Statistics worthy of Sharing (2024)

22/06/2021

The statistic:
The graph shows the evolution of our global energy mix; how much energy is coming from which source.

Interpretation:
This graph is from the book Numbers don’t lie by Vaclav Smill. It shows how our global energy mix has evolved. Fossil fuels are still our primary energy source with 85,1% of energy in 2017, a reduction of 1,5% with respect to 1992. Often in the media, it seems that wind and solar energy are advancing rapidly. Yet, in reality, it does not grow as fast as you would think. For example, in 1992 they counted for 0.5% of the energy supply, 25 years later, it grew by 900% to 4,5% of the energy supply. Additionally, If you consider that only 27% of CO2 comes from electricity production, it translates into an even smaller potential CO2 reduction. However, transportation is responsible for 16% of CO2, and has the potential to be electrified, increasing the importance of energy as a means of CO2 reduction.
Source:
Books:
Vaclav Smill- Numbers don't lie
Bill Gates - How to avoid a climate disaster
https://ourworldindata.org/energy-mix

14/06/2021

The statistic:
The 2 maps illustrate the change across counties in the Republican two-party vote share between the 2016 and 2012 elections and in exposure to robots between the immediate years prior to the two elections. Darker shades represent bigger positive changes in exposure to robots and in the Republican two-party vote share.

The interpretation:
These two maps retrieved from the paper “Political machinery: did robots swing the 2016 US presidential election?” (2018), suggest that there is a positive correlation between the change in exposure to robots and the change in the Republican two-party vote share. To test if this relationship is causal, the authors ran a series of OLS regressions controlling for demographic and labour market characteristics. Although the effect decreased when further controls were added, the relationship was positive and statistically significant at traditional significance levels in all specifications. In their most conservative and preferred estimate, each additional unit increase in exposure to robots increased the Republican two-party vote share by on average 1.309 percentage points. Using this estimate, they found that if the use of robots had not increased in the years preceding the election, Hillary Clinton would have won Michigan, Pennsylvania, and Wisconsin, hence changing the result of the elections. Although this result has to be interpreted carefully, it shed some light on the potential effects that automation can have on political activism. Indeed, as already documented in a previous post, exposure to robots may significantly decrease wages and employment. In addition, when interpreting the elephant graph we identified the replacement of labour by robots as one factor explaining the stagnation of middle-class income in western countries. According to a poll, when unemployed workers are asked about the main reason for their situation, more than a third identify automation (Hamel et al., 2014). Thus, this paper shows that workers hurt by automation technologies are more likely to vote for radical political change, which Trump embodied in 2016.

References:
Frey, C., Berger, T., & Chen, C. (2018). Political machinery: did robots swing the 2016 US presidential election? Oxford Review of Economic Policy, 34(3), 418–442. https://doi.org/10.1093/oxrep/gry007

Hamel, L., Firth, J., and Brodie, M. (2014), Kaiser Family Foundation/New York Times/CBS News Non-Employed Poll.

06/06/2021

The statistic:
This graph is called the “elephant graph” and shows how income has grown in each percentile of the income distribution. It shows how income has evolved between 1980 and 2016, and shows to a certain extent the winners and losers of globalisation.

The interpretation:
This graph is from the world inequality database, and shows a great improvement in low-income countries such as China and India, who benefited from the globalisation of supply chains and were able to lift people out of poverty. However, the poorest countries did not improve as much because of lack of infrastructure, are too dangerous to be included into the global economy or lack the protection of property rights. In the upper half of the graph are the middle-income people in developed nations eg. Europe, US, Canada.. The average “western citizen” did not experience the same dramatic increase in income as many of their jobs were replaced by robots, moved to other continents for cost-saving purposes, or did not exist anymore. This is one potential explanation for the rise of populism, as the middle class in western countries are not improving at the same pace as the rest of the world. Lastly, there is the top 1%. These are the real winners of globalisation and captured 27% of the growth between 1980 and 2016. This is because some companies were able to benefit massively from globalisation as new markets opened up for their goods and services and return on capital increased.

