全球经济展望报告-PDF版

世界银行集团（WBG）：2021全球数字能力研究报告（英文版）（89页）.pdf

这项关于数字经济数字能力的全球研究是预由 Samia Melhem(领导数字发展)领导的世界银行团队削减数字开发全球实践专家)，由 Astrid Herdis 共同领导 Jacobsen(全球数字开发.

2021-08-16 89页 5星级

世界银行：全球生产率：趋势、推动力与政策（英文版）（460页）.pdf

据估计，2019年全世界共有1490万项专利生效。美国(310万)、中国(270万)和日本(210万)的专利数量最多。在全球5820万商标注册活动中，生效数量最多的是中国(2520万)，其次是美国(2.

2021-08-02 460页 5星级

国际货币基金组织（IMF）：2021年世界经济展望报告（7月刊）（英文版）（21页）.pdf

自2021年4月世界经济展望(WEO)预测以来，各国的经济前景进一步分化。疫苗接种已成为全球经济复苏分裂为两大阵营的主要断层线：一是有望在今年晚些时候实现活动进一步正常化的阵营(几乎所有发达经济体)，.

2021-07-30 21页 5星级

新加坡金融管理局（MAS）：2021年新加坡宏观经济回顾报告（英文版）（120页）.pdf

在本期审查中，特写A指出了数字解决方案如何越来越多地被用于应对气候变化带来的挑战，并突出了包括绿色金融在内的各个部门的实例和机遇。我们感谢杰拉德乔治教授和西蒙J。D来自新加坡管理大学(SMU)的Schillebeeckx为这篇文章做贡献。我们要感谢全球预测模型网络提供的特殊功能B，它描述了MAS使用的全球预测模型的主要特征，并说明了如何使用该模型来模拟解决政策相关问题的替代方案。在与东盟 3宏观经济研究办公室(AMRO)合作编写的方框A中，使用了一个多国多部门模型来估计区域全面经济伙伴关系(RCEP)对成员国经济的影响。我们也很高兴展示方框B，其中概述了新加坡劳动力市场政策应对COVID-19危机各个阶段的演变。我们也感谢Sumit Agarwal教授和Bernard Yeung教授，他们来自新加坡国立大学(NUS)商学院和亚洲金融与经济研究局(ABFER)，他们对Box C做出了贡献。该框强调了通胀预期和家庭消费的关键见解，这些见解在央行研究协会2020年年会的ABFER会议上提出。他们强调消费者价格预期在影响关键经济变量方面的重要性，因此货币政策制定者需要了解这些预期的形成。最后，我们要感谢彼得威尔逊教授协助编辑这篇评论的各个部分。在其2020年10月的货币政策声明中，新加坡金融管理局将新台币政策区间的升值率保持在每年0%，政策区间的宽度或其中心水平不变。鉴于核心通胀前景疲弱，经评估，这一政策立场是适当的。尽管今年核心通胀率预计将上升并逐渐转为正值，但仍将远低于长期平均水平。在过去六个月里，新台币的净入学率在政策区间的中点上方略有波动。新币净汇率小幅升值，部分反映出随着全球风险情绪改善，新币兑大多数储备货币走强。三个月期新加坡元综合银行同业拆借利率基本持平于0.4%。全球经济增长的前景已经稳固，应该为新加坡经济的持续复苏提供支持。尽管如此，2021年的产出仍将低于潜在水平。尽管预计今年MAS核心通胀率将从目前的低水平逐步上升，但仍将低于历史平均水平。贸易和工业部4月14日发布的预测显示，新加坡2021年第一季度经季节性调整后的经济季度环比增长率为2.0%，低于2020年第四季度的3.8%。第一季度的连续放缓主要是由建筑业推动的，活动继续受到工地安全距离措施的限制。相比之下，在医药产量反弹的背景下，制造业恢复正增长。一年前，GDP在连续三个季度下降后，第一季度微涨0.2%。

2021-07-27 120页 5星级

墨卡托中国研究所：中国的下个世纪：经济管理、数字治理和国家安全（英文版）（78页）.pdf

点击查看更多墨卡托中国研究所：中国的下个世纪：经济管理、数字治理和国家安全（英文版）（78页）.pdf精彩内容。

2021-07-26 78页 5星级

全球发展中心（CGD）：中巴经济走廊及其对巴基斯坦经济的影响（英文版）（27页）.pdf

中国的海外投资项目，特别是在“一带一路”倡议下，正在受到学术界和政策界的广泛审查。然而，没有足够的经验证据来评估其影响。本文采用差异中的方法和世界银行编制的一对新的政府支出和经济活动数据集，考察了中巴经济走廊对巴基斯坦的局部影响。研究发现，2013年CPEC的宣布伴随着CPEC地区政府支出的不成比例增长。然而，在最初宣布后的六年里，CPEC并没有直接促进沿途地区经济活动的显著增长。在过去十年中，对中国资本流入发展中国家的分析急剧增加，但中国国内和国外资本流动的特点之间存在着持续的脱节。在中国，官方媒体一贯强调中国对发展中国家的贷款和投资对发展的影响。在美国，有关中国资本外流的学术和媒体讨论往往强调其债务和地缘战略影响，往往忽略了这些资本外流对受援国支出、基础设施和经济活动的地面影响。这种脱节在巴基斯坦最为普遍。自从中国第一次宣布中巴经济走廊(CPEC)一项620亿美元的基础设施投资计划，旨在将中国西部与巴基斯坦港口城市瓜达尔连接起来以来，中国媒体一直将该项目称为“巴基斯坦增长的基石”。1但尽管外部分析人士提出了不少建议在谴责中国在巴基斯坦基础设施上的大规模支出对地缘战略的影响的文献中，中国关于这一支出对经济影响的说法基本上没有得到审查。这项研究，委托CGD和作者大卫兰德里博士，试图解决这一差距的文献。Landry博士对世界银行的两个新数据集采用了差异中的差异方法，对CPEC的宣布如何影响巴基斯坦在次国家一级的支出和经济活动进行了描述性分析。作者发现，尽管巴基斯坦政府在2013年宣布该项目后，在CPEC邻近地区的支出明显增加，但迄今为止，这些支出中几乎没有导致经济活动的增长。这些发现提出了几个关键问题，需要在未来几年进一步分析。为什么中国宣布建设基础设施会引发巴基斯坦政府国内消费习惯的转变?在巴基斯坦政府支出发生这种变化之后，非CPEC邻近地区的情况如何?为什么巴基斯坦和中国政府增加投资的前景未能在CPEC邻近地区产生经济活动收益?虽然这项研究只提供了中国对外发展举措的局部效应的一瞥，但它提供了一个新的模型，用以研究中国发展金融在次国家一级对受援国的影响。此外，它还提供了一个框架，让人们了解中国自己对国际发展的看法，重点是中国发展支出的实际接受者，而不是其地缘政治背景。

2021-07-26 27页 5星级

国际货币基金组织（IMF）：2021年世界经济展望报告（4月刊）（英文版）（191页）.pdf

这是自COVID-19被宣布为全球大流行以来的一年，也是生命和生计遭受严重损失的一年。与世界各地的许多人一样，制定世界经济展望的团队也因疫情蔓延而失去了亲人。全世界不断上升的人口伤亡和数百万人仍然失业.

2021-07-22 191页 5星级

花旗银行：2021年中期展望报告：后疫情时代新经济的新投资组合（英文版）（55页）.pdf

我非常高兴地向大家介绍2021年中期展望走向后科维德时代：新经济的新投资组合。这是自今年早些时候花旗环球财富(Citi Global Wealth)成立以来的第一版。我们的使命是为全球所有客户提供最好.

2021-07-21 55页 5星级

国际政治研究所：疫情后的中国：经济复苏与对世界的挑战（英文版）（183页）.pdf

自2019年12月以来震撼中国的冠状病毒大流行对中国国民经济的韧性构成了严峻考验。这一前提对中国政府来说一点也不积极，中国政府在进入危机之际，已经受到了与美国贸易战的深刻影响、危险的内部再平衡以及在国内债务不断增加的情况下进行金融去杠杆化的必要性的削弱。这场大流行进一步加剧了这些挑战，2020年，中国GDP仅增长2.3%这是历史上最糟糕的表现之一。然而，这一次，对于中国来说，过去又是一个序幕。在过去的20年里，中国在应对重大危机的影响方面取得了巨大的成功，例如2007-08年的金融危机或2003年SARS疫情爆发后的金融危机。如今，尽管2020年的经济表现相当疲弱，但随着零售消费、投资和贸易这三个关键经济指标的迅速复苏，中国经济仍在迅速复苏。如果说有什么区别的话，那就是经济过热，2021年第一季度GDP同比增长18%。特别是零售消费的增长表明，领导层成功地重建了消费者信心。与全球金融危机余波中的应对措施相比，中国应对全球经济衰退的方式的特点是，中国的精英们在很大程度上追求创新。尽管中国的经济体制与欧洲截然不同，但在危机时期，中国仍然是一个有影响力的比较词，至于对2020年全球需求和产出下降的反应，财政刺激是迄今为止帮助经济走出危机的最重要政策，尽管人们不应忘记，与全球金融危机后相比，今天中国依靠更有限的工具箱来重启经济。事实上，后冠状病毒时代的中国已将国内经济放在首位，而复苏措施的目的并不像过去那样是为了支持世界其他地区的增长。从这个意义上讲，一个关键的指标是中国所称的“新投资项目”的框架：中国的投资现在更具选择性，优先考虑国内消费和国民经济的数字化，而不利于传统的大型基础设施项目。此外，北京领导层采取的货币政策表明，北京愿意在大流行后的复苏中进行创新，这一政策显然没有其他国家采取的措施那么激进，而且包括了非常具体的目标，重点是中小企业，即：这个国家劳动力市场的“引擎”。有鉴于此，本报告探讨了中国如何设计和实施后危机复苏战略，同时关注中国在短期和中期内必须克服的内外部危机。特别是，报告有两条主线。首先，它关注中国的国内情况，详细描述了流感大流行对经济和政治体系的影响，以及对中国复苏政策选择的影响。其次，它着眼于大流行对国家国际战略的影响，主要着眼于与美国的经贸关系的未来和“一带一路倡议”(BRI)的前景。

