{"title":"超高速宽带对劳动收入的影响:一种事件研究方法","authors":"Laura Abrardi, Carlo Cambini, Lorien Sabatino","doi":"10.1080/10438599.2023.2275211","DOIUrl":null,"url":null,"abstract":"ABSTRACTWe investigate the impact of ultra-fast broadband connections on labor income and employment. We use panel data for Italian municipalities for the period 2012–2019 and we exploit the staggered roll-out of ultra-fast broadband started in 2015. Through an event study approach, we find evidence of endogeneity between ultra-fast broadband roll-out and labor market outcomes. To identify causal relationships, we use income from pensions to implement the estimator developed by [Freyaldenhoven, S., C. Hansen, and J. M. Shapiro. 2019. “Pre-Event Trends in the Panel Event-Study Design.” American Economic Review 109 (9): 3307–3338. https://doi.org/10.1257/aer.20180609.]. We find that access to ultra-fast broadband increases the income of the self-employed by 1.3% but has no impact on workers. Such an effect is mostly driven by a rise in self-employed workers, which is concentrated in urban areas, and in municipalities at the top and bottom quartiles of labor income.KEYWORDS: Ultra-fast broadbandfiber-based networkslabor incomeself-employed workersJEL CODES: L96D24D22 AcknowledgmentsWe would like to thank the Editor, three anonymous Referees, as well as Fabio Landini, Giovanni Cerulli and the participants to the SIE 2022 (Torino) and SIEPI 2022 (L'Aquila) for useful comments and suggestions to previous versions of the paper. We are grateful to Mario Mirabelli (TIM-LAB) and Francesco Nonno (OpenFiber) for providing us with access to and guidance on the broadband data used in this paper. The views expressed herein represent those of the authors and do not reflect in any case the opinions of the companies and institutions that provided the data and funding.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Indeed, starting in 2018, the Italian government has increased the financial resources from 0.5 to 7 billion Euros for UBB. In 2021, the Italian Government has decided to use part of the Next Generation EU funds to finalize the deployment of UBB infrastructure throughout the country, with around 3.6 billion Euros of public expenditure.2 The two papers also differ in the UBB variable used. While we consider a dummy variable describing the availability of a UBB access in a municipality in a given year, Abrardi and Sabatino (Citation2023) use the number of years since UBB was introduced in a given municipality.3 Higher broadband speed levels may also affect property prices (Ahlfeldt, Koutroumpis, and Valletti Citation2017) and firms' location decisions (Canzian, Poy, and Schüller Citation2019; Duvivier Citation2019).4 The Digital Agenda for Europe specifies the goals in terms of network coverage and service adoption for the whole European population. See https://www.europarl.europa.eu/factsheets/en/sheet/64/digital-agenda-for-europe for more.5 https://www.agcom.it/documents/10179/1571667/Documento+generico+08-11-2014+1415441917492/d34cc914-c150-4fd7-a383-a0c39c9d7670?version=1.16 Before 2015 only a few large cities such as Milan and Bologna enjoyed fiber-based connections realized by the local telecommunication operator.7 Open Fiber deployment plan can be found here: https://openfiber.it/area-infratel/piano-copertura/.8 For privacy reasons, data are missing when municipalities have less than three taxpayers for a particular category of income. This explains the lower number of observations for self-employed income, as in small municipalities there may be less than three self-employed workers. For the calculation of total labor income, we treat missing values as zeroes.9 Results are not affected by different clustering methods.10 Since our sample covers from 2012 to 2019, then r={−7,−6,…,0,+1,..,+4}.11 In Italy, the pension benefit is indexed to the accumulated lifelong contributions valorized with the nominal GDP growth rate (as a five-year moving average).12 The Italian government introduced some (limited) flexibility only after 2019, by allowing early retirement under specific age and contribution conditions (i.e. workers must be no less than 62 years old and have made qualifying contributions for not less than 38 years) (OECD Citation2021).13 In most industrialized countries, the growth of wages in recent decades has been lower than that of labor productivity, resulting in a decline in the share of value added attributable to paid