Facundo Alvaredo, Lucas Chancel, Thomas Piketty, Emmanuel Saez, Gabriel Zucman. (2017). The elephant curve of global inequality and growth. Wid.World. https://wid.world/document/elephant-curve-global-inequality-growth-wid-world-working-paper-2017-20/

30/05/2021

Interpretation:
The statistic retrieved from the paper “Don't throw away your printed books: A meta-analysis on the effects of reading media on reading comprehension” by Delgado et al. (2018), shows that overall, paper-based reading has an advantage compared to digital-based reading. As a matter of facts, most of the 38 considered studies found lower reading outcomes for digital-based reading compared to paper-based reading. Although the mean of these effects seems to be quite low (−0.21), the authors argue that it is relevant in an educational context since it represents ⅔ of the yearly growth in reading comprehension during elementary school. In recent years, the educational system has become more and more digitized, a phenomenon that has become more pronounced since the covid crisis. However, the results of this paper show that digitalisation is not always desirable. Indeed, providing students with paper-based reading might be preferred to an equivalent digital version, especially in contexts of time pressure (e.g. exams) in which the advantages of paper-based reading are even stronger.

Reference:
Delgado, P., Vargas, C., Ackerman, R., & Salmerón, L. (2018). Don’t throw away your printed books: A meta-analysis on the effects of reading media on reading comprehension. Educational Research Review, 25, 23–38. https://doi.org/10.1016/j.edurev.2018.09.003

23/05/2021

The statistic
According to the MIT, which did over 1000 simulations with different energy mixes to obtain net-zero by 2050. All cheapest paths included nuclear energy, helping renewables to a zero-carbon future.

The interpretation:
According to the report “The Future of Nuclear Energy in a Carbon-Constrained World” by MIT, nuclear energy is needed in all cheapest paths to a carbon-free future. The main reason why nuclear energy is so important is that it can run at all times. While solar and wind energy are dependent on the sun and wind. There is not always sun and wind to obtain energy, and it is especially difficult for regions far from the equator as sun and wind are season dependent, meaning that wind turbines and solar panels can not provide at their energy production capacity. However, it should also be noted that according to the report, new nuclear plants in the US and Western Europe are not advised due to the high capital costs.
Sources:

MIT. (2018). The Future of Nuclear Energy in a Carbon-Constrained World.https://energy.mit.edu/wp-content/uploads/2018/09/The-Future-of-Nuclear-Energy-in-a-Carbon-Constrained-World.pdf
Gates, B. (2021). How to Avoid a Climate Disaster: The Solutions We Have and the Breakthroughs We Need. Knopf.

19/05/2021

The statistic:
The graph shows the returns to education at the first and ninth deciles for 15 European countries and the US.

The interpretation:
This statistic retrieved from the article “Does education reduce wage inequality?: Quantile regression evidence from 16 countries” by Pedro Martins and Pedro Pereira (2004), shows that the return to education is higher for people at the top of the wage distribution (9th decile) than for people at the bottom of the distribution (1st decile). Points along the 45-degree line indicate that the return to education in the 9th decile and 1st decile of the wage distribution are equal, while points above the line indicate higher returns for individuals at the top of the distribution. It can be observed that, except for Greece, all the countries exhibit higher returns for those with higher earnings. This result is in contrast with the traditional argument that simple investments in education reduce inequality. Although partly true, this kind of reasoning neglects the heterogeneity in schooling-related earnings increment across the wage distribution.

Source:
Martins, P., & Pereira, P. (2004). Does education reduce wage inequality?: Quantile regression evidence from 16 countries. Labour Economics, 11(3), 355–371. https://doi.org/10.1016/j.labeco.2003.05.003

16/05/2021

The statistic:
According to the Living Planet Report, there is an average rate of decline in animal population size of 68% between 1970 and 2016. It looks at the evolution of The Living Planet Index (LPI), which tracks the abundance of almost 21,000 populations of mammals, birds, fish, reptiles and amphibians around the world. For two decades it has used the trends that emerge as a measure for changes in biodiversity. The building blocks for this indicator are wildlife population datasets gathered from almost 4,000 sources.

