Pub Date : 2023-11-06DOI: 10.1080/09645292.2023.2277120
Loredana Cultrera, François Rycx, Giulia Santosuosso, Guillaume Vermeylen
ABSTRACTUsing a unique pan-European dataset, we rely on two alternative measures of over-education and control stepwise for four groups of covariates in order to interpret the over-education wage penalty in light of theoretical models. Firstly, it appears that a significant fraction (i.e. between 1/5 and 1/3) of PhD holders in Europe are genuinely over-educated. Secondly, these genuinely over-educated PhD holders are found to face a substantial wage penalty (ranging from 15 to almost 30%) with respect to their well-matched counterparts. Finally, unconditional quantile regressions highlight that the over-education wage penalty among PhD holders increases greatly along the wage distribution.KEYWORDS: Phd graduatesover-educationover-skillingjob satisfactionwagesEuropeJEL CODES: J21J24 Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 In the European Union, the statistics point in the same direction: the number of newly enrolled doctoral students aged between 24 and 35 increased by almost 27% between 2013 and 2018 (from around 71,000 to almost 90,000), while the number of doctoral students rose from around 735,000 to 779,000 between 2013 and 2019 (European Commission Citation2020; Eurostat, Citation2023). Furthermore, in 2019, the number of new doctorate holders was around 121,000 in the EU-28 (Eurostat, Citation2023).2 This said, it should be noted that a significant number of people embark on a thesis for reasons other than obtaining a job requiring a PhD. Intrinsic motivation and intellectual development are also important drivers (Hnatkova et al. Citation2022). In addition, studies show that many PhD graduates, despite holding jobs for which a PhD is not essential (and for which they are therefore likely to be over-educated), can nevertheless leverage their degree to improve their career prospects. More specifically, as Boman et al. (Citation2021) point out, in many jobs, a doctorate, even if not required, is desired or valued, so that the person with a doctorate has a more interesting and rewarding job, which also makes it easier to access more responsibility, promotion or other benefits (pecuniary or otherwise).3 The term ‘voluntary’ should be interpreted with caution as it may obviously be a constrained choice.4 The study by Ermini, Papi, and Scaturro (Citation2017), based on four cohorts of Italian doctoral graduates (relating to the years 2004, 2006, 2008 and 2010), also finds that jobs held by doctoral graduates in academia and the research sector are more often associated with a successful match. The analysis by Boman et al. (Citation2021), which is based on a career tracking survey of doctoral graduates between 2006 and 2020 in nine European universities, concludes that almost half of doctoral graduates are employed in jobs that do not require a doctorate, but also that overeducation is most prevalent outside universities and research institutions.5 By relying on the WA method, over-education is co
{"title":"The over-education wage penalty among PhD holders: a European perspective","authors":"Loredana Cultrera, François Rycx, Giulia Santosuosso, Guillaume Vermeylen","doi":"10.1080/09645292.2023.2277120","DOIUrl":"https://doi.org/10.1080/09645292.2023.2277120","url":null,"abstract":"ABSTRACTUsing a unique pan-European dataset, we rely on two alternative measures of over-education and control stepwise for four groups of covariates in order to interpret the over-education wage penalty in light of theoretical models. Firstly, it appears that a significant fraction (i.e. between 1/5 and 1/3) of PhD holders in Europe are genuinely over-educated. Secondly, these genuinely over-educated PhD holders are found to face a substantial wage penalty (ranging from 15 to almost 30%) with respect to their well-matched counterparts. Finally, unconditional quantile regressions highlight that the over-education wage penalty among PhD holders increases greatly along the wage distribution.KEYWORDS: Phd graduatesover-educationover-skillingjob satisfactionwagesEuropeJEL CODES: J21J24 Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 In the European Union, the statistics point in the same direction: the number of newly enrolled doctoral students aged between 24 and 35 increased by almost 27% between 2013 and 2018 (from around 71,000 to almost 90,000), while the number of doctoral students rose from around 735,000 to 779,000 between 2013 and 2019 (European Commission Citation2020; Eurostat, Citation2023). Furthermore, in 2019, the number of new doctorate holders was around 121,000 in the EU-28 (Eurostat, Citation2023).2 This said, it should be noted that a significant number of people embark on a thesis for reasons other than obtaining a job requiring a PhD. Intrinsic motivation and intellectual development are also important drivers (Hnatkova et al. Citation2022). In addition, studies show that many PhD graduates, despite holding jobs for which a PhD is not essential (and for which they are therefore likely to be over-educated), can nevertheless leverage their degree to improve their career prospects. More specifically, as Boman et al. (Citation2021) point out, in many jobs, a doctorate, even if not required, is desired or valued, so that the person with a doctorate has a more interesting and rewarding job, which also makes it easier to access more responsibility, promotion or other benefits (pecuniary or otherwise).3 The term ‘voluntary’ should be interpreted with caution as it may obviously be a constrained choice.4 The study by Ermini, Papi, and Scaturro (Citation2017), based on four cohorts of Italian doctoral graduates (relating to the years 2004, 2006, 2008 and 2010), also finds that jobs held by doctoral graduates in academia and the research sector are more often associated with a successful match. The analysis by Boman et al. (Citation2021), which is based on a career tracking survey of doctoral graduates between 2006 and 2020 in nine European universities, concludes that almost half of doctoral graduates are employed in jobs that do not require a doctorate, but also that overeducation is most prevalent outside universities and research institutions.5 By relying on the WA method, over-education is co","PeriodicalId":46682,"journal":{"name":"Education Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135589903","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}
ABSTRACTThe COVID-19 pandemic has forced a shift from traditional face-to-face instruction to online learning. We analyze how this shift has affected learning outcomes, using a rich data set from a financial literacy training of schoolteachers in Armenia. Online training worked well for relatively simple skills (acquiring theoretical financial knowledge) but less well than in-person training for more complex tasks (learning how to teach financial literacy to students). We also found that the deterioration of training success in the online cohort is stronger among social studies teachers than among math teachers. AcknowledgementsWe are very thankful to the editor and two anonymous referees whose comments helped us to substantially improve this paper. We are also grateful to Andrew Dustan, Kayleigh McCrary, Pedro Sant'Anna and Zaruhi Sahakyan, as well as attendees at the 2022 meeting of the Armenian Economic Association for helpful discussions, and to the staff of the Consumer Rights Protection and Financial Education Center, in particular Araks Manucharyan, for providing the data used in this article. The views expressed in this paper are those of the authors and do not necessarily represent the views or policies of the Central Bank of Armenia.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Even after the pandemic ends, online learning will have an important role in the future, as it can be more cost-effective (OECD Citation2020), or meet diverse learning needs (UNESCO Citation2020).2 Armenia has about 1420 primary and secondary schools, so each cohort consists of about 355 schools. The average school in Armenia is much smaller than in the United States, serving around 270 students in the relevant age range, and sending about 4 teachers to the financial literacy training.3 Almost all teachers also participated in the pre-test before the training took place. Those who did not (<10 across both years combined) were dropped from the dataset.4 The test questions contain a ‘don't know’ answer option.5 The 2020 Covid death rate was 1180 per million population in Armenia as a whole (1572 per million population in Yerevan). The 2021 Covid death rate was 1810 per million population in Armenia as a whole, and 2400 per million in Yerevan. Measured by these death rates, the pandemic was approximately 50% more severe in the in-person year 2021 than in the online year.6 Approximately 50 percent of our sample are ‘rich’ under this definition. Because teacher salaries are relatively flat, this variable depends mostly on the teacher's partner's income.7 For example, some clusters outside Yerevan are composed of schools from relatively urban areas, e.g. from the second-largest city, while other clusters contain primarily rural schools.8 Both the predicted and the actual pre-scores in 2021 are also quite close to the average pre-score of the 2020 cohort (46.3).9 Since teachers receive a salary that varies only slightly with
