Polling Results Should Indeed be Worrying for the Democrats

The Washington Post and ABC News jointly sponsor a regular poll of American voters on a range of political issues, including whom they would vote for in upcoming Congressional elections.  Their most recent poll, released on November 14, showed that registered voters nationally would favor a Republican over a Democrat in their local congressional district race, by a margin of 10 percentage points.  The pollsters noted that this margin is the largest margin Republicans have enjoyed in all the polls they have conducted asking this question, in a series that goes back decades.

Numerous pundits soon followed with commentary on this, with articles such as “Why the generic ballot is so ominous for Democrats”, and “Democrats face a 2022 superstorm”, and “Democrats Shouldn’t Panic.  They Should Go Into Shock”.  Viewed in a longer-term context, and not simply in terms of the current polling margin in favor of the Republicans, should there indeed be such a concern if you are a Democrat?  As we will discuss in this post, the answer is yes.

The chart at the top of this post shows the responses that have been given to this question in the Washington Post / ABC News polls over the last two decades.  The figures for the past polling results are provided online here.  The polls asking this specific question have been undertaken in the periods leading up to each midterm election, normally starting about one year before the midterm election date and then repeated every few months until the November election.  There is then a gap – normally of three years – before they start the cycle for the next midterm election.

The specific question asked is:  “If the election for the U.S. House of Representatives were being held today, would you vote for (the Democratic candidate) or (the Republican candidate) in your congressional district? Would you lean toward the (Democratic candidate) or toward the (Republican candidate)?”  They also allow for possible responses of some other candidate, or neither, or would not vote, or no opinion, which I have combined into a single “Other / No Opinion” category in the chart.

Some points to note:

a)  The swing in preferences seen in the current midterm cycle is not unusual.  One saw a similar swing in favor of the Republicans in the period leading up to the 2010 election, and in favor of the Democrats in the periods leading up to the 2006 and 2018 elections.

b)  The initial polling results (one year before the midterm elections) were generally a pretty good predictor of the final outcome.  The polling results generally fluctuated within a relatively more narrow range in the multiple polls in the year leading up to the midterm itself.  The year 2014 was an exception, where the early indication was that Republican support had declined, but with it then recovering prior to the election.

c)  The “Other / No Opinion” category generally fluctuated within a range of roughly 5 percentage points, and in an understandable pattern.  Uncertainty on whom (if any) to vote for generally rose in the three years since the prior midterm, and then fell as one got closer to the election date.

d)  The substantial swings (from three years earlier) found in the initial polls in several of the cases (for the 2006, 2010, and 2018 midterms), coupled with the relatively smaller fluctuations then found in the year leading up to the election, were thus highly predictive.  The electoral results were net gains for the Democrats of 31 seats in 2006 and 41 seats in 2018, and a net gain for the Republicans of 64 seats in 2010.

Based on this pattern, Democrats can expect to lose a substantial number of seats in the 2022 midterms.  The closest parallel is to 2010, for several reasons.  Both 2010 and 2022 were (or will be) the first midterms of a newly elected Democratic president (Obama and Biden).  In the period leading up to 2010, Democrats saw their polling result fall (in the initial poll one year before the 2010 midterm, from three years before) by 9 percentage points, while that of the Republicans rose by 7 percentage points.   In the new poll one year before the 2022 midterm, the Democrats have seen the same fall of 9 percentage points in their polling result, while the preference for the Republicans rose again by 7 percentage points.

The Democrats had a net loss of 64 seats in the House of Representatives in 2010.  These early polling results suggest they could expect a similar result in 2022.

There are, of course, a number of provisoes:

a)  As they always say when investing in the stock market:  “Past performance is no guarantee of future returns.”  While there may have been this pattern in several of the recent midterm election cycles, there is no guarantee that pattern will continue.

b)  Furthermore, that pattern is based on a very small number of cases.  There have only been five midterm elections in the last 20 years, and substantial swings in voter preferences in only three of them.  And only one case (2010) with a Democratic president in his first term in office.  it is dangerous to generalize from figures for such a small number of election cycles.  But it would not be helpful to go back further in time as the political environment has changed, with more of a left-right polarization now than one had before.

c)  There will also be an impact from the substantial gerrymandering of congressional district lines now being redrawn to reflect the new census numbers.  The Supreme Court in 2019 ruled that it would not intervene in this practice when it considered two cases brought before it (of North Carolina and Maryland).  Each had egregiously gerrymandered district lines, and there were open and public statements in each from the politicians who had drawn those district lines that they had done so for the greatest possible partisan advantage that they could manage.

The Supreme Court nevertheless ruled that gerrymandering was not reviewable by any federal court, on a 5-4 vote where the five in the majority were the five Republican appointees to the court.  As a result, a number of states are now redrawing district lines to maximize partisan advantage.  And the ruling heavily favored Republicans, given their control of a larger number of states where politicians are allowed to draw the district lines that they will then be running in.  Republicans at the state level have full control of redrawing the lines for 184 congressional districts this year, while Democrats have full control in states where lines for just 75 districts will be redrawn.  In part this is because several of the larger Democratically controlled states (including California, Washington, and Colorado) now use independent, nonpartisan, commissions to draw the district lines.

The consequences of this gerrymandering will be on top of what one should expect from shifts in voter preferences.  And the margin in seats in the House that gives Democrats control of the chamber is only five following the 2020 election.  Already by this point, with only a small number of states having completed the redistricting process, a mid-November analysis at the New York Times concluded that Republicans will pick up a net of five congressional seats, and thus gain control of the House, even if the voting numbers in each locale were the same as what they were in 2020.  It would simply be a consequence of the newly drawn lines.

