34 (Author at Cato Institute) https://www.cato.org/ en Six Models Project a Drop in COVID-19 Deaths as States Open Up https://www.cato.org/blog/six-models-project-drop-covid-19-deaths-states-open Alan Reynolds <p>“Closely watched and scrutinized projections by <a name="_Hlk39655018" id="_Hlk39655018">University of Washington researchers nearly doubled the expected number of COVID-19 deaths to about 135,000 by August [4<sup>th</sup>], based on the easing of social distancing measures</a>,” reports The <em><a href="https://www.wsj.com/articles/the-tricky-math-behind-coronavirus-death-predictions-11588719034">Wall Street Journal</a></em>. Not really. That’s just a&nbsp;convenient excuse. The estimate was doubled because it was obviously wrong.</p> <p>At the time of my April 6&nbsp;blog, “<a href="https://www.cato.org/blog/tracking-white-houses-favorite-epidemic-curve">Tracking the White House’s Favorite Epidemic Curve</a>,” the “Murray Model” from The Institute for Health Metrics and Evaluation (IHME) was projecting 81,766 deaths by August 4. I&nbsp;wrote that “If COVID-19 deaths [per day] do <em>not</em> stop climbing soon after mid‐​April, then Chris Murray’s IHME model may begin to appear too optimistic.”</p> <p>As it happens, daily deaths did seem to peak at 2216 on April 16 and were then flat or down for few days, hitting a&nbsp;low of 1965 on April 26 (before rising back to 2343 on May 1). In the first week of April, while the curve appeared to be flattening, the Murray Model reduced projected deaths to total only 60,000, which would have required daily deaths to drop like a&nbsp;stone – more like a&nbsp;cliff than a&nbsp;curve. It did not take long for reality to outrun that&nbsp;estimate.</p> <p>An April 30 news story in <a href="https://www.statnews.com/2020/04/30/coronavirus-death-projections-compare-causes-of-death/">Stat</a> noted, “On Wednesday, April 29, the country blew past 60,000, more than three months before the Institute for Health Metrics and Evaluation projected. IHME, whose model has been criticized by many epidemiologists, now says the most likely death toll on Aug. 4&nbsp;will be 72,433, though it could be as low as about 60,000 (obviously impossible) and as high as 115,000.” That still‐​incredible scenario, suggesting there would be only 12,000 more deaths ahead over the next three months (about 133 per day), is the one that had to be doubled to 134,475 through August 4. It wasn’t because of any easing of mandatory social distancing rules, whose efficacy is <a href="https://www.wsj.com/articles/risk-based-social-distancing-is-key-to-reopening-11588672800">assumed</a> rather than proven, but because 72,433 was no more realistic than 60,000.</p> <p>The <em>Wall Street Journal</em> theory – that the IHME “nearly doubled the expected number of COVID-19 deaths … based on the easing of social distancing measures”– is easy to disprove. The online projections are available state‐​by‐​state, making it easy for anyone to see that daily deaths are <em>still projected to decline</em> in states that eased social distancing rules.</p> <p>The IHME modelers may have encouraged the <em>Journal</em> writers’ incorrect interpretation of why their death estimates had to be fixed by fabricating a<a href="https://www.washingtonpost.com/health/leading-model-projects-some-states-could-reopen-by-may-4-not-so-fast-say-other-modelers/2020/04/17/6366f866-80f6-11ea-9040-68981f488eed_story.html"> flawed state grading scheme</a> for social distancing mandates (which <a href="https://www.cato.org/blog/epidemiological-models-cant-tell-states-when-open-or-how">I&nbsp;criticized here</a>). In reality, the reason the IHME’s total projected deaths have increased is not because daily deaths are projected to rise (on the contrary, deaths still fall quickly) but because deaths are now expected to remain <em>relatively </em>higher for dozens<em> more days</em>.</p> <p>Focusing on daily deaths, state infection <a href="https://rt.live/">reproduction rates</a> and hospitalization admissions is more informative than “confirmed” cases, which are the tip of an invisible iceberg (because nobody tests those with no symptoms or symptoms to mild to seek medical attention) and sensitive to the intensity of testing.</p> <p>The recent increase in IHME’s cumulative death estimate over six months does not mean COVID-19 deaths rates have gotten worse, only that their projections have gotten better.</p> <p>None of the epidemiological models can claim to have much predictive value for more than a&nbsp;month ahead. So, what do they say about COVID-19 deaths in May? Ryan Best and Jay Boice gathered <a href="https://projects.fivethirtyeight.com/covid-forecasts/">six model predictions</a> of total U.S. deaths through May 31 ­–from IHME, MIT, Columbia, Northeastern, Los Alamos and the University of Texas. Aside from the just‐​revised IHME model, the average prediction of the other five ranged from 93,000 to 114,000, with a&nbsp;median of 100,000.</p> <p>Having already reached 72,050 by May 5, reaching 100,000 by the end of the month would leave 27,950 more to go in 26&nbsp;days or 1,075 deaths per day. That sounds worse that it is. Since there were 2,343 deaths on May 1, all models predict daily deaths must <em>fall sharply</em> in order to average less than half that many daily deaths over the whole month. The newest mid‐​range estimate from the IHME, for example, shows daily deaths down to 924 by May 31. Contrary to misdirected news stories about one model expecting more deaths than it did before, all six of these leading models expect a&nbsp;very significant drop in the pace of COVID-19 deaths this month.</p> Wed, 06 May 2020 12:34:34 -0400 Alan Reynolds https://www.cato.org/blog/six-models-project-drop-covid-19-deaths-states-open Epidemiological Models Can’t Tell States When to Open or How https://www.cato.org/blog/epidemiological-models-cant-tell-states-when-open-or-how Alan Reynolds <p><span>With more than a&nbsp;dozen states starting to <a href="https://www.cnn.com/interactive/2020/us/states-reopen-coronavirus-trnd/">relax mandatory social distancing</a> edicts, some organizations are offering advice to states on </span>when they might prudently alleviate or decentralize current mandates.<span> The most ambitious effort is an offshoot of a&nbsp;model from the Institute for Health Metrics and Evaluation (<a href="https://covid19.healthdata.org/united-states-of-america">IHME</a>) at the University of Washington. Having been made famous in White House briefings by Doctors Birx and Fauci, that “Murray Model” could conceivably be influencing federal and state official about the proper timing and design of state decisions to relax mandates.</span></p> <p><span>I assembled a&nbsp;table “Evaluating State COVID-19 Rules” to examine IHME recommended dates for states to begin “opening up” businesses, shopping, recreation and employment, while adding two sources of relevant facts. </span></p> <p><span><a name="_Hlk39139101" id="_Hlk39139101">Evaluating State COVID-19 Rules</a></span></p> <table> <tbody> <tr> <td></td> <td> <p><span>Deaths </span></p> <p><span>per Million </span></p> </td> <td> <p><span>Reproduction</span></p> <p><span>Rate (Rt) </span></p> </td> <td> <p><span>IHME </span></p> <p><span>Opening Date </span></p> </td> <td> <p><span>IHME</span></p> <p><span>Score</span></p> </td> </tr> <tr> <td> <p><span>AK </span></p> </td> <td> <p><span>12</span></p> </td> <td> <p><span>0.82</span></p> </td> <td> <p><span>N/A</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>AL</span></p> </td> <td> <p><span>50</span></p> </td> <td> <p><span>0.85</span></p> </td> <td> <p><span>19‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>AR</span></p> </td> <td> <p><span>19</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>28‐​Jun</span></p> </td> <td> <p><span>3</span></p> </td> </tr> <tr> <td> <p><span>AZ</span></p> </td> <td> <p><span>42</span></p> </td> <td> <p><span>1.01</span></p> </td> <td> <p><span>6‐​Jul</span></p> </td> <td> <p><span>3</span></p> </td> </tr> <tr> <td> <p><span>CA</span></p> </td> <td> <p><span>48</span></p> </td> <td> <p><span>0.9</span></p> </td> <td> <p><span>20‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>CO</span></p> </td> <td> <p><span>133</span></p> </td> <td> <p><span>0.9</span></p> </td> <td> <p><span>1‐​Jun</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>CT</span></p> </td> <td> <p><span>583</span></p> </td> <td> <p><span>0.88</span></p> </td> <td> <p><span>17‐​Jun</span></p> </td> <td> <p><span>4</span></p> </td> </tr> <tr> <td> <p><span>DC</span></p> </td> <td> <p><span>278</span></p> </td> <td> <p><span>0.93</span></p> </td> <td> <p><span>27‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>DE</span></p> </td> <td> <p><span>144</span></p> </td> <td> <p><span>1.08</span></p> </td> <td> <p><span>20‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>FL</span></p> </td> <td> <p><span>57</span></p> </td> <td> <p><span>0.79</span></p> </td> <td> <p><span>21‐​Jun</span></p> </td> <td> <p><span>4</span></p> </td> </tr> <tr> <td> <p><span>GA</span></p> </td> <td> <p><span>101</span></p> </td> <td> <p><span>0.81</span></p> </td> <td> <p><span>28‐​Jun</span></p> </td> <td> <p><span>4</span></p> </td> </tr> <tr> <td> <p><span>HI</span></p> </td> <td> <p><span>11</span></p> </td> <td> <p><span>0.79</span></p> </td> <td> <p><span>11‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>IA</span></p> </td> <td> <p><span>43</span></p> </td> <td> <p><span>1.06</span></p> </td> <td> <p><span>1‐​Jul</span></p> </td> <td> <p><span>4</span></p> </td> </tr> <tr> <td> <p><span>ID</span></p> </td> <td> <p><span>36</span></p> </td> <td> <p><span>0.84</span></p> </td> <td> <p><span>19‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>IL</span></p> </td> <td> <p><span>166</span></p> </td> <td> <p><span>0.91</span></p> </td> <td> <p><span>21‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>IN</span></p> </td> <td> <p><span>149</span></p> </td> <td> <p><span>1.05</span></p> </td> <td> <p><span>22‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>KS</span></p> </td> <td> <p><span>44</span></p> </td> <td> <p><span>1.05</span></p> </td> <td> <p><span>28‐​Jun</span></p> </td> <td> <p><span>3</span></p> </td> </tr> <tr> <td> <p><span>KY</span></p> </td> <td> <p><span>51</span></p> </td> <td> <p><span>0.9</span></p> </td> <td> <p><span>22‐​Jun</span></p> </td> <td> <p><span>4</span></p> </td> </tr> <tr> <td> <p><span>LA</span></p> </td> <td> <p><span>386</span></p> </td> <td> <p><span>0.76</span></p> </td> <td> <p><span>26‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>MA</span></p> </td> <td> <p><span>462</span></p> </td> <td> <p><span>0.9</span></p> </td> <td> <p><span>21‐​Jun</span></p> </td> <td> <p><span>4</span></p> </td> </tr> <tr> <td> <p><span>MD</span></p> </td> <td> <p><span>169</span></p> </td> <td> <p><span>0.92</span></p> </td> <td> <p><span>27‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>ME</span></p> </td> <td> <p><span>38</span></p> </td> <td> <p><span>0.97</span></p> </td> <td> <p><span>19‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>MI</span></p> </td> <td> <p><span>358</span></p> </td> <td> <p><span>0.69</span></p> </td> <td> <p><span>21‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>MN</span></p> </td> <td> <p><span>54</span></p> </td> <td> <p><span>1.06</span></p> </td> <td> <p><span>7‐​Jun</span></p> </td> <td> <p><span>4</span></p> </td> </tr> <tr> <td> <p><span>MO</span></p> </td> <td> <p><span>54</span></p> </td> <td> <p><span>0.89</span></p> </td> <td> <p><span>17‐​Jun</span></p> </td> <td> <p><span>4</span></p> </td> </tr> <tr> <td> <p><span>MS</span></p> </td> <td> <p><span>80</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>1‐​Jun</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>MT</span></p> </td> <td> <p><span>14</span></p> </td> <td> <p><span>0.86</span></p> </td> <td> <p><span>18‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>NC</span></p> </td> <td> <p><span>36</span></p> </td> <td> <p><span>0.95</span></p> </td> <td> <p><span>13‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>ND</span></p> </td> <td> <p><span>19</span></p> </td> <td> <p><span>1.09</span></p> </td> <td> <p><span>20‐​Jul</span></p> </td> <td> <p><span>2</span></p> </td> </tr> <tr> <td> <p><span>NE</span></p> </td> <td> <p><span>29</span></p> </td> <td> <p><span>1.14</span></p> </td> <td> <p><span>7‐​Jul</span></p> </td> <td> <p><span>3</span></p> </td> </tr> <tr> <td> <p><span>NH</span></p> </td> <td> <p><span>45</span></p> </td> <td> <p><span>0.97</span></p> </td> <td> <p><span>17‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>NJ</span></p> </td> <td> <p><span>725</span></p> </td> <td> <p><span>0.91</span></p> </td> <td> <p><span>28‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>NM</span></p> </td> <td> <p><span>53</span></p> </td> <td> <p><span>0.94</span></p> </td> <td> <p><span>5‐​Jun</span></p> </td> <td> <p><span>4</span></p> </td> </tr> <tr> <td> <p><span>NV</span></p> </td> <td> <p><span>77</span></p> </td> <td> <p><span>0.94</span></p> </td> <td> <p><span>23‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>NY</span></p> </td> <td> <p><span>1180</span></p> </td> <td> <p><span>0.83</span></p> </td> <td> <p><span>28‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>OH</span></p> </td> <td> <p><span>69</span></p> </td> <td> <p><span>0.92</span></p> </td> <td> <p><span>15‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>OK</span></p> </td> <td> <p><span>53</span></p> </td> <td> <p><span>0.85</span></p> </td> <td> <p><span>N/A</span></p> </td> <td> <p><span>4</span></p> </td> </tr> <tr> <td> <p><span>OR</span></p> </td> <td> <p><span>24</span></p> </td> <td> <p><span>0.91</span></p> </td> <td> <p><span>30‐​May</span></p> </td> <td> <p><span>4</span></p> </td> </tr> <tr> <td> <p><span>PA</span></p> </td> <td> <p><span>161</span></p> </td> <td> <p><span>0.93</span></p> </td> <td> <p><span>28‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>RI</span></p> </td> <td> <p><span>226</span></p> </td> <td> <p><span>0.93</span></p> </td> <td> <p><span>22‐​Jun</span></p> </td> <td> <p><span>4</span></p> </td> </tr> <tr> <td> <p><span>SC</span></p> </td> <td> <p><span>39</span></p> </td> <td> <p><span>0.94</span></p> </td> <td> <p><span>14‐​Jun</span></p> </td> <td> <p><span>4</span></p> </td> </tr> <tr> <td> <p><span>SD</span></p> </td> <td> <p><span>13</span></p> </td> <td> <p><span>0.89</span></p> </td> <td> <p><span>2‐​Jul</span></p> </td> <td> <p><span>2</span></p> </td> </tr> <tr> <td> <p><span>TN</span></p> </td> <td> <p><span>28</span></p> </td> <td> <p><span>0.9</span></p> </td> <td> <p><span>1‐​Apr</span></p> </td> <td> <p><span>4</span></p> </td> </tr> <tr> <td> <p><span>TX</span></p> </td> <td> <p><span>25</span></p> </td> <td> <p><span>0.74</span></p> </td> <td> <p><span>15‐​Jun</span></p> </td> <td> <p><span>4</span></p> </td> </tr> <tr> <td> <p><span>UT</span></p> </td> <td> <p><span>15</span></p> </td> <td> <p><span>0.9</span></p> </td> <td> <p><span>7‐​Jul</span></p> </td> <td> <p><span>3</span></p> </td> </tr> <tr> <td> <p><span>VA</span></p> </td> <td> <p><span>58</span></p> </td> <td> <p><span>0.93</span></p> </td> <td> <p><span>27‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>VT</span></p> </td> <td> <p><span>75</span></p> </td> <td> <p><span>0.8</span></p> </td> <td> <p><span>24‐​Mar</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>WA</span></p> </td> <td> <p><span>108</span></p> </td> <td> <p><span>0.88</span></p> </td> <td> <p><span>31‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>WI</span></p> </td> <td> <p><span>52</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>22‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>WV</span></p> </td> <td> <p><span>21</span></p> </td> <td> <p><span>0.81</span></p> </td> <td> <p><span>10‐​May</span></p> </td> <td> <p><span>5</span></p> </td> </tr> <tr> <td> <p><span>WY</span></p> </td> <td> <p><span>12</span></p> </td> <td> <p><span>1.08</span></p> </td> <td> <p><span>1‐​Jun</span></p> </td> <td> <p><span>3</span></p> </td> </tr> </tbody> </table> <p><span>The first column in the table shows COVID-19 <a href="https://www.worldometers.info/coronavirus/country/us/">deaths per million</a> by state. The national average death rate is 179 per million, yet 31 of the 50 states have experienced fewer than 60 deaths per million. On the basis of experience, these 31 states (at least less‐​populated counties within the states) appear to be relatively low‐​risk places to experiment with relaxing restrictions. </span></p> <p><span>The second column shows the Reproduction Rate (Rt) in the latest week. In this series, “<a href="https://rt.live/">(R<sub>t</sub>) represents the effective reproduction rate</a> of the virus.” That is, if each person affects one other then Rt equals 1. The curve is then flat, since everyone cured is matched by someone newly infected. While Rt is below 1.0, on the other hand, that means new cases are smaller than the number cured so the number still infected is declining. </span></p> <p><span>By the week ending April 25, 42 states had an Rt below 1.0. Among some of the first to lighten restrictions, Rt was down to 0.83&nbsp;in Florida and Oklahoma by April 30, 0.78&nbsp;in Georgia and 0.75&nbsp;in Texas. </span></p> <p><span>Soon after famously projecting that U.S. deaths would total only 60,000­ –a number already exceeded­– the IHME model’s latest <a href="https://www.geekwire.com/2020/coronavirus-modelers-raise-projected-u-s-death-toll-lengthen-timeline-state-state-recovery/">update</a> “raises the outlook for the cumulative U.S. death toll through Aug. 4 … to a&nbsp;new figure of 74,073.” The new curve projects that <em>nationwide</em> <em>daily</em> deaths will drop to 0–10 by July 1. But that is said to depend on maintaining “current social distancing… until infections [are] minimized and containment implemented.” That may explain why these modelers recently began <a href="https://www.thedenverchannel.com/news/coronavirus/key-model-used-by-white-house-says-social-distancing-measures-should-continue-into-may-or-june">advising states</a> what to do and when to do it. “A recent analysis from the Institute for Health Metrics and Evaluation,” noted an April 22 <a href="http://www.oudaily.com/coronavirus/oklahoma-amid-coronavirus-experts-recommend-continued-social-distancing-statewide/article_7b2967a8-84b7-11ea-9fbb-abc317feee3d.html">local news</a> story, “suggests that Oklahoma may be among several states that need to continue social distancing until late June or early July.” Oklahoma Governor Kevin Stitt ignored that advice and allowed haircuts.</span></p> <p><span>The IHME team created a&nbsp;1–6-point scorecard awarding the most points for the most severe “current social distancing” rules. They use these scores to propose specific dates in May‐​July when each state is granted approval to begin graduating from business and school closures and universal home isolation to more‐​targeted “containment strategies… [such as] testing, contact tracing, isolation, and limiting gathering size.” </span></p> <p><span>The table’s third column “IHME Opening Date”– shows when “relaxing social distancing may be possible with containment strategies.” The last column shows each state’s “IHME Score,” which I&nbsp;argue is mainly what determines each state’s date for relaxing “social distancing” as they define it. </span></p> <p><span>The modelers “evaluate [State] government‐​mandated social distancing measures… <a href="http://www.healthdata.org/covid/faqs">based on the New Zealand Government alert system</a> Level 4″ (which was eased to <a href="https://www.nzherald.co.nz/nz/news/article.cfm?c_id=1&amp;objectid=12325362">Level 3</a> on April 28). They rate the virtue of states by how many of six equal‐​weighted broad categories of restrictions they implemented. A&nbsp;perfect score of 6&nbsp;would require (1) closing nonessential businesses, (2) closing nonessential services, (3) closing all schools, (4) banning large gatherings, (5) issuing stay‐​at‐​home orders, and (6) “travel severely limited.”</span></p> <p><span>No state scored more than 5, because no state could meet the New Zealand standard for domestic or international immobility. New Zealand banned foreign travelers on March 12 (because 5&nbsp;people died out of 4.8 million) following other island nations Hong Kong and Singapore. New Zealand’s March 23 Level 4&nbsp;plan also decreed “Public transit is closed.” Period. </span></p> <p><span>Since no U.S. state lived up to New Zealand’s travel and transit bans, that left the highest possible score at 5. As a&nbsp;result, the unparalleled risks of proximity to strangers in New York’s subways, trains, busses and airports became irrelevant to scorekeepers who granted New York the same 5‐​point safety score as states with no mass transit or international airports. So, the IHME gives New York its blessing to relax social distancing after May 27. All others states that checked five boxes are likewise sanctioned to open in May, some a&nbsp;week or two earlier. </span></p> <p><span>States with a&nbsp;score of 4, on the other hand, have to wait until June before doing anything different. And states with a&nbsp;score of 2&nbsp;or 3&nbsp;have to wait until July when the IHME model predicts zero COVID-19 deaths nationwide (we may not believe that, but they should because it’s their model). </span></p> <p>In short, the timing recommended for opening a&nbsp;state’s economy depends on points earned on the basis of adherence to New Zealand’s formerly tough rules. However, the IHME makes a&nbsp;contrary claim that the “timeline is based on the date by which our model projects that COVID-19 infections will drop below 1&nbsp;per 1&nbsp;million people.” Yet the model projects only <em>deaths </em>per day, not “infections” (most of which are asymptomatic and uncounted) or even cases (which are sensitive to testing). Also, the model tracks<em> actual</em> deaths, not deaths per million.</p> <p>Even if the dates for relaxing social distancing depended on projected <em>deaths </em>falling to 1&nbsp;per day (rather than infections per million) that goal has been met by South Dakota ever since April 8&nbsp;which you might think would make the state safe to open up entirely. South Dakota never closed anything but schools and large gatherings, in fact, which is why it is cursed with the lowest IHME score of 2. So, this remarkably safe state is admonished by the IHME to keep its two little rules in place until “after July 5, 2020.”</p> <p>The fact that some states have a&nbsp;larger number of social distancing rules cannot explain why some had fewer deaths than others. On the contrary, South Dakota and 18 other states had fewer than 60 deaths per million regardless of their sub‐​par IHME scores of 2–4 and their resulting late dates for changing current rules in any unapproved way (such as confining home isolation to the sick and vulnerable)</p> <p>Policies to deal with epidemics should be evaluated more on actual results and less on illusions that anyone really knows which schemes will prove more cost‐​effective and durable than others.</p> <p>After graphically comparing mitigation policies with results among many states and countries, and finding no clear connections, <a href="https://medium.com/@yinonweiss/coronavirus-shutdown-effectiveness-visualized-part-2-1a6e7b97649d">Yinon Weiss</a> concluded that “legal shut downs may only play a&nbsp;small role in the slowing down of the spread when compared to other factors such limiting travel to/​from hot spots, degree of public transportation use, washing hands, cancelling high transmission events such as conferences, and whether people are staying home when sick or exposed.”</p> <p><span>The first columns of my table provide two relevant measures to discern how well states are actually coping with COVID-19, rather than relying on malleable model projections or subjective points scored in the IHME 1–5 virtue award contest. New tests, cases, hospitalizations and deaths also bear watching for potential hot spots, of course. Thankfully, no infectious disease has ever before been so quickly and transparently monitored by so many private and state online trackers. </span></p> <p><span>If federal or state officials are instead considering relying on dates and scores from the University of Washington’s IHME team to decide what to do and when to do it, they should carefully reconsider delegating such authority to unaccountable technocrats to make social, health and economic choices that will deeply affect many millions of American lives. </span></p> Thu, 30 Apr 2020 14:57:01 -0400 Alan Reynolds https://www.cato.org/blog/epidemiological-models-cant-tell-states-when-open-or-how How One Model Simulated 2.2 Million U.S. Deaths from COVID-19 https://www.cato.org/blog/how-one-model-simulated-22-million-us-deaths-covid-19 Alan Reynolds <p>When it came to dealing with an unexpected surge in infections and deaths from SARS‐​CoV‐​2 (the virus causing COVID-19 symptoms), federal and state policymakers understandably sought guidance from competing epidemiological computer models. On March 16, a 20‐​page report from Neil Ferguson’s team at Imperial College London quickly gathered enormous attention by producing enormous death estimates. Dr. Ferguson had previously publicized almost equally <a href="https://www.spectator.co.uk/article/six-questions-that-neil-ferguson-should-be-asked/amp">sensational death estimates</a> from mad cow disease, bird flu and swine flu.</p> <p> </p><div data-embed-button="promo_block" data-entity-embed-display="view_mode:block_content.full" data-entity-embed-display-settings="internal:/research/covid-19" data-entity-type="block_content" data-entity-uuid="3100ed49-a16e-4270-8d9b-8c9aa44dea27" class="align-center embedded-entity" data-langcode="en"><a href="/research/covid-19"> <div class="promo-block clearfix spacer--standout block--standout bg--standout block p-standard"> <div class="block--inner"> <h3 class="mb-md-4 heading"> <a href="https://www.cato.org/research/covid-19">Frequently Asked Questions about COVID-19</a> </h3> <ul><li><strong>How should the government approach this pandemic?</strong> <ul><li><a href="https://www.cato.org/publications/commentary/covid-19-response-critical-guidelines-policymakers">COVID-19 Response: Critical Guidelines for Policymakers</a></li> </ul></li> <li><strong>Does immigration affect COVID-19 rates?</strong> <ul><li><a href="https://www.cato.org/blog/no-mr-president-immigration-not-correlated-covid-19-united-states">“No, Mr. President, Immigration Is Not Correlated with COVID-19 in the United States</a>,” by Alex Nowrasteh and Andrew C. Forrester</li> </ul></li> <li><strong>How do we keep our liberty during this crisis?</strong> <ul><li>“<a href="https://www.cato.org/publications/commentary/preventing-liberty-becoming-coronavirus-fatality">Preventing Liberty from Becoming a Coronavirus Fatality,”</a> by Ted Galen Carpenter</li> </ul></li> <li><strong>Need help with homeschooling?</strong> <ul><li><a href="https://www.cato.org/publications/commentary/i-homeschool-kids-here-are-6-ideas-parents-while-schools-are-closed">“I Homeschool My Kids. Here Are 6 Ideas for Parents While Schools Are Closed,”</a> by Kerry McDonald</li> </ul></li> </ul><div class="mt-md-4 mt-standard field field-name-field-call-to-action"> <span class="hs-cta-wrapper" id="hs-cta-wrapper-d3308680-4313-468d-b361-e7a6da28ca98"><span class="hs-cta-node hs-cta-d3308680-4313-468d-b361-e7a6da28ca98" id="hs-cta-d3308680-4313-468d-b361-e7a6da28ca98"><a href="https://cta-redirect.hubspot.com/cta/redirect/4957480/d3308680-4313-468d-b361-e7a6da28ca98" target="_blank"><img class="hs-cta-img" id="hs-cta-img-d3308680-4313-468d-b361-e7a6da28ca98" src="https://no-cache.hubspot.com/cta/default/4957480/d3308680-4313-468d-b361-e7a6da28ca98.png" alt="Learn more" /></a></span> //--&gt; </span> </div> </div> </div> </a></div> <p><a href="https://www.nytimes.com/2020/03/17/world/europe/coronavirus-imperial-college-johnson.html">The New York Times</a> quickly ran the hot news about this new COVID-19 estimate:</p> <blockquote><p>The report, which warned that an uncontrolled spread of the disease could cause as many as 510,000 deaths in Britain, triggered a sudden shift in the government’s comparatively relaxed response to the virus. American officials said the report, which projected up to 2.2 million deaths in the United States from such a spread, also influenced the White House to strengthen its measures to isolate members of the public.