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Journal of Economic Structures最新文献

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Economic impacts of biodiesel policy in Indonesia: a computable general equilibrium approach. 印尼生物柴油政策的经济影响:一个可计算的一般均衡方法。
Q1 Economics, Econometrics and Finance Pub Date : 2022-01-01 Epub Date: 2022-11-09 DOI: 10.1186/s40008-022-00281-9
Sahara, Ahmad Dermawan, Syarifah Amaliah, Tony Irawan, Salsa Dilla

The Government of Indonesia has been promoting the advancement of the biodiesel sector to fulfill its commitment to support clean energy, energy security, and rural development. This paper examines the economic impact of the biodiesel sector using a computable general equilibrium model. Besides analyzing the impacts on the national macroeconomic conditions, other sectors, and household incomes, our model has also included a regional block to capture the impact of the biodiesel mandate on regional growth. Two simulations were performed: (1) fulfillment of the 30% biodiesel blending target (B30 mandate), and (2) Simulation 1 combined with the European Union's biodiesel trade ban resulting in an export reduction of 5.18%. The results show that the two simulations provide positive impacts on macroeconomic variables, including real gross domestic product and real wages. However, the B30 mandate and the combined effect of the EU trade ban still yield an inflationary effect in the short term. They also potentially reduce the production of several agricultural products-such as sugarcane, fruits, vegetables, and soybeans-leading to an increase in food prices. The policy implications highlight that the current B30 mandate and EU ban cannot automatically improve the fuel trade balance.

印度尼西亚政府一直在推动生物柴油行业的发展,以履行其支持清洁能源、能源安全和农村发展的承诺。本文使用可计算的一般平衡模型考察了生物柴油部门的经济影响。除了分析对国家宏观经济条件、其他部门和家庭收入的影响外,我们的模型还包括一个区域区块,以捕捉生物柴油授权对区域增长的影响。进行了两个模拟:(1)实现30%生物柴油混合目标(B30要求),(2)模拟1结合欧盟生物柴油贸易禁令导致出口减少5.18%。结果表明,两种模拟对实际国内生产总值和实际工资等宏观经济变量均有正向影响。然而,B30的授权和欧盟贸易禁令的综合影响在短期内仍会产生通胀效应。它们还可能减少几种农产品的产量,如甘蔗、水果、蔬菜和大豆,从而导致食品价格上涨。政策影响突显出,目前的B30授权和欧盟禁令无法自动改善燃料贸易平衡。
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引用次数: 6
Applying the input–output price model to identify inflation processes 运用投入产出价格模型识别通货膨胀过程
Q1 Economics, Econometrics and Finance Pub Date : 2021-12-13 DOI: 10.21203/rs.3.rs-1152331/v1
M. Przybyliński, A. Gorzałczyński
We try to examine the potential of input–output price model to identify mechanisms of price formation and transmission. Contrary to previous research that focused on overcoming the specific limitations of the model, we test its overall performance. In the presented study, the historical values of the commonly used consumer price index were decomposed according to the classic input–output price model for an open economy. A sequence of ex post simulations under various assumptions was used to identify the sources of inflation. This study required the use of input–output tables in current and previous year’s prices. The proposed method of decomposition might be a starting point to create a framework for studying different aspects of inflation process.
我们试图检验投入产出价格模型的潜力,以确定价格形成和传导机制。与之前专注于克服模型特定局限性的研究相反,我们测试了其整体性能。在本研究中,根据开放经济的经典投入产出价格模型,对常用的消费者价格指数的历史值进行了分解。使用了一系列在各种假设下的事后模拟来确定通货膨胀的来源。这项研究要求在当年和上年的价格中使用投入产出表。所提出的分解方法可能是创建一个研究通货膨胀过程不同方面的框架的起点。
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引用次数: 1
Correction to: Relaxing the import proportionality assumption in multi-regional input–output modelling 修正:放宽多区域投入产出模型中的进口比例假设
Q1 Economics, Econometrics and Finance Pub Date : 2021-12-01 DOI: 10.1186/s40008-021-00258-0
Simon Schulte, A. Jakobs, S. Pauliuk
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引用次数: 0
A new method of identifying key industries: a principal component analysis 识别关键行业的一种新方法:主成分分析
Q1 Economics, Econometrics and Finance Pub Date : 2021-11-22 DOI: 10.21203/rs.3.rs-1053053/v1
Lefteris Tsoulfidis, I. Athanasiadis
This article using the principal components analysis identifies key industries and groups them into particular clusters. The data come from the US benchmark input–output tables of the years 2002, 2007, 2012 and the most recently published input–output table of the year 2019. We observe some intertemporal switches of industries both between and within the top clusters. The findings further suggest that structural change is a slow-moving process and it takes time for some industries to move from one cluster to the other. This information may be proved important in the designation of effective economic policies by targeting key industries and also for the stability properties of the economic system.
本文使用主成分分析确定关键行业,并将其分组到特定的集群中。数据来自美国2002年、2007年、2012年的基准投入产出表和最新公布的2019年投入产出表。我们观察到一些行业在顶级集群之间和内部的跨期转换。研究结果进一步表明,结构变化是一个缓慢的过程,一些产业从一个集群转移到另一个集群需要时间。这些信息对于制定针对关键行业的有效经济政策以及经济系统的稳定性具有重要意义。
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引用次数: 1
Compilation and analysis of international from-whom-to-whom financial stock table for Japan, Korea, the United States, and China 日本、韩国、美国、中国的国际个人金融存量表的编制与分析
Q1 Economics, Econometrics and Finance Pub Date : 2021-11-18 DOI: 10.1186/s40008-021-00254-4
Satoru Hagino, Jiyoung Kim
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引用次数: 0
Innovation, firm productivity, and export survival: firm-level evidence from ASEAN developing countries 创新、企业生产率与出口生存:来自东盟发展中国家的企业层面证据
Q1 Economics, Econometrics and Finance Pub Date : 2021-11-16 DOI: 10.1186/s40008-021-00251-7
Utumporn Jitsutthiphakorn
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引用次数: 4
Legal environment and corporate finance: evidence from the Italian manufacturing industry 法律环境与企业融资:来自意大利制造业的证据
Q1 Economics, Econometrics and Finance Pub Date : 2021-11-01 DOI: 10.1186/s40008-021-00252-6
G. Falavigna, R. Ippoliti
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引用次数: 1
Do Information and Communications Technology (ICT) and financial development contribute to economic diversification? Evidence from sub-Saharan Africa 信息和通信技术(ICT)和金融发展是否有助于经济多样化?来自撒哈拉以南非洲的证据
Q1 Economics, Econometrics and Finance Pub Date : 2021-10-12 DOI: 10.1186/s40008-023-00299-7
O. Owolabi, A. Adedeji, B. Aderounmu, Asa-Ruth Oboko Oku, T. Ogunbiyi
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引用次数: 2
Financial structure, cycle, and instability 金融结构、周期和不稳定性
Q1 Economics, Econometrics and Finance Pub Date : 2021-09-30 DOI: 10.1186/s40008-022-00275-7
Kenshiro Ninomiya
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引用次数: 2
Assessing the dynamic tourism inter-industry linkages and economic structural changes in Cambodia’s economy 评估旅游业行业间的动态联系和柬埔寨经济的经济结构变化
Q1 Economics, Econometrics and Finance Pub Date : 2021-09-15 DOI: 10.1186/s40008-021-00249-1
Chantha Hor
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引用次数: 1
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Journal of Economic Structures
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