# HouseholdBench: Evaluating Large Language Models as Predictors of Household Economic Behavior

arXiv: https://arxiv.org/abs/2610.07563 · HTML: https://mlsanity.com/paper/2610.07563/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 93 references that are in the map.
- First posted on arXiv 6 Oct 2026.

## Builds on (most cited first, 17 of 17)

- [DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence](https://mlsanity.com/paper/2606.19348/) (2026)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals](https://mlsanity.com/paper/2411.10109/) (2024)
- [AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society](https://mlsanity.com/paper/2502.08691/) (2025)
- [LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra](https://mlsanity.com/paper/2507.15815/) (2025)
- [Be.FM: Open Foundation Models for Human Behavior](https://mlsanity.com/paper/2505.23058/) (2025)
- [Using LLMs to Model the Beliefs and Preferences of Targeted Populations](https://mlsanity.com/paper/2403.20252/) (2024)
- [LABOR-LLM: Language-Based Occupational Representations with Large Language Models](https://mlsanity.com/paper/2406.17972/) (2024)
- [When Can Digital Personas Reliably Approximate Human Survey Findings?](https://mlsanity.com/paper/2605.10659/) (2026)
- [How Well Do LLMs Predict Human Behavior? A Measure of their Pretrained Knowledge](https://mlsanity.com/paper/2601.12343/) (2026)
- [Simulating Macroeconomic Expectations in Survey Experiments with LLM-based Economic Agents](https://mlsanity.com/paper/2505.17648/) (2025)
- [From Demographics to Survey Anchors: Evaluating LLM Agents for Modeling Retirement Attitudes](https://mlsanity.com/paper/2605.16303/) (2026)
- [Synthetic Personalities: How Well Can LLMs Mimic Individual Respondents Using Socio-Economic Microdata?](https://mlsanity.com/paper/2606.04592/) (2026)
- [Inflation Attitudes of Large Language Models](https://mlsanity.com/paper/2512.14306/) (2025)
- [BehaviorBench: Benchmarking Foundation Models for Behavioral Science Tasks](https://mlsanity.com/paper/2606.24162/) (2026)
- [Mitigating Identity Essentialism in LLM Agents with Longitudinal Life Trajectories](https://mlsanity.com/paper/2608.19621/) (2026)
- [Can Large Language Models Anticipate Behavioral Responses to Social Policies? A Case of Pension Enrollment Prediction among China's Flexible Workers](https://mlsanity.com/paper/2609.05189/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have the potential to meet a key goal in economics: a quantitative model of household decision making, across a variety of settings. Yet existing evaluations cover few surveys and outcomes, and do not study how households adjust to changing economic conditions. We introduce a new evaluation, HouseholdBench, which unites 6 U.S. household surveys and 32 prediction tasks spanning numeric, categorical and probabilistic outcomes, related to consumption, income, labor, expectations, and housing. Using past behavior, demographics and macroeconomic conditions, the tasks test whether LLMs predict behavior, including how households adjust to changes in various policies. We evaluate 13 proprietary and open-weight LLMs against a no-change baseline and a gradient-boosted tree model. Most LLMs outperform the no-change baseline, including for policy response tasks -- with the best model lowering error for numeric outcomes by 12.2%. Across most tasks, gradient-boosted trees rank first; leading proprietary LLMs approach their performance, but open-weight models lag. LLMs exhibit systematic over- and underprediction across different tasks. We identify methods that enable a 4 billion parameter open-weight model to match proprietary models' performance: fine-tuning and aggregating 16 predictions per observation. Improvements generalize to policy-response tasks, which are excluded from fine-tuning. We release our datasets, code, and leaderboard on our website: https://jn-huang.github.io/householdbench