新冠肺炎疫情迫使传统的面对面教学向在线学习转变。我们利用来自亚美尼亚教师金融知识培训的丰富数据集，分析了这种转变对学习成果的影响。对于相对简单的技能(获得理论金融知识)，在线培训效果很好，但对于更复杂的任务(学习如何向学生传授金融知识)，在线培训不如面对面培训好。我们还发现，在线队列培训成功的恶化在社会学科教师中比在数学教师中更强烈。我们非常感谢编辑和两位匿名审稿人，他们的意见帮助我们大大改进了本文。我们还要感谢Andrew Dustan, Kayleigh McCrary, Pedro Sant'Anna和Zaruhi Sahakyan，以及亚美尼亚经济协会2022年会议的与会者进行了有益的讨论，并感谢消费者权益保护和金融教育中心的工作人员，特别是Araks Manucharyan，为本文提供了使用的数据。本文中表达的观点是作者的观点，并不一定代表亚美尼亚中央银行的观点或政策。披露声明作者未报告潜在的利益冲突。注1即使疫情结束后，在线学习也将在未来发挥重要作用，因为它可以更具成本效益(OECD Citation2020)，或满足多种学习需求(UNESCO Citation2020)亚美尼亚有大约1420所小学和中学，因此每个队列由大约355所学校组成。亚美尼亚的平均学校规模比美国小得多，大约有270名相关年龄段的学生，大约有4名教师参加了金融知识培训在培训开始前，几乎所有的老师都参加了预测试。那些没有达到这一目标的人(两年内的总和<10)从数据集中被删除试题中有一个“不知道”的选项2020年，亚美尼亚全国的新冠肺炎死亡率为每百万人1180例(埃里温为每百万人1572例)。2021年，亚美尼亚全国的新冠肺炎死亡率为每百万人1810例，埃里温为每百万人2400例。根据这些死亡率来衡量，2021年的大流行比在线年严重约50%根据这个定义，我们的样本中大约有50%是“富有的”。因为教师的工资是相对稳定的，这个变量主要取决于教师伴侣的收入例如，埃里温以外的一些组群由来自相对城市地区的学校组成，例如来自第二大城市的学校，而其他组群主要包括农村学校2021年的预估分和实际预估分也非常接近2020年的平均预估分(46.3分)由于教师的工资与工作经验的差异很小(兼职教师除外)，家庭收入的大部分差异是由于没有或没有第二收入来源。家庭收入如何影响金融知识最合理的途径是，较富裕的家庭与正规金融机构有更多的互动(例如，在银行有储蓄账户)考虑一个没有任何人口控制的模型的结果是有趣的。虽然治疗效果仍然是显著负的(对于TS和TMS;并且对于FLS不显著)，效应的大小减少了大约10%到25%，例如，对于表3中的第一次回归，从−4.26到−3.08;在第三次回归中，从- 6.22到- 5.27。这种变化的方向是直观的，因为治疗组的教师比对照组的教师更优秀(例如，治疗组有更多的数学教师和更富有的教师)。因此，如果我们不考虑人口结构的变化，估计的在线负面影响的大小将会更小虽然一般能力和从指令中获得的收益之间存在正相关的报道(例如Kliegl, Smith和Baltes (Citation1990);Kwon and Lawson (Citation2000);Verhaeghen和Marcoen (Citation1996))，负相关也很常见(例如Gaultney, Bjorklund和Goldstein (Citation1996);Traut, Guild, and Munakata (Citation2021))。
{"title":"Can teachers learn online? – evidence from Armenia during the COVID-19 pandemic","authors":"Naneh Hovanessian, Gevorg Minasyan, Armen Nurbekyan, Mattias Polborn, Tigran Polborn","doi":"10.1080/09645292.2023.2273224","DOIUrl":"https://doi.org/10.1080/09645292.2023.2273224","url":null,"abstract":"ABSTRACTThe COVID-19 pandemic has forced a shift from traditional face-to-face instruction to online learning. We analyze how this shift has affected learning outcomes, using a rich data set from a financial literacy training of schoolteachers in Armenia. Online training worked well for relatively simple skills (acquiring theoretical financial knowledge) but less well than in-person training for more complex tasks (learning how to teach financial literacy to students). We also found that the deterioration of training success in the online cohort is stronger among social studies teachers than among math teachers. AcknowledgementsWe are very thankful to the editor and two anonymous referees whose comments helped us to substantially improve this paper. We are also grateful to Andrew Dustan, Kayleigh McCrary, Pedro Sant'Anna and Zaruhi Sahakyan, as well as attendees at the 2022 meeting of the Armenian Economic Association for helpful discussions, and to the staff of the Consumer Rights Protection and Financial Education Center, in particular Araks Manucharyan, for providing the data used in this article. The views expressed in this paper are those of the authors and do not necessarily represent the views or policies of the Central Bank of Armenia.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Even after the pandemic ends, online learning will have an important role in the future, as it can be more cost-effective (OECD Citation2020), or meet diverse learning needs (UNESCO Citation2020).2 Armenia has about 1420 primary and secondary schools, so each cohort consists of about 355 schools. The average school in Armenia is much smaller than in the United States, serving around 270 students in the relevant age range, and sending about 4 teachers to the financial literacy training.3 Almost all teachers also participated in the pre-test before the training took place. Those who did not (<10 across both years combined) were dropped from the dataset.4 The test questions contain a ‘don't know’ answer option.5 The 2020 Covid death rate was 1180 per million population in Armenia as a whole (1572 per million population in Yerevan). The 2021 Covid death rate was 1810 per million population in Armenia as a whole, and 2400 per million in Yerevan. Measured by these death rates, the pandemic was approximately 50% more severe in the in-person year 2021 than in the online year.6 Approximately 50 percent of our sample are ‘rich’ under this definition. Because teacher salaries are relatively flat, this variable depends mostly on the teacher's partner's income.7 For example, some clusters outside Yerevan are composed of schools from relatively urban areas, e.g. from the second-largest city, while other clusters contain primarily rural schools.8 Both the predicted and the actual pre-scores in 2021 are also quite close to the average pre-score of the 2020 cohort (46.3).9 Since teachers receive a salary that varies only slightly with ","PeriodicalId":46682,"journal":{"name":"Education Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135271454","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}
Pub Date : 2023-10-13DOI: 10.1080/09645292.2023.2265594
Jennifer L. Steele