The interpretation:
As shown on the graph, we have an average decline of 68% of the animal population size since 1970. The decline of animal population size is the largest in South America followed by Africa losing 94% and 65% respectively. According to the Living Planet Report 2020 by The Zoological Society London and WWF, there are 5 main causes: Changes in land and sea use, including habitat loss and degradation, Species overexploitation, Invasive species, Pollution and Climate change. Habitat loss of animals is by far the biggest cause of loss of wildlife, over all continents, followed by the species' overexploitation. According to the report, it is clear that we, humans, have caused this loss of wildlife, mostly unintentionally, by not thinking about the consequences of our actions; the things we eat, the things we buy and the choices we made. Companies that choose profits over the natural world and governments that do not pass strong enough laws to stop this from happening are also fueling biodiversity loss. People also refer to this loss of wildlife as the 6th mass extinction. What we humans often forget is that the species at the top of the food chain suffered the most in the past 5 mass extinction. In the current world, we humans are at the top of the food chain making us vulnerable to loss of wildlife.

Source:
WWF (2020) Living Planet Report 2020 - Bending the curve of biodiversity loss. Almond, R.E.A., Grooten M. and Petersen, T. (Eds). WWF, Gland, Switzerland.

09/05/2021

The statistic:
The two graphs show the impact of exposure to robots from 1993 to 2007 in commuting zones (geographical area) on changes in employment and weekly wages from 1990 to 2007 in the US. Exposure to robots in each commuting zone is measured as the sum over industries of the local employment share of industry times the national pe*******on of robots into that industry. The robots considered in the paper are industrial robots and are defined by the International Federation of Robotics as “an automatically controlled, reprogrammable, and multipurpose [machine]”. The dots' size indicate the share of the 1990 US working age population in the corresponding commuting zone.

The interpretation:
The graphs retrieved from the paper “Robots and Jobs: Evidence from US Labor Markets” by Acemoglu and Restrepo (2020) show that wages and employment significantly decrease with exposure to robots. Indeed, the blue regression line is negative and significant in both graphs. In addition, the coefficients are still significantly negative when the top 1% of the commuting zones with the highest exposure to robots is excluded (red dotted line). Theoretically, there are different mechanisms through which robots or other automation technologies can impact the labour market. The negative impact comes from the displacement of workers. As a matter of facts, estimations from the World Bank predict that 57 percent of jobs in the OECD could be automated in the next two decades. However, there are also positive effects through increasing productivity of workers and creation of new activities. This is what happened during the interwar period. Around this time, the telegraph has been replaced by better communication technologies and there has been a lot of improvement in the transportation sector. However, although some professional activities disappeared because of technological improvement, the new activities that appeared absorbed those displaced workers and the progress in education allowed to facilitate this reallocation of labour. Thus, technological progress has been associated with improvements of the condition of workers. However, other historical events have shown how the displacement effect could hurt workers, such as in early 1800 when the Luddites rebelled against the introduction of textile machinery, which threatened their skilled craft. Given the rapid pace at which automation technologies and artificial intelligence are developing, it is important to be prepared and develop programs that would help mitigate the negative impact of displacement. In addition, as has been shown in the interwar period, education might help to reallocate workers to more skill-intensive sectors.

Source:
Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, vol 128(6), pages 2188-2244.

05/05/2021

The statistic:
The histograms show how Indian farmers performed before and after harvest on cognitive tests. Raven’s test is a common component in IQ tests and measures the capacity to think logically independent of acquired knowledge. Response time and errors rates are measured on Stroop tests. For instance, in such tests participants can be faced with “555” and have to quickly respond “3” (the number of 5 in the sequence) instead of “5”.

The interpretation:
These histograms are results from research done by Princeton and Harvard University to understand better whether poverty has an impact on cognitive performance. To do so, the researchers measured the performance on Raven’s test, along with response time and error rates on Stroop tests, before and after sugarcane farmers in India got their harvest. The Indian sugarcane farmers were used as they earned all their money after the harvest making them “richer” after harvest and “poorer” before the harvest as they had less money left. As can be observed in the histograms, the performance of farmers on Raven's test was lower pre-harvest. In addition, they took more time to respond and made more errors on Stroop tests. The research found that the drop in IQ is equivalent to 13 points, which is a lot considering a mean of 100 and standard deviation of 15. The main explanation the researchers gave is that poverty-related concerns consume mental resources, leaving less cognitive resources for other tasks. Quoting the researchers: “Being poor means coping not just with a shortfall of money, but also with a concurrent shortfall of cognitive resources. The poor, in this view, are less capable not because of inherent traits, but because the very context of poverty imposes load and impedes cognitive capacity.”. Additionally, according to the researchers, the drop in IQ is equivalent of losing a full night sleep before taking the test.

