Genshin Impacts on Chinese trade

During the pandemic, when people were isolated at home, some people discovered a passion for sourdough. Others picked up a book. But plenty of people got (more) heavily into gaming. In late 2020, Genshin Impact was launched into that environment, and immediately exploded in popularity despite being released by a Chinese gaming studio little known to Western gamers. The interesting thing about Genshin Impact is that it doesn't 'Westernise' its Chinese foundations, and through that it may have opened a window to Chinese culture that many Western gamers wouldn't otherwise have noticed.

What effect, if any, did this have? That is essentially the question that this new article by Tianyu Wang (Jiangsu Provincial Academy of Social Sciences) and co-authors, published in the journal China Economic Review (sorry I don't see an ungated version online), tries to answer. Specifically, they look at the impact on Chinese exports, using a difference-in-differences (DiD) strategy. This involves comparing trade between China and countries with more, or less, exposure to Genshin Impact, between the period before and after its release (which they set as October 2020, the first full month after the open beta of Genshin Impact was released on 28 September 2020). Their data is monthly export data from China to other countries, from the UN Comtrade database.

However, there are a couple of oddities with the analysis. First, Wang et al. control for a variety of variables in their regression model. However, two of the variables they control for are the log of GDP and the log of GDP per capita. Because their model is a log-linear model, this means that they are unnecessarily controlling for GDP twice. To see why, consider this equation:

lnY = a + blnX + cln[X/Z]

You can think of X as GDP and Z as population, so X/Z is GDP per capita. Since ln[X/Z] is equal to [lnX - lnZ], that equation is really:

lnY = a + blnX + clnX - clnZ = a + [b+c]lnX - clnZ

So, the coefficients on both GDP and GDP per capita are not directly interpretable and a bit awkward. The coefficient on log GDP per capita in their model is actually the negative of a coefficient on log population, while the coefficient on log GDP is incorrect. Fortunately though, this just adds unnecessary complexity to their model. It doesn't bias the coefficients in the rest of the model.

Second, Wang et al. use Google Trends data as the treatment variable. This seems appropriate, because Google Trends will pick up differences in cross-country interest in Genshin Impact. Specifically, they create a Google Trends Index (GTI) that captures the search intensity for their term of interest. However, in their main analysis, they don't use a GTI based on searches for 'Genshin Impact'. Instead, they use a GTI based on searches for 'Sony'. Their explanation for that is:

There is evidence indicating that Sony and miHoYo maintain a very close relationship, and that Sony has played an important role in the global promotion of Genshin Impact.

They also say that:

...regressing China's exports directly on Genshin Impact GTI is highly endogenous...

Both of those statements may be true, and Wang et al. provide a variety of evidence in support of the close relationship between Sony and Genshin Impact. However, they don't provide similar evidence for why searches for 'Genshin Impact' would be endogenous in a way that searches for 'Sony' wouldn't. One possibility is that they are worried that search intensity for 'Genshin Impact' is correlated with countries' pre-existing closeness to China, or with pre-existing interest in Chinese cultural products. A difference-in-differences strategy, especially one that controls for country-level differences in pre-treatment trade, should already be controlling for those issues. However, time-varying shocks that are correlated with both Genshin Impact searches and Chinese exports after 2020 would remain. For example, the Genshin Impact GTI would also capture changes in favourability of views towards China that change for reasons other than Genshin Impact. Using the 'Sony' GTI may therefore reduce one problem, but it also introduces another, since Sony searches could reflect many things unrelated to Genshin Impact or China.

Fortunately, Wang et al. do report results based on the GTI for 'Genshin Impact' in their online appendix, and the results are not so different from what they get with the 'Sony' GTI. Apparently, this was suggested by one of the journal reviewers. Honestly, I think the results based on the 'Genshin Impact' GTI are the more plausible results, so I'm going to focus on them. And in those results, reported in Table D6 in the online appendix, they find that following the open beta release of Genshin Impact, every one-unit higher GTI for 'Genshin Impact' for a country is associated with a 0.215 percent increase in exports from China to that country. Unfortunately, they don't report the summary statistics for the 'Genshin Impact' GTI, so it is difficult to interpret. It is also difficult to interpret because the GTI is a normalised measure of search intensity relative to all Google searches in a given country and period. However, for comparison, the effect using the 'Genshin Impact' GTI is slightly larger than what they report for the 'Sony' GTI, which is a 0.186 percent increase in exports for each one-unit higher 'Sony' GTI.

Either way, the results suggest that countries where Genshin Impact was a bigger phenomenon experienced larger increases in exports from China than countries where Genshin Impact was less impactful. Wang et al. then turn to the mechanisms that might explain this change, using Pew Global Trends and Attitudes data. They report that:

Although we do not find evidence that Genshin Impact improved favorable perceptions of China, we do find evidence that it reduced unfavorable perceptions. This effect is primarily driven by a decline in mild aversion; there is no significant change in strong aversion. This result is intuitive—individuals who strongly dislike China are unlikely to revise their views solely because of a video game.

They also find that media narratives became more positive following Genshin Impact's release, for countries where the 'Sony' GTI was higher. However, this result is only suggestive as it was statistically insignificant.

One interesting final aspect of the paper is that Wang et al. used data on cultural distance to further explore the results, finding that:

...as bilateral cultural distance increases, the promotional effect of Genshin Impact on China's exports significantly diminishes.

So, Genshin Impact had a larger trade impact for countries with greater cultural similarity to China. That suggests that, while it might be an interesting narrative to suggest that Genshin Impact exposed the world to China, improving perceptions of China and increasing trade, the effect was actually concentrated on the countries that were already most similar to China.

This paper presents some interesting findings. However, it clearly isn't the last word on whether the international sharing of cultural products can have tangible effects on international trade, beyond their effects on the trade of the cultural product itself. It would be interesting to see if there are similar impacts for Korean cultural products, for example, or Bollywood movies (or Nollywood movies, for that matter).

Turkish inflation drives consumers to incur extreme shoe-leather costs

Inflation imposes costs on people. One of the costs of inflation is that it gives people strong incentives to spend time and effort avoiding higher prices. They can do that by reducing their cash holdings, searching harder for low prices, or, in extreme cases, travelling to shop elsewhere. When inflation is high, and prices are increasing rapidly, consumers have a strong incentive to spend a lot of time doing these things. Economists call these shoe-leather costs, because when consumers have to walk around a lot of stores in order to compare prices, their shoes wear out. At least, that's a literal explanation of the term. In an age where prices are published online, the actual act of 'walking around to compare prices' is a lot easier on the shoes. Or is it? An extreme example has been playing out recently, as reported in Bloomberg last November (paywalled, but you can find an ungated version here):

Almost every month, Cihan Citak gets into his car, passport in hand, and sets off from Istanbul to Alexandroupolis, a Greek seaside city 40 kilometers (25 miles) from the Turkish border. After a roughly four-hour drive, he walks the crowded aisles of the local supermarket, filling his cart with wine, cheese and other groceries that cost a fraction of what they do back home...

Cross-border retail has become routine for many who found that Turkey’s surging food prices and stronger lira make Greece a cheaper alternative for everyday purchases. The trend, while not new, is accelerating: 6% of all Turks crossing the border to Greece in the first nine months of the year were on a shopping run, the highest share of overall travelers since at least 2012, data from the country’s statistics agency show.

When inflation causes people to drive four hours in order to find lower prices, you know the shoe-leather costs must be high. The inflation rate in Türkiye is over 30 percent. That isn't hyper-inflation, but it is very high. For comparison in New Zealand, the inflation rate spiked at about 7 percent just after the pandemic, but that was the highest it had been in over 30 years. Inflation more recently has been between 2.5 and 3.5 percent, which is higher than the Reserve Bank's mandate to keep inflation between one and three percent in the medium to long term.

All of that is to say that Türkiye’s much higher inflation creates much stronger incentives for consumers to incur shoe-leather costs to avoid higher prices than is currently the case in New Zealand

[HT: New Zealand Herald, also paywalled]

This week in research #128

Here's what caught my eye in research over the past week:

• Ruggles tests Richard Easterlin's argument that the economic and social prospects of a generation are influenced by the size of the cohort relative to adjacent cohorts, and finds using US data from 1910 to 2040 that the theory fits the data well for the period from 1940 to 1980 but fails in later decades, although baby boomers exiting the labour force will likely lead to increases in wages in the future
• de Bondt and Sun (with ungated earlier version here) use ChatGPT to classify activity sentiment scores from Purchasing Managers’ Index (PMI) news releases, then use those scores to 'nowcast' GDP, finding that on average, out-of-sample forecast accuracy improves by about 20% apart from the two most recent years
• Skali et al. (open access) find that better-looking Swiss politicians are not more prone to rent-seeking through interest group affiliations, and do not deviate more from their voters' preferences
• Jin, Karim, and Schulze (open access) find that Islamist terror attacks created significant negative abnormal returns in American and European markets, but the stock market effects of other terror attacks were almost nil

In other news, I wrote a quick take on the New Zealand Budget as part of The Conversation's coverage this week. That article also has a drop-down menu at the bottom that summarises the key Budget announcements in each area

Try this: Taxed

Today was Budget Day in New Zealand. The government revealed its forecasts of future revenue and its spending plans. There is a good summary of this on The Conversation (disclaimer: I wrote the blurb at the top of that summary).

The problem with the Budget is that the numbers are large, and it is difficult to get a good sense of the relative magnitudes. How do you interpret $1.18 billion in spending on rail network renewal and upgrades?

One of my recent students, Tyler Dunseath, created the Taxed website, that uses your income to work out how much tax you pay (weekly, fortnightly, monthly, or annually), then apportions that tax to the various categories of spending from the government accounts. So, for example, if your weekly income is $1000 before tax, and you don't adjust for ACC, KiwiSaver, or student loan repayments, you pay $165.77 in tax. Of that, $56.79 goes to social security and welfare, $37.40 goes to health, $24.25 goes to education, and so on. The results give you a better sense of how taxes are distributed.

Of course, there are a number of caveats, the biggest of which is that government services are a bundle, and while Taxed might make it seem like you could in theory say, "I don't want to pay $0.32 per week for international peacekeeping", it doesn't work that way. Moreover, a lot of government spending is on services that are public goods and therefore non-excludable, so even if you could opt out of paying for them, you would still receive the benefits of them.

Second, government receives some income that is earmarked for particular purposes. For example, the fuel excise tax is earmarked for the National Land Transport Fund. So, your income tax isn't distributed in exact proportion to the government's spending on different categories, because less of your income tax goes towards transport.

Third, the site doesn't account for the taxes we pay on goods and services (GST, or excise taxes on alcohol, tobacco, or fuel), or the user charges we pay.

With those caveats in mind though, Taxed is a pretty cool way of showing how the government's spending is distributed, and in a way that most people are more likely to understand than the millions or billions of dollars cited in the budget.

Enjoy!

[HT: Tyler Dunseath]