2021-07-21 183页 5星级

经合组织（OECD）：2021年印度尼西亚经济调查报告（英文版）（59页）.pdf

经过20年的持续稳定增长，这场大流行引发了一场“完美风暴”。2020年，印尼国内生产总值(GDP)收缩，印尼的一些脆弱性凸显出来，尽管史无前例的政策干预限制了这种损害。2020年的衰退是普遍的。大多数.

2021-07-14 59页 5星级

美联储：2021年上半年美国货币政策报告（英文版）（75页）.pdf

联邦公开市场委员会(FOMC)坚定地致力于履行国会赋予的促进最大就业、稳定物价和适度长期利率的法定任务。委员会力求向公众尽可能清楚地解释其货币政策决定。这种明晰有助于家庭和企业作出知情的决策，减少经济.

2021-07-14 75页 5星级

城市未来中心（CUF）：布鲁克林不断增长的创新经济（英文版）（21页）.pdf

与过去十年中的许多其他城市中心一样，布鲁克林在医疗、零售、餐饮和教育领域的就业增长强劲。但由于布鲁克林在创新经济方面取得的巨大成功，布鲁克林的经济发展速度已经超过了其他大多数地方。正如这些数据的简要细节所示，布鲁克林是全国少数几个在创新经济增长中占据重要份额的地区之一，创新经济是由技术、创造力和发明推动的一系列行业，正是这些行业推动着美国高薪就业的增长。在过去十年中，布鲁克林在这些创新产业方面的表现超过了纽约市其他地区，这些产业为纽约人增加了数千个高薪工作岗位，有助于该区经济的多样化，并使布鲁克林在未来几年有望大幅增长的经济领域拥有重要的竞争优势。城市未来中心(CUF)的这项分析发现，布鲁克林受益于创新经济所有三个核心领域的持续增长：科技初创企业、创意公司和下一代制造商和制造商。胡国的所有主要技术中心，布鲁克林区自2008以来的启动增长率仅次于旧金山。布鲁克林356%的增长率超过了纽约(308%)、费城(290%)、洛杉矶(279%)和芝加哥(270%)。我们对来自Crunchbase的数据进行了详细分析，Crunchbase是一个领先的全球数据库，利用公共、私人和自我报告的混合来源跟踪科技型初创企业，数据显示布鲁克林在2018年拥有1205家科技型初创企业，而2008年只有264家。布鲁克林目前拥有纽约市9.2%的科技初创企业，高于2000年的6.3%，而且比以往任何时候都高。从2007年到2017年，布鲁克林科技行业的就业人数增长了175%，比曼哈顿86%的增长率高出一倍多。布鲁克林的初创企业集中在媒体娱乐(249家初创企业)、商业和购物(174家)、金融服务(102家)以及数据和分析(81家)领域。但在过去三年里，布鲁克林在一些新兴领域也出现了显著增长，包括人工智能(23家初创企业)、区块链(14家)和虚拟现实(8家)。过去十年，布鲁克林创意产业的就业岗位增长了155%，大大超过了曼哈顿创意经济16%的增长速度。

2021-07-14 21页 5星级

香港特别行政区：2021年第一季度经济报告（英文版）（154页）.pdf

香港经济在2021第一季度明显复苏，出口强劲增长，全球需求急剧反弹。实际国内生产总值(GDP)(1)恢复了7.9%的可观同比增长，结束了连续六个季度的萎缩。经季节性调整的季度间比较(2)，实际国内生产总值显著增长5.4%，连续三个季度增长。然而，经济复苏不平衡，总体经济活动仍低于衰退前的水平。为了实现更广泛的经济复苏，社会各界必须共同努力，控制疫情，并积极参与疫苗接种计划。由于许多主要市场的进口需求回升，一季度商品出口总额激增。对中国大陆的出口激增，对美国和欧盟的出口增长强劲。亚洲其他主要市场的出口也明显回升。服务出口下降幅度明显收窄。尽管入境旅游业仍然低迷，但随着全球经济复苏和活跃的区域贸易流动，跨境运输和商业服务业有所改善，金融服务出口继续扩大。国内需求进一步复苏，但仍相对低迷。私人消费支出在第一季度仅小幅增长，即使在比较基数极低的情况下也是如此，因为第四波本地疫情扰乱了消费活动，特别是在本季度初，出境旅游受到严重阻碍。在企业前景不那么悲观的情况下，整体投资支出持续温和增长。劳动力市场在第一季度受到显著压力，不过随着疫情消退，劳动力市场在本季度后期趋于稳定。经济复苏转化为更明显的劳动力市场复苏可能需要一段时间。经季节性调整的失业率从2020年第四季度的6.6%上升到2021年2月结束的三个月期间的17年高点7.2%，然后在2021年第一季度下降到6.8%，就业不足率从2020年第四季度的3.4%上升到截至2021年2月的三个月期间的4.0%，然后在2021年第一季度小幅下降到3.8%。一季度住宅地产市场活跃。贸易活动进一步回升，而持平价格恢复到2%的增长。一季度居民消费价格压力进一步缓解。这主要归因于食品通胀的缓解和私人住房租金的大幅下降。由于整体经济活动仍低于衰退前水平，其他主要消费物价指数构成部分的价格压力仍然非常温和。

2021-07-14 154页 5星级

城市未来中心（CUF）：让纽约人为推动纽约经济的科技工作做好准备（英文版）（16页）.pdf

到2020年，纽约市的就业岗位比COVID-19流感爆发时减少了约60万个。但即使在过去一年的经济灾难中，纽约的一些雇主仍在招人。虽然从仓储、运输到医疗等领域都在创造新的就业岗位，但技术岗位的增长速度最快。这项对Burning Glass Technologies收集的纽约市就业岗位数据的分析显示，在流感大流行期间，技术岗位在总招聘需求中领先于所有其他职业。尽管流感大流行推动了医疗行业招聘的激增，但在2020年4月至11月期间，科技行业(67923人)的职位空缺多于医疗行业(60266人)。科技行业的招聘需求也是金融行业的两倍多，是营销行业的三倍多，而且几乎是酒店和教育需求的五倍。从4月到11月，总共有近五分之一(18%)的职位是技术职位。从4月到11月，软件开发人员/工程师的职位空缺总数(21268个)超过了其他任何职业，除医生(12899个)外，其他每一个职位的空缺都超过一倍。但在高需求中，开发商远不是唯一的技术角色。在前50名最受欢迎的职位中，技术职位包括11个，包括IT项目经理(4104)、网络工程师/架构师(3066)、web开发人员(2678)、网络/信息安全工程师/分析员(2670)、计算机支持专家(2541)、计算机系统工程师/架构师(2438)、数据挖掘分析员(2182)、系统分析员(1939)，用户界面/用户体验设计师/开发者(1921)。重要的是，科技也推动了对高薪工作的需求。科技职业占总招聘职位的18%多一点，但对于平均起薪在8万美元或以上的职位来说，却占据了高达40.1%的需求。事实上，在138个薪水在8万美元或以上的职业中，有41个是技术职业。这项分析还发现，与其他任何领域相比，科技领域的不同职业都有更大的招聘需求。例如，从4月到11月，19个科技职业至少有1000个职位空缺，相比之下，14个医疗保健职业，10个金融业，6个酒店业，只有4个文书和行政职位空缺。此外，我们的分析发现，许多其他职业的需求强劲，这些职业不完全是技术角色，而是具有主要的技术成分，或者在技术部门广泛存在。其中包括业务开发/销售经理(6837人，需求排名第五)、营销经理(6762人，总体排名第六)、业务/管理分析师(6208人，总体排名第八)、产品经理(3531人，总体排名第十八)和招聘人员(2046人，总体排名第42)等。在纽约市从4月到11月公布的所有职位空缺中，有55%的职位需要强大的数字技能。

2021-07-14 16页 5星级

经济及社会理事会：2021年实现可持续发展目标进展报告（英文版）（32页）.pdf

1.2020年是人类历史上不平凡的一年。迄今为止，COVID-19大流行已经夺走了300多万人的生命，摧毁了全球经济，颠覆了人类生活的各个领域。2.在大流行病爆发之前，在执行可持续发展目标的一些重要领.