Coupling the gerrymandering with the shift in the preferences for Democrats vs. Republicans found in the polling results, there is every reason to expect Democrats will lose control of the House.  This in itself is not surprising.  Since the presidency of John Quincy Adams in 1826, the party of the incumbent president has lost seats in the House in the midterm after their first term election in all cases other than the sole exceptions of Franklin Roosevelt in 1934 and George W. Bush in 2002.  With the Democrats holding a majority in the House of just five seats, most have expected that Republicans will gain control after the 2022 midterms.

But the polling results, on top of the gerrymandering as well as the historical norm, suggest the Democratic losses are likely to be large.  Furthermore, the losses are most likely to be in the more competitive districts, which are more likely to be currently represented by the more moderate Democrats.  Thus the remaining Democrats in Congress following the 2022 election are likely to be the ones further to the left.  That is, the center of the Democrats is likely then to be shifted to the left, just as the center of the Republicans has shifted in recent years to the right.

Polarization, already large, would grow.

There Have Been Real Consequences From Not Taking Covid Seriously

A.  Introduction

Earlier posts on this blog have documented that vaccination rates against Covid-19 have been systematically lower in accordance with the share of a state’s vote for Trump in the 2020 election, and that mask-wearing to protect the individuals and those around them have also been systematically lower.  The higher the share voting for Trump in a state, the lower the share vaccinated and the lower the share wearing masks.

Those choices have had consequences.  As shown in the charts above, it should not then be surprising that states with a higher share of their vote for Trump have seen, on average, a higher number of cases of Covid-19 (per 10,000 of population) as well as a higher number of deaths.  The relationship is statistically a very strong one.  While many factors affect the likelihood of being infected with Covid-19 and of dying from it (including factors such as urban density, extent of travel, health status of the population, adequacy of the health care system, and more), political identification by itself appears to be a strong and independent factor.

In what is literally a life and death issue, one would have thought that rational self-interest would have dominated.  It has not.  Following a review of the data, this post will discuss some possible reasons why.

B.  The Relationship Between the Incidence of Covid-19 Cases and Deaths and the Share Voting for Trump

The figures at the top of this post plot the relationship between the number of cases of Covid-19 in a state (per 10,000 of population), or the number of deaths (also per 10,000), and the share in the state who voted for Trump in 2020.  The Covid data come from the CDC.  It was downloaded October 26, but since case and death counts from the states may not be fully reported to the CDC for up to a week to ten days, I used October 15 as the end date for the analysis here.  “Cases” are confirmed cases, and “deaths” are deaths as a consequence of Covid-19, both as defined in the CDC guidance for how these should be recorded.

For the start date I used July 1, 2020.  This came at the end of the first wave of Covid-19 cases and deaths.  Cases and deaths in this first wave were excluded for two reasons.  One is that the first wave arrived suddenly in mid-March and with an intensity that surprised many.  The nation was unprepared, with little done to prepare for the disease that was spreading around the world as Trump was claiming it was all under control, that it was “going to disappear”, and that it would soon “go away”.  Also, the CDC had bungled the initial testing (where testing was more readily accessible in parts of Africa than in the US in the key initial months), so the full extent of the developing problem was not clear until it hit.  The response, and the then only possible response, was to quickly institute lockdowns, and this was soon done in all 50 states.  The lockdowns were effective, albeit costly, and by late April the approach had succeeded in starting to bring down the daily number of new cases.  Case numbers continued to fall in May and into June.

But starting in early May, disparate decisions were taken across the different states on how fast to lift the lockdown measures.  Some opened up early and with little guidance on or advocacy for the wearing of masks, while others opened up more cautiously.  But with the opening up, and the refusal by a significant share of the population to wear masks and to follow social distancing recommendations, the daily number of new cases stopped falling and by around mid-June began to rise again.  The daily number of deaths followed a similar pattern but with a lag of about two weeks, and so began to rise around the end of June. Thus July 1 can be taken as a turning point – the end of the first wave and the start of the second.  While differences across the states had already started to develop from early May (when decisions were taken on how rapidly to open up), the consequences of the varying approaches only became clear as the second wave started to build.  On average across the nation, this was around July 1.

The second reason to exclude this first wave is that the quality of the data for that initial period was poor.  The Trump administration was slow in launching and then ramping up testing, with testing limited even well into April to those who showed obvious symptoms or who had been in close contact with someone with a confirmed case.  Thus many cases were missed.  While testing has been far from perfect throughout this pandemic, it was much worse in the earlier months than it was later.  For this reason as well, excluding the estimates from the earlier months will provide a better measure of how successful or not the different states were as they responded to the pandemic in their different ways after the initial lockdowns.

Excluding the first wave leads to the exclusion of 6% of confirmed cases and 18% of deaths from the overall totals as of October 15, 2021.  Most thus remained.  Note also the disparity in these figures.  That the official figures recorded that just 6% of the confirmed cases in the US (as of October 15) were in this initial, first wave, period, while this same period recorded 18% of deaths, strongly suggests that cases were significantly undercounted in that first wave.

The charts then show the incidence of total confirmed cases of Covid-19, or deaths from it, per 10,000 of population, over the period from July 1, 2020, to October 15, 2021, with this plotted against the share of the vote that Trump received in that state in 2020.  The relationship is a strong one:  The higher the share of the state vote for Trump, the higher the incidence of Covid-19 cases and of deaths.  Taking averages, the average number of confirmed cases over this period per 10,000 in the states won by Trump was 1,461 (i.e. 14.6% of their population) vs. 1,113 in the states won by Biden.  That is, there were on average 31% more cases in the states won by Trump.  The number of deaths from Covid-19 came to 21.2 per 10,000 in the states won by Trump vs. 15.3 in the states won by Biden, or 38% more in the states won by Trump.

But averaging across all the states won by Trump or by Biden is not terribly meaningful as there will be a mix of voters in every state.  Furthermore, there were a number of states where the vote was close to 50/50.

It is thus more meaningful to examine the trend across the different states, as a function of the share voting for Trump.  This trend is provided in the regression line shown in each chart, where simple, linear, ordinary least squares regression was used.  The statistical relationship found was very strong, and especially so for the regression for the number of cases of Covid-19.  The R-squared (a measure of how much of the variation in the values is accounted for by the regression line alone) was extremely high for such a cross-state sample as here – at 0.63 for the number of Covid-19 cases and a still high 0.36 for the number of Covid-19 deaths.  (R-squared values can vary between 1.0, in which case the regression line explains 100% of the variation across states, and 0.0, in which case the regression line explains none of the variation.)

The higher correlation (the higher R-squared) observed in the relationship for the number of cases than in the relationship for the number of deaths is what one would expect.  To die from the disease, one must first have caught it.  Hence this will depend on the number of cases in the state.  But deaths from it will then depend on additional factors such as the age structure of the population, general health conditions (obesity rates, for example), as well as the availability and quality of health care services (hospitals, for example).  These factors will vary by state, and hence add additional variation to that found for the number of confirmed cases.

The slope of the regression line is an estimate of how many additional cases of (or deaths from) Covid-19 to expect (per 10,000) for each 1% point higher share of the vote for Trump.  For each additional 1% point in the share of the vote for Trump in a state, there were on average 23.8 more cases (per 10,000 of population) of Covid-19 during the period examined, and on average 0.36 more deaths (per 10,000).  The t-statistics for these slope coefficients were both extremely high, at 9.1 for the number of cases and 5.2 for the number of deaths.  A t-statistic of 2.0 or higher is generally taken to be an indicator that the relationship found is statistically significant (as it implies that in 95% of the cases, the slope is something different from zero – a slope of zero would imply no relationship).  A t-statistic of 3.5 would raise that significance to 99.9%.  The t-statistics here of 9.1 and 5.2 are both far above even that mark.

One can also use the regression lines to address the question of what the impact would have been on Covid-19 cases and deaths if everyone behaved as Biden voters did (or as Trump voters did).  The regression lines look at how the incidence of cases or deaths change based on each additional percentage point in the vote for Trump.  If one extrapolates this to the extreme case of zero votes for Trump (and hence a “pure” Biden vote), one can estimate what cases and deaths would have been if all behaved as Biden voters did.

This is a straight line, i.e. linear, extrapolation of the effects, and the limitations from this assumption will be addressed in a moment.  But using linearity, the effects are easily calculated by simply inserting zeroes for the Trump share of the vote into the regression equations, so that one is left with the constants of +96.94 for the number of cases (per 10,000 of population) and -0.69 for the number of deaths.  That is, there would have been a predicted 97 (per 10,000) cases of Covid-19 over this period in the US rather than the actual figure of 1,261 (per 10,000).  This is 92% lower.  And the number of deaths would have been essentially zero (and indeed would have reached zero with still some share voting for Trump – based on the regression equation coefficients it would have been at the 2% point share for the Trump vote).

Are these results plausible?  Would cases and deaths have fallen by so much if all of the population had behaved (in terms of wearing masks, social distancing, getting vaccinated once vaccines became available, and other such behaviors) as the Biden voters did?  The answer is yes.  Indeed, the linear extrapolation is conservative, as infectious diseases such as Covid-19 spread exponentially.

If in some state each infected person infects, on average, two further people, the number infected will double in each time period for the disease.  This is exponential growth, with a reproduction rate of two in this example – a doubling in each period.  For Covid-19, the time period from when a person is infected to when that person may, on average, spread it to another, is a week and a half.  A person becomes infectious (can spread it to others) about one week after they became infected with the disease, and then can infect others for about a week (with the average then at the half-way point of that week).  Thus 100 cases of active infections in some region would double to 200 in that time period of a week and a half, then to 400 in the next time period, and so on.  If, in contrast, responsible behavior (such as vaccinations and mask-wearing) reduces the reproduction rate to one-half rather than two, then 100 cases will lead to 50 in the next time period, to 25 in the next, and so on down to zero.

In any given state there is a mix of Biden voters and Trump voters.  While there are many factors that matter, if these two identities reflect, on average, differing shares of people that do or do not choose to be vaccinated, wear masks, and so on, then the average reproduction rate will vary depending on the relative shares of such voters.  That average reproduction rate will be lower in states with a higher share of Biden voters, and for a sufficiently high share of Biden voters (a sufficiently low share of Trump voters), there will be an exponential decline in new infections from Covid-19.  The linear extrapolation based on the regression equations would thus be a conservative estimate of the number of cases to expect when most of the population behaves as the Biden voters have.

There are, of course, many factors that enter into whether a person is infected by someone with Covid-19, and whether they then die from the infection they got from someone.  But the charts and the regression results suggest that the share of the population in a state voting for Biden or for Trump is, by itself, strongly correlated with how likely that was.  Why?

C.  Personal Behavior and Political Identity

The fact of, and then the consequences from, this political divide for infection by Covid should not be a surprise to anyone.  As noted before, Trump voters are far less likely to be vaccinated or to wear masks to protect themselves and others from this highly infectious, and deadly, disease.  This then translates into higher infection rates, and the higher infection rates then to higher deaths.

One sees this unwillingness to be vaccinated also in surveys.  The most recent of the regular surveys by the Kaiser Family Foundation (published on October 28) found that 90% of Democrats had received at least the first dose of the Covid vaccine, while only 61% of Republicans had.  Furthermore, 31% of Republicans declared they would “definitely not” be vaccinated, while just 2% of Democrats held that view.  Gallup surveys have found similar results, with a survey from mid-September finding that 92% of Democrats had received at least the first dose of the Covid vaccine, but that only 56% of Republicans had.  And 40% of Republicans in that survey said they are not planning on being vaccinated ever, while only 3% of Democrats said that.

Not surprisingly, one then sees this reflected in state politics.  Republican governors (such as Abbott of Texas and DeSantis of Florida) have gone so far as to issue executive orders to block private companies from protecting their staff and their customers from this disease, and even to prohibit local school boards from taking measures to protect schoolchildren.

The direct result is that the virus that causes Covid-19 has continued to spread.  An infectious disease such as Covid-19 will only persist as long as it is being spread on to others.  It cannot survive on its own.  The issue, then, is not just that someone refusing to wear a mask or to be vaccinated is highly likely to catch the disease, but that that person is likely to spread it to others.  While Republican governors such as Abbott and DeSantis have said this is a matter of “personal freedom”, it is not that at all.  No one is free to do harm to others.  It is the same reason why there are laws against drunk driving.  Drunk drivers are more likely to cause crashes (not all of the time, but often), and those crashes will harm others, up to and including killing others.  Spreading Covid-19 is similar, up to and including that those who become infected may die from it.

For whatever causal reason, the facts themselves are clear.  But why has a significant share of the population chosen to behave this way?  This is now more speculative, and goes into an area that I openly acknowledge is not my area of expertise.  With that proviso, some speculation.

It is clear that political identity has played a central role, where Trump from the start treated the then developing pandemic as an issue where you were either with him – and his assertion that he had it all under control – or against him.  This started with Trump’s assertion in an interview on January 22, 2020 (from Davos, Switzerland) that he had no worries, that “we have it totally under control”, and that “It’s going to be just fine”.  This claim continued through February (as cases were growing in the US), where on February 27 he said “It’s going to disappear.  One day it’s like a miracle.  It will disappear.”  And in campaign rallies in February, he claimed to his cheering supporters that he had been doing a superb job in stopping the virus and that any charge to the contrary was simply a “hoax” coming from the Democrats.

Thus, from the start, Trump made the issue a political one.  If you were a true supporter of Trump you could not treat the disease as something of concern – Trump had taken care of it.  Any assertion that the developing pandemic was in fact serious, and needed to be addressed, was a “hoax” perpetrated by the Democrats.

Trump then continued to assert all would soon be well, saying on March 10 that “it will go away”, on April 29 that “This is going away.  It’s gonna go.  It’s gonna leave.  It’s gonna be gone.”, on May 11 that “we have prevailed”, on June 17 that “It’s fading away.”, and on July 19 that “It’s going to disappear”.  But more than 600,000 Americans have died since July 19, 2020, not far short of the 651,000 Americans who have died in battle in all of America’s wars since 1775.  From the start of the pandemic, more than 750,000 Americans have now died.

Trump’s politicization of Covid-19 was then amplified when, at the April 3 press conference in which he announced the CDC recommendation that everyone should wear face masks when going out, he immediately then added that he would not himself wear a face mask.  Face masks are highly effective in hindering the spread from person to person of the virus that causes Covid-19, and until vaccines became available, were the best way to hinder that spread.  But wearing a face mask is also highly visible.  For those who saw themselves as supporters of Trump, and believed what he said (that the virus was going away, that he had it under control, and that any concerns over this were merely a hoax promoted by the Democrats), then it was not surprising that many would follow Trump’s highly public example and not wear a mask either.  Some even went so far as to shoot, and kill, store personnel when told they should wear a face mask inside some store.

It is not surprising that such views would then carry over to vaccination.  Having rationalized not wearing a mask, it is easy to rationalize a refusal to be vaccinated.  And rationalizations could easily be found just by watching Fox News.  In the six months from April through September this year, for example, Fox News chose to air a claim undermining vaccination on all but two of those more than 180 broadcast days.  Many were also exposed to claims that can only be described as truly bizarre, such as that the vaccination will be secretly inserting a microchip into your body for the government to track you, with Bill Gates behind it all; or that it will make you magnetic with this managed through 5G telecom towers; or that it will re-write your body’s DNA; and more.

One can therefore easily come up with rationalizations not to be vaccinated, of varying degrees of plausibility, if you are predisposed against it.  But many of those providing such rationalizations must have realized that their rationalizations often did not make much sense.  Rather, their decisions appear to have been driven more by a visceral or emotional reaction (vaccinations just “feel” wrong) than as an outcome of a rational process.  That is, the decision not to be vaccinated was made first, based on emotions or feelings, with the rationalizations then arrived at later to justify a decision that had already been made.  (Such a process is in accord with the “social intuitionist” model of Jonathan Haidt, where decisions are made first, in a visceral reaction based on emotion, while rationalizations then come later to justify that decision.)

In the case of Covid-19, those decisions on vaccination (and earlier on wearing masks) were made in accordance with political identity – a perceived loyalty to Trump – rather than in recognition of the very real risks that would follow if one contracted Covid-19.  Wearing a mask or accepting a vaccination would simply be “wrong” and disloyal.

I have found it astonishing how strong this emotional reaction has apparently become.  Covid-19 is new (it did not even exist just two years ago), it is deadly (where on average about 1.5% of those infected have died – with a much higher fatality rate than this average for those who are older or who have other health issues), and may have serious long-term ill effects even for those who do not die from it.  Yet this visceral reaction appears to have been so powerful that many supporters of Trump still refuse to be vaccinated, despite the risk of genuine life and death consequences.

I should hasten to add that not all voters for Trump have refused to be vaccinated.  Indeed, according to the surveys, about 60% (a majority) have as of October.  There are also highly vocal partisans on the left who have refused to be vaccinated.  Their reasons are likely very different from that of the typical Trump voter, but the underlying cause appears still to be intuitive – the feeling that such vaccinations are simply “wrong”.  But the issue is that the relative shares of the two groups have been very different:  A far higher share of those who voted for Trump have refused vaccination than is the case for those who voted for Biden.  The consequences are as shown in the charts at the top of this post.

