This study investigates how integrating persona variables—demographic, social, and behavioral factors—into large language models (LLMs) affects their ability to simulate diverse perspectives in subjective NLP tasks. The research finds that persona variables account for less than 10% of the variance in annotations in existing subjective NLP datasets. However, incorporating persona variables via prompting provides modest but statistically significant improvements in LLM predictions. Persona prompting is most effective in samples where many annotators disagree, but their disagreements are relatively minor. A linear relationship is observed between the correlation of persona variables and human annotations, with stronger correlations leading to more accurate LLM predictions. In a zero-shot setting, a 70b model with persona prompting captures 81% of the annotation variance achievable by linear regression trained on ground truth annotations. However, for most subjective NLP datasets, where persona variables have limited explanatory power, the benefits of persona prompting are limited. The study also explores the effectiveness of LLMs in simulating personas when the importance of persona variables varies. It finds a linear relationship between the target and predicted R² values, with the best-performing model capturing 81% of the target R². The study concludes that persona prompting is not reliable for simulating different perspectives in existing NLP tasks due to the low explanatory power of persona variables in most datasets. The research recommends caution in using LLMs for simulation purposes, especially in NLP tasks where persona variables' influence is likely weak. It also suggests more strategic dataset design to improve the predictability of LLM simulations. The study acknowledges limitations, including the subjectivity of human behavior and the potential biases in LLM simulations. Ethical considerations are also discussed, including the use of anonymized datasets and the potential risks of LLM simulations. The study highlights the importance of evaluating LLM simulation capabilities independently for each language, without translation.This study investigates how integrating persona variables—demographic, social, and behavioral factors—into large language models (LLMs) affects their ability to simulate diverse perspectives in subjective NLP tasks. The research finds that persona variables account for less than 10% of the variance in annotations in existing subjective NLP datasets. However, incorporating persona variables via prompting provides modest but statistically significant improvements in LLM predictions. Persona prompting is most effective in samples where many annotators disagree, but their disagreements are relatively minor. A linear relationship is observed between the correlation of persona variables and human annotations, with stronger correlations leading to more accurate LLM predictions. In a zero-shot setting, a 70b model with persona prompting captures 81% of the annotation variance achievable by linear regression trained on ground truth annotations. However, for most subjective NLP datasets, where persona variables have limited explanatory power, the benefits of persona prompting are limited. The study also explores the effectiveness of LLMs in simulating personas when the importance of persona variables varies. It finds a linear relationship between the target and predicted R² values, with the best-performing model capturing 81% of the target R². The study concludes that persona prompting is not reliable for simulating different perspectives in existing NLP tasks due to the low explanatory power of persona variables in most datasets. The research recommends caution in using LLMs for simulation purposes, especially in NLP tasks where persona variables' influence is likely weak. It also suggests more strategic dataset design to improve the predictability of LLM simulations. The study acknowledges limitations, including the subjectivity of human behavior and the potential biases in LLM simulations. Ethical considerations are also discussed, including the use of anonymized datasets and the potential risks of LLM simulations. The study highlights the importance of evaluating LLM simulation capabilities independently for each language, without translation.