全球经济展望报告-PDF版

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域方面正在取得进展，例如，在减少贫穷、改善妇幼保健、增加电力供应和促进两性平等方面。然而，在许多情况下，这些进展的速度还不够快。在一些真正具有变革意义的领域，如减少不平等、减少碳排放和解决饥饿问题，进展要么停滞，要么逆转。简而言之，到2020年初，世界还没有走上实现2030年目标的轨道。3.由于大流行仍在许多地区肆虐，可持续发展目标进一步偏离轨道的程度尚不完全清楚。然而，正如本报告所表明的那样，这一流行病显然已经在一些领域产生了非常重大的影响，破坏了几十年的发展努力。4.这一点在可持续发展目标1中尤其明显，在该目标中，与流行病有关的经济衰退在2020年又使1.19亿至1.24亿人陷入赤贫，进一步加剧了消除贫穷的挑战，如冲突、气候变化和自然灾害。这场危机也加剧了不平等。到2020年，相当于2.55亿个全职工作岗位流失，另有1.01亿儿童和青年低于最低阅读水平，抹去了过去20年取得的教育成果。据估计，由于这一流行病，今后十年将有多达1 000万女孩面临童婚风险。5.与COVID-19相关的经济放缓对减缓气候危机几乎没有什么作用。初步数据显示，2020年全球温室气体排放量增加，而2020年全球平均气温比工业化前水平高出约1.2，危险地接近巴黎协定要求的1.5上限。此外，世界未能实现2020年的目标，即在2015-2020年期间，阻止生物多样性丧失，每年损失1000万公顷森林。6.本报告还表明，支持可持续发展目标转型所需的实施手段受到了19世纪危机的负面影响。2020年，资金流动大幅下降：全球外国直接投资流量下降了40%，流向中低收入国家的汇款流量下降了7%。据预测，2020年全球商品贸易额将比2019年下降5.6%。这一流行病的众多财政影响正在导致许多国家陷入债务困境。而官方发展援助净额在2020年增加到1610亿美元。

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. 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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.

2021-07-12 31页 5星级

欧盟委员会：2021年欧洲经济预测报告（英文版）（46页）.pdf

卫生状况的改善以及随之而来的病毒控制措施的持续放松，使我们能够逐步恢复到大流行之前我们所知道的自由，并使我们的经济恢复运转。夏季中期预测根据最新发展，特别是考虑到以下因素，重新评估了欧盟及其成员国的经.

2021-07-09 46页 5星级

硅谷银行（SVB）：2021年第二季度全球经济报告（英文版）（32页）.pdf

随着经济继续复苏，增长和通胀预期正在提高。美联储(fed)采取了一种结果驱动的方法，将等待数据表明经济复苏是稳固的，然后再上调联邦基金目标区间。由于前端收益率被锚定在低位，而曲线腹部及其后显著变陡，因.

2021-07-07 32页 5星级

Vanguard：经济与市场展望：冠状病毒时代的预测（英文版）（40页）.pdf

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、草地和一个圆形剧场为特色，作为滨水表演项目的一部分，接待艺术家、乐队和艺人。哥本哈根居民也将有一个新的场地，他们可以在水上闲逛。2020年4月，澳大利亚建筑师马歇尔布莱彻和丹麦公司studiofokstrot公布了哥本哈根群岛的规划。“parkipelago”将由城市港湾中的一组漂浮“岛屿”组成，人们可以在这里放松、游泳、钓鱼，甚至观赏星空。此前，类似的岛屿原型CPH也取得了成功。哥本哈根群岛是用钢铁和回收的漂浮材料建造的，覆盖着茂盛的树木和绿色植物，固定在海底。每个岛屿的下侧将为水生生物的繁衍提供空间。普雷希特设计了Parc de la Distance，一个位于维也纳一片空地上的迷宫式公园。高大的树篱将公园内的许多路线隔开，让人们在遵守社会距离准则的同时探索绿地。这项设计旨在克服在流感大流行期间，公共公园关闭大门所带来的挑战。在英国，工程公司奥雅纳(Arup)正在为公共空间建造新的座位区，让人们可以远离社会，这是利物浦无墙的一部分。这个项目是与分生组织设计公司和利物浦市议会合作设计的，将在城市周围安装可适应的模块化单元，称为“混合街道家具”。绿地正逐渐成为城市规划的未来。长期以来，城市户外景点一直是吸引世界各地城市居民的资产，随着COVID-19病毒的爆发，人们对新鲜空气和开放空间的渴望加剧。城市设计师们正在为更持久的解决方案指明前进的方向，在巨额投资的支持下创造新的文化空间。usic

2021-07-01 217页 5星级

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商界领袖和各国政府都承认，持续的长期价值创造需要一种新的经济模式，这种模式不那么依赖廉价、容易获得的材料和能源，而且能够恢复和再生自然资本。埃伦麦克阿瑟基金会迄今为止的研究表明，循环经济是一个明显的价值创造机会。随着许多政策制定者对这一前景看好的模式产生兴趣，他们设想自己可以在创造适当的有利条件方面发挥重要作用，并酌情为开启这一模式指明方向。本报告从国家和政策制定者的角度看待循环经济的机遇，旨在为政策制定者提供一个可行的工具，帮助他们加速向循环经济过渡。为决策者提供循环经济工具是埃伦麦克阿瑟基金会领导的合作成果，丹麦商业管理局和丹麦环境保护局是主要贡献者，尤其是在丹麦试点阶段。该工具包是与丹麦和国际利益攸关方合作开发的，其中包括主要决策者、企业和学术界人士。麦肯锡商业与环境中心(MCBE)提供了分析支持。向循环经济过渡可以带来更具创新性、弹性和生产力的经济的持久利益。这项研究中进行的建模表明，在丹麦，它可以导致额外的GDP增长0.8-1.4%，创造7000-13000个工作岗位，减少3-7%的碳足迹，减少5-50%的选定材料的原始资源消耗。这些估计是2035年的，只考虑生产部门和医院，占丹麦经济的25%。他们没有考虑到向可再生能源的进一步转变。虽然这种估计必然依赖于一些假设，并与不确定性有关，但它们证实了越来越多的研究结果，即向循环经济过渡对经济增长、创造就业和环境的影响可能是积极的。许多循环经济机会具有良好的潜在盈利能力，但往往存在限制进一步扩大规模或阻碍发展步伐的非金融障碍。政策制定者可以在帮助企业克服这些障碍方面发挥重要作用。障碍包括现有法规的意外后果(例如。对阻碍再制造产品贸易和运输的废物的定义)、社会因素，如公司和决策者缺乏发现和抓住循环经济机会的经验，以及市场失灵，如信息不完善(如企业需要修复，拆卸和再制造产品)和不可解释的外部性(例如。碳排放)。除了创造有利条件外，决策者还可以酌情为向循环经济过渡确定方向。

2021-06-30 177页 5星级

世界旅游业理事会（WTTC）：2021年可持续发展领导力报告（英文版）（12页）.pdf

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2021-06-29 12页 5星级

莱坊国际（Knight Frank）：2021年财富报告（英文版）（88页）.pdf

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2021-06-28 88页 5星级

麦肯锡（McKinsey）：2021年美国黑人经济状况报告（英文版）（152页）.pdf

自从美国成立以来，种族就塑造了个人和社区的命运。尽管有几代人的聪明才智和努力，美国黑人发现他们的机会受到了不适当和系统的限制，种族不平等几乎渗透到社会和经济的各个方面。麦肯锡黑人经济流动研究所(McK.

2021-06-23 152页 5星级

麦肯锡：放眼疫情之外：世界经济能比输给新冠病毒更多吗？（英文版）（15页）.pdf

一年多来，全世界一直在与SARS-CoV-2和这一大流行病的经济影响作斗争。尽管全球还有大量的工作要做，但领导COVID-19退出的国家让我们看到了心灵如何转变，如何与朋友和家人团聚，如何回归更正常的.

2021-06-23 15页 5星级

国际金融公司（IFC）：可持续发展委员会：结构与实践（英文版）（92页）.pdf

可持续性是公司长期业绩的关键。在这个复杂的、相互联系的、快节奏的世界里，几乎没有一个概念与我们所面临的挑战有那么大的关联。然而，这也是一个被过度使用和经常被误解的概念。可持续性包括经济、环境和社会层面.

2021-06-23 92页 5星级

麦肯锡：在新冠疫情后的经济复苏中消费者、工人和公司的下一步是什么（英文版）（16页）.pdf

COVID-19改变了我们的生活和工作方式，在大流行消退很久之后，它将改变我们的行为。公司迅速采取行动部署数字和自动化技术，大大加快了危机前发展速度慢得多的趋势。工作远程化，购物、娱乐，甚至药品在线化.

2021-06-17 16页 5星级

印度品牌价值基金会（IBEF）：未来十年印度经济的游戏规则变革者（英文版）（268页）.pdf

“结构转型”是近来在印度经济背景下使用的一个突出的流行语。这主要是由于正在采取一系列政策举措，以确保经济为长期增长做好准备。2016-17年印度经济调查还指出，印度不仅是世界上增长最快的经济体之一，以.

2021-06-11 268页 5星级

欧盟委员会：2021年经济政策协调：克服新冠疫情支持经济复苏和经济现代化（英文版）（18页）.pdf

欧洲联盟采取了前所未有的行动来抗击COVID-19流行病，缓冲危机的影响，使我们的经济走上强劲、可持续和包容性增长的道路。在过去一年中，欧盟和成员国表现出果断和团结一致的态度，采取了一系列政策措施，以.

2021-06-08 18页 5星级

贝莱德（BlackRock）：2021年全球展望报告（英文版）（27页）.pdf

这与经济周期衰退和复苏无关。重新启动仍然是令人惊讶的上升-但我们不应该从这么大的数字推断。我们相信，活动将平息，周期将恢复。长期收益率正朝着更高的方向发展，但低于人们通常理解的水平。我们看到各国央行倾.

2021-06-08 27页 5星级

IRi：2021年第一季度新兴增长点（英文版）（43页）.pdf

到第1季度，家庭消费仍保持在类似水平。随着机动性的提高，我们预计这一提升与。腐蚀正常，但仍高于前COVID-19。提高流动性正在影响早餐谷类食品等类别，因为居家消费“合适的尺寸”和高于正常水平的居家消.

2021-06-04 43页 5星级