This article explores the challenges and opportunities faced by the Bank for Agriculture and Agricultural Cooperatives (BAAC) in Thailand from a microfinance perspective. It examines the role of BAAC as a specialized financial institution in assisting underprivileged households and small businesses in accessing financial services. The study highlights the political exploitation of BAAC for populist strategies and the negative impact of corruption on the effectiveness of its operations. Additionally, it discusses the rice-pledging policy in Thailand, which was driven by political motivations and resulted in significant losses for the government. The article emphasizes the need for sustainable development strategies and decreased political interference to enhance the performance of BAAC and effectively support farmers and the poor in Thailand.
{"title":"Challenges and Opportunities for the Bank for Agriculture and Agricultural Cooperatives in Thailand: A Microfinance Perspective","authors":"Worrawoot Jumlongnark","doi":"arxiv-2409.03157","DOIUrl":"https://doi.org/arxiv-2409.03157","url":null,"abstract":"This article explores the challenges and opportunities faced by the Bank for\u0000Agriculture and Agricultural Cooperatives (BAAC) in Thailand from a\u0000microfinance perspective. It examines the role of BAAC as a specialized\u0000financial institution in assisting underprivileged households and small\u0000businesses in accessing financial services. The study highlights the political\u0000exploitation of BAAC for populist strategies and the negative impact of\u0000corruption on the effectiveness of its operations. Additionally, it discusses\u0000the rice-pledging policy in Thailand, which was driven by political motivations\u0000and resulted in significant losses for the government. The article emphasizes\u0000the need for sustainable development strategies and decreased political\u0000interference to enhance the performance of BAAC and effectively support farmers\u0000and the poor in Thailand.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"143 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study explores compounding impacts of climate change on power system's load and generation, emphasising the need to integrate adaptation and mitigation strategies into investment planning. We combine existing and novel empirical evidence to model impacts on: i) air-conditioning demand; ii) thermal power outages; iii) hydro-power generation shortages. Using a power dispatch and capacity expansion model, we analyse the Italian power system's response to these climate impacts in 2030, integrating mitigation targets and optimising for cost-efficiency at an hourly resolution. We outline different meteorological scenarios to explore the impacts of both average climatic changes and the intensification of extreme weather events. We find that addressing extreme weather in power system planning will require an extra 5-8 GW of photovoltaic (PV) capacity, on top of the 50 GW of the additional solar PV capacity required by the mitigation target alone. Despite the higher initial investments, we find that the adoption of renewable technologies, especially PV, alleviates the power system's vulnerability to climate change and extreme weather events. Furthermore, enhancing short-term storage with lithium-ion batteries is crucial to counterbalance the reduced availability of dispatchable hydro generation.
{"title":"Ensuring resilience to extreme weather events increases the ambition of mitigation scenarios on solar power and storage uptake: a study on the Italian power system","authors":"Alice Di Bella, Francesco Pietro Colelli","doi":"arxiv-2409.03593","DOIUrl":"https://doi.org/arxiv-2409.03593","url":null,"abstract":"This study explores compounding impacts of climate change on power system's\u0000load and generation, emphasising the need to integrate adaptation and\u0000mitigation strategies into investment planning. We combine existing and novel\u0000empirical evidence to model impacts on: i) air-conditioning demand; ii) thermal\u0000power outages; iii) hydro-power generation shortages. Using a power dispatch\u0000and capacity expansion model, we analyse the Italian power system's response to\u0000these climate impacts in 2030, integrating mitigation targets and optimising\u0000for cost-efficiency at an hourly resolution. We outline different\u0000meteorological scenarios to explore the impacts of both average climatic\u0000changes and the intensification of extreme weather events. We find that\u0000addressing extreme weather in power system planning will require an extra 5-8\u0000GW of photovoltaic (PV) capacity, on top of the 50 GW of the additional solar\u0000PV capacity required by the mitigation target alone. Despite the higher initial\u0000investments, we find that the adoption of renewable technologies, especially\u0000PV, alleviates the power system's vulnerability to climate change and extreme\u0000weather events. Furthermore, enhancing short-term storage with lithium-ion\u0000batteries is crucial to counterbalance the reduced availability of dispatchable\u0000hydro generation.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt
Emerging marketplaces for large language models and other large-scale machine learning (ML) models appear to exhibit market concentration, which has raised concerns about whether there are insurmountable barriers to entry in such markets. In this work, we study this issue from both an economic and an algorithmic point of view, focusing on a phenomenon that reduces barriers to entry. Specifically, an incumbent company risks reputational damage unless its model is sufficiently aligned with safety objectives, whereas a new company can more easily avoid reputational damage. To study this issue formally, we define a multi-objective high-dimensional regression framework that captures reputational damage, and we characterize the number of data points that a new company needs to enter the market. Our results demonstrate how multi-objective considerations can fundamentally reduce barriers to entry -- the required number of data points can be significantly smaller than the incumbent company's dataset size. En route to proving these results, we develop scaling laws for high-dimensional linear regression in multi-objective environments, showing that the scaling rate becomes slower when the dataset size is large, which could be of independent interest.
{"title":"Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry","authors":"Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt","doi":"arxiv-2409.03734","DOIUrl":"https://doi.org/arxiv-2409.03734","url":null,"abstract":"Emerging marketplaces for large language models and other large-scale machine\u0000learning (ML) models appear to exhibit market concentration, which has raised\u0000concerns about whether there are insurmountable barriers to entry in such\u0000markets. In this work, we study this issue from both an economic and an\u0000algorithmic point of view, focusing on a phenomenon that reduces barriers to\u0000entry. Specifically, an incumbent company risks reputational damage unless its\u0000model is sufficiently aligned with safety objectives, whereas a new company can\u0000more easily avoid reputational damage. To study this issue formally, we define\u0000a multi-objective high-dimensional regression framework that captures\u0000reputational damage, and we characterize the number of data points that a new\u0000company needs to enter the market. Our results demonstrate how multi-objective\u0000considerations can fundamentally reduce barriers to entry -- the required\u0000number of data points can be significantly smaller than the incumbent company's\u0000dataset size. En route to proving these results, we develop scaling laws for\u0000high-dimensional linear regression in multi-objective environments, showing\u0000that the scaling rate becomes slower when the dataset size is large, which\u0000could be of independent interest.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Huaqing Xie, Xingcheng Xu, Fangjia Yan, Xun Qian, Yanqing Yang
GDP is a vital measure of a country's economic health, reflecting the total value of goods and services produced. Forecasting GDP growth is essential for economic planning, as it helps governments, businesses, and investors anticipate trends, make informed decisions, and promote stability and growth. While most previous works focus on the prediction of the GDP growth rate for a single country or by machine learning methods, in this paper we give a comprehensive study on the GDP growth forecasting in the multi-country scenario by deep learning algorithms. For the prediction of the GDP growth where only GDP growth values are used, linear regression is generally better than deep learning algorithms. However, for the regression and the prediction of the GDP growth with selected economic indicators, deep learning algorithms could be superior to linear regression. We also investigate the influence of the novel data -- the light intensity data on the prediction of the GDP growth, and numerical experiments indicate that they do not necessarily improve the prediction performance. Code is provided at https://github.com/Sariel2018/Multi-Country-GDP-Prediction.git.
