As usual, all data can be directly downloaded from the interactive visualistion.
And the interactive visualisations can be turned into a static image via the option of the downward arrow in the bottom right corner of the visualisation.
In the chart below we have plotted rate of natural population increase (i.e. combination of birth and death rate) vs. child mortality for all countries from 1970-2015. This, of course, makes the chart a little busy but shows a largely consistent downward trend. We have added an option in the bottom left of the chart to "highlight typical countries from each continent". Selecting this will highlight one country from each continent which we thought was fairly representative and showed this trend well. This should be useful in conveying that the general trend is consistent across the world and not just regionally or context-focused.
For this one, we have two charts you may be interested in using. The first chart below shows the relationship between child mortality and level of prosperity (GDP per capita). It shows quite clearly the correlation between the two, but by pressing 'play' we can also see the tendency for countries to move towards the bottom right with time.
The second chart shows fertility rate (average children per woman) versus level of prosperity (GDP per capita). Again, watching these trends through time shows nicely the gravitation towards the bottom right corner as levels of prosperity rise.
For this one, the graph below, found in our Life Expectancy entry here is probably best to use.
We have just written a new paper on this question. A short write up of it is here – if you want to read the entire paper just send me an email.
This chart shows the divergence in the US:
This chart shows that the US is an outlier and that the divergence is (much) smaller in other countries:
And this chart shows what accounts for the 'decoupling' that we saw in the US:
FRED is a good source for this – for example this one.
I have made the simple chart below for you detailing suicide rate (per 100,000 individuals) in the United States over the last decade or so. It's also worth noting that others at OWID did a great long-run look at suicides in the 'Suicides' entry, found here. Esteban and Max also did a great update of the 'Happiness and Life Satisfaction' entry, found here. There may be some charts or information in those which would be useful for you in this section.
Here is the latest data by the CDC.
All the details on the measures are here at CDC: https://www.cdc.gov/drugoverdose/data/analysis.html
For this one, we have lots of information and charts at our entry on Population growth, in this section here in particular.
We have plotted the price trends for both durables and services in a single chart for the USA in the chart below. We have also added this to our "Technological Progress" entry here, with the following descriptor:
"In the chart below we see the price changes in goods and services in the United States from 1997-2017, measured as the percentage price change since 1997. Positive values indicate an increase in prices since 1997, and negative values represent a price decline. Here we see a distinct divide between consumer durables and technologies (which have typically seen a price decline), and service-based purchases (which have increased in price).
A combination of industrial offshoring, technological innovation and economies of scale have resulted in a price decline in goods such as televisions (-96% since 1997), software (-67%), toys (-69%), and clothing (-4%). In contrast, the prices of goods and services such as education, childcare, medical care, and housing have increased significantly, rising by 150%, 110%, 100% and 58%, respectively.
The observed rise in costs of services may be partly attributed to the so-called ‘Baumol’s cost disease‘, which is an important exception from the general regularity that the pay for labor – the wage – reflects the productivity of labor. Baumol’s cost disease describes the phenomenon whereby wages rise in jobs which have experienced little improvements in labor productivity in order to compete with salaries in other sectors.
Examples of service-based roles such as nursing, healthcare, childcare and education have experienced little productivity growth relative to manufacturing sectors which have seen continued improvements through technological innovation. In order to retain employees in service-based roles, salaries have risen in order to remain competitive with industrial sectors; this increase in pay has occurred despite minimal gains in productivity. This may in part explain why the cost of education, healthcare and other services have risen faster than the general rate of inflation."
In the chart below we have mapped (and can also be viewed in "chart" mode) per capita healthcare expenditure by country. It is also worth noting here that we also have this blog post by Max on healthcare spending and life expectancy; there are some great charts in there which may fit with what you are looking for (and are welcome to use).
For this I pulled together data on oil & gas employment records from the US Bureau of Labor Statistics and oil & gas rig count in the US from 1970-2017. I wasn't entirely sure how it was going to turn out, but you see the relationship between the two in the chart below.
There is certainly a decoupling, which you were looking for! I'm not sure if this pattern is what you were hoping for, but it is the story that the data tells. You are welcome this chart if it is useful for you, but also welcome to disregard if not.
In the chart below we have plotted the referenced data for the non-commercial distance flight records. We have also included this as a short explainer in our entry on Technological progress, found here.
There are a couple of charts we have included here which may be useful for you to choose from. The first is the cost per Mb of human genome DNA sequence over time (based on NHGRI Genome Sequencing Program data).
The second chart is the one you highlighted that would be best for you to use- the number of genome base pairs which could be sequenced for a given economic value. Having crunched the numbers on this, we are pretty sure the graph from the Medium article you linked to is incorrect. We got another member of our team, who did his degree in exactly this field to take a look over this, and he has confirmed that the article has majorly overestimated the costs of sequencing. Instead, in the second chart, we have calculated and plotted the number of DNA base-pairs which can be sequenced for one US$. I have included details of the calculation step I took to derive these figures in the "sources" tab of the chart.
We have also added this as a short explainer to our entry on Technological Progress, found here. It's worth noting (and as we mention in our entry), that the cost of sequencing a full human genome are slightly higher than summing all of the necessary Megabases. We have also graphed the cost of the full human genome, here. You can decide which of these (if any) is most suited to what you want to convey.
Here you cited Zucman et al's Distributional National Accounts work. This data is now available directly in the very good World Wealth & Income Database.
There you find for example data on Personal Wealth to Net National Income Ratios.
In the chart below I have plotted the price of lab-grown meat, and the average annual price of 100% ground beef in the US (based on data from the US Bureau of Labor Statistics). Note that for consistency (and as is often best to do so they can be compared with other food commodities) I have recalculated these as the price per kilogram (in US$). Details of the conversion factor I used are in the "sources" tab of the chart.
You are more than welcome to use this- the data is perfectly valid in any case- but it's probably worth noting that the cost of lab-grown meat at the moment is exactly that: the cost of production at a very small lab scale. There are likely to be significant (and possibly economically challenging) issues in terms of scaling this up to a level close to commercial-scale. Knowing a little about synthetic proteins and meats personally, I see this to be incredibly challenging. This transition (which is likely reflected in other select technologies) could be quite complex from the perspective of rates of technological progress, economies of scale and the overall economics of production. I hope to develop a much better understanding of the different elements of technological progress for future work at OurWorldinData, so cannot really offer a more concrete explanation of what this transition across the scales of production may look like at the moment.
I have tried to be explicit in the subtitle of this chart (and in the source tab) that this the costing for lab-scale production. It is still an example of rapid progress, regardless, so you can see if it fits in with your book at some stage.