Oxford Research Encyclopedia of Economics and Finance最新文献

{"title":"Political Economy of Protection","authors":"Xenia Matschke","doi":"10.1093/acrefore/9780190625979.013.322","DOIUrl":"https://doi.org/10.1093/acrefore/9780190625979.013.322","url":null,"abstract":"The political economy of protection is a field within economics, but it has significant overlap with its sister discipline, political science. For a political economy of protection, one needs at a minimum two types of economic agents: political decision makers who provide protection, and economic agents who are protected or even actively seek protection. The typical political economy scenario leads to an economic outcome that is not Pareto-optimal: From a general welfare perspective, the political interaction is not desirable. An important task of political economy research is to explain why and how political interaction takes place. For the first part of the question, it appears clear that if protection is actively sought, the protection seeker intends to benefit from his activities. However, if the policymakers were truly interested in Pareto optimality and welfare maximization, they would refuse to protect. Hence a crucial assumption in the political economy literature is that the politicians’ objective function differs from the general welfare function. For the second part of the question, theoretical political economy models consider either the election campaign phase when politicians are eager to win a majority of votes (preelection models) or the phase when the politicians have been elected and may benefit from the spoils associated with holding office (postelection models). Whereas in the election phase, politicians have an incentive to cater to the interests of that part of the electorate that is considered pivotal for the election outcome, in the postelection phase they may be open to, for example, special interest group (SIG) influences from which they derive utility.\u0000 A first wave of theoretical political economy models originates from the 1980s. Building on these early advances, more elaborate models have been proposed. The most prominent one is the Grossman–Helpman protection for sale (PfS) model. It delivers a postelection general equilibrium framework of trade policy determination. In this common agency model, industry interest groups act as principals and offer the government a menu of contracts of campaign contributions in exchange for trade policy. The PfS model predicts that industries that lobby for protection will obtain trade protection in equilibrium, whereas nonlobbying industries will face import subsidies. Numerous papers have evaluated the PfS model empirically and found that the implied weight on contributions in the governmental welfare function and the implied share of the population represented by lobbies are both very high. Remedies for this surprising result exist, but it has also been argued that the found empirical regularities may be spurious.\u0000 At the beginning of the 21st century, the majority of political economy literature is still theoretical, but better data availability increasingly offers the opportunity to empirically test theoretical results. A number of challenges remain for the political economy lit","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125489179","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}

{"title":"Econometrics for Modelling Climate Change","authors":"Jennifer L. Castle, D. Hendry","doi":"10.1093/acrefore/9780190625979.013.675","DOIUrl":"https://doi.org/10.1093/acrefore/9780190625979.013.675","url":null,"abstract":"Shared features of economic and climate time series imply that tools for empirically modeling nonstationary economic outcomes are also appropriate for studying many aspects of observational climate-change data. Greenhouse gas emissions, such as carbon dioxide, nitrous oxide, and methane, are a major cause of climate change as they cumulate in the atmosphere and reradiate the sun’s energy. As these emissions are currently mainly due to economic activity, economic and climate time series have commonalities, including considerable inertia, stochastic trends, and distributional shifts, and hence the same econometric modeling approaches can be applied to analyze both phenomena. Moreover, both disciplines lack complete knowledge of their respective data-generating processes (DGPs), so model search retaining viable theory but allowing for shifting distributions is important. Reliable modeling of both climate and economic-related time series requires finding an unknown DGP (or close approximation thereto) to represent multivariate evolving processes subject to abrupt shifts. Consequently, to ensure that DGP is nested within a much larger set of candidate determinants, model formulations to search over should comprise all potentially relevant variables, their dynamics, indicators for perturbing outliers, shifts, trend breaks, and nonlinear functions, while retaining well-established theoretical insights. Econometric modeling of climate-change data requires a sufficiently general model selection approach to handle all these aspects. Machine learning with multipath block searches commencing from very general specifications, usually with more candidate explanatory variables than observations, to discover well-specified and undominated models of the nonstationary processes under analysis, offers a rigorous route to analyzing such complex data. To do so requires applying appropriate indicator saturation estimators (ISEs), a class that includes impulse indicators for outliers, step indicators for location shifts, multiplicative indicators for parameter changes, and trend indicators for trend breaks. All ISEs entail more candidate variables than observations, often by a large margin when implementing combinations, yet can detect the impacts of shifts and policy interventions to avoid nonconstant parameters in models, as well as improve forecasts. To characterize nonstationary observational data, one must handle all substantively relevant features jointly: A failure to do so leads to nonconstant and mis-specified models and hence incorrect theory evaluation and policy analyses.","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134564338","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}

{"title":"A Survey of Econometric Approaches to Convergence Tests of Emissions and Measures of Environmental Quality","authors":"Junsoo Lee, J. Payne, M. Islam","doi":"10.1093/acrefore/9780190625979.013.668","DOIUrl":"https://doi.org/10.1093/acrefore/9780190625979.013.668","url":null,"abstract":"The analysis of convergence behavior with respect to emissions and measures of environmental quality can be categorized into four types of tests: absolute and conditional β-convergence, σ-convergence, club convergence, and stochastic convergence. In the context of emissions, absolute β-convergence occurs when countries with high initial levels of emissions have a lower emission growth rate than countries with low initial levels of emissions. Conditional β-convergence allows for possible differences among countries through the inclusion of exogenous variables to capture country-specific effects. Given that absolute and conditional β-convergence do not account for the dynamics of the growth process, which can potentially lead to dynamic panel data bias, σ-convergence evaluates the dynamics and intradistributional aspects of emissions to determine whether the cross-section variance of emissions decreases over time. The more recent club convergence approach tests the decline in the cross-sectional variation in emissions among countries over time and whether heterogeneous time-varying idiosyncratic components converge over time after controlling for a common growth component in emissions among countries. In essence, the club convergence approach evaluates both conditional σ- and β-convergence within a panel framework. Finally, stochastic convergence examines the time series behavior of a country’s emissions relative to another country or group of countries. Using univariate or panel unit root/stationarity tests, stochastic convergence is present if relative emissions, defined as the log of emissions for a particular country relative to another country or group of countries, is trend-stationary.\u0000 The majority of the empirical literature analyzes carbon dioxide emissions and varies in terms of both the convergence tests deployed and the results. While the results supportive of emissions convergence for large global country coverage are limited, empirical studies that focus on country groupings defined by income classification, geographic region, or institutional structure (i.e., EU, OECD, etc.) are more likely to provide support for emissions convergence. The vast majority of studies have relied on tests of stochastic convergence with tests of σ-convergence and the distributional dynamics of emissions less so. With respect to tests of stochastic convergence, an alternative testing procedure accounts for structural breaks and cross-correlations simultaneously is presented. Using data for OECD countries, the results based on the inclusion of both structural breaks and cross-correlations through a factor structure provides less support for stochastic convergence when compared to unit root tests with the inclusion of just structural breaks.\u0000 Future studies should increase focus on other air pollutants to include greenhouse gas emissions and their components, not to mention expanding the range of geographical regions analyzed and more robust analysis of the ","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116431710","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}

{"title":"School Choice and Accountability","authors":"E. Greaves, S. Burgess","doi":"10.1093/acrefore/9780190625979.013.650","DOIUrl":"https://doi.org/10.1093/acrefore/9780190625979.013.650","url":null,"abstract":"School choice and accountability are both mechanisms initially designed to improve standards of education in publicly provided schools, although they have been introduced worldwide with alternative motivations such as to promote equality of access to “good” schools. Economists were active in the initial design of school choice and accountability systems, and continue to advise and provide evidence to school authorities to improve the functioning of the “quasi-market.”\u0000 School choice, defined broadly, is any system in which parents’ preferences over schools are an input to their child’s allocation to school. Milton Friedman initially hypothesized that school choice would increase the diversity of education providers and improve schools’ productivity through competition. As in the healthcare sector and other public services, “quasi-markets” can respond to choice and competition by improving standards to attract consumers. Theoretical and empirical work have interrogated this prediction and provided conditions for this prediction to hold. Another reason is to promote equality of access to “good” schools and therefore improve social mobility. Rather than school places being rationed through market forces in the form of higher house prices, for example, school choice can promote equality of access to popular schools. Research has typically considered the role of school choice in increasing segregation between different groups of pupils, however, due to differences in parents’ preferences for school attributes and, in some cases, the complexity of the system.\u0000 School accountability is defined as the public provision of school-performance information, on a regular basis, in the same format, and using independent metrics. Accountability has two functions: providing incentives for schools, and information for parents and central authorities. School choice and accountability are linked, in that accountability provides information to parents making school choices, and school choice multiplies the incentive effect of public accountability. Research has studied the effect of school accountability on pupils’ attainment and the implications for teachers as an intermediate mechanism.","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"101 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113954446","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}

