Macro Roundup

defense spending, adjusted for ppP. In ppP terms the US is being outspent by China and Russia combined and that American number includes funding for ongoing operations in the middle east which aren’t relevant vis-à-vis peers, “… I construct a military-PPP exchange rate for 59 countries, using publicly available data. Intuitively this breaks down each country’s military budget into three broad categories, personnel, operations, and equipment, and then constructs a relative unit cost ratio across counties for each component. The military-PPP exchange rate is then the average of relative equipment prices; relative wages for personnel, adjusted for skill levels; and the GDP-PPP exchange rate to measures operations costs - where the budget share of each component is the appropriate weight. Thus, the military-PPP exchange rate will take account of factors such as low salaries for personnel and appropriately weight these costs in the overall defence budget. The approach yields values that can be interpreted as a first-order approximation to the true relative military unit cost ratio, under standard assumptions of cost minimisation and constant returns to scale...In market exchange rate terms, these three countries have combined military spending in 2019 that amounts to $378 billion, compared to the US’s $734 billion. In military-PPP terms, however, their combined spending is $840 billion (Figure 1). Hence, in real terms, the US military budget in 2019 is not larger than the next eleven countries. Rather it is smaller than the next three and similar to the spending of China and Russia combined….”

Peter Robertson, "Debating defence budgets: Why military purchasing power parity matters," Center For Economic and Policy Research, October 9, 2021, https://voxeu.org/article/why-military-purchasing-power-parity-matters

Debating defence budgets: Why military purchasing power parity matters

In his 2016 State of the Union address, US President Obama claimed that “he United States of America is the most powerful nation on Earth. Period. It’s not even close. We spend more on our military than the next eight nations combined.”

Since then, US military spending has only increased, and today, measured at market exchange rates, it is greater than the next 11 countries combined (Stockholm International Peace Research Institute 2021, Peterson Foundation 2021). This apparent difference makes a mockery of US defence budget requests and of claims that any country poses a significant threat.1

In this column, I show that the perception of the US military ‘spending supremacy’ is misplaced. Specifically, the conventional use of market exchange rates to compare across countries dramatically overstates US real military spending relative to other countries and, likewise, understates the real spending in countries such as China, India, and Russia.

What is militaryppP?

The need to adjust for local price differences when making international comparisons of real GDP or standards of living is well recognised. Because relative prices differ across countries, a dollar spent on non-traded goods and services can go a lot further in some countries than in others. This is known as the Penn effect or the Balassa-Samuelson effect, and reflects the fact that the market exchange rate only arbitrages the price of traded goods, so international relative prices of non-traded goods can differ widely from the exchange rate.

The same applies to military spending. Soldiers in the Chinese People’s Liberation Army (PLA), for example, receive far lower salaries than the equivalent US personnel (The Economist 2021). Thus, the PLA’s salary budget, if converted to US dollars at the market exchange rate, would pay for far fewer US army personnel than what it actually pays for in China.2

In response to this, international comparisons of military spending are sometimes made using GDP-purchasing power parity (PPP) exchange rates. These, however, are designed to compare average prices, not military costs; they could overstate military spending since procurement doesn’t necessarily depend on local labour costs (Gilboy and Heginbotham 2012, Frankel 2014).

The alternative views and exchange rate concepts have long been a source of frustration for defence planners, particularly with the enormous variations they imply for China, which has been as much as 200% (Crane 2005).

In fact, neither GDP-PPP exchange rates nor market exchange rates provide the ‘correct’ ratio for converting spending across two countries into real values. The appropriate exchange rate is one that tells us the relative price or unit cost of defence services across countries - that is, a ‘military-PPP’ exchange rate.

The problem in constructing a military-PPP exchange rate is that output prices and quantities of defence services are unobservable and, perhaps unsurprisingly, few countries have shown much inclination to share the necessary input cost data. There is also a problem of substitution bias, which arises because countries will choose different combinations of inputs - such as personnel and equipment - because they have different relative prices.

