Yves here. This new approach for assessing the cost of war shows severe costs to the citizens of Donetsk. Imagine what economic consequences for Ukraine will be now that the conflict has become more intense and destructive that the civil war in Donetsk was through 2019.
By Nicolas Gomez Parra, Research Fellow Inter-American Development Bank; Harun Onder, Senior Economist, The World Bank; and Erhan Artuc, Senior Economist, The World Bank Group. Originally published at VoxEU
Estimating the economic impact of a war is a daunting task. Conventional indicators like casualties, infrastructure damages, and the effects on GDP provide useful benchmarks, but they fail to capture the complex impact of wars. This column highlights a new method for estimating this impact by using a preference-based approach as revealed by people’s mobility patterns. A case study of the conflict in Eastern Ukraine between 2014 and 2019 shows a large lower-bound welfare loss for Donetsk residents, equivalent to between 27.72% and 39.74% of income loss for a duration of ten years.
Wars have complex economic and social consequences. Conflict-driven deaths, physical destruction, and economic disorganisation levy unambiguously heavy tolls on societies. However, accounting for the whole burden of wars on human wellbeing is challenging. On the one hand, it is generally difficult to measure the intangible fallout of wars, such as institutional degradation, erosion of social trust, and eruption of psycho-social trauma. On the other, even otherwise measurable factors, including common economic indicators like prices, employment, and trade, may not be recorded accurately in times of conflict, which renders a systematic assessment difficult. With these challenges, economic impact assessments of wars often reflect only a subset of the broad and persistent misery engendered by large-scale human violence.
A New Approach to Estimating the Economic Impact of War
In economics, mobility of people is often characterised as a discrete choice problem, where agents choose a location to migrate to, or remain, from a set of potential destinations. Hotz and Miller (1993) showed that there is a mapping from choice patterns to agents’ utility. There is a very simple intuition behind this mapping: if agents are more likely to choose an option, it is because choosing that option gives them more net utility compared to other choices. A similar approach was previously used by Artuc et al. (2010) for estimating the welfare generated by mobility using labour flows and by Arkolakis et al. (2012) for calculating income gains from trade using trade flows.
To better account for the complex welfare impacts of wars in data-constrained environments, in a recent paper (Artuc et al. 2022), we propose an alternative approach based on a mapping from migration choices to welfare, following the intuition of Hotz and Miller (1993). This approach relies on a general and flexible model, where agents choose among a finite number of destinations. The theoretical framework preserves generality in multiple ways. For example, it remains agnostic about the components of location-specific utility (e.g. wages, local amenities, and others), it does not rely on a specific formulation of expectation formation (such as perfect foresight versus stochastic outcomes), and it can be implemented with different time preference and risk aversion assumptions (e.g. myopic versus forward-looking with a time discount, or risk-averse versus risk-neutral). In contrast to the previous applications, this method requires only partially observed outflow data as opposed to stayer data, as the latter may not be observed during an active conflict.
The model is estimated using gross bilateral migration patterns before the onset of the conflict. This helps first to establish how economic agents regard spatial welfare differentials by voting with their feet. Next, we use the estimated model to infer the magnitude of the conflict-driven welfare shock from partially observed migration patterns after the onset of the conflict. Thus, an increase in migration outflows to a subset of possible destinations, together with the estimated responsiveness of agents to welfare differentials across regions, yield a measure of how agents perceive conflict-driven welfare shock.
Application to the Conflict in Donbas, 2014-2019 (not 2022!)
The conflict in the eastern territories of Ukraine began in 2014 and remained regionally contained throughout our analysis. About 38% of the combined territories of Donetsk and Luhansk oblasts (less than 4% of total Ukrainian land) remained outside the Ukrainian government’s control, which was demarcated by a 457 kilometre line of contact. Despite the hostilities, migration was not prohibited and remained feasible for Ukrainians. They routinely crossed the contact line for family visits, shopping, and to use ATMs to withdraw pensions (World Bank 2021). About 1.4 million internally displaced people (IDPs) were registered officially in this period. While half of the IDPs remained in Donbas, the other half moved elsewhere in Ukraine. Interestingly, the post-conflict migration outflow exhibited a pattern similar to the pre-conflict migration outflows (Figure 1).