2021-07-12 32页 5星级

PBO：加拿大家庭财富分布的顶尾估计（英文版）（31页）.pdf

June 17, 2020ESTIMATING THE TOP TAIL OF THEFAMILY WEALTH DISTRIBUTION INCANADAPowered by TCPDF (www.tcpdf.org) The Parliamentary Budget Officer (PBO) supports Parliament by providing economic and financial analysis for the purposes of raising the quality of parliamentary debate and promoting greater budget transparency and accountability. PBO has developed a modelling approach to estimate the top tail of the family wealth distribution in Canada. The modelling approach produces a new micro database of high-net-worth families to undertake analytical and costing work. This report describes the approach to constructing the database and showcases its analytical capabilities. PBO wishes to acknowledge Professor Jim Davies, who provided valuable technical clarifications related to estimating the top tail of the family wealth distribution, and officials from Statistics Canadas Survey of Financial Security (SFS) Team, who answered many questions related to the SFS. Lead Analyst: Nigel Wodrich, Analyst Contributor: Aidan Worswick, Analyst This report was prepared under the direction of: Xiaoyi Yan, Director Nancy Beauchamp, Carol Faucher, Jocelyne Scrim and Rmy Vanherweghem assisted with the preparation of the report for publication. For further information, please contact pbo-dpbparl.gc.ca Yves Giroux Parliamentary Budget Officer RP-2021-007-S_e Table of Contents Executive Summary 1 1. Introduction 3 2. Measuring family wealth in Canada 4 3. Database construction 7 4. Database capabilities 8 Modelling approach and assumptions 11 Initial data alignment 11 Rich list data incorporation 12 Pareto interpolation 14 Iterative calibrations 17 Summary statistics 19 Future database development 21 References 22 Notes 25 Estimating the top tail of the family wealth distribution in Canada 1 Executive Summary The Parliamentary Budget Officer (PBO) has developed a modelling approach to estimate the top tail of the family wealth distribution in Canada. Its main purpose is to address underreported and missing data of high-net-worth families in the Survey of Financial Security Public Use Microdata File (SFS PUMF). Drawing on the National Balance Sheet Accounts (NBSA), the modelling recalibrates the SFS PUMF to add a synthetic dataset of families with wealth over $3 million. This modelling work produced a new analytical resource, the High-net-worth Family Database (HFD). HFD enables PBO to produce cost estimates and analysis of measures affecting Canadian families with wealth in the millions and billions of dollars. Using HFD, PBO finds that Canadas wealthiest families have significantly more wealth than recorded in the SFS PUMF. HFD increases the wealth share of the top 1 per cent of families by 12 percentage points compared with the SFS PUMF (Table ES-1). The discrepancy is likely due to sampling and non-sampling errors, especially higher survey non-response among high-net-worth families, in the SFS. Family wealth distribution, SFS PUMF and HFD, by selected quantiles, Canada, 2016 Family wealth quantile SFS PUMF Share of total wealth HFD Share of total wealth (per cent) (per cent) Top 0.01% 0.4 5.6 Top 0.1% 3.1 12.1 Top 0.5% 9.2 20.5 Top 1% 13.7 25.6 Top 5% 33.0 43.4 Top 10% 47.6 56.4 Top 20% 67.2 73.5 Middle 40% 30.5 25.3 Bottom 40% 2.3 1.2 Sources: PBO calculations of the SFS PUMF; PBO High-net-worth Family Database This report describes the modelling approach used to produce the synthetic dataset of high-net-worth families, to incorporate it into the SFS PUMF, and to align aggregate values in the combined dataset with those in the NBSA. It will serve as a reference for future PBO work on the topic as it arises. Table ES-1 Estimating the top tail of the family wealth distribution in Canada 2 HFD was constructed using publicly-available data. Additional documentation is available upon request. Estimating the top tail of the family wealth distribution in Canada 3 1. Introduction During the 2019 federal election, the Parliamentary Budget Office (PBO) estimated the financial cost of electoral proposals of political parties upon request.1 One such request was made to estimate the fiscal revenues of an annual tax on the net wealth of high-net-worth families above $20 million.2 PBO faced a key barrier to meet the request: The lack of a publicly available micro database that reliably assesses high-net-worth families in Canada. For example, Statistics Canadas principal family wealth microdata product, the Survey of Financial Security Public Use Microdata File (SFS PUMF), reports families with wealth up to only $27 million. By contrast, the lowest entry on Canadian Business magazines list of the 100 “Richest People” had a wealth of $875 million. To address the data gap, PBO developed a modelling approach to reliably estimate the top tail of the family wealth distribution in Canada. This approach consisted of adapting a straight-forward Pareto interpolation technique in Bach et al. (2014) and Saez and Zucman (2019). The technique creates a synthetic dataset bridging wealth microdata from the SFS PUMF and the Canadian Business (CB) magazines Richest People List. This synthetic dataset enabled PBO to fulfil the electoral costing request with a two-page cost estimate, published in September 2019. Since the federal election, PBO decided to build on that work and develop a functional analytical tool of high-net-worth families. To do so, the modelling approach used in the election underwent several refinements. The most significant of these was applying a modified ordinary least squares (OLS) regression and iterative calibration procedure developed in Vermeulen (2016) and (2018). The refined approach aligns the aggregate asset, liabilities, and net worth values in the re-estimated family wealth distribution with those in the National Balance Sheet Accounts (NBSA). As a result of these refinements, what was reported in PBOs electoral proposal cost estimate is not directly comparable with the results in this report. The ultimate product from this modelling work is the High-net-worth Family Database (HFD). HFD was constructed using publicly available data from year-end 2016, the most recent date all sources reported data. It will be used to undertake analytical and costing work on high-net-worth families as it arises. To showcase the kind of analytical work that is feasible using HFD, summary statistics from the database are presented in Section 4 and Appendix B of the report. These results are for illustrative purposes and may differ from analysis of a specific measure using HFD. Estimating the top tail of the family wealth distribution in Canada 4 2. Measuring family wealth in Canada For the purposes of this report, PBO measured family wealth in terms of marketable net worth: the amount of money left to a family if it liquidates all its financial and non-financial assets and paid off all its liabilities.3,4Canadian families collectively hold significant wealth. According to Statistics Canadas National Balance Sheet Accounts (NBSA), which record the stock of assets, liabilities and net worth for each institutional sector, at the end of 2019 Canadas household sector held $11.7 trillion in total net worth. That figure is approximately five times larger than Canadas annual GDP.5 Real estate ($5.8 trillion) and mortgages on that real estate ($1.5 trillion) are the single largest asset and liabilities categories, respectively (Figure 2-1). Household assets, liabilities and net worth, Canada, 2019 Q4 Source: PBO calculations of Statistics Canada Table 36-10-0580-01 (National Balance Sheet Accounts for the household sector, 2019 Q4) The distribution of wealth among households is heavily skewed toward the wealthiest families.6 In Canada, a small proportion of families at the top of the distribution possess net worth that is orders of magnitude higher than the countrys median net worth (Figure 2-2). The high concentration of wealth among a small number of families makes it difficult to reliably measure wealth at the very top of the distribution. This difficulty is evident in Figure 2-1 Financial assets($7.5T)Non-financial assets($6.5T)Total liabilities($2.3T)Net worth($11.7T)Currency & Deposits($1.6T)Real estate($5.8T)Mortgages($1.5T)Net worth($11.7T)Listed & Unlisted Shares($1.2T)Consumer durables ($0.7T)Consumer credit ($0.7T)Mutual Funds($1.5T)Life insurance & pensions($2.8T) -=Estimating the top tail of the family wealth distribution in Canada 5 the Survey of Financial Security Public Use Microdata File (SFS PUMF), Statistics Canadas national survey to measure Canadians net worth. The wealthiest family observed in the 2016 SFS PUMF had a net worth of only $27 million;7 the survey did not report any wealthier families, for several potential reasons (Box 2-1). Distribution of family net worth, Survey of Financial Security Public Use Microdata File, 2016 Source: PBO calculations using the 2016 SFS PUMF There are at least four general approaches that can be taken to improve estimates of the top tail of the family wealth distribution. The first involves compiling dossiers on each high-net-worth family, much like the Forbes Worlds Billionaires list. The second uses individual income tax returns to capitalize the incomes reported by taxpayers. The third uses estate tax records to back out the wealth recorded by the deceased and makes certain assumptions about how the recorded wealth of the deceased relates to the actual wealth of the living. The fourth consists of adjusting the family wealth distribution in national surveys like the SFS PUMF using data from other sources. This last approach is PBOs preferred approach and is further developed in the next section. -5051015202530 - 20 40 60 80 100$ millionsFamily PercentileMedian net worth($0.3 million)Top net worth($27.3 million)Figure 2-2 Estimating the top tail of the family wealth distribution in Canada 6 Box 2-1 Limitations of national wealth surveys in measuring high-net-worth families There are several plausible reasons national wealth surveys, like Canadas SFS, are limited in measuring and analyzing high-net-worth families. Surveys may be subject to sampling errors if the surveyed sample is not representative of the population, including at the top of the family wealth distribution. Response errors, where families inaccurately report, willingly or not, the value of their assets and liabilities, may bias estimates for high-net-worth families. Certain large asset and liabilities values in the SFS PUMF are also subject to top-coding, where they are replaced with a maximum value. While this procedure ensures the confidentiality of released data, it also reduces top wealth shares (see Appendix A.3). The most impactful limitation may be differential unit non-response, the tendency of high-net-worth families to be less likely to participate in surveys. If high-net-worth families are undersampled and the survey weights of those that are sampled are not adequately scaled upwards, top wealth shares will be underestimated. While Statistics Canada reports the overall response rate (70.3 per cent for the 2016 SFS), little is publicly-known about the incidence of differential unit non-response in the SFS. There is evidence from the U.S. of a positive correlation between wealth and the rate of unit non-response in its main wealth survey, the Survey of Consumer Finances (Kennickell & Woodburn, 1997) Statistics Canada attempts to address differential unit non-response among high-net-worth families by oversampling geographic areas known to have higher income and believed to have higher wealth (Statistics Canada, 2018a). However, similar approaches to oversample high-net-worth families using geographic or income-stratified geographic information in several European countries have been shown to be of limited effectiveness in accurately measuring the wealth of high-net-worth families (Vermeulen, 2018). Estimating the top tail of the family wealth distribution in Canada 7 3. Database construction PBOs High-net-worth Family Database (HFD) was constructed using data from three sources: 1. The Survey of Financial Security Public Use Microdata File.8 The SFS PUMF is Canadas national net worth survey. Statistics Canada surveys a representative sample of over 12,000 resident economic families on their major financial and non-financial assets and debts.9 HFD uses the most recently-published iteration of the SFS PUMF, from 2016. 