Homemade Foreign Trading

Over the past two decades, China has taken steps to facilitate international participation in its capital markets, including Qualified Foreign Institutional Investors (QFII) and Renminbi QFII (RQFII), which allow licensed international institutional investors to directly invest in Chinese securities. Among all the accesses to Chinese capital markets, Stock Connect, which launched on November 17, 2014, and is the newest “opening-up” effort from Chinese policy makers, quickly became the dominant investment channel for foreign investors.

Stock Connect is distinctive in that it represents one of the greatest innovations in Chinese capital markets. The program achieves the goal of international financial integration (in certain stock/bond markets) with the rest of world, but without opening up China’s capital account. It does so by enabling investors from Hong Kong and overseas areas—but also qualified investors from Mainland China—to directly trade eligible shares listed on the other market via their local exchanges, without the need to adapt to the operational practices of the other market. More importantly, investors on each side can only use their funds to trade securities in the specified market(s) on the other side, without further access to the rest of the economy in the other market. 

However, there is a dark side to this market. The authors show that Stock Connect creates regulatory loopholes for opportunistic mainland investors to arbitrage by “round-tripping.” More specifically, the authors present evidence that a group of “homemade” mainland investors—likely Chinese corporate insiders for the purpose of identity concealment—engage in cross-border trading via the connect program as if they were “foreign investors.” 

Why would someone conceal their identity? Researchers have explored such motivations as tax evasion, tunneling, and market misleading, but this new work also examines round-tripping of insiders who choose to profit on their non-public information through the Stock Connect program. Round-tripping has gained prominence as the mainland and Hong Kong exchanges recently reached an agreement on the further expansion of eligible stocks under Stock Connect. 

How does the Stock Connect program help conceal investors’ identities? In contrast to the mainland exchanges, which adopt a see-through surveillance scheme for trading and clearing, under Hong Kong’s jurisdiction there are financial intermediaries (brokers or custodians) who hold their clients’ securities under the names of intermediaries. During the first three years after the launch of the Stock Connect program in 2014, northbound trading (or the trading of China Connect Securities by Hong Kong and overseas investors through Stock Connect) adopted the scheme that is consistent with Hong Kong’s jurisdiction. Therefore, the Stock Connect program offers an opportunity for domestic traders in mainland markets to disguise themselves by trading eligible A-shares of connected firms indirectly.

Before describing the authors findings, it is important to note a key piece of legislation that the authors call a “game changer.” In a joint announcement made by the two regulators on both sides on August 24, 2018, the Stock Connect program established a system whereby northbound custodians are required to assign a unique identifier to their northbound clients. This allows the mainland regulator to identify the actual beneficial owner of each northbound trade and to deal with irregular mainland investors.

Please see the full working paper for a more detailed description of how Stock Connect has reshaped trading and dealing within and through mainland China; in brief, the authors employ a comprehensive dataset on northbound custodian holdings operated in the Hong Kong exchange to explore irregular trading activities and to address the question: Who are more likely to exploit the advantage of disguising themselves through the connect program? They find the following:

• Beginning with a study of return predictability of northbound flows from different origins in the Chinese A-share market, the authors find that although the trading activities of less prestigious foreign custodians and cross-operating mainland custodians were informative in the early days of the Stock Connect, their northbound flows have become uninformative about future stock since regulations were introduced to crack down on homemade foreign trading. 
• In China, state-owned enterprises (SOEs) and non-SOEs differ in government scrutiny in ways that might make non-SOEs better accommodating insiders as homemade foreign investors. Meanwhile, centrally administrated SOEs have more levels of administration and hence lack information transparency, which also creates space for homemade foreign trading. Consistent with these hypotheses, the authors find that for both central-SOEs and non-SOEs, the return predictability of northbound flows from problematic custodians fell after the reform, and the reduction is significantly below that of northbound flows from other custodians.
• Finally, concurrent trading activities of northbound investors from problematic custodians and mainland inside sellers become relatively infrequent after the regulatory reform. 

The effort to crack down on cross-border regulatory arbitrage continues. As of July 25, 2022, northbound brokers are no longer allowed to set up trading accounts for mainland investors. This presumably leads to an elevated transaction cost and litigation risk for engaging in homemade foreign trading in China, and, as the authors suggest, may encourage the flow of genuine foreign investment into the emerging capital market and improve market efficiency.

Non-Tariff Trade Barriers in the U.S.-China Trade War

When the US and China engaged in a trade war in 2018 and 2019 there was much focus on the multiple rounds of tariff hikes between the two countries. However, there was also abundant anecdotal evidence about non-tariff regulatory mechanisms imposed by China to stifle purchase of US exports, like inspection delays on certain products, onerous permit requirements, and other targeted efforts to restrain exports from the United States to China.

Non-tariff barriers can have large effects on trade and welfare, but their opaqueness makes them difficult to measure. In this paper, the authors employ Chinese customs level data available through the Tsinghua China Data Center, along with a demand theory model, to infer the use of non-tariff barriers in the U.S.-China trade dispute between 2018 and 2020. This includes China’s use of regulatory measures in 2018 and 2019 at the height of the trade war to punish American exporters, as well as in 2020 to benefit American exporters in China’s effort to end the trade war.

First, the authors estimate the use of non-tariff trade barriers by China in its trade battle with the United States in 2018 and 2019, and in the first year of the purchase agreement in 2020. They first estimate the elasticities of demand for US products in China relative to products made by other countries, and the elasticity of supply of exports to China, to find that:

• Foreign export supply curves are essentially horizontal, which suggests that the incidence of higher Chinese trade barriers—whether tariffs or non-tariff regulations—is entirely borne by Chinese consumers.

The authors then use the estimates of the demand elasticities to back out the changes in non-tariff barriers as the residual of changes in imports of US products relative to imports from other countries of the same product, after controlling for the effect of tariffs. These estimates suggest that:

• Non-tariff barriers on US imports to China increased significantly in 2018 and 2019, by an average of 56 percentage points (in tariff equivalent units) for agricultural products and by 17 percentage points for manufactured products.  And in the first year of the Phase 1 agreement in 2020, some of the increase in non-tariff barriers on US agricultural exports was reversed.
• The use of non-tariff barriers was also much more targeted towards specific products compared to the tariffs, and applied largely to non-state importers.  For example, the tariff equivalent of non-tariff barriers increased by almost 300 percentage points in 2018 and 2019 for such categories as “oil-seeds,” “cereals,” and “ores, slag and ash.”

The authors also employ a demand theory model to estimate the effect of trade barriers, including tariffs and non-tariff barriers, on Chinese welfare to find that:

• About 50% of the overall decline in US exports to China between 2017 and 2019 was due to higher non-tariff trade barriers, and the other half due to higher tariffs. However, most of the welfare loss incurred by China from the trade war was due to non-tariff trade barriers.
• Specifically, trade barriers imposed in 2018 and 2019 lowered Chinese welfare in 2019 by $40 billion, with 93% of the welfare loss coming from the use of non-tariff trade barriers. This welfare loss is about six times larger than an equivalent import decline due to higher tariffs. Non-tariff barriers are more costly compared to tariffs because they apply to some importers and not others, which results in misallocation and because non-tariff barriers do not generate revenues.

While the authors focus on the 2018-2019 US-China trade war, they offer similar examples of other recent disputes to illustrate the broader impact of non-tariff regulations in trade disputes. For example, when Canadian authorities arrested Meng Wangzhou, the CFO of Huawei, Chinese authorities retaliated on Canadian exports with similar opaque regulatