Students from different socioeconomic backgrounds exhibit persistent gaps in test scores, gaps that can translate into unequal educational and labor-market outcomes later in life. In many assessments, performance reflects not only what students know, but also how effectively they can communicate that knowledge. This distinction is especially salient in writing assessments, where scores jointly reward the substance of students' ideas and the way those ideas are expressed. As a result, observed score gaps may conflate differences in underlying content with differences in expressive skill. A central question, therefore, is how much of the socioeconomic-status (SES) gap in scores is driven by differences in what students say versus how they say it. We study this question using a large corpus of persuasive essays written by U.S. middle- and high-school students. We introduce a new measurement strategy that separates content from style by leveraging large language models to generate multiple stylistic variants of each essay. These rewrites preserve the underlying arguments while systematically altering surface expression, creating a "generated panel" that introduces controlled within-essay variation in style. This approach allows us to decompose SES gaps in writing scores into contributions from content and style. We find an SES gap of 0.67 points on a 1-6 scale. Approximately 69% of the gap is attributable to differences in essay content quality, Style differences account for 26% of the gap, and differences in evaluation standards across SES groups account for the remaining 5%. These patterns seems stable across demographic subgroups and writing tasks. More broadly, our approach shows how large language models can be used to generate controlled variation in observational data, enabling researchers to isolate and quantify the contributions of otherwise entangled factors.

翻译：不同社会经济背景的学生在考试成绩上存在持续差距，这种差距可能转化为未来教育和劳动力市场的不平等结果。在许多评估中，表现不仅反映学生掌握的知识，还反映他们有效传达知识的能力。这种区分在写作评估中尤为显著——评分同时考量学生观点的实质内容与表达方式。因此，观察到的分数差距可能混淆了深层内容差异与表达技能差异。核心问题在于：社会经济地位（SES）导致的分数差距中，多大程度源于"说什么"的差异，多大程度源于"怎么说"的差异。本研究通过分析美国初高中学生撰写的大量议论文来探讨此问题。我们提出一种新的测量策略：利用大语言模型为每篇论文生成多种文体变体，在保留核心论点的同时系统化改变表层表达，从而构建"生成面板"，实现对文体特征的受控组内变异。该方法使我们能够将写作分数的SES差距分解为内容贡献与形式贡献。研究发现：在1-6分量表上存在0.67分的SES差距，其中约69%可归因于论文内容质量差异，26%源于文体差异，剩余5%来自不同SES群体间的评分标准差异。该模式在不同人口亚群和写作任务中保持稳定。从更广泛意义而言，本研究展示了大语言模型如何通过生成观测数据的受控变异，帮助研究者分离并量化原本相互交织的影响因素。