国内生产总值是衡量一个国家经济健康状况的重要指标，反映了商品和服务生产的总价值。预测 GDP 增长对经济规划至关重要，因为它有助于政府、企业和投资者预测趋势，做出明智决策，并促进稳定和增长。虽然之前的大多数工作都集中在预测单个国家的 GDP 增长率或使用机器学习方法，但在本文中，我们对使用深度学习算法预测多国情况下的 GDP 增长进行了全面研究。对于只使用 GDP 增长值的 GDP 增长预测，线性回归通常优于深度学习算法。但是，对于带有选定经济指标的 GDP 增长的回归和预测，深度学习算法可能优于线性回归。我们还研究了新数据--光照强度数据对 GDP 增长预测的影响，数值实验表明它们并不一定能提高预测性能。代码见https://github.com/Sariel2018/Multi-Country-GDP-Prediction.git。
{"title":"Deep Learning for Multi-Country GDP Prediction: A Study of Model Performance and Data Impact","authors":"Huaqing Xie, Xingcheng Xu, Fangjia Yan, Xun Qian, Yanqing Yang","doi":"arxiv-2409.02551","DOIUrl":"https://doi.org/arxiv-2409.02551","url":null,"abstract":"GDP is a vital measure of a country's economic health, reflecting the total\u0000value of goods and services produced. Forecasting GDP growth is essential for\u0000economic planning, as it helps governments, businesses, and investors\u0000anticipate trends, make informed decisions, and promote stability and growth.\u0000While most previous works focus on the prediction of the GDP growth rate for a\u0000single country or by machine learning methods, in this paper we give a\u0000comprehensive study on the GDP growth forecasting in the multi-country scenario\u0000by deep learning algorithms. For the prediction of the GDP growth where only\u0000GDP growth values are used, linear regression is generally better than deep\u0000learning algorithms. However, for the regression and the prediction of the GDP\u0000growth with selected economic indicators, deep learning algorithms could be\u0000superior to linear regression. We also investigate the influence of the novel\u0000data -- the light intensity data on the prediction of the GDP growth, and\u0000numerical experiments indicate that they do not necessarily improve the\u0000prediction performance. Code is provided at\u0000https://github.com/Sariel2018/Multi-Country-GDP-Prediction.git.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper derives 'scaling laws' -- empirical relationships between the amount of training compute used for a Large Language Model (LLM) and its performance -- for economic outcomes. In a preregistered experiment, 300 professional translators completed 1800 tasks with access to one of thirteen LLMs with differing model training compute sizes (or a control). Our results show that model scaling substantially raises productivity: for every 10x increase in model compute, translators completed tasks 12.3% quicker, received 0.18 s.d. higher grades, and earned 16.1% more per minute (including bonus payments). Further, the gains from model scaling are much higher for lower-skilled workers who gain a 4x larger improvement in task completion speed. These results imply further frontier model scaling -- which is currently estimated at 4x increase per year -- may have significant economic implications.
{"title":"Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Translation","authors":"Ali Merali","doi":"arxiv-2409.02391","DOIUrl":"https://doi.org/arxiv-2409.02391","url":null,"abstract":"This paper derives 'scaling laws' -- empirical relationships between the\u0000amount of training compute used for a Large Language Model (LLM) and its\u0000performance -- for economic outcomes. In a preregistered experiment, 300\u0000professional translators completed 1800 tasks with access to one of thirteen\u0000LLMs with differing model training compute sizes (or a control). Our results\u0000show that model scaling substantially raises productivity: for every 10x\u0000increase in model compute, translators completed tasks 12.3% quicker, received\u00000.18 s.d. higher grades, and earned 16.1% more per minute (including bonus\u0000payments). Further, the gains from model scaling are much higher for\u0000lower-skilled workers who gain a 4x larger improvement in task completion\u0000speed. These results imply further frontier model scaling -- which is currently\u0000estimated at 4x increase per year -- may have significant economic\u0000implications.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ann M. Furbush, Anna Josephson, Talip Kilic, Jeffrey D. Michler
We examine the impact of livelihood diversification on food insecurity amid the COVID-19 pandemic. Our analysis uses household panel data from Ethiopia, Malawi, and Nigeria in which the first round was collected immediately prior to the pandemic and extends through multiple rounds of monthly data collection during the pandemic. Using this pre- and post-outbreak data, and guided by a pre-analysis plan, we estimate the causal effect of livelihood diversification on food insecurity. Our results do not support the hypothesis that livelihood diversification boosts household resilience. Though income diversification may serve as an effective coping mechanism for small-scale shocks, we find that for a disaster on the scale of the pandemic this strategy is not effective. Policymakers looking to prepare for the increased occurrence of large-scale disasters will need to grapple with the fact that coping strategies that gave people hope in the past may fail them as they try to cope with the future.