employment (Istat Citation2018). The growth rate of payroll wages has been particularly low in Italy, where average wages declined by around 5% from 2006 to 2015 (Istat Citation2018).14 To ease the comparison with the baseline model, we report fixed effect results in Appendix Table A1. As can be seen, results are qualitatively the same but generally larger in magnitude, consistent with the positive bias detected so far. Interestingly enough, OLS estimates suggest a positive impact on per capita self-employment income, which however is not confirmed by the FHS estimates.15 According to Istat data, the unemployment rate in Southern regions in 2019 was 17.9%, versus 6.6% in the North-West. See http://dati.istat.it.16 The share of the population with tertiary education in 2020 in Italy is 21.3% in the North, 24.2% in the Center, and 16.2% in the South. Data are available at https://italiaindati.com/laureati-in-italia/.17 We report fixed effect estimates of the heterogeneous effects in Appendix Tables A2 and A3. As can be seen, the results are again qualitatively similar and slightly larger in magnitude, thus increasing the confidence in our main results.18 From a geographical perspective, Italy is partitioned into 610 LLS, 107 provinces, and 20 administrative regions.19 The first stage F-test is well below 10.Additional informationFundingWe acknowledge financial support from TIM-LAB (Turin) and Ministero dell'Istruzione, dell'Università e della Ricerca, Award TESUN - 83486178370409, finanziamento dipartimenti di eccellenza, CAP. 1694 TIT. 232 ART. 6.","PeriodicalId":51485,"journal":{"name":"Economics of Innovation and New Technology","volume":"4 8","pages":"0"},"PeriodicalIF":3.2000,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The impact of ultra-fast broadband on labor income: an event study approach\",\"authors\":\"Laura Abrardi, Carlo Cambini, Lorien Sabatino\",\"doi\":\"10.1080/10438599.2023.2275211\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACTWe investigate the impact of ultra-fast broadband connections on labor income and employment. We use panel data for Italian municipalities for the period 2012–2019 and we exploit the staggered roll-out of ultra-fast broadband started in 2015. Through an event study approach, we find evidence of endogeneity between ultra-fast broadband roll-out and labor market outcomes. To identify causal relationships, we use income from pensions to implement the estimator developed by [Freyaldenhoven, S., C. Hansen, and J. M. Shapiro. 2019. “Pre-Event Trends in the Panel Event-Study Design.” American Economic Review 109 (9): 3307–3338. https://doi.org/10.1257/aer.20180609.]. We find that access to ultra-fast broadband increases the income of the self-employed by 1.3% but has no impact on workers. Such an effect is mostly driven by a rise in self-employed workers, which is concentrated in urban areas, and in municipalities at the top and bottom quartiles of labor income.KEYWORDS: Ultra-fast broadbandfiber-based networkslabor incomeself-employed workersJEL CODES: L96D24D22 AcknowledgmentsWe would like to thank the Editor, three anonymous Referees, as well as Fabio Landini, Giovanni Cerulli and the participants to the SIE 2022 (Torino) and SIEPI 2022 (L'Aquila) for useful comments and suggestions to previous versions of the paper. We are grateful to Mario Mirabelli (TIM-LAB) and Francesco Nonno (OpenFiber) for providing us with access to and guidance on the broadband data used in this paper. The views expressed herein represent those of the authors and do not reflect in any case the opinions of the companies and institutions that provided the data and funding.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Indeed, starting in 2018, the Italian government has increased the financial resources from 0.5 to 7 billion Euros for UBB. In 2021, the Italian Government has decided to use part of the Next Generation EU funds to finalize the deployment of UBB infrastructure throughout the country, with around 3.6 billion Euros of public expenditure.2 The two papers also differ in the UBB variable used. While we consider a dummy variable describing the availability of a UBB access in a municipality in a given year, Abrardi and Sabatino (Citation2023) use the number of years since UBB was introduced in a given municipality.3 Higher broadband speed levels may also affect property prices (Ahlfeldt, Koutroumpis, and Valletti Citation2017) and firms' location decisions (Canzian, Poy, and Schüller Citation2019; Duvivier Citation2019).4 The Digital Agenda for Europe specifies the goals in terms of network coverage and service adoption for the whole European population. See https://www.europarl.europa.eu/factsheets/en/sheet/64/digital-agenda-for-europe for more.5 https://www.agcom.it/documents/10179/1571667/Documento+generico+08-11-2014+1415441917492/d34cc914-c150-4fd7-a383-a0c39c9d7670?version=1.16 Before 2015 only a few large cities such as Milan and Bologna enjoyed fiber-based connections realized by the local telecommunication operator.7 Open Fiber deployment plan can be found here: https://openfiber.it/area-infratel/piano-copertura/.8 For privacy reasons, data are missing when municipalities have less than three taxpayers for a particular category of income. This explains the lower number of observations for self-employed income, as in small municipalities there may be less than three self-employed workers. For the calculation of total labor income, we treat missing values as zeroes.9 Results are not affected by different clustering methods.10 Since our sample covers from 2012 to 2019, then r={−7,−6,…,0,+1,..,+4}.11 In Italy, the pension benefit is indexed to the accumulated lifelong contributions valorized with the nominal GDP growth rate (as a five-year moving average).12 The Italian government introduced some (limited) flexibility only after 2019, by allowing early retirement under specific age and contribution conditions (i.e. workers must be no less than 62 years old and have made qualifying contributions for not less than 38 years) (OECD Citation2021).13 In most industrialized countries, the growth of wages in recent decades has been lower than that of labor productivity, resulting in a decline in the share of value added attributable to paid employment (Istat Citation2018). The growth rate of payroll wages has been particularly low in Italy, where average wages declined by around 5% from 2006 to 2015 (Istat Citation2018).14 To ease the comparison with the baseline model, we report fixed effect results in Appendix Table A1. As can be seen, results are qualitatively the same but generally larger in magnitude, consistent with the positive bias detected so far. Interestingly enough, OLS estimates suggest a positive impact on per capita self-employment income, which however is not confirmed by the FHS estimates.15 According to Istat data, the unemployment rate in Southern regions in 2019 was 17.9%, versus 6.6% in the North-West. See http://dati.istat.it.16 The share of the population with tertiary education in 2020 in Italy is 21.3% in the North, 24.2% in the Center, and 16.2% in the South. Data are available at https://italiaindati.com/laureati-in-italia/.17 We report fixed effect estimates of the heterogeneous effects in Appendix Tables A2 and A3. As can be seen, the results are again qualitatively similar and slightly larger in magnitude, thus increasing the confidence in our main results.18 From a geographical perspective, Italy is partitioned into 610 LLS, 107 provinces, and 20 administrative regions.19 The first stage F-test is well below 10.Additional informationFundingWe acknowledge financial support from TIM-LAB (Turin) and Ministero dell'Istruzione, dell'Università e della Ricerca, Award TESUN - 83486178370409, finanziamento dipartimenti di eccellenza, CAP. 1694 TIT. 232 ART. 6.\",\"PeriodicalId\":51485,\"journal\":{\"name\":\"Economics of Innovation and New Technology\",\"volume\":\"4 8\",\"pages\":\"0\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2023-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Economics of Innovation and New Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/10438599.2023.2275211\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Economics of Innovation and New Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/10438599.2023.2275211","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
摘要
