Uruguay — in biotech, renewables, precision agriculture, and hydrogen technologies — sectors not central today but structurally feasible tomorrow.

2/4
We combine real BACI-CEPII trade data with synthetic shock scenarios (tariffs, demand, exchange rates) generated via GANs to build hybrid trade networks.
The GNN learns which products can increase a country’s Economic Complexity Index (ECI) — even under global disruption.

Augmenting trade complexity analysis with deep learning: an AI-based framework for small open economies This paper presents an AI-based framework to augment trade complexity analysis for strategic diversification in small open economies. Using BACI-CEPII trade data and synthetic scenarios generated v...

🧵 1/4
Global trade is fragmenting. My new paper in Applied Economics Letters introduces an AI-based framework that uses Graph Neural Networks (GNNs) and Generative Adversarial Networks (GANs) to predict where diversification and resilience truly emerge.
🔗 doi.org/10.1080/1350... #EconSky

Found on RePEc/IDEAS: ideas.repec.org/a/bba/j00009... 💡

Graph of the rise in productivity after the shift to remote work at a call centre in Turkiye showing more calls processed per hour. The Covid-19 pandemic sparked a large and lasting shift to remote work. This column draws on detailed administrative data from a major call centre in Türkiye to show that a permanent shift to fully remote work can expand recruitment and raise productivity without compromising service quality. The transition increased the share of women, including married women, as well as employees from rural areas and smaller towns. Productivity gains were driven by quieter home environments and more efficient communication. But the benefits of in-person onboarding persist, even in a permanently remote work model.

Data from a call centre in Türkiye show that a permanent shift to fully #remotework can expand recruitment and raise #productivity without compromising service quality.
C Giray Aksoy, N Bloom, S Davis, V Marino, C Özgüzel
cepr.org/voxeu/column...
#EconSky

🙃 #data #econsky #stats #databsky #science #publicHealth #WomeninSTEM

"Un profesor titular en la Universidad Complutense de Madrid gana unos 35.000 euros al año. En la Universidad de Michigan, un profesor promedio gana 207.000 dólares (unos 195.000 euros). Es decir, que, en cuatro años, un académico en Michigan cobra lo que uno en España recibiría en dos décadas."

¿Fuga de cerebros en EE UU? La oportunidad de oro que Europa está a punto de desaprovechar Muchos investigadores en el país norteamericano están más dispuestos que nunca a mudarse. Pero si queremos que consideren venirse, tenemos que ponernos serios

¿Fuga de cerebros en EE UU? La oportunidad de oro que Europa está a punto de desaprovechar
elpais.com/ciencia/2025...