{"title":"Coping or Hoping? Livelihood Diversification and Food Insecurity in the COVID-19 Pandemic","authors":"Ann M. Furbush, Anna Josephson, Talip Kilic, Jeffrey D. Michler","doi":"arxiv-2409.02285","DOIUrl":"https://doi.org/arxiv-2409.02285","url":null,"abstract":"We examine the impact of livelihood diversification on food insecurity amid\u0000the COVID-19 pandemic. Our analysis uses household panel data from Ethiopia,\u0000Malawi, and Nigeria in which the first round was collected immediately prior to\u0000the pandemic and extends through multiple rounds of monthly data collection\u0000during the pandemic. Using this pre- and post-outbreak data, and guided by a\u0000pre-analysis plan, we estimate the causal effect of livelihood diversification\u0000on food insecurity. Our results do not support the hypothesis that livelihood\u0000diversification boosts household resilience. Though income diversification may\u0000serve as an effective coping mechanism for small-scale shocks, we find that for\u0000a disaster on the scale of the pandemic this strategy is not effective.\u0000Policymakers looking to prepare for the increased occurrence of large-scale\u0000disasters will need to grapple with the fact that coping strategies that gave\u0000people hope in the past may fail them as they try to cope with the future.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jeffrey D. Michler, Dewan Abdullah Al Rafi, Jonathan Giezendanner, Anna Josephson, Valerien O. Pede, Elizabeth Tellman
Impact evaluations of new technologies are critical to assessing and improving investment in national and international development goals. Yet many of these technologies are introduced and promoted at times and in places that lack the necessary data to conduct a strongly identified impact evaluation. We present a new method that combines remotely sensed Earth observation (EO) data, recent advances in machine learning, and socioeconomic survey data so as to allow researchers to conduct impact evaluations of a certain class of technologies when traditional economic data is missing. To demonstrate our approach, we study stress tolerant rice varieties (STRVs) that were introduced in Bangladesh more than a decade ago. Using 20 years of EO data on rice production and flooding, we fail to replicate existing RCT and field trial evidence of STRV effectiveness. We validate this failure to replicate with administrative and household panel data as well as conduct Monte Carlo simulations to test the sensitivity to mismeasurement of past evidence on the effectiveness of STRVs. Our findings speak to conducting large scale, long-term impact evaluations to verify external validity of small scale experimental data while also laying out a path for researchers to conduct similar evaluations in other data poor settings.
