Tales Of All Countries

[Vella Massart]

Accordingto conventional wisdom, a fiscal consolidation is likely to contract real aggregate demand. It has often been argued, however, that this conclusion is misleading as it neglects the role of expectations of future policy: if the fiscal consolidation is read by the private sector as a signal that the share of government spending in GDP is being permanently reduced, households will revise upwards their estimate of their permanent income, and will raise current and planned consumption. Only the empirical evidence can sort out which of these two contending views about fiscal policy is more appropriate -- i.e how often the contractionary effect of a fiscal consolidation prevails on its expansionary expectational effect. This paper brings new evidence to bear on this issue drawing on the European exercise in fiscal rectitude of the 1980s, and focusing, in particulars on its two most extreme cases -- Denmark and Ireland. We find that at least in the experience of these two countries the expectations' view has a serious claim to empirical relevance.

Then 9/11 struck, and all U.S. government-supported projects in Afghanistan were frozen. That particular food security project became a thing of the past. The world had made a dynamic change, and Afghanistan was at its epicenter.

One night we had dinner at the home of a couple from another agency in this consortium. They had invited a few of us over for stew, cooked in a pressure cooker on their small two-burner propane stove. A nurse from Switzerland arrived late and seemed somewhat distant while we all got acquainted. Over dinner she and I started to talk, during which I sensed something was wrong. I eventually asked her if she was okay. That led to a heartbreaking story. Just before dinner, as the local medical professional, she had been called to the village close to the one we were at. Seven children had been playing with a land mine they found in the dirt close by. Her job was to confirm each of their deaths as the families identified the remains of their bodies. As you can imagine, she was shaken to the core by this. We listened to her cry and tell the story. Although many mines had been laid during the warlord conflict and were still deployed, it was believed these had been laid by the Taliban as another way to exert terror.

Clean water is one of the most basic of all human needs. Without it children, families, and entire communities struggle just to survive. Poverty, sickness, and even death linger wherever people lack a source of clean, safe water.

The United Nations Food and Agriculture Organization estimates that 811 million in the world suffer from under-nourishment. By providing the gift of food, you will save the lives of thousands of hungry children and their families in developing countries like Haiti, Malawi, Guatemala and Nicaragua.

The country / origin of the tale is taken from the website Andrew Lang's Fairy Books which also has translations of the tales.

The casual user should be aware that not all the tales in Lang Fairy Books are fairy tales / folk tales There are several

stories from Hans Christian Anderson. A Voyage to Lilliput is from Swift's Gulliver's Travels.

It tells a stark story of health inequality\u2014\u201Dthe most shocking and the most inhumane\u201D form of injustice, as Martin Luther King, Jr. once said, with almost twenty years of expected life separating the coastal states from the red belt in the South and Appalachia. The fact that the map\u2019s designers decided to represent the spectrum of life expectancy on a red-blue gradient gave it an implicit\u2014and not entirely inappropriate\u2014political spin. Blur your eyes a little and the red regions could be a map of Trump support, or\u2014more consequentially\u2014states that have refused the Obamacare Medicare expansion.

Politics aside, you will not be surprised to hear that I was delighted to see this map go viral. The power of data visualization as a tool for making sense of what, quite literally, ails us is an old obsession of mine, dating back to John Snow\u2019s map from 1854, and William Farr\u2019s country-vs-city life tables from the early 1840s. New ways of seeing patterns in health data can be as lifesaving in the long run as new therapeutic drugs or clinical treatments. And of course, I am a big believer in the utility of the life expectancy unit of measurement, having written an entire book (and co-produced a TV series) about that particular datapoint. I think you can make the case that life expectancy at birth is the single most revealing datapoint for measuring how well a nation or region is functioning\u2014more revelatory than GDP or educational attainment, or any other yardstick. If you\u2019re trying to get a sense of how well a given society is doing, asking how long people live on average is a great place to start.

Viewed by that standard, the life expectancy map of the US tells a tale of two countries. The gap between the US county with the highest life expectancy\u2014Summit County, Colorado, with an LE of nearly 87\u2014and the lowest\u2014Oglala Lakota County in South Dakota\u2014is a full twenty years. Overall, the United States ranks about 50th in life expectancy worldwide, but if coastal, urban America broke off and formed its own country, it might well rank in the top ten. If Appalachia formed its own country, it would be about 70th in the world rankings, roughly in the same league as Algeria and Iran.

If you know something about the history, the map is an extraordinary display of one of the most important demographic trends of the past two centuries: the great reversal of urban/rural health outcomes\u2014a transformation that began with Snow and Farr\u2019s work in the middle of the 19th century. A century and half ago, the most deadly places to live in industrialized countries were the big cities. Today it\u2019s the countryside that kills you. Here in Brooklyn, for instance, average life expectancy is about five years higher than the overall US average\u2014that\u2019s not affluent brownstown Brooklyn, mind you, that\u2019s all 2.8 million residents of Kings County.

There\u2019s one other important point that should be made about the life expectancy map that is not immediately visible from just looking at it: the striking disparities you see there are not exclusively reflecting an underlying problem with our healthcare system. A huge part of what is driving the historic decline in life expectancy over the past few years is the spike in deaths in teenagers and young adults, thanks to opioid overdoses, suicide, gun deaths, and traffic accidents. (As I was writing this post, David Wallace-Wells published an excellent op-ed diving into this point in more detail.) Overall LE expectancy numbers can be dramatically altered when young people die. (The high rate of infant and childhood mortality for most of human history is why LE was so shockingly low\u2014somewhere in the low to mid-30s\u2014for so long.) The same principle applies when a large cohort of 20-year-olds start dying. Even though the map also happens to roughly coincide with vaccination rates, COVID is less responsible for the variation in LE here, because COVID mortalities are so skewed towards the elderly. The closest thing that I can think of to what we are seeing with young people in the red zone on this map is what the US lived through in 1918-1919, when the combination of the war and the Great Influenza (which for complicated reasons was disproportionately lethal for young people, and relatively mild for older folks) killed a significant number of otherwise health young adults. That comparison should be a wake-up call for all of us.

It\u2019s somewhat ironic contemplating a map like this in light of the many new studies that have been released over the past few months suggesting that we are on the verge of some significant breakthroughs involving our understanding of the aging process itself\u2014including the possibility of slowing it down significantly. This is something I touched on a bit in Extra Life, but only at the very end of the book\u2014most of it was focused on health innovations that prevented illnesses like smallpox or the reduction of catastrophic events like automobile crashes. But for the past year or so, I\u2019ve been working on a project that looks specifically at the prospects for radical life extension, and what the societal and ethical implications of such a project might be. (In many ways, this investigation parallels some of the questions I raised in \u201CThe Man Who Broke The World,\u201D my Times Magazine piece from last month.) I\u2019ll have more to say about this in the next installment of the newsletter, but I\u2019ve written a long essay\u2014almost a pamphlet, really\u2014on these issues, and I\u2019m going to serialize it here for paying Adjacent Possible subscribers over the next few months. I\u2019m really excited about both the format\u2014longer than a magazine article, but shorter than a proper book\u2014and the subject matter. I think we may well be on the verge of a significant societal conversation about the implications of anti-aging interventions; this series should be helpful in getting you all up to speed for that debate. Or at least help you decide if you really want to live forever!

The rise of economic inequality is one of the most hotly debated issues today in the US (Furman 2016) and indeed in the world. Yet economists and policymakers alike face important limitations when trying to measure and understand the rise of inequality.

One major problem is the disconnect between macroeconomics and the study of economic inequality. Macroeconomics relies on national accounts data to study the growth of national income, while the study of inequality relies on individual or household income, survey, and tax data. Ideally all three sets of data should be consistent, but they are not. The total flow of income reported by households in survey or tax data adds up to barely 60% of the national income recorded in the national accounts, with this gap increasing over the past several decades.1

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