</p> </blockquote> <p>A month later that 2.2 million estimate was still being used (without revealing the source) by President Trump and Doctors Fauci and Birx to imply that up to <a href="https://www.cato.org/blog/did-mitigation-save-two-million-lives">two million lives had been saved</a> by state lockdowns and business closings and/​or by federal travel bans.</p> <p>The following summary of the Ferguson/​Imperial College report provides clues about how the model came to generate such dramatic conclusions:</p> <blockquote><p>In the (unlikely) absence of any control measures or spontaneous changes in individual behavior, we would expect a peak in mortality (daily deaths) to occur after approximately 3 months. In such scenarios, given an estimated R0 of 2.4, we predict 81% of the G.B. and U.S. populations would be infected over the course of the epidemic… In total, in an unmitigated epidemic, we would predict approximately 510,000 deaths in G.B. and 2.2 million in the U.S., not accounting for the potential negative effects of health systems being overwhelmed on mortality.</p> </blockquote> <p>This worst‐​case simulation came up with 2.2 million deaths by simply assuming that <a href="https://www.hpnonline.com/infection-prevention/screening-surveillance/article/21130206/covid19-predicted-to-infect-81-of-us-population-cause-22-million-deaths-in-us">81% of the population</a> gets infected ­–268 million people– and that 0.9% of them die. It did <em>not </em>assume health systems would have to be overwhelmed to result in so many deaths, though it did make that prediction.</p> <p>Neither the high infection rate nor the high fatality rate holds up under scrutiny.</p> <p>To project that nearly everyone becomes infected the report had to assume that each person infects 2.4 others and those people, in turn, infect 2.4 others and so on, with the result that the number infected doubles roughly every four days. This 2.4 “reproduction number” (R0) was “based on … the early growth‐​rate of the epidemic in Wuhan.” But the reproduction number always appears highest during the early phase of an epidemic (partly due to increased testing) and has now fallen to nearly zero in China.</p> <p>The reproduction number is not a constant, but a variable that depends on many other things, from humidity and sunlight to human behavior.</p> <p>Suppose an infected man walks into a small elevator with three other people and begins coughing. The other three get infected from droplets in the air or from virus on objects (such as elevator buttons) they touch before touching their faces. In this case, we observe an R0 of 3.0. But if the coughing man is wearing a mask then perhaps one person does not become infected by inhaling the virus, so the R0 falls to 2.0. If the other two quickly use an alcohol‐​based hand sanitizer before touching their face, or wash their hands, then nobody becomes infected and the R0 falls to zero.</p> <p>The worst‐​case Imperial College estimate of 2.2 million deaths if everyone does “nothing” did <em>not</em> simply mean no government lockdowns, as a March 31 White House graph with two curves implied. It meant nobody avoids crowded elevators, or wears face masks, washes their hands more often, or buys gloves or hand sanitizer. Everyone does literally nothing to avoid danger.The Ferguson team knew that was unrealistic, yet their phantasmal 2.2 million estimate depended on it. As they reticently acknowledged, “it is highly likely that there would be significant spontaneous change in population behavior even in the absence of government‐​mandated interventions.” An earlier <a href="https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/873723/03-potential-effect-of-non-pharmaceutical-interventions-npis-on-a-Covid-19-epidemic-in-the-UK.pdf">February 20</a> brief said, “Some social distancing is to be expected, even in the absence of formal control measures.”</p> <p>The obvious reality of voluntary self‐​protective actions makes it incorrect to allude to the extreme Ferguson death estimate, consciously or not, as evidence that heavy‐​handed government interventions prevented <a href="https://www.wsj.com/articles/americans-need-forbearance-not-more-stimulus-11587422691">“hundreds of thousands”</a> of deaths. In fact, the Imperial College team <a href="https://www.forbes.com/sites/bjornlomborg/2020/04/09/save-lives-and-avoid-a-catastrophic-recession/#5a57037c6f92">did <em>not </em>recommend “a complete lockdown</a> which … prevents people going to work.”</p> <p>The key premise of 81% of the population being infected should have raised more alarms than it did. Even the deadly “<a href="https://www.livescience.com/worst-epidemics-and-pandemics-in-history.html">Spanish Flu</a>” (H1N1) pandemic of 1918–19 infected no more than <a href="https://web.archive.org/web/20160923152823/http:/www.flu.gov/pandemic/history/1918/">28% of the U.S. population</a>. The next H1N1 “Swine Flu” pandemic in 2009-10, infected 20-<a href="https://www.cidrap.umn.edu/news-perspective/2013/01/study-puts-global-2009-pandemic-h1n1-infection-rate-24">24%</a> of Americans.</p> <p>To push the percentage infected up from 20–28% to an unprecedented 81% for COVID-19 required assuming the number of cases and/​or deaths keeps doubling every three or four days for months (deaths were predicted to peak July 20). And that means assuming the estimated reproduction number (R0) of 2.4 remains high, and people keep mingling with different groups, until nearly everyone gets infected. Long before 8 out of 10 people became infected, however, a larger and larger percentage of the population would have recovered from the disease and become immune, so a smaller and smaller share would still remain susceptible.</p> <p>Little more than a month after the outbreak exploded in March, COVID-19 curves are already flattening conclusively in many different countries with quite different government mitigation policies. By April 16, it was taking <a href="https://ourworldindata.org/coronavirus">60 days for the number of deaths to double in China</a> – not 4 days. The worldwide average was up to 11 days, including 17 days in Italy, 18 days in Taiwan, and 24 in South Korea.</p> <p>In short, the Imperial College projection that 81% of the U.S. population could be infected if everyone just did literally nothing to protect themselves or others is inconsistent with rational risk avoidance, history and recent experience. Even with a much smaller percentage infected, however, deaths could still end up extremely high if nearly 1% of those infected died, as the Ferguson team assumed.</p> <p>The assumed 0.9% death rate (within a range of 0.4% to 1.4%) was tweaked from a smaller estimate in a study of deaths in China by <a href="https://www.medrxiv.org/content/10.1101/2020.03.09.20033357v1">Robert Verrity</a> and others, which found a “<em>case</em> fatality rate” (CFR) of 1.38% among known and tested cases. By assuming that such confirmed cases underestimated actual infections by only about half, they inferred an “<em>infection </em>fatality rate” (IFR) of 0.66%.</p> <p>Epidemiologists have since found growing <a href="https://www.medrxiv.org/content/10.1101/2020.03.24.20042291v1">evidence</a> that the number of <a href="https://www.statnews.com/2020/03/17/a-fiasco-in-the-making-as-the-coronavirus-pandemic-takes-hold-we-are-making-decisions-without-reliable-data/">undetected cases with few symptoms or none</a> is much larger than merely doubling the <a href="https://reason.com/wp-content/uploads/2020/04/Bommer-Vollmer-2020-COVID-19-detection-April-2nd.pdf">small number</a> of known and tested cases. A review of such research by the Oxford University <a href="https://www.cebm.net/covid-19/global-covid-19-case-fatality-rates/">Centre for Evidence‐​Based Medicine</a> finds “a presumed estimate for the COVID-19 IFR somewhere between 0.1% and 0.36%.” A middling estimate of 0.22% would by itself reduce the infamous 2.2 million death estimate to half a million even if 81% were somehow infected.</p> <p><a href="https://www.wsj.com/articles/is-the-coronavirus-as-deadly-as-they-say-11585088464">Eran Bendavid and Jay Bhattacharya</a> of the Stanford School of Medicine, with 15 others, conducted serological tests for COVID-19 antibodies from a representative sample of 3,300 people from Santa Clara County, CA. The high percentage showing proof of having been cured of undetected asymptomatic cases indicates that between 48,000 to 81,000 people in Santa Clara county had already been infected and cured by the time they were tested on April 3–4. Those numbers are 50 to 85 times larger than the number of known, confirmed cases. They correspond to “an infection fatality rate of 0.12–0.2%” – similar to the flu (which nonetheless killed a CDC‐​estimated 61,000 in the 2017/18 season by infecting millions).</p> <p>The Santa Clara antibody testing strongly suggests there must be sizable islands or clusters of people elsewhere in the U.S. who now have some immunity, which would substantially reduce the future risk of community spread. This is one reason any “rebound” in the fourth quarter would likely be more easily contained, even aside from the fact that we’re all much better educated, equipped and prepared if hot spots flare up in the fall. Because a newer and better <a href="https://covid19.healthdata.org/united-states-of-america">Washington University IHME model</a> ends with August 4, its low estimate of COVID-19 deaths (under 61,000 as of April 15) misses five months of 2020 and is therefore surely too optimistic for the whole year. Yet the IHME estimates will nonetheless prove enormously closer to reality than the archaic overstuffed Imperial College prediction of 2.2 million deaths.</p> <p>The trouble with being too easily led by models is we can too easily be misled by models. Epidemic models may seem entirely different from economic models or climate models, but they all make terrible forecasts if filled with wrong assumptions and parameters.</p> Tue, 21 Apr 2020 15:05:51 -0400 Alan Reynolds https://www.cato.org/blog/how-one-model-simulated-22-million-us-deaths-covid-19 Did Mitigation Save Two Million Lives? https://www.cato.org/blog/did-mitigation-save-two-million-lives Alan Reynolds <p>In the April 16 White House briefing, President Trump again said, as he often has before, that “models predicted between 1.5 and 2.2 million deaths” if we had not endured the various economic shutdowns imposed by the Governors of 42 States. The severity and breadth of those statewide shutdowns was initially encouraged, and is now justified, by just one dramatic statistic. That number was the 2.2 million U.S. deaths supposedly at risk from COVID-19.</p> <p>The famed 2.2 million estimate first reached viral status in the March 31 White House briefing by Doctors Anthony Fauci and Deborah Birx. They displayed a graph with two bell‐​shaped epidemic curves placed on top of each other. Both curves estimate deaths per day which rise to a peak and then fall.</p> <p> </p><div data-embed-button="image" data-entity-embed-display="view_mode:media.blog_post" data-entity-type="media" data-entity-uuid="1b5ee904-1b61-4086-9962-a2cc5b96befc" class="align-left embedded-entity" data-langcode="en"> <img srcset="/sites/cato.org/files/styles/pubs/public/2020-04/Two%20Curves.jpg?itok=EougLFfG 1x, /sites/cato.org/files/styles/pubs_2x/public/2020-04/Two%20Curves.jpg?itok=2M40qfXW 1.5x" width="700" height="394" src="/sites/cato.org/files/styles/pubs/public/2020-04/Two%20Curves.jpg?itok=EougLFfG" alt="Two Curves" typeof="Image" class="component-image" /></div> <p>The steeper of the two curves was painted black and marked “Pandemic Outbreak: No Interventions.” It showed an estimated total of 1.5 to 2.2 million deaths from COVID-19 in what appears to be a relatively short period. The White House graph showed no dates, but the source of that now‐​famous 2.2 million estimate (as explained later) predicted U.S. deaths would keep rising until June 20.</p> <p>The second “flattened” curve rises more slowly but also peaks later (in, say, August) with a more prolonged period of deaths than the steep curve (where more deaths happen more quickly). The flatter curve is labeled “With Interventions” and also100,000 to 240,000 deaths. Contrary to the delayed timing shown in the lighter graph, the architects of that more‐​delayed or flattened curve believe COVID-19 deaths have already peaked.</p> <p>Based on the two curves, President Trump <a href="https://www.politico.com/news/2020/04/01/trump-coronavirus-millions-saved-160814">repeatedly remarked</a> that State government mitigation plans saved two million lives – the difference between one curve’s estimate of 2.2 million and the other’s very different estimate of roughly 200,000. The graphs certainly do create that impression, although White House briefings by Fauci and Birx never spelled it out. They instead emphasized that the goal of “flattening the curve” was merely to buy time and avoiding overwhelming the hospitals, saving some lives (but not two million) by not running out of hospital beds and ventilators.</p> <p>Where did the two curves come from? At first, many thought the estimates came from the White House experts. <a href="https://www.cnbc.com/2020/03/30/white-house-coronavirus-expert-predicts-up-to-200000-us-coronavirus-deaths.html">CNBC </a>reported, “Dr. Birx said the projections by Dr. Anthony Fauci that U.S. deaths could range from 1.6 million to 2.2 million deaths is a worst‐​case scenario if the country did ‘nothing’ to contain the outbreak, but said even ‘if we do things almost perfectly,’ she still predicts up to 200,000 U.S. deaths.”</p> <p>The steep curve with 2.2 million deaths was not from Dr. Fauci, however, but from Neal Ferguson’s team at <a href="https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf">Imperial College</a> London. As for the flatter curve, Dr. Birx later attributed it to Chris Murray’s team at the University of Washington Institute for Health Metrics and Evaluation (<a href="https://covid19.healthdata.org/united-states-of-america">IHME</a>). However, even the highest IHME death estimates never approached 200,000 (unlike death estimates on that graph) and their mid‐​range estimates have lately been reduced from 93,000 to 69,000. Like the Imperial College model and others, the IHME model greatly overestimated the need for hospital beds and ventilators. But because it incorporates new facts, such errors get corrected.</p> <p>The March 16 Imperial College paper, unlike the IHME model, is a month old and cannot be undone without losing face. Even with “the most effective mitigation strategy examined,” that study predicted, “the surge limits for both general ward and ICU beds would be exceeded by at least 8‐​fold under the more optimistic scenario.” Yet there are no signs the U.S. will ever need 8 times as many hospital beds, or that even New York City is critically short of beds or urgent care facilities.</p> <p>If the point of comparing two graphs was to show estimated deaths with and without “interventions,” then there was no reason to use two models rather than one. The same Imperial College model that warned of 2.2 million U.S. deaths with no interventions also predicted 1.1 to 1.2 million deaths –not 100,000 to 240,000– even with “the most effective mitigation strategy.” The Imperial College recommendations for “most effective mitigation” focused on social distancing for those over 70 and isolation of only those infected and their contacts, rather than banning jobs or closing all restaurants and beaches. An effective strategy would be targeted and localized, consistent with the new federal guidelines for gradually easing restrictions on the least‐​risky counties, populations, activities and businesses.</p> <p>For the White House science team to meld the Ferguson and Murray curves into a single graph was quite inappropriate, because they are quite different. The Murray/​IHME model continually monitors actual data on state and national deaths and hospitalization, and adjusts the projected epidemic curve to incorporate new information.</p> <p>The Ferguson/​Imperial College estimates, by contrast, came from a month‐​old (March 16) simulation model for high‐​density areas which constructed hypothetical scenarios using inflexible parameters about infection rates and other variables culled from obsolete studies of the earliest phase of the outbreak in Wuhan, China. In my next blog, I will attempt to explain how and why the Imperial College report may have ended up with such extreme projections of hospital bed shortages and deaths.</p> <p>Dr. Fauci and Dr. Birx have done an outstanding job of explaining how the new coronavirus spreads and what we can all do to reduce the risk of getting infected or infecting others. Federal advice about proper handwashing and alcohol‐​based hand sanitizers, and (belatedly) the value of masks, helped reinforce the natural incentive of people to protect themselves. Federal bans on travel from China and Europe clearly saved lives. Many of the broad statewide mitigation mandates, though not yet efficiently targeted, nonetheless saved lives.</p> <p>But it was disingenuous for the White House team to imply–by wrongly comparing epidemic curves from two different models–that these mitigation strategies may have saved two million American lives. The British model that once postulated a scenario in which 2.2 million U.S. lives could be at risk was simply wrong, and references to it should stop.</p> Fri, 17 Apr 2020 12:54:07 -0400 Alan Reynolds https://www.cato.org/blog/did-mitigation-save-two-million-lives The COVID-19 Data We Have May Not Be The Data We Need https://www.cato.org/blog/covid-19-data-we-have-may-not-be-data-we-need Alan Reynolds <p>COVID-19 statistics that are easiest for reporters to find and explain are often the ones we keep hearing about in daily news reports. A perennial favorite is the <a href="https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6">John Hopkins University</a> graphical database which is constantly updated to add up the cumulative number of “confirmed” cases, deaths and recoveries since January 21 for separate countries and the world.</p> <p>To switch the focus from these familiar multi‐​month totals to what is happening now, however, I built this simple graph of daily new deaths and daily new confirmed cases. It does suggest recent flattening, though professional optimism about reaching a peak is partly based on <a href="https://apps.npr.org/dailygraphics/graphics/covid-state-deaths-20200407/">key states</a> rather than national totals.</p> <p> </p><div data-embed-button="image" data-entity-embed-display="view_mode:media.blog_post" data-entity-type="media" data-entity-uuid="111f115d-7ec8-42c6-bc0c-f6b778c07be4" class="align-center embedded-entity" data-langcode="en"> <img srcset="/sites/cato.org/files/styles/pubs/public/2020-04/NEW%20U.S.%20COVID%2019%20CASES%20AND%20DEATHS%20.jpg?itok=8bf1HBIt 1x, /sites/cato.org/files/styles/pubs_2x/public/2020-04/NEW%20U.S.%20COVID%2019%20CASES%20AND%20DEATHS%20.jpg?itok=z1IHMdxb 1.5x" width="700" height="415" src="/sites/cato.org/files/styles/pubs/public/2020-04/NEW%20U.S.%20COVID%2019%20CASES%20AND%20DEATHS%20.jpg?itok=8bf1HBIt" alt="new cases and deaths" typeof="Image" class="component-image" /></div> <p>The cumulative data we see repeatedly in the daily news is not always the data we need. “Confirmed cases” in the John Hopkins data means cases that were tested. This makes it sensitive to the intensity of testing and also limits the count to those sick enough to be tested. It also exaggerates the apparent death rate by excluding the majority of COVID-19 infections, those having mild or no symptoms.</p> <p>The “number recovered” would ultimately add up to 99% of infections if 1% died. Yet the number recovered seen in the John Hopkins site always remains very low. That is because states don’t keep track of what happened to every sick person, and nobody knows about the vast majority who recover at home.</p> <p>As these cumulative multi‐​month totals go, the most relevant one is missing. That would be “active” cases – all those infected minus those who recovered or died. But the published numbers don’t tell us how many Americans are now or ever have been infected, nor how many recovered. Compared with confirmed cases and recovered cases, death numbers are more meaningful and solid (despite ambiguity about <a href="https://www.cdc.gov/nchs/data/nvss/coronavirus/Alert-2-New-ICD-code-introduced-for-COVID-19-deaths.pdf?fbclid=IwAR0PGhCEPqdxb0vWlUR8MQLcgUm-lGKcCdPXf6gxne7KyFWB86fG8xLtNao">attributing death</a> to COVID-19 despite underlying conditions).</p> <p>Cumulative totals include old history. The numbers we need would tell us whether or not the rate of change is slowing for <a href="https://apps.npr.org/dailygraphics/graphics/covid-state-deaths-20200407/">new deaths</a>. New deaths are a good proxy for new cases (as the graph shows), and also for new <a href="https://www.npr.org/sections/health-shots/2020/03/31/824665834/are-hospitals-seeing-a-surge-of-coronavirus-patients-some-officials-arent-saying">hospitalizations</a> with a lag. But death figures are clearer and easier to track.</p> <p>The <a href="https://www.cato.org/blog/tracking-white-houses-favorite-epidemic-curve">Murray Model</a> I discussed on April 6 updates a model‐​built epidemic curve with actual daily deaths by state. It has already reduced mid‐​range estimates COVID-19 total deaths from about 81,000 to 61,000. But the model’s highest estimates more than double that figure, and even the mid‐​range curve (which fits best so far) can rise or fall with new data. The main point is that I do believe the model’s focus on real‐​time death data is the best single indicator we have right now or will have later without a lot more testing, including random samples and testing for immunity.</p> Fri, 10 Apr 2020 15:26:02 -0400 Alan Reynolds https://www.cato.org/blog/covid-19-data-we-have-may-not-be-data-we-need Tracking the White House’s Favorite Epidemic Curve https://www.cato.org/blog/tracking-white-houses-favorite-epidemic-curve Alan Reynolds <p>New York Governor Cuomo recently said he thinks New York City deaths from COVID-19 may be near an “apex.” White House advisers Dr. Deborah Birx and Dr. Anthony Fauci refer to the same phenomenon as a “peak” or flattening of the bell‐​shaped epidemic curve. ­When we reach that peak, daily reports on the number of coronavirus deaths should stop doubling every five days (from 661 March 31 to 1212 on April 5) and instead begin to level off and then decline.</p> <p>Governor Cuomo and the White House team share the same expectation that we’re nearing a&nbsp;peak because they share the admirably transparent “Chris Murray Model” from The Institute for Health Metrics and Evaluation (IHME) at the University of Washington.</p> <p>For the nation as whole, the model says the nationwide peak in daily new COVID-19 deaths is expected to peak at 3,130 on April 16&nbsp;(see the dotted line in “Deaths per Day” graph <a href="https://covid19.healthdata.org/projections">here</a>). That&nbsp;3,130 figure is the mid‐​point of a&nbsp;range which could go much higher. Yet the latest daily deaths have been a&nbsp;bit&nbsp;lower than mid‐​range projections.</p> <p>If actual deaths more‐​or‐​less follow the middle path then level off and fall that could result in a&nbsp;total of 81,766 COVID-19 deaths, according to the model’s latest run. But that&nbsp;overall death total&nbsp;depends on&nbsp;a&nbsp;rapid downturn in deaths from late April to late June, which may look&nbsp;too good to be true until we really see it. Allowing some elbow room for slower improvement, the White House team has estimated up to 240,000 deaths from COVID-19 – which would be nearly four times the <a href="https://www.cdc.gov/flu/about/burden/past-seasons.html">61,000</a> U.S. deaths suffered in the 2017–18 flu season.</p> <p>If COVID-19 deaths do <em>not</em> stop climbing soon after mid‐​April, then Chris Murray’s IHME model may begin to appear too optimistic. But there is nothing secret about it, so anyone can easily check. So long as reality keeps matching the mid‐​range curve fairly well, it is fascinating to watch and a&nbsp;little encouraging.</p> Mon, 06 Apr 2020 15:14:52 -0400 Alan Reynolds https://www.cato.org/blog/tracking-white-houses-favorite-epidemic-curve COVID-19 Deaths and Incredible WHO Estimates https://www.cato.org/blog/covid-19-deaths-incredible-who-estimates Alan Reynolds <p>“Death Toll Hits 9&nbsp;as Outbreak Spreads,” was the scary <em>Wall Street Journal </em>headline in print before it was toned down <a href="https://www.wsj.com/articles/confirmed-coronavirus-cases-outside-china-pass-10-000-11583228968">online</a>. COVID-19 deaths at a&nbsp;nursing home and hospital in Washington state were&nbsp;unrelated to the virus <em>spreading</em> “across the U.S.” The facts tell us much more about the exceptionally high risks of fatal infection from COVID-19 (or pneumonia or flu) among elderly people living close together in nursing homes or hospitals, many of them already sick.</p> <p>The ongoing COPD-19 outbreak in Kirkland Washington at the Life Care nursing home and Evergreen hospital represents high‐​risk concentrations of vulnerable seniors. Among those who died in Washington, <a href="https://q13fox.com/2020/03/03/seventh-coronavirus-death-in-washington-state-happened-last-week-at-harborview-medical-center/">all but two were in their 70s or 80s</a> (the other two in their 50s) and most had “<a href="https://www.spokesman.com/stories/2020/mar/03/seventh-death-in-western-washington-attributed-to-/">underlying health conditions</a>.” Evergreen hospital has two more in critical condition, in their 70s and 90s, both with underlying conditions.</p> <p>What we just learned from Washington was already known from China’s experience A&nbsp;February 24 article by Katarina Zimmer in <em><a href="https://www.the-scientist.com/news-opinion/why-some-covid-19-cases-are-worse-than-others-67160">The Scientist</a> </em>provides an excellent summary:</p> <p>“The latest data from China stem from an analysis of nearly 45,000 confirmed cases, and on the whole suggest that the people most likely to develop severe forms of COVID-19 are those with pre‐​existing illnesses and the elderly.While less than 1&nbsp;percent of people who were otherwise healthy died from the disease, the fatality rate for people with cardiovascular disease was 10.5 percent. That figure was 7.3 percent for diabetes patients and around 6&nbsp;percent for those with chronic respiratory disease, hypertension, or cancer. While overall, 2.3 percent of known cases [in China] proved fatal—which many experts say is likely an overestimate of the mortality rate, given that many mild cases might go undiagnosed—patients 80&nbsp;years or older were most at risk, with 14.8 percent of them dying.”</p> <p><em>The Scientist</em>&nbsp;article&nbsp;point about the death rate being “less than 1&nbsp;percent” among the healthy and also the point about the 2.3% Chinese estimate being a “an overestimate” because “many mild cases might go undiagnosed” underscore <a href="https://www.cato.org/blog/misleading-arithmetic-covid-19-death-rates">similar points I&nbsp;made</a> in a&nbsp;March 2&nbsp;blog. My concern is that misinformation about COVID-19 has fueled excessive fear of the virus by greatly exaggerating the actual death rate per hundred people infected (the infection fatality rate).</p> <p>I suggested that if we took unreported mild cases into account, the actual death rate among infected people outside China may be as low as 0.5%. Skeptics greeted death rates of 0.5–1.0% as Panglossian heresy. Yet the rigorous February 10 study I&nbsp;cited, from <a href="https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-2019-nCoV-severity-10-02-2020.pdf">Imperial College London</a>, concluded the global infection fatality ratio was about 1.0. My estimate is lower than 1.0 because I&nbsp;exclude China where (I argue) high fatality rates in Wuhan were exaggerated by overcounting institutionalized elderly with serious infection.</p> <p>On February 29, Denise Grady, <em>The New York Times’</em> veteran health and medicine reporter, independently came to&nbsp;conclusions not unlike those&nbsp;Katarina Zimmer and I&nbsp;did within a&nbsp;few days of each other – namely than fewer than 1% of people infected with COVID-19 are likely to die from it, and that&nbsp;the odds of death are lower that that for healthy non‐​elderly people.&nbsp;Ms. Grady explained&nbsp;these facts in&nbsp;<em>The New York Times</em>&nbsp;as follows [with emphasis&nbsp;added]:</p> <p>“Early estimates of the coronavirus death rate from China, the epicenter of the outbreak, have been around 2&nbsp;percent. But a&nbsp;new report on 1,099 cases from many parts of China, published on Friday in The New England Journal of Medicine, finds a&nbsp;lower rate: 1.4 percent. The coronavirus death rate may be even lower, if — <em>as most experts suspect</em> — <em>there are many mild or symptom‐​free cases that have not been detected.</em> The true death rate could <a href="https://www.nejm.org/doi/full/10.1056/NEJMe2002387" target="_blank">turn out to be similar to that of a&nbsp;severe seasonal flu</a>, below 1&nbsp;percent, according to an editorial published in the journal by Dr. Anthony S. Fauci and Dr. H. Clifford Lane, of the National Institute of Allergy and Infectious Diseases, and Dr. Robert R. Redfield, director of the Centers for Disease Control and Prevention.”</p> <p>Despite two health science writers and one economist trying to warn people that widely hyped mortality rates greatly exaggerate the risk (not to mention Drs. Fauci, Lane and Redfield) exaggerated estimates continue to grab the headlines.</p> <p>On March 4, <em>The New York Times</em> reported, “Dr. Tedros Adhanom Ghebreyesus, the organization’s director general, said in a&nbsp;news conference in Geneva that… ‘Globally, <a href="https://www.nytimes.com/2020/03/04/world/coronavirus-news.ht">about 3.4 percent of reported Covid‐​19 cases have died</a>.” “By comparison,” he added, ” seasonal flu generally kills far fewer than 1&nbsp;percent of those infected.”</p> <p>To the newspaper’s&nbsp;credit, <em>The New York Times</em> apparently felt obliged to caution readers that the WHO’s 3.4 percent death rate is quite implausible, if not wildly inaccurate:&nbsp;“The figure does not include mild cases that do not require medical attention and is skewed by Wuhan, where the death rate is several times higher than elsewhere in China. It is also quite possible that there are many undetected cases that would push the mortality rate lower. Still, it was the first time that the organization had offered a&nbsp;global mortality rate for the disease.”</p> <p>Ironically, my previous blog quoted Dr. Tedros Adhanom saying, “Most people will have mild disease and get better without needing any special care.”</p> <p>Because <em>most</em> cases are mild, as he said, and because mild cases are excluded <em>by definition</em> from “reported cases,” the WHO’s alleged 3.4% mortality rate is nothing more than&nbsp;sensationalist nonsense.</p> Wed, 04 Mar 2020 15:05:55 -0500 Alan Reynolds https://www.cato.org/blog/covid-19-deaths-incredible-who-estimates The Misleading Arithmetic of COVID-19 Death Rates https://www.cato.org/blog/misleading-arithmetic-covid-19-death-rates Alan Reynolds <p>Assuming the number of people who have reportedly died from COVID-19 is reasonably accurate, then the percentage of infected people who die from the disease (the death rate) must surely have been <em>much lower</em> than the 2–3% estimates commonly reported. That is because the number of infected people is much larger than the number tested and reported.</p> <p>The triangle graph, from a February 10 study from <a href="https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-2019-nCoV-severity-10-02-2020.pdf">Imperial College London</a>, shows that most people infected by COVID-19 are never counted as being infected. That is because, the Imperial College study explains, “the bottom of the pyramid represents the likely largest population of those infected with either mild, non‐​specific symptoms or who are asymptomatic.”</p> <p> </p><div data-embed-button="image" data-entity-embed-display="view_mode:media.blog_post" data-entity-type="media" data-entity-uuid="ce6c8c1e-b8e8-4460-9708-be9b13f51ae6" class="align-left embedded-entity" data-langcode="en"> <img srcset="/sites/cato.org/files/styles/pubs/public/2020-03/IMPERIAL%20COLLEGE%20SPECTRUM%20OF%20CASES%2C%20color.jpg?itok=tMdUrFbm 1x, /sites/cato.org/files/styles/pubs_2x/public/2020-03/IMPERIAL%20COLLEGE%20SPECTRUM%20OF%20CASES%2C%20color.jpg?itok=cmjeTXiT 1.5x" width="700" height="502" src="/sites/cato.org/files/styles/pubs/public/2020-03/IMPERIAL%20COLLEGE%20SPECTRUM%20OF%20CASES%2C%20color.jpg?itok=tMdUrFbm" alt="COVID-19 Cases" typeof="Image" class="component-image" /></div> <p>As the Director General of the World Health Organization (WHO), Tedros Adhanom, explained in his <a href="https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---28-february-2020">February 28 briefing</a>, “Most people will have mild disease and get better without needing any special care.” Several studies have found that about 80% of all the COVID-19 cases have relatively minor symptoms which end without severe illness and therefore remain unreported.</p> <p>A Chinese study in the <a href="https://jamanetwork.com/journals/jama/fullarticle/2762130">Journal of the American Medical Association</a>, February 20, found a “case‐​fatality rate” of 2.3%, meaning 1,023 died out of 44,672 cases. But the total sample in that study (72,314) included an additional 27,642 non‐​confirmed cases, and simply including cases (let alone <em>unreported </em>minor cases) would have reduced the fatality rate to 1.4%. China‐​based estimates are largely confined to cases with the most severe symptoms, so it should be no surprise that the reported death rate among severe cases is much higher than it would be if the data also included “most people” who “have a mild disease and get better.” The risk of infecting more people is also more severe in China data, since severe cases are concentrated and transmitted in hospitals. This can also lead to overstated estimates of how many people are infected by someone with COVID-19, including the WHO “reproduction number” estimate of 1.4–2.5, which is mainly based on evidence from China.</p> <p>As the graph indicates, other countries include more non‐​severe cases than China does, notably by testing incoming travelers who arrive with a cough and fever. Even after casting a slightly wider net, however, the number of confirmed cases probably captures only about 30% of the actual number.</p> <p>By the morning of March 2, there had been 89,253 confirmed cases of COVID-19 reported around the world, with about 96% of those in Asia. For comparison, the were an estimated <a href="https://www.who.int/gho/hiv/en/">37.9 million</a> people living with HIV in 2018.</p> <p>It is worth noting that have also been 45,393 known <em>recoveries</em> from COVID-19 (compared to 3048 cumulative deaths) and, importantly, <a href="https://www.statista.com/chart/20943/new-daily-confirmed-covid-19-cases-and-recoveries/">recoveries have been outnumbering new cases</a>.</p> <p>What about the relatively small number of COVID-19 cases <em>outside China</em>? In his February 28, the Director General of WHO reported that “Outside China, there are now 4351 cases in 49 countries, and 67 deaths.” Deaths of 67 divided by 4351 seems to demonstrate a death rate of 1.5%. But such calculations are highly misleading. They assume the denominator of that ratio (4351) is as accurate as the numerator (67). Yet people with “mild cases who get better” are unlikely to <em>ever</em> be included in the denominator.</p> <p>If the WHO estimate of 4351 confirmed cases amounted to 30% of the actual number infected outside of China at that time, for example, then the combined total of both unreported and confirmed cases would be 4351 divided by 0.30 or 14,503. In that case, the actual death rate would 67 divided by 14,503, or less than one half of one percent (0.46%). Also, such death rates in the recent past are likely to come down over time, because they happened before promising new clinical trials of antiviral drugs that proved effective against more deadly viruses such as SARS, HIV and Ebola.</p> <p>For perspective, the <a href="https://www.cdc.gov/about/history/sars/timeline.htm">SARS</a> coronavirus killed 774 people out of 8096 known cases in 2003, which was a death rate of 9.6% before it vanished the next year. Bird flu in 1997 was predicted to be a deadly pandemic, but it killed very few people before it disappeared. In its February 22 U.S. <a href="https://www.contagionlive.com/news/cdc-reports-13-million-flu-cases-thus-far-in-201920-season">Influenza Surveillance Report</a>, “CDC estimates that so far this season there have been at least 32 million flu illnesses… and 18,000 deaths from flu.” Dividing 18,000 by 32 million implies a low U.S. death rate of .0138% from the flu. Looking at the death rate alone is obviously not enough: We also have to look at the numbers of people infected, and the duration of the epidemic, which is why the flu killed so many more people than SARS. Still, it is important to avoid scaring people about the risk of death from COVID-19 by continuing to ignore the fact that the vast majority of cases “have mild disease and get better without needing any special care.”</p> Mon, 02 Mar 2020 11:54:47 -0500 Alan Reynolds https://www.cato.org/blog/misleading-arithmetic-covid-19-death-rates Simon Johnson Claims the Warren Health Plan Is a Gift to U.S. Businesses https://www.cato.org/blog/simon-johnson-claims-warren-health-plan-gift-us-businesses Alan Reynolds <p><span>An advisor to the Warren campaign, <a href="https://www.wsj.com/articles/warren-has-the-remedy-for-health-costs-11575229582">Simon Johnson</a> of MIT, has written an impressively fact‐​free <em>Wall Street Journal </em>article claiming Senator Warren’s “remedy for health care costs” would be a&nbsp;wonderful gift to American businesses.</span></p> <p><span>“Americans currently spend nearly 18% of gross domestic product on health <span>care… </span> and a&nbsp;great deal of this burden falls directly on companies.” He claims “this dead weight gets heavier each year” and “companies cannot by themselves easily constrain health‐​insurance premiums.” The impression is that businesses shoulder a&nbsp;large and rising share of total spending on health care. And unlike all other employee compensation (such as salaries and retirement benefits) this is said to be just a “deadweight cost” that “get heavier each year” yet remains “unpredictable.” </span></p> <p><span>According to the <a href="https://www.cato.org/companies%20cannot%20by%20themselves%20easily%20constrain%20health-insurance%20premiums">Centers for Medicare and Medicaid Services</a> (CMS), however, “the private business share of overall health care spending remained fairly steady since 2010.” That steady 20% share is neither “a great deal of” the total nor did it “get heavier each year.” What <em>has</em> gotten heavier each year is the burden of Medicare and Medicaid which, unlike private companies, obviously cannot “constrain health care premiums” –­ including those collected in the form of taxes.<br><br> Federal, state and local governments accounted for 48% of spending in 2017, says CMS, compared with 34% for private insurance. If someone is spending too&nbsp;much, the private sector seems the least likely culprit. And Johnson doesn’t even pretend that health spending will be any smaller “over the next 10&nbsp;years.”</span></p> <p><span>Johnson does claim out‐​of‐​pocket health spending “would drop nearly to zero,” ostensibly because Warren’s plan would allegedly have no premiums, copays or deductibles (until the money ran out). But out of pocket health care spending can’t possibly drop to zero because it includes over‐​the‐​counter medicines and supplements, and many other goods and services not normally covered by Medicare (except private Advantage plans) such as hearing aids and eyeglasses, dental care, massages, etc.</span></p> <p><span>Johnson claims “costs would drop immediately” because, for example, Warren’s plan hopes to pay 70 percent below current Medicare prices for brand‐​name prescription drugs (without killing too many people). But Medicare’s current prescription drug plan (Part D) is run by competing private insurance companies, and they already drive tough bargains. </span></p> <p><span>“The health‐​care burden hurts American business,” says Johnson, due to “the onerous contribution most companies are required to make through employer‐​sponsored insurance.” Would the Warren plan end that “onerous contribution”? Of course not. They’re counting on it to pay 43% of the cost of the plan.&nbsp;<br><br><em>Washington Post</em> fact‐​checker Glenn Kessler explains: “Instead of employers continuing to give that money to insurance companies, Warren proposes that businesses direct 98 percent to the federal government [$8.8 trillion] and keep 2&nbsp;percent for themselves… ”<br><br> Would the Warren plan prohibit the next government from requiring employers to pay a&nbsp;vastly more “onerous contribution” in the future? Of course not. The Warren plan would also raise taxes on corporations and their investors by some $6.3 trillion, according to Kessler, so any ephemeral promise of reducing companies’ insurance premiums by 2% for a&nbsp;year or two is hardly great news for American business. </span></p> Mon, 02 Dec 2019 15:22:11 -0500 Alan Reynolds https://www.cato.org/blog/simon-johnson-claims-warren-health-plan-gift-us-businesses Optimal Top Tax Rates: A Review and Critique https://www.cato.org/publications/cato-journal/optimal-top-tax-rates-review-critique Alan Reynolds <div class="lead mb-3 spacer--nomargin--last-child text-default"> <p>Several prominent economists who advocate more egalitarian use of taxes and transfers to redistribute income have used selective (and arguably low) estimates of the “elasticity of taxable income” (ETI) to suggest that U.S. individual income tax rates of 73–83 percent at high incomes would be “socially optimal” in the sense of maximizing revenue available for political redistribution.</p> </div> , <div class="mb-3 spacer--nomargin--last-child text-default"> <p>Proponents of major increases or reductions in U.S. marginal tax rates have long cited historical evidence to support their policy recommendations. Elasticity of taxable income estimates are simply a&nbsp;relatively new summary statistic used to illustrate observed behavioral responses to past variations in marginal tax rates. They do so by examining what happened to the amount of income reported on individual tax returns, in total and at different levels of income, before and after major tax changes.</p> <p>The ETI compares the percentage change in reported taxable income (i.e., income after deductions) to the percentage change in the net‐​of‐​tax rate (i.e., the portion of marginal income a&nbsp;taxpayer is allowed to keep, which equals 1&nbsp;minus the marginal tax rate). Thus, if the marginal tax rate decreases from 60 to 40 percent, the net‐​of‐​tax share will increase from 40 to 60 percent and taxpayers will have an incentive to earn and/​or report more taxable income, other things being constant. ETI measures the strength of that response.</p> <p>For example, if a&nbsp;reduced marginal tax rate produces a&nbsp;substantial increase in the amount of taxable income reported to the IRS, the elasticity of taxable income is high. If not, the elasticity is low. ETI incorporates effects of tax avoidance as well as effects on incentives for productive activity such as work effort, research, new business start‐​ups, and investment in physical and human capital.</p> <p>ETI estimates, in turn, have been used by economists to estimate various concepts of an ideal or “optimal” tax rate within a&nbsp;linear flat rate tax system or a&nbsp;nonlinear progressive tax system. What is optimal from the point of economic efficiency or incentives, however, is not necessarily optimal if the government’s priority (or the economist’s priority) is to maximize tax revenue collected from high incomes, ostensibly for the purpose of redistributing that extra revenue to the poor.</p> <p>To estimate a&nbsp;redistributive‐​optimal or revenue‐​maximizing top tax rate, Diamond and Saez (<a href="#ch08_ref13">2011</a>: 171) claim that, if the relevant ETI is 0.25, then the revenue‐​maximizing top tax rate is 73 percent. Such estimates, however, do not refer to the top federal income tax rate, as is frequently implied (<a href="#ch08_ref25">Krugman 2011</a>), but to the combined marginal rate on income, payrolls, and sales at the federal, state, and local level. I&nbsp;find that, with empirically credible changes in parameters, the Diamond‐​Saez formula can more easily be used to show that top U.S. federal, state, and local tax rates are already too high rather than too low. By also incorporating dynamic effects — such as incentives to invest in human capital and new ideas — more recent models estimate that the long‐​term revenue‐​maximizing top tax rate is between 22 and 49 percent, and one study (<a href="#ch08_ref23">Judd et al. 2018</a>: 1) finds that, in certain cases, the optimal marginal tax rate on the top income is negative, which was also the conclusion of Stiglitz (<a href="#ch08_ref51">1987</a>).</p> <p>Piketty, Saez, and Stantcheva (<a href="#ch08_ref38">2014</a>: 233) likewise claim the relevant ETI is only 0.2, which lifts their redistributive‐​optimal top tax rate to 83 percent (effectively on all income — including corporate income, dividends, and capital gains — to minimize opportunities for tax avoidance). But they add that “the optimal top tax rate … actually goes to 100 percent if the real supply‐​side elasticity is very small” (ibid.: 232).</p> <p>They support the claim that 83 percent top tax rates on all income would be harmless by comparing <em>percentage point</em> changes in top individual tax rates from about 1960 to 2009 among 18 OECD countries with their per capita GDP growth rates. Yet percentage point changes from 1960 to 2009 cannot tell us whether tax rates were high or low during most of the many years between those distant end points. Piketty, Saez, and Stantcheva’s comparison of long‐​term GDP growth rates with percentage point changes in top tax rates simply shows that countries like Germany and Japan reduced top tax rates to 50–53 percent in the 1950s, decades before the United States and United Kingdom did the same. If Germany, Switzerland, France, or Spain had cut their top tax rates by as many percentage points as the United States has since 1960, their top tax rates would now be well below zero.</p> <p>Piketty, Saez, and Stantcheva (<a href="#ch08_ref38">2014</a>) imply that top corporate executives are the main target of their 83 percent marginal tax, and that high CEO pay is mainly just wasteful rent. Their alleged evidence for a “nonconventional bargaining model” and “CEO rent‐​extraction” rests mainly on an undocumented claim that the “use of stock‐​options has exploded in the post‐​1986 period, i.e., after top tax rates went down” (ibid.: 261). Evidence shows the opposite — namely, that stock‐​based executive compensation exploded after <em>1993 when top tax rates went up</em> (<a href="#ch08_ref19">Gorry, Hubbard, and Mathur 2018</a>: 16).</p> <p>These authors argue that an 83 percent marginal rate on top incomes could greatly reduce <em>pretax</em> pay of allegedly overpaid CEOs. But that appears incongruous with their claim that the 83 percent tax rate could also maximize revenue. I&nbsp;also find the combined compensation of the top five executives in S&amp;P 1000 firms accounted for less than 6&nbsp;percent of top 1&nbsp;percent income in 2005, which narrows the relevance of an unsubstantiated “CEO rent‐​extraction” hypothesis.</p> <h2>Conflicting Views about Elasticity and Effects on Long‐​Term Prosperity</h2> <p>In 2019, a&nbsp;University of Chicago survey asked a&nbsp;panel of economic experts (<a href="#ch08_ref11">Chicago Booth 2019</a>) whether or not they agreed that “Raising the top federal marginal tax on earned personal income to 70 percent … would raise substantially more revenue (federal and state combined) without lowering economic activity.” Among those answering, 20 economists disagreed and 8&nbsp;agreed. This result reflects considerable professional disagreement about the parameters used to estimate optimal top tax rates, notably ETI estimates, and what they imply for tax policy.</p> <p id="note-1">Elasticity of taxable, or perhaps gross income (<a href="#ch08_ref10">Chetty 2009</a>), can be “a sufficient statistic to approximate the deadweight loss” from tax disincentives and distortions (<a href="#ch08_ref45">Saez 2001</a>: 212). Although recent studies define revenue‐​maximization as “optimal,” Goolsbee (<a href="#ch08_ref18">1999</a>: 39) rightly emphasizes, “The fact that efficiency costs rise with the square of the tax rate are likely to make the optimal rate well below the revenue‐​maximizing rate.“<sup><a href="#fn08-01">1</a></sup></p> <p>If the estimated <em>ETI for high‐​income taxpayers</em> (not all taxpayers) is relatively high, that suggests that past increases in top marginal tax rates were associated with little or no increases in tax revenue because economic activity was discouraged and tax avoidance encouraged. With an ETI of 1.0 or more at top incomes, the reduction in reported income would offset the higher tax rate (on reduced taxable income) leaving no increase in revenue.</p> <p>In 2009, Chetty observed that, “The empirical literature on the taxable income elasticity has generally found that elasticities are large (0.5 to 1.5) for individuals in the top percentile of the income distribution.… This finding has led some to suggest that reducing top marginal tax rates would generate substantial efficiency gains” (<a href="#ch08_ref10">Chetty 2009</a>: 1). For example, Gruber and Saez (<a href="#ch08_ref20">2002</a>: 28) wrote, “These findings [about the ETI being highest at the highest incomes] may have important implications for the optimal tax structure, suggesting a&nbsp;tax system which is progressive on average but not on the margin, with… marginal rates that are flat or falling with income.”</p> <p>Since about 2011, however, scholars who had previously argued that reducing top marginal tax rates would be economically optimal (to minimize distortions and disincentives) began theorizing that increasing top tax rates might be socially optimal (to maximize income redistribution). This metamorphosis required discounting evidence that elasticities are large (0.5 to 1.5) for individuals in the top percentile. And it required assuming or asserting that the highest, most distortive marginal tax rates on labor, capital, and entrepreneurship could be greatly increased without impairing incentives or lowering economic activity.</p> <h2>When the United States Tried 70–92 Percent Tax Rates in 1951–63, Revenues Were Below Average</h2> <p>From 1951 to 1963, the top U.S. federal tax on individual income was 91–92 percent and the lowest rate was 20 percent, yet revenues from individual income taxes were only 7.5 percent of GDP (<a href="#ch08_ref35">OMB 2019</a>: Table 2.3). From 1982 to 1990, the top rate was first reduced to 50 percent and then to 28 percent, yet revenues rose to 8&nbsp;percent of GDP. The top tax rate was subsequently increased twice — in 1991 and 1993 — climbing to 39.6 percent, yet revenues from 1991 to 1996 fell to 7.7 percent of GDP. Finding a&nbsp;revenue‐​maximizing top tax rate is evidently not as easy as it may appear.</p> <p>Following the advice of Piketty, former French president François Hollande briefly experimented with a&nbsp;super tax in 2012–14, with a&nbsp;top rate of 75 percent on incomes above 1&nbsp;million euros (and also raising the next‐​highest rate from 41 percent to 45 percent). Real GDP growth fell below 0.5 percent in 2012–13 and unemployment rose to nearly 10.5 percent. The 75 percent tax rate was abandoned in favor of a&nbsp;45 percent top rate after raising only trivial sums on paper (160 million euros in 2014, compared with a&nbsp;previously estimated 30 billion), while arguably losing more government revenue as a&nbsp;result of a&nbsp;nearly stagnant economy and accelerated exodus of affluent expatriates (<a href="#ch08_ref32">Murphy and John 2014</a>). In fact, relatively high personal income tax rates in France never raised much revenue. According to OECD Revenue Statistics (2018: 70, Table 3.8), personal income tax revenues in 2016 amounted to 8.6 percent of GDP in France, 10 percent in Germany, and 10.4 percent in the United States. Like all European welfare states, France relies mainly on regressive payroll taxes and VAT.</p> <p>Those who claim it is different this time — that tax rates of 70 percent or more in the United States would raise more government revenue than they did in the past — bear a&nbsp;burden of proof.</p> <p>Economists’ use of ETI estimates to advocate a&nbsp;steep marginal U.S. tax rate on the highest incomes gained prominence with a&nbsp;study by Diamond and Saez (<a href="#ch08_ref13">2011</a>) because of their supposition about an “optimal” revenue‐​maximizing top tax rate (τ*). They argued that, if the Pareto parameter is 1.5 and the ETI is 0.25, then “τ* =1/(1 + 1.5 × 0.25) = 73 percent” (ibid.: 171). If that formula is accepted uncritically, then the conclusion follows from the premises. But neither the formula itself, nor the two parameters (Pareto and elasticity) need be accepted uncritically. If this was a&nbsp;recipe for baking a&nbsp;cake, it might be prudent to question both the recipe and the ingredients.</p> <p>Diamond and Saez (<a href="#ch08_ref13">2011</a>: 171) described the 0.25 ETI as “a mid‐​range estimate from the empirical literature.” Yet that range was subjectively defined as 0.1–0.4 by Saez, Slemrod, and Giertz (<a href="#ch08_ref48">2012</a>: 42) who cited estimates as high as 1.99 at top incomes. The bottom of that alleged range was defined by only one study: “Gruber and Saez’s elasticity estimate for broad income, 0.12, [which] is notably smaller than their corresponding estimate for taxable income” (ibid.: 39). That is, the uniquely low floor of the alleged 0.1–0.4 range (0.12) was not an estimate of the elasticity of taxable income at all. And the mid‐​point of the selective 0.1–0.4 range (0.25) is in no sense an average of estimates from the empirical literature.</p> <p>Diamond and Saez acknowledge, however, that any average ETI <em>for all taxpayers</em> (let alone the low 0.25 figure they selected) is too low to be used to estimate marginal tax rates for the top 1&nbsp;percent:</p> <blockquote>In the current tax system with many tax avoidance opportunities at the higher end, the elasticity <em>e</em> is likely to be higher for top earners than for middle incomes, possibly leading to [optimal] decreasing marginal tax rates at the top.… However, the natural policy response should be to close tax avoidance opportunities, in which case the assumption of constant elasticities might be a&nbsp;reasonable benchmark [<a href="#ch08_ref13">Diamond and Saez 2011</a>: 174].</blockquote> <p>This suggests that in an ideal but unobserved world, where high‐​income taxpayers could find no way to reduce the amount of reported income subjected to a&nbsp;marginal tax rate of 73 percent, the authors’ unusually low ETI of 0.25 for average incomes might conceivably be a&nbsp;reasonable benchmark for high incomes. In actual experience, they quietly acknowledge, 0.25 is an unreasonably low ETI benchmark for high incomes.</p> <p>In the empirical literature, an ETI of at least 0.4 is the most common estimate for all taxpayers, and an ETI of at least 0.8 would be a&nbsp;conservative estimate for top 1&nbsp;percent taxpayers.</p> <p>Mathur, Slavov, and Strain (<a href="#ch08_ref30">2012</a>) surveyed 11 academic and governmental elasticity estimates for taxable income from 1987 to 2009, and the average ETI was 0.72 (after excluding short‐​run estimates and using the midpoint whenever a&nbsp;range of estimates was offered). In a&nbsp;meta‐​regression analysis of 51 U.S. and international studies, Neisser (2017) found average ETI estimates of 0.54 for gross income and 0.67 for taxable income. Among recent studies, Burns and Ziliak (<a href="#ch08_ref09">2017</a>) estimate the all‐​taxpayer ETI as 0.4, and Kumar and Liang (<a href="#ch08_ref26">2018</a>) as 0.46 to 0.7.</p> <p id="note-2">Saez (<a href="#ch08_ref46">2004</a>: 123) found, as others have, that “those taxpayers with very high incomes are much more responsive to changes in taxation than taxpayers in the middle or upper‐​middle class.” He estimated the 1960–2000 elasticity of <em>gross</em> income before deductions was about 0.7 (actually 0.59 to 1.58) for taxpayers in the top 1&nbsp;percent, after highly elastic capital gains and deductions are excluded, and acknowledged that “elasticities of taxable income [which allows for deductions] are likely to be larger” (ibid.: 120).<sup><a href="#fn08-02">2</a></sup></p> <p>The hypothetical estimate of 0.25 used by Diamond and Saez (<a href="#ch08_ref13">2011</a>) is implausibly low even for middle‐​income taxpayers and impossibly low for the high‐​income taxpayers targeted by their proposed 73 percent rate.