This paper examines the role of heterogeneity in a real business cycle model, which traditionally has not fully captured the relative volatility of hours to output. Men and women have different cyclical volatilities in hours worked, which is robust to different filtering methods. This empirical regularity is used to motivate a standard RBC model augmented to allow for two different agents following Jaimovich et al. (2013). These two agents have identical utility functions, but face different elasticities of labor demand due to their different complementarities with capital. These estimated elasticities find that women are more complementary to capital. The calibrated model generates the cyclical volatility of work hours by gender and for the total hours worked that matches the U.S. data better than the traditional representative agent model. I then explore other extensions to this model including investigating the stability of the estimated labor demand elasticities and allowing for various Frisch elasticities of labor supply. This paper demonstrates that allowing for even broad levels of heterogeneity in a simple framework can increase the model’s tractability with the data. Since gender is important to explain U.S. business cycle dynamics, we need to carefully consider heterogeneity when analyzing counter-cyclical economic policy, as it may not have symmetric effects across assorted groups.

本文研究了异质性在真实商业周期模型中的作用，而传统商业模型通常无法完全捕获小时数相对于产出的相对波动性。男性和女性在工作小时数上具有不同的周期性波动性，这对于不同的过滤方法是有力的。这个经验规律性被用来激励标准的RBC模型，该模型被增强以允许遵循Jaimovich等人的两种不同的代理人。（2013）。这两个主体具有相同的效用函数，但是由于它们与资本的互补性不同，因此面临着劳动力需求的不同弹性。这些估计的弹性发现，女性对资本的补充更大。校正后的模型会按性别生成工作时间的周期性波动，并且与美国代表数据相比，与美国数据匹配的工作总小时数要好得多。然后，我探索对该模型的其他扩展，包括调查估计的劳动力需求弹性的稳定性，并考虑劳动力供应的各种弗里施弹性。本文证明，在一个简单的框架中甚至允许广泛的异质性水平都可以提高该模型对数据的可处理性。由于性别对于解释美国商业周期动态很重要，因此在分析反周期经济政策时，我们需要仔细考虑异质性，因为它可能不会对各个群体产生对称影响。本文证明，在一个简单的框架中甚至允许广泛的异质性水平都可以提高该模型对数据的可处理性。由于性别对于解释美国商业周期动态很重要，因此在分析反周期经济政策时，我们需要仔细考虑异质性，因为它可能不会对各个群体产生对称影响。本文证明，在一个简单的框架中甚至允许广泛的异质性水平都可以提高该模型对数据的可处理性。由于性别对于解释美国商业周期动态很重要，因此在分析反周期经济政策时，我们需要仔细考虑异质性，因为它可能不会对各个群体产生对称影响。