2. The National Balance Sheet Accounts. The NBSA aggregate the individual balance sheets of households across the economy and reports their aggregate financial assets, non-financial assets, liabilities, and ultimately net worth.10 HFD uses NBSA data from 2016 Q4, the date that aligns most closely with the vintages of the SFS PUMF and CBs Richest People List used in the database.11 3. Canadian Business magazines Richest People List. CB conducts journalistic and market research to compile a list of the 100 wealthiest Canadian citizens.12 HFD uses CBs 2017 Richest People List, which was published in December 2016 and corresponds most closely with the 2016 SFS PUMF. PBO followed Vermeulens (2016) elegant approach to address missing and underreported data of high-net-worth families in the SFS PUMF and build HFD. First, the aggregate values of financial assets, non-financial assets, and total liabilities in the SFS PUMF were adjusted to align with the corresponding totals by category in the NBSA. Second, data from CBs Richest People List were added to the SFS PUMF. Third, the resulting joint dataset was used to run a modified OLS regression that would determine the shape of the wealth distribution for the missing and underreporting families and bridge the top of the SFS PUMF and the bottom of the CB Richest People List. Fourth, the results from the modified OLS regression were applied to create a new synthetic dataset of high-net-worth families. Fifth, the synthetic dataset was merged with the joint dataset from the second step. The addition of the synthetic dataset generally creates more assets and liabilities than there are in the NBSA, which leads to sixth step: to reduce (or increase) each of the financial assets, non-financial assets, and total liabilities in the SFS PUMF by an adjustment factor and returning to the second step to repeat the procedure iteratively until the value of the financial assets, non-financial assets, and total liabilities in the final, integrated dataset (combining NBSA-adjusted SFS PUMF, the synthetic dataset, and CBs Richest People List) are equal to those in the NBSA. Estimating the top tail of the family wealth distribution in Canada 8 The modelling approach used to construct HFD is described in greater detail in Appendix A. 4. Database capabilities PBO generated summary statistics of HFD to showcase its analytical capabilities. Using HFD, PBO finds that Canadas wealthiest families have significantly more wealth than recorded in the SFS PUMF. The wealth share of the top 1 per cent of families increases by 12 percentage points in HFD compared with the SFS PUMF (Table 4-1). Family wealth distribution, SFS PUMF and HFD, by selected quantiles, Canada, 2016 Family wealth quantile SFS PUMF Share of total wealth HFD Share of total wealth (per cent) (per cent) Top 0.01% 0.4 5.6 Top 0.1% 3.1 12.1 Top 0.5% 9.2 20.5 Top 1% 13.7 25.6 Top 5% 33.0 43.4 Top 10% 47.6 56.4 Top 20% 67.2 73.5 Middle 40% 30.5 25.3 Bottom 40% 2.3 1.2 Sources: PBO calculations of the SFS PUMF; PBO High-net-worth Family Database Appendix B presents additional summary statistics for year-end 2016, HFDs base period when each of its sources most recently reported data. Analyzing high-net-worth families in subsequent periods requires making certain assumptions about the evolution of families and their wealth since the end of 2016. To illustrate the kinds of assumptions required to bring HFD forward, PBO also generated summary statistics on high-net-worth families for year-end 2019. PBO assumed that, since 2016: - The composition of families (number of people, age, etc.) has remained constant across the wealth distribution;13 Table 4-1 Estimating the top tail of the family wealth distribution in Canada 9 - The number of families has grown at the same rate as the number of individuals, and this growth has been uniform across the wealth distribution;14 - Aggregate financial assets, non-financial assets, and total liabilities have grown at the same rate as indicated in the NBSA, and this growth has been proportional across the family wealth distribution. Following these assumptions, PBO applied two adjustments to HFD. First, the weight of each observation was increased by growth rate of Canadas population between 2016 Q4 and 2019 Q4. Second, the financial assets, non-financial assets, and total liabilities of each observation was increased proportionally, until their aggregate totals matched those in the NBSA in 2019 Q4. The resulting summary statistics are presented in Tables 4-2 and 4-3. Both tables highlight the strong concentration of wealth among Canadas high-net-worth families. Family wealth distribution, by selected quantiles, Canada, 2019 Family wealth quantile Wealth threshold Number of families Total wealth Share of total wealth ($ millions) (thousands) ($ billions) (per cent) Top 0.01% 143.1 1.6 654 5.6 Top 0.1% 29.3 16.0 1,427 12.2 Top 0.5% 9.7 79.7 2,410 20.6 Top 1% 6.1 159.3 3,010 25.7 Top 5% 2.3 796.7 5,107 43.7 Top 10% 1.6 1,593.5 6,629 56.7 Top 20% 1.0 3,186.9 8,633 73.8 Middle 40% 0.1-1.0 6,373.8 2,932 25.1 Bottom 40% under 0.1 6,373.8 132 1.1 Source: PBO High-net-worth Family Database; PBO calculations based on Statistics Canadas Quarterly Demographic Estimates and the NBSA Table 4-2 Estimating the top tail of the family wealth distribution in Canada 10 Wealth distribution, by selected wealth thresholds, Canada, 2019 Family wealth threshold Families with wealth above: Number of families Total wealth Share of total wealth (thousands) ($ billions) (per cent) $1 billion 0.1 221 1.9 $500 million 0.2 333 2.8 $250 million 0.7 488 4.2 $100 million 2.7 785 6.7 $50 million 7.2 1,097 9.4 $25 million 19.4 1,525 13.0 $10 million 76.3 2,377 20.3 $5 million 206.6 3,271 28.0 $2.5 million 699.1 4,871 41.6 $1 million 3,123.7 8,570 73.3 Source: PBO High-net-worth Family Database; PBO calculations based on Statistics Canadas Quarterly Demographic Estimates and the NBSA Table 4-3 Estimating the top tail of the family wealth distribution in Canada 11 Modelling approach and assumptions Initial data alignment PBO performed an initial adjustment to the SFS PUMF microdata so that the aggregate values of assets, liabilities, and net worth aligned with the corresponding totals by category for the household sector in the NBSA. While the SFS PUMF and the NBSA both estimate household net worth, there are procedural and conceptual distinctions between the two sources that lead to slightly different estimates.15 Most obviously, the SFS PUMF is derived from a survey with confidence intervals on its estimates; the NBSA measure stocks and flows in capital and financial accounts but because certain household categories are calculated as residuals from other sectors, the NBSA have a margin of error of their own. The SFS does not sample the territories and certain population groups representing two per cent of the population. Certain assets and liabilities are also measured differently. For example, the NBSA do not record the value of collectibles such as art work; the two sources measure credit card debt differently, the main reason Statistics Canada (2019a) identifies for under-coverage of total liabilities in the SFS PUMF (Table A1-1). Concordance between the SFS PUMF and the NBSA Household Sector, 2016 SFS PUMF NBSA Coverage ($ billions) ($ billions) (SFS/NBSA) Financial assets 5,845 6,468 0.904 Non-financial assets 6,193 5,934 1.043 Total liabilities 1,751 2,062 0.850 Net worth 10,287 10,339 0.995 Sources: PBO calculations of the 2016 SFS and Statistics Canada Table 36-10-0580-01 Notes: NBSA totals reflect results for 2016 Q4. Business equity was counted as a financial asset. Totals may not add due to rounding. Nevertheless, there are several reasons for which it is desirable to bring the SFS PUMF into alignment with the NBSA. Alignment can compensate for underreporting in national wealth surveys (Vermeulen, 2016). Unlike the SFS and its predecessor, the Survey of Consumer Finances (SCF), the NBSA have Table A1-1 Estimating the top tail of the family wealth distribution in Canada 12 been estimated on a consistent basis over time (Davies and Di Matteo, 2020). This consistency allows for better comparison of the family wealth distribution estimates going back in time. The NBSA are also estimated and released more frequently (quarterly) than the SFS (triennially). The higher frequency provides opportunities to update estimates in non-survey years of the SFS. Finally, alignment with the household sector of the NBSA permits analyses of the overall position of households relative to other economic sectors included in the NBSA (Statistics Canada, 2019a). For some of these same reasons, Statistics Canada also performs alignment between the SFS and the NBSA in its Distributions of Household Economic Accounts (DHEA) dataset. To bring the SFS PUMF into alignment with the NBSA, PBO first classified each asset and debt variable from the SFS PUMF into three large categories: financial assets;16 non-financial assets; and total liabilities.17 For each category, PBO calculated an adjustment factor as the inverse of the “coverage” calculation in Table A1-1. We increased (decreased) the financial assets, non-financial assets, and total liabilities values for each family in the SFS PUMF by the relevant adjustment factor. Since each family has a unique portfolio of assets and liabilities, their net worth varies differently with this adjustment procedure.18 Rich list data incorporation The next procedure consisted of adding wealth data from a rich list to the NBSA-adjusted SFS PUMF. The motivation to augment the SFS PUMF with rich list data is to improve the accuracy of the subsequent regression analysis that estimates Pareto parameters used in the imputation of the missing and underreported high-net-worth families.19 Vermeulen (2018) demonstrates that the addition of even a small number of entries from a rich list significantly improves the accuracy of interpolated top tail estimates, enough that there is almost no estimation bias.20 In Canada there are two prominent, publicly available rich lists: the Forbes list of the worlds billionaires, which includes Canadian entries; and Canadian Business (CB) magazines Richest People List. PBO elected the latter for HFD, following Davies and Di Matteo (2020). They note that CBs list contains billionaires missing in the Forbes list and includes entries below Forbes US$1 billion cut-off.21 Before they could be added to the NBSA-adjusted SFS PUMF, data from CBs Richest People List required adjustment.22 Estimating the top tail of the family wealth distribution in Canada 