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).

Cultural distance matters more for the spread of democracy than geographical distance

Over the last year, I've been working towards some exciting new projects using measures of cultural distance. So, I was interested to read this new article by Thanos Kyritsis (University of Auckland), Luke Matthews (RAND Corporation), David Welch and Quentin Atkinson (both University of Auckland), published in the journal Evolutionary Human Sciences (open access, with a non-technical summary available on The Conversation). In the article, Kyritsis et al. look at how differences in democracy between countries are related to the cultural and geographical distance between them, and whether changes in democracy over time in one country are related to the level of democracy in other countries that are culturally close and geographically close to the first country.

Kyritsis et al. develop measures of cultural distance based on linguistic distance and religious distance, and use three different measures of democracy: Polity 5 (covering 1800-2018), Vanhanen’s Index of Democracy (covering 1810-2012), and the Freedom in the World index (covering 1972-2020). Overall, their dataset covers 220 years, with 221 modern and historical nations, and 41,638 observations in total. They also distinguish between different waves of democracy:

...an initial ‘slow’ wave beginning in the US and culminating in the emergence of several European democracies at the end of the First World War (1828–1926); a second wave linked to the process of decolonisation following the end of the Second World War (1945–1962); and a third wave comprising a succession of transitions in Western Europe, Latin America, the Pacific, Eastern Europe after the fall of communism, and sub-Saharan Africa (1974 to present)...

In the first part of their analysis, Kyritsis et al. regress the difference in democracy between two countries on the distances between them (linguistic, religious, and geographical). They run this analysis cross-sectionally for each year in their dataset, and find that there were:

...independent effects of all three predictors on each democracy indicator over the time period covered by our data... While the effects of linguistic and religious ancestry were slightly attenuated in this combined model, when present, they remained generally better predictors of all three democracy indicators than geography. Over the last 50 years, linguistic and religious ancestry accounts for up to 12.3% and 17.4% of variance in pairwise differences in democracy, respectively, compared with 1.2% for geographical proximity...

Looking at how the cross-sectional relationships have changed over time, they find that:

...across all three democracy indicators, linguistic ancestry is an increasingly important predictor beginning mid-way through the second wave (circa 1955) and plateauing (or, in the case of Polity 5, declining somewhat) in the third wave from about 1990 to the present. Likewise, religious ancestry becomes an increasingly important predictor of similarity in all three democracy measures from approximately the beginning of the third wave, circa 1975, plateauing and then declining somewhat from circa 2000 to the present... 

The relationships between geographical distance and differences in democracy were not as large as for the two measures of cultural distance, and the relationship was inconsistent over time. Overall, this suggests a stronger effect of cultural distance on democracy than the effect of geographical distance.

Turning to their second analysis, of changes in democracy over time, Kyritsis et al. find that:

The democratic status of nations’ linguistic relatives is the only effect to show a consistently positive trend across all three democratic outcome measures for the duration of the time series. The language ancestry effect is strongest, and statistically significant for most of the third wave of democratisation across all outcome measures. Unsurprisingly, since democratic status tends to persist, most of the variation in nations’ democracy indicators at T2 is explained by their democracy at T1, but linguistic ancestry accounts for a non-trivial component of the remaining variation, explaining up to 17.4% of variation across outcome measures in the third wave. The democratic status of nations’ religious relatives shows no consistent effect until the third wave, when we see a sustained positive trend across all outcome measures, consistent with our cross-sectional analyses, with religious ancestry explaining up to 11.1% of the variation in democratic outcomes during this period. Also in accordance with our cross-sectional analyses, the effects of a nation’s geographical neighbours on its democratic outcomes tend to be positive, although these geographical effects show more variation through time and across outcome measures.

Essentially, the longitudinal analysis results confirm what they found in the cross-sectional analysis over time. Democracy appears to diffuse more readily between cultural 'neighbours' than between geographical neighbours. My future research will build on similar themes, where cultural distance appears to matter more than geographical distance. I look forward to sharing some of that in a future post.

