The development of Generative AI enables large-scale automation of product design. However, this automated process usually does not incorporate consumer preference information from a company’s internal dataset. Meanwhile, external sources such as social media and user-generated content (UGC) websites often contain rich product design and consumer preference information, but such information is not utilized by companies in design generation. We propose a semi-supervised deep generative framework that integrates consumer preferences and external data into product design, allowing companies to generate consumer-preferred designs in a cost-effective and scalable way. We train a predictor model to learn consumer preferences and use predicted popularity levels as additional input labels to guide the training of a Continuous Conditional Generative Adversarial Network (CcGAN). The CcGAN can be instructed to generate new designs of a certain popularity level, enabling companies to efficiently create consumer-preferred designs and save resources by avoiding developing and testing unpopular designs. The framework also incorporates existing product designs and consumer preference information from external sources, which is particularly helpful for small or start-up companies who have limited internal data and face the “cold-start” problem. We apply the proposed framework to a real business setting by helping a large self-aided photography chain in China design new photo templates. We show that our proposed model performs well in generating appealing template designs for the company.

China relaxed its strict One-Child Policy to universally allow couples to have two children in 2016. Although the new policy suggests an improvement in welfare for couples, as they now have more freedom to achieve their desired fertility levels, it has the drawback of possibly increasing gender inequality both in the labor market and within the household. This paper starts with a difference-in-difference method to show that the new policy increased the gender wage gap between women and men and negatively affected the intrahousehold bargaining power of women. Motivated by this empirical pattern, I then build and estimate a dynamic collective household model to quantify the welfare impact of the new policy on both genders using a novel machine learning method and indirect inference. The results suggest that the welfare cost of the Two-Child Policy for women is equivalent to 6.00% of lifetime consumption, while the welfare benefit of the policy for men is equivalent to 7.23% of lifetime consumption. Policy experiments suggest that implementing anti-discrimination laws for women in the labor market significantly improves women’s welfare while providing public childcare subsidies is most effective in stimulating fertility in the post-policy era.

This paper proposes a novel method to identify the One-Child Policy’s impact on couples’ childbearing using self-reported survey measures. We use couples’ pre-policy ideal number of children together with the answers in the post-policy period to back out the counterfactual number of children without the One-Child Policy. Findings indicate a significant average re- duction of 0.2714 children per couple in 2014 due to the policy. Variations in policy effects are explored across educational, urban/rural, and occupational groups, with highly educated urban women in government jobs experiencing the most pronounced impact. Sub-region analysis suggests significant policy stringency differences among provinces.

Small businesses/entrepreneurs increasingly rely on crowdfunding platforms to raise funds for their entrepreneurial projects. While such a financing strategy enables entrepreneurs and funders to interact with fewer geographic constraints, we find empirical evidence consistent with strong local biases among funders in online crowdfunding markets. What drives these biases? Our results suggest that in addition to funders’ local preferences, information frictions play a more important role in driving the local biases, preventing high-quality entrepreneurial projects from being identified and funded by investors outside their regions. Exploring the role of platform-design features, we find that, with the presence of strong local biases, not reveal- ing locational information of projects could be welfare improving. Business strategies could be tailored for different project categories to help mitigate non-local funders’ informational disadvantages and foster local entrepreneurship.