As noted before, the cause for this relationship cannot be known with certainty, and what I have presented here should be viewed as speculative on my part.  There may well be other explanations.  For example, a related but somewhat different explanation would be that a common third factor explains both the tendency of some to vote for Trump and also to be resistant to vaccinations.  Those in this group may put faith in conspiracy theories (including, but not limited to, terrible consequences from being vaccinated), distrust authority, proudly but stubbornly insist on doing the opposite of whatever is recommended, and for such reasons not only refuse to be vaccinated but also vote for Trump.

Whatever the explanation, the results have been tragic.  This has also been a lesson in how strongly some will keep to a held position, even as they have seen prominent figures, and sometimes friends or even family members, come down with this disease.  When an issue becomes one of identity, it appears that even with such tragic consequences there will be many who steadfastly refuse to change.

The World Bank Doing Business Report: Changes in Rank May Not Mean What They May Appear to Mean

A.  The Issue

The World Bank has been publishing its Doing Business report annually, from the first released in September 2003 (and titled Doing Business in 2004) until the one released in October 2019 (and titled Doing Business 2020).  It has always been a controversial report, criticized for a number of different reasons.  But it has also been one that gained a good deal of attention – from the news media, from investors, and from at least certain segments of the public.  And because it received a good deal of attention (indeed more than any other World Bank report), governments paid attention to what it said, especially about them.

It is not my intention here to review those criticisms.  They are numerous.  Rather, in this post I will present a different approach to how the results could have been presented, which would have been more informative and which might have quieted some (but not all) of the criticisms.

One feature of the report, which was also the most widely discussed aspect of the report, was its ranking of countries in terms of their (assessed) environment for doing business. Because the Doing Business reports were widely cited, countries paid attention to those rankings and by various methods sought to improve their rankings.  These were often positive and productive, with countries seeking to simplify business regulation so that businesses could operate more effectively.  While some criticized this as simplistic, it is difficult to see a rationale, for example, justifying that in Venezuela (in 2020), to start a business would require completing 20 different procedures normally requiring (given the times for review and other requirements) an estimated 230 days.  In contrast, in New Zealand, starting a business involves only one procedure and can be completed in half a day.  Of course, few new businesses in Venezuela actually take 230 days to get started.  Either they are simply ignored (with bribes then paid to the police to leave them alone despite their violation of the regulations), or bribes are paid to obtain the licenses without going through the complex processes.

Given the prominence of the report, some countries undertook to improve their rankings through less positive means.  Sometimes the system could be gamed in various ways (e.g. to enact some “reform”, but then to limit its application narrowly so as to fit the letter, but not the spirit, of what was being assessed).  Or sometimes pressure could be applied on those submitting the data to slant it in a favorable direction.

And sometimes a country would try to apply political pressure on World Bank management in order to secure more favorable treatment.  A recently released independent investigation (commissioned by the World Bank Board, and undertaken by the law firm WilmerHale), found that there was such pressure (or at least perceived such pressure) brought by China on Bank management to ensure its ranking in the 2018 report would not fall.  At the instigation of the president’s offiice, and then overseen by the then #2 at the World Bank (Kristalina Georgieva – now the head of the IMF), Bank staff were directed to re-examine the ratings that had been assigned to China, and indeed re-examine the methodology more broadly, to see whether with some set of changes China’s ranking would not fall.  According to the WilmerHale report, several different approaches were considered and tried until one was found which would lead to the desired outcome.  In the almost final draft of the report, just before its planned publication, China’s ranking would have fallen from #78 in 2017 to #85 in 2018 – a fall of 7 places.  But with the last minute changes overseen by Georgieva, China’s ranking in 2018 became #78 – the same as in 2017.  And that was what was then published.

B.  An Alternative Approach

This focus on rankings is misguided, although not surprising given how the results are presented.  A country may well have enacted significant measures improving its business environment, but if countries a bit below it in the previous year’s rankings did even more, then the country could see a fall in its ranking despite the reforms it had undertaken.  And that fall in ranking would then often be interpreted in the news media (as well as by others) as if the business environment had deteriorated.  In this example, it had not.  Rather, it just did not improve as much as others.  But this nuance could easily be missed, and often was.

A different presentation in the Doing Business reports could have addressed this, and might have made the report a bit less controversial.  Rankings, by their nature, are a zero-sum game, where a rise in the ranking of one country means a fall in the ranking of another.  While this is needed in a sports league, where a tiny difference in the seasonal won/loss record can determine the ranking and hence who goes to the playoffs, it is not the same for the business environment.  Investors want to know what is good and what is not so good in an absolute sense, and small differences in rankings are of no great consequence.  And while some might argue that countries are competing in a global market for investor interest, there are far more important issues for any investor than some small difference in rankings.  For example, China and Malta were ranked similarly for several years in the mid-2010s, but whether one was a bit above or a bit below the other would be basically irrelevant to an investor.

An alternative presentation of the findings, based on comparison to an absolute rather than relative scale, would have been more meaningful as well as less controversial.  Specifically, countries could have been compared not against each other in every given year to see if their rankings had moved up or down, but rather to what the set of scores were (and consequent rankings were) in some base year.  That is, one would see whether their business environment had gotten better or worse in terms of the base year set of scores and consequent rankings (what one could call that base year’s “ladder” of scores).  The Doing Business project had that data, reported the underlying scores, and used those scores to produce its rankings.  But there was no systematic presentation of how the business environments may have changed over time, with this then compared to what the rankings were in some base period.

There were, I should note, figures provided in the Doing Business reports on the one-year changes in scores for each individual country, but few paid much attention to them.  They were for one-year changes only, and did not show the more meaningful cumulative changes over a number of years.  Nor did they then show how such changes would affect the country’s position on an understandable scale, such as what the ratings were across countries in a base year.

In this new approach, the comparison would be to an absolute scale (the scores and consequent rankings in the base year), not a relative ranking in each future period.  The issue is analogous to different types of poverty measures.  Sometimes poverty in a country is measured as the bottom 10% of the population (ranked by income).  This is a useful measure for certain things (such as how to target various social programs), but will of course always show 10% of the population as being “poor” regardless of how effective the social programs may have been.  In contrast, one could have an absolute measure of poverty (for many years the World Bank used the measure of $1 per day of income per person), and one could then track how many people were moving out of poverty (or not) by this measure.  Similarly, with the relative ranking of the 190 countries covered in the Doing Business reports, one will always find a mean ranking of 95, regardless of what countries may have done to improve their business environment.  It is, obviously, simply a relative measure, and not terribly useful in conveying what has been happening to the business environment in any given country.  Yet everyone focuses on it.

Any comparisons over time of these scores must also be over periods where the methodological approach used (precisely what is measured, and what weights are assigned to those measures) has not changed.  Otherwise one is comparing apples to oranges, where changes in the scores may reflect the methodological changes and not necessarily changes in the business environment of the countries.  Such changes in the methodological specifics have been criticized by some, as such changes in methodology will, in itself, lead to changes in rankings.  But one should expect periodic changes in the methodological approach used, as experience is gained and more is learned.

C.  The Results

The 2016 to 2020 period was one where the methodological specifics used in the Doing Business reports did not change, and hence one can make a meaningful comparison of scores over this period.  The data needed are what they specifically call the “Doing Business Scores”, which are the absolute values for the indices used to measure the business environment.  They range from 100 (for the best possible) to 0 (for the worst).  These scores have (at least until now) been made publicly available in the online Doing Business database.  I used these to illustrate what could be done to present the Doing Business results based on an absolute, rather than relative, scale.

The results are presented in a table at the end of this post, and readers might want to take a brief look at that table now to see its structure.  The base year is 2016, and the calculations are based on the changes in the country’s Doing Business Scores over the period from 2016 to 2020.  No country scores 100, and the top-ranked country (New Zealand) had an overall score of 87.1 in 2016 and slightly less at 86.8 in 2020.  I re-normalized the scores to set the top score (New Zealand’s 87.1 in 2016) to 100, with the rest then scaled in proportion to that.

The scores are thus shown as a proportion to what the best overall score was in 2016.  And this was done not just for the 2016 scores but also for the 2020 scores.  Importantly, the 2020 scores are not taken as a proportion of the best score for any country in 2020, but rather as a proportion of what the best score was in 2016.  Those scores are shown in the last two columns of the table, first for 2016 (as a proportion of what the best score was in 2016 – New Zealand at 87.1), and then for 2020 (again as a proportion of what the best score was in 2016 – New Zealand’s 87.1).  Note that on this scaling, New Zealand in 2016 would be 100.00, while in 2020 its rating would fall very slightly to 99.66 (as the Doing Business Score for New Zealand fell slightly from 87.1 in 2016 to 86.8 in 2020, and 86.8 is 99.66% of the 2016 score of 87.1).

The first two columns in the table show the rankings of countries, as the Doing Business reports have them now, for 2016 and then for 2020.  The sequence in the table is according to the ranking in 2016.  The third (middle) column then shows where a country would have ranked in 2016, had they had then the business environment that they had in 2020.  These are shown as fractional “rankings”, where, for example, if a country’s score in 2020 was halfway between the scores of countries ranked #10 and #11 in 2016, then that country would have a “rank” of 10.5.  That is, that country would have ranked between those ranked #10 and #11 in 2016, had they had a business environment in 2016 that they in fact had in 2020.

This now provides an absolute measure of country performance over time.  It is a comparison to where they would have ranked in 2016 had they had then what their policies later were.  This will then provide a more accurate account of what has been happening in the business environment in the country.  Take the case of Germany, for example.  It ranked #16 in 2016 and then lower at #22 in 2020.  Many would interpret this as a business environment that had deteriorated over the period.  But that is in fact not the case.  The absolute score was 91.27 in 2016 and a slightly improved 91.50 in 2020.  The business environment became slightly better (as assessed) between those two years, and it would have moved up a bit to a “rank” of 15.3 if it had had in 2016 the business environment it had in 2020.  But because a number of countries below Germany in 2016 saw a larger improvement in their business environment by 2020 than that of Germany, the relative ranking of Germany fell to #22.

Some of the differences could be large.  El Salvador, for example, ranked #80 in 2016 and fell to #91 in the traditional (relative) ranking for 2020.  But its business environment in fact improved significantly over the period, where the business environment it had in 2020 would have placed it at a ranking of 70.0 in 2016.  That is, it would have seen an improvement by 10 positions between the two periods rather than a deterioration of 11.  But other countries moved around as well, changing the relative ranking even though in absolute terms the business environment in El Salvador became significantly better.

D.  The Politics

One can understand how politicians in some country could grow frustrated if, after a period where they pushed through substantial (and possibly politically costly) reforms that aimed to improve their business environment, they then found that their Doing Business ranking nonetheless declined.  One might try to explain that other countries were also changing, and that despite their improvement other countries improved even more and hence pushed them down in rank.  This can follow when the focus is on relative rankings.  But this is not likely to be very convincing, as what the politicians see reported in the news media is the relative rankings.

A calculation of how the ratings would have changed in absolute terms would not suffer from this problem.  Countries would be credited for what they accomplished, not based on how what they did compares to what other countries might have done.  If all countries improved, then one would see that reflected when an absolute scale is used.  But when the focus is on relative rankings, any move up in rank must be matched by someone else moving down.  It is a zero-sum game.