The challenge, therefore, is to find the best estimate of countries’ real defence budgets given the available data. In Robertson (2021), I construct a military-PPP exchange rate for 59 countries, using publicly available data. Intuitively this breaks down each country’s military budget into three broad categories, personnel, operations, and equipment, and then constructs a relative unit cost ratio across counties for each component. The military-PPP exchange rate is then the average of relative equipment prices; relative wages for personnel, adjusted for skill levels; and the GDP-PPP exchange rate to measures operations costs - where the budget share of each component is the appropriate weight.

Thus, the military-PPP exchange rate will take account of factors such as low salaries for personnel and appropriately weight these costs in the overall defence budget. The approach yields values that can be interpreted as a first-order approximation to the true relative military unit cost ratio, under standard assumptions of cost minimisation and constant returns to scale.3

US military spending is about the same as China's and Russia's combined

Looking at military spending this way reveals that real military spending in many countries is indeed far larger than the values implied by market exchange rates. To see this, rather than looking at the US relative to the next eight countries, we can focus on just three other countries - China, India, and Russia.

In market exchange rate terms, these three countries have combined military spending in 2019 that amounts to $378 billion, compared to the US’s $734 billion. In military-PPP terms, however, their combined spending is $840 billion (Figure 1). Hence, in real terms, the US military budget in 2019 is not larger than the next eleven countries. Rather it is smaller than the next three and similar to the spending of China and Russia combined.

Real defence spending versus military power

Whether the US is spending too much or not enough is a hotly debated issue andppP concepts are sometimes invoked (e.g Korb 2021 and Greenwalt 2021). As tragically demonstrated by the collapse of the Afghan National Army, however, spending isn’t everything. Comparing real military spending across countries amounts to comparing military inputs, not military output. Military output, or ‘power’, also depends on a country’s defence strategy, alliances, force multipliers and other non-budget factors.

This, however, is true of all international comparisons of military spending, irrespective of what exchange rate concept is used. Using military-PPP does not remove the need for an expert eye to how effective a country’s spending is. It does, nevertheless, provide a more solid economic basis for thinking about these issues.

Caution is also required since the military-PPP values discussed here are based on very aggregate data and involve approximations. They can be improved upon, particularly with more disaggregated data. The key strength of the military-PPP approach is it sets out an exchange rate concept that has a clear economic meaning.

With these caveats, the results show that market exchange rates dramatically understate the real military spending of many countries including China, India, and Russia. Defence analysts will have their views on what this means in terms of relative capabilities. But evaluations of security risks can only be improved if they avoid exchange rate misperceptions.

References

Beinart, P (2021), “Let’s cut our ridiculous defense budget”, New York Times, 7 April.

The Economist (2020), “Buck for the bang: Nominal spending figures understate China’s military might”, 1 May.

Frankel, J (2014), “China is not yet number one”, VoxEU.org, 9 May.

FederalPay.org (2021), “United States Army - O-6 colonel”, accessed 25 September.

Gilboy, G J, and E Heginbotham (2012), Chinese and Indian strategic behaviour: Growing power and alarm, New Delhi: Cambridge University Press.

Greenwalt, B (2021), “China already outspends US military? Discuss”, Breaking Defense, 26 May.

Korb, L (2021), “China is not outspending US on defense; ’22 budget is enough”, Breaking Defense, 7 June.

Peter G Peterson Foundation (2021), “The US spends more on defense than the next 11 countries combined”, 19 July.

Robertson, P E, and A Sin (2017), “Measuring hard power: China’s growth and military capacity”, Defence and Peace Economics 28(1): 91-111.

Robertson, P E (2015), “China’s military and growing political power”, VoxEU.org, 30 March.

Robertson, P E (2021), “The real military balance: International comparisons of defense spending”, Review of Income and Wealth (forthcoming).

The South China Morning Post (2021), “China to boost pay for PLA forces after years of sweeping reforms”, 21 January.

Stockholm International Peace Research Institute (2021), SIPRI military expenditure database, accessed June 2021.

Zakaria, F (2021), “The Pentagon is using China as an excuse for huge new budgets”, Washington Post, 18 March.

Endnotes

1 Fareed Zakaria, writing in March 2021, argued, “America’s ‘edge’ over China is more like a chasm … China spends around $250 billion on its military, a third as much as the United States” (Zakaria 2021). Similarly Peter Beinart criticised US President Biden’s 2022 Department of Defense budget, arguing that “China spends less than one-third as much on defense as the United States” (Beinart 2021).

2 As an example, a colonel in the US army earns a salary of $7,100 to $12,638 per month (FederalPay.org 2021). Converting this to renminbi at the market exchange rate gives enough renminbi to pay for two to four colonels in the People’s Liberation Army, given a monthly salary of RMB 20,000 as reported by The South China Morning Post (2021).

3 Robertson (2021) discusses the implication of various standard index number approaches including bilateral and multilateral indices. The method builds on previous work on China in Robertson (2015) and Robertson and Sin (2017).

4 Military spending is also an annual flow, whereas capabilities depend on past spending assets and experience.

Note Wikipedia’s ranking of defense spending by countryhttps://en.wikipedia.org/wiki/List_of_countries_by_military_expenditures

Every year between 2015 and 2018 an average of 40,641 individuals between ages 18 and 64 died of opioid overdoses. A large majority of them, 37,596, did not have a college degree. The rest, 3,045 of them, were college graduates. The benchmark economy matches these statistics exactly. The downward spiral from opioid use is portrayed in Figure 8. It shows how the college- and non-college-educated' expected utilities steadily decline as they move through the stages of opioid addiction. The descent appears fairly gradual until one hits the addiction stage and, of course, the loss in utility associated with death is large. While utility is always higher for college graduates, the relative utility values of death to nonuse are roughly the same for both types of individuals. Backin 2000, the number of opioid-related deaths was only 8,179 (7,549 deaths among non-college and 629 among college graduates). Between 2000 and 2018, more than 400,000 individuals between the ages of 18 and 64 died from opioid overdoses. What can account for the dramatic rise in opioid use and overdose deaths during the last two decades? The model is now used as a quantitative laboratory to answer this question. Three candidates are entertained; namely, the fall in opioid prices, more powerful prescriptions, and longer length prescriptions…The calibrated model is then used to highlight the forces underlying the recent opioid epidemic. Through the eyes of the model, there were two key forces. The first force isthe decline in prices for both prescription and black market opioids. Thishad a big effect. The second force isthe increase in the dosages per prescription meted out by doctors. Thisalso had a significant impact. The fact that doctors kept pain suffers on prescription opioids for a longer period of time had little effect. Last, an analysis is conducted onmedical interventions that reduce either the probability of becoming addicted or the odds of an addict dying from an overdose. While such interventions are valued by consumers, theyincrease the number of opioid users.Reducing the odds of addiction can result in even more deaths due to the rise in users…The developed framework is taken to the US data for both the college- and non-college educated populations. The quantitative analysis has three key steps. The first step is the estimation of Markov chains characterizing the movements in and out of misuse and addiction. Death is an absorbing state. The model is calibrated in the second step to match the estimated transitions from the Markov chains for both the college- and non-college educated. The framework fits the US data well. A check is performed on the calibration, by examining whether the model's prediction on the relationship between prescription opioid access and opioid deaths is consistent with the cross-state evidence about this. The calibrated framework is then used to decompose the rise in opioid usage. The analysis suggests that drops in the price of both Rx and illicit opioids combined with increases in Rx dosages were the primary drivers of the opioid epidemic.

The evidence, “…In 2019 the age-adjusted death rate from an opioid overdose was 21.6 per 100,000 people. This compares with 12.9 for kidney disease, 14.2 from suicides, 14.7 for influenzas, 21.6 from diabetes, and 161.5 from heart disease (the leading cause of death in the United States). Opioid overdose deaths place in the top 10 leading causes of death in the United States. As can been from Figure 1, prior to 2015 most of these opioid deaths arose from prescription (Rx) overdoses, but after that they came from synthetic opioids; in particular, fentanyl. (The sources for all the data displayed in the figures are presented in Appendix B.) The overdose death rate was much higher for those without a college degree compared with those who had one. The rise in the death rate from synthetic opioids is particularly marked for the non-college educated population….”

Jeremy Greenwood, Nezih Guner and Karen Kopecky, "The Downward Spiral," National Bureau Of Economic Research, https://www.nber.org/papers/w29764

“…Figure 4 shows that the out-of-pocket expense for prescription opioids has fallen by a factor of 3 since 2001. This has been attributed to two factors. First, the advent of generic prescription opioids. Second, the expansion of social programs such as Medicare and Medicaid that subsidized the purchase of opioids, as can be seen from Figure 5. For the college educated Medicaid is less important than private payers while for the non-college educated the reverse is true. The share of opioids prescriptions funded by the government grew from 17 percent in 2001 to 60 percent in 2010. The vast majority of opioids went to people who needed relief from pain caused by either disability or illness. Over the same period the street price of opioids dropped by a factor of 3. This has been chalked up to both the illegal imports of inexpensive powerful synthetic opioids, for example fentanyl, from China and elsewhere. Additionally, the diversion of opioids from legal sources onto the black market via fraudulent prescription, family and friends giving away and/or selling their prescriptions, and theft. The rise of illegal imports is ascribed to the tightening of prescriptions and the introduction of a tamper-proof form of OxyContin. The upshot is that opioids are much less expensive now than they were in 2001. Likewise, the introduction of low-cost heroin at the beginning of the 20th century was due to the banning of smoking opioids and the increased restrictions on the use of cocaine…”

“.. Starting around the year 2000 there was a dramatic increase in number of opioid prescriptions per person for both the college- and non-college-educated populations, as shown in Figure 3. The non-college educated were much more likely to have an opioid prescription than the college educated. The former often work in occupations involving physical labor. Additionally, the amount of Rx opioids consumed, conditional on a prescription, also rose. Again, this was particularly true for those without a college degree. The price of prescription opioids has fallen dramatically since 2000…”

In the months after Texas banned all but the earliest abortions in September,the number of legal abortions in the state fell by about half. But two new studies suggest the total number among Texas women fell by far less — around 10 percent — because of large increases in the number of Texans who traveled to a clinic in a nearby state or ordered abortion pills online.

Margot Sanger-Katz, Claire Cain Miller and Quoctrung Bui, "Most Women Denied Abortions by Texas Law Got Them Another Way,"New York Times, March 7, 2022, https://www.nytimes.com/2022/03/06/upshot/texas-abortion-women-data.html

Most Women Denied Abortions by Texas Law Got Them Another Way

Two groups of researchers at the University of Texas at Austin counted the number of women using these alternative options. They found that while the Texas law — which prohibits abortion after fetal cardiac activity can be detected, or around six weeks — lowered the number of abortions, it did so much more modestly than earlier measurements suggested.

Combined, the data points to what may happen to abortion access if the Supreme Court decides to overturn Roe v. Wade when it rules on another abortion law this summer. The data shows the limitations of laws restricting abortion. Yet it also shows how restrictions erect significant obstacles, which will cause some women to carry unwanted pregnancies to term.

“The law has not done anything to change people’s need for abortion care; it has shifted where people are getting their abortion,” said Kari White, principal investigator of the university’s Texas Policy Evaluation Project and the lead researcher on the new out-of-state abortion study. She expressed surprise at how few abortions were prevented by such a sweeping set of restrictions: “The numbers are way bigger than we expected. It’s pretty astounding.”

But for the architects of the Texas law, even a modest reduction in abortions is a success.

“There’s no hesitation from our side to declare this a victory for actually protecting pre-born children from elective abortion,” said John Seago, the legislative director of Texas Right to Life, who was involved in the creation of the law. “We’re realists around here, so the best we can do is incentivize women to have their children.”

Gov. Greg Abbott, a Republican who said the bill “ensures that the life of every unborn child who has a heartbeat will be saved from the ravages of abortion” when he signed it, declined to comment on the new numbers.

As state legislatures await a Supreme Court ruling and take stock of the Texas experience this year, several have passed new abortion restrictions, even if they conflict with Roe. On Thursday night, the Florida Legislature voted to ban most abortions after 15 weeks. Somewhere between 21 and 26 states are expected to ban or substantially restrict abortion if the Supreme Court permits it. On Monday, an effort by Senate Democrats to codify abortion rights into federal law failed to attract enough votes.

Each month in the period between September 2021, when the Texas law went into effect, and the end of the year, an average of 1,400 women went to one of seven nearby states, according to one of the new studies, released Sunday. That was 12 times as many as typically sought abortions out of state before the law.

The study included seven nearby states: New Mexico, Oklahoma, Louisiana, Arkansas, Kansas, Mississippi and Colorado. Nearly half of Texans who traveled went to Oklahoma, and a quarter to New Mexico. It counted Texans who visited 34 of 44 clinics, so the total was probably higher.

An average of 1,100 women ordered abortion pills online each month from Aid Access, an overseas service that sends pills in the mail while sidestepping U.S. abortion restrictions, by connecting women with European doctors and Indian pharmacies. That is more than triple the number who ordered pills in an average month before the law, according to the second study, published last week in JAMA Network Open.

Before, there was an average of 11 requests a day. Immediately after, that spiked to 138 requests a day, and has leveled out at about 30. The study could not determine if all medication requests resulted in abortions.

“The law is semi-effective; it will not stop all abortions,” said Abigail R.A. Aiken, an author of the study, who teaches public affairs and leads a research group studying self-managed abortion at the University of Texas at Austin.

Those who were unable to get abortions are most likely to be poor, a variety of research has found. It’s expensive to travel to another state and pay for transportation, child care and lodging in addition to the procedure.

The new data covers the most common alternative methods to the in-state clinics, but does not include all Texans who got abortions. An unknown number of women most likely used other means, such as ordering pills from online pharmacies that have not published their sales numbers; crossing the border to Mexico to buy over-the-counter pills; traveling to additional states to get abortions; or using herbs or other methods for self-managing abortions.

If Roe is overturned, the same patterns may not hold nationwide, because abortion access would be even harder than it has been for Texans.

Recent research has shown that abortion pills outside formal health care settings are accessible, reliable and effective — and that information about Aid Access is increasingly being shared online. But some women don’t know it’s an option. “It’s been the question that has been on my mind for the last 10 years: How do you reach the ones who cannot find you?” said Rebecca Gomperts, the physician who runs Aid Access.

Also, it is technically illegal to sell prescription medicine to American patients from another country without a prescription from a doctor licensed in the United States. Enforcement is difficult, however, even as Texas and some other states have explicitly restricted medication abortion.

Without Roe, clinics would close in wide swaths of the South and Midwest. The closures would increase average driving distances to the nearest clinic to about 280 miles, up from 35 miles, for women in states without one, found research by Caitlin Knowles Myers, an economist at Middlebury College, and colleagues.

Research on past abortion laws has shown that longer distances tend to reduce abortions, as the challenges of travel mount. Groups offering financial and logistical support to Texas women said donations had decreased after a bump when the law went into effect.

The groups added they would not have enough resources to help women in so many states. Remaining clinics would most likely be overwhelmed with patients. Trust Women, which has an abortion clinic in Oklahoma, is seeing 10 times as many Texans there as it used to. That causes a ripple effect. Many Oklahomans can’t get local care and have to seek it elsewhere, said Rebecca Tong, an executive director of the clinics.

The clinics have tried to expand to meet demand, but especially in Oklahoma, where abortion would be outlawed if Roe were overturned, it’s hard to recruit doctors, she said: “Some would be leaving a full-time job with benefits, and for what?”

At Hope Medical in Shreveport, La., two-thirds of patients are now from Texas, up from one-fifth before the Texas law went into effect. The clinic used to do the majority of abortions before nine weeks, but now most patients are in their late first or early second trimester, because of the longer wait times for appointments.

“What ultimately is happening is because we are so busy and we can’t work any faster, we’re seeing women whose pregnancies were further along,” said Kathaleen Pittman, the clinic’s administrator. “It’s happening not just to Texas women but to Louisiana women because they’re having to wait too.”

Still, clinics are preparing for an even bigger surge if Roe is overturned, planning to expand capacity if abortion remains legal in their state — or, if not, to open over state lines where it is legal; offer more guidance through telemedicine; or offer pre-abortion care.

Kristina Tocce, the medical director of Planned Parenthood of the Rocky Mountains, said the influx of Texas patients had provided a preview of a possible future, in which Colorado, which is expected to keep abortion fully legal, could become a post-Roe abortion hub.

“What is going to happen when this happens to more and more states?” she said. “I don’t know, but we can’t absorb 26 states going dark.”