Source:
Mani, A., Mullainathan, S., Shafir, E., & Zhao, J. (2013). Poverty Impedes Cognitive Function. Science, 341(6149), 976–980. https://doi.org/10.1126/science.1238041

01/05/2021

The statistic:
This graph shows how the top income tax rate evolved over time.

The interpretation:
The graph shows that taxes used to be low at the beginning of the 20 century. However, it sharply rose around the first world war as countries needed money to fund the war. It later slightly decreased after the first world war but then rose again during the second world war. After the second world war did not decrease significantly, there are a couple of reasons for this, according to Thomas Piketty: firstly, the world had to be rebuilt after the war. Secondly, western countries entered the ideological cold war in which there was a competition between capitalism and communism. The West had to tax the rich in order to provide services for the poor citizens. Providing a high growth rate of standard of living for the poor was important at that time as if the poor would be disappointed, they might support the communist ideology. But as soon as the soviet union fell and thus the cold war, western countries did not have the pressure that the poor might support the communist ideology. There was, therefore, no more need to provide an as high growth of standard of living for the poor and western countries, therefore, reduced the top tax rate. Additionally, it was during these times that there was the new economic thinking of trickle-down economics by Thatcher in the UK and Reagan in the US. They believed that cutting tax rates for the rich would let the rich take more risks and invest more, which would create jobs.
Source:
Capital and ideology book by Thomas Piketty

28/04/2021

The statistic:
This graph shows the mitigation curves, the amount of CO2 reduction required to keep global warming below 1,5 degrees.

The interpretation:
This graph is from the IPCC (The Intergovernmental Panel on Climate Change), which are climate scientists all around the world to report the current situation of our planet made by the United Nations. In 2016 all countries agreed on the Paris agreement to aim for a global warming temperature of 1.5°C in order to minimise the collateral damage that global warming could create. This graph, in a nutshell, verbalises that if we keep CO2 pollution constant at the rate of 2019 we have only 9 years, from 2019 on, left before we have consumed our whole carbon budget to keep global warming below 1.5°C, to avoid this we have to reduce CO2 by 18%/year which is very challenging and nearly impossible. Even worse, this is if we keep global CO2 emission constant at the rate of 2019, which will probably not happen as there is more CO2 pollution each year as people leave poverty and demand CO2 intensive services and goods, meaning that in reality, we have less than nine years. Furthermore, this graph also shows that if we had only started trying to reduce CO2 pollution in the year 2000, only a 4%/year reduction would be necessary, which was certainly achievable.

Source
Sanwal, M., Wang, C., Zheng, X., & Yang, X. (2019). What National Decision‐makers Need From The IPCC: Special Reports With New Insights. Global Policy, 10(4), 728–732. https://doi.org/10.1111/1758-5899.12717

24/04/2021

The statistic: The statistic shows the number of trade disputes filed by the US in each year between 1995 and 2014. In addition, the US president in each of those years is specified.

The interpretation:
The statistic, retrieved from the paper “Suspiciously timed trade disputes” from Paola Conconi et al. (2017), suggests that there is a pattern of increasing trade disputes as we get closer to the end of each president’s first term, when it is still possible to run for office. There is no clear pattern for their second term. For instance, the Obama administration waited until 2 months before his re-election to file a trade dispute in the World Trade Organization (WTO) against Chinese export subsidies to car parts. It is no coincidence that the automobile industry, which would benefit from the trade dispute, was an important industry in the swing state of Ohio. The same evidence can be found for the George W. Bush presidency, as a dispute was filed to the WTO against the EU for subsidizing Airbus at the end of his first term. The authors argue that there are three potential explanations for this re-election effect: the trade dispute was delayed, the dispute was pushed too soon, or the dispute would not have been implemented at all if it wasn’t for re-election motives. Since there is no evidence that these trade disputes were weaker cases, cases that the US is more likely to lose, the most plausible explanation is that these cases were delayed. This shows how political games can delay the implementation of policies and hence hurt the concerned industries.