13 Unlike the SFS PUMF, CB includes non-resident Canadians in its accounting of the 100 Richest Canadians. As a result, PBO dropped non-resident Canadians from the CB dataset, similar to MacDonald (2018). In addition, several CB entries refer to extended families comprising multiple family units. These include entries entitled “family”, “brothers”, and “estate”, or that otherwise listed multiple people who were not married. By contrast, the SFS PUMF family unit consists of economic families and persons not in an economic family (unattached individuals). PBO developed an approach to split extended families in the CB into constituent economic families. We used public sources to identify the generation(s) controlling the family wealth. Each sibling (and cousin, if applicable) within the controlling generation(s), as well as their living parent(s) (and uncles and aunts, if applicable), was treated as a unique economic family. We assumed that the extended familys reported wealth resides exclusively and entirely within the identified constituent economic families. We also assumed that the extended familys wealth is divided evenly among its constituent economic families. Finally, we dropped all split entries that fell below the lowest entry ($875 million) on the CB list. This final procedure ensured that the top of the wealth tail, above $875 million, comprised of a complete population of families above that level to avoid bias in the subsequent regression analysis to estimate Pareto parameters. Following this splitting procedure, the cleaned CB dataset included 80 resident economic families. Each held a wealth of at least $875 million, and collectively they held $197 billion in wealth. PBO added the cleaned CB data to the NBSA-adjusted SFS PUMF, creating a joint dataset (see Figure A2-1). Each CB observation was assigned a weight of 1, reflecting that each observation represents a one family unit. Estimating the top tail of the family wealth distribution in Canada 14 Family wealth distribution in the joint dataset,23 2016 Sources: PBO calculations of the 2016 SFS PUMF and Canadian Business Richest People List, 2017 Pareto interpolation PBO used the joint dataset to impute the missing and underreported high-net-worth families. To do so, PBO referred to the modified OLS regression approach for complex survey designs in Vermeulen (2018). The resulting estimated Pareto parameters were then used to interpolate the missing and underreported high-net-worth families. A key assumption for this imputation procedure is that the top of the family wealth tail exhibits a Pareto distribution. This assumption has been widely applied in the literature on wealth distributions, including in Canada. Davies and Shorrocks (1999) characterize the notion that the top wealth tail follows a Pareto distribution as an “enduring feature” of the wealth distribution. Brzozowski et al. (2010) assume that the top decile of the SFS is Pareto-distributed in their comparison of different statistical methods to impute top-coded observations into the SFS PUMF. Ogwang (2011) finds that CBs Richest People List from 1999 to 2008 displays Pareto power law24 behaviour using modified OLS and MLE estimation methods. Davies and Di Matteo (2020) assume that the top wealth tail follows a Pareto distribution in their analysis of the evolution of top family wealth shares in Canada between 1892 and 2016. Vermeulen (2018) notes another key assumption: that the national wealth survey and rich list datasets “are consistent with the same Pareto 1 10 100 1,000 10,000 100,000 1,000,000 10,000,00001101001,00010,000Economic familiesWealth ($ millions)Number of economic families within each one-million-dollar wealth bracketSFS PUMFsCanadian BusinessFigure A2-1 Estimating the top tail of the family wealth distribution in Canada 15 distribution”. PBO makes that assumption, but its a cautious one for two reasons. The first is due to the reliability and substance of documentation available on CBs methodology. The most recent CB methodology that PBO could locate dates from 2006.25 The methodology provides useful information about CBs approach, at least for that year. The methodology indicates that at least certain debts (privately-owned companies, real estate) are ascertained or estimated, and deducted from total assets. However, the methodology also states that “intentionally conservative estimates” are used to valuate private investments and that “its safe to assume the Rich 100 are worth more than the stated amount” (Canadian Business, 2006). Davies and Di Matteo (2020) note that the problems of rich list data compilation are reduced by the scrutiny the lists attract and the refinements the lists undergo as they are repeated annually (CB has been compiling a rich list since 1999). While its reasonable to assume that CB approximates the wealth of the highest-net-worth Canadians, its unclear what, if any, bias there may be in its dataset. The second note of caution in assuming the joint dataset lies on the same Pareto distribution is due to top-coding in the SFS PUMF. The SFS PUMF is top-coded such that a certain number of the largest values on some of the assets and debts are replaced with a maximum value to ensure the confidentiality of each observation disclosed in public use files. However, it also reduces the wealth of the top families in the SFS PUMF relative to SFS data available at a Research Data Centre (RDC), which is not top-coded.26 Brzozowski et al. (2010) reported that the wealth share of the top 1 percent of families was approximately 1.5 percentage points lower in the 1999 SFS PUMF (13.2 percent) than in the 1999 SFS RDC data (15.7 percent). The degree of top-coding in the 2016 SFS PUMF is not reported publicly, and PBO has not analyzed the extent of top-coding or its potential bias on the Pareto estimates. In theory, this potential bias is reduced by estimating the Pareto parameters over a sufficiently large segment of the top tail of the joint dataset; a larger segment should include, proportionally, fewer top-coded families, reducing the potential bias those families could introduce. To apply Vermeulens (2018) regression approach, PBO first isolated a subset of observations the joint dataset with wealth over which the regression would be run. The choice of an appropriate or even a best-fit is unclear and often determined case by case.27 In Vermeulens (2018) re-estimation of top wealth shares in 10 European countries and the U.S., the choice of depends, in part, on the method used in each countrys national wealth survey to oversample high-net-worth families, who are less likely to respond to such surveys. Countries that oversample using individual information, such as income tax information (the U.S.) or taxable wealth information (Spain, France), were each tested with ranging from 500,000 to 10 million. By contrast, countries that oversample using income-stratified geographic information (Germany, Belgium), geographic information only (Austria, Portugal), or no oversampling at all (Italy, Estimating the top tail of the family wealth distribution in Canada 16 Netherlands) were each tested with ranging from 500,000 to only 2 million. In those countries, there were too few observations above thresholds higher than 2 million to accurately estimate Pareto parameters. Canadas SFS does not appear to use individual information to oversample high-net-worth families. The survey stratifies each province into rural and urban areas. In rural areas, the SFS uses geographic information from the Labour Force Survey area frame to select a multi-stage sample. In urban areas, the SFS uses information from the Socioeconomic indicators File (SEF) T1 Family File (T1FF), such as age and income, to stratify the Address Register into groups of dwellings predicted to have similar wealth (Statistics Canada, 2018b). The urban stratum for the highest wealth represents the top 5 percent of each province.28 PBO thus narrowed the range of appropriate in the Canadian context to between $750,000 and $3 million, an approximate conversion of the euro values of used in Vermeulen (2018) for countries that also use geographic and income-stratified geographic information to oversample high-net-worth families. Vermeulen (2018) and Chakraborty et al. (2019) highlight a trade-off when selecting a specific : A lower threshold will increase the sample size for the regression leading to a more reliable Pareto estimation, but at the risk of potentially including observations that do not follow Pareto tail behaviour. Ultimately, PBO chose the upper-bound of the range of appropriate : $3 million. Compared with national wealth surveys in European countries that oversampled using geographic or income-stratified geographic information, Canadas SFS PUMF has comparatively many more observations at the 2 million / $3 million threshold.29 The choice of a higher permits more observations from the NBSA-adjusted SFS PUMF to be retained post-interpolation while maintaining a robust sample size to undertake the regression estimate of Pareto parameters. Having chosen a , PBO ranked all observations with wealth , = 1, | from the joint dataset in descending order of their wealth. Each observation with wealth and weight was defined in terms of , the average weight of all observations with wealth equal or greater than , and , the average weight of the wealthiest observations (=1). Vermeulen (2018) proposes one final specification to the regression. Gabaix and Ibragimov (2011) found that log-rank-log-size OLS regressions were systematically, strongly biased in small samples. Vermeulen (2018) therefore reduces the rank of each observation in the regression by . The modification reduces the bias to a leading order. Estimating the top tail of the family wealth distribution in Canada 17 The resulting modified OLS regression is described by: ln ( 0.5) = ln() (ln() ln () The estimated coefficient from the above regression is the Pareto parameter, which determines the shape of re-estimated top tail of the family wealth distribution. In general, a higher results in a fatter top tail and a higher concentration of wealth. The estimated coefficient was applied to a standard Pareto cumulative distribution function over a given wealth interval , | : (,) = 1 1 The above cumulative distribution function (,) yields estimates between 0 and 1 for the probability that a family in the top tail will have wealth between and . Following Chakraborty et al. (2019), cumulative distribution estimates were converted into the number of synthetic families within the wealth interval , by multiplying the distribution function (,) by , the sum of the weights of all observations with wealth , = 1, | . Like Davies and Di Matteo (2020), PBO retained the cleaned CB entries without interpolation. The resulting synthetic dataset consists of families with wealth between ($3 million) and , the wealth of the lowest entry from the cleaned CB dataset ($875 million). After replacing observations from the joint dataset with wealth , =1, | with the synthetic dataset, PBO created an integrated dataset. The integrated dataset combines families from the NBSA-adjusted SFS PUMF with wealth under ; families from the synthetic dataset with wealth between and ; and families from the cleaned CB with wealth equal or higher than . Iterative calibrations Substituting high-net-worth families in the NBSA-adjusted SFS PUMF with the interpolated synthetic and cleaned CB datasets creates a problem: Families in the new integrated dataset possess more aggregate wealth than the household sector in the NBSA. PBO followed Vermeulen (2016) to implement an iterative calibration procedure that aligns aggregate asset, liabilities, and net worth values in the integrated dataset with those in the NBSA. The first step requires returning to the NBSA-adjusted SFS PUMF (the product of Appendix A.1). For families with wealth in the NBSA-adjusted SFS PUMF, PBO calculated three ratios: aggregate financial assets to aggregate net worth; aggregate non-financial assets to aggregate net worth; Estimating the top tail of the family wealth distribution in Canada 18 and aggregate total liabilities to aggregate net worth. The ratios were then applied uniformly to synthetic and CB families in the integrated dataset to divide their wealth into constituent asset and liabilities values. In the next step, PBO calculated aggregate values for financial assets, non-financial assets, and total liabilities across the entire integrated dataset. The aggregate values in the integrated dataset were compared with their corresponding values in the NBSA. To the extent that integrated dataset aggregate values were higher (lower) than the NBSA, PBO applied a downward (upward) revision to the adjustment factors applied to the original SFS PUMF data in Appendix A.1. From there, PBO re-estimated the Pareto parameters in Appendix A.3 and repeated this adjustment and re-estimation procedure iteratively until the aggregate values of financial assets, non-financial assets, and total liabilities were aligned to their corresponding values in the NBSA. This procedure typically required several repetitions to produce NBSA-calibrated values for all assets and liabilities. The final, calibrated value of the parameter, which determines the shape of the family wealth distribution, was 1.45.30 The final adjustment factors applied to SFS PUMF data to bring the integrated dataset into alignment with the NBSA are presented in Table A4-1. Altogether, financial and non-financial assets were reduced by 5.8 percent and 13.0 percent, respectively. Total liabilities were adjusted up by 12.8 per cent, reflecting the significantly lower reported liabilities in the SFS PUMF compared with the NBSA. Estimating the top tail of the family wealth distribution in Canada 19 Adjustment factors applied to the SFS PUMF to align aggregate asset and liabilities values in the integrated dataset with the NBSA Initial alignment Iterative calibrations Overall Appendix A.1 Appendix A.4 (A.1*A.4) Financial assets 1.106 0.852 0.942 Non-financial assets 0.959 0.907 0.870 Total liabilities 1.176 0.959 1.128 Source: PBO calculations The iterative calibration procedure was repeated until aggregate values for assets and debts were within 0.00001 per cent of the corresponding values in the NBSA. PBO applied a very small, proportional adjustment to the financial assets, non-financial assets, and total liabilities of all families in the integrated dataset to fully align their aggregate values with those in the NBSA. The resulting dataset is the High-net-worth Family Database (HFD). Summary statistics Tables B-1 and B-2 present summary statistics from HFD for its base year 2016. The HFDs results are comparable to other precedents in the literature: wealth shares in Table B-1 are comparable to Davies and Di Matteo (2020); the number and wealth of families in Table B-2 are similar to Wealth-X (2017); and the HFDs overall finding of significant upward revisions to top wealth shares relative to national wealth surveys dovetails results in Bach et al. (2015), Vermeulen (2016) and (2018), and Davies and Di Matteo (2020). For reference in interpreting the summary statistics, the calibrated HFD represents approximately 15,349,000 families that collectively possess $10.3 trillion in wealth. Table A4-1 Estimating the top tail of the family wealth distribution in Canada 20 Family wealth distribution, by selected quantiles, Canada, 2016 Family wealth quantile Wealth threshold Number of families Total wealth Share of total wealth ($ millions) (thousands) ($ billions) (per cent) Top 0.01% 130.5 1.5 574 5.6 Top 0.1% 26.7 15.3 1,254 12.1 Top 0.5% 8.9 76.7 2,117 20.5 Top 1% 5.5 153.4 2,644 25.6 Top 5% 2.1 767.5 4,488 43.4 Top 10% 1.4 1,534.9 5,829 56.4 Top 20% 0.9 3,069.9 7,599 73.5 Middle 40% 0.1-0.9 6,139.7 2,613 25.3 Bottom 40% under 0.1 6,139.7 128 1.2 Source: PBO High-net-worth Family Database Family wealth distribution, by selected wealth thresholds, Canada, 2016 Wealth threshold Families with wealth above: Number of families Total wealth Share of total wealth (thousands) ($ billions) (per cent) $1 billion 0.1 184 1.8 $500 million 0.2 277 2.7 $250 million 0.6 408 3.9 $100 million 2.2 656 6.3 $50 million 6.2 925 8.9 $25 million 16.7 1,287 12.5 $10 million 63.7 1,994 19.3 $5 million 173.8 2,751 26.6 $2.5 million 549.5 3,983 38.5 $1 million 2,699.0 7,246 70.1 Source: PBO High-net-worth Family Database Table B-1 Table B-2 Estimating the top tail of the family wealth distribution in Canada 21 Future database development Future work on HFD will be guided by topics of relevance to parliamentarians, the availability of new data sources, and the evolution of the academic literature on measuring top wealth shares. PBO wishes to verify whether top-coding in the SFS PUMF introduces bias to the estimation of Pareto parameters. This analysis can be done by constructing HFD using SFS data from a Statistics Canada Research Data Centre (RDC), where observations are not top-coded, and comparing the SFS PUMF and SFS RDC versions of HFD. Statistics Canada collected data for its next iteration of the SFS between September and December 2019 (Statistics Canada, 2019b). Its unclear when the new public use microdata file will be available. PBO plans to adapt HFD to the most recent publicly available version of the SFS, which will be conducted triennially going forward. Future database development may also have to contend with the potential loss of an existing data source. The rich list used to construct HFD came from CBs Richest People 2017, which corresponds to data from 2016. While CB published a list for 2018 (corresponding to data from 2017), PBO has not been able to locate a 2019 publication of this list. If CB has discontinued publication of an annual rich list, PBO will consider alternative rich lists, such as the Forbes Worlds Billionaires List, to update HFD. Finally, there is the potential for future research to offer opportunities to refine the modelling approach used to construct HFD. Topics of interest include the identification of a best-fit , the wealth threshold at which Pareto interpolation begins; a more refined approach to divide the wealth of synthetic high-net-worth families into constituent asset and liabilities categories; the possibility to estimate more granular categories of assets and liabilities of high-net-worth families; and the consideration of incorporating non-marketable forms of wealth in estimates of the top tail of the family wealth distribution. PBO plans to monitor the academic literature for new theories and methodologies that could refine or enhance HFD. Estimating the top tail of the family wealth distribution in Canada 22 References Auten, G., & Splinter, D. (2019). Income Inequality in the United States: Using Tax Data to Measure Long-term Trends (Working paper). Retrieved from http:/ Bach, S., Beznoska, M., & Steiner, V. (2014). A Wealth Tax on the Rich to Bring Down Public Debt? Revenue and Distributional Effects of a Capital Levy in Germany. Fiscal Studies, 35(1), 67-89. Bach, S., Thiemann, A., & Zucco, A. (2015). The Top Tail of the Wealth Distribution in Germany, France, Spain, and Greece. Retrieved from Deutsches Institut fr Wirtschaftsforschung website: https:/www.diw.de/documents/publikationen/73/diw_01.c.513261.de/dp1502.pdf Brzozowski, M., Gervais, M., Klein, P., & Suzuki, M. (2010). Consumption, income, and wealth inequality in Canada. Review of Economic Dynamics, 13(1), 52-75. Canadian Business (2006). Rich 100 Methodology. Retrieved from Internet Archive website: https:/web.archive.org/web/20060318021430/http:/ Canadian Business (2016). Canadas Richest People: The Complete Top 100 Ranking (2017). Retrieved from https:/ Catherine, S., Miller, M., & Sarin, N. (2020). Social Security and Trends in Inequality. Manuscript in progress. Retrieved from the SSRN website: https:/ Chakraborty, R., Kavonius, I. K., Prez-Duarte, S., & Vermeulen, P. (2019). Is the Top Tail of the Wealth Distribution the Missing Link between the Household Finance and Consumption Survey and National Accounts?. Journal of Official Statistics, 35(1), 31-65. Davies, J. (1993). The Distribution of Wealth in Canada. Research in Economic Inequality, 4(1), 159-180. Davies, J., & Shorrocks, A. (1999). The Distribution of Wealth. In A. B. Atkinson and F. Bourguignon (Eds.), Handbook of Income Distribution: Volume 1 (pp. 605-675). New York, NY: North-Holland. Davies, J., Shorrocks, A., & Lluberas, R. (2018). Global Wealth Databook 2018. Retrieved from the Credit Suisse website: https:/www.credit- Davies, J., & Di Matteo, L. (2020). Long Run Canadian Wealth Inequality in International Context. Review of Income and Wealth. Advance online publication. https:/doi.org/10.1111/roiw.12453 Estimating the top tail of the family wealth distribution in Canada 23 Gabaix, X., & Ibragimov, R. (2011). Rank 1/2: A Simple Way to Improve the OLS Estimation of Tail Exponents. Journal of Business & Economic Statistics, 29(1), 24-39. Gu, W., & Wong, A. (2010). Estimates of Human Capital in Canada: The Lifetime Income Approach (Statistics Canada Catalogue no. 11F0027M No. 062). Retrieved from https:/www150.statcan.gc.ca/n1/en/pub/11f0027m/11f0027m2010062-eng.pdf?st=5C89HjQP Kennickel, A., & Woodburn, R. L. (1997). Consistent Weight Design for the 1989, 1992 and 1995 SCFs, and the Distribution of Wealth (Working paper). Retrieved from the Federal Reserve website: https:/www.federalreserve.gov/econresdata/scf/files/wgt95.pdf Kennickel, A., & Woodburn, R. L. (1997). Consistent Weight Design for the 1989, 1992 and 1995 SCFs, and the Distribution of Wealth (Working paper) Supplemental material. Retrieved from the Federal Reserve website: https:/www.federalreserve.gov/econresdata/scf/files/wgt95app.pdf MacDonald, D. (2018). Born to Win: Wealth concentration in Canada since 1999. Retrieved from the Canadian Centre for Policy Alternatives website: https:/www.policyalternatives.ca/sites/default/files/uploads/publications/National Office/2018/07/Born to Win.pdf Ogwang, T. (2011). Power laws in top wealth distributions: evidence from Canada. Empirical Economics, 41(2), 473-486. Parliamentary Budget Officer (2019a). Cost Estimate of Election Campaign Proposal Net Wealth Tax. Retrieved from https:/www.pbo-dpb.gc.ca/web/default/files/Documents/ElectionProposalCosting/Results/32630202_EN.pdf Parliamentary Budget Officer (2019b). Evaluation of Election Proposal Costing 2019. Retrieved from https:/www.pbo-dpb.gc.ca/web/default/files/Documents/Reports/ADM001/ADM001_en.pdf Saez, E., & Zucman, G. (2019, January 18). Letter to Senator Elizabeth Warren.Retrieved from https:/gabriel-zucman.eu/files/saez-zucman-wealthtax-warren.pdf Statistics Canada (2017). Quarterly Demographic Estimates: October to December 2016 (Catalogue no. 91-002-X). Retrieved from https:/www150.statcan.gc.ca/n1/en/pub/91-002-x/91-002-x2016004-eng.pdf?st=6pE9ffng Statistics Canada (2018a). Survey of Financial Security: Detailed information for 2016. Retrieved from https:/www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SDDS=2620 Statistics Canada (2018b). 2016 Survey of Financial Security: Public Use Microdata User Guide. Retrieved from Statistics Canadas Survey of Financial Security: Public Use Microdata File. Statistics Canada (2018c). User Guide: Canadian System of Macroeconomic Accounts (Catalogue no. 13-606-G). Retrieved from https:/www150.statcan.gc.ca/n1/en/catalogue/13-606-G Estimating the top tail of the family wealth distribution in Canada 24 Statistics Canada (2019a). Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2018, technical methodology and quality report (Catalogue no. 13-604-M2019001). Retrieved from https:/www150.statcan.gc.ca/n1/en/pub/13-604-m/13-604-m2019001-eng.pdf?st=NR7VbCfH Statistics Canada (2019b). Survey of Financial Security. Retrieved from https:/www.statcan.gc.ca/eng/survey/household/2620 Statistics Canada (2020a, February 28). Gross domestic product, income and expenditure, fourth quarter 2019 Catalogue no. 11-001-X. Retrieved from https:/www150.statcan.gc.ca/n1/en/daily-quotidien/200228/dq200228a-eng.pdf?st=SjHJHBRW Statistics Canada (2020b). National Balance Sheet Accounts Table: 36-10-0580-01. Retrieved from https:/www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3610058001 Statistics Canada (2020c). Quarterly Demographic Estimates: October to December 2019 (Catalogue no. 91-002-X). Retrieved from https:/www150.statcan.gc.ca/n1/en/pub/91-002-x/91-002-x2019004-eng.pdf?st=CxSrE1dx Vermeulen, P. (2016). Estimating the Top Tail of the Wealth Distribution. The American Economic Review, 106(5), 646-650. Vermeulen, P. (2018). How Fat is the Top Tail of the Wealth Distribution?. Review of Income and Wealth, 64(2), 357-387. Wealth-X (2017). World Ultra Wealth Report 2017. Retrieved from https:/ Weil, D. (2015). Capital and Wealth in the Twenty-First Century. The American Economic Review, 105(5), 34-37. Estimating the top tail of the family wealth distribution in Canada 25 1 Parliamentary Budget Officer (2019b). Over four months leading up to the 2019 federal election, PBO costed over 200 electoral proposal requests from political parties. 2 Parliamentary Budget Officer (2019a). More specifically, PBO was requested to estimate the revenues from “introducing an annual net wealth tax on Canadian resident economic families equal to 1% of net wealth above $20 million” for which “all asset and liabilities will be included in the net wealth tax base, except wealth won in lotteries”. 3 This definition is the same as that in the Survey of Financial Security (Statistics Canada, 2018b) and forms the statistical foundation of PBOs modelling in this report. For the purposes of this report, the definition applies equally to the terms “net worth” and “wealth”, which are used interchangeably. 4 There is an emerging literature on whether to include non-marketable forms wealth in the estimation of household wealth and wealth shares and, if so, how. Weil (2015) describes human capital and public transfer wealth as the two most quantitatively important forms of “wealth-like objects” that are not captured by measures of market wealth. Catherine et al. (2020) focus on the public transfer wealth; they develop an approach to incorporate Social Security wealth to the measurement of household wealth in the U.S. They find that this addition attenuates increases in wealth inequality since 1989 and reduces top wealth shares compared with other recent literature. Though these other forms of wealth, due to their non-marketable nature, may be less tangible and difficult to measure, they also represent significant stores of value in Canada. Gu and Wong (2010) produced estimates for human capital wealth in Canada using a lifetime earnings approach; they found that in 2007, the stock of human capital wealth was $16.4 trillion. By comparison, the (marketable) net worth of the household sector in that same year, as recorded by the National Balance Sheet Accounts (NBSA), was only $6.0 trillion (Statistics Canada, 2020b). Social security also represents a significant store of value in Canada. The NBSA includes in its social security funds sub-sector the Canada Pension Plan (CPP) and Quebec Pension Plan (QPP) (Statistics Canada, 2018c). At year-end 2019, the net worth of this sector was valued at $0.5 trillion (Statistics Canada, 2020b). The NBSA does not accord this net worth to the household sector, but rather to the general government sector. Other social protection “pay-as-you-go” programs, such as federal Old Age Security (OAS) and the Guaranteed Income Supplement (GIS), are not included in the NBSAs social security sub-sector because those programs do not hold accumulated assets; however, even these transfer programs arguably constitute a form of wealth for households (Catherine et al., 2020). 5 Canadas GDP at market prices in the fourth quarter 2019 was $2.3 trillion (Statistics Canada, 2020a). Notes Estimating the top tail of the family wealth distribution in Canada 26 6 The concept of a “family” in this report is equivalent to the concept of a “family unit” in Statistics Canada (2018b). This includes economic families and or a person not in an economic family (unattached individual). Statistics Canada (2018b) defines an economic family as “a group of two or more persons who live in the same dwelling and are related to each other by blood, marriage, common law or adoption.” It defines a person not in an economic family as “a person living either alone or with others to whom he or she is unrelated, such as roommates or a lodger.” 7 This wealthiest observation in the 2016 SFS PUMF represents 965 economic families in the general population. 8 This analysis is based on Statistics Canadas Survey of Financial Security Public Use Microdata, 2016, which contains anonymous data collected in the Survey of Financial Security. All computations on these microdata were prepared by the Parliamentary Budget Officer (PBO). The responsibility for the use and interpretation of these data is entirely that of the PBO. 9 For more information, see the Survey of Financial Security: Public Use Microdata User Guide, 2016 (Statistics Canada, 2018b). 10 For more information, see the Canadian System of Macroeconomic Accounts User Guide (Statistics Canada, 2018c). 11 2016 Q4 is the quarter corresponding most closely to the 2016 SFS collection period. According to Statistics Canada (2019b), the 2016 SFS was collected between 9 September 2016 and 6 December 2016. In addition, as stated in the main text, CBs Richest People List was also published in 2016 Q4 (December 2016). 12 For more information, see CBs Rich 100 methodology (Canadian Business, 2006). 13 Auten & Splinter (2019) demonstrate the importance of making assumptions regarding the evolution of family composition when estimating top income shares over time. The authors data shows differential changes to family composition across the income distribution (e.g., outside the top of the distribution, there is a declining marriage rate, declining family size, and increasing numbers of single-parent households). All things being equal, such differential changes to family composition over time can be expected to cause changes in the distribution of income among families. It would not be surprising to find that differential changes to family composition can also affect top wealth shares. 14 Growth in the number of families was approximated by the growth rate in the population reported in Statistics Canadas Quarterly Demographic Estimates between 2016 Q4 and 2019 Q4. The approximation was necessary because the number of economic families in 2019 Q4 was not available at the publication date. The annual growth rates of the population and of the number of economic families have been within 0.3 per cent of each other since 2012. 15 Statistics Canada (2019a) provides an excellent exposition of the conceptual differences between the SFS and the NBSA. 16 Financial assets were calculated using employer pension plans valued on a termination basis, rather than a going concern basis. Statistics Canada (2018b) provides a helpful description of the distinction between the two valuation methods. Estimating the top tail of the family wealth distribution in Canada 27 17 Variables were placed according to the mapping presented in Statistics Canada (2019a), except that PBO retained the value of collectibles in the SFS PUMF. 18 Vermeulen (2016) posits that this adjustment procedure tends to disproportionately increase the wealth of wealthier households, since national wealth surveys tend to underreport financial assets and financial assets represent a higher share of richer families portfolios relative to poorer ones. This dynamic also occurs in the Canadian data. 19 An approach that leverages data from household wealth surveys and rich list data to estimate the top tail of the family wealth distribution is used, among others, in Davies (1993), Bach et al. (2014), Bach et al. (2015), Vermeulen (2016), Davies et al. (2017), Vermeulen (2018), Chakraborty et al. (2019), and Davies and Di Matteo (2020). 20 Vermeulen (2018) develops a Monte Carlo study to demonstrate the utility of adding rich lists when estimating top tail. The results show that the addition of a rich list to survey data in the regression to estimate Pareto parameters causes the interpolated wealth tail to be estimated with an upward or downward bias of only 0.01. 21 Davies and Di Matteo (2020) provide a helpful discussion on the differences between the Forbes list and Canadian Business Richest People List and present a comparison of the entries from each list. 22 Bach et al. (2014), Bach et al. (2015), and Davies and Di Matteo (2020) similarly undertake rich list cleaning procedures before incorporating rich list data into national wealth survey microdata. 23 Economic families with negative net worth in the SFS PUMF are not presented in Figure A2-1. According to the SFS PUMF, there were 878,482 economic families with negative net worth in 2016. 24 In non-formulaic terms, the Pareto power law as applied to the family wealth distribution asserts that the wealth of the th wealthiest family in the population is inversely proportional to its rank. 25 Canadian Business (2006). The methodology was retrieved using the internet archiving website “Wayback Machine”. 26 Though the SFS Master File is not top-coded, the weighting procedure of the survey methodology may reduce the weights of some high-net-worth families even in the Master File. Statistics Canada (2018b) discloses, as part of the weighting procedure, that “influential observation are identified, and weights are reduced for a small number of extreme observations.” 27 Vermeulen (2018) explains that it is unclear where the Pareto-distributed top tail of the wealth distribution starts. He addresses the uncertainty by presenting estimates using six different thresholds ranging from 500,000 to 10 million. 28 Disclosed to PBO in correspondence with analysts from the SFS Team at Statistics Canada. 29 The 2016 SFS PUMF includes 638 observations with wealth greater than $3 million. By contrast, no country in Vermeulen (2018) using geographic or income-stratified geographic information to over-sample high-net-worth families had more than 100 observations with wealth greater than 2 million. Estimating the top tail of the family wealth distribution in Canada 28 30 This value of falls within Davies and Di Matteos (2020) range of estimates to perform top tail imputation on historical Canadian wealth survey data.