Such a change in approach might have made the Doing Business reports politically more palatable.  It might also have reduced the pressure from countries such as China.  Throughout the period of 2016 to 2020, China was taking actions which, in terms of the Doing Business measures, were leading to consistently higher absolute scores each year, including for 2018.  With an absolute scale, such as that proposed here, one would have seen those year-by-year improvements in China’s position relative to where it would have been in a base year ranking (2016 for example).  The improvements were relatively modest in 2017 and 2018, and then much more substantial in 2019 and 2020.  But despite that (modest) improvement in 2018, China’s relative ranking in that year would have fallen 7 places to #84 from the #78 rank in the published 2017 report.  China found this disconcerting, and the WilmerHale report describes how Bank staff were then pressured to come up with a way to keep China’s (relative) ranking no worse than the #78 position it had in 2017.  And they then did.

At this point, however, a move to a presentation in terms of an absolute scale is too late for the Doing Business project as it has operated up to now.  Following the release of the WilmerHale report, the World Bank announced on September 16 that it would no longer produce the Doing Business report at all.  Whatever it might do next, if anything, will likely be very different.


2016  Rank 2020 Rank Rank with 2020 policy but 2016 ladder 2016 fraction of 2016 best 2020 fraction of 2016 best
New Zealand 1 1 1.1 100.00 99.66
Singapore 2 2 1.4 97.47 98.97
Denmark 3 3 1.8 97.01 97.93
Hong Kong, China 4 4 1.8 96.79 97.93
United States 5 5 4.4 95.98 96.44
United Kingdom 6 8 5.3 95.64 95.87
Korea, Rep. 7 6 4.4 95.41 96.44
Norway 8 9 7.4 93.92 94.83
Sweden 9 10 7.8 93.69 94.14
Taiwan, China 10 15 10.5 93.34 92.88
Estonia 11 18 10.9 92.42 92.54
Australia 12 14 10.1 92.31 93.23
Finland 13 20 12.7 91.96 92.08
Canada 14 23 15.7 91.62 91.39
Ireland 15 24 15.7 91.62 91.39
Germany 16 22 15.3 91.27 91.50
Latvia 17 19 12.3 90.82 92.19
Iceland 18 26 19.0 90.70 90.70
Lithuania 19 11 9.0 90.70 93.69
Austria 20 27 20.5 90.47 90.36
Malaysia 21 12 9.3 90.24 93.57
Georgia 22 7 4.9 89.90 96.10
North Macedonia 23 17 10.8 89.44 92.65
Japan 24 29 22.8 88.98 89.55
Poland 25 40 27.0 88.29 87.72
Portugal 26 37 25.8 87.72 87.83
Switzerland 27 36 25.6 87.72 87.94
United Arab Emirates 28 16 10.5 87.60 92.88
Czech Republic 29 41 28.0 87.37 87.60
France 30 32 25.2 87.37 88.17
Mauritius 31 13 9.3 87.26 93.57
Spain 32 30 23.0 87.14 89.44
Netherlands 33 42 30.0 86.68 87.37
Slovak Republic 34 45 32.7 85.88 86.80
Slovenia 35 38 25.8 85.76 87.83
Russian Federation 36 28 22.2 85.07 89.78
Israel 37 34 25.4 83.81 88.06
Romania 38 55 36.7 83.47 84.16
Bulgaria 39 61 41.0 83.24 82.66
Belgium 40 46 33.7 83.12 86.11
Cyprus 41 52 36.6 82.66 84.27
Thailand 42 21 13.0 82.55 91.96
Italy 43 58 37.3 82.32 83.70
Mexico 44 60 40.0 82.20 83.12
Croatia 45 51 36.5 81.97 84.50
Moldova 46 48 35.5 81.97 85.42
Chile 47 59 38.5 81.75 83.35
Hungary 48 53 36.6 81.63 84.27
Kazakhstan 49 25 15.7 81.40 91.39
Montenegro 50 50 36.3 81.06 84.73
Serbia 51 44 32.5 80.37 86.91
Luxembourg 52 72 51.5 79.45 79.91
Armenia 53 47 35.3 79.33 85.53
Turkey 54 33 25.2 79.33 88.17
Colombia 55 65 50.8 79.10 80.48
Belarus 56 49 35.7 78.87 85.30
Puerto Rico 57 66 50.8 78.87 80.48
Costa Rica 58 74 52.0 77.73 79.45
Morocco 59 54 36.6 77.38 84.27
Peru 60 76 57.0 77.15 78.87
Rwanda 61 39 25.8 77.04 87.83
Greece 62 79 57.3 76.81 78.53
Bahrain 63 43 31.0 76.46 87.26
Qatar 64 77 57.0 76.35 78.87
Oman 65 68 51.0 76.12 80.37
Jamaica 66 71 51.4 76.00 80.02
South Africa 67 84 61.5 76.00 76.92
Botswana 68 87 67.0 75.20 76.00
Azerbaijan 69 35 25.4 75.09 88.06
Mongolia 70 80 57.9 74.97 77.84
Bhutan 71 89 67.3 74.51 75.77
Panama 72 86 63.0 74.28 76.46
Tunisia 73 78 57.0 74.17 78.87
Ukraine 74 64 50.7 73.71 80.60
Bosnia and Herzegovina 75 90 69.0 73.59 75.09
St. Lucia 76 93 75.7 72.90 73.13
Kosovo 77 56 36.8 72.33 84.04