Source:
Conconi, P., De Remer, D., Kirchsteiger, G., Trimarchi, L., & Zanardi, M. (2017). Suspiciously timed trade disputes.

21/04/2021

The statistic: The statistic shows the coefficients of the explanatory variables of multivariate probit models along with the 90% confidence intervals.

The interpretation:
The statistic, retrieved from the paper “Almost identical but still treated differently: hiring discrimination against foreign-born and domestic born minorities” by Susanne Veit & Lex Thijsen (2021), shows that there is discrimination in the application process of publicly advertised vacancies. The researchers sent fictitious job applications to various vacancies and only changed the birthplace and origins of the applicants while keeping constant the other characteristics. The response was considered positive if the employer signalled interest or asked for further information. The research discussed in the paper only concerned job applications from Germany, the Netherlands, Norway, Spain, and the U.K. The results are striking: domestic-born children of immigrants and foreign-born ethnic minorities have a significantly lower probability to receive a positive response compared to majority candidates. In addition, minority group members of European origin and minority group members of Middle Eastern or African origin both have a much lower likelihood of receiving a positive response, the effect being significantly higher for the latter. Finally, there seems to be a hierarchy in the probability of receiving a positive response. Indeed, the probability of receiving a positive response is the highest for majority applicants, followed by domestic-born European minorities, foreign-born European minorities, domestic-born minorities of Middle Eastern and African origin, and foreign-born minorities of the same origin. Overall, this research shows that, although some improvements have been made, there is still a high level of discrimination against minorities in the EU when considering job applications.

Source: Susanne Veit & Lex Thijsen (2021) Almost identical but still treated differently: hiring discrimination against foreign-born and domestic-born minorities, Journal of Ethnic and Migration Studies, 47:6, 1285-1304, DOI: 10.1080/1369183X.2019.1622825

18/04/2021

The statistic:
The Food and Agriculture Organization of the United Nations (FAO) screens the evolution of forests worldwide. Their 2020 report on the state of the world’s forests provides insights on the state of global forests and their evolution over the last decades.

The interpretation:
In terms of deforestation, there is a clear tendency for lower net forest area loss over the last decades. Simply put, the rate at which deforestation takes place has decreased by 37%, coming from -0.19% per year between 1990-2000 to -0.12% per year between 2010-2020. Even though the trend is positive, the world has lost approximately the size of Libya in forest cover over the last 30 years. Furthermore, this statistic represents the net change in area covered, where some regions exhibit gains in forest areas (Europe and Asia) while others suffer from a higher deforestation rate (South America, Africa). Africa became the worst performing continent in terms of hectares lost per year in the last decade, beating South America which managed to reduce its area covered loss per year by 50% between 2000 and 2020. Agriculture for both commercial and local/subsistence use is found to be the main driver of deforestation, accounting for more than 70% of the total area lost in southern hemisphere continents.

Furthermore, forests in protected areas (national parks, conservation areas, game reserves) are found to represented around 18% of the global forest area and are increasing at an average rate of 17 million hectares per year since 1992 (+-2.5% per year), where tropicals forest are found to be the biggest beneficiaries from these protections. Nevertheless, this rate is increasing at a lower level over the last decade.

Overall, even though there is improvement in the statistics over the last decades, the targets of the Sustainable Development Goals (SDG) of 2030 are not met yet and are supposedly not attainable for the deadline. The current decrease in the area covered undermines the growth of biodiversity and the overall ecosystem which will in turn also affect the development towards the 2030’ SDGs.

For further information and the full report:
FAO and UNEP. 2020. The State of the World’s Forests 2020. Forests, biodiversity and people. Rome. https://doi.org/10.4060/ca8642en

14/04/2021

The statistic: The statistic shows the share of global vaccination going to the wealthiest 27 countries and their share of the global population.