{"title":"Spatial Models in Econometric Research","authors":"L. Anselin","doi":"10.1093/acrefore/9780190625979.013.643","DOIUrl":"https://doi.org/10.1093/acrefore/9780190625979.013.643","url":null,"abstract":"Since the late 1990s, spatial models have become a growing addition to econometric research. They are characterized by attention paid to the location of observations (i.e., ordered spatial locations) and the interaction among them. Specifically, spatial models formally express spatial interaction by including variables observed at other locations into the regression specification. This can take different forms, mostly based on an averaging of values at neighboring locations through a so-called spatially lagged variable, or spatial lag. The spatial lag can be applied to the dependent variable, to explanatory variables, and/or to the error terms. This yields a range of specifications for cross-sectional dependence, as well as for static and dynamic spatial panels.\u0000 A critical element in the spatially lagged variable is the definition of neighbor relations in a so-called spatial weights matrix. Historically, the spatial weights matrix has been taken to be given and exogenous, but this has evolved into research focused on estimating the weights from the data and on accounting for potential endogeneity in the weights.\u0000 Due to the uneven spacing of observations and the complex way in which asymptotic properties are obtained, results from time series analysis are not applicable, and specialized laws of large numbers and central limit theorems need to be developed. This requirement has yielded an active body of research into the asymptotics of spatial models.","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133061024","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}

{"title":"The Effects of Parental Job Loss on Children’s Outcomes","authors":"Jenifer Ruiz-Valenzuela","doi":"10.1093/acrefore/9780190625979.013.654","DOIUrl":"https://doi.org/10.1093/acrefore/9780190625979.013.654","url":null,"abstract":"Severe economic downturns are typically characterized by a high incidence of job losses. The available evidence suggests that job losers suffer short-run earning losses that persist in the long run, are more likely to remain unemployed, suffer negative health impacts, and experience an increased likelihood of divorce. Job losses have therefore the potential to generate spillover effects for other members of the household, including children. This comes about because most of the negative consequences of job loss have a direct effect on variables that enter both the production function of cognitive achievement and the health production function. Workers who lose their jobs are likely different from those who remain employed in ways that are unobserved to the researcher and that might, in turn, affect child outcomes. Omitted variable bias poses a challenge to obtaining causal estimates of parental job loss. The way the literature has tried to approximate the ideal experiment has mainly depended on whether the child outcome under analysis could be observed both before and after the shock (i.e., both before and after parental job loss), normally relying on job losses coming from plant closures or downsizes and/or individual fixed effects. A survey of the literature shows that father’s job losses seem to have a detrimental impact on outcomes measuring children’s health and school performance. The impact of mother’s job losses on these same outcomes is mixed (including negative, null, and positive impacts). The impact on more long-term outcomes is less clear, with very mixed findings when it comes to the effect of parental job loss on college enrollment, and small impacts on earnings. In many studies, though, average effects mask important differences across subgroups: the negative impact of parental job loss seems to be mostly concentrated on disadvantaged households.","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115963700","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}

{"title":"Education and Economic Growth","authors":"E. Hanushek, Ludger Woessmann","doi":"10.1093/acrefore/9780190625979.013.651","DOIUrl":"https://doi.org/10.1093/acrefore/9780190625979.013.651","url":null,"abstract":"Economic growth determines the future well-being of society, but finding ways to influence it has eluded many nations. Empirical analysis of differences in growth rates reaches a simple conclusion: long-run growth in gross domestic product (GDP) is largely determined by the skills of a nation’s population. Moreover, the relevant skills can be readily gauged by standardized tests of cognitive achievement. Over the period 1960–2000, three-quarters of the variation in growth of GDP per capita across countries can be accounted for by international measures of math and science skills. The relationship between aggregate cognitive skills, called the knowledge capital of a nation, and the long-run growth rate is extraordinarily strong.\u0000 There are natural questions about whether the knowledge capital–growth relationship is causal. While it is impossible to provide conclusive proof of causality, the existing evidence makes a strong prima facie case that changing the skills of the population will lead to higher growth rates.\u0000 If future GDP is projected based on the historical growth relationship, the results indicate that modest efforts to bring all students to minimal levels will produce huge economic gains. Improvements in the quality of schools have strong long-term benefits.\u0000 The best way to improve the quality of schools is unclear from existing research. On the other hand, a number of developed and developing countries have shown that improvement is possible.","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114064297","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}

{"title":"Financial Economics of United States Slavery","authors":"Rajesh P. Narayanan, Jonathan B. Pritchett","doi":"10.1093/acrefore/9780190625979.013.761","DOIUrl":"https://doi.org/10.1093/acrefore/9780190625979.013.761","url":null,"abstract":"Financial economics reveals that slaves were profitable investments and that the rate of return from owning slaves was at least as high as the return on comparable investments. The profitability of slavery depended on both the productivity and the market valuation of slaves. Owners increased the productivity of slaves by developing better strains of cotton, employing more efficient systems of production (gang labor), and using force and coercion (whippings). Efficient markets facilitated the interregional transfer of labor, and selective sales devastated slave families. Market studies show that slave prices reflected the capitalized value of labor and that they varied based on labor productivity. The profitability of slaves and the availability of efficient markets made slaves attractive investment vehicles for storing wealth. Their attractiveness as investments, however, may have had some other costs. Several studies argue and provide evidence that investment in slaves supplanted investment in other forms of physical and human capital, much to the detriment of southern industrialization and development. Besides serving as investment vehicles, slaves also facilitated financing. A growing body of work provides evidence that slaves were pledged as collateral to obtain credit.","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126068699","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}

