研究成果｜陈志钢教授团队在经济学SSCI期刊CWE及CER发文三篇

研究内容

本文探讨了将中国居民膳食转变为健康参考膳食（中国居民平衡膳食宝塔、EAT-柳叶刀膳食、地中海膳食和少肉膳食）的健康和环境影响。首先，本文估算了包含居民在家和在外消费的食物消费量，以便更好地反映中国居民膳食结构变迁。其次，采用估算的最新食物消费数据分析了当前中国居民膳食与健康参考膳食的差距，并综述了缩小该差距对中国居民健康的影响。最后，使用中国农业产业模型模拟了不同健康参考膳食情景下的温室气体排放变化，该模型是多市场的农产品局部均衡模型，共涵盖了30种农产品和农产品加工品。

研究结果

结果表明，当前中国膳食与健康参考膳食存在一定差距，主要表现在谷物以精制米面为主、缺乏粗粮，肉类消费过量，全谷物、水果、豆类和奶类等消费不足。采取中国居民平衡膳食宝塔、EAT−柳叶刀膳食、地中海膳食和少肉膳食的建议，中国与膳食有关慢性疾病患病率和死亡率将会显著下降。同时，也会使农业活动温室气体排放显著下降。模拟结果显示，在四种健康参考膳食的模拟方案下，温室气体排放将分别比2030年预计温室气体排放量减少1.5−2.0亿吨（下降18%−25%），比2020年预计温室气体排放量减少0.6−1.2亿吨（下降9%−16%）。其中，与2030年预计温室气体排放量相比，少肉膳食对温室气体排放减少的贡献最大（25%），其次是地中海膳食（22%）和EAT−柳叶刀膳食（21%），最后是中国居民平衡膳食宝塔（18%）。

研究贡献

本文对现有文献的贡献主要有以下几点。第一，首次利用1997-2019年包含居民在家和在外消费的数据描述了中国居民膳食结构变迁；第二，采用最新估算的消费数据分析了当前中国膳食与健康参考膳食（如中国居民平衡膳食宝塔和EAT−柳叶刀膳食）之间的差距；第三，使用农产品局部均衡模型定量估算了中国采取四种健康参考膳食后温室气体排放的减少量。在模拟过程中，我们考虑了未来社会经济发展的变化，包括人口变化、经济发展、居民收入变化和技术变化等因素。

英文摘要

Diets are key determinants of nutrition and health and play a significant role in the environment. In this article, we aim to (i) describe dietary transitions and health in China and the consequent environmental challenges; (ii) identify differences between current Chinese diets and healthy reference diets; (iii) conduct a systematic review assessing the health impacts of four reference diets on the Chinese population, and (iv) simulate changes in greenhouse gas emissions under different diet scenarios. The results show differences between the Chinese diets and reference diets, with the current Chinese diet including mainly grains (especially refined rice), excessive meat consumption, and insufficient consumption of fruit and milk. If all Chinese consumers adopt one of the healthy reference diets all the time, the incidence of diet-related chronic disease and mortality would be significantly reduced. Such dietary shifts would also reduce greenhouse gas emissions by 146–202 million tons (18–25 percent) compared with the projected emissions level in 2030.

研究内容

本文通过自上而下的宏观-微观模拟分析方法，将宏观经济部门的增长预测与基于2018 年中国家庭追踪调查的个体数据相链接，依据Zhang等（2020）基于社会核算矩阵乘数分析法估计的疫情对宏观经济冲击作为外生变量输入微观模拟模型，分析疫情对农民工个体和家庭收入的影响，以及对居民收入分配和贫困的影响。

研究发现

在疫情期间，农民工及其家庭受到的冲击很大，大约70%的农民工可能会失去部分工资收入，尤其在中小微企业工作的农民工受到的影响最大。农民工收入的减少将直接导致农民工汇款下降，使得约50%的留守家庭收入减少，平均减少幅度超过45%。近13%的非贫困家庭受疫情影响后可能陷入贫困。疫情也导致贫困差距指数上升，意味着许多贫困家庭将更加贫困。