The interpretation:
This statistic retrieved from the Bloomberg Vaccine Tracker shows that the distribution of vaccines in the world is highly unequal. Indeed, The wealthiest 27 countries have 39.3% of the vaccination while accounting for only 11,1% of the world population. In other words, the wealthiest countries vaccinate 25 times faster than the poorest countries. Thus, although in some countries, most of the population at risk will be vaccinated in the next few months, the covid crisis is likely to last globally if nothing is done to help the lowest income nations. Indeed, as long as the virus prevails in some part of the globe, new variants can develop and come back to the Western countries.
Source:
Bloomberg (2021). The world's wealthiest countries are getting vaccinated 25 times faster. available from: https://www.bloomberg.com/news/articles/2021-04-09/when-will-world-be-fully-vaccinated-pace-is-2-400-faster-in-wealthy-countries

10/04/2021

The statistic:
The graph shows the evolution of the average relative poverty rate by age group since the mid-1980 in 18 OECD countries. The relative poverty rate is defined as half the median-equivalised income

The interpretation:
The graph shows that average poverty risks have shifted from the old to the young. Indeed, the relative poverty rate increased by 7.6 percentage points for the young aged 18-25 since the mid-1980s. By contrast, the rate decreased for the elderly aged 75 or more by around 4 percentage points. A factor that contributed to this divergence was the 2008 financial crisis, which has hit younger generations the most. Nowadays, there is a clear division in the labour market between the old, who often have permanent contracts with strong protection rights, and the young, who often have to rely on part-time contracts with little to no protection against dismissal. For instance, in Japan, out of the 20 million people counted as “non-regular employees”, meaning that they have temporary or part-time contracts with no pensions or other benefits and low wages, ⅔ are women and most of the men are young (Bill Emmott, 2017). The same phenomenon can be observed in France, as in 2012 the french labour minister judged that only one young worker in five could expect to move from a temporary to a permanent contract (Peet John, 2012). In addition, the covid crisis has worsened the situation of the young by impacting education, lowering the amount of students' jobs and reducing employment opportunities for newly graduates. Finally, heavy investment in healthcare (although necessary), can be considered as a further transfer from the young to the elderly population. This type of intergenerational inequality is rarely mentioned in the mainstream media, but is essential to understand the frustration of the younger population.
Source:
Preventing ageing unequally. OECD. (2017). https://read.oecd-ilibrary.org/employment/preventing-ageing-unequally_9789264279087-en

07/04/2021

The statistics:
The tables show the “newsworthiness” of disasters by disaster and continent. Newsworthiness is how likely a certain event has to get news coverage. The sample used was 5212 natural disasters between 1968 and 2002.

The interpretation:
The academic research paper looks at how likely some event is to get into the news, by looking at the 5212 natural disasters between 1968 and 2002 and how this newsworthiness affected economic relief. The academics investigated how much news coverage of a certain disaster gets in the US to better understand the parameters of a disaster that makes it newsworthy. The following two tables show partial results of the study. The first table's last column shows how many people have to die from a certain disaster to get equal news coverage as the death of a volcano eruption. The second table is more shocking, it shows how many people have to die in a disaster to have equal coverage as one death in Europe, in other words, 91 deaths in the pacific of a disaster is estimated to get as much media coverage as 1 death in Europe from a disaster and 38920 people have to die from famine to get equal coverage as 1 person dying of a volcano eruption. Obviously, you have to take into consideration that this was between 1968 and 2002 in the US. This was before the creation of smartphones and social media and thus results are likely to be different. But it still gives an interesting insight on how much attention a death from a certain disaster gets.

Source:
Eisensee, T., & David Strömberg. (2007). NEWS DROUGHTS, NEWS FLOODS, AND U. S. DISASTER RELIEF*. The Quarterly Journal of Economics, 693-728.

Statistics worthy of Sharing

22/06/2021

14/06/2021

06/06/2021

30/05/2021

23/05/2021

19/05/2021

16/05/2021

09/05/2021

05/05/2021

01/05/2021

28/04/2021

24/04/2021

21/04/2021

18/04/2021

14/04/2021

10/04/2021

07/04/2021

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