My Profile

• Emergence of Slavery 

CSL MPI

Works other than the working paper derived from this project:

1. "Slavery as a process of maintaining extreme inequality", Open Peer Commentary accepted in Behavioral and Brain Sciences, publication in process (2026)

2. Online interactive map for anthropological records: Major empirical background of this project

3. Substack post with ~ 400 reads (in English + in Chinese): Starting from Jaadugar / 从《穹庐下的魔女》谈起

4. Video on BiliBili (the YouTube in China) with ~ 15,000 plays (in Chinese): 为什么希塔拉想为主人复仇？

Slavery has taken strikingly different forms across human history, yet quantitative research still treats it as the mere negation of freedom. One of the sharpest contrasts concerns the fate of the enslaved: some societies integrated slaves as full members, whereas others held them in permanent bondage. Ecological accounts, which explain slavery as a response to conditions favoring surplus production, predict when slavery appears but not whether the enslaved are integrated. We argue that inter-societal conflict closes this gap. We develop a cultural evolutionary model in which societies divide their populations between free citizens and warriors, raid one another for captives, produce a surplus, and then fight to loot it. Three institutions compete: raiding without enslavement, enslaving captives for maximal extraction, and enslaving captives but later integrating them as free members who help defend the surplus. Integration proves adaptive precisely where conflict is intense, because additional defenders are then worth more than the extra surplus that harsher treatment would have yielded; as the productivity of slave labor rises, non-integrating exploitation prevails instead. Ethnographic data on Indigenous societies of the Pacific Coast of North America support these predictions. Measuring conflict exposure by the raiding frequency of a society's neighbors, we find that greater exposure predicts a higher probability of integrating slaves, a pattern that neither ecology nor cultural affiliation alone can account for. Slavery is better understood as a conditional process than as a fixed status.

• Modern Slavery and Mistrust 

(Working Paper)

CSL MPI 

Works other than the working paper derived from this project:

1. Substack post: How is human trafficking data accessible to the public and researchers?

Human trafficking and exploitation cause substantial direct damages to millions of victims annually, yet their wider societal repercussions remain unknown. We examine whether such ‘modern slavery’ erodes social capital — proxied by interpersonal trust — in two severely affected countries that provide all required data at sub-national levels: Romania and India. We find robust links between victimization rates and reduced trust in both countries. Moreover, instrumental variables analyses leveraging Romania’s geography indicate that this association is causal and tracks human traffickers’ recruitment patterns. More broadly, we demonstrate that contemporary human trafficking’s harms parallel those documented for past forms of slavery.

• Tricked into trouble: Deception, threat, and coercion in exploitative labor relations

(Working Paper)

CSL MPI 

Exploitative labor conditions are a massive global challenge, generating substantial illicit gains for delinquent employers. However, their strategic logic remains poorly understood. Here, we study the three practically most relevant forms of exploitative employer behavior in a principal-agent setting: deception, threat, and coercion. We analyze principals’ incentives for using these means, their welfare consequences, and the effects of introducing licensing to mitigate prevalent deception. We find that exploiters’ use of deception harms not only agents but also legitimate employers who are forced to compensate agents for the risk of exploitation. Moreover, we observe that increasing the costs of exploitation does not necessarily improve social welfare, as it can incentivize more employers to use milder forms of exploitation. Overall, we improve the economic understanding of exploitative labor relations by separating threat and coercion, integrating deception, providing insights into resulting market distortions, and identifying crucial pitfalls for seemingly first-best policy interventions.

• Wisdom of the Crowd: Crowd Analysis Project ---- Contributed one set of algorithms 

(https://woccap.com/)

CSL MPI 

Till Vater and I contributed four algorithms in this project, aiming to aggregate the crowd's most predictive guesses on four fields: climate, economics, politics, and sports. The algorithm's aggregated crowd guess will be validated by real-world data. Our algorithm implemented machine learning techniques (random forest) and automated the key predictor selection process (SHAP). Our algorithm is ranked as the fourth-best-performing algorithm in the field of politics, and we won 100 EUR for accurately predicting the decile of the reviewed algorithm. 

• Master Thesis: Robust Machine Learning for Food Security Forecasting (1.0/1.0) 

(https://github.com/yudingshechu/Masterarbeit)

Uni Bonn

The food insecurity issue is serious in eastern Africa, facing the shocks such as the Covid pandemic and war in Ukraine, a robust machine learning is needed for food insecurity forecasting. This study combines the Uganda National Households Survey data and other open-source data to predict household food insecurity before and during Covid. It is shown that models based on decision trees behave robustly when facing the shock of Covid. These tree-based models provide flexibility for policymakers to trade off the cost and benefit of food insecurity aiding. We also find that demographic and asset features provide the most prediction power. Finally, tree-based methods are robust against limited features or un-updated training data when facing a shock, implying they are robust in practical scenarios. 

Later, this led to my first publication: "How robust are machine learning approaches for improving food security amid crises? - Evidence from COVID-19 in Uganda" (with Lukas Kornher and Clara Brandi), World Development, 196(2025), doi:10.1016/j.worlddev.2025.107171 

• Literature Review of machine learning implementation in food security 

ZEF

Systematically collect, review, and summarize relevant papers, and explain machine learning algorithms in detail. Later, this ended up in my first publication: "How robust are machine learning approaches for improving food security amid crises? - Evidence from COVID-19 in Uganda" (with Lukas Kornher and Clara Brandi), World Development, 196(2025), doi:10.1016/j.worlddev.2025.107171 

• Term Paper of Research Module in Econometrics and Statistics 

(https://github.com/yudingshechu/Research-Module-Yield-Curve-Estimation-RKHS)

Uni Bonn

Replicated the implementation of RKHS non-parametric estimation in yield curve estimation

Used simulated data to show the advantages of RKHS estimation over other traditional estimations

• Final Project of Effective Programming Practices for Economists 

(https://github.com/yudingshechu/EPP-Final-Project)

Uni Bonn

Clean and manipulate large volumes of China's national survey data

Replicated a DID paper with an automatically generated project template (pytask)

• Final Project of Computational Statistics

(https://github.com/yudingshechu/Computational_Statistics/tree/Final-Project)

Uni Bonn

Used simulated data to implement LASSO in instrumental regression

Compared the post-LASSO instrumental regression algorithm in different simulated cases