</p> <h2>Correcting for a&nbsp;Trend May Be Incorrect</h2> <p>Income observed from individual tax returns, which Piketty and Saez rely on, can be greatly affected by changes in tax rates and regulations, such as the Tax Reform Act of 1986 (TRA86). The reduction in top tax rates to 28 percent in 1988–90, from 50–70 percent in prior years, encouraged massive income shifting of business and professional income from corporate tax returns to individual tax returns, via S‐​corporations, partnerships, and LLCs. This change created a&nbsp;surge in top incomes reported on individual tax returns, which was largely the result of changed accounting rather than changed incomes.</p> <p>Income shifting was only one of many behavioral responses to raising (or reducing) the portion of top incomes taxpayers are allowed to keep; Feldstein (<a href="#ch08_ref15">2011</a>) enumerates numerous others. He found the reported taxable incomes of taxpayers who faced 49–50 percent marginal tax rates in 1985 surged by 44.8 percent between 1985 and 1988, while their net‐​of‐​tax share rose by 42 percent (from 50.5 to 72 percent) after the top tax rate was reduced to 28 percent. The greater increase in reported income implies an ETI larger than one.</p> <p id="note-3">Saez (<a href="#ch08_ref46">2004</a>) claimed Feldstein’s similar 1995 estimates of the ETI during TRA86 were too high because they failed to correct for a&nbsp;secular upward trend in the top 1&nbsp;percent’s share of total income. To the extent that such an upward trend was in itself reflecting changed incentives to earn and report more income on individual tax returns, however, then it would be misleading to “correct” for what were to a&nbsp;considerable extent behavioral responses.<sup><a href="#fn08-03">3</a></sup></p> <p>Piketty and Saez (<a href="#ch08_ref36">2003</a>) estimate that the top 1&nbsp;percent’s share of total income increased by 11 percentage points between 1960 and 2015, but Auten and Splinter (<a href="#ch08_ref03">2018</a>) find it increased by only 0.3 percentage points. As argued by Reynolds (<a href="#ch08_ref43">2012</a>), Auten and Splinter find most of the apparent rise in the top 1&nbsp;percent’s share of total income in Piketty and Saez’s data has been the result of (1) behavioral responses to lower tax rates and (2) exclusion of a&nbsp;large and rapidly growing amount of government transfers and untaxed employer benefits from the denominator — total before‐​tax income.</p> <h2>Weak Formulas and Strong Assumptions</h2> <p>A Pareto probability distribution describes a&nbsp;situation where most of the data pertain to the upper tail of a&nbsp;curve, such as 20 percent owning 80 percent of the land in Italy in Pareto’s example. A&nbsp;Pareto parameter for income gauges the “thickness” of the tail above, say, the threshold defining the top 1&nbsp;percent. In 2017, that group included more than 1.7 million U.S. families earning more than $463,320 before taxes, or $422,810 if capital gains are excluded, according to Piketty and Saez (<a href="#ch08_ref36">2003</a> with updated data March 2019: Table 0).</p> <p>The larger the number describing the Pareto parameter (“Pareto‐​Lorenz α&nbsp;coefficient”), the “thinner” the distribution and the less pretax income is in the upper tail of the distribution (<a href="#ch08_ref45">Saez 2001</a>: 211). A&nbsp;Pareto parameter of 3.0 is much “thinner” than 1.5, for example. “If the distribution is thin,” explains Saez (ibid.: 212), “then raising the top rate for high income taxpayers will raise little additional revenue.” Diamond and Saez (<a href="#ch08_ref13">2011</a>) chose a&nbsp;thick parameter of 1.5 and their optimal tax rate calculation implicitly assumes that number is given or constant, regardless of behavioral responses to changing marginal tax rates. This assumption, like their choice of a&nbsp;low ETI to represent high‐​income taxpayers, greatly affects the result.</p> <p>Before examining how varying the parameters of the Saez (<a href="#ch08_ref45">2001</a>) formula affects the estimated revenue‐​maximizing top tax rate, it may be helpful to examine assumptions behind the formula itself that have been the subject of some controversy. Even if the parameters were precise and permanent, the formula could not provide incontestable answers to the question it sets out to answer.</p> <p id="note-4">Fairness as defined in the optimal tax literature does not suggest tax systems should aim to reduce inequality regardless of the distribution of ability.<sup><a href="#fn08-04">4</a></sup> Even if top incomes reported on yearly tax returns were not so undeniably sensitive to tax rates, they would still be a&nbsp;poor proxy for long‐​term income or for personal differences in the ability to earn income.</p> <p>Saez (<a href="#ch08_ref45">2001</a>) uses income reported on U.S. individual tax returns, and estimates a&nbsp;Pareto parameter and ETI (also from tax returns), in order to simulate differences in ability, endowments, or productivity — which would supposedly approximate “ability to pay” high marginal tax rates. To derive optimal tax rates from tax return data is treacherous, however, because income observed in tax returns is itself dependent on tax rates: When marginal tax rates go up, reported top incomes go down.</p> <p>As Mankiw, Weinzierl, and Yagan (<a href="#ch08_ref29">2009</a>: 5) note:</p> <blockquote>The planner can observe income, which depends on both ability and effort, but the planner can observe neither ability nor effort directly. If the planner taxes income in an attempt to tax those of high ability, individuals will be discouraged from exerting as much effort to earn that income.</blockquote> <p class="BodyTextFlush">However, they add, “Estimating the distribution of ability is a&nbsp;task fraught with perils. For example, when Saez (<a href="#ch08_ref45">2001</a>) derives the ability distribution from the observed income distribution, the exercise requires making assumptions on many topics at and beyond the frontier of the optimal tax literature” (<a href="#ch08_ref29">Mankiw, Weinzierl, and Yagan 2009</a>: 152).</p> <p>Tanninen, Tuomala, and Tuominen (<a href="#ch08_ref52">2019</a>: 25–26) explain that</p> <blockquote>Saez (<a href="#ch08_ref45">2001</a>) … assumes that the labor supply elasticity is constant [which] is contradicted by a&nbsp;growing body of evidence. He further assumes a&nbsp;linear tax schedule in inferring the skill distribution for the earnings distribution. This … seems particularly inappropriate in optimal nonlinear taxation. The strong assumptions required for structural identification of the model reduced the confidence of the optimal tax schedule calculations [such as the 73 percent figure in Diamond and Saez (<a href="#ch08_ref13">2011</a>)].</blockquote> <p>The Saez formula for estimating a&nbsp;revenue‐​maximizing top tax rate in a&nbsp;nonlinear tax system is “a simple generalization of the well‐​known formula for the flat tax rate maximizing tax revenue… [which is based on] the average elasticity over all taxpayers” (<a href="#ch08_ref44">Saez 1999</a>: 68). The flat tax rate formula says the revenue‐​maximizing rate for all taxpayers equals 1/ (1 + <em>e</em>). The Saez formula (<a href="#ch08_ref44">1999</a>, <a href="#ch08_ref45">2001</a>) calculates a&nbsp;comparable flat tax for only the top incomes on the assumptions that the Pareto parameter would be unaffected by changing the top tax rate and that those subject to the top rate share the same ETI (which is not to be confused with the average elasticity over all taxpayers). Giertz (<a href="#ch08_ref17">2004</a>: 16) uses the flat tax version to demonstrate that “under a&nbsp;single‐​rate tax system … an ETI of 0.40 [for all taxpayers] would imply a&nbsp;revenue‐​maximizing income tax rate of 70 percent [for all taxpayers].” But the lower 0.25 ETI (<em>e</em>) used in Diamond and Saez (<a href="#ch08_ref13">2011</a>), implies a&nbsp;higher revenue‐​maximizing flat tax rate of 80 percent (τ* = 1/ (1 + 0.25) = 0.80).</p> <p>Early efforts to estimate such a&nbsp;revenue‐​maximizing flat tax assumed all income came from labor, and homogeneous individuals differed only in their ability (skill or productivity), not their effort. A&nbsp;revenue‐​maximizing flat tax formula based on these assumptions was initially expressed simply as a&nbsp;function of the labor supply elasticity, but later adapted to encompass elasticity of taxable income in general. Newer estimates of a&nbsp;revenue‐​maximizing tax rate in a&nbsp;progressive tax system still retain the restrictive assumptions of the older flat tax formula — namely, that income comes only from labor and that people differ only by ability.</p> <p>Judd et al. (<a href="#ch08_ref23">2018</a>: 1–2) find this one‐​dimensional approach unrealistic:</p> <blockquote>The Mirrlees (1971) optimal tax analysis and much of the literature that followed assumed that people differ only in their productivity, and shared common preferences over consumption and leisure.… A&nbsp;more realistic model would account for multi‐​dimensional heterogeneity. For example, some high ability people have low income because they prefer leisure, or the life of a&nbsp;scholar and teacher. In contrast, some low ability people have higher‐​than‐​expected income because circumstances, such as having to care for many children, motivate them to work hard.</blockquote> <p>Economists or government officials who hope to tax “from each according to their ability,” cannot meaningfully judge ability simply by grouping people by income reported on one year’s income. Thus, Judd et al. (<a href="#ch08_ref23">2018</a>: 3) ask, “If a&nbsp;person has low income, is it because he is a&nbsp;middle‐​aged individual with low ability, or is it a&nbsp;young person with high‐​ability at the beginning of a&nbsp;steep life‐​earnings profile? The government may want to help the former, but not the latter.”</p> <p>When Judd et al. examine “three‐​dimensional heterogeneity combining heterogeneous ability, elasticity of labor supply, and basic needs,” they find more scope for taxpayers to respond to redistributive tax policies in ways that make such policies counterproductive and limit their feasible scope. In fact, they simulate “cases where the [optimal] marginal tax rate on the top income is negative” (<a href="#ch08_ref23">Judd et al. 2018</a>: 4). In a&nbsp;classic essay on “Pareto Efficient and Optimal Taxation,” Stiglitz (<a href="#ch08_ref51">1987</a>: 50) likewise argued that, in a&nbsp;general equilibrium analysis, Pareto‐​efficient optimal taxation requires “the government to impose a&nbsp;negative marginal tax rate on the more productive individuals.”</p> <p>The one‐​dimensional view of taxpayer homogeneity in the old flat tax models remains at the core of the nonlinear model used by Diamond and Saez (<a href="#ch08_ref13">2011</a>). If these formulas are to be believed, the low ETI of 0.25 and low Pareto parameter of 1.5&nbsp;in Diamond and Saez imply a&nbsp;revenue‐​maximizing flat tax rate of 80 percent for all taxpayers or a&nbsp;similar revenue‐​maximizing top tax rate of 73 percent collected from only about 1&nbsp;percent of all taxpayers.</p> <h2>Estimated Top Tax Rate Models Are One‐​Dimensional, Short‐​Term, and Static</h2> <p>The fact that a&nbsp;nonlinear optimal flat tax formula ends up postulating a&nbsp;revenue‐​maximizing flat tax of 80 percent (by assuming parameters are given and unrelated to the new tax regime) underscores the importance of the Chicago Booth survey question at the start of this article about how such a&nbsp;draconian tax regime could possibly raise more revenue without decreasing economic activity.</p> <p>The whole concept of an 80 percent flat tax seems an arcane academic abstraction. Nobody has any evidence about what the ETI (or Pareto parameter) might look like if people were faced with an 80 percent marginal tax on every dollar earned above a&nbsp;standard deduction. Such an ETI would surely not be anything remotely close to 0.25 — probably more than 1.00. With an 80 percent flat tax, we might expect many more people to switch from earning taxable income to demanding transfer payments, and many surviving businesses to relocate to other countries or disappear into an underground tax‐​free sector paid with cash, barter, or digital currency.</p> <p>The nonlinear calculation of a&nbsp;revenue‐​maximizing rate has much in common with the flat tax version, including the key detail that elasticity estimates and Pareto parameters created from data collected while U.S. marginal tax rates were fairly low are simply assumed to remain unchanged if marginal tax rates were instead much higher.</p> <p>Unlike its flat tax cousin, the nonlinear Saez (<a href="#ch08_ref45">2001</a>) formula purports to estimate a&nbsp;very high “optimal” tax rate for only a&nbsp;tiny fraction of taxpayers, but also be “revenue‐​maximizing” for <em>only</em> that tiny fraction. The nonlinear optimal tax formula is silent about what happens after the first year to economic activity and tax revenue below the top tax bracket. Yet what happens at the top can have discouraging long‐​term effects, for example, on the effort, education, and investment of others not yet in the top tax bracket. Even if a&nbsp;high top tax rate increased total revenue from the top tax bracket in the short run, negative dynamic effects could depress long‐​run revenue collected from the totality of income, payroll, sales, property, and other taxes.</p> <p>As Jaimovich and Rebelo (<a href="#ch08_ref21">2017</a>: 267) put it, “The Diamond‐​Saez calculation suffers from an important shortcoming: it considers only the static effect of taxation on current tax revenue.” It ignores dynamic effects by implicitly assuming that the growth rate of the economy is invariant with respect to the tax rate. The Jaimovich and Rebelo dynamic model finds, “Low or moderate tax rates have a&nbsp;small impact on long‐​run growth rates. But as tax rates and other disincentives to investment rise, their negative impact on growth rises dramatically” (ibid.: 266).</p> <p>Zajac (<a href="#ch08_ref53">1995</a>: 11) warns, “Economists who focus on a&nbsp;static aspect of an economy run the danger of doing beautiful work on the wrong problem.” A&nbsp;dynamic perspective would view a&nbsp;73 percent marginal tax rate on high salaries as a&nbsp;tax on the expected future return from investing time and money in human capital. Diamond and Saez (<a href="#ch08_ref13">2011</a>: 175) recognize that “a more progressive tax system could reduce incentives to accumulate human capital in the first place,” so “the elasticity <em>e</em> should reflect not only short‐​run labor supply responses but also long‐​run responses through education and career choices.” Badel, Huggett, and Luo (<a href="#ch08_ref04">2018</a>: 16) argue that, once those long‐​run human capital incentives are taken into account, the “Laffer curve peaks at a&nbsp;top rate equal to 49 percent.”</p> <p>Jones (<a href="#ch08_ref22">2019</a>: 12) takes it further, adding Schumpeterian effects of top taxes on inventions, innovation, and technological change and noting that “Diamond and Saez (<a href="#ch08_ref13">2011</a>) … do not consider any interaction effects between the efforts of top earners and the wages earned by workers outside the top.” In Jones’s idea‐​based exogenous growth model, “high incomes are the prize that motivates entrepreneurs to turn a&nbsp;basic research insight that results from formal R&amp;D into a&nbsp;product or process that ultimately benefits consumers. High marginal tax rates reduce this effort and therefore reduce innovation and the incomes of everyone in the economy” (ibid: 2). His model suggests that “incorporating ideas as a&nbsp;driver of economic growth cuts the optimal top marginal tax rate substantially relative to the basic Saez calculation” (ibid.: 39). In one simulation, “the rate that incorporates innovation and maximizes a&nbsp;utilitarian social welfare function is just 22 percent” (ibid: 3).</p> <h2>A Pareto Parameter Cannot Be Assumed Constant, or Independent of Top Tax Rates</h2> <p>The upper tail in the United States became thicker after the highest marginal tax rates at the federal level came down from 70 percent to 28.0–39.6 percent as the very highest incomes (top 0.001 percent) grew rapidly — partly due to switching from the corporate tax. Observing such a&nbsp;thick tail in the post‐​TRA86 U.S. economy might appear to imply there is ample income available to tax at high marginal rates, as Diamond and Saez take for granted. But that conclusion would require assuming (1) that the increased amount of high income visible on individual tax return data since 1988 did not happen precisely because marginal tax rates were lower, and that (2) observed high ETI at the highest incomes would not cause many high incomes to shrink or vanish from U.S. individual tax returns if top marginal tax rates were sharply increased.</p> <p>Like the ETI, the Pareto parameter is not an iron law but a&nbsp;variable that was higher and often rising <em>before</em> top tax rates on incomes and capital gains were repeatedly reduced after 1978–82 (<a href="#ch08_ref04">Badel et al. 2018</a>: Figure 11). Atkinson and Piketty (<a href="#ch08_ref02">2010</a>) find the U.S. Pareto (α) coefficient for the top 1&nbsp;percent fell from 2.33&nbsp;in 1979 to 1.67&nbsp;in 1988 when the 28 percent top tax took effect for both salaries and capital gains. It fell again to 1.6&nbsp;in 2004 when the top federal tax rate was cut to 15 percent on dividends and capital gains and to 35 percent on salaries. Since the Atkinson and Piketty series ends at 2005, and the 1.6 Saez and Stantcheva (<a href="#ch08_ref49">2018</a>) estimate is for 2007, those unusually low parameter estimates from 2005 to 2007 probably reflect the unusually low top tax rates on capital and labor from 2004 to 2012.</p> <p>Those who now want to put top tax rates back up to the rates prevailing in the 1970s are implicitly assuming that doing so would not push the Pareto parameter back up. The U.S. time series and international cross section evidence suggest otherwise.</p> <p>Lundberg (<a href="#ch08_ref28">2017</a>) assembled recent Pareto parameter estimates for 27 countries from country‐​specific studies, the World Wealth and Income Database (WID) and the Luxembourg Income Study (LIS). The LIS focuses on labor income, which Lundberg argues is more accurate and relevant for Scandinavian countries, which have a&nbsp;low flat tax on capital income. Several countries have no tax on capital gains — including Belgium, the Netherlands, Switzerland, New Zealand, and South Korea — making LIS estimates based on labor income arguably more comparable to other countries.</p> <p>Pareto parameter estimates differ because of inclusion or exclusion of capital income (e.g., 2.20 or 2.59 for France) and are sometimes ambiguous. In general, however, Pareto parameter estimates are generally highest for countries with the highest top tax rates on labor income: 3.35&nbsp;in the Netherlands, 3.18&nbsp;in Sweden, 3.14&nbsp;in Austria, and 3.04&nbsp;in Denmark. Pareto parameter estimates are likewise generally lowest in countries with the lowest top tax rates on both labor and capital, such as 1.61&nbsp;in the United States, 1.73&nbsp;in Switzerland, 1.75&nbsp;in Malaysia, 1.79&nbsp;in Taiwan and the United Kingdom, and 1.81&nbsp;in South Korea.</p> <p>The Diamond‐​Saez (<a href="#ch08_ref13">2011</a>) assumption of a&nbsp;constant Pareto parameter of 1.5 is lower than the 2.0 norm (for labor income) suggested by Saez (<a href="#ch08_ref44">1999</a>, <a href="#ch08_ref45">2001</a>) and much lower than it was when the United States had high top tax rates or than it is in European countries with high top tax rates.</p> <p>Using either a&nbsp;higher Pareto parameter or higher ETI within the disputed formula of Diamond and Saez greatly lowers their calculated optimal top tax rate. Saez (<a href="#ch08_ref46">2004</a>: 129) noted that even with a&nbsp;low Pareto parameter of 1.6 and an ETI of 0.5 (below his own estimate for top 1&nbsp;percent incomes) “the Laffer rate would 55.6 percent, not much higher than the combined maximum federal, state, Medicare, and sales tax rate.” By 2019, however, the top marginal tax rate was above 55.6 percent in most states.</p> <p>In the Diamond and Saez formula, the concept of a&nbsp;revenue‐​maximizing top tax rate in 2019 must include the 37 percent top federal rate plus nondeductible state and local income and sales tax rates, the 2.9 percent Medicare tax and 0.9 percent surtax, and the 3.8 percent Obamacare net investment tax. Adding an average state income tax of 6.4 percent (<a href="#ch08_ref27">Loughead and Wei 2019</a>) and an average state and local sales tax of 6.4 percent (<a href="#ch08_ref12">Cummings 2019</a>) raises the 2019 U.S. top marginal tax rate to 57.4 percent nationwide. But the top tax rate can be even higher than 57.4 percent in 9&nbsp;states with top income tax rates from 8.8 percent to 13.3 percent and 11 with sales tax rates of 8.1 percent to 9.5 percent. In 2019, the top tax rate in San Francisco was 45 percent at the federal level plus 14.8 percent state and city income tax, plus 8.5 percent state and city sales tax for a&nbsp;total top tax rate of 66.2 percent. The same calculation for New York City adds up to a&nbsp;66.6 percent top rate.</p> <p>Once we replace the inappropriate all‐​income ETI of 0.4 with a&nbsp;modest <em>high‐​income</em> ETI of 0.8, while keeping the same controversial Saez formula, that results in an optimal federal‐​state‐​local top tax rate of 43.9 with the lowest Pareto parameter in the empirical literature of 1.6, or 38.5 percent with a&nbsp;Pareto parameter of 2.0.</p> <p>In short, with empirically plausible changes in parameters, the Diamond and Saez formula can more easily be used to show that top U.S. federal and state tax rates are already <em>too high</em> rather than too low.</p> <h2>Piketty, Saez, and Stantcheva Justify a&nbsp;Top Tax Rate of 83–100 Percent</h2> <p>Piketty, Saez and Stantcheva (<a href="#ch08_ref38">2014</a>), in another celebrated study, use essentially the same Diamond‐​Saez formula of adding a&nbsp;given Pareto parameter to an assumed ETI to justify a&nbsp;top federal‐​state tax rate of 83 percent. They begin with a&nbsp;long‐​term ETI estimate of 0.52 for the top 1&nbsp;percent in the United States between 1960–64 and 2004-08. They also find about the same multidecade 0.47 ETI for the top 1&nbsp;percent among 18 OECD countries, but pointedly note (ibid.: 255) that “the elasticity… increased sharply to 0.6–0.8&nbsp;in the period 1981–2010,” so that adding the 1960s and 1970s to the average is what makes it look so low. The fact that the OECD ETI estimate for the top 1&nbsp;percent is 0.6–0.8 after 1981 suggests the post‐​1981 U.S. ETI estimate would likewise be closer to 0.8 (and to other estimates) if their average had properly excluded the extraneous 1965–1980 period when the top U.S. tax rate was unchanged.</p> <p>After conjecturing that only 0.2 (at most) of their watered‐​down 0.5 five‐​decade ETI is “due to supply‐​side effects generating more activity,” Piketty, Saez, and Stantcheva (<a href="#ch08_ref38">2014</a>: 233) end up deducing (via subtraction) that three‐​fifths of their 50‐​year 0.5 ETI (0.3) must therefore be the supposedly preventable result of tax avoidance and/​or “bargaining” clout (called “CEO rent‐​extraction”).</p> <p>Piketty, Saez, and Stantcheva (<a href="#ch08_ref38">2014</a>: 239) theorize that “marginal tax rates affect the rewards to bargaining effort and can hence affect the level of such bargaining efforts.” To verify the “main channel” of their “nonconventional bargaining model,” they rely on an incorrect claim that the “use of stock‐​options has exploded in the post‐​1986 period, i.e., after top tax rates went down” (ibid.: 261). They also claim paying executives of public companies in stock or options is “a zero‐​sum game transfer from the bottom 99 percent to the top 1&nbsp;percent” (ibid.: 249). On the contrary, grants of restricted stock or stock options that pay off are entirely financed by the company’s stockholders through dilution.</p> <p>In reality, stock‐​based executive compensation did not explode “after top tax rates went down” after 1986, but after top tax rates went <em>up</em> in 1993. That was partly because section 162(m) of the 1993 tax law denied companies any deduction for the cost of executive compensation above $1 million for salary and bonuses, but not for “performance‐​based” stock options or restricted stock (<a href="#ch08_ref42">Reynolds 2005</a>). But it was also because two <em>higher</em> tax rates, 36 percent and 39.6 percent, were added in 1993.</p> <h2>CEO Stock Options Exploded after Top Tax Rates Went Up, Not Down</h2> <p>Gorry, Hubbard, and Mathur (<a href="#ch08_ref19">2018</a>: 16) find that “the share of [executive stock] options awarded increased from about 18 percent of total compensation in 1992 to 23 percent by 2005. There was an even larger increase in restricted stock grants, from 4&nbsp;percent to 13 percent, over the same period.” However, the percentage of S&amp;P 500 firms offering CEO stock options fell from 70 percent in 2009 to 56 percent in 2017, according to Bout, Cruz and Wilby (<a href="#ch08_ref08">2019</a>), despite the 2013 increase in top marginal tax rates. New FASB rules in 2006 requiring expensing of the estimated value of stock options grants made restricted stock more attractive for many firms.</p> <p id="note-5">The first key point from Gorry, Hubbard, and Mathur (<a href="#ch08_ref19">2018</a>) is that the deferral of taxes after 1992 — through shifting compensation to stock and stock options — was clearly an example of <em>avoidance elasticity</em> to delay and thus dilute the bite of <em>higher</em> top tax rates in 1993. This is the antitheses of the Piketty‐​Saez‐​Stantcheva (<a href="#ch08_ref38">2014</a>) supposition about the surge of options after 1992 being a “bargaining” response to the reduction in top marginal rates in 1988.<sup><a href="#fn08-05">5</a></sup></p> <p>A second point, which I&nbsp;derive from the data in Gorry, Hubbard, and Mathur (<a href="#ch08_ref19">2018</a>), is that CEO compensation in the largest U.S. corporations accounts for a&nbsp;surprisingly small share of top 1&nbsp;percent income. Even including all top five executives in the S&amp;P 1000 (not just CEOs, who are supposed to have special bargaining clout) they report, “[A]verage total compensation increased from $866,987&nbsp;in 1992 to $1,852,074&nbsp;in 2005” in 1991 dollars. That is, the 5,000 executives combined earned a&nbsp;total of $9.26 billion in 2005&nbsp;in 1991 dollars, which translates into $13.17 billion in 2005 dollars.</p> <p>In the Piketty and Saez estimates for 2005, the average income among 729,405 tax units in the top 1&nbsp;percent was $310,062, which adds up to $225.16 billion. The $13.17 billion in total compensation of top five executives in the 1,000 largest U.S. corporations therefore accounted for just 5.8 percent of the total income of the top 1&nbsp;percent in 2005. Even if bargaining theory explained compensation of CEOs plus four other top executives in large corporations (as it cannot in the case of stock options) that would still leave 94.2 percent of top 1&nbsp;percent income unexplained.</p> <p>To buttress their bargaining theory, Piketty, Saez, and Stantcheva (<a href="#ch08_ref38">2014</a>: 239) write, “Bakija, Cole, and Heim (<a href="#ch08_ref06">2012</a>) have recently shown that executives, managers, supervisors, and financial professionals account for 70 percent of the increase in the share of national income going to the top 0.1 percent.” But that amorphous mixture of unrelated occupations has no connection to the CEOs of large public corporations who are alleged to have unique discretionary bargaining power over corporate boards. Bakija, Cole, and Heim (<a href="#ch08_ref06">2012</a>: 49, Table A.1) include “supervisors in any field except finance or government” in public and private firms, whether incorporated or not. Then they add all sorts of financial professionals such as hedge fund managers and private equity partners, plus what Piketty and Saez (2004: Table 2) call “Capitalists and Rentiers” — that is, “bankers, real‐​estate brokers, stock and bond brokers, insurance brokers, all other brokers, and capitalists: investors and speculators.”