{"title":"Impact Evaluations in Data Poor Settings: The Case of Stress-Tolerant Rice Varieties in Bangladesh","authors":"Jeffrey D. Michler, Dewan Abdullah Al Rafi, Jonathan Giezendanner, Anna Josephson, Valerien O. Pede, Elizabeth Tellman","doi":"arxiv-2409.02201","DOIUrl":"https://doi.org/arxiv-2409.02201","url":null,"abstract":"Impact evaluations of new technologies are critical to assessing and\u0000improving investment in national and international development goals. Yet many\u0000of these technologies are introduced and promoted at times and in places that\u0000lack the necessary data to conduct a strongly identified impact evaluation. We\u0000present a new method that combines remotely sensed Earth observation (EO) data,\u0000recent advances in machine learning, and socioeconomic survey data so as to\u0000allow researchers to conduct impact evaluations of a certain class of\u0000technologies when traditional economic data is missing. To demonstrate our\u0000approach, we study stress tolerant rice varieties (STRVs) that were introduced\u0000in Bangladesh more than a decade ago. Using 20 years of EO data on rice\u0000production and flooding, we fail to replicate existing RCT and field trial\u0000evidence of STRV effectiveness. We validate this failure to replicate with\u0000administrative and household panel data as well as conduct Monte Carlo\u0000simulations to test the sensitivity to mismeasurement of past evidence on the\u0000effectiveness of STRVs. Our findings speak to conducting large scale, long-term\u0000impact evaluations to verify external validity of small scale experimental data\u0000while also laying out a path for researchers to conduct similar evaluations in\u0000other data poor settings.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study examines the inter-organizational and spatial mobility patterns of East German inventors following the fall of the Berlin Wall. Existing research often overlooks the role of informal institutions in the mobility decisions of inventors, particularly regarding access to and transfer of knowledge. To address this gap, we investigate the unique circumstances surrounding the dissolution of the German Democratic Republic, which caused a significant shock to establishment closures and prompted many inventors to change their jobs and locations. Our sample comprises over 25,000 East German inventors, whose patenting careers in reunified Germany post-1990 are traced using a novel disambiguation and matching procedure. Our findings reveal that East German inventors in technological fields where access to Western knowledge was facilitated by industrial espionage were more likely to pursue inter-organizational mobility and continue their inventive activities in reunified Germany. Additionally, inventors from communities with strong political support for the ruling socialist party encountered difficulties in sourcing knowledge through weak ties, resulting in a lower likelihood of continuing to patent. However, those who overcame these obstacles and continued to produce inventions were more likely to relocate to West Germany, leaving their original social contexts behind.
{"title":"Inventor Mobility After the Fall of the Berlin Wall","authors":"Paul Hünermund, Ann Hipp","doi":"arxiv-2409.01861","DOIUrl":"https://doi.org/arxiv-2409.01861","url":null,"abstract":"This study examines the inter-organizational and spatial mobility patterns of\u0000East German inventors following the fall of the Berlin Wall. Existing research\u0000often overlooks the role of informal institutions in the mobility decisions of\u0000inventors, particularly regarding access to and transfer of knowledge. To\u0000address this gap, we investigate the unique circumstances surrounding the\u0000dissolution of the German Democratic Republic, which caused a significant shock\u0000to establishment closures and prompted many inventors to change their jobs and\u0000locations. Our sample comprises over 25,000 East German inventors, whose\u0000patenting careers in reunified Germany post-1990 are traced using a novel\u0000disambiguation and matching procedure. Our findings reveal that East German\u0000inventors in technological fields where access to Western knowledge was\u0000facilitated by industrial espionage were more likely to pursue\u0000inter-organizational mobility and continue their inventive activities in\u0000reunified Germany. Additionally, inventors from communities with strong\u0000political support for the ruling socialist party encountered difficulties in\u0000sourcing knowledge through weak ties, resulting in a lower likelihood of\u0000continuing to patent. However, those who overcame these obstacles and continued\u0000to produce inventions were more likely to relocate to West Germany, leaving\u0000their original social contexts behind.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"143 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We build on the interpretation of the Economic Complexity method as Correspondence Analysis (CA), and propose that the Canonical form of CA (CCA), which originated in the ecology literature, can be used to calculate multi-dimensional economic complexity. The traditional (CA) way of calculating economic complexity includes no "external" information such as countries' development characteristics to facilitate interpretation of "complexity". This has led to a wide range of fairly ad hoc interpretations of economic complexity on the basis of ex-post correlation to a long list of other variables. By the ex-ante inclusion of a number of country variables in the construction of the complexity indicators, CCA enables better interpretation, also in the case of multi-dimensional indicators. The analysis is further facilitated by another element of the ecologists' toolbox, the so-called biplots, which are CCA-based graph embeddings that represent a lower-dimensional product-space in which products and countries are positioned together, in mutual correspondence to each other. We show that in this way, CCA provides a richer account of development in many of its aspects, especially economic growth.