ABSTRACTThe question of why postsecondary institutions produce different labor market outcomes is difficult to answer due to unobserved student characteristics. Here, I leverage students' geographic proximity to three classifications of postsecondary institutions – earnings-enhancing, competitive, and Historically Black Colleges and Universities (HBCUs). Using a nationally representative sample, I estimate attainment and earnings effects of first attending each type. Attending an institution classified as earnings-enhancing increases humanities credit completion, degree attainment, and early-career wages. Among underrepresented students, living closest to an HBCU strongly predicts HBCU enrollment. This yields higher STEM credit completion but lower early-career wages, suggesting possible labor market bias.Abbreviations: Competitive: Barron's Top 3 Selectivity Tier Institution; HBCU:Historically Black College or University; HSI: High-Success Institution; STEM: Science; Technology; Engineering; and Mathematics; Underrepresented Minority (URM): Black; Indigenous; or Hispanic/LatinxHIGHLIGHTSNearest-college attributes predict college choice for many high school students, especially those living near HBCUs.Colleges previously linked to students' wage mobility yield higher earnings by students' mid-20s.Higher earnings effects coincide with higher humanities credit completion, bachelor's completion, and postbaccalaureate training.HBCU attendance relative to other options yields higher STEM credit completion, but lower early-career wages.HBCU attendance relative to no college also increases humanities credit completion and bachelor's degree completion.KEYWORDS: Human capitalsalary wage differentialsinstitutional effectsinstrumental variablescollege proximity Disclosure statementNo potential conflict of interest was reported by the author.Notes1 Chetty et al. (Citation2017) also found high variation in the ‘mobility rates’ of institutions, which they defined as the product of institutions' success rates and the fraction of bottom-quintile students enrolled in them.2 ELS:2002 provides cross-sectional base-year weights for each school and student to reflect both the inverse probability of selection, which is known from the sampling design, and the probability of nonresponse, which is estimated from student and school attributes at baseline. The dataset also includes panel weights for use in longitudinal analyzes across the other survey waves. I do not employ the ELS weights in this analysis because my identification strategy, instrumental variables analysis, in effect assigns greater weight to respondents who are sensitive to the set of geographic instrumental variables. Applying sampling and non-response weights may therefore distort the internal validity of the IV analysis (Solon, Haider, and Wooldridge Citation2015).3 The four HBCUs also classified as high-success institutions are Howard University, Morehouse College, Spelman College, and Xavier Universi
摘要高等教育机构为什么会产生不同的劳动力市场结果这个问题很难回答，因为没有观察到学生的特征。在这里，我利用学生在地理上接近三种高等教育机构——提高收入、竞争和传统黑人学院和大学(HBCUs)。使用一个具有全国代表性的样本，我估计了首次参加每种类型的成就和收入效应。进入一所被归类为提高收入的院校就读，可以提高人文学科学分的完成程度、学位的获得程度和早期职业生涯的工资。在代表性不足的学生中，住得离HBCU最近强烈地预示着HBCU的入学率。这导致更高的STEM学分完成率，但较低的早期职业工资，表明可能存在劳动力市场偏见。竞争性:Barron's Top 3 Selectivity Tier Institution;HBCU:历史上的黑人学院或大学;恒生指数:高成功机构;茎:科学;技术;工程;和数学;少数族裔(URM):黑人;本土的;最近的大学属性预测了许多高中生的大学选择，尤其是那些住在hbcu附近的学生。以前与学生工资流动性挂钩的大学在学生25岁左右的时候收入更高。较高的收入效应与较高的人文学科学分完成度、学士学位完成度和学士学位后培训相吻合。与其他选择相比，就读HBCU的学生可以获得更高的STEM学分，但较低的早期职业工资。HBCU的出勤率相对于没有大学的人也增加了人文学科学分的完成率和学士学位的完成率。关键词:人力资本工资工资差异制度效应工具变量大学接近性披露声明作者未报告潜在利益冲突。注1 Chetty等人(Citation2017)还发现，院校的“流动性”存在很大差异，他们将其定义为院校的成功率与最低五分之一的学生入学比例的乘积ELS:2002提供了每个学校和学生的横截面基年权重，以反映从抽样设计中已知的选择的逆概率，以及从基线时的学生和学校属性估计的无响应概率。该数据集还包括用于其他调查波的纵向分析的面板权重。我没有在这个分析中使用ELS权重，因为我的识别策略，工具变量分析，实际上给那些对地理工具变量集敏感的受访者分配了更大的权重。因此，应用抽样和非响应权重可能会扭曲IV分析的内部有效性(Solon, Haider, and Wooldridge Citation2015)同样被列为高成功院校的四所hbcu分别是霍华德大学、莫尔豪斯学院、斯佩尔曼学院和路易斯安那州泽维尔大学。4我也尝试过进入一所高stem院校，但这组地理指标并不能预测我能否进入这类院校。
{"title":"Which college types increase earnings? Estimates from geographic proximity","authors":"Jennifer L. Steele","doi":"10.1080/09645292.2023.2265594","DOIUrl":"https://doi.org/10.1080/09645292.2023.2265594","url":null,"abstract":"ABSTRACTThe question of why postsecondary institutions produce different labor market outcomes is difficult to answer due to unobserved student characteristics. Here, I leverage students' geographic proximity to three classifications of postsecondary institutions – earnings-enhancing, competitive, and Historically Black Colleges and Universities (HBCUs). Using a nationally representative sample, I estimate attainment and earnings effects of first attending each type. Attending an institution classified as earnings-enhancing increases humanities credit completion, degree attainment, and early-career wages. Among underrepresented students, living closest to an HBCU strongly predicts HBCU enrollment. This yields higher STEM credit completion but lower early-career wages, suggesting possible labor market bias.Abbreviations: Competitive: Barron's Top 3 Selectivity Tier Institution; HBCU:Historically Black College or University; HSI: High-Success Institution; STEM: Science; Technology; Engineering; and Mathematics; Underrepresented Minority (URM): Black; Indigenous; or Hispanic/LatinxHIGHLIGHTSNearest-college attributes predict college choice for many high school students, especially those living near HBCUs.Colleges previously linked to students' wage mobility yield higher earnings by students' mid-20s.Higher earnings effects coincide with higher humanities credit completion, bachelor's completion, and postbaccalaureate training.HBCU attendance relative to other options yields higher STEM credit completion, but lower early-career wages.HBCU attendance relative to no college also increases humanities credit completion and bachelor's degree completion.KEYWORDS: Human capitalsalary wage differentialsinstitutional effectsinstrumental variablescollege proximity Disclosure statementNo potential conflict of interest was reported by the author.Notes1 Chetty et al. (Citation2017) also found high variation in the ‘mobility rates’ of institutions, which they defined as the product of institutions' success rates and the fraction of bottom-quintile students enrolled in them.2 ELS:2002 provides cross-sectional base-year weights for each school and student to reflect both the inverse probability of selection, which is known from the sampling design, and the probability of nonresponse, which is estimated from student and school attributes at baseline. The dataset also includes panel weights for use in longitudinal analyzes across the other survey waves. I do not employ the ELS weights in this analysis because my identification strategy, instrumental variables analysis, in effect assigns greater weight to respondents who are sensitive to the set of geographic instrumental variables. Applying sampling and non-response weights may therefore distort the internal validity of the IV analysis (Solon, Haider, and Wooldridge Citation2015).3 The four HBCUs also classified as high-success institutions are Howard University, Morehouse College, Spelman College, and Xavier Universi","PeriodicalId":46682,"journal":{"name":"Education Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135854503","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}
Pub Date : 2023-10-09DOI: 10.1080/09645292.2023.2266591
My Nguyen
ABSTRACTThis paper examines the spillover effects of maternal education in elementary classrooms in the context of Vietnam. Drawing from the sample of students who are randomly assigned to classrooms, we find that exposure to classmates whose mothers are well-educated positively influences student achievement. The heterogeneity analyses reveal that the magnitude of the effects tends to be larger for students from advantaged backgrounds. Exploring the mechanisms, we find that higher academic aspiration and motivation as well as an improved learning environment are potential pathways to the favorable impacts of peers’ maternal education.KEYWORDS: Peer effectmaternal educationlearningstudent achievement Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Changes in teachers’ and mothers’ behavior can also be potential pathways (Wang Citation2021; Chung and Zou Citation2023). These are mentioned in Section 5.5 but unfortunately the lack of data makes it impossible for us to empirically test these pathways.2 School data were only collected for the younger cohort3 Vietnam is categorized into eight socioeconomic regions: North-West, North-East, Red River Delta, North Central Coast, South Central Coast, South-East, Central Highlands, and Mekong River Delta. Furthermore, all major urban centers (Hanoi, Ho Chi Minh City, Da Nang, Hai Phong, and Ba Ria-Vung Tau) are regarded as a new region – the Cities region (Nguyen Citation2008). Five regions were selected for the YLP study, based on the following criteria: (i) regions in the North, Central and South, (ii) regions consisting of urban, rural, and mountainous areas, (iii) regions having a larger (than the national average) poor population, and (iv) regions reflecting some unique factors of the country, such as natural disaster and war consequences. The Young Lives team then selected one province from each region: Lao Cai (North-East region), Hung Yen (Red River Delta), Da Nang (Cities), Phu Yen (South Central Coast), and Ben Tre (Mekong River Delta).4 In the full sample that includes students who are randomly assigned to classrooms and students who are not randomly assigned to classrooms, there are 3,284 students and 176 classrooms (Table 1, Columns 4-6).5 On average, a student in our sample misses around 1.15 days of class during the academic year while the maximum of absent days each student has is 9 days. Approximately 15% of students have the number of absences greater than the class average.