Figure 1 Migration outflows and forced displacement from Donbas, annual averages
Notes: Panel a shows gross migration outflows from Donetsk and Luhansk oblasts between 2008 and 2012 by destination, with average annual numbers in parentheses. Panel b does the same by using annualized official internal displacement numbers (IDPs) between 2014 and 2019. Source: Artuc et al. (2022).
The empirical analysis for inter-regional migration before 2014 yields a migration elasticity parameter between 0.46 and 0.68 depending on the agents’ risk aversion and time preferences (Figure 2). These are comparable with the estimates in the literature (Kennan and Walker, 2011 find approximately 0.5 for the United States). Using these estimates, and following an inversion equation as discussed above, we then map moving probabilities onto welfare, and compute the welfare shocks implied by the post-conflict migration outflows from Eastern Ukraine.
Figure 2 Estimates for migration elasticity parameter
Notes: IV regression results. Error bars show 95% confidence intervals. σ denotes the degree of relative risk aversion in a CRRA utility, and β is the time discount factor.
Source: Artuc et al. (2022).
Finally, following the literature (e.g. Onder et al. 2019), we compute the equivalent income losses implied by these negative welfare shocks. Specifically, we answer the following question: What would be the rate an income loss amortised over ten years that makes an average individual equally worse off as the conflict? Our lower-bound estimates for Donetsk oblast show an equivalent income loss for ten years of between 27.72% and 38.06%, depending on agents’ time preferences, vis-a-vis the average pre-conflict income when agents are risk neutral. This range is equivalent to an income loss of between 30.93% and 39.74% when agents are risk averse with a constant relative risk-aversion parameter equal to one. The magnitude of welfare loss is similar for the Luhansk oblast, equivalent to between 28.04% and 38.50% when agents are risk neutral, and between 28.18% and 36.43% when agents are risk averse.
To test for robustness, we repeat the analysis using different subsets of possible destinations and ensure that our results are not driven by increased attractiveness of a particular destination. For example, we exclude oblasts on the western border and those with large airports, use only neighbours of Donbas, use only use the ten smallest oblasts, and finally use only the ten largest oblasts. These tests suggest that a sharp decline in the utility in Donbas was the main factor behind a sudden but balanced increase in mobility to non-conflict regions within Ukraine (as seen in Figure 1), rather than destination-specific positive shocks or an unbalanced decline in moving costs.
Synopsis
The new approach highlighted in this column has several desirable properties. First, it relies on the revealed preferences of economic agents to account for what matters in assessing the welfare impact of wars. A preference-based welfare concept better captures the intangible consequences of wars (e.g. cultural effects or trauma from physical and sexual violence) as it covers both pecuniary and non-pecuniary factors affecting individual wellbeing. Second, because we abstain from specifying the composition of welfare – thus largely delegating the model selection problem to economic agents themselves – data constraints are significantly relaxed. Our approach only relies on migration away from conflict, which is typically well-recorded by humanitarian organisations for coordinating assistance, and only from a subset of potential migration destinations, not all of them. Overall, the ability to ease a pervasive model-selection problem and circumvent potentially prohibitive data constraints help to achieve a more complete inference about the welfare impact of wars.
It is also important to note that these estimates are driven by the (ex-ante) perceived welfare shocks by economic agents, which trigger migration decisions, and they should be interpreted as the lower bounds of the welfare impact. When conflicts reduce welfare in non-conflict areas significantly or boost mortality drastically, the actual impact can be larger.
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That panel that ‘shows gross migration outflows from Donetsk and Luhansk oblasts between 2008 and 2012 by destination’. I notice from some of the names that those people fled to other Russian speaking areas but is this panel only half the story? By now there must be going towards a million people that left the Donbass to go to Russia itself for protection from the Ukrainians. Should that not be part of that chart? The numbers are hug so must be accounted for.
But when it talked about estimating the cost of the war, I was thinking about an interview I saw earlier today with a DPR tank commander call-sign Radik. He was talking about his work and talked about the time when a Javelin hit his tank and killed his friend Tolya and how he was only able to escape from his tank through the bottom hatch. Since then his tank was repaired and now he has written “For Tolya” on the barrel. But when you looked past the bulky uniform and the huge tanker’s helmet, you could see the young miner that he was before the war. He will fight his own demands after the war – if he survives – and it is young men and women like him that really have to be tallied in the cost of this war-
https://en.wikipedia.org/wiki/Humanitarian_situation_during_the_war_in_Donbas#Refugees
900,000 left Ukraine for Russia between 2014 and the start of the SMO. Over 3 million have left for Russia since. Russia has taken the largest number of refugees of all countries, the next being Poland with 1.9 million. Of course many will return to Eastern Ukraine from Russia to rebuild their lives, how many Ukrainians from the West will return? Most of the ‘refugees’ form western Ukraine (which has not really been effected by military action until recently) left for a better life in the EU, better wages and the European freebies on offer, how many will want to leave that for the uncertainty and poverty of whatever is left of western Ukraine (quite possibly in Polish hands by that juncture)
There were also many young men in 2014 that came from western parts of Ukraine to join the militias “defending the constitution”. That’s why the general use of the pseudonyms among militias – to protects their relatives still living in the western parts.
Even during the SMO I’ve seen it mentioned that men born in Donbass, moved to work in the west and then pressed into Ukrainian army have surrendered and changed sides at the first opportunity.
Probably not more than a few thousand since 2014, though.
In 2016, various UN connected sources said 1 million to Russia and 500,000 to Belarus, although some may have returned.
Joe Biden and every congress pawn that voted for funding this atrocity are war criminals.
Our family will not file income taxes nor purchase one discretionary item for the remainder of this rotten regime and its corruptconomy.
This got me thinking about the consequences and costs of hate. Everything about this conflict started with hatred of the Russians for no good reasons, there would be no conflict here if it wasn’t for Russophobia – from the Ukrainian side because they deem Russians, Poles and Roma to be a lower form of human, from the American side…well, for the same reason I guess, because after Glasnost there was no longer an ideological or political basis for hate, and yet there it is.
So war is the economic consequence of bigotry and hate but, at the same time, also causes the social trauma which in turn leads to bigotry and hate, especially intergenerational of the sort where the reasons are long since forgotten, are no longer known. There will be a cost long beyond this displacement, it will be carried forward.
And then I think about, well, we don’t seem to hate the Germans anymore, we seem to have considered Germans as much the victims of Nazism, we let them rebuild, lessons were learned, there was forgiveness. Bygones seem to be bygones as well with Japan and Vietnam, it’s quite remarkable how both societies don’t have a serious grudge with the Americans. The Chinese, however, haven’t even done anything yet, there’s no overt act of aggression from them, yet the anti-Chinese sentiment right now is quite excessive. Russia hasn’t done anything that the US hasn’t already done, also excessive.
So beyond studying the cost of war, there’s an opportunity here to study the cost of unwarranted and unjustified hate, how it leads to war, and also the cost benefits of settling differences, forgiveness, despite legitimate grievances. For a while it looked like the Russia story was going to be an example of the latter, now it’s a case study for the former.
It might be about the love of money and power. “We” meaning the neoconartists, the fossil fuel industry, the Democrats, the Republicans, but not the American people. “We” love LNG exporters, credit card companies, GMO producers, patent holders, so much so, that a phony war to benefit them short and long term is needed to eliminate those autarkic Russians who don’t need and won’t buy their products.
Oops, forgot the MIComplex and their stock values. Europe’s economy? Collateral damage, no more significant than an Afghan wedding party which when vaporized by a drone, is part of the WOT, which “we” love.
The real haters are Trotsky’s grandchildren who seem to be populating the State Department and are inciting this.
Ally Versus Ally: America, Europe and the Siberian Pipeline Crisis – book by Anthony Blinken 1987.
Some people have been planning this for a long time.
I don’t think hate is organic or prior to the needs of those who want a war. I think the hate is created and stoked as a preparation for war. Post-coup Ukraine textbooks have apparently taken up the task, and the Russians became a very convenient bogeyman when the Clinton campaign needed an excuse for losing to Trump, but the ‘Blame Russia First’ (BRF) movement arising from the Trump shock was only possible due to diligent (and less so) long-term project of recycling cold war propaganda by US Neocons, the ideological fluffers for the arms industry.
Exactly. There is always some hate there for intensely person reasons but when it’s decided that war is what those in charge need to further their desires they have to overcome that terrible tendency most people have of not wanting to kill each other.
Hate is the propaganda tool of the monied, both domestically and internationally.
Get two subsets of the masses to hate each other and they will not join in going after the smaller group of monied.
The hate for Russia may well be in order to try to replace Putin with a globalist that will reinstate the Yeltsin “free market” access to Russian raw materials and energy reserves.
But they seems to have underestimated the support Putin had with the masses. Likely because they were listening mostly to westernized online “intellectuals” etc.
But when it comes to China it is “personal”. As China kinda played the west by offering cheap labor and then running off with the engineering data of the products. China has yet to establish anything close to the “intellectual property” enforcement that the west is accustomed to. Wander around Shenzhen and you will find many a small shop making copies of both each other’s products and those of the west.
Ironically this is not that far off from how USA industrialized back in the day. Look up “Slater the traitor” as the British like to call him, for one glorious example.
I think you have this exactly backwards. The fundamental ‘problem’ with Russia is that it won’t submit and be economically exploited. Everything else flows from that.
Precisely, and this has been going on for quite a while.
In the early days of the American Republic, US-Russian relations were good, and during our Civil War, Abraham’s only major diplomatic friend in Europe was Tsar Aleksander II, Autocrat of all the Russia’s.
The sea change was in the early 1880s, when the US embarked on the course of Empire, laying down a new steel navy to replace the rotting wooden navy built for the Civil War, with the intention of becoming a globally dominant maritime power.
In this process, we shifted Russia from “Distant but occasionally helpful friend” to “Blot upon the concience of Humanity, to be contained, punished, made to look like Us, and made to accept Our leadership.”
In 1900, Alfred Thayer Mahan, our Seapower guru, proposed alliance between ourselves, the British Empire, the German Empire, and the Japanese Empire, to contain Imperial Russia until collapse. Since then, the only Russian governments the US government have actually liked were the defeated, collapsed, helpless, bankrupt, compliant Russia of Alexander Kerensky, and the defeated, collapsed, helpless, bankrupt, compliant Russia of Boris Yeltsin. We even despised Nicholas II, until he was safely dead.
So you’re absolutely correct, we have despised Putin ever since he destroyed the independent political power of our proteges, the Oligarchs in 2003. Things have just escalated from there.
Intangibles may be more consequential than tangibles. Gonzalo Lira posted a commentary yesterday about the affect of the casualties on him. Statistical analysis cannot measure the immeasurable – the shell shock, the PTSD, the trauma (thoughts trail off to tears).
Recently i found myself wondering how many “suicide by cop” were military veterans of the last forever wars.
A very good question.
This is a pointless exercise where economists profess to earn their keep, ridiculously pretending that everything can be translated into some form of individual choice. The authors are forgetting the profusion of giant war loans, euphemistically called ‘aid’, which have been provided against various forms of collateral and promises of privatization. For sure whoring the future of Ukraine to any willing corporation and investment fund should count among the costs of war.
Much of the country’s arable land, resources, productive capacity, and earnings over the next ten generations must have been pawned by now, and this extractive operation will continue for a very long time, since Zelensky has put Blackrock in charge with the postwar reconstruction of the economy. This is like making the Devil abbot of the nunnery.
God help Ukrainians and their rump state, they will be a dirtpoor colony, with Putler being blamed for their miserable life under the boot of imperial capitalism.
There is a method to trade territory that doesn’t seem to be much in use since the 1800s: simply buying the territory in question. Worked for Alaska and Louisiana. Probably would have cost the Russian state considerably less than they’re expending now in material and lives.