</p> <p>Bakija, Cole, and Heim (<a href="#ch08_ref06">2012</a>: 41, Table 6) realize the combined incomes of “executives, managers, supervisors, and financial professionals” are not informative about top CEO compensation in publicly‐​traded corporations — the sole theme of the “CEO rent extraction” hypothesis of Piketty, Saez, and Stantcheva (<a href="#ch08_ref38">2014</a>). In an Addendum to Table 6, Bakija, Cole, and Heim struggle to narrow their focus from successful nonfinancial salaried businesspersons to the incomes of all executives (top to bottom) in all public corporations (large and small). They did so by collecting IRS data on incomes of executives in nonfinancial businesses who collect more salary than business income. By that measure, salaried corporate executives with incomes in the top 1&nbsp;percent earned 2.23 percent of national income in 1979, 2.24 percent in 1993, and an unchanged 2.22 percent in 2004 and 2005. Meanwhile, the top percentile’s share of national income rose from 9.18 percent in 1979 to 16.97 percent in 2005. Contrary to the Piketty‐​Saez‐​Stantcheva CEO rent‐​extraction hypothesis, Bakija, Cole, and Heim end up estimating that executives in nonfinancial public corporations (unlike those in private firms) accounted for a&nbsp;much smaller share of total top 1&nbsp;percent income in 2005 (13.1 percent) than they had in 1979 (24.3 percent).</p> <p>Bakija, Cole, and Heim (<a href="#ch08_ref06">2012</a>: 12) note that “Kaplan and Rauh [<a href="#ch08_ref24">2013</a>] … have argued that executives of publicly traded firms represent too small of a&nbsp;share of top income earners in the U.S. to be able to explain much of the rise in top income shares.” Their own estimates not only confirm Kaplan and Rauh (<a href="#ch08_ref24">2013</a>), but further demonstrate that executive pay of publicly traded nonfinancial firms could not possibly account for <em>any</em> of the rise in the top 1&nbsp;percent income share from 1979 to 2005 because, in their estimates, public corporate executives’ share of national income did not rise at all between those years.</p> <p>By asserting that (1) CEOs simply will not bother to bargain for allegedly “zero‐​sum” stock options if faced with an 83 percent marginal rate, and that (2) tax avoidance is easily preventable (as explained later) by simply taxing capital gains and corporate profits at the same 83 percent rate, Piketty, Saez, and Stantcheva (<a href="#ch08_ref38">2014</a>: 233) claim to be left with a&nbsp;supposed ETI estimate of just “0.2 (at most)” as their residual estimate of the real, supply‐​side loss from a&nbsp;high marginal tax rate. That 0.2 ETI forms the basis for their “socially optimal” 83 percent top tax rate (ibid.).</p> <p>Remarkably, they add that “the optimal top tax … actually goes to 100 percent if the real supply‐​side elasticity is very small” (ibid.: 232). They settle for 83 percent rather than 100 percent because of practical concerns that “some real responses could be somewhat dampened by government policies” (ibid.: 235). Otherwise, a&nbsp;100 percent top bracket remains the ethical optimum, because, “we assume that the average social marginal welfare weight among top bracket income earners is zero. In that case, the government sets to maximize tax revenue raised from top bracket taxpayers” (ibid.: 234.). This demonstrates, as Feldstein (<a href="#ch08_ref16">2012</a>: 282) remarked in a&nbsp;similar context, an “implicit assumption that ‘society’ owns everyone’s potential earnings.”</p> <p>Confiscating all U.S. income above the threshold defining the top tax bracket ($612,350 for couples under 2019 law) would supposedly be the “socially optimal” way to maximize revenue “if the real supply‐​side elasticity is very small.” However, that assumes the ETI, Pareto parameter, and GDP estimates made when top tax rates were reasonable would not change after the top tax rate became unreasonable. The elasticity and Pareto parameter might appear low <em>before</em> an 83–100 percent tax was imposed, but that does not mean they would remain small after that happened.</p> <p>The amount of revenue available for redistribution depends on <em>average</em> tax rates (not the top marginal rate) times the tax base (taxable income and wealth). The future size of the high‐​income tax base cannot simply be assumed to be unaffected by a&nbsp;100 percent tax rate, regardless what ETI is deployed to reach such an inexplicable definition of optimality. People are not apt to work up to their full potential if added compensation for added effort is zero.</p> <p>The notion that a&nbsp;100 percent marginal tax rate could ever “maximize tax revenue raised from top bracket taxpayers” is a&nbsp;<em>reductio ad absurdum</em>. And that same abstract analysis becomes only marginally more credible by substituting the number 83 percent for 100 percent.</p> <h2>Mertens and Olea Find Lower Marginal Tax Rates Raise GDP</h2> <p>Two defining themes of the Piketty, Saez, and Stantcheva (<a href="#ch08_ref38">2014</a>) narrative — that the ETI for the top 1&nbsp;percent is low and mostly unrelated to real activity — are contradicted by Mertens and Olea (<a href="#ch08_ref31">2018</a>: 1803). They find “short‐​run tax elasticities of reported income of around 1.2” based on time series from 1946 to 2012 for the top 1 percent, and the elasticities are “positive and statistically significant for other income groups.” They also find:</p> <blockquote>Marginal rate cuts lead to increases in real GDP and declines in unemployment that are broadly consistent with existing macro results … The associated short‐​run reported income elasticity for the top 1&nbsp;percent is estimated to be around 1.5. In the short run, a&nbsp;top marginal rate cut is estimated to raise real GDP, to lower aggregate unemployment and to have a&nbsp;measurable positive effect on incomes outside of the top 1&nbsp;percent.… Targeted cuts for the bottom 99 percent also generate positive effects on reported incomes and aggregate economic activity, but with a&nbsp;delay of several years [ibid.: 1805].</blockquote> <p>Mertens and Olea (<a href="#ch08_ref31">2018</a>) cite a&nbsp;number of other macro and labor supply studies demonstrating a&nbsp;strong dynamic connection between marginal tax rates and long‐​term real activity, including labor force participation, lifetime work hours, and entrepreneurial innovation. Stantcheva, for example, coauthored a&nbsp;paper finding (as Jones [<a href="#ch08_ref22">2019</a>] hypothesized) that “taxes matter for innovation: higher personal and corporate income taxes negatively affect the quantity and quality of inventive activity and shift its location at the macro and micro levels” (<a href="#ch08_ref01">Akcigit et al. 2018</a>:1).</p> <p>Yet Piketty, Saez, and Stantcheva (<a href="#ch08_ref38">2014</a>) did not confront any of the vast literature connecting marginal tax rates to economic performance. All they did was to obfuscate the issue by testing a&nbsp;hypothesis that nobody advanced — namely, that economic growth should be expected to be affected by the total <em>percentage point change</em> in top tax rates between two dates separated by decades rather than by whether and when those rates were high or low. Unsurprisingly, they “find no evidence of a&nbsp;correlation between growth in real GDP per capita and the drop in the top marginal tax rate in the period 1960 to the present” (ibid.: 232).</p> <h2>Percentage Point Changes in Tax Rates Cannot Tell Us If Rates Were High or Low</h2> <p>Piketty, Saez, and Stantcheva (<a href="#ch08_ref38">2014</a>: 256) use a&nbsp;scatter diagram to compare percentage point changes in top individual tax rates with economic growth among 18 OECD countries from about 1960–64 to various years from 2005-09. They conclude that “countries experiencing the largest increases in top income shares (the United States and the United Kingdom) have growth rates that are comparable to those of Germany, or Denmark who did not experience large top rate cuts” (ibid.: 257). They add that Spain and Switzerland also “did not experience any significant top rate cut” since the early 1960s (ibid.: 252).</p> <p>Comparing percentage point changes in top tax rates between two data points separated by decades is vacuous. In 1960–64, the top tax rate was extremely high in the United States (91 percent) and United Kingdom (88 percent), but only 53 percent in Germany, 44 percent in Switzerland, and 40 percent in Spain. In a&nbsp;1964 “Comparison of European and United States Tax Structures,” Eckstein and Tanzi (<a href="#ch08_ref14">1964</a>: 247, 250) observed:</p> <blockquote>The extent of nominal [tax] progression is lower on the continent than here, but if the proposed tax program is enacted [which reduced all U.S. marginal rates by 30 percent in 1964–65], our tax system will no longer differ so greatly even in this respect.… The current proposals … remove the most objectionable feature of our direct‐​tax system, the extreme progression of the income tax.</blockquote> <p>Eckstein and Tanzi (<a href="#ch08_ref14">1964</a>: 251, 244–45) remarked that France exempted individual capital gains and about 30 percent of labor income, and noted that, in Germany, “The tax system was the main instrument of economic policy for growth.… After 1950, the … burden of direct taxation was made substantially lighter, both on business and on households.”</p> <p>Reynolds (<a href="#ch08_ref39">1996</a>: 200) explains that German “income‐​tax rates were slashed from 95 percent on incomes above $15,000 at the time of the Allied occupation to a&nbsp;maximum of 53 percent on incomes of $250,000 by the early 1950s.” The United States, by contrast, never had a&nbsp;combined top federal and state tax rate as low as 53 percent before 1987 and the top rate in 2019 was much higher than 53 percent in most states. Meanwhile, Germany recently reduced the top tax rate to 47.5 percent and Switzerland to 40 percent.</p> <p>Neither Germany, Switzerland, nor Spain could possibly have cut their top tax rates by 63 percentage points as the United States did after 1960 (from 91 percent to 28 percent), since that would have left their top tax rates well below zero.</p> <p>Japan looked like one of the two fastest‐​growing economies in the original publication of Piketty, Saez, and Stantcheva, which started from the early1960s, but drops to fifth place in their more recent op‐​eds, which begin with 1975. That is because Japan’s economy grew by 9.8 percent a&nbsp;year from 1952 to 1973 by emulating German tax strategy (before reversing course since then). In 1950 the top income tax rate in Japan was cut from 86 percent to 50 percent. “From 1950 to 1974, Japan cut taxes every year (except 1960) often by greatly increasing the income thresholds at which the higher tax rates applied, or by enlarging deductions and exemptions [particularly for savings]” (<a href="#ch08_ref40">Reynolds 1998</a>: 3).</p> <p>The individual income tax is, of course, only one of many taxes affecting a&nbsp;country’s attractiveness as a&nbsp;place to work, invest, and do business. Even Diamond and Saez (<a href="#ch08_ref13">2011</a>) include the marginal effect of payroll and sales taxes. And it would be misleading to not even mention corporate tax rates when discussing economic growth policies in Ireland and several other business‐​friendly tax regimes.</p> <p>Even limiting cross‐​section comparisons to top individual tax rates, the narrow selection of 18 OECD countries in Piketty, Saez, and Stantcheva (<a href="#ch08_ref38">2014</a>) leaves out all the bustling economies that halved their top tax rates in Asia, Latin America, Africa, and Eastern Europe (<a href="#ch08_ref41">Reynolds 2004</a>). Since 1979, the highest income tax rate was cut from 55 percent to 22 percent in Singapore, from 89 percent to 40 percent in South Korea, from 60 percent to 30 percent in India, from 60 percent to 28 percent in Malaysia, and from 50 percent to 30 percent in Indonesia. Chile cut the top tax rate from 60 percent to 35 percent, Brazil from 55 percent to 27.5 percent, Colombia from 56 percent to 33 percent, and Bolivia from 48 percent to 13 percent. Mauritius cut the top tax rate from 50 percent in 1979 to 15 percent and Botswana from 75 percent to 25 percent. Economic growth has been famously vigorous in all of these cases, among others.</p> <p>If judged by the change in top individual tax rates rather than their typical level, Hong Kong could never be used to test the hypothesis that low marginal tax rates are conducive to rapid growth because Hong Kong <em>always</em> kept the top tax rate below 25 percent (e.g., 17 percent in recent years).</p> <h2>Profits and Capital Gains Would Also Be Taxed at 83 Percent</h2> <p>When considering the impact of the proposed 83 percent top tax rate on the U.S. economy, it is important to understand that the formula used to define an 83 percent tax rate as “optimal” is based on the assumption that high‐​income tax avoidance is “fully eliminated.” Eliminating opportunities for tax avoidance requires applying the same 83 percent tax rate to all income, including realized capital gains and corporate income. Although Piketty, Saez, and Stantcheva (<a href="#ch08_ref38">2014</a>) title their paper “Optimal Taxation of Top Labor Incomes,” they necessarily endorse comparable tax rates on capital income. That is because all opportunities to shift income from taxable personal income into corporate earnings, capital gains, or dividends taxed at a&nbsp;lower rate must be eradicated by tax “reform.”</p> <p>If high personal incomes were taxed at 83 percent while C‐​corporations were taxed at recent U.S. rates of 21–35 percent, many businesses and professionals would soon become closely held C‐​corporations to retain earnings within the firm. Similarly, if large salaries and royalties were taxed at 83 percent while long‐​term capital gains were taxed at recent U.S. rates of 20–28 percent, many executives and celebrities would negotiate to be paid in assets expected to appreciate, such as growth stocks or collectibles.</p> <p>As Piketty, Saez, and Stantcheva (<a href="#ch08_ref38">2014</a>: 231–32, 238) put it, the “second elasticity (avoidance) becomes irrelevant,” if and only if we assume that “differential treatment of different income forms” is “eliminated by reforming the tax system.” Thus, “reform” clearly means the same 83 percent top tax rate must be applied to individual and corporate income, capital gains and dividends, tax‐​free municipal bond interest, and any other source of income, including in‐​kind employee benefits.</p> <p>Although Piketty, Saez, and Stantcheva (<a href="#ch08_ref38">2014</a>) must assume literally zero tax avoidance elasticity to arrive at their diminutive 0.2 ETI for the top 1&nbsp;percent, they actually just hope “the tax‐​avoidance elasticity could likely be substantially reduced” by imposing an 83 percent rate on all different forms of both labor and capital income, while also hoping for “international cooperation” (ibid.: 238). Without reducing avoidance elasticity to zero, however, their estimate of 0.2 ETI is incorrect on their own terms, and so too is the resulting 83 percent estimate of the optimal top tax rate.</p> <p>Unfortunately, an 83 percent tax on capital gains and corporate profits could easily result in very few U.S. investment gains or profitable corporations left to tax. It is difficult to imagine a&nbsp;single country that might agree to cooperate in trying to match or enforce such unprecedented taxes. To blithely assume an 83 percent marginal tax on salaries, dividends, interest, corporate profits, and capital gains in one nation would have no adverse effect on that country’s long‐​term relative allure as a&nbsp;magnet for human and financial capital would require a&nbsp;novel growth theory that has yet to be invented.</p> <p>By using such wishful devices to stamp out tax avoidance and CEO bargaining clout, Piketty, Saez, and Stantcheva (<a href="#ch08_ref38">2014</a>) reduce their already diluted 0.5 elasticity estimate down to an illusory 0.2. That is how they are able to proclaim that, if the ETI at the highest incomes was 0.2 (which it is not), that “corresponds to a&nbsp;socially optimal top tax rate τ* = 83 percent” (ibid.: 233).</p> <p>Piketty, Saez, and Stantcheva (<a href="#ch08_ref38">2014</a>: 234) refer to 83 percent as “redistributive optimal” (regardless of excess burden inefficiency) simply because it is “the rate set to maximize tax revenue raised from top bracket earners.” This is somewhat paradoxical because the same authors frequently advocate high marginal rates as a&nbsp;way to reduce <em>pretax</em> incomes of top earners, including alleged CEO rents from bargaining, and thus shrink the tax base of the highest tax rates.</p> <p>A key concluding proclamation in a&nbsp;PowerPoint presentation by Saez at the University of Chicago, October 9, 2014, was that “high top tax rates reduce the pretax income gap without visible effect on economic growth” (<a href="#ch08_ref47">Saez 2014</a>). But his comment about economic growth rests precariously on the shaky Piketty‐​Saez‐​Stantcheva (<a href="#ch08_ref38">2014</a>) comparison of changes in OECD top tax rates since 1960. And his goal of using high top tax rates to “reduce the pretax income” of the rich is difficult to reconcile with his claim that high top rates will also raise the most tax revenue. High marginal tax rates cannot both minimize pretax top incomes and maximize revenue from top incomes. If “the point of high top marginal income tax rates is to constrain the immoderate … accumulation of riches,” as Saez and Zucman (<a href="#ch08_ref50">2019</a>) insist in a&nbsp;<em>New York Times</em> op‐​ed, then such a&nbsp;deliberate shrinkage of top incomes and wealth would almost certainly result in a&nbsp;sustained shrinkage in taxes collected from top incomes and wealth.</p> <p>If an 83 percent top marginal rate could greatly reduce the reported amount of “immoderate” income, as Saez and Zucman promise, that means they must be assuming the highest‐​income taxpayers will respond strongly to higher marginal tax rates by lowering their taxable incomes. That is, the argument that steep marginal tax rates will result in a&nbsp;steep drop in <em>pretax</em> high incomes presumes <em>high elasticity of income</em> at high incomes. That, in turn, implies an elasticity of gross income among top taxpayers of <em>at least</em> 0.7 (<a href="#ch08_ref46">Saez 2004</a>) — unlike the hypothetical 0.2 ETI number that Piketty, Saez, and Stantcheva fashioned to justify an 83 percent top tax rate</p> <h2>Conclusion</h2> <p>Improbably low estimates of the elasticity of taxable income, combined with statistical formulas based on controversial assumptions, have been used to predict a&nbsp;top marginal tax rate that supposedly maximizes short‐​term revenue on high incomes (though not necessarily in total or in the long run). Although these estimates refer to a&nbsp;combined federal and state marginal income, payroll, and sales tax rate, they are frequently misused in debates about the federal income tax rates alone. Such estimates have been cited by journalists and political figures as proof that the top <em>federal</em> tax rate could be safely raised to 70 percent or more, supposedly without damaging economic activity.</p> <p>Using the Diamond and Saez (<a href="#ch08_ref13">2011</a>) formula, despite its shortcomings, I&nbsp;find the calculated revenue‐​maximizing federal‐​state‐​local top tax rate could range from 38.5 percent to 43.9 percent with parameters consistent with the empirical literature. Yet I&nbsp;estimate that top marginal rates already average 57.4 percent nationwide and exceed 60 percent in major cities.</p> <p>Piketty, Saez, and Stantcheva (<a href="#ch08_ref38">2014</a>) devise the lowest ETI of 0.2 and highest marginal tax rate of 83 percent by starting with an unusually low estimate of an ETI of 0.5 for the top 1&nbsp;percent, which includes 1965–80 when the top tax rate was unchanged. They claim that the low 0.5 figure can be assumed to drop to 0.2&nbsp;in the future if (1) CEOs bargain less aggressively after compensation is taxed at an 83 percent rate, and (2) if tax avoidance stops after an 83 percent tax is applied to capital gains, and corporate profits and nations cooperate in tax harmonization and enforcement. These hypotheses appear speculative and empirically unsubstantiated. The authors also assume, questionably, that the future volume of taxable capital gains, corporate profits, and GDP would be unaffected by an 83 percent marginal tax rate.</p> <p>The Piketty, Saez, and Stantcheva comparison of GDP growth rates with percentage point changes in top tax rates between two data points separated by decades does not show that lower marginal tax rates are unrelated to GDP growth as they claim, but only that countries such as Germany and Japan reduced top tax rates in the 1950s, decades before the United States and United Kingdom.</p> <p>Raising the top tax rate to 83 percent on all personal income from labor and capital, as Piketty, Saez, and Stantcheva in effect propose, is quite unlikely to be a&nbsp;revenue‐​maximizing rate if, as Saez and Zucman (<a href="#ch08_ref50">2019</a>) affirm, the actual objective is to greatly reduce <em>pretax</em> incomes of those who would otherwise be reporting much higher income in the affected top tax bracket.</p> <p>This article has questioned the elasticity estimates, Pareto parameters, and static formulas used to estimate revenue‐​maximizing flat or progressive tax rates, and it has disputed the multidecade cross‐<sub></sub>country data cited by Piketty, Saez, and Stantcheva to justify their conjecture that marginal tax rates as high as 83 percent on high incomes would not diminish long‐​term economic progress.</p> <p>The Saez (<a href="#ch08_ref45">2001</a>) formula used to estimate an optimal top tax rate in a&nbsp;nonlinear tax system is derived from a&nbsp;formula designed for a&nbsp;linear flat tax system, and both have been used to produce almost equally extreme results. In both formulas, the use of low estimates of ETI and Pareto parameter to validate flat or progressive marginal tax rates of 70–83 percent treats those parameters as if they were constants rather than variables likely to be affected by major changes in marginal tax rates.</p> <p>Mechanical bookkeeping estimates of a&nbsp;short‐​term static revenue‐​maximizing flat tax of 80 percent or top progressive tax rate of 73–83 percent neglect effects on the long‐​term dynamics of economic growth, including incentives for human capital and innovation. They sidestep the most vital questions about “lowering economic activity.”</p> <p>Any tax penalty on adding to personal income is also a&nbsp;penalty on adding to national income. Income that is not created is also not taxed. Higher marginal tax penalties on the rewards from added education and innovation erode the dynamic long‐​term growth of the economy and therefore cannot be revenue‐​maximizing over time, because growth of real government revenues ultimately depends on growth of taxable income and wealth.</p> <h2>References</h2> <p><a id="ch08_ref01"></a>Akcigit, U.; Grigsby, J.; Nicholas, T.; and Stantcheva, S. (2018) “Taxation and Innovation in the 20th Century.” NBER Working Paper No. 24982 (October).</p> <p><a id="ch08_ref02"></a>Atkinson, A. B., and Piketty, T., eds. (2010) <em>Top Incomes: A&nbsp;Global Perspective</em>. New York: Oxford University Press.</p> <p><a id="ch08_ref03"></a>Auten, G., and Splinter, D. (2018) “Income Inequality in the United States: Using Tax Data to Measure Long‐​Term Trends” (August 23). Available at <a href="http://davidsplinter.com/AutenSplinter-Tax_Data_and_Inequality.pdf">http://​david​splin​ter​.com/​A​u​t​e​n​S​p​l​i​n​t​e​r​-​T​a​x​_​D​a​t​a​_​a​n​d​_​I​n​e​q​u​a​l​i​t​y.pdf</a>.</p> <p><a id="ch08_ref04"></a>Badel, A.; Huggett, M.; and Luo, W. (2018) “Taxing Top Earners: A&nbsp;Human Capital Perspective.” Georgetown University (July 9). Available at <a href="http://faculty.georgetown.edu/mh5/research/top-earners.pdf">http://​fac​ul​ty​.george​town​.edu/​m​h​5​/​r​e​s​e​a​r​c​h​/​t​o​p​-​e​a​r​n​e​r​s.pdf</a>.</p> <p><a id="ch08_ref05"></a>Badel, A.; Daly, M.; Huggett, M.; and Nybom, M. (2018) “Top Earners: Cross‐​Country Facts.” Federal Reserve Bank of St. Louis <em>Review</em> 100 (3): 237–57.</p> <p><a id="ch08_ref06"></a>Bakija, J.; Cole, A.; and Heim, B. (2012) “Jobs and Income Growth of Top Earners and the Causes of Changing Income Inequality: Evidence from U.S. Tax Return Data.” Department of Economics Working Papers 2010–22, Williams College (January).</p> <p><a id="ch08_ref07"></a>Blomquist, S., and Simula, L. (2019) “Marginal Deadweight Loss When the Income Tax is Nonlinear.” <em>Journal of Econometrics</em> 211 (1): 47–60.</p> <p><a id="ch08_ref08"></a>Bout, A.; Cruz, P.; and Wilby, B. (2019) “S&amp;P 500 CEO Compensation Increase Trends.” Harvard Law School Forum on “Corporate Governance and Financial Regulation” (February 6). Available at <a href="https://corpgov.law.harvard.edu/2019/02/06/sp-500-ceo-compensation-increase-trends-2">https://​corp​gov​.law​.har​vard​.edu/​2​0​1​9​/​0​2​/​0​6​/​s​p​-​5​0​0​-​c​e​o​-​c​o​m​p​e​n​s​a​t​i​o​n​-​i​n​c​r​e​a​s​e​-​t​r​e​nds-2</a>.</p> <p><a id="ch08_ref09"></a>Burns, S. K., and Ziliak, J. P. (2017) “Identifying the Elasticity of Taxable Income.” <em>Economic Journal</em> 127 (March): 297–329.</p> <p><a id="ch08_ref10"></a>Chetty, R. (2009) “Is the Taxable Income Elasticity Sufficient to Calculate Deadweight Loss?” <em>American Economic Journal: Economic Policy</em> 1 (2): 31–52.</p> <p><a id="ch08_ref11"></a>Chicago Booth (2019) “IGM Forum: Top Marginal Tax Rates” (January 19). Available at <a href="http://www.igmchicago.org/surveys/top-marginal-tax-rates">www​.igm​chica​go​.org/​s​u​r​v​e​y​s​/​t​o​p​-​m​a​r​g​i​n​a​l​-​t​a​x​-​rates</a>.</p> <p><a id="ch08_ref12"></a>Cummings, J. (2019) “State and Local Sales Tax Rates, Midyear 2019.” Tax Foundation <em>Fiscal Fact</em> No. 663 (July).</p> <p><a id="ch08_ref13"></a>Diamond, P., and Saez, E. (2011) “The Case for a&nbsp;Progressive Tax: From Basic Research to Policy Recommendations.” <em>Journal of Economic Perspectives</em> 25 (4): 165–90.</p> <p><a id="ch08_ref14"></a>Eckstein, O., and Tanzi, V. (1964) “Comparison of European and United States Tax Structures and Growth Implications.” In NBER and the Brookings Institution (eds.), <em>The Role of Direct and Indirect Taxes in the Federal Reserve System</em>, 217–93. Princeton, N.J.: Princeton University Press.</p> <p><a id="ch08_ref15"></a>Feldstein, M. (2011) “The Tax Reform Act of 1986: Comment on the 25th Anniversary.” NBER Working Paper No. 17531 (October).</p> <p><a id="ch08_ref16"></a>__________ (2012) “The Mirrlees Review.” <em>Journal of Economic Literature</em> 50 (3) 781–90.</p> <p><a id="ch08_ref17"></a>Giertz, S. (2004) “Recent Literature on Taxable‐​Income Elasticities: Technical Paper 2004–16.” Congressional Budget Office (December 1). Available at <a href="http://www.cbo.gov/publication/16189">www​.cbo​.gov/​p​u​b​l​i​c​a​t​i​o​n​/​16189</a>.</p> <p><a id="ch08_ref18"></a>Goolsbee, A. (1999) “Evidence on the High‐​Income Laffer Curve from Six Decades of Tax Reform.” Brookings Panel on Economic Activity (September). Available at <a href="https://faculty.chicagobooth.edu/austan.goolsbee/research/laf.pdf">https://​fac​ul​ty​.chicago​b​ooth​.edu/​a​u​s​t​a​n​.​g​o​o​l​s​b​e​e​/​r​e​s​e​a​r​c​h​/​l​a​f.pdf</a>.</p> <p><a id="ch08_ref19"></a>Gorry, A.; Hubbard, R. G.; and Mathur, A. (2018) “The Elasticity of Taxable Income in the Presence of Intertemporal Income Shifting.” NBER Working Paper No. 24351 (April).</p> <p><a id="ch08_ref20"></a>Gruber, J., and Saez, E. (2002) “The Elasticity of Taxable Income: Evidence and Implications.” <em>Journal of Public Economics</em> 84 (1): 1–32.</p> <p><a id="ch08_ref21"></a>Jaimovich, N., and Rebelo, S. (2017) “Nonlinear Effects of Taxation on Growth.” <em>Journal of Political Economy</em> 125 (1): 265–91.</p> <p><a id="ch08_ref22"></a>Jones, C. (2019) “Taxing Top Incomes in a&nbsp;World of Ideas.” NBER Working Paper No. 25725 (April).</p> <p><a id="ch08_ref23"></a>Judd, K. L.; Ma, D.; Saunders, M. A.; and Su, C-L. (2018) “Optimal Income Taxation with Multidimensional Taxpayer Types.” Stanford University Working Paper (July 1).</p> <p><a id="ch08_ref24"></a>Kaplan, S. N., and Rauh, J. (2013) “It’s the Market: The Broad‐​Based Rise in the Return to Top Talent.” <em>Journal of Economic Perspectives</em> 27 (3): 35–56.</p> <p><a id="ch08_ref25"></a>Krugman, P. (2011) “Taxing Job Creators.” <em>New York Times</em> (November 22).</p> <p><a id="ch08_ref26"></a>Kumar, A., and Liang, C-Y. (2018) “Estimating Taxable Income Responses with Elasticity Heterogeneity.” Federal Reserve Bank of Dallas Working Paper No.1611.