摘要本文以越南为研究对象，探讨小学课堂上母亲教育的外溢效应。从随机分配到教室的学生样本中，我们发现，接触母亲受过良好教育的同学对学生的成绩有积极影响。异质性分析表明，对于来自优越背景的学生，这种影响的幅度往往更大。研究发现，更高的学习动机和学习环境的改善是同伴母亲教育产生良好影响的潜在途径。关键词:同伴效应母亲教育学习学生成绩披露声明作者未报告潜在利益冲突。注1教师和母亲行为的改变也可能是潜在的途径(Wang Citation2021;Chung and Zou [j]。这些都在5.5节中提到，但不幸的是，缺乏数据使我们无法对这些途径进行实证测试3越南被划分为八个社会经济区域:西北部、东北部、红河三角洲、中北部海岸、中南部海岸、东南部、中部高地和湄公河三角洲。此外，所有主要城市中心(河内、胡志明市、岘港、海防和巴黎头市)都被视为一个新的区域-城市区域(Nguyen Citation2008)。根据以下标准，选择了五个地区进行YLP研究:(i)北部，中部和南部地区，(ii)由城市，农村和山区组成的地区，(iii)贫困人口较多(高于全国平均水平)的地区，以及(iv)反映该国某些独特因素的地区，例如自然灾害和战争后果。然后Young Lives团队从每个区域中选择一个省份:老街(东北地区)、洪颜(红河三角洲)、岘港(城市)、富颜(中南部海岸)和本崔(湄公河三角洲)在包括随机分配到教室的学生和非随机分配到教室的学生的完整样本中，有3,284名学生和176个教室(表1，第4-6列)平均而言，我们样本中的学生在学年中缺课约1.15天，而每个学生的缺课天数最多为9天。大约15%的学生缺课次数超过班级平均水平。
{"title":"Spillover effects of maternal education in elementary classroom: evidence from Vietnam","authors":"My Nguyen","doi":"10.1080/09645292.2023.2266591","DOIUrl":"https://doi.org/10.1080/09645292.2023.2266591","url":null,"abstract":"ABSTRACTThis paper examines the spillover effects of maternal education in elementary classrooms in the context of Vietnam. Drawing from the sample of students who are randomly assigned to classrooms, we find that exposure to classmates whose mothers are well-educated positively influences student achievement. The heterogeneity analyses reveal that the magnitude of the effects tends to be larger for students from advantaged backgrounds. Exploring the mechanisms, we find that higher academic aspiration and motivation as well as an improved learning environment are potential pathways to the favorable impacts of peers’ maternal education.KEYWORDS: Peer effectmaternal educationlearningstudent achievement Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Changes in teachers’ and mothers’ behavior can also be potential pathways (Wang Citation2021; Chung and Zou Citation2023). These are mentioned in Section 5.5 but unfortunately the lack of data makes it impossible for us to empirically test these pathways.2 School data were only collected for the younger cohort3 Vietnam is categorized into eight socioeconomic regions: North-West, North-East, Red River Delta, North Central Coast, South Central Coast, South-East, Central Highlands, and Mekong River Delta. Furthermore, all major urban centers (Hanoi, Ho Chi Minh City, Da Nang, Hai Phong, and Ba Ria-Vung Tau) are regarded as a new region – the Cities region (Nguyen Citation2008). Five regions were selected for the YLP study, based on the following criteria: (i) regions in the North, Central and South, (ii) regions consisting of urban, rural, and mountainous areas, (iii) regions having a larger (than the national average) poor population, and (iv) regions reflecting some unique factors of the country, such as natural disaster and war consequences. The Young Lives team then selected one province from each region: Lao Cai (North-East region), Hung Yen (Red River Delta), Da Nang (Cities), Phu Yen (South Central Coast), and Ben Tre (Mekong River Delta).4 In the full sample that includes students who are randomly assigned to classrooms and students who are not randomly assigned to classrooms, there are 3,284 students and 176 classrooms (Table 1, Columns 4-6).5 On average, a student in our sample misses around 1.15 days of class during the academic year while the maximum of absent days each student has is 9 days. Approximately 15% of students have the number of absences greater than the class average.","PeriodicalId":46682,"journal":{"name":"Education Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135147214","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}
ABSTRACTThis paper evaluates economies of scale and scope, and the merger effect among national universities in Japan. We apply SUR for the total translog cost function in FY2014 and FY2018. The main results are: (i) there exist economies of scale as a whole university; (ii) but there exist no clear economies of scope except for in research; (iii) there are cost saving effects with mergers among single colleges, but not in the case of mergers of general universities and medical colleges, (iv) both the costs of a public and a private university are higher than those of a national university.KEYWORDS: Higher educationEconomies of scaleEconomies of scopeMerger of universities Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 In Table A1 (Appendix), studies of the stochastic cost frontier approach, which estimates economies of scale and scope, are listed. Other studies using this approach to estimate the efficiency level of universities are Stevens (Citation2005), McMillan and Chan (Citation2006), Agasisti and Salerno (Citation2007), Lenton (Citation2008), Kempkes and Pohl (Citation2008, Citation2010), Yamasaki and Itaba (Citation2009, Citation2010), Johnes and Johnes (Citation2009), Johnes and Schwarzenberger (Citation2011), Mamun (Citation2012), Suhara (Citation2014), Johnes and Johnes (Citation2016), Gralka (Citation2018), Agasisti and Gralka (Citation2019), Fu et al. (Citation2019), Maeda (Citation2020).2 As we note in the literature