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全球COVID-19大流行造成了现代史上最剧烈、最深刻的短期经济收缩。即使一些国家成功控制了疫情，全球病例数仍在继续增长。卫生和经济的双重危机远未结束。主要的经济影响是由必要的政府强制封锁和社会疏远措施共同推动的。随着这些措施的缓解，以及除非爆发需要广泛更新限制的疫情，我们预计随着生产潜力的恢复，经济活动将初步恢复。但我们预计此后经济将缓慢复苏，消费者不愿恢复面对面的交流。最终活动恢复到新的正常状态，很可能涉及到相对于前病毒轨迹的永久性疤痕，可能要到2021年底或之后才会发生。正如Vanguard所倡导的那样，中国的政策反应令人印象深刻地大胆和迅速。我们看到货币政策一直保持宽松，直到2021年，而且进一步的财政支持似乎是可能的。目前极低的融资成本应能减轻公共债务增加带来的负担。考虑到长期的产能过剩，通胀可能会保持在较低水平。我们预测的风险主要与健康结果有关，并且倾向于下降。尽管我们的基线预测显示，随着病毒传播事件的发生，需要局部封锁，病毒将逐渐恢复工作，但我们的下行预测显示，COVID-19病毒将进一步蔓延，并在全国范围内重新实施限制。金融市场回报预期有所改善，股市估值看起来更具吸引力。但仍存在相当大的不确定性，市场可能进一步调整。我们建议投资者关注长期预期回报，拥抱全球多元化，避免在动荡的市场中拖延时间。在Vanguard于2019年底发布的经济与市场展望(the New Age of Uncertability)中，我们将全球经济展望描述为增长放缓至低于趋势的水平，持续的地缘政治不确定性和不可预测的决策打压了经济活动。对贸易战持续或升级的担忧，特别是美国与美国之间的贸易战。中国;英国退欧对英国和欧洲前景的不确定性影响;而未知的未知可能性(如香港的民间动荡、新兴市场的民粹主义和市场不稳定)有可能把全球经济的不同部分抛在一边。由于通胀率即使不算太低，但仍将保持温和，货币政策在可预见的未来看来仍将保持宽松，预计财政政策对支撑政策刺激的贡献微乎其微。但随着各国央行的效率可能达到极限，以及长期的不确定性可能损害生产潜力，我们看到了全球经济可能陷入低增长均衡的重大风险。然而，即使在我们最悲观的下行预测中，我们也没有预见到一场全球性流行病的毁灭性冲击，这场流行病将在人力成本、经济活动缩减和金融市场混乱等方面对全球经济造成严重破坏。我们强调的更大经济不确定性的破坏性影响无疑已经加剧。企业、家庭和市场参与者需要改变计划，以应对经济活动前所未有的巨大波动、重大的货币和财政政策措施以及资产价格波动。也许更具挑战性的是，需要对政府强制关闭经济活动和限制人们行动作出反应，以及需要考虑到医疗状况的巨大不确定性。

2021-07-06 40页 5星级

智威汤逊：2021年未来趋势和变化报告（英文版）（217页）.pdf

从2021年春天开始，曼哈顿将有一个新的浮岛公园，位于哈德逊河。这座占地两英亩的小岛由希瑟威克工作室设计，亿万富翁夫妇巴里迪勒和黛安冯弗斯滕贝格构思，将扩大纽约有限的绿地，并将以草坪、花园、草地和一个.

2021-07-01 217页 5星级