</p> <p><a id="ch08_ref27"></a>Loughead, K., and Wei, E. (2019) “State Individual Income Tax Rates and Brackets.” Tax Foundation Fiscal Fact No. 643 (March).</p> <p><a id="ch08_ref28"></a>Lundberg, J. (2017) “The Laffer Curve for High Incomes.” Uppsala University Working Paper No. 2017:9.</p> <p><a id="ch08_ref29"></a>Mankiw, N. G.; Weinzierl, M.; and Yagan, D. (2009) “Optimal Taxation in Theory and Practice.” <em>Journal of Economic Perspectives</em> 23 (4): 147–74.</p> <p><a id="ch08_ref30"></a>Mathur, A.; Slavov, S.; and Strain, M. R. (2012) “Should the Top Marginal Income Tax Rate Be 73 Percent?” <em>Tax Notes</em> (November 19): 905–15.</p> <p><a id="ch08_ref31"></a>Mertens, K., and Olea, J. L. M. (2018) “Marginal Tax Rates and Income: New Time Series Evidence.” <em>Quarterly Journal of Economics</em> 133 (4): 1803–84.</p> <p><a id="ch08_ref32"></a>Murphy, H., and John, M. (2014) “France Waves Discreet Goodbye to 75 Percent Super‐​Tax.” Reuters (December 14).</p> <p><a id="ch08_ref33"></a>Neisser, C. (2017) “The Elasticity of Taxable Income: A&nbsp;Meta‐​Regression Analysis.” ZEW Discussion Papers, No. 17–032, Zentrum für Europäische Wirtschaftsforschung. Available at <a href="http://www.econstor.eu/bitstream/10419/168333/1/896808637.pdf">www​.econ​stor​.eu/​b​i​t​s​t​r​e​a​m​/​1​0​4​1​9​/​1​6​8​3​3​3​/​1​/​8​9​6​8​0​8​6​3​7.pdf</a>.</p> <p><a id="ch08_ref34"></a>OECD (Organisation for Economic Cooperation and Development) (2018) Revenue Statistics 2018: Tax Revenue Trends in the OECD. Available at <a href="https://read.oecd-ilibrary.org/taxation/revenue-statistics-2018_rev_stats-2018-en#page1">https://​read​.oecd​-ili​brary​.org/​t​a​x​a​t​i​o​n​/​r​e​v​e​n​u​e​-​s​t​a​t​i​s​t​i​c​s​-​2​0​1​8​_​r​e​v​_​s​t​a​t​s​-​2​0​1​8​-​e​n​#​page1</a>.</p> <p><a id="ch08_ref35"></a>OMB (Office of Management and Budget) (2019) “Historical Tables.” <em>Budget of the United States Government</em>. Available at <a href="http://www.whitehouse.gov/omb/historical-tables">www​.white​house​.gov/​o​m​b​/​h​i​s​t​o​r​i​c​a​l​-​t​ables</a>.</p> <p><a id="ch08_ref36"></a>Piketty, T., and Saez, E. (2003) “Income Inequality in the United States, 1913–1998.” <em>Quarterly Journal of Economics</em> 118 (1): 1–39.</p> <p><a id="ch08_ref37"></a>__________ (2004) “Income Inequality in the United States, 1913–2002” (November). Available at <a href="https://eml.berkeley.edu/~saez/piketty-saezOUP04US.pdf">https://eml.berkeley.edu/~saez/piketty-saezOUP04US.pdf</a>.</p> <p><a id="ch08_ref38"></a>Piketty, T.; Saez, E.; and Stantcheva, S. (2014) “Optimal Taxation of Top Labor Incomes: A&nbsp;Tale of Three Elasticities.” <em>American Economic Journal: Economic Policy</em> 6 (1): 230–71.</p> <p><a id="ch08_ref39"></a>Reynolds, A. (1996) “Economic Prosperity Is No Mystery.” <em>Orbis</em> (Spring) 199–213.</p> <p><a id="ch08_ref40"></a>__________ (1998) “Toward Meaningful Tax Reform in Japan.” Presentation to a&nbsp;Keidanren‐​Cato Institute Symposium (April 6). Available at <a href="http://www.researchgate.net/publication/255614652_Toward_Meaningful_Tax_Reform_in_Japan">www​.research​gate​.net/​p​u​b​l​i​c​a​t​i​o​n​/​2​5​5​6​1​4​6​5​2​_​T​o​w​a​r​d​_​M​e​a​n​i​n​g​f​u​l​_​T​a​x​_​R​e​f​o​r​m​_​i​n​_​Japan</a>.</p> <p><a id="ch08_ref41"></a>__________ (2004) “Marginal Tax Rates.” <em>The Library of Economics and Liberty Encyclopedia</em>. Available at <a href="http://www.econlib.org/library/Enc/MarginalTaxRates.html">www​.econ​lib​.org/​l​i​b​r​a​r​y​/​E​n​c​/​M​a​r​g​i​n​a​l​T​a​x​R​a​t​e​s​.html</a>.</p> <p><a id="ch08_ref42"></a>__________ (2005) “Compensation, Journalism and Taxes.” In W. A. Niskanen (ed.), <em>After Enron: Lessons for Public Policy,</em> 245–82. Lanham, Md.: Rowman &amp;&nbsp;Littlefield.</p> <p><a id="ch08_ref43"></a>__________ (2012) “The Misuse of Top 1&nbsp;Percent Income Shares as a&nbsp;Measure of Inequality.” Cato Institute Working Paper No. 9 (October 4).</p> <p><a id="ch08_ref44"></a>Saez, E. (1999) “Optimal Income Tax Rates and Elasticities: A&nbsp;Summary.” <em>Proceedings: Annual Conference on Taxation and Minutes of the Annual Meeting of the National Tax Association</em> 92 (1999): 64–71. Available at <a href="http://www.jstor.org/stable/41954637">www​.jstor​.org/​s​t​a​b​l​e​/​4​1​9​54637</a>.</p> <p><a id="ch08_ref45"></a>__________ (2001) “Using Elasticities to Derive Optimal Income Tax Rates.” <em>Review of Economic Studies</em> 68: 205–20.</p> <p><a id="ch08_ref46"></a>__________ (2004) “Reported Income and Marginal Tax Rates, 1960–2000: Evidence and Policy Implications.” In J. Poterba (ed.), <em>Tax Policy and the Economy,</em> Vol. 18: 117–73. Cambridge, Mass.: MIT Press for the National Bureau of Economic Research.</p> <p><a id="ch08_ref47"></a>__________ (2014) “Income and Wealth Inequality: Evidence and Policy Implications.” Video Lecture at University of Chicago Neubauer Collegium for Culture and Society (October 9). Available at <a href="https://neubauercollegium.uchicago.edu/directors_lecture/2014_15_directors_lectures/emmanuel_saez">https://​neubauer​col​legium​.uchica​go​.edu/​d​i​r​e​c​t​o​r​s​_​l​e​c​t​u​r​e​/​2​0​1​4​_​1​5​_​d​i​r​e​c​t​o​r​s​_​l​e​c​t​u​r​e​s​/​e​m​m​a​n​u​e​l​_saez</a>.</p> <p><a id="ch08_ref48"></a>Saez, E.; Slemrod, J.; and Giertz S. H. (2012) “The Elasticity of Taxable Income with Respect to Marginal Tax Rates: A&nbsp;Critical Review.” <em>Journal of Economic Literature</em> 50 (1): 3–50.</p> <p><a id="ch08_ref49"></a>Saez, E., and Stantcheva, S. (2018) “A Simpler Theory of Optimal Capital Taxation.” <em>Journal of Public Economics</em>162 (June): 120–42.</p> <p><a id="ch08_ref50"></a>Saez, E., and Zucman, G. (2019) “Alexandria Ocasio-Cortez’s Tax Hike Idea Is Not About Soaking the Rich. It’s About Curtailing Inequality and Saving Democracy.” <em>New York Times</em> (January 22).</p> <p><a id="ch08_ref51"></a>Stiglitz, J. E. (1987) “Pareto Efficient and Optimal Taxation and the New Welfare Economics.” NBER Working Paper No. 2189 (March).</p> <p><a id="ch08_ref52"></a>Tanninen, H.; Tuomala, M.; and Tuominen, E. (2019) <em>Inequality and Optimal Redistribution.</em> New York: Cambridge University Press.</p> <p><a id="ch08_ref53"></a>Zajac, E. E. (1995) <em>The Political Economy of Fairness.</em> Cambridge Mass.: MIT Press.</p> <p><sup><a id="fn08-01">1</a></sup> Blomquist and Simula (<a href="#ch08_ref07">2019</a>) simulate with an average ETI of 0.4 and Pareto parameter of 2.0 that the incremental deadweight loss in 2006 would be a&nbsp;little over 44 cents for each additional dollar raised from an equal (in percentage points) increase in marginal tax rates <em>for all taxpayers</em>. But substituting an academic ETI estimate of 1.04 for <em>the highest</em> tax brackets raises the deadweight loss to one dollar per dollar of added revenue in their formula. And the efficiency loss (or excess burden) becomes much higher with less generous assumptions about the linearization of the tax schedule.</p> <p><sup><a id="fn08-02">2</a></sup> Gruber and Saez (<a href="#ch08_ref20">2002</a>) estimated a&nbsp;lower 0.57 ETI for “high incomes,” but their definition of high incomes includes some incomes too low to be in the top 1&nbsp;percent ($100,000) while excluding all income above $1 million. That million‐​dollar cap left out superstars, top CEOs, major investors, top professionals, and small businesses — where theory and evidence find the ETI is highest.</p> <p><sup><a id="fn08-03">3</a></sup> Goolsbee (<a href="#ch08_ref18">1999</a>: 24) notes that, because high incomes are highly cyclical, estimates of their ETI “may differ depending on the state of the business cycle.” Yet making cyclical corrections to ETI estimates could be misleading if higher marginal tax rates contributed to recessions — as may have happened in 1932, 1937, 1970, 1981 (via bracket creep) or 1991.</p> <p><sup><a id="fn08-04">4</a></sup> If high marginal tax rates effectively reduced pretax or posttax incomes in the upper tail (rather than, say, changing the form of income or moving it into cash, offshore accounts, or retained corporate earnings), that would reduce inequality by definition — if inequality is defined solely by income at the top. But that alone would not redistribute income unless (1) government receipts actually increased over time and (2) any incremental tax receipts were given to low‐​income people rather than used for other political priorities.</p> <p><sup><a id="fn08-05">5</a></sup> Using the EXECUCOMP database for top five executives at S&amp;P 1000 firms from 1992 to 2005 (only a&nbsp;small fraction of all top 1&nbsp;percent income from labor and capital), Gorry, Hubbard, and Mathur (<a href="#ch08_ref19">2018</a>) find that accounting for increased deferral raises their estimated ETI for these executives from 0.8 to 2.24, but that only 0.31 of that is from reduced labor supply (an incomplete gauge of potential real effects).</p> </div> Mon, 30 Sep 2019 03:00:00 -0400 Alan Reynolds https://www.cato.org/publications/cato-journal/optimal-top-tax-rates-review-critique A Different Look at After‐​Tax Income Inequality https://www.cato.org/blog/different-look-after-tax-income-inequality Alan Reynolds <p>Every presidential candidate promises to “reduce income inequality” by raising tax rates on the rich and increasing transfer payments (including tax credits and in‐​kind benefits) for the middle class. Yet the widely‐​used <a href="https://www.urban.org/sites/default/files/publication/99455/how_different_studies_measure_income_inequality_0.pdf">flawed data</a> from Thomas Piketty and Emmanuel Saez <em>exclude </em>both taxes and transfers. Income measures that exclude taxes and transfers cannot tell us whether taxes or transfers are high or low, and cannot be directly affected by higher taxes on some or higher transfers to others (because such policies are ignored in the data). <br /><br /><br /> A simple table adapted from the 2017 Consumer Expenditure Survey, from the Bureau of Labor Statistics, may be sufficient to show how crucial it is to take account of taxes (including refundable tax credits), and also to adjust average income for the different number of people and workers per household. <br /></p> <p> </p><div data-embed-button="image" data-entity-embed-display="view_mode:media.blog_post" data-entity-type="media" data-entity-uuid="00cabfc9-5814-4c64-9b06-82a315cbb0b5" data-langcode="und" class="embedded-entity"> <img srcset="/sites/cato.org/files/styles/pubs/public/wp-content/uploads/after_tax_income_by_quintile.jpg?itok=vC4A_izW 1x, /sites/cato.org/files/styles/pubs_2x/public/wp-content/uploads/after_tax_income_by_quintile.jpg?itok=2oE2CylI 1.5x" width="700" height="294" src="/sites/cato.org/files/styles/pubs/public/wp-content/uploads/after_tax_income_by_quintile.jpg?itok=vC4A_izW" alt="After Tax Income by Quintile" typeof="Image" class="component-image" /></div> <p>Incomes are shown by fifths (“quintiles”), with the lowest 20% on the left and highest on the right. <br /><br /><br /> The second row shows the “lower limit” of pretax income needed to counted be in each quintile. The next two rows show mean (average) income before and after taxes. <br /><br /><br /> The column at the far right shows a ratio of highest to lowest income, called the 80/20 ratio, which a common gauge of inequality. It shows that the highest 20% earned 16.5 times as much as the lowest 20% before taxes, but only 12.5 times as much after taxes. <br /><br /><br /> But simply adjusting household income for taxes is not enough. Average incomes cannot be properly compared between the highest and lowest quintiles because there are three times as many people per consumer unit (household) in the highest 20% as there are in the lowest. And there are four times as many workers in the highest 20% as there are in the lowest. <br /><br /><br /> By adjusting for different household size, we find the highest 20% earned only 6.5 times as much after‐​tax income per person as the lowest 20%. <br /><br /><br /> But income is likely to be higher in households with two or more workers than it is in households with no workers or only one. So, that last row measures average after‐​tax income per worker in the highest and lowest quintiles (and those in between). <br /><br /><br /> By further adjusting for the different number of earners, the highest 20% earned only 3 times per worker as much as the lowest 20%, after taxes. <br /><br /><br /> Properly understood, the facts about U.S. after‐​tax income distribution and growth are insufficiently alarming to justify the political misuse of questionable pretax, pretransfer income statistics as a false argument for redistributing after‐​tax income.</p> Thu, 25 Jul 2019 14:41:00 -0400 Alan Reynolds https://www.cato.org/blog/different-look-after-tax-income-inequality 1973: The Year John Kenneth Galbraith Made Socialism Mainstream https://www.cato.org/blog/1973-year-john-kenneth-galbraith-made-socialism-mainstream Alan Reynolds <p>I started writing about economic issues in 1971, first in <em>Reason</em> then <em>National Review</em>. One of my most serious early articles –­and certainly the most unread–­ was a&nbsp;2800‐​word critique of John Kenneth Galbraith in <em>The Intercollegiate Review</em>, posing as a&nbsp;book review with the mildly disrespectful title “<a href="https://www.researchgate.net/publication/333356286_Irrelevant_Anachronism_A_review_of_John_Kenneth_Galbraith_Economics_and_the_Public_Purpose_1973">Irrelevant Anachronism</a>.”&nbsp;<br><br /> <br> Ken Galbraith and I&nbsp;met years later, when he was invited to comment about my presentation at a&nbsp;<a href="https://www.youtube.com/watch?v=i_Yg-mlKAAo">1987 debate at Harvard</a> [recorded by C‐​Span] about “The Disappearing Middle Class” on a&nbsp;panel with Lester Thurow, Barry Bluestone and Frank Levy.&nbsp;<br><br /> <br> In Paul Krugman’s ill‐​tempered 1994 book, <em>Peddling Prosperity</em> [which I&nbsp;reviewed as <a href="http://www.pkarchive.org/others/krugman3.html">“Peddling Pomposity”</a>], he called Galbraith “the first celebrity economist,” adding that “he has never been taken seriously by his academic colleagues, who regard him as more of a&nbsp;media personality.” <br><br /> <br> Today, Krugman is a&nbsp;leading celebrity economist and media personality. But he never approached the pop chart supremacy and political clout that Galbraith once had. Galbraith was, for example, the uncontested bandleader behind the deafening drumbeat for Nixon’s price controls in August 15, 1971. <br><br /> <br> My September 24, 1971 cover story for <em>National Review</em>, <a href="https://object.cato.org/sites/cato.org/files/articles/reynolds-nixon-controls.pdf">“The Case Against Wage and Price Controls”</a> began by dismembering the arguments behind Galbraith’s briefly victorious argument that, “The seemingly obvious remedy for the wage‐​price spiral is to regulate prices and wages by public authority” [from <em>The New Industrial State</em>, 1967]. <br><br /> <br> Once the central government can tell workers what their labor is worth and tell businessmen how to price their products, that is about as far as we can possibly get from a&nbsp;free market, and Nixon’s New Economic Policy was perhaps as close as the U.S. ever came to full‐​blown socialism (aside from rationing in major wars). The only thing worse would be allowing the government make virtually all decisions about what producers can produce and consumers can consume ­–­ otherwise known as “socialism.”&nbsp;<br><br /> <br> In his 1973 book, <em>Economics and the Public Purpose</em>, Galbraith found a “socialist imperative” for virtually every product or service of much importance. As in the case of his campaign for wage and price controls, this clarion call for socialism fit in with the temper of the times and did not generate the concern or skepticism the word sometimes arouses today. <br><br /> <br> When Americans today wonder what “socialism” means, they could do worse than recall how the quite mainstream commentator John Kenneth Galbraith defined it in 1973. Newsweek provided a&nbsp;concise summary on October 1, 1973 with Arthur Cooper’s glowing review of Galbraith’s book, <em>Economics and the Public Purpose</em> (also the topic of my review about its quaint irrelevance).&nbsp;<br><br /> <br> In the tradition of New Deal regulatory protagonists <a href="https://www.law.gmu.edu/assets/files/publications/working_papers/1219ContractualTheory.pdf">Berle and Means</a> (whose inspiration he acknowledged in many books), Galbraith wrote of a “bureaucratic symbiosis” between the federal government and the “planning system” of giant corporations and their “technostructure” of lawyers, scientists, engineers and lobbyists.&nbsp;<br><br /> <br> Cooper explained:&nbsp;<br> </p> <blockquote><p>Galbraith is certain that the people are being exploited by a [corporate‐​dominated] planning system whose interests run increasingly counter to their best interests… [and is] blunt about what is required to rectify the situation‐ “a new socialism.” This socialism demands various actions: <br><br /> <br> •&nbsp;Set up “full organization under public ownership of the weak parts of the market system‐ housing, medical care and transportation.” <br><br /> <br> •&nbsp;Encourage small‐​business men and firms in the market system to form trade associations, with governmental regulation of prices and extend coverage of the minimum wage as well as a&nbsp;major increase in the amount. <br><br /> <br> •&nbsp;Abandon the unrealistic goal of full employment and institute instead a&nbsp;guaranteed or alternative income for those who cannot find satisfactory work. <br><br /> <br> •&nbsp;Convert “fully mature corporations” into fully public [government‐​owned] corporations. This would mean public purchase of stock for fixed‐ interest‐​bearing securities so that capital gains would accrue to the public treasury. Such public corporations as Renault and the Tennessee Valley Authority are run this way now. <br><br /> <br> •&nbsp;Also convert large specialized weapons firms doing more than half their business with the government into full public corporations. “The large weapons firms are already socialized except in name”-e.g., Lockheed and General Dynamics. <br><br /> <br> •&nbsp;Impose a&nbsp;public authority to coordinate different areas of the planning system. Thus, the promotion of electrical use by appliance firms will not run absurdly ahead of the utilities’ ability to supply electricity. <br><br /> <br> •&nbsp;Establish “a special presumption” in favor of public support of the arts. <br><br /> <br> Admittedly not a “revolutionary,” Galbraith allows that all this will come about only through political processes‐ once politics itself is emancipated from the grip of the planning system. Since he believes the Republican Party is “the instrument of the planning system,” Galbraith’s hopes repose in the McGovern wing of the Democratic Party. Will Galbraith’s ideas, which may be “radical” but certainly sound sensible, work? Maybe time will tell. But John Galbraith sounds like an idea whose time has come.</p> </blockquote> <p>Mr. Cooper’s 1973 hope that the time had come for socialism proved a&nbsp;decidedly premature forecast, thanks in part to (1) George McGovern’s unprecedented presidential defeat and (2) the stagflationary disaster resulting from Nixon’s 1971–74 policy of mixing a&nbsp;deliberately debased dollar with Galbraithian <a href="https://imprimis.hillsdale.edu/farewell-to-wage-and-price-controls-july-1974/">wage and price controls</a>. <br><br /> <br> Belief in socialism requires innocently trusting politicians and bureaucrats to make all your decisions for you, typically by promising to give you goodies that some other chump is expected to pay for. This inevitably involves greatly limiting individual choices: the fewer choices are left, the more “socialist” the system has become. “Single payer,” for example, means a&nbsp;single choice. Take it or leave it. Second or third choices become illegal. <br><br /> <br> If a&nbsp;single choice from the bossy political duopoly was better than many in the marketplace, we might as well replace all U.S. restaurants with a&nbsp;chain of federal cafeterias, and allow production and sales of only one people’s car in only one color. <br><br /> <br>&nbsp;</p> Tue, 18 Jun 2019 13:56:00 -0400 Alan Reynolds https://www.cato.org/blog/1973-year-john-kenneth-galbraith-made-socialism-mainstream Alan Reynolds blog post, “The Smoot‐​Hawley Tariff and the Great Depression,” is cited on Bloomberg TV https://www.cato.org/multimedia/media-highlights-tv/alan-reynolds-blog-post-smoot-hawley-tariff-great-depression-cited Mon, 03 Jun 2019 11:41:00 -0400 Alan Reynolds https://www.cato.org/multimedia/media-highlights-tv/alan-reynolds-blog-post-smoot-hawley-tariff-great-depression-cited Alan Reynold’s article, “Is it True that 40% of Americans Can’t Handle a $400 Emergency Expense?,” is cited on KHOW’s The Ross Kaminsky Show https://www.cato.org/multimedia/media-highlights-radio/alan-reynolds-article-it-true-40-americans-cant-handle-400 Thu, 09 May 2019 11:33:00 -0400 Alan Reynolds https://www.cato.org/multimedia/media-highlights-radio/alan-reynolds-article-it-true-40-americans-cant-handle-400 Is it True that 40% of Americans Can’t Handle a $400 Emergency Expense? https://www.cato.org/blog/it-true-40-americans-cant-handle-400-emergency-expense-0 Alan Reynolds <p>Governor <a href="https://www.wsj.com/articles/im-running-to-save-capitalism-11557090143">John Hickenlooper</a>, writing in <em>The Wall Street Journal</em>, repeats a misleading interpretation of one answer to a Federal Reserve poll question that is frequently used to suggest many Americans are in dire financial straits: “Forty percent of Americans in 2017 didn’t have enough savings to cover a $400 medical emergency or car repair, according to the Federal Reserve.” <br /><br /><br /> But that is not the question that was asked, and it certainly is not the answer. <br /><br /><br /> The question was about how people would <em>choose</em> to pay a $400 “emergency expense” — not whether or not they could pay it out of savings (or checking) if they wanted to. Respondents were also free to choose more than one way of paying the extra $400 (“please selects all that apply”), so the answers add up <strong>143%</strong> rather than 100%. Even if 100% said they could pay an extra $400 with cash, there could still be more than 40% who would choose a different method. <br /><br /><br /> It turns out that 86% would pay cash or charge it and then pay off the bill at the next statement (many consumers autopay credit card bills from checking accounts). Some (11%) said they might borrow some or all of it from a friend or family member, but that probably means a spouse or parent in most cases (respondents included full‐​time students). <br /><br /><br /><a href="https://www.federalreserve.gov/publications/appendix-b-consumer-responses-to-survey-questions.htm">Report on the Economic Well‐​Being of U.S. Households (SHED)</a>&#13;<br /></p> <p> </p><div data-embed-button="image" data-entity-embed-display="view_mode:media.blog_post" data-entity-type="media" data-entity-uuid="fb56a448-decd-49b4-b6ac-979388369575" data-langcode="und" class="embedded-entity"> <img srcset="/sites/cato.org/files/styles/pubs/public/wp-content/uploads/40-cannot-pay-400.jpg?itok=WIkRFWEc 1x, /sites/cato.org/files/styles/pubs_2x/public/wp-content/uploads/40-cannot-pay-400.jpg?itok=C__QV81a 1.5x" width="700" height="523" src="/sites/cato.org/files/styles/pubs/public/wp-content/uploads/40-cannot-pay-400.jpg?itok=WIkRFWEc" alt="Media Name: 40-cannot-pay-400.jpg" typeof="Image" class="component-image" /></div> <p>A related follow‐​up question asked if a $400 “emergency expense” would prevent paying all other bills in full an on time, and 85% said it would not. <br /><br /><br /> Question EF5B. <strong>How would a $400 emergency expense that you had to pay impact your ability to pay your other bills this month</strong>&#13;<br /></p> <table><tbody><tr><td><strong>Response</strong></td> <td><strong>Percent</strong></td> </tr><tr><td>I would still be able to pay all of my other bills in full</td> <td>85</td> </tr><tr><td>I could not pay some other bills or would only make a partial payment on some of them</td> <td>14</td> </tr><tr><td>Refused</td> <td>1</td> </tr></tbody></table><p>Note: Number of unweighted respondents = 9,670. <br /><br /><br /><a href="https://www.federalreserve.gov/publications/appendix-b-consumer-responses-to-survey-questions.htm">https://​www​.fed​er​al​re​serve​.gov/​p​u​b​l​i​c​a​t​i​o​n​s​/​a​p​p​e​n​d​i​x​-​b​-​c​o​n​s​u​m​e​r​-​r​e​s​p​o​n​s​e​s​-​t​o​-​s​u​r​v​e​y​-​q​u​e​s​t​i​o​n​s.htm</a>&#13;<br /><br /><br /> There are many credible ways to measure economic well‐​being (such as real after‐​tax income and/​or wealth), but giving a few thousand people a multiple‐​choice exam about how they might prefer to pay an unexpected $400 expense is not one of them.</p> Mon, 06 May 2019 10:22:00 -0400 Alan Reynolds https://www.cato.org/blog/it-true-40-americans-cant-handle-400-emergency-expense-0 Roger McNamee’s Facebook Critique https://www.cato.org/blog/roger-mcnamees-facebook-critique Alan Reynolds <p>In a recent <em>Time </em>magazine article, <a href="http://time.com/magazine/us/5505429/january-28th-2019-vol-193-no-3-u-s/">Roger McNamee</a> offers an agitated criticism of Facebook, adapted from his book <em>Zucked: Waking Up to the Facebook Catastrophe</em>.  Facebook “has a huge impact on politics and social welfare,” he claims, and “has done things that are truly horrible.”  Facebook, he says, is “<a href="http://nymag.com/intelligencer/2019/02/facebook-investor-roger-mcnamee-is-now-a-company-nemesis.html">terrible for America</a>.”&#13;<br /> &#13;<br /> McNamee suggests his “history with the company made me a credible voice.” From 2005 to 2015, McNamee was one of a half dozen managing directors of Elevation Partners, an $1.9 billion private equity firm that bought and sold  shares in eight companies, including such oldies as <a href="https://dealbook.nytimes.com/2014/07/18/after-tough-years-elevation-partners-profits-on-sale-of-forbes-media/">Forbes</a> and Palm.  U2 singer Bono was a co-founder. Other partners included two former executives from Apple and one from Yahoo.  Another is married to the sister of Facebook’s COO.  Such investors are not necessarily disinterested observers, much less policy experts.&#13;<br /> &#13;<br /> Between <a href="http://fortune.com/2011/01/11/timeline-where-facebook-got-its-funding/">November 2009</a> and June 2010 Elevation Partners invested $210 million for 1% of Facebook.  That was early, but two years after Microsoft made a larger investment.  Back then, McNamee and other investors had facetime with Zuckerberg. &#13;<br /> &#13;<br /> McNamee supposedly became alarmed while perusing “Bay Area for Bernie” on Facebook and finding suspicious memes critical of Hillary.  Later, he imagined the Brexit vote must be due to misleading Facebook posts (as if British tabloids and TV were silent).  “Brexit happens in June,” <a href="http://nymag.com/intelligencer/2019/02/facebook-investor-roger-mcnamee-is-now-a-company-nemesis.html">he says</a>, “and then I think, Oh my god, what if it’s possible that in a campaign setting, the candidate that has the more inflammatory message gets a structural advantage from Facebook? And then in August, we hear about Manafort, so we need to introduce the Russians into the equation.” &#13;<br /> &#13;<br /> He suggests goofy Facebook ads by <a href="https://www.cato.org/publications/commentary/disconnected-dots-between-meddling-collusion">Russian trolls</a> stole the U.S. election from Clinton. Actually, the Mueller indictment said the Internet Research Agency “allegedly used social media and other internet platforms to address <a href="https://www.wired.com/story/russia-indictment-twitter-facebook-play-both-sides/">a wide variety of topics</a>” <em>to inflame political debates</em>, frequently taking both sides of divisive issues.  Such political trolling for fun and profit (clicks generate advertising money) is commonplace in Russia, and also <a href="https://www.washingtonpost.com/national/nothing-on-this-page-is-real-how-lies-become-truth-in-online-america/2018/11/17/edd44cc8-e85a-11e8-bbdb-72fdbf9d4fed_story.html">at home</a> in the USA.&#13;<br /> &#13;</p> <p>McNamee’s political agenda is partly a matter of endorsing aggressive discretionary use of antitrust prosecution, which <a href="https://object.cato.org/sites/cato.org/files/serials/files/regulation/2018/3/regulation-v41n1-8-updated.pdf">I have analyzed</a> at length in <em><a href="https://www.econlib.org/reynolds-on-the-return-of-antitrust/">Regulation</a></em>.  