研究贡献

文章的主要贡献有以下三个方面：第一，从研究议题上看，现有文献对农民工这一脆弱群体的影响研究较少，特别是少有研究关注疫情对以农民工汇款为主要收入来源的留守家庭的影响。文章弥补了现有的研究的不足。第二，从分析数据来看，目前聚焦疫情影响的研究主要使用宏观数据，而由于数据采集的滞后性，缺乏有代表性的个体微观数据。文章采用了微观模拟模型，评估了疫情对农民工微观个体及其家庭的冲击，进而分析了对不同人群影响的差异。第三，从政策含义上看，文章揭示了农民工及其家庭在冲击下的返贫风险，因此，需要采取更多针对性更强的政策措施来保障农民工及其家庭的收入。

英文摘要

Chinese migrant workers are very exposed to the shocks caused by the COVID-19 pandemic. Falling remittances adversely affect their families who rely on remittance incomes. The impacts of COVID-19 on migrants and remittance-receiving households are assessed using a nationally representative household dataset and a microsimulation model. We found about 70 percent of migrant workers lost part of their wage income during the pandemic lockdown period and rural migrants working in small and medium enterprises were affected the most. This led to about 50 percent of remittance-receiving households being affected adversely by falling remittances, and the average decline in such income was more than 45 percent. Nearly 13 percent of pre-pandemic nonpoor remittance-receiving households could fall into poverty, raising the poverty rate among remittance-receiving households by 4 percentage points. Many households that were poor prior to the pandemic became more impoverished. The results indicate that social protection programs targeting vulnerable migrants and their families at home are important.

研究内容：

在发展中国家广泛实施的分权式贫困瞄准项目中，瞄准效果不佳受到了广泛关注。不同于以往的文献重点关注精英俘获导致的瞄准失误现象，本文在中国精准扶贫政策实施的背景下，探讨了村民社会资本对贫困瞄准的影响。实证研究使用了来自2017年西部地区国家级贫困县3个行政村17个自然村的独特普查数据，并采用主成分分析法将互惠、帮工时间、礼金支出、政治联系指标合并为一个村民社会资本水平指数。基于工具变量法的估计结果证明了社会资本水平更高的农户更有可能成为精准扶贫政策的受益者。相较于贫困户，非贫困户通常有着更高的社会资本水平，从而可以通过动员其社会资本水平来获取本应分配给贫困人口的项目名额，进而导致瞄准偏误。村民社会资本对贫困瞄准的这种影响在控制了政治精英俘获效应后仍然存在。以上研究发现表明，村民社会资本是中国农村分权式贫困瞄准出现瞄准偏误的根本原因，同时也表明社会资本并不是穷人的资本。

研究结果：

首次实证考察了村民社会资本对贫困瞄准偏误的影响。研究结果显示：第一，村民社会资本是造成中国农村贫困定位误差的根本原因。第二，在“回头看”政策实施之后，政治精英捕获现象基本被消除。研究结果表明，在分权式贫困瞄准政策中，村民社会资本并不是穷人的资本。

英文摘要：

Poor targeting performance is a common concern in the increasingly implemented decentralized targeted antipoverty programs in developing countries. Different from previous literature that focuses on targeting errors caused by elite capture, we explore the role of villager social capital as a whole in poverty targeting in the context of China's Targeted Poverty Alleviation (TPA) policy. The empirical analysis uses a unique census-type data from three administrative and seventeen natural villages in the poverty-stricken county in Western China in 2017. Villager social capital is measured by a proxy index by combing reciprocity, support time, gift expenses, and political connection of villagers. We verify that the villager with rich villager social capital is more likely to be a beneficiary of TPA by using instrumental variable estimation. The nonpoor can mobilize their higher level of social capital than the poor to capture the beneficiary quotas that should be allocated to the poor, resulting in mistargeting. Such effect persists after controlling political elite capture effects. The findings point out villager social capital is the root cause of poor targeting in decentralized targeting programs in rural China and also lend new support from China to the classic debate on social capital is not the capital of the poor.