review, it is reasonable and common that three university output measures be used—undergraduate education, graduate education, and research. In fact, as for types of education, education for undergraduate and graduate students differs because graduate education is more related to research, an activity distinct from education. The main goal of research is the production of papers, patents, etc. Therefore, we use these three output measures.3 According to Christensen and Greene (Citation1976), estimating cost function alone leads to multicollinearity problems because the information of the input share equations cannot be used. As a result, the explanatory variables in a translog cost function are inaccurate parameter estimates. As the simultaneous method of the cost function and the input share equations as a system increases the degree of freedom, the accuracy of the estimates increases, compared to a case of a single estimation method using the cost function only.4 As for output measure, universities of more than 95% in undergraduate education, of more than 93% in graduate education and of more than 96% in research satisfy the monotonicity condition. As for input factor price, universities of 100% in both labor and material and other prices, and of more than 98% in capital price satisfy the monotonicity condition.5 The partial derivatives of input prices are stable with little variation across models. As for university type, the partial derivatives of labor prices are higher i
摘要本文对日本国立大学合并的规模经济、范围经济和合并效果进行了评价。我们对2014财年和2018财年的总超对数成本函数应用SUR。主要结果是:(1)大学整体存在规模经济效应;(ii)但不存在明显的范围经济效益(研究除外);(三)单一学院之间的合并有节约成本的效果，但普通大学和医学院合并没有这种效果;(四)公立大学和私立大学的成本都高于国立大学。关键词:高等教育规模经济范围经济大学涌现披露声明作者未报告潜在利益冲突注1表A1(附录)列出了估计规模经济和范围经济的随机成本前沿法的研究。其他使用这种方法估计大学效率水平的研究包括Stevens (Citation2005)， McMillan and Chan (Citation2006)， Agasisti and Salerno (Citation2007)， Lenton (Citation2008)， Kempkes and Pohl (Citation2008, Citation2010)， Yamasaki and Itaba (Citation2009, Citation2010)， Johnes and Johnes (Citation2009)， Johnes and Schwarzenberger (Citation2011)， Mamun (Citation2012)， Suhara (Citation2014)， Johnes and Johnes (Citation2016)， Gralka (Citation2018)，1 . Agasisti and Gralka (Citation2019)， Fu et al. (Citation2019)， Maeda (Citation2020)正如我们在文献综述中指出的那样，使用三种大学产出衡量标准——本科教育、研究生教育和研究——是合理和普遍的。事实上，就教育类型而言，本科生和研究生的教育是不同的，因为研究生教育更多地与研究有关，是一种与教育不同的活动。研究的主要目标是产生论文、专利等。因此，我们使用这三个输出度量Christensen和Greene (Citation1976)认为，由于无法利用输入份额方程的信息，单独估计成本函数会导致多重共线性问题。因此，超对数成本函数中的解释变量是不准确的参数估计。由于成本函数和输入份额方程作为一个系统的同时方法增加了自由度，与仅使用成本函数的单一估计方法相比，估计的准确性提高了在产出指标上，95%以上的本科院校、93%以上的研究生院校和96%以上的科研院校满足单调性条件。在投入要素价格方面，劳动力、材料和其他价格均达到100%的高校，资本价格达到98%以上的高校满足单调性条件投入价格的偏导数是稳定的，模型之间的差异很小。就大学类型而言，劳动力价格偏导数在劳动力成本比高的单专业院校和教育类院校中较高。5 .在医学院，材料的部分衍生品和其他价格较高Chung (Citation1994)的理论表明，当σkk为0时，则输入要素之间的关系为替代关系，但当σkl<0时，则为互补关系。σkk = C⋅CkkCkCkσkl = C⋅CklCkClCkk =∂2 C∂wk2, Ckl =∂2 C∂周∂西城,Ck =∂C∂周,Cl =∂C∂西城,k, l = l, k、M (k≠l) 7我们对2014财年和2018财年之间的结构性变化进行了统计检验。Wald检验结果显示，FY2014和FY2018的常数项发生了变化。因此，我们包括2018财年的假人至于选取非常小的没有零产出的数字来计算范围经济，也有一些研究。例如，Kim和Clark (Citation1988)取供水行业样本平均值的10%。Goldberg等人(Citation1991)使用100万美元作为证券行业中非常小的数字。Rezvanian和Mehdian (Citation2002)在商业银行行业的样本中取最小值。因此，本研究认为0.001与样本均值相比已经足够小。
{"title":"Economies of scale and scope, merger effects, and ownership difference: an empirical analysis of universities in Japan","authors":"Fumitoshi Mizutani, Tomoyasu Tanaka, Noriyoshi Nakayama","doi":"10.1080/09645292.2023.2260574","DOIUrl":"https://doi.org/10.1080/09645292.2023.2260574","url":null,"abstract":"ABSTRACTThis paper evaluates economies of scale and scope, and the merger effect among national universities in Japan. We apply SUR for the total translog cost function in FY2014 and FY2018. The main results are: (i) there exist economies of scale as a whole university; (ii) but there exist no clear economies of scope except for in research; (iii) there are cost saving effects with mergers among single colleges, but not in the case of mergers of general universities and medical colleges, (iv) both the costs of a public and a private university are higher than those of a national university.KEYWORDS: Higher educationEconomies of scaleEconomies of scopeMerger of universities Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 In Table A1 (Appendix), studies of the stochastic cost frontier approach, which estimates economies of scale and scope, are listed. Other studies using this approach to estimate the efficiency level of universities are Stevens (Citation2005), McMillan and Chan (Citation2006), Agasisti and Salerno (Citation2007), Lenton (Citation2008), Kempkes and Pohl (Citation2008, Citation2010), Yamasaki and Itaba (Citation2009, Citation2010), Johnes and Johnes (Citation2009), Johnes and Schwarzenberger (Citation2011), Mamun (Citation2012), Suhara (Citation2014), Johnes and Johnes (Citation2016), Gralka (Citation2018), Agasisti and Gralka (Citation2019), Fu et al. (Citation2019), Maeda (Citation2020).2 As we note in the literature review, it is reasonable and common that three university output measures be used—undergraduate education, graduate