But he goes much further than that.   He would empower politicians to both dismantle and constrict McNamee’s suspiciously selective list of disfavored firms (which absolves <a href="https://venturebeat.com/2018/03/10/google-and-apple-are-in-a-tight-race-to-acquire-the-most-promising-ai-startups/">Apple</a>, Microsoft and others).  He also wants government to coddle, bankroll and subsidize the sort of start-ups he’d probably like to invest in – after they’re subsidized and protected.  And he wants “public health services to counter internet addiction.”&#13;<br /> &#13;<br />  “We can create a political movement,” says McNamee; “We can insist on government intervention.”  He wants “to set limits on the markets in which monopoly-class [?] players like Facebook, Google and Amazon can operate.” “The economy would benefit from breaking them up. . . I favor regulation as way to reduce harmful behavior.”  &#13;<br /> &#13;<br /> There is no coherent argument for “limits on markets,” however, since McNamee doesn’t try to explain (nobody could) how Amazon owning a newspaper or grocery store, or Google buying <a href="https://www.trustradius.com/products/google-marketing-platform/competitors">DoubleClick</a>, or Microsoft acquiring <a href="https://www.cato.org/publications/commentary/microsofts-acquisition-github-not-anticompetitive">GitHub</a> could reduce competition in those markets. &#13;<br /> &#13;<br /> Facebook owns one of many photo-sharing services (Instagram) that has to compete with email, and Facebook owns one        two of many messaging services (WhatsApp and Messenger), among others from Apple and many others, that struggles to compete with texting. &#13;<br /> &#13;<br /> Instagram, WhatsApp and Snapchat compete with others for mobile photo sharing, and with YouTube for video sharing, but they aren’t really comparable to Facebook, Twitter, LinkedIn or Google Plus for sharing opinions, news and links.  “Breaking-up” Facebook by forcing the sale of Instagram and WhatsApp to different owners (e.g., private equity firms) would make no discernable difference to consumer choices or competition.  &#13;<br /> &#13;<br /> McNamee’s proposed regulation of “harmful behavior” would invite political censorship and propaganda.  So too would his proposed subsidies and protection from competition for new firms deemed “civically responsible” by politicians and bureaucrats. “In exchange for adopting a benign business model, perhaps based on subscriptions, startups would receive protection from the giants. Given that social media is practically a public utility,” he claims, “I think it is worth considering more aggressive strategies, including government subsidies . . . [because] civically responsible social media may be essential to the future of the country. The subsidies might come in the form of research funding, capital for startups, tax breaks and the like.”&#13;<br /> &#13;<br /> McNamee’s scheme for inviting ambitious political operatives to force Facebook to submit to being micro-managed as a regulated public utility is because he is confident that most common folk (unlike himself) are easily duped.  It is his <em>noblesse oblige</em> to launch a political movement to protect the <em>lumpenproletariat </em>from their childish foolishness. &#13;<br /> &#13;<br /> The trouble, as he sees it, is that people friend people they agree with, “so each persons’ News Feed becomes a unique reality, a filter bubble that creates the illusion that most people the user knows believe the same thing. Showing users only posts they agree with was good for Facebook’s bottom line,” he claims, “but some research showed it also increased polarization.”  Such sharing of news and opinion among Facebook friends, says McNamee, has “aggravated the flaws in our democracy while leaving citizens ever less capable of thinking for themselves.” &#13;<br /> &#13;<br /> Why is the tendency of like-minded people friend or follow each other any more of a “threat to democracy” (as McNamee calls it) than conservative Republicans watching Fox News and leftist Democrats watching MSNBC? Should that be banned too? And how is McNamee’s proposed Social Media Czar supposed to ban liberals or conservatives (who are said to be increasingly incapable of thinking) from using social media to befriend or follow like-minded people?  After all, the FCC and FTC can’t even control robocalls and spam texts.&#13;<br /> &#13;<br /> Many uses of the Internet involve some personal data about identity, contacts, sometimes politics. Many “likes” might a strong clue of which party a Facebook user will vote for – but that is easier to find from public voter registration and campaign donation.  <a href="https://tosdr.org/#google">Google</a> and <a href="https://tosdr.org/#apple">Apple</a> collect and share data that may reveal your interests, though that can be largely thwarted by deleting your browser and search history. Sites like Facebook or Instagram don’t acquire sensitive data about banking, health or criminal offenses.  In the case of Facebook, McNamee’s feigned hysteria about privacy and <a href="https://docs.microsoft.com/en-us/sql/analysis-services/data-mining/data-mining-algorithms-analysis-services-data-mining?view=sql-server-2017">data-mining algorithms</a> is mainly about tracking our online shopping and searches to guess <em>what we want to buy</em>.  Personally, I find some targeted ads useful but most are easy to ignore.&#13;<br /> &#13;<br /> If you hate commercials on TV, cut the cable cord. If you hate commercials on Facebook, close your account.  Yet most Facebook users apparently find the social benefits still exceed the privacy costs. That is <em>their</em> choice to make. After a year of criticism from regulators, politicians and professional “privacy advocates,” Facebook’s fourth quarter earnings were <a href="https://www.cpomagazine.com/data-privacy/after-a-year-of-privacy-scandals-facebook-is-back-to-business-as-usual/">up 30%</a> from a year earlier and the number of active monthly users was up 9%. &#13;<br /> &#13;<br /> Facebook and Google, like other enormously valuable internet services, are free because of advertising.  McNamee’s vague vision of “human-driven social networks” would apparently be “based on subscriptions” and taxpayer subsidies, including some sort of federal protection from “giant” domestic and foreign competitors (which at least implies banning takeovers, even though the chance of a big buyout is precisely what motivates many start-ups).  &#13;<br /> &#13;<br /> Very few <a href="https://www.ionos.com/digitalguide/online-marketing/social-media/the-best-facebook-alternatives/">Facebook alternatives</a> have attracted more than a few million users and survived with no ads and no fees.  Vero, for example, lured three million users by offering “access to Vero <a href="https://www.vero.co/announcement/">free for life</a>. . . until further notice” while adding that they “will confirm the start date and pricing of Vero subscriptions soon.”  That business plan begins with early freeloaders then hopes for later suckers. Any subscription-based social medium could work only if everyone we wanted to reach was also willing to pay the fee, which is statistically unlikely.  That’s why McNamee lobbies to force taxpayers to provide “human-driven social networks” like Vero with socialist start-up capital and endless subsidies.&#13;<br /> &#13;<br /> An author’s political agenda often drives the arguments, which explains why extreme rhetoric about hypothetical “crises” in the future are typically abused to excuse extreme proposals for government meddling in the present.  McNamee turns out to be just another missionary for paternalistic big government to throttle successful big tech firms, subsidize less-promising firms, and protect the gullible masses from being persuaded by Facebook posts to make what he regards as politically undesirable choices. </p> <p></p> Mon, 18 Feb 2019 12:10:00 -0500 Alan Reynolds https://www.cato.org/blog/roger-mcnamees-facebook-critique A Federal Shutdown Is an Annoyance — Interest on $22 Trillion in Debt Is a Problem https://www.cato.org/publications/commentary/federal-shutdown-annoyance-interest-22-trillion-debt-problem Alan Reynolds <div class="lead mb-3 spacer--nomargin--last-child text-default"> <p>President Trump’s decision to shut down the government because Congress was supposedly spending $5.7 billion&nbsp;too little, rather than too much, was hardly a&nbsp;traditional budget priority for conservatives or libertarians. But the overheated partisan feud distracted attention from a&nbsp;much larger issue that made the shutdown possible — namely, that Congress has&nbsp;still&nbsp;not enacted a&nbsp;budget. And the budget the president proposed is too fat.</p> </div> , <div class="mb-3 spacer--nomargin--last-child text-default"> <p>When fiscal year 2019 began on Oct. 1, Congress had enacted&nbsp;<a href="http://www.crfb.org/papers/qa-everything-you-should-know-about-government-shutdowns" target="_blank">only seven</a>&nbsp;of the required twelve appropriations, and even departments among the seven did not have full‐​year appropriations. From Sept. 18 to Dec. 19, Congress only passed short‐​term bills to fund neglected agencies for a&nbsp;month or two. President Trump finally refused to sign another temporary patch through February 8th unless it included $5.7 billion to extend and improve the southern border barriers (far less than the&nbsp;<a href="https://www.reuters.com/article/us-usa-budget-mulvaney/trump-budget-asks-more-than-200-billion-for-infrastructure-border-security-budget-director-idUSKBN1FW03N" target="_blank">$18 billion</a>&nbsp;Trump had first requested). But even if Congress added that $5.7 billion to the last temporary patch, it still would still have left the government with no budget after February 8th.</p> <p>Even if the president’s $5.7 billion mission was accepted, that would soon leave 99.999 percent of the U.S. budget in limbo.&nbsp;The remaining $4.4 trillion in the budget is the elephant in this room and that big animal needs a&nbsp;diet.&nbsp;Federal spending rose by a&nbsp;relatively modest 3.1 percent a&nbsp;year from 1993 to 2000, and the economy and American citizens seemed content.&nbsp;Federal spending then increased by 6.6 percent from 2001 to 2006, and by 8.5 percent a&nbsp;year from 2007 to 2010.<u></u></p> <p>Far from being a “stimulus,” as advertised, runaway spending from 2001 to 2010 was&nbsp;at the expense of the private sector,&nbsp;which performed poorly. Rising federal purchases absorbed resources that would otherwise have been available to expand private enterprises.&nbsp;Rising transfer payments discouraged people from participating in the labor force.</p> <p>With spending rising much faster than the economy, federal spending jumped from 17.6 percent of GDP in 2001 to a&nbsp;record 24.4 percent in 2009 and 23. percent in 2010-11. Sequester limits temporarily brought spending down to a&nbsp;still‐​high level of 20.3 percent of GDP by 2014 though spending rose to 21 percent of GDP last year.</p> <p>In just five years, 2007 to 2012, national debt held by the public (rather than by Social Security and other trust funds) doubled from 35.2 percent in 2007 to 70.4 percent of GDP by 2012.&nbsp;The debt/​GDP ratio has again been creeping up since 2015 — to 76.5 percent in 2017, 78.8 percent in 2018, and reaching 81.9 percent by 2022.</p> <p>Allowing the national debt to rise so much faster than the economy that supports it means a&nbsp;growing share of the federal budget (if we had a&nbsp;federal budget) will be devoted to paying interest to Treasury bondholders, many of whom are foreign.</p> <p>The burden of government is measured by what it spends, not how it pays for it. Within limits, national debt can be&nbsp;<a href="http://economics.virginia.edu/sites/economics.virginia.edu/files/macro/Blanchard.pdf" target="_blank">rolled over</a>&nbsp;indefinitely, selling new bonds to repay old, so long as the interest rate is below the growth of nominal GDP (real growth plus inflation).</p> <p>However, a&nbsp;national debt approaching $22 trillion has to be serviced. Net interest paid out on the public debt increased 19.4 percent between the last quarters of 2017 and 2018, according&nbsp;to the&nbsp;<a href="https://www.cbo.gov/publication/54905" target="_blank">Congressional Budget Office</a>, and exceeded the cost of Medicaid. The Trump administration projects interest expense rising from 1.4 percent of GDP in 2017 ($263 billion) to 2.4 percent in 2023 ($619 billion).</p> <p>Over the years, Congress has carelessly delegated sweeping discretionary authority to presidents to impose tariffs, stop immigrants or start wars without congressional legislation or approval. Presidential authority to shut down the government, by contrast, is ceded only by congressional inaction — by failure to enact a&nbsp;budget before the current fiscal year began. The absence of any budget to put some bounds on federal spending is a&nbsp;much more troublesome issue than a&nbsp;temporary partial government shutdown, annoying as that surely is.</p> </div> Fri, 25 Jan 2019 13:24:00 -0500 Alan Reynolds https://www.cato.org/publications/commentary/federal-shutdown-annoyance-interest-22-trillion-debt-problem Alan Reynolds’ paper, “Obama’s Soak‐​the‐​Rich Tax Hikes Won’t Work,” is cited on The Larry Elder Show https://www.cato.org/multimedia/media-highlights-radio/alan-reynolds-paper-obamas-soak-rich-tax-hikes-wont-work-cited Fri, 25 Jan 2019 10:06:00 -0500 Alan Reynolds https://www.cato.org/multimedia/media-highlights-radio/alan-reynolds-paper-obamas-soak-rich-tax-hikes-wont-work-cited Trade War with China Slashed U.S. Exports 26.3% as U.S. Imports Rose 38.5% https://www.cato.org/blog/trade-war-china-slashed-us-exports-263-us-imports-rose-385 Alan Reynolds <p>The Trump Administration’s trade warfare with China began in earnest last March 22<sup>nd</sup> (following steel and aluminum tariffs that primarily hit other countries). U.S. and Chinese tariffs on each other’s goods then <a href="https://en.wikipedia.org/wiki/China%E2%80%93United_States_trade_war_(2018%E2%80%93present)">escalated repeatedly</a> through <a href="https://www.bbc.com/news/business-44529600">September 18</a> with threats of much more the same by March 1 of this year. <br /><br /><br /> The effect so far has been quite different from what President Trump first promised and still keeps pretending. In fact, U.S. goods <em>exports to China (excluding services) fell by 26.3% from March through October</em>, while <em>U.S. imports from China rose by 36.5%</em>. <br /></p> <p> </p><div data-embed-button="image" data-entity-embed-display="view_mode:media.blog_post" data-entity-type="media" data-entity-uuid="f09987c7-fc7e-48d9-a350-31c795c329b9" data-langcode="und" class="embedded-entity"> <img srcset="/sites/cato.org/files/styles/pubs/public/wp-content/uploads/u.s._china_trade_in_goods_2018.jpg?itok=ty-Wl_r9 1x, /sites/cato.org/files/styles/pubs_2x/public/wp-content/uploads/u.s._china_trade_in_goods_2018.jpg?itok=ojmADz5W 1.5x" width="700" height="418" src="/sites/cato.org/files/styles/pubs/public/wp-content/uploads/u.s._china_trade_in_goods_2018.jpg?itok=ty-Wl_r9" alt="US China Trade Data " typeof="Image" class="component-image" /></div> <p>U.S./China trade data were supposed to be updated for November on January 8, but that potential embarrassment was mercifully postponed by President Trump’s government shutdown. Yet Reuters, using Chinese data, estimates the U.S. trade deficit with China <a href="https://www.cnbc.com/2019/01/14/china-2018-full-year-december-trade-exports-imports-trade-balance.html?__source=newsletter%7Cmorningsquawk">rose 17%</a> last year. The table makes that estimate look low. <br /><br /><br /> Meanwhile, the Trump shutdown is rationalized by his fanciful untruths about <a href="http://reason.com/blog/2019/01/13/our-porous-border-and-other-myths-at-the">people and drugs</a> “flooding across the border” <em>on foot</em> between checkpoints, rather than in planes, trains, ships, trucks, and cars (not to mention overstaying visas). <br /><br /><br /> Oddly enough, Mr. Trump waited until<em> after</em> Republicans had lost the House to demand more billions for “The Wall” (as though the Executive Branch wrote the laws). <br /><br /><br /> If the end result of political feuding over a border wall turns out to be half as big a fiasco as President Trump’s trade war, how could he hope to run for reelection on the blatant failure of his two noisiest campaign issues? But it might not be too late for Trump to quietly discard his losing cards and pivot toward more promising games and issues. <strong> </strong></p> Mon, 14 Jan 2019 14:45:00 -0500 Alan Reynolds https://www.cato.org/blog/trade-war-china-slashed-us-exports-263-us-imports-rose-385 Martin Feldstein vs. Fed Chairman Powell and Irving Fisher https://www.cato.org/blog/martin-feldstein-vs-fed-chairman-powell-irving-fisher Alan Reynolds <p>In a November 27 <em>Wall Street Journal</em> article, <a href="https://www.wsj.com/articles/raise-rates-today-to-fight-a-recession-tomorrow-1543276851">“Raise Rates Today to Fight a Recession Tomorrow,”</a> Martin Feldstein reminded us he has been repeatedly cheerleading since 2013 for the Fed to raise interest rates faster and higher “to prevent the overvaluation of assets” whose prices “will collapse when long‐​term interest rates rise.” I <a href="https://www.cato.org/blog/no-aboveaverage-pe-ratio-does-not-show-stocks-are-overpriced">critiqued</a> one of Feldstein’s similar articles in 2017. <br /><br /><br /> November 27 was an odd time to be fretting about overvaluation. The day before Mr. Feldstein’s article appeared, a headline in the same newspaper – “Stocks, Bonds Face Year in Red” – observed that “stocks, bonds and commodities are staging a rare simultaneous retreat” <br /><br /><br /> Yet Feldstein urged the Fed to keep pushing short‐​term rates higher (3.4% “will not be high enough”) to somehow ease the pain of a supposedly inevitable increase in long‐​term interest rates (even if inflation stays near 2%), and to also make it easier to lower short‐​term rates in response to some future recession, a recession probably caused by the Fed raising rates too much (see graph). <br /><br /><br /> Mr. Feldstein defined “overvalued assets” in terms of historical averages. “The price‐​earnings ratio is nearly 40% above its long‐​term average,” he warned. But that is because long‐​term interest rates are more than 50% <em>below</em> their long‐​term average. The yield on 10‐​year Treasury bonds averaged 6.5% since 1970, ranging from 1.8% in 2012 to 13.9% in 1981. <a href="https://www.cato.org/blog/reynolds-model-stock-prices">The p/​e ratio almost always moves higher when long‐​term interest rates move lower.</a> <br /><br /><br /> Why are long‐​term interest rates so low? Because inflation has remained persistently low, and because the Fed can’t push short‐​term rates much above inflation for long without provoking asset liquidation and recession. The graph uses the GNP deflator (blue line) to gauge inflation because it covers the whole economy and is available over many decades. The fed funds rate clearly rises with higher inflation and falls with lower inflation, so the notion of raising the funds rate to reduce inflation is self‐​contradictory. Even before <a href="https://pubs.aeaweb.org/doi/pdf/10.1257/jep.26.4.185">Irving Fisher</a> (1896) economists such as Thornton and Mill understood that nominal interest rates rise and fall with inflation, with real interest rates being cyclical but relatively stable. <br /></p> <p> </p><div data-embed-button="image" data-entity-embed-display="view_mode:media.blog_post" data-entity-type="media" data-entity-uuid="2fa057b8-2783-4d9a-8660-d04b06a336bb" data-langcode="und" class="embedded-entity"> <img srcset="/sites/cato.org/files/styles/pubs/public/wp-content/uploads/fed_funds_rates_tracks_inflation.png?itok=TjHCO1jC 1x, /sites/cato.org/files/styles/pubs_2x/public/wp-content/uploads/fed_funds_rates_tracks_inflation.png?itok=prIRgroi 1.5x" width="700" height="282" src="/sites/cato.org/files/styles/pubs/public/wp-content/uploads/fed_funds_rates_tracks_inflation.png?itok=TjHCO1jC" alt="Fed funds rate tracks inflation" typeof="Image" class="component-image" /></div> <p>When Feldstein predicted a stock market crash “when long‐​term interest rates rise” he explicitly did <em>not</em> predict a rise in inflation. His prediction relied instead on a key conceptual ambiguity: What does a “normal” interest rate mean, and why should we presume that global bond markets gravitate toward such a historical norm? <br /><br /><br /> Former Senator <a href="https://www.wsj.com/articles/the-debt-threat-to-the-economy-1544486040">Phil Gramm and Michael Solon</a>, writing in the December 11 <em>Wall Street Journal</em>, redefine “normal” to mean arbitrarily excluding the high interest rates of 1977–82 and also the low interest rates of 2009–2018. After further subtracting inflation, this leaves them with a postwar trimmed average rate of <em>1.2%</em> for real Treasury “borrowing costs” (presumably a weighted blend of short‐​term and long‐​term rates). “This suggests,” they conclude, “that if the Fed could meet its 2% inflation target during this recovery, Treasury borrowing costs might stay close to the 3.2% range.” <br /><br /><br /> The authors nevertheless raise concerns that <em>if</em> borrowing costs rose to 4.8% – which implies 3.6% inflation – it could become difficult to roll over the accumulated Obama‐​era debt without the Fed monetizing too many Treasury bills and bonds (paying for them by crediting bank reserves that currently pay interest). They conclude, convincingly, that firm spending caps and making peace on trade would make the future economy far more predictable and secure. <br /><br /><br /> The day <em>after</em> Feldstein’s article, Fed Chairman Jerome Powell questioned the wisdom of continually raising short‐​term interest rates regardless of economic reality. His comments greatly <em>increased</em> prices of stocks and bonds until “<a href="https://www.marketwatch.com/story/here-are-the-worst-performing-stocks-on-tariff-man-tuesday-2018-12-04">Tariff Man Tuesday</a>” terrified world investors. Yet Chairman Powell’s changing conjectures about the fed funds rate being either near or far from to some unknowable “neutral rate” seem nearly as arbitrary and unsettling as Mr. Feldstein’s divinations about U.S. long‐​term rates reverting to an ancient average. <br /><br /><br /> To sum this all up: 1. Stock prices have been high relative to earnings because bond yields have been low; 2. Bond yields have been low because the fed funds rate has been low; 3. The fed funds rate has been low because inflation has been low. <br /><br /><br /> Anyone predicting a sizable increase in long‐​term interest rates must also be predicting a sizable increase in inflation. Because <a href="https://www.cato.org/blog/inflation-largely-global-phenomenon-1">inflation is largely a global phenomenon</a>, however, it would be extremely challenging to persuasively predict higher inflation (and therefore higher bond yields) while much of the world economy is struggling to expand, the dollar has been rising and commodity prices falling.</p> Tue, 11 Dec 2018 15:01:00 -0500 Alan Reynolds https://www.cato.org/blog/martin-feldstein-vs-fed-chairman-powell-irving-fisher The 1990 Bush “Tax Increase” Reduced Taxes https://www.cato.org/blog/1990-bush-tax-increase-reduced-taxes Alan Reynolds <p>The late President G.H.W. Bush famously reneged on his “no new taxes” pledge and signed the “Bush tax increase” on November 5, 1990, to take effect the following January.&nbsp;The new law was intended to raise more revenue from high‐​income households and unincorporated businesses.&nbsp;It was supposed to raise revenue partly by raising the top tax rate from 28% to 31% but more importantly by phasing‐​out deductions and personal exemptions as income on a&nbsp;joint return climbed above $150,00&nbsp;(the phase‐​outs were called <a href="http://congressionalresearch.com/RS22464/document.php?study=The+PEP+and+Pease+Provisions+of+the+Federal+Individual+Income+Tax">the PEP and Pease provisions</a>).&nbsp;<br><br /> <br> Treasury estimates expected revenues after the 1990 budget deal to be higher by a&nbsp;half‐​percent of GDP.&nbsp;What happened instead is that <a href="https://www.whitehouse.gov/omb/historical-tables/">revenues <em>fell</em> from 17.8% of GDP in 1989 to 17.3% in 1991, and then to 17% in 1992 and 1993.</a>&nbsp;Instead of rising from 17.8% of GDP to 18.3% as initial estimates assumed, revenues fell to 17%.&nbsp;In fact, revenues did not climb back to the 1989 level of 17.8% of GDP until 1995, despite much higher excise taxes since 1991. <br><br /> <br> Another way to gauge the 1990 and 1993 tax increase is to measure the revenue gains in real 2009 dollars, adjusted for inflation.&nbsp;According to Table 1.3 of the Historical Statistics in the U.S. Budget, real revenues (in 2009 dollars) soared from $1,308.8 billion in 1980 to $1,654.6 billion in 1990 (26.4%), as the top tax rate fell from 70% to 28%.&nbsp;After the Bush tax increases in 1991 and retroactive Clinton tax increases in 1993, by contrast, revenues were virtually no higher in 1993 than they had been before – $1,655.7 billion.&nbsp;GDP in 1993 was a&nbsp;bit larger than in 1990 but revenues fell as a&nbsp;percent of GDP despite higher excise taxes. <br><br /> <br> A&nbsp;recession began in October 1990, just as the intended tax increase was being enacted.&nbsp;To blame the weak revenues of 1991–93 entirely on that brief recession begs the obvious question: To what extent was a&nbsp;recession that began with a&nbsp;tax increase caused or at least worsened by that tax increase?&nbsp;<br><br /> <br> Some describe the Bush tax increase of 1990 act of great political courage and <a href="https://www.cato.org/publications/commentary/budget-blunders-1990-are-no-blueprint-2011">bipartisan cooperation</a> which supposedly helped shrink the budget deficit “<a href="http://www.washingtonpost.com/read-their-lips-for-the-origins-of-todays-deficit-fight-look-to-1990/2011/07/25/gIQAC0jHdI_story.html">by $492 billion</a> … over just five years.”&nbsp;But that figure too was (1) just an <em>estimate</em>, (2) only 30% of it was ostensibly to come from higher taxes, and (3) most of the hoped‐​for added revenue was not from higher income tax on couples earning over $150,000 but from higher <em>excise</em> taxes on gasoline, alcohol, tobacco, telephones, etc.&nbsp;The gas tax went up a&nbsp;nickel; the beer tax was doubled.&nbsp;Nearly 10% of the revenue windfall was expected from a&nbsp;new luxury tax on cars, yachts, airplanes, furs, and jewelry which devastated those businesses (contributing to the recession) before being repealed in less than a&nbsp;year. <br><br /> <br> Journalists who look back at what happened to tax revenues after tax rates were raised or lowered, such as <em>Washington Post</em> fact checker Glenn Kessler, commonly rely on an updated version of a&nbsp;1998 <a href="https://www.treasury.gov/resource-center/tax-policy/tax-analysis/Documents/WP-81.pdf">working paper</a> by Treasury economist Jerry Tempalski.&nbsp;However, Tempalski only presented <em>estimated </em>effects on revenues, not actual effects.&nbsp;“Treasury estimates a&nbsp;bill when it is enacted… and sometimes reestimates a&nbsp;bill for several subsequent January budgets,” Tempalski explained, but some of “the first post‐​enactment estimates proved not very accurate.”&nbsp;Tax changes were often phased‐​in or phased‐​out, yet “the estimates… include no adjustment to capture the long‐​run, fully‐​phased‐​in effect of the tax bills.”&nbsp;Early estimates looked ahead only two years, later ones covered four. <br><br /> <br> These antiquated revenue estimates tell us <em>nothing</em> about what actually happened after tax laws were changed.&nbsp;They only tell us what notoriously erroneous revenue estimators<em> expected</em>.&nbsp;Yet the Tempalski estimates have been repeatedly cited as evidence that lower tax rates never even come close to “paying for themselves”&nbsp;by such leading journalists as <em>Washington Post</em> fact‐​checker <a href="https://www.washingtonpost.com/news/fact-checker/wp/2017/12/07/history-lesson-do-big-tax-cuts-pay-for-themselves/">Glenn Kessler</a> and Lori Robertson of <span><a href="http://www.factcheck.org/2017/05/trumps-tv-campaign-ad/">FactCheck​.org</a></span>, and even by the chief economist for <em>Tax Analysts</em>,&nbsp;<span><a href="http://www.taxhistory.org/www/features.nsf/Articles/BA3E17E50FBB8E1085257F7200487369?OpenDocument">Martin A. Sullivan</a></span>.