Visualizing Strike Activity in China

In my last post, I suggested that the likelihood of social unrest in China is probably higher than a glance at national economic statistics would suggest, because those statistics conceal the fact that economic malaise is hitting some areas much harder than others and local pockets of unrest can have national effects (ask Mikhail Gorbachev about that one). Near the end of the post, I effectively repeated this mistake by showing a chart that summarized strike activity over the past few years…at the national level.

So, what does the picture look like if we disaggregate that national summary?

The best current data on strike activity in China come from China Labour Bulletin (CLB), a Hong Kong–based NGO that collects incident reports from various Chinese-language sources, compiles them in a public data set, and visualizes them in an online map. Those data include a few fields that allow us to disaggregate our analysis, including the province in which an incident occurred (Location), the industry involved (Industry), and the claims strikers made (Demands). On May 28, I downloaded a spreadsheet with data for all available dates (January 2011 to the present) for all types of incidents and wrote an R script that uses small multiples to compare strike activity across groups within each of those categories.

First, here’s the picture by province. This chart shows that Guangdong has been China’s most strike-prone province over the past several years, but several other provinces have seen large increases in labor unrest in the past two years, including Henan, Hebei, Hubei, Shandong, Sichuan, and Jiangsu. Right now, I don’t have monthly or quarterly province-level data on population size and economic growth to model the relationship among these things, but a quick eyeballing of the chart from the FT in my last post indicates that these more strike-prone provinces skew toward the lower end of the range of recent GDP growth rates, as we would expect.

sparklines.province

Now here’s the picture by industry. This chart makes clear that almost all of the surge in strike activity in the past year has come from two sectors: manufacturing and construction. Strikes in the manufacturing sector have been trending upward for a while, but the construction sector really got hit by a wave in just the past year that crested around the time of the Lunar New Year in early 2015. Other sectors also show signs of increased activity in recent months, though, including services, mining, and education, and the transportation sector routinely contributes a non-negligible slice of the national total.

sparklines.industry

And, finally, we can compare trends over time in strikers’ demands. This analysis took a little more work, because the CLB data on Demands do not follow best coding practices in which a set of categories is established a priori and each demand is assigned to one of those categories. In the CLB data, the Demands field is a set of comma-delimited phrases that are mostly but not entirely standardized (e.g., “wage arrears” and “social security” but also “reduction of their operating territory” and “gas-filing problem and too many un-licensed cars”). So, to aggregate the data on this dimension, I created a few categories of my own and used searches for regular expressions to find records that belonged in them. For example, all events for which the Demands field included “wage arrear”, “pay”, “compensation”, “bonus” or “ot” got lumped together in a Pay category, while events involving claims marked as “social security” or “pension” got combined in a Social Security category (see the R script for details).

The results appear below. As CLB has reported, almost all of the strike activity in China is over pay, usually wage arrears. There’s been an uptick in strikes over layoffs in early 2015, but getting paid better, sooner, or at all for work performed is by far the chief concern of strikers in China, according to these data.

sparklines.demands

In closing, a couple of caveats.

First, we know these data are incomplete, and we know that we don’t know exactly how they are incomplete, because there is no “true” record to which they can be compared. It’s possible that the apparent increase in strike activity in the past year or two is really the result of more frequent reporting or more aggressive data collection on a constant or declining stock of events.

I doubt that’s what’s happening here, though, for two reasons. One, other sources have reported the Chinese government has actually gotten more aggressive about censoring reports of social unrest in the past two years, so if anything we should expect the selection bias from that process to bend the trend in the opposite direction. Two, theory derived from historical observation suggests that strike activity should increase as the economy slows and the labor market tightens, and the observed data are consistent with those expectations. So, while the CLB data are surely incomplete, we have reason to believe that the trends they show are real.

Second, the problem I originally identified at the national level also applies at these levels. China’s provinces are larger than many countries in the world, and industry segments like construction and manufacturing contain a tremendous variety of activities. To really escape the ecological fallacy, we would need to drill down much further to the level of specific towns, factories, or even individuals. As academics would say, though, that task lies beyond the scope of the current blog post.

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