{"title":"Value-Added Estimates of Teacher Effectiveness: Measurement, Uses, and Limitations","authors":"Jessalynn K. James, S. Loeb","doi":"10.1093/acrefore/9780190625979.013.647","DOIUrl":"https://doi.org/10.1093/acrefore/9780190625979.013.647","url":null,"abstract":"Since the turn of the 21st century, an abundant body of research has demonstrated that teachers meaningfully contribute to their students’ learning but that teachers vary widely in their effectiveness. Measures of teachers’ “value added” to student achievement have become common, and sometimes controversial, tools for researchers and policymakers hoping to identify and differentiate teachers’ individual contributions to student learning. Value-added measures aim to identify how much more a given teacher’s students learn than what would be expected based on how much other, similar students learn with other teachers. The question of how to measure value added without substantial measurement error and without incorrectly capturing other factors outside of teachers’ control is complex and sometime illusory, and the advantages and drawbacks to any particular method of estimating teachers’ value added depend on the specific context and purpose for their use. Traditionally, researchers have calculated value-added scores only for the subset of teachers with students in tested grades and subjects—a relatively small proportion of the teaching force, in a narrow set of the many domains on which teachers may influence their students. More recently, researchers have created value-added estimates for a range of other student outcomes, including measures of students’ engagement and social-emotional learning such as attendance and behavioral incidences, which may be available for more teachers. Overall, teacher value-added measures can be useful tools for understanding and improving teaching and learning, but they have substantial limitations for many uses and contexts.","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114271168","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}

{"title":"Economic History of Hawai‘i","authors":"S. La Croix","doi":"10.1093/acrefore/9780190625979.013.687","DOIUrl":"https://doi.org/10.1093/acrefore/9780190625979.013.687","url":null,"abstract":"Hawai‘i became one of the last two major land areas on the planet to be settled when Polynesians from Tahiti and the Marquesas Islands navigated voyaging canoes to Hawai‘i in the 11th or 12th century. Settlers brought plants and animals needed to start taro farms modeled on those in their homelands and established chiefdoms using traditional norms of behavior and governance institutions from their home societies. Sometime round 1400, Hawaiians lost contact with the outside world and remained isolated for the next 350–400 years. During this period, competing states emerged, ruled by a sharply differentiated elite (ali‘i) and supported by agricultural surpluses sufficient to support religious and artisan specialists and construction of hundreds of monumental temples. Contact with the outside world was reestablished in 1778 and led to major demographic, economic, and political change: Exposure to outside diseases led to a massive decline in the Native Hawaiian population over the next 125 years; integration with global product markets transformed Hawai‘i’s economy; and warfare among competing states led to the emergence of a centralized monarchy after 1795 that incorporated and adapted some Western political institutions. In 1820, Protestant missionaries brought a foreign religion to Hawai‘i, helped develop a Hawaiian alphabet, and established mission schools that brought literacy to much of the population. A two-decade boom (1812–1833) in harvesting and trading sandalwood with American ships overlapped with a 50-year period in which hundreds of Pacific whaling ships visited Hawai‘i annually to hire Hawaiian sailors and purchase provisions and services. Sugar plantations spread from 1835, expanded rapidly during the U.S. Civil War, and fell back with peace in 1865. An 1876 trade treaty with the United States exempted Hawai‘i sugar firms from the high U.S. tariff on sugar, and they responded by expanding production tenfold by 1883, using immigrant labor from China, Portugal, and Japan. Problems with renegotiating the treaty led to a rebellion by a mostly Caucasian militia group in 1886 that culminated in the overthrow of Queen Lili‘uokalani in 1893. The United States annexed Hawai‘i in 1898 and established a colonial “territorial” government that persisted until Hawai‘i was admitted to the U.S. economic and political union in 1959 as its 50th state. Pineapple and sugar industries expanded under protection of U.S. tariffs and with employment of migrant labor from Japan, Europe, Korea, the Philippines, and Puerto Rico. Japan’s attack on Pearl Harbor in 1941 was followed by imposition of martial law and the buildup of a large U.S. military presence. The economy struggled after the war until the introduction of jet plane passenger service in 1958 prompted millions of tourists from the United States, Japan, and other countries to visit Hawai‘i each year. The tourism boom, institutional reforms of statehood, and population growth ignited an economic boom t","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133045075","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}