&nbsp;<br><br /> <br> In the same vein, estimated revenue effects of the 1990 “tax increase” are still being cited as if they are facts rather than discredited old estimates.&nbsp;When discussing tax increases (or tax cuts), journalists and economists must take care to distinguish between intended effects on revenue and actual effects.&nbsp;Fact checkers can’t fact check the old estimates because they’re not facts. Estimates are just estimates.&nbsp;<br><br /> <br>&nbsp;</p> Mon, 10 Dec 2018 17:00:00 -0500 Alan Reynolds https://www.cato.org/blog/1990-bush-tax-increase-reduced-taxes Everything You Need to Know About Net or Gross Saving Rates https://www.cato.org/blog/everything-you-need-know-about-net-or-gross-saving-rates Alan Reynolds <p>Writing in <em>Project Syndicate</em>, <a href="https://www.project-syndicate.org/commentary/america-low-saving-rate-weak-fundamentals-by-stephen-s-roach-2018-02">Stephen Roach</a>, former chief economist for Morgan Stanley, declares the U.S. economy’s foundations fundamentally unsound:&#13;<br /> &#13;</p> <blockquote><p>“America’s net national savings rate – the sum of saving by businesses, households and the government sector – stood at just 2.1% of [gross] national income in the third quarter of 2017.  That is only one third of the 6.3% of the average that prevailed in the final three decades of the twentieth century. . . America. . . is saving next to nothing.  Alas, the story doesn’t end there. To finance consumption and growth, the U.S. borrows surplus saving from abroad to compensate for the domestic shortfall.  All that borrowing implies a large balance of payments deficit with the rest of the world which spawns an equally large trade deficit.”   </p> </blockquote> <p>This alleged “savings crisis” has popped up periodically since the 1980s when there’s a Republican in White House, such as <a href="https://townhall.com/columnists/alanreynolds/2006/02/16/a-negative-savings-rate-n766399">2006</a> when I wrote about it.&#13;<br /> &#13;<br /> Roach believes it “important to think about saving in ‘net’ terms, which excludes the depreciation of obsolete or worn-out capacity in order to assess how much the economy is putting aside to fund the expansion of productive capacity.”  &#13;<br /> &#13;<br /> Dividing <em>net </em>savings by <em>gross</em> national income subtracts a semi-arbitrary <a href="https://www.oecd-ilibrary.org/docserver/9789264068476-8-en.pdf?expires=1539366078&amp;id=id&amp;accname=guest&amp;checksum=604F47F5F3F44C13A3E3FFAF1110790C">estimate of depreciation</a> from the numerator but not from the denominator. Dividing net by gross shrinks the resulting savings/income ratio. For Roach to suggest that more net savings could in any sense pay for more “consumption and growth” is misleading at best.  Don’t expect a discount on a new car because you hope to pay with net savings, after subtracting estimated depreciation.&#13;<br /> &#13;<br /> The amount of money needed for new plants and equipment is <em>gross</em>, not net. And it is the dollar gap between <em>gross</em> investment and <em>gross</em> saving that needs to be financed by attracting foreign investment.  Mr. Roach calls foreign investment in U.S. equity (stocks) or real property “borrowing,” but that’s not how we describe the same investments if made by a U.S. resident.&#13;<br /> &#13;</p> <p> </p><div data-embed-button="image" data-entity-embed-display="view_mode:media.blog_post" data-entity-type="media" data-entity-uuid="6de2215d-50c5-4800-9e2d-f7a74916d08c" data-langcode="und" class="embedded-entity"> <img srcset="/sites/cato.org/files/styles/pubs/public/wp-content/uploads/gross_and_net_saving.png?itok=x33fLXY3 1x, /sites/cato.org/files/styles/pubs_2x/public/wp-content/uploads/gross_and_net_saving.png?itok=WogOeqwJ 1.5x" width="700" height="282" src="/sites/cato.org/files/styles/pubs/public/wp-content/uploads/gross_and_net_saving.png?itok=x33fLXY3" alt="Gross and Net Saving and Investment Rates" typeof="Image" class="component-image" /></div> <p>The blue line in the first graph shows gross savings as a percentage of gross national income (GNI). The red line shows gross private domestic investment as a percentage of GDP, which is quite <a href="https://www.thebalance.com/gross-national-income-4020738">similar to</a> GNI (GDP excludes income of foreigners spent in the U.S. and remitted income of Americans living abroad).  &#13;<br /> &#13;<br /> The dotted green line is <em>net savings divided by gross income</em> – the extraneous ratio that worries Mr. Roach.  The green line appears to fall much more than the blue line simply <em>because estimated depreciation</em> rose from 12.3% of national income in 1969 to 15.9% in 2017 -- as the capital stock shifted from structures to rapidly-depreciating high-tech. Because rising depreciation estimates are subtracted from saving yet added to income, the downward tilt of the green line is exaggerated by the oddity of dividing net savings by gross income. &#13;<br /> &#13;<br /> A declining net savings rate since the mid-1960s did not thwart fixed investment, though recessions always do.  Real <a href="https://fred.stlouisfed.org/series/W173RX1A020NBEA">net domestic fixed investment</a> nearly tripled from $379.9 billion in 1983 (in 2009 dollars) to over $1 trillion by 2005-2006, and has again been heading up since the 2008-09 recession.&#13;<br /> &#13;<br /> In the second graph, the ups and downs in the net savings rate (green line) do <em>not</em> track or explain the movements in net exports (exports minus imports). The U.S. runs a capital surplus and current account deficit when the economy is growing briskly.  Trade deficits shrink just before, during and right after recessions.  &#13;<br /> &#13;</p> <p> </p><div data-embed-button="image" data-entity-embed-display="view_mode:media.blog_post" data-entity-type="media" data-entity-uuid="0ee583a4-f35d-40dc-a59b-bbcfa0f36562" data-langcode="und" class="embedded-entity"> <img srcset="/sites/cato.org/files/styles/pubs/public/wp-content/uploads/net_saving_and_net_exports.png?itok=PTCMnWR6 1x, /sites/cato.org/files/styles/pubs_2x/public/wp-content/uploads/net_saving_and_net_exports.png?itok=HQnnCJ_6 1.5x" width="700" height="282" src="/sites/cato.org/files/styles/pubs/public/wp-content/uploads/net_saving_and_net_exports.png?itok=PTCMnWR6" alt="Net Saving does not explain net exports" typeof="Image" class="component-image" /></div> <p>When previous “net savings” anxieties appeared, they were used as a rationale for raising taxes.  In accounting, unlike economics, it sounds simple to raise national savings by reducing the government’s negative savings (budget deficits).  If we carelessly assume that higher taxes have no bad effects on the economy or private savings, budget deficits would then fall with higher taxes and national saving (the sum of public and private saving) would rise.  In this simplistic bookkeeping, <a href="https://www.cato.org/publications/commentary/would-more-taxes-equal-more-savings">more taxes are <em>defined</em> as being identical to more savings</a>.     &#13;<br /> &#13;<br /> There are big problems with assuming a $100 million tax-financed cut in the deficit equals a $100 million increase in national savings.  One is that politicians’ favorite targets for new taxes are savers and savings – retained corporate profits, dividends, interest, capital gains and high incomes in general.  If successful firms and families pay more in taxes, they’ll have less to save.</p> <p>But the biggest problem with assuming smaller budget deficits add to national savings is that it is rarely true.  Smaller deficits (particularly surpluses) are frequently <em>offset by lower private savings. </em> &#13;<br /> &#13;</p> <p> </p><div data-embed-button="image" data-entity-embed-display="view_mode:media.blog_post" data-entity-type="media" data-entity-uuid="8a24f181-465f-4fd7-b297-15ecaa6fc623" data-langcode="und" class="embedded-entity"> <img srcset="/sites/cato.org/files/styles/pubs/public/wp-content/uploads/govt_and_private_saving.png?itok=Cn51pIHv 1x, /sites/cato.org/files/styles/pubs_2x/public/wp-content/uploads/govt_and_private_saving.png?itok=Pcu_1AEj 1.5x" width="700" height="282" src="/sites/cato.org/files/styles/pubs/public/wp-content/uploads/govt_and_private_saving.png?itok=Cn51pIHv" alt="Govt and Private Saving are inversely related" typeof="Image" class="component-image" /></div> <p>The last graph compares recent changes in government savings (red line) with changes in private saving (blue) in billions of 2009 dollars.  When the red line falls sharply (2000-2002 and 2006-2009), that indicates a rising budget deficit.  Each time the red line fell, however, the blue line rose nearly as much -- leaving total national saving little changed. Conversely, when the deficit was greatly reduced from 2011 to 2014, private savings was greatly reduced too, with little net effect on total public and private saving.&#13;<br /> &#13;<br /> This <em>inverse relationship between public and private savings</em> is not unique to the United States, nor to the last 30 years.  In “<a href="https://www.researchgate.net/publication/328248064_Toward_a_Reconstruction_of_Macroeconomics">A Reconstruction of Macroeconomics</a>” (1992), I displayed graphs for the U.K., Sweden, Norway, and Japan to show the household savings rates fell dramatically (sometimes into negative territory) when these countries moved from budget deficits to surpluses for a few years in the 1978-92 period.  This is consistent with Ricardian Equivalence (taxpayers regard more national debt as their own, so they save to pay more future taxes), but perhaps also consistent with simpler cyclical explanations (people save more in recessions to rebuild lost wealth, and do the opposite in boom times).&#13;<br /> &#13;<br /> We do not live in a closed economy, where new investment might have to be financed from flows of new domestic saving rather than from stocks of appreciated assets.  Global capital finds investment opportunities around the world, and foreign firms and investors find many of the best opportunities in the USA. More capital is better than less, and a dollar is a dollar.&#13;<br /> &#13;<br /> Since the purpose of saving is to add to wealth, the best measure of saving is the addition to wealth.  In the first quarter of 2018, household net worth was a record <a href="https://fred.stlouisfed.org/series/HNONWPDPI">685% of disposable income</a> according to the Federal Reserve – up from 548% six years earlier.   When the value of accumulated wealth rises that much, annual additions to the stockpile (saving) become far less urgent or significant.&#13;<br /> &#13;<br /> Dire warnings of a looming savings crisis have been reported many, many times before, always in ways that are agitated, confused, mistaken and irrelevant.&#13;<br /> &#13;<br /> The net savings rate <em>does not explain or predict</em> investment, trade deficits, interest rates, or anything else worthy of concern.  </p> <p></p> Fri, 12 Oct 2018 16:15:39 -0400 Alan Reynolds https://www.cato.org/blog/everything-you-need-know-about-net-or-gross-saving-rates Phillip Cagan’s 1984 Reflections on a Gold‐​Convertible Currency https://www.cato.org/blog/phillip-cagans-1984-reflections-gold-convertible-currency Alan Reynolds <p>Milton Friedman published <em>Studies in the Quantity Theory of Money</em> in 1956, a&nbsp;seminal anthology of papers from five economists, leading with “The Monetary Dynamics of Hyperinflation” –the recent PhD dissertation of <a href="https://www.legacy.com/obituaries/nytimes/obituary.aspx?n=phillip-david-cagan&amp;pid=158606680">Phillip Cagan (1927–2012)</a>, which became an instant classic.&nbsp;So, Cagan was thought to be a “Monetarist” a&nbsp;dozen years before that phrase was even coined by my UCLA teacher, <a href="https://www.nytimes.com/1989/05/10/obituaries/prof-karl-brunner-is-dead-at-73-economist-and-early-monetarist.html">Karl Brunner</a>. <br><br /> <br> Soon after August 15, 1971 when President Nixon opted to renege on the Bretton Woods pledge to convert foreign official dollar reserves into gold on demand (rather than simply devalue the dollar/​gold ratio), we entered a&nbsp;long and painful period of extremely high worldwide inflation. <br><br /> <br> Even as measured by the gentler “core” CPI (less food and energy), U.S. inflation averaged 9% from 1974 through 1981, reaching 12.2% in 1980.&nbsp;When President Reagan took office in January 1981, the Fed had pushed the fed funds rate above 19% — up from 9% six months earlier. <br><br /> <br> We can’t always fix such big problems by thinking small, so the prolonged stagflation of 1968 to 1982 led several economists to propose fundamental, even radical monetary reform, preferably on a&nbsp;global scale.&nbsp;Such ambitious reform plans commonly involved making dollars convertible in tangible assets, such as gold or a&nbsp;group of commodities. <br><br /> <br> I&nbsp;was invited to testify before the 1982 Gold Commission, perhaps because of a&nbsp;decade of published and personal connections to <a href="https://www.researchgate.net/publication/327871323_The_Purge_of_Chicago_Economists_1971">Milton Friedman</a> and Karl Brunner.&nbsp;I&nbsp;had echoed conventional objections to a&nbsp;gold standard before, and was probably expected to do so again.&nbsp;But that would have been too facile. I&nbsp;instead took the occasion to review periods of long and impressive <a href="https://www.researchgate.net/publication/325906027_Alan_Reynolds_Gold_and_Economic_Boom_1792-1926">prosperity when currencies were linked (or re‐​linked) to gold</a>, invariably followed by instability and crises when they weren’t. <br><br /> <br> Other economists attempted to replicate some key advantages of being able to convert dollars to gold and vice‐​versa at a&nbsp;predictable guaranteed rate, yet do so without using gold. In 1983, Greenfield and Yeager proposed the “<a href="http://www.econ.mq.edu.au/Econ_docs/econ841_student_presentations/Greenfield_Yeager-Ak.pdf">Black‐​Fama‐​Hall</a>” system (melding similar analyses of Fischer Black, Gene Fama and Robert Hall) in which the unit of account would be defined by convertibility into a&nbsp;basket of commodities, rather than just gold and/​or silver. <br><br /> <br> Chicago School monetarists were generally quite critical of any of these ideas, except, as we later learned, Phil Cagan.&nbsp;<br><br /> <br> After Brunner moved to the University of Rochester and his star pupil Alan Meltzer to Carnegie‐​Melon, they held legendary Carnegie‐​Rochester conferences which I&nbsp;attended.&nbsp;<br><br /> <br> After the conference on April 15–16, 1984 I&nbsp;kept the paper by Phillip Cagan of Columbia University, “The Report of the Gold Commission (1982)” later&nbsp;reprinted in <a href="https://www.sciencedirect.com/journal/carnegie-rochester-conference-series-on-public-policy/vol/20/suppl/C">Carnegie‐​Rochester Conference Series on Public Policy 20 (1984)</a> 247–268.&nbsp;In it, Cagan flirted with hopeful thoughts about hypothetical hybrid standards, such as Black‐​Fama‐​Hall, but not before he said this about gold: <br> </p> <blockquote><p>The appeal of the gold standard… is that it solves to problems.&nbsp;First, if control over the quantity of transactions balances becomes more difficult and discretionary policy is unable to achieve reasonable stability of the price level, convertibility can provide the needed control of the relevant monetary quantities for stabilizing the price level.&nbsp;Second, even if monetary policy continues to be capable of achieving stability of the price level, discretionary control may still fail to do so, as in the past, because of inadequate determination or inability to pursue polices that are successful (for political or other reasons). Convertibility provides a&nbsp;mechanism for making a&nbsp;commitment to price stability. <br><br /> <br> I&nbsp;see no escape from the conclusion, inherent in the position of the advocates of gold, that only a&nbsp;convertible monetary system is sufficiently free of discretion to guarantee that it will achieve price stability… If one is looking for some kind of long‐​last commitment of a&nbsp;constitutional nature, a&nbsp;convertible monetary system seems to be the only practical possibility.</p> </blockquote> Tue, 25 Sep 2018 16:12:00 -0400 Alan Reynolds https://www.cato.org/blog/phillip-cagans-1984-reflections-gold-convertible-currency A Contemporary Economist’s Account of the “Crowning Folly of Tariff of 1930” https://www.cato.org/blog/contemporary-economists-account-crowning-folly-tariff-1930 Alan Reynolds <p>“[T]here came another folly of government intervention in 1930 transcending all the rest in significance. In a&nbsp;world staggering under a&nbsp;load of international debt which could be carried only if countries under pressure could produce goods and export them to their creditors, we, the great creditor nation of the world, with tariffs already far too high, raised our tariffs again. The Hawley‐​Smoot Tariff Act of June 1930 was the crowning folly of the who period from 1920 to 1933…. <br><br /> <br> Protectionism ran wild all over the world.&nbsp;Markets were cut off.&nbsp;Trade lines were narrowed.&nbsp;Unemployment in the export industries all over the world grew with great rapidity, and the prices of export commodities, notably farm commodities in the United States, dropped with ominous rapidity…. <br><br /> <br> The dangers of this measure were so well understood in financial circles that, up to the very last, the New York financial district retained hope the President Hoover would veto the tariff bill.&nbsp;But late on Sunday, June 15, it was announced that he would sign the bill. This was headline news Monday morning. The stock market broke twelve points in the New York Time averages that day and the industrials broke nearly twenty points. The market, not the President, was right.” <br><br /> <br><em>– Dr. Benjamin M. Anderson [chief economist at Chase National Bank 1920–39], Economics and the Public Welfare: A&nbsp;Financial and Economic History of the United States, 1914–1946 (</em><em>Indianapolis, Liberty Press, 1979, pp. 229–230)</em></p> Thu, 20 Sep 2018 14:28:00 -0400 Alan Reynolds https://www.cato.org/blog/contemporary-economists-account-crowning-folly-tariff-1930 About 1,100 Puerto Rican Deaths from Maria — NOT 2,795 or 4,645 https://www.cato.org/blog/about-1100-puerto-rican-deaths-maria-not-2795-or-4645 Alan Reynolds <p>The estimated number of above-average "excess deaths" in Puerto Rico <em>attributed </em>to Hurricane Maria (Sept 20, 2017) is a difficult figure to estimate objectively.  Puerto Rico’s official figure of 64 deaths by December 9, 2017 (which the President remembered) counted only those deaths <em>directly </em>attributed to the storm and confirmed by medical examiners.  Most of the <em>direct</em> deaths from Katrina were from <em>drowning </em>– which is much easier to attribute to the storm than many other causes of death. Studies of Puerto Rican deaths from Maria aspire to account for a wide range of <em>indirect</em> effects that are presumed (not proven) to be consequences of the storm such as suicides and heart attacks, infectious diseases, and damage to electricity and therefore to dialysis and respirator equipment.&#13;<br /> &#13;<br /> Among at least eight major studies of direct and indirect effects on mortality attributed to Maria, two outliers stand out as being 3-5 times larger than the others, which all cluster around 1000. The first big number was from Harvard. On September 13, <em>Time</em> said, “<a href="https://www.nejm.org/doi/full/10.1056/NEJMsa1803972">Harvard’s report</a>, which was based on systematic household surveys throughout Puerto Rico, reached an estimate of <a href="http://time.com/5395369/death-tolls-hurricane/">4,645 storm-related deaths</a> between September and December 2017, many as a result of ‘delayed or interrupted health care.’”  Nonsense. The Harvard study extrapolated from only 15 deaths reported in a survey of 3299 households to estimate that “<a href="https://github.com/c2-d2/pr_mort_official/blob/master/misc/faq.md">between 793 and 8498</a> people died . . . up to the end of 2017.” By adding 793 and 8498 and dividing the result by 2, <em>Time</em> and others came up with a totally meaningless “average” which were widely reported with predictable sensationalism: “The hurricane that struck Puerto Rico in September was responsible for <em>more deaths than the Sept. 11 attacks and Hurricane Katrina combined</em>,” exclaimed <em><a href="https://www.thedailybeast.com/hurricane-maria-killed-more-people-than-katrina-and-911-combined-harvard-study-finds">The Daily Beast</a>."</em> In reality, these “estimates of death from people who were interviewed” are little better than an opinion poll, and finding 15 deaths out of a sample of 3299 can’t plausibly be multiplied into 4645 for the whole island.&#13;<br /> &#13;<br /> The latest sensational <a href="https://prstudy.publichealth.gwu.edu/sites/prstudy.publichealth.gwu.edu/files/reports/Acertainment%20of%20the%20Estimated%20Excess%20Mortality%20from%20Hurricane%20Maria%20in%20Puerto%20Rico.pdf">estimate of 2,975</a> excess deaths <em>over six months</em> is from an August 28 report from the Milken Institute School of Public Health at George Washington University  (GWU) commissioned by the Government of Puerto Rico. The study mentions two “scenarios” (census and displacement) yet only publicized the one with the biggest number: “Total excess mortality post-hurricane <em>using the migration displacement scenario</em> is estimated to be 2,975 (2,658-3,290) for the total study period of September 2017 through February 2018.” &#13;<br /> &#13;<br /> The 2,975 estimate only applies to the “displacement scenario.”  That is, the study “estimates cumulative excess net migration from Puerto Rico in the months from September 2017 through February 2018 and subtracts this from the census population estimates in these months.”  The population fell by about 8%, mainly due to migration rather than death, so the fact that there were more deaths than average after the hurricane means the <em>death rate</em> (deaths per thousand) rose more than the unadjusted statistics would suggest because the population is smaller.  But this issue is the <em>number </em>of deaths, not the death rate, and displacement (migration) did not make that number any higher than half a dozen other studies found (about 1000) much less three times higher.  &#13;<br /> &#13;</p> <p>Trying to explain the high “displacement scenario” estimate, <a href="https://www.vox.com/2018/9/13/17855414/trump-hurricane-maria-puerto-rico-death-toll">Eliza Barclay at VOX</a> writes, “The ideal way to calculate the death toll from a hurricane, disaster researchers say, generally, is to count all the deaths in the time since the event, and then compare that number to the average number of deaths in the same time period from previous years. Subtract the average number from the current number and that’s the death toll.”  Unfortunately, the GWU “displacement scenario” estimate does <em>not </em>do that.  What it does instead is to compare what actually happened with hypothetical simulations of <em>what might have happened</em> without the storm.  Those projections come from “a series of generalized linear models (GLMs). . . accounting for trends in population . . . in terms of age, sex, seasonality and residence by municipal level of socioeconomic development.”  And the estimates “also considered Puerto Rico’s consistently high emigration during the prior decade and dramatic population displacement after the hurricane.” Such complexity adds uncertainty.&#13;<br /> &#13;<br /> The August 28 GWU report claimed to be “the first to use actual death certificates and other mortality data in order to estimate a more precise mortality count due to Hurricane Maria.” On the contrary, an earlier Aug 2 study in the <em><a href="https://jamanetwork.com/journals/jama/fullarticle/2696479">Journal of the American Medical Association</a></em>, by professors from Penn State and the University of Texas, had already used death certificate data to (as Ms. Barclay recommended) “count all the deaths in the time since the event and then compare that number to the average number of deaths in the same period from previous years.” Yet that ideal method found the number of excess deaths was <strong>1,139</strong> from September through December of last year.  As the <strong>Table </strong>from that paper shows, "excess deaths" means the number above the 2010-2016 average.  Since 90% of these atypical deaths happened in September and October, it appears quite plausible to attribute most of them to Hurricane Maria.  That is consistent with five previous credible estimates of  Puerto Rican deaths due to Maria, which, as a <em><a href="https://www.washingtonpost.com/amphtml/news/fact-checker/wp/2018/06/02/did-4645-people-die-in-hurricane-maria-nope/?noredirect=on">Washington Post fact checker</a></em> noted in June were “all . . . roughly around 1,000 deaths.” &#13;<br /> &#13;</p> <p> </p><div data-embed-button="image" data-entity-embed-display="view_mode:media.blog_post" data-entity-type="media" data-entity-uuid="185cb7dc-e99c-46ab-bbdc-04880cbce0af" data-langcode="und" class="embedded-entity"> <img srcset="/sites/cato.org/files/styles/pubs/public/wp-content/uploads/puerto_rican_excess_deaths_attributed_to_maria_9-20-17.png?itok=gHmYD37u 1x, /sites/cato.org/files/styles/pubs_2x/public/wp-content/uploads/puerto_rican_excess_deaths_attributed_to_maria_9-20-17.png?itok=h9PNyTNj 1.5x" width="700" height="272" src="/sites/cato.org/files/styles/pubs/public/wp-content/uploads/puerto_rican_excess_deaths_attributed_to_maria_9-20-17.png?itok=gHmYD37u" alt="Puerto Rico deaths by month" typeof="Image" class="component-image" /></div> <p>The rationale for the study’s novel choice of a six-month time frame was to find out if things are getting better.  But <em>the more months pass after the disaster, the more arbitrary it appears to attribute deaths to the disaster</em>, since an estimated 77% of those who died were seniors.&#13;<br /> &#13;<br /> In marked contrast to the JAMA paper (where 90% of the deaths happened near the time of the hurricane) <em>only <strong>42.7%</strong> of the GWU study’s simulated 2,975 deaths occurred in September and October of 2017</em>.  Another 27.8% occurred in November and December, and 29.5% occurred in January and February of 2018.  That timing seems counterintuitive and implausible, suggesting the September storm <em>has lately been becoming more fatal</em> rather than less.  &#13;<br /> &#13;<br /> To attribute deaths over a six-month period to the hurricane <em>per se</em> is inherently difficult and subjective. What the Milken report calls “a failing health system “ and “multiple cascading failures in critical infrastructure” (telecom and power) may largely reflect negligence by Commonwealth or city governments, notably the island’s <a href="https://www.economist.com/united-states/2017/10/19/the-story-of-puerto-ricos-power-grid-is-the-story-of-puerto-rico">mismanaged government-owned electric utility</a>, <a href="https://www.wired.com/story/why-cant-we-fix-puerto-ricos-power-grid/">Prepa</a>&#13;<br /> &#13;<br /> To attribute the estimated 6-month deaths to FEMA, as some have, is even less believable. By August 8, FEMA reported it had awarded “more than $3 billion in Public Assistance funds . . . to the government of Puerto Rico and municipalities” for Hurricane María-related costs. “This is a massive job and it has taken <a href="https://www.fema.gov/disaster/4339">a massive effort by everybody: the Government of Puerto and the municipalities, federal agencies, voluntary and faith-based organizations and the private sector</a>,” said Federal Coordinating Officer Michael Byrne. &#13;<br /> &#13;<br /> The questionable 2,975 GWU estimate of hurricane-related deaths, like the unbelievable 4,645 Harvard estimate before it, is being widely misused as a criticism of emergency relief efforts by FEMA and numerous private charities, rather than to either the sheer magnitude of destruction to an isolated island, or to any shortcomings of local Puerto Rican efforts. &#13;<br /> &#13;<br /> In short, an actual 4-month count closer to 1,100 for above-average Puerto Rican deaths in the wake of Maria appears much more transparent and statistically relevant than the 6-month statistical simulation of 2,975 now being used.</p> <p></p> Mon, 17 Sep 2018 16:35:00 -0400 Alan Reynolds https://www.cato.org/blog/about-1100-puerto-rican-deaths-maria-not-2795-or-4645