Fiji 78 101 86.0 72.10 70.61
Vietnam 79 70 51.3 71.87 80.14
El Salvador 80 91 70.0 71.64 74.97
China 81 31 24.3 71.53 88.75
Malta 82 88 67.1 71.53 75.89
Indonesia 83 73 51.5 71.30 79.91
Guatemala 84 96 80.0 70.84 71.87
Uzbekistan 85 69 51.1 70.84 80.25
Dominica 86 111 92.0 70.61 69.46
San Marino 87 92 74.0 70.49 73.71
Trinidad and Tobago 88 105 90.0 70.49 70.38
Kyrgyz Republic 89 81 57.9 70.38 77.84
Tonga 90 103 88.0 70.38 70.49
Kuwait 91 83 59.0 69.69 77.38
Zambia 92 85 62.0 69.46 76.81
Samoa 93 98 83.0 69.12 71.30
Uruguay 94 102 86.0 69.12 70.61
Namibia 95 104 88.0 68.89 70.49
Nepal 96 94 76.7 68.54 72.56
Vanuatu 97 107 90.3 68.08 70.15
Antigua and Barbuda 98 113 92.7 67.97 69.23
Saudi Arabia 99 62 44.0 67.97 82.20
Sri Lanka 100 99 83.8 67.97 70.95
Seychelles 101 100 85.0 67.74 70.84
Philippines 102 95 79.0 66.82 72.10
Albania 103 82 58.0 66.70 77.73
Paraguay 104 124 100.5 66.70 67.85
Kenya 105 57 36.8 66.59 84.04
Dominican Republic 106 115 95.0 66.48 68.89
Barbados 107 128 106.0 66.25 66.48
Brunei Darussalam 108 67 50.8 66.02 80.48
Bahamas, The 109 119 95.3 65.56 68.77
Ecuador 110 129 107.0 65.56 66.25
St. Vincent and the Grenadines 111 130 111.0 65.56 65.56
Ghana 112 116 95.0 65.44 68.89
Eswatini 113 121 96.5 65.33 68.31
Argentina 114 126 101.0 65.10 67.74
Jordan 115 75 54.5 65.10 79.22
Uganda 116 117 95.0 64.98 68.89
Honduras 117 133 116.6 64.41 64.64
Papua New Guinea 118 120 95.7 64.29 68.66
Brazil 119 125 100.5 63.83 67.85
St. Kitts and Nevis 120 139 126.5 63.83 62.69
Belize 121 134 120.5 63.61 63.72
Iran, Islamic Rep. 122 127 101.6 63.61 67.16
Lesotho 123 122 96.8 63.38 68.20
Egypt, Arab Rep. 124 114 94.5 62.80 69.00
Lebanon 125 143 127.7 62.80 62.34
Solomon Islands 126 136 122.5 62.80 63.49
India 127 63 48.5 62.57 81.52
West Bank and Gaza 128 118 95.0 62.23 68.89
Nicaragua 129 142 127.3 62.11 62.46
Grenada 130 146 131.0 61.54 61.31
Cabo Verde 131 137 123.4 61.31 63.15
Palau 132 145 129.8 60.96 61.65
Cambodia 133 144 129.6 60.73 61.77
Mozambique 134 138 123.4 60.62 63.15
Maldives 135 147 131.3 60.16 61.19
Tajikistan 136 106 90.0 59.47 70.38
Guyana 137 135 120.5 58.55 63.72
Burkina Faso 138 151 136.5 58.21 59.01
Marshall Islands 139 153 137.3 58.09 58.44
Pakistan 140 108 90.5 57.86 70.03
Côte d’Ivoire 141 110 91.0 57.75 69.69
Mali 142 148 133.0 57.75 60.73
Bolivia 143 150 136.1 57.18 59.36
Malawi 144 109 90.7 57.06 69.92
Tanzania 145 140 127.0 57.06 62.57
Senegal 146 123 97.0 56.95 68.08
Benin 147 149 135.0 55.91 60.16
Nigeria 148 131 113.0 55.57 65.33
Lao PDR 149 154 137.7 55.34 58.32
Micronesia, Fed. Sts. 150 158 150.0 55.22 55.22
Zimbabwe 151 141 127.0 54.88 62.57
Sierra Leone 152 162 151.3 53.85 54.54
Togo 153 97 82.0 53.85 71.53
Suriname 154 163 151.3 53.27 54.54
Niger 155 132 113.5 53.16 65.21
Comoros 156 160 150.7 53.04 54.99
Gambia, The 157 155 142.0 53.04 57.75
Burundi 158 165 153.2 52.35 53.73
Sudan 159 171 160.5 52.24 51.44
Kiribati 160 164 153.0 52.12 53.85
Djibouti 161 112 92.0 50.86 69.46
Guinea 162 156 146.2 50.86 56.72
Algeria 163 157 147.3 50.75 55.80
Gabon 164 168 160.4 50.52 51.66
Ethiopia 165 159 150.3 50.29 55.11
Mauritania 166 152 136.9 50.29 58.67
São Tomé and Principe 167 169 160.4 50.29 51.66
Syrian Arab Republic 168 176 170.5 49.37 48.22
Iraq 169 172 160.6 49.25 51.32
Myanmar 170 166 153.2 48.34 53.73
Cameroon 171 167 157.2 48.11 52.93
Madagascar 172 161 151.1 48.11 54.76
Bangladesh 173 170 160.4 46.96 51.66
Guinea-Bissau 174 174 167.8 46.38 49.60
Equatorial Guinea 175 178 172.8 45.92 47.19
Liberia 176 175 167.8 45.92 49.60
Afghanistan 177 173 163.5 45.12 50.63
Timor-Leste 178 181 176.9 45.12 45.24
Congo, Rep. 179 180 176.7 44.66 45.35
Yemen, Rep. 180 187 188.0 44.09 36.51
Haiti 181 179 173.4 43.28 46.73
Angola 182 177 172.6 43.17 47.42
Chad 183 182 182.3 40.53 42.37
Congo, Dem. Rep. 184 183 182.6 39.49 41.56
Venezuela, RB 185 188 188.2 39.15 34.67
South Sudan 186 185 183.8 37.66 39.72
Libya 187 186 186.5 37.43 37.54
Central African Republic 188 184 182.9 36.74 40.87
Eritrea 189 189 188.9 24.00 24.80
Somalia 190 190 190.0 23.19 22.96