Want to track the impact of the Trump administration’s tariffs? The Tax Foundation has made that possible with its new tariff tracker. In the introduction, the analysts explain,
The Trump administration has imposed and threatened several rounds of tariffs in 2018, and other countries have responded to these measures in kind. Using the Tax Foundation Taxes and Growth Model, we analyze the effects of enacted, threatened, and retaliatory tariffs on the United States economy. Tariffs damage economic well-being, and lead to a net loss in production and jobs, and lower levels of income.
According to the Tax Foundation model, the tariffs enacted so far by the Trump administration would reduce long-run GDP by 0.06 percent ($15 billion) and wages by 0.04 percent and eliminate 48,585 full-time equivalent jobs. If the Trump administration enacts additional tariffs on automobiles and parts and additional Chinese tariffs, GDP would fall by an additional 0.3 percent ($89.60 billion), resulting in 0.2 percent lower wages and 277,825 fewer full-time equivalent jobs.
Other countries have also announced intentions to enact tariffs on U.S. exports. If these tariffs are fully enacted, we estimate that U.S. GDP would fall another 0.05 percent ($12 billion) and cost an additional 38,182 full-time equivalent jobs.
If all tariffs announced thus far were fully enacted, U.S. GDP would fall by 0.47 percent ($117.6 billion) in the long run, effectively offsetting one-quarter of the long-run impact of the Tax Cuts and Jobs Act. Wages would fall by 0.33 percent and employment would fall by 364,593.
Several years ago, I linked to a Brookings post that highlighted parenting as having a massive effect on children’s outcomes. I was reminded of it while reading a 2016 post at the World Bank’s Development Impact blog. The author David McKenzie, Lead Economist in the Development Research Group, writes,
I was surprised by a paper by Todd Schoellman in the most recent AEJ Macro which argues that parents, not country, are what matters for early childhood development.
He studies the adult outcomes of refugees who immigrated to the U.S. as children, but who differed in the age of arrival. The main analysis is on IndoChinese refugees fleeing the Vietnam War and Khmer Rouge. The thought experiment is to compare the labor market outcomes and completed education of a refugee who arrived at age 1 to one who arrived at age 3 or 4. The latter had 2 or 3 more years of that critical early childhood period in a poor country (Vietnam, Laos and Cambodia) where conflict was going on, and then both come to the U.S. and grow up there. The key result is seen in this graph – which shows that adult wages are no different for those who arrived at age 4 or 5 versus those who arrive at age 0 or 1:
Schoellman argues (according to McKenzie)
that the theory most consistent with this data is that parents, rather than country environment, are the most important inputs to early childhood human capital formation. Of course one can argue that country environment in turn shapes parents – it determines parental education, parental wealth, etc. But the result that parents, and not goods or place, matter is a surprising one. By the way, if you are worried about external validity, he shows the same flatness of log wages with respect to age of arrival between 0 and 5 also holds for Ethiopian and Afghani refugees, Cuban immigrants, Mexican immigrants, and for the set of immigrants from poor countries as a whole.
If you want to argue about another type of external validity, I would argue that the U.S. may not be very good at providing early childhood care for refugees (at least at the time of the study) – this is not discussed in the paper, but it seems likely to me that the U.S. is a worse place to receive early childhood care if you are a poor family than most European countries or Australia and New Zealand – so perhaps it is only when you get into public schools here that you start to get the benefits. The type of data the authors have doesn’t tell us anything about what early childhood educational facilities, if any, were available to these refugee kids.
You can read a working paper version of the study here.
The question is cheeky, but appropriate given how “check your privilege” is all the rage. A new working paper suggests that high unemployment rates are actually found in wealthy countries rather than poorer ones. The researchers summarize,
[C]laims about unemployment in poorer countries are all over the map, with some studies finding that the highest unemployment rates in the world are in Sub-Saharan Africa, at around 30% per year (World Bank 2004), while others posit that unemployment is almost non-existent in developing countries, since people there are too poor to have the luxury of not working (Fields 2004, Squire 1981).
In our paper (Feng et al. 2018), we contribute to the study of unemployment by building a data set of average unemployment rates covering 84 countries of all income levels. To address the issue of data comparability, we intentionally bypass existing data banks, such as those of the ILO, and build our own measures of unemployment from the ground up using household survey data. We draw on 199 nationally representative household surveys conducted in various recent years (but mostly between 2000 and 2010). Importantly, our data cover not just the rich countries of the world, but many poor nations, including a dozen from sub-Saharan Africa and many more from the middle of the world income distribution.
…We define work as wage employment, self-employment, and unpaid work in a family farm or business. We exclude those doing home production of services like cooking, cleaning, and childcare. Our search measures cover job search activity in some recent period, like the last week (in our preferred metrics). We compute unemployment rates in each survey as the number of adults not working but searching for work / (number working + those not working and searching). We then average over all surveys for each county in our data.
What we find is that average unemployment rates are in fact substantially lower in poor countries than in rich countries. In the poorest quartile of the world income distribution, unemployment averages around 2.5%, while in the richest quartile, around 8% of the labour force is unemployed on average. Interestingly, we find that the higher unemployment rates of richer economies hold for workers of all ages, for men and women separately, and within both urban and rural areas. Thus, our data suggest that unemployment is largely a rich-country phenomenon, rather than a feature of underdevelopment.
Why is this the case?
The figure below plots the ratio of unemployment for the low educated to that of the high educated against the log of GDP per capita. In the poorest countries, this ratio is below one, meaning that the low educated are less likely to be unemployed than the high educated. The exact opposite is true of the richest countries, all of whom have ratios above one. For instance, in the US, the ratio is 2.3, meaning that those without high school education are more than twice as likely to unemployed as those that finished high school (or went on to college or beyond). Whereas one might have thought this pattern is a universal feature of labour market outcomes, our data show that unemployment concentrated among the less educated is actually confined to richer economies.
The authors explain,
Countries differ only by their productivity in the modern sector, with the traditional sector offering the same (low) level of output per worker in all countries. This assumption is central to our analysis and based on the emerging consensus that cross-country productivity differences are skill-biased, rather than affecting unskilled and skilled tasks and workers equally (Caselli and Coleman 2006, Malmberg 2016).
The model predicts that in countries with low modern-sector productivity levels, the bulk of the workforce sorts into the traditional sector. Only workers with the highest skill levels choose the modern sector. Then, as modern-sector productivity rises – our proxy for ‘development’ – more and more lower skilled workers are drawn into the modern sector. This raises overall unemployment, as more workers now search for jobs, with some of them being unemployed each period in equilibrium. As in the data, our model predicts that development also brings a rise in the ratio of unemployment for the less- to more-skilled. The reason is most of the higher skilled workers are already in the modern sector and searching for wage jobs in poor economies. As modern-sector productivity grows, it is the less skilled workers that switch sectors, and their unemployment rate rises faster as a result.
They conclude,
Our research suggests that unemployment is largely a feature of advanced economies. In poor countries, only the most skilled workers search for wage jobs, while most of the less skilled workers select into traditional self-employment activities. Thus, few workers in poor economies are actually unemployed in practice. In advanced economies, the modern wage-paying sectors have relatively high productivity levels and employ the bulk of the labour force. As a consequence, as modern firms and positions come and go, workers are occasionally cast into unemployment. While unemployment per se is undesirable, the high underlying productivity level of the modern sector is not.
Paul Krugman once famously said, “Productivity isn’t everything, but in the long run it is almost everything. A country’s ability to improve its standard of living over time depends almost entirely on its ability to raise its output per worker.”[ref]The Age of Diminished Expectations: U.S. Economic Policy in the 1990s, 3rd ed., pg. 11.[/ref] Harvard’s Greg Mankiw writes, “Almost all variation in living standards is attributable to differences in countries’ productivity[.]”[ref]Principles of Economics, 7th ed., pg. 13.[/ref] This study demonstrates at least two important points:
The United Nations Sustainable Development Goals (SDGs) are targets for global development adopted in September 2015, set to be achieved by 2030. All countries of the world have agreed to work towards achieving these goals.
Our SDG Tracker presents data across all available indicators from the Our World in Data database, using official statistics from the UN and other international organizations. It is the first publication that tracks global progress towards the SDGs and allows people around the world to hold their governments accountable to achieving the agreed goals.
The 17 Sustainable Development Goals are defined in a list of 169 SDG Targets. Progress towards these Targets is agreed to be tracked by 232 unique Indicators. Here is the full list of definitions.
This new version of our SDG-Tracker was launched on 28th June 2018. We will keep this up-to-date with the most recent data and SDG developments through to the end of the 2030 Agenda.
Pro-choicers frequently claim that making abortion illegal won’t decrease the number of abortions; it will only decrease the number of safe, legal abortions. They suggest that there is no practical use to restricting abortion legally and that if pro-lifers really cared about decreasing abortion rates, they would focus on decreasing unplanned pregnancies (through better access to contraception, better sex education, etc.)
So pro-choicers claim.
But there’s a lot of research to show that abortion law affects abortion rates–and not just legal abortion rates, but total abortion rates. Studies often measure the changes in fertility in areas where abortion access recently changed. Secular Pro-Life has compiled a list of such studies if you’re interested.
I’ve now had a few conversations where I point out this reality, and the pro-choice person’s response is to claim that even if abortion restrictions have some nonzero effect on abortion rates, that effect is dwarfed by the decrease in abortions thanks to contraception access. It’s easy for me to believe that both more access to contraception and less access to abortion will decrease abortion rates, and personally I’m for taking both approaches. But the claim that the effect of contraception access trounces the effect of abortion access sounds like just a slightly watered down version of the false claim that abortion access doesn’t affect abortion rates at all. That is, it’s an ad hoc, ill-founded claim to justify our country’s incredibly liberal abortion laws, but the evidence (at least what I’ve seen so far) doesn’t bear it out.
While the drop mirrors the closure of abortion clinics nationwide, experts say the figure is likely down to more effective use of contraception and the falling pregnancy rate.
The article references this CDC report, which has found a net decrease in the abortion rate (number of abortions per 1,000 women age 15-44) of 22% (from 15.6 to 12.1). This is great news, but it’s not clear from the CDC report the extent to which different factors contributed to the decrease. The CDC authors explain
One factor that might have contributed to this decrease is the increase that occurred during the same period in the use of the most effective forms of reversible contraception, specifically intrauterine devices and hormonal implants, which are as effective as sterilization at preventing unintended pregnancy (102–105). Although use of intrauterine devices and implants has increased in recent years, use of these methods remains low in comparison with use of oral contraceptives and condoms, both of which are less effective at preventing pregnancy (102,104).
So contraception likely played a role, but the CDC can’t quantify it, and they still find that the most effective forms of contraception are not used much compared to the less effective forms. They certainly aren’t asserting that the entire 22% decrease is due solely to contraception access, and their report doesn’t attempt to compare the effects of contraception access to the effects of abortion access.
There are studies that looked at both factors. For example, this Guttmacher report found that between access to the Pill and access to abortion, abortion was associated with a birth rate decrease twice that for the pill.
Among white minors, having had access to the pill was associated with a 9% drop in the overall birthrate and an 8% drop in the rate of nonmarital first births. In this same group, access to an abortion was correlated with a 17% decline in the nonmarital birthrate and a 16% decline in the rate of nonmarital first births.
Another study found that, for women under age 19, “liberalized abortion policy predicts a 34 percent decline in motherhood” whereas “the results do not provide evidence that pill policies had a substantial effect.” The author explains
The birth control pill’s effects on family formation are theoretically ambiguous: The pill was a technological innovation in contraception, but with a failure rate of about 9 percent in the first year of typical use (Trussell, 2004), it still provides an imperfect means of preventing pregnancy. Trends in sexual behavior suggest that any reductions in unintended pregnancies among teens due to safer, pill-protected sex were offset by large increases in sexual activity. Difference-in-difference estimates also provide little evidence to support the view that pill policies had a substantial influence on age at first birth and marriage. Results in Goldin and Katz (2002) and Bailey (2006, 2009) that suggest otherwise are not robust to reasonable perturbations of the authors’ research designs including addressing discrepancies in the legal codings, choosing alternative data sets, and/or adjusting sample selection procedures. Rather, the results robustly point to policies governing abortion, a second, less lauded but more certain means of preventing unwanted births, as the driving force behind delayed family formation in the 1970s. [Emphasiss added]
This study is not a perfect comparison to claims about more modern contraception. The idea is that the most effective forms of contraception (e.g. IUDs instead of the Pill) do a better job of decreasing unintended pregnancy rates because even if users increase their sexual activity as a result, the increase in risk-taking behavior does not offset the decrease in risk these more effective contraceptive methods provide.
Note also that research suggests when abortion is legalized the abortion rate increases more than the birth rate decreases. See Footnote 8 of this report, p8 of the PDF, which explains in part:
Note, however, that the decline in births is far less than the number of abortions, suggesting that the number of conceptions increased substantially –and example of insurance leading to moral hazard. The insurance that abortion provides against unwanted pregnancy induces more sexual conduct or diminished protections against pregnancy in a way that substantially increases the number of pregnancies. [Emphasis added]
People are less cautious about avoiding pregnancy when they know they can get abortions as a back up option. This idea is further substantiated by a study published in the June 2015 edition Perspectives on Sexual and Reproductive Health which concluded:
Women who lived in a state where abortion access was low were more likely than women living in a state with greater access to use highly effective contraceptives rather than no method (relative risk ratio, 1.4). Similarly, women in states characterized by high abortion hostility (i.e., states with four or more types of restrictive policies in place) were more likely to use highly effective methods than were women in states with less hostility (1.3).
This research also suggests that teasing out the effects of abortion access compared to contraception use may prove challenging, since the two appear to be inversely correlated.
So with that brief overview of just a few studies, so far these are the conclusions I’m drawing:
