Abstract: This paper uses China’s 2016 Pollutant Discharge Permits System (PDPS) to evaluate its impact on innovation in the food processing industry. We begin by exploring the entry and exit decisions of food processing firms. The findings suggest that stricter environmental enforcement following the PDPS leads to decreased local firm entry but increased neighboring firm entry, indicating that new firms tend to locate in neighboring regions with less stringent regulations. Building upon this pattern, we argue that the PDPS’s positive impact on firm innovation is primarily driven by increased industry agglomeration—a dynamic directly stimulated by heterogeneous regulatory pressures.

Abstract: Precise and reliable measurement of food insecurity rates not only informs and shapes public food policy but also serves as an indicator of the effectiveness of broader public policies aimed at enhancing household welfare. This paper tests the consistency of the measurement of household-level food insecurity using the USDA’s “Food Security Module” questionnaire, an instrument in use for almost 30 years. Applying Tree-based item response models, known as IRTree models, this study verifies areas of the FSM questionnaire and data coding process that are effective and reveals other parts that could benefit from improvements. The results confirm the validity of current method, which treats invalid responses, such as “Don’t know” or “Refused” or “No response”, as random missing data. Furthermore, the results support the practice of treating all affirmative responses, such as “Often true” and “Sometimes true” as equivalent when determining the level of food insecurity among households. However, the “screen-out” process before the second stage of the questionnaire leads to information loss. Moreover, the analysis reveals racial discrepancies in the measurement outcomes, with food insecurity rates being underestimated for “Black or African American” and “American Indian or Alaska Native” groups relative to others. 

Abstract: This study investigates the trade implications of agricultural Voluntary Sustainability Standards (VSSs), with a novel distinction between Quality-Enhancement (QE) and Labor Welfare (LW) certifications. While VSSs are increasingly adopted to address sustainability concerns in agriculture, their trade effects remain ambiguous. We develop a theoretical gravity model grounded in Melitz (2002) to explore how product quality improvements and labor welfare gains influence trade, and test these mechanisms empirically. Decomposing trade into intensive and extensive margins, we find that QE-VSSs significantly boost both trade volume and new trade relationships in the short term, with effects tapering after four years. LW-VSSs, in contrast, show delayed benefits: while their early impact on trade is limited, they enhance labor efficiency and trade volume in the longer run. Heterogeneity analyses reveal that developing exporters trading with developed importers benefit most from both types of standards, especially under non-mandatory frameworks. We also find that VSS adoption is associated with a reduction in income disparity, suggesting that sustainability standards may support more inclusive global trade. Our findings highlight the differentiated trade mechanisms of QE- and LW-VSSs and underscore the need for tailored policy support to balance sustainability objectives with trade competitiveness.

Abstract: Yield data is fundamental for assessing nutrient application efficiency. While relative yield is widely used for its comparability, normalization can cause significant information loss. This study reframes yield metric selection as a model evaluation problem to determine the most accurate representation of crop response. We evaluated seven yield metrics with three agronomic models, comparing estimates of a critical soil phosphorus threshold for robustness and plausibility against a regional guideline of 30 mg kg⁻¹. Models using raw yield consistently provided the best fit, with threshold estimates (28.9–34.6 mg kg⁻¹) closely aligning with the benchmark. Conversely, normalized metrics like relative yield performed poorly, generating highly variable and biased estimates. These findings establish that raw yield is the most statistically robust and economically relevant metric for quantifying crop response, holding important implications for nutrient management.