education, and research. In fact, as for types of education, education for undergraduate and graduate students differs because graduate education is more related to research, an activity distinct from education. The main goal of research is the production of papers, patents, etc. Therefore, we use these three output measures.3 According to Christensen and Greene (Citation1976), estimating cost function alone leads to multicollinearity problems because the information of the input share equations cannot be used. As a result, the explanatory variables in a translog cost function are inaccurate parameter estimates. As the simultaneous method of the cost function and the input share equations as a system increases the degree of freedom, the accuracy of the estimates increases, compared to a case of a single estimation method using the cost function only.4 As for output measure, universities of more than 95% in undergraduate education, of more than 93% in graduate education and of more than 96% in research satisfy the monotonicity condition. As for input factor price, universities of 100% in both labor and material and other prices, and of more than 98% in capital price satisfy the monotonicity condition.5 The partial derivatives of input prices are stable with little variation across models. As for university type, the partial derivatives of labor prices are higher i","PeriodicalId":46682,"journal":{"name":"Education Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134885414","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}
Pub Date : 2023-09-11DOI: 10.1080/09645292.2023.2252620
David Contreras
This paper examines the presence of systematic differences in teachers' grading behaviour across gender and whether these can be attributed to teacher bias. This study measures these differences by comparing teachers' grades with national exams, which are externally and anonymously marked. Consistent with the literature, the gender gap in teacher grading is against boys. Using a dataset with gender gaps at class-subject level – which allows to follow teachers in different classes over time – this study shows that teachers' grading behaviour is not persistent across classes. Results suggest that gender grading gaps are explained by differences in students' behaviour.
{"title":"Gender differences in grading: teacher bias or student behaviour?","authors":"David Contreras","doi":"10.1080/09645292.2023.2252620","DOIUrl":"https://doi.org/10.1080/09645292.2023.2252620","url":null,"abstract":"This paper examines the presence of systematic differences in teachers' grading behaviour across gender and whether these can be attributed to teacher bias. This study measures these differences by comparing teachers' grades with national exams, which are externally and anonymously marked. Consistent with the literature, the gender gap in teacher grading is against boys. Using a dataset with gender gaps at class-subject level – which allows to follow teachers in different classes over time – this study shows that teachers' grading behaviour is not persistent across classes. Results suggest that gender grading gaps are explained by differences in students' behaviour.","PeriodicalId":46682,"journal":{"name":"Education Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135939224","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}
Pub Date : 2023-09-11DOI: 10.1080/09645292.2023.2254516
Marcelo Castro, Breno da Cruz
{"title":"Effects of a large-scale program for the construction of daycare and preschool centers on cognitive skills and female employment","authors":"Marcelo Castro, Breno da Cruz","doi":"10.1080/09645292.2023.2254516","DOIUrl":"https://doi.org/10.1080/09645292.2023.2254516","url":null,"abstract":"","PeriodicalId":46682,"journal":{"name":"Education Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135982209","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}
Pub Date : 2023-08-05DOI: 10.1080/09645292.2023.2244702
Katikar Tipayalai, Chayaton Subchavaroj
{"title":"Assessing the spatial impact of educational attainment on poverty reduction in Thailand","authors":"Katikar Tipayalai, Chayaton Subchavaroj","doi":"10.1080/09645292.2023.2244702","DOIUrl":"https://doi.org/10.1080/09645292.2023.2244702","url":null,"abstract":"","PeriodicalId":46682,"journal":{"name":"Education Economics","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49303907","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}
Pub Date : 2023-08-04DOI: 10.1080/09645292.2023.2243550
Vladana Djinovic, N. Giannakopoulos
{"title":"Home computer ownership and educational outcomes of adolescents in Greece","authors":"Vladana Djinovic, N. Giannakopoulos","doi":"10.1080/09645292.2023.2243550","DOIUrl":"https://doi.org/10.1080/09645292.2023.2243550","url":null,"abstract":"","PeriodicalId":46682,"journal":{"name":"Education Economics","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48395150","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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