- September 1, 2020 Economy
China Shock in Reverse: How Regional Economies Have Fared 20 Years after WTO Accession
Jessica Redmond was a MacroPolo Summer Associate, where she spent 10 weeks conceptualizing, researching, and executing on this project. Jessica is now taking a gap year from the Harvard Kennedy School. All analysis and findings presented in this project are her own. For questions regarding this project, please reach out to Jessica directly (firstname.lastname@example.org) and you can follow her on Twitter @jessica22792408.
China officially became the 143rd member of the Word Trade Organization (WTO) in December 2001, completing a key step of the “reform and opening up” process that began in 1978. As well as reducing trade barriers, China’s WTO entry also forced reforms in the domestic economy—from labor markets and factor prices to state-owned enterprises (SOEs)—that were designed to unlock growth potential across the country.
Twenty years later, there is little doubt that China is a trade superpower. Its total trade had skyrocketed from $500 billion in 2001 to $4.6 trillion in 2019 (see Figure 1), the impact of which rippled across the global economy. That impact is also partly responsible for intensified polarization on global trade over the last decade, particularly among advanced economies.
Figure 1. China’s Trade Value Rose Nine-Fold Since WTO Accession
Source: National Bureau of Statistics (NBS).
Debates have revolved around who benefits from trade in the age of globalization and who bears the costs. Those debates reached a crescendo in the United States with the publication of the famous “China Shock” paper in 2016, which quantified the localized impact of Chinese imports on the manufacturing-focused US rust belt. The authors recognized the overall benefits of trade but found that adjustment to trade is slower than expected, with costs falling disproportionately on local economies where industries most exposed to import competition are concentrated. It appears, then, that economic openness can result in unequal distribution of gains and costs within national economies, especially those as large and diverse as the United States.
But openness runs both ways. Relatively little attention has been paid to how openness has affected China’s regional economies. After nearly 20 years of WTO membership, we now have more robust and historical data to begin considering whether the impacts of trade, and associated foreign direct investment (FDI) patterns, have been dispersed widely or in a concentrated manner within China. Have all regional economies gained equally? This project is an initial foray into how China’s openness to the global economy may have resulted in its own “winners” and “losers” (see Map).
Interactive Map: Regional Trade and FDI Patterns from 1993-2019
Note: Trade and FDI are represented as % of provincial GDP and % of provincial gross capital formation respectively. The higher the percentage, the darker the color of each province, which can be viewed as proxy for the relative degree of “openness. Circle diameters represent the absolute volumes of trade and FDI.
Source: NBS; WIND.
Based on this data, the assessment that follows attempts to answer the questions above by examining whether trade exposure had distributional impacts across several indicators: regional rural-urban inequality, GDP per capita growth, and the rate of urbanization. This is of course only a preliminary exploration and does not consider the many factors beyond trade affecting these trends. Much more work needs to be done in this area, but here are some initial findings:
- Since WTO accession, the coastal provinces of Zhejiang and Jiangsu emerge as clear “winners” in terms of growth in trade and the proportion of overall trade originating from those two provinces. Heilongjiang, Liaoning, and Jilin provinces (Northeast rust belt) are the “losers,” having seen their levels of trade and FDI stagnate and the region as a whole having run a trade deficit in goods over the last four years.
- Although a disproportionate amount of trade activity occurs along the east coast, there is little evidence of an uneven distribution of costs based on measures such as provincial rural-urban inequality, GDP per capita growth, or rate of urbanization (used as a proxy for industrial development). This does not mean costs were not incurred, but does seem to imply that China adjusted rapidly so the costs do not appear to be locally concentrated.
- We hypothesize that a key contributor to the muted localized impact from trade is China’s labor market flexibility. Unlike the United States, China’s hundreds of millions of migrant workers were likely more willing and able to move across geographies and sectors. The impacts of trade may have also been absorbed by the Chinese state that supported industries or regions that faced economic headwinds. More work needs to be done to explore whether the Chinese state’s role, as well as other factors such as China’s level of economic development, helped to alleviate adjustment costs from trade exposure.
“Winners” and “Losers” in China’s post-WTO Trade Explosion
Clear Winners: Zhejiang and Jiangsu
Unsurprisingly, the east coast has continued to dominate trade activity and FDI over the last two decades, in large part because they had first-mover advantage and received favorable treatment such as establishing Special Economic Zones. These dynamics may have had self-reinforcing effects on FDI as foreign firms chose locations based on factors such as human capital, infrastructure, and per capita income.
But zooming in, there have been notable shifts of trade activity among east coast subregions. Jiangsu and Zhejiang, the two easternmost provinces, have seen the largest gains in trade activity and overall prominence in China’s total trade. Shanghai, Zhejiang, and Jiangsu combined make up nearly 35% of total trade as of 2019, up from 28.5% in 2001 and 16% in 1994.
Clear Losers: Heilongjiang, Jilin, and Liaoning
These northeast provinces (dongbei) are often referred to as China’s rust belt and have experienced relatively flat growth in trade, despite being one of the more industrialized regions initially and with Liaoning being a coastal province. Regional share of total trade has fallen from 5% in 2001 to 3% in 2019. In addition, all three provinces have run a trade deficit in goods since 2016.
These rust belt provinces have had to deal with poor performing SOEs, bad investment climate, and local protectionism. The region was hit hard by unemployment and social discontent in the early 2000s as a result of dramatic SOE reforms. It has also fallen behind on technology, making it an unattractive destination for global manufacturing following WTO Accession.
Relative Gains: Henan, Hunan, Anhui, Chongqing, Sichuan, and Guangxi
Although still far below the level of the east coast, the central and western regions saw relatively large gains in trade activity after 2010. For example, Chongqing, Sichuan, and Guangxi combined have seen their share of total trade increase by 3.4% since 2010, compared to an average growth of only 0.8% between 2001-2010.
These regional economies relied more heavily on agriculture and therefore were not as well placed to capitalize on WTO accession immediately. But the Chinese government’s push to “go West” starting in the mid-2000s seemed to have had some success in shifting trade and FDI patterns. In addition, other factors such as the wage gap between coastal and inland regions and cheaper land costs, as well as the 2008 stimulus, can help to explain rising investment in the region.
Relative losses: Guangdong, Hainan, and Fujian
Guangdong, as the site of China’s first SEZ and with its proximity to Hong Kong, has always reigned as a leading trade hub. Guangdong alone still remains the province with the highest amount of trade activity regionally, but rising wages and land costs in “Tier 1” cities such as Shenzhen and Guangzhou have led to a relative shift of trade away from this regional hub. The Guangdong-Hainan-Fujian region has fallen from a 2001 peak share of 40% of total trade to 27% in 2019.
The beneficiaries of Guangdong’s relative loss appear to be Tier 2 cities further up the east coast—in particular, mid-coastal cities such as Ningbo, Nanjing, and Hangzhou. They have seen tremendous trade growth, likely due to their lower costs and proximity and linkages to Shanghai’s port.
Inequality, Growth, and Trade Adjustment
Given the uneven distributions of trade activity and FDI, and the varied levels of economic development across China, have the gains and costs of trade also been concentrated? Traditional models tend to assume trade adjustment happens in the long-term and eventually disperses the impacts broadly. But of course, not all regions adjust to trade quickly or at the same speed. Therefore, where trade-exposed industries are concentrated and where trade happens matter in terms of impact, as the “China Shock” paper and other similar studies have demonstrated.
Localized impacts from trade can now be tested, given what we know of China’s competitive advantage in manufacturing, disadvantage in agriculture, and where trade has been concentrated. To do so, we examined shifts in several economic indicators that would be affected by trade: 1) rural-urban inequality; 2) GDP per capita growth; and 3) rate of urbanization.
Based on these indicators, there appears to be little evidence that regional trade exposure has led to benefits being concentrated in the east coast and the costs concentrated in the inland regions. Rather it appears the impacts of trade have been felt broadly across the country.
Each of the maps below shows the change of the respective indicators from 2002 to 2019. Darker shading indicates more gains in terms of income equality, GDP per capita growth, and urbanization at the provincial level.
1. Comparing the change in rural-urban income inequality (in percentage points)Note: Positive values indicate a narrowing of the rural-urban average wage gap. For example, if rural average income was 60% lower than urban average wage in 2002, and 59% lower than urban average income in 2019, the map would show a value of 1.
Source: Author’s own calculations, NBS data.
In terms of regional inequality, we considered the average of rural and urban incomes for a province over the specified time period. As FDI inflows and trade activity tend to favor urban areas, we would expect to see greater increases in rural-urban inequality within the east coast relative to other regions because the former has the greatest trade exposure.
But the evidence is mixed at best. Although Jiangsu and Shanghai, two provinces with the highest concentration of trade, both saw small increases in income inequality (4.3 and 1.9 percentage points, respectively), in general provinces with higher trade exposure tend to have had smaller increases, or even decreases, in income inequality. Taking into account other factors that could have affected income inequality at the provincial level weakens this relationship further.
More broadly, income inequality narrowed for most of China since 2001, including along the east coast. Although Tibet saw a significant narrowing of its average income gap by 16.5 percentage points (likely because it had a low base), coastal Zhejiang also experienced significant narrowing of the gap of 7.5 percentage points. Overall, it is difficult to see a clear pattern of greater trade openness leading to greater income inequality.
2. Comparing differences in GDP per capita growth (% change)
Note: Values indicate the change in GDP per capita values from 2002 to 2019, as a % of 2002 GDP per capita values. For example, if GDP per capita doubled from 2002 to 2019, the map would show a value of 100%.
Source: Author’s own calculations, NBS data.
When comparing GDP per capita growth across provinces, we would expect lower GDP per capita growth in inland, agricultural regions that are more exposed to trade, such as the central and western regions. This is because we assume agriculture is more susceptible to import competition in China’s case because of its comparative disadvantage in the sector and because it reduced trade protection against agricultural imports as part of its WTO accession.
Yet the more “agro regions” of China have seen some of the greatest per capita GDP growth, particularly Guizhou (1326% increase since 2002), Yunnan (794% increase since 2001), and Anhui (920% increase since 2001).
Overall, we see convergence between the agro regions and the east coast in per capita GDP. A clear outlier is the northeast rust belt region, which has seen much slower growth than the surrounding regions and has seen falling GDP per capita since 2015.
3. Urbanization and trade adjustment (in percentage points)
Note: Values indicate the change in the ratio of urban residents:total residents for each province from 2005 to 2018. For example, if the ratio of Urban:Total residents in a province was 50 in 2005 and 51 in 2019, the map would show a value of 1.
Source: Author’s own calculations, NBS data.
One potential reason for the concentrated costs of trade is the slow pace of regional adjustments to trade shocks. In particular, this could mean labor market inflexibility where workers are not moving across industries or regions quickly in response to depressed wages or jobs. To see whether China has adjusted to trade more rapidly, we can consider changes in urbanization. That’s because urbanization is a good proxy gauge for industrialization, since the latter requires the population to move from farms to cities where manufacturing is concentrated.
Given that the impact of trade should lead to higher returns on manufacturing and lower returns on agricultural land, we would expect to see workers moving from the countryside to urban regions as part of the trade adjustment. Again, we broadly see convergence across China, with the less open and rural regions seeing urban residents increase at a faster rate than the already industrialized coast.
Central China has seen a slightly faster rate of urbanization, particularly Henan (+21.1 percentage points), Shaanxi (+20.9 percentage points), and Guizhou (+20.7 percentage points), though coastal provinces such as Hebei have also seen sizable shifts (+18.7 percentage points). The northeast rust belt region once again stands out as a laggard with much lower rates of urbanization, despite low levels of trade openness.
The extent to which China’s provinces have experienced the effects of its opening to the world economy differ drastically. Yet as demonstrated above, China appears to have quickly adjusted to this unexpected trade boom leading to dispersed, rather than concentrated, effects from openness over the last 20 years.
One explanation is that China’s labor market post-WTO accession behaved much like how the traditional model expected—the assumption that workers will move to where wages and employment are rising, both geographically and sectorally. China’s massive migrant labor force was quite mobile and perhaps could more easily shift from agriculture to light manufacturing. In contrast, lower labor market flexibility in import-facing localities and difficulties in shifting from manufacturing to services for US workers may have been a factor in the slower adjustment to trade exposure. This is supported by evidence that labor mobility in US manufacturing has been declining across states in the last few decades.
In addition, any concentrated trade impacts may be dwarfed by other factors in China’s dramatic economic development, including government policies that sought to mitigate those impacts through subsidies, massive infrastructure projects, or other protections of domestic agriculture and industry. Trends such as privatization and economic decentralization may be more significant explanatory variables that have had larger impacts on the indicators examined above.
One clear parallel between the United States and China is that both of their “rust belts” have seen relative decline, though for different reasons. The US rust belt adjusted slowly to import competition, while China’s rust belt failed to capture the benefits from the country’s post-WTO trade boom and has stagnated for decades. These outcomes speak to policy decisions and economic trends beyond trade, such as technology changes in the manufacturing industry and the legacy of sclerotic SOEs.
More uncertainties lie ahead, however. China is in the midst of a significant economic transition, as it aims to shed low-end manufacturing and climb the value-added ladder, while simultaneously trying to generate more consumption-led growth. These uncertainties are only exacerbated by the disruptions brought by the Covid-19 crisis.
How China manages its transition will have profound implications on current trade patterns and their impacts. A key area to watch is whether China can maintain its labor market flexibility as it moves into high-tech manufacturing and services. An aging labor force, rising labor costs, and ongoing difficulties with hukou reform could hinder labor mobility and make the costs of trade more prominent and concentrated over time.
 Author’s own calculation based on NBS data. Average increase in total trade (Exports + Imports) for central provinces from 2010 to 2019 was $37.7 billion, compared to $16.8 billion in 2001-2010. The western region saw an average increase of $23 billion from 2010-2019 and $9 billion from 2001-2010. In comparison, both the east coast (2010-2019: $108 billion increase; 2001-2010: $215 billion increase) and northeast (2010-2019: $8 billion increase, 2001-2010: $25 billion increase) saw slowing growth.
 The findings from the paper have generated considerable debate. Many maintain the view that the decline of Western manufacturing was in many ways inevitable, and that the “China Shock” ascribes incorrectly the causes of economic malaise in manufacturing-focused regions. Although the “China Shock” paper recognizes these longer term trends, the authors nonetheless hold that import shocks from China accelerated the process of decline and resulted in localized depressive economic effects.
 Author’s own calculation based on NBS data. Difference is expressed as the difference between urban and rural wages as a percentage of urban wages:
The methodology for calculating average rural and urban incomes changed between 2012 and 2013. This change had an average impact of 3.4 percentage points on differences between wages, with a standard deviation of 1.6 percentage points. Average year-on-year changes (including 2012-2013) were 0.87 percentage points on average with standard deviation of 1 percentage point. Absolute changes over the period were 6.74 percentage points from 2002 to 2019 with a standard deviation of 3.36.
 Without accounting for other factors, a 10-percentage point increase in trade as a % of GDP is associated with the average rural-urban wage gap rising by 1.7 percentage points.
 A simple fixed-effects panel regression suggests that rural-urban wage differences are positively correlated with trade openness, but the effect is small and similar to the impact of increased privatization and decentralization.
 The “China Shock” paper argued both that US regions with higher concentrations of “import-facing” industries, primarily manufacturing in this case, saw generalized downside effects on local labor markets such as job loss and stagnant wages.
 Data on urban residents as a proportion of total residents are only available from 2005-2018.
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