摘要本文研究超高速宽带连接对劳动收入和就业的影响。我们使用了2012-2019年意大利各市的面板数据,并利用了2015年开始的超高速宽带的交错部署。通过事件研究方法,我们发现了超高速宽带部署与劳动力市场结果之间存在内生性的证据。为了确定因果关系,我们使用养老金收入来实现由[Freyaldenhoven, S., C. Hansen, and J. M. Shapiro. 2019]开发的估计器。“小组事件研究设计中的事件前趋势”经济研究,2009(9):397 - 398。https://doi.org/10.1257/aer.20180609。]。我们发现,超高速宽带的接入使个体经营者的收入增加了1.3%,但对工人没有影响。这种影响主要是由个体经营者的增加所驱动的,这些个体经营者集中在城市地区,以及劳动收入最高和最低四分之一的城市。关键词:超高速宽带光纤网络劳动收入个体劳动者jel代码:L96D24D22致谢我们要感谢编辑,三位匿名审稿人,以及Fabio Landini, Giovanni Cerulli和SIEPI 2022(都灵)和SIEPI 2022(拉奎拉)的参与者对本文以前版本的有用意见和建议。我们感谢Mario Mirabelli (TIM-LAB)和Francesco Nonno (OpenFiber)为我们提供了本文中使用的宽带数据的访问和指导。本文所表达的观点代表作者的观点,在任何情况下都不反映提供数据和资金的公司和机构的观点。披露声明作者未报告潜在的利益冲突。注1事实上,从2018年开始,意大利政府已将UBB的财政资源从5亿欧元增加到70亿欧元。2021年,意大利政府决定使用部分下一代欧盟基金在全国范围内完成UBB基础设施的部署,公共支出约为36亿欧元这两篇论文所使用的UBB变量也有所不同。当我们考虑一个虚拟变量来描述某一特定年份某一城市UBB接入的可用性时,Abrardi和Sabatino (Citation2023)使用了自UBB在某一特定城市引入以来的年数更高的宽带速度水平也可能影响房地产价格(Ahlfeldt, Koutroumpis和Valletti Citation2017)和公司的选址决策(Canzian, Poy和sch<s:1> ller Citation2019;Duvivier Citation2019) 4。欧洲数字议程规定了整个欧洲人口的网络覆盖和服务采用方面的目标。更多信息请参见https://www.europarl.europa.eu/factsheets/en/sheet/64/digital-agenda-for-europe。5 https://www.agcom.it/documents/10179/1571667/Documento+generico+08-11-2014+1415441917492/d34cc914-c150-4fd7-a383-a0c39c9d7670?version=1.16在2015年之前,只有米兰和博洛尼亚等少数大城市享有由当地电信运营商实现的光纤连接Open Fiber部署计划可以在这里找到:https://openfiber.it/area-infratel/piano-copertura/.8出于隐私原因,当市政当局的特定收入类别的纳税人少于三个时,数据就会丢失。这解释了自雇收入的观察数字较低,因为在小城市,自雇工人可能少于三个。在计算总劳动收入时,我们把缺失值当作零结果不受不同聚类方法的影响由于我们的样本涵盖2012年至2019年,那么r={−7,−6,…,0,+1,…,+4}.11在意大利,养恤金福利以累积终身缴款为指数,以名义国内生产总值增长率(作为五年移动平均值)计算意大利政府仅在2019年之后引入了一些(有限的)灵活性,允许在特定年龄和缴款条件下提前退休(即工人必须不低于62岁,并已作出不少于38年的合格缴款)(OECD Citation2021)在大多数工业化国家,近几十年来工资的增长低于劳动生产率的增长,导致可归因于有偿就业的增加值份额下降(Istat Citation2018)。意大利的工资增长率特别低,从2006年到2015年,意大利的平均工资下降了约5% (Istat Citation2018)为了便于与基线模型进行比较,我们在附录表A1中报告了固定效应结果。可以看出,结果在质量上是相同的,但通常在量级上更大,与迄今为止检测到的正偏差一致。有趣的是,OLS的估计表明对人均自营职业收入的积极影响,然而,这并没有得到FHS的估计的证实。
The impact of ultra-fast broadband on labor income: an event study approach
ABSTRACTWe investigate the impact of ultra-fast broadband connections on labor income and employment. We use panel data for Italian municipalities for the period 2012–2019 and we exploit the staggered roll-out of ultra-fast broadband started in 2015. Through an event study approach, we find evidence of endogeneity between ultra-fast broadband roll-out and labor market outcomes. To identify causal relationships, we use income from pensions to implement the estimator developed by [Freyaldenhoven, S., C. Hansen, and J. M. Shapiro. 2019. “Pre-Event Trends in the Panel Event-Study Design.” American Economic Review 109 (9): 3307–3338. https://doi.org/10.1257/aer.20180609.]. We find that access to ultra-fast broadband increases the income of the self-employed by 1.3% but has no impact on workers. Such an effect is mostly driven by a rise in self-employed workers, which is concentrated in urban areas, and in municipalities at the top and bottom quartiles of labor income.KEYWORDS: Ultra-fast broadbandfiber-based networkslabor incomeself-employed workersJEL CODES: L96D24D22 AcknowledgmentsWe would like to thank the Editor, three anonymous Referees, as well as Fabio Landini, Giovanni Cerulli and the participants to the SIE 2022 (Torino) and SIEPI 2022 (L'Aquila) for useful comments and suggestions to previous versions of the paper. We are grateful to Mario Mirabelli (TIM-LAB) and Francesco Nonno (OpenFiber) for providing us with access to and guidance on the broadband data used in this paper. The views expressed herein represent those of the authors and do not reflect in any case the opinions of the companies and institutions that provided the data and funding.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Indeed, starting in 2018, the Italian government has increased the financial resources from 0.5 to 7 billion Euros for UBB. In 2021, the Italian Government has decided to use part of the Next Generation EU funds to finalize the deployment of UBB infrastructure throughout the country, with around 3.6 billion Euros of public expenditure.2 The two papers also differ in the UBB variable used. While we consider a dummy variable describing the availability of a UBB access in a municipality in a given year, Abrardi and Sabatino (Citation2023) use the number of years since UBB was introduced in a given municipality.3 Higher broadband speed levels may also affect property prices (Ahlfeldt, Koutroumpis, and Valletti Citation2017) and firms' location decisions (Canzian, Poy, and Schüller Citation2019; Duvivier Citation2019).4 The Digital Agenda for Europe specifies the goals in terms of network coverage and service adoption for the whole European population. See https://www.europarl.europa.eu/factsheets/en/sheet/64/digital-agenda-for-europe for more.5 https://www.agcom.it/documents/10179/1571667/Documento+generico+08-11-2014+1415441917492/d34cc914-c150-4fd7-a383-a0c39c9d7670?version=1.16 Before 2015 only a few large cities such as Milan and Bologna enjoyed fiber-based connections realized by the local telecommunication operator.7 Open Fiber deployment plan can be found here: https://openfiber.it/area-infratel/piano-copertura/.8 For privacy reasons, data are missing when municipalities have less than three taxpayers for a particular category of income. This explains the lower number of observations for self-employed income, as in small municipalities there may be less than three self-employed workers. For the calculation of total labor income, we treat missing values as zeroes.9 Results are not affected by different clustering methods.10 Since our sample covers from 2012 to 2019, then r={−7,−6,…,0,+1,..,+4}.11 In Italy, the pension benefit is indexed to the accumulated lifelong contributions valorized with the nominal GDP growth rate (as a five-year moving average).12 The Italian government introduced some (limited) flexibility only after 2019, by allowing early retirement under specific age and contribution conditions (i.e. workers must be no less than 62 years old and have made qualifying contributions for not less than 38 years) (OECD Citation2021).13 In most industrialized countries, the growth of wages in recent decades has been lower than that of labor productivity, resulting in a decline in the share of value added attributable to paid employment (Istat Citation2018). The growth rate of payroll wages has been particularly low in Italy, where average wages declined by around 5% from 2006 to 2015 (Istat Citation2018).14 To ease the comparison with the baseline model, we report fixed effect results in Appendix Table A1. As can be seen, results are qualitatively the same but generally larger in magnitude, consistent with the positive bias detected so far. Interestingly enough, OLS estimates suggest a positive impact on per capita self-employment income, which however is not confirmed by the FHS estimates.15 According to Istat data, the unemployment rate in Southern regions in 2019 was 17.9%, versus 6.6% in the North-West. See http://dati.istat.it.16 The share of the population with tertiary education in 2020 in Italy is 21.3% in the North, 24.2% in the Center, and 16.2% in the South. Data are available at https://italiaindati.com/laureati-in-italia/.17 We report fixed effect estimates of the heterogeneous effects in Appendix Tables A2 and A3. As can be seen, the results are again qualitatively similar and slightly larger in magnitude, thus increasing the confidence in our main results.18 From a geographical perspective, Italy is partitioned into 610 LLS, 107 provinces, and 20 administrative regions.19 The first stage F-test is well below 10.Additional informationFundingWe acknowledge financial support from TIM-LAB (Turin) and Ministero dell'Istruzione, dell'Università e della Ricerca, Award TESUN - 83486178370409, finanziamento dipartimenti di eccellenza, CAP. 1694 TIT. 232 ART. 6.
期刊介绍:
Economics of Innovation and New Technology is devoted to the theoretical and empirical analysis of the determinants and effects of innovation, new technology and technological knowledge. The journal aims to provide a bridge between different strands of literature and different contributions of economic theory and empirical economics. This bridge is built in two ways. First, by encouraging empirical research (including case studies, econometric work and historical research), evaluating existing economic theory, and suggesting appropriate directions for future effort in theoretical work. Second, by exploring ways of applying and testing existing areas of theory to the economics of innovation and new technology, and ways of using theoretical insights to inform data collection and other empirical research. The journal welcomes contributions across a wide range of issues concerned with innovation, including: the generation of new technological knowledge, innovation in product markets, process innovation, patenting, adoption, diffusion, innovation and technology policy, international competitiveness, standardization and network externalities, innovation and growth, technology transfer, innovation and market structure, innovation and the environment, and across a broad range of economic activity not just in ‘high technology’ areas. The journal is open to a variety of methodological approaches ranging from case studies to econometric exercises with sound theoretical modelling, empirical evidence both longitudinal and cross-sectional about technologies, regions, firms, industries and countries.