Here I want to do a very interesting analysis: a comparison of the strength of cities’ fiscal revenues. It is well known that more and more cities are highly dependent on land sales revenue for their finances, so the first table I give here is the ranking of 31 major Chinese cities in terms of their dependence on land finance in 2020. Note that the tax revenue data I give in the table below is the full-caliber revenue, that is, it also includes the portion of tax revenue paid to the central government, not only the local-level tax revenue. The subject of “government fund income” is actually the income from land sales, which is a summary of various land revenues of local governments.
According to the above table, the national land finance dependency is 31.5%, which is the baseline. Cities below the baseline have a low dependency on land finance, while cities above the baseline have a high dependency. So it looks like the top five cities are, in order: Shenzhen, Beijing, Shanghai, Tianjin and Nanchang. And the bottom five cities are, in order: Fuzhou, Zhengzhou, Xi’an, Nanjing and Changchun. Shenzhen, which has the lowest dependence on land finance, has a dependence of only 13.6%; while Fuzhou, which has the highest dependence, has a dependence of 54.2%, with more than half of its revenue coming from land sales, which is a very scary thing.
Ladies and gentlemen, if we only analyze to this point, then this public number is just like other tawdry public numbers, and we can’t talk about the premier data guru at all. For the above data, we must analyze further in depth. You should know that each city has the obligation to pay state taxes to the central government, and after deducting the paid portion, the rest of the money can be spent by itself. So, next we have to calculate the actual size of the 31 cities to pay all of them, the calculation is not difficult, the amount of payment = full-caliber revenue – public expenditure. If this contribution exceeds the revenue from land sales, it means that the city is relatively healthy financially. As long as it is no longer obligated to pay up, it can completely abandon real estate, which is very strong. Based on such calculation rules, we calculated the fiscal toughness indicator in the table below. For the record, folks, the following table is well worth looking at carefully, over and over again, and it is very, very interesting.
Before we talk about the fiscal toughness indicator, let’s talk about an aside, these 31 cities, the top three highest fiscal contribution scale, in order, are Shanghai Beijing and Shenzhen, especially Shanghai, in 2020 enough to contribute 906.7 billion, 24.28 million Shanghai people, per capita contribution of 37,300 yuan. This is a very shocking thing. You know that the money paid to the central government will be used to subsidize the central and western finances in the form of transfer payments. So the people of Shanghai are feeding their compatriots in the Midwest with their own blood and sweat, with 996 and 007, with premature aging and sudden death.
To cut to the chase, of the 31 cities, only 7 have positive financial toughness indicators, that is, the size of the contribution exceeds the revenue from land sales. I must list them here in order: Shanghai, Beijing, Shenzhen, Guangzhou, Hangzhou, Dongguan, and Foshan. And, I can responsibly say that in terms of the whole country, these are actually the only seven cities that can achieve positive values for the fiscal toughness indicator. Although this table does not exhaust all the first and second-tier cities in the country, in fact, it is not even close to one or two, basically all here. Very few cities do not appear in this table, but only because the construction and maintenance level of the local taxation website is too poor, and the release of relevant data for 2020 is too slow, and the fiscal strength of such cities, of course, is not strong.
The remaining 24 cities are all negative on the fiscal strength index, which means they all have a high dependence on land finance. In particular, Nanning, Harbin, Shijiazhuang, Changchun, Wuhan, Zhengzhou and Chongqing, the seven cities, the scale of financial contribution itself is negative, meaning that these seven cities did not make a dime to the central financial contribution, in turn, also need the central financial subsidies, in fact, to Shanghai, an excellent city to subsidize. Of course, which Wuhan in 2020 is a special case, in fact, Wuhan in 2019 also contributed more than 40 billion, the economic performance of Wuhan in 2020 is not good can not be blamed on Wuhan. Chongqing is also, is also slightly better in the city, under the jurisdiction of those mountain counties and cities are very poor, financially can not be self-sufficient, but also understandable. As for the other five cities, all provincial capitals, that is really a little excuse can not find, need to reflect on why the financial can not earn money.
We take the second table and the first one to do a comparison, in the position of the last five overlap, is undoubtedly the most dangerous financial cities, the possibility of financial collapse in a minute. Well, the recurrence of just two cities: Zhengzhou and Fuzhou. Zhengzhou in particular, the scale of financial contributions is still negative, while Fuzhou at least also contributed 15.8 billion it. From this point of view, Zhengzhou has to grow a snack. I hope that in 2021, a major event like the default on the payment of Henan Yong Coal bonds, which almost knocked the whole country’s urban investment bond issue down, will not happen again. Go Zhengzhou people!
Well, that’s the situation. As far as these 31 key cities are concerned, 24 of them are highly dependent on revenue from land sales, accounting for 77.4%. This ratio is already very high. If there is a trend of falling house prices in 2021, resulting in falling land prices and land abortions, the finances of these 24 cities will immediately be in an untenable state, and it will be extremely difficult to maintain financial balance even if they stop making financial contributions altogether.
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