The US-China Customs War and China’s Economy: Evidence from Night Lights
The US-China Customs War and China’s Economy: Evidence from Night Lights

The US-China Customs War and China’s Economy: Evidence from Night Lights

The Impact of the US-China Customs War on China’s Economy: New Evidence from Night Lights

In early 2018, the Trump administration began a series of tariff measures that would gradually raise tariffs on China’s exports to the United States over the next two years. For each round of these U.S. tariffs, the Chinese government responded with retaliation duties on goods imported from the United States. By September 2019, both sets of tariffs had been extended to cover the vast majority of products traded between the two countries.1 These tariffs negatively affected bilateral trade flows (Amiti et al. 2019, Fagjelbaum et al. 2019, 2020) and have become a persistent thorn in the side of US-China relations.

Did US tariffs on Chinese exports have a negative impact on economic activity in China, and if so, how much? Similarly, did China’s retaliatory tariffs affect domestic production, given that these would in principle have increased the cost of US input?

There is now a fair amount of empirical evidence on the effects of this ‘customs war’ on the US economy. Researchers have documented that there has been an almost complete transfer of the tariff burden to US prices (Amiti et al. 2019, Cavallo et al. 2021). Tariffs have also negatively impacted domestic consumption (Waugh 2019), investment (Amiti et al. 2020) and employment (Flaaen and Pierce 2019).

In comparison, less is known about the impact (if any) that the customs war has had on China’s economy, which motivates the two research questions posed above. This is not due to a lack of interest in these issues, but rather reflects limitations on data availability. In particular, China’s statistical agencies do not regularly disclose data on output and employment at the sub-national level or for detailed industries; such data that becomes available are often subject to reporting delays.2

Given these data limitations, we turn to satellite readings of brightness at night. Night light measurements have come into increasing use as a proxy for the intensity of man-made activity in otherwise data-scarce environments. The economic growth and development literature has shown that night light is strongly correlated with more conventional measures of economic performance such as GDP (Chen and Nordhaus 2011, Henderson et al. 2012). Night light data have also been used recently to study short-term responses to macroeconomic shocks across locations in a country (Chodorow-Reich et al. 2019).3 The night light data gives us a degree of spatial resolution – at the level of 11 km x 11 km lattice cells – which would be difficult to achieve with other data sources. Satellite readings are also less of a concern for data manipulation.

Figure 1 provides an example of the variation across locations that night light data reveals. The figure plots brightness at night in Suzhou Prefecture City in Q1 / 2018 and Q1 / 2019, respectively. Night lights dimmed more in the two highlighted regions – Huqiu New & Hi-Tech Zone and Suzhou Industrial Park – compared to the rest of Suzhou.4 The decrease in night light intensity was greater in these two production zones, which have a high export intensity, suggesting a possible link to the customs war. If export demand were hit by U.S. tariffs, the resulting decline in production and labor demand would reduce the light from factory night shifts and from workers’ dormitories, which are often close to China’s industrial areas.

figure 1 Night light intensity in Suzhou in Q1 / 2018 and Q1 / 2019

More formally, we perform this analysis using a panel dataset with quarterly observations collected for the nearly 100,000 lattice cells spanning mainland China. In order to isolate the effects of the tariffs in a credible way, we implement an empirical strategy with shift sharing. To assess the impact of US tariffs on Chinese exporters, we construct a measure of a network location’s exposure to these tariffs that makes use of the composition of net exports before the tariffs. Intuitively, locations in China would be more vulnerable if they initially specialized in selling products to the United States, which were later subjected to major tariff increases. At the same time, we construct an analogous measure of a net location’s exposure to China’s retaliatory tariffs, using the composition of pre-tariff imports of intermediates and capital goods, as well as the product-level retaliatory tariffs that were subsequently imposed. The empirical strategy we follow requires detailed information on the structure of China’s trade flows at the grid level. We construct this by geo-locating companies in 2016 Chinese customs data for grid locations using web-based map services (Google Maps and Amap).

Our key result is that places in China that were more exposed to US tariffs experienced a larger decline in night light intensity, indicating a decline in local economic activity (Chor and Li 2021). These results are obtained using a specification that includes lattice and prefecture time-fixed effects. We also check extensively for pre-trends that may be associated with other initial grid characteristics, such as openness to trade (initial grid exports per capita, grid intermediate imports per capita) and overall economic development (initial grid average night light intensity). We also show that our results are robust when we check for a location’s possible exposure to simultaneous exchange rate movements or VAT adjustments.

In contrast, we find that exposure to China’s retaliatory tariffs had a negligible effect on night light at the grid level. We present some suggestive evidence as to why this was the case. China’s tariff cut for its most favored nation (MFN) offset some of the bite from retaliation rates. Chinese importers also appear to have approached alternative country sources for their input. Last but not least, there is the possibility that Chinese firms replaced domestic inputs, although we lack direct data to confirm this.

As another step in our analysis, we map the above impact on night light intensity to more conventional economic results – specifically GDP per capita. per capita and manufacturing business. To do this, we follow the statistical model in Henderson et al. (2012) and estimate the necessary structural elasticity that maps changes in night light to changes in GDP per. per capita (manufacturing employment, respectively), using prefecture-level data from the Chinese City Statistical Yearbook 2012-2012. With these elasticities, we can then calculate the derived changes in the respective economic results that match the changes in night light intensity induced by the US customs shocks.

Figure 2 summarizes this derived effect of US tariffs across grid placements. The lattice cells are first arranged according to the severity of the exposure to the US tariffs and grouped into population-weighted percentile containers. We find that the negative impact of US tariffs was very skewed across locations. Up to 70% of China’s population experienced zero or minimal direct exposure to US tariffs. On the other hand, the tail most 2.5% of China’s population most directly exposed experienced a shock in the United States of 9.1 percentage points; this corresponds to a decrease of 2.52% in GDP per capita. per capita and a 1.62% reduction in manufacturing employment compared to unaffected networks. The lattice cells in this most tariff-exposed container were not only assembled in coastal areas; instead, these cells could be found in close to two-thirds of China’s prefectures.

Figure 2 Effects of export duty shock across population percentages

To summarize, our results confirm that the tariffs imposed by the United States on exports from China had a negative impact on economic performance in the short term. There are still more open questions with political implications. To what extent will the continued application of these tariffs complicate and slow down the recovery of China’s economy from the Covid-19 pandemic? What are also the long-term consequences of these tariffs, as their impact on companies’ investment decisions plays out more?

References

Amiti, M, SJ Redding and D Weinstein (2019), “The Impact of the 2018 Trade War on US Prices and Welfare”, Journal of Economic Perspectives 33 (4): 187-210.

Amiti, M, SH Kong and D Weinstein (2020), “The Effect of the US-China Trade War on US Investment”, NBER Working Paper No. 27114.

Cavallo, A, G Gopinath, B Neiman and J Tang (2021), “Tariff throughput at the border and in the store: evidence from US trade policy”, American Economic Review: Insights 3 (1): 19-34.

Chen, X and WD Nordhaus (2012), “Using Brightness Data as a Proxy for Economic Statistics”, Proceedings of the National Academic of Sciences 108 (21): 8589-8594.

Chodorow-Reich, G, G Gopinath, P Mishra and A Narayanan (2019), “Cash and the Economy: Evidence from the Demonization of India”, Quarterly Journal of Economics 135 (1): 57-103.

Chor, D and B Li (2021), “Illuminating the Effects of the US-China Customs War on China’s Economy”, NBER Working Paper No. 29349.

Cui, C and L SZ Li (2021), “The Effect of the US-China Trade War on New Chinese Enterprises”, Financial Letters 203: 109846.

Donaldson, D and A Storeygard (2016), “The View from Above: Applications of Satellite Data in Economics”, Journal of Economic Perspectives 30 (4): 171-198.

Fajgelbaum, PD, PK Goldberg, PJ Kennedy and AK Khandelwal (2019), “Back to protectionism”, VoxEU.org, November 7th.

Fajgelbaum, PD, PK Goldberg, PJ Kennedy and AK Khandelwal (2020), “The return to protectionism”, Quarterly Journal of Economics 135 (1): 1-55.

Flaaen, A and J Pierce (2019), “Deviation from the Impacts of the 2018-2019 Tariffs on a Globally Connected U.S. Manufacturing Sector”, Working Paper.

He, C, K Mau and M Xu (2021a), “Trade Shock and Corporate Recruitment Decisions: Evidence from Job Advertisements of Chinese Companies in the Trade War”, Work economy 71: Article 102021.

He, C, K Mau and M Xu (2021b), “The effects of the US-China trade war on corporate job postings”, VoxEU.org, July 15th.

Henderson, JV, A Storeygard and DN Weil (2012), “Measuring Economic Growth from Outer Space”, American Economic Review 102 (2): 994-1028.

Waugh, ME (2019), “Consumption response to trade shocks: evidence from the US-China trade war”, NBER Working Paper No. 26353.

Endnotes

1 At the height of the ‘customs war’ in September 2019, US tariffs on China had risen by 20.7 percentage points for the average HS 6-digit product; these affected 14.2% of the 2017 value of China’s total exports. The retaliation rates imposed by China averaged 16.6 percentage points; these affected 5.6% of the 2017 value of China’s total imports.

2 Existing studies of the impact of tariffs on China have similarly addressed the use of new data sources: He et al. (2021a, 2021b) examine data on online job postings, while Cui and Li (2021) look at registrations of new business registrations.

3 See Donaldson and Storeygard (2016) for a study on the use of satellite-based data in economic research.

4 The change in average log night lights was -0.105, -0.085 and -0.067 for New & Hi-tech Zone, Industrial Park and the rest of Suzhou, respectively.

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