我们将经济复杂性方法解释为对应分析法（CA），并在此基础上提出，源于生态学文献的对应分析法的卡农形式（CCA）可用于计算多维经济复杂性。传统的（CA）经济复杂性计算方法不包括 "外部 "信息，如国家的发展特征，以方便对 "复杂性 "的解释。这就导致了对经济复杂性的一系列相当特别的解释，其依据是与一长串其他变量的事后相关性。通过在构建复杂性指标时事先纳入一些国家变量，共同国家评估可以更好地进行解释，在多维指标的情况下也是如此。生态学家工具箱中的另一个元素--所谓的双图（biplots）--进一步促进了分析的进行，双图是基于 CCA 的图嵌入，代表了一个低维度的产品空间，在这个空间中，产品和国家被定位在一起，相互对应。我们表明，通过这种方式，CCA 可以更丰富地描述发展的许多方面，尤其是经济增长。
{"title":"Reinterpreting economic complexity in multiple dimensions","authors":"Önder Nomaler, Bart Verspagen","doi":"arxiv-2409.01830","DOIUrl":"https://doi.org/arxiv-2409.01830","url":null,"abstract":"We build on the interpretation of the Economic Complexity method as\u0000Correspondence Analysis (CA), and propose that the Canonical form of CA (CCA),\u0000which originated in the ecology literature, can be used to calculate\u0000multi-dimensional economic complexity. The traditional (CA) way of calculating\u0000economic complexity includes no \"external\" information such as countries'\u0000development characteristics to facilitate interpretation of \"complexity\". This\u0000has led to a wide range of fairly ad hoc interpretations of economic complexity\u0000on the basis of ex-post correlation to a long list of other variables. By the\u0000ex-ante inclusion of a number of country variables in the construction of the\u0000complexity indicators, CCA enables better interpretation, also in the case of\u0000multi-dimensional indicators. The analysis is further facilitated by another\u0000element of the ecologists' toolbox, the so-called biplots, which are CCA-based\u0000graph embeddings that represent a lower-dimensional product-space in which\u0000products and countries are positioned together, in mutual correspondence to\u0000each other. We show that in this way, CCA provides a richer account of\u0000development in many of its aspects, especially economic growth.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"125 19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142192978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper analyses the price effects and tax pass-through of a VAT increase from 7% to 19% on restaurant services in Germany as of January 1, 2024. The Synthetic Control Method (SCM) is used to identify the causal effects of this reform using prices of goods and services unaffected by the tax change as a counterfactual for restaurant prices. Immediately in January, 31% of the tax increase was passed on to consumer prices. Pass-through increased to 58% in the following six months, which corresponds to a causal consumer price increase of about 6.5%. The presumed increase in demand for gastronomy services due to hosting the UEFA Euro 2024 tournament did not alter the path of price adjustments compared to previous months.
{"title":"Price effects and pass-through of a VAT increase on restaurants in Germany: causal evidence for the first months and a mega sports event","authors":"Matthias Firgo","doi":"arxiv-2409.01180","DOIUrl":"https://doi.org/arxiv-2409.01180","url":null,"abstract":"This paper analyses the price effects and tax pass-through of a VAT increase\u0000from 7% to 19% on restaurant services in Germany as of January 1, 2024. The\u0000Synthetic Control Method (SCM) is used to identify the causal effects of this\u0000reform using prices of goods and services unaffected by the tax change as a\u0000counterfactual for restaurant prices. Immediately in January, 31% of the tax\u0000increase was passed on to consumer prices. Pass-through increased to 58% in the\u0000following six months, which corresponds to a causal consumer price increase of\u0000about 6.5%. The presumed increase in demand for gastronomy services due to\u0000hosting the UEFA Euro 2024 tournament did not alter the path of price\u0000adjustments compared to previous months.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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