[HT: The Conversation]

Social disorganisation and crime over two centuries

Social disorganisation theory is the idea that differences (or changes) in family structures and community stability are a key contributor to differences (or changes) in crime rates between different places (or times). It suggests that neighbourhoods that are more unstable (higher social disorganisation) will have higher crime rates than neighbourhoods that are less unstable (lower social disorganisation), ceteris paribus (holding everything else constant). It also suggests that if neighbourhoods become more unstable over time, crime rates will increase, ceteris paribus. I've mostly encountered social disorganisation theory in relation to the effect of alcohol outlets on crime. In that context, things become tricky, because alcohol outlets tend to locate in more unstable (higher social deprivation) neighbourhoods, which may also have higher crime because of social disorganisation. So, disentangling the effects of alcohol outlets from the effects of social disorganisation more generally is difficult.

The broader literature on social disorganisation theory faces issues as well, because of potential reverse causation and endogeneity. If you want to test the effect of neighbourhood instability on crime, you have to recognise that not only can instability cause crime, but crime can cause instability. Finding ways of dealing with these issues is important.

So, I was interested to read this recent article by Zeresh Errol (Monash University), Jakob Madsen (University of Western Australia), and Solmaz Moslehi (Monash University), published in the Journal of Economic Behavior and Organization (sorry, I don't see an ungated version online). They use annual data covering the period 1840 to 2018 for 16 countries: Australia, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, the Netherlands, Norway, Sweden, Switzerland, the U.K. and the U.S. Their data includes crime rates (per 100,000 population), family structure (proxied by the divorce rate and the share of out-of-wedlock births), community structure (proxied by the urbanisation rate), GDP per capita, and the proportion of the population aged 15-29 years. The crime rates are disaggregated into property crime, violent crime, homicide, robbery and assault. Errol et al. then look at the relationship between the family structure and community structure variables and crime rates (controlling for the other variables, along with country and time fixed effects).

However, remember that endogeneity is a problem here for the family structure variables, so any simple regression analysis is not going to tell us about the causal relationship between family structure and crime. Errol et al. solve this problem using instrumental variables analysis. Essentially, this involves finding an instrument that affects the endogenous variable (divorce or out-of-wedlock birth rate), but has no direct effect on the outcome variable (crime rates). In a unique twist to this paper, Errol et al. use the weighted average of the family structure variables in all other countries as instruments for the family structure variables in country i. For example, for Australia's divorce rate, they use as an instrument the weighted average of the divorce rates in all other countries. The weighted average of other countries' rates is a valid instrument because divorce rates should be related across countries, but the divorce rate in Australia should not exert any effect on the crime rate in Belgium (or any other country). You can tell a similar story for out-of-wedlock birth rates.

Now, rather than using the straight average of all other countries, Errol et al. use a weighted average, where the weights are based on the linguistic distance between the countries. So, data from countries where the main language is more similar will have a greater weight in the calculation of the average. This is quite an exciting aspect of the article to me, because it relates closely to some ongoing work I have been doing on using cultural distance measures in new and exciting ways (more on that in future posts).

Looking at the IV regression results, Errol et al. run separate regressions for the five crime rates, and separately using the divorce rate and the out-of-wedlock birth rate as their proxy for family structure. Across these various models, they find that:

...the coefficients of Div and Owed are significantly positive in seven of the ten cases, where two of the insignificant coefficients pertain to homicide.

In other words, higher divorce rates and out-of-wedlock birth rates cause an increase in crime rates. The size of the coefficients is a little difficult to interpret, given that what Errol et al. report is a coefficient that sums several lagged values. However, they do seem to be meaningful in size, given that Errol et al. note for the ordinary least squares (not IV) regression, that:

Based on the coefficients of the 10-year first difference estimates of Div (Owed), a one standard deviation increase in Div (Owed), is associated with a 15.4(1.8) and 174.4(153.3) percentage point increase in the rates of violent crime and property crime, respectively...

So, this paper demonstrates the important of social disorganisation in understanding differences in crime rates over an extremely long time period. However, it also illustrates a potentially fruitful way of constructing instruments for instrumental variables analyses, using cultural (or, in their case, linguistic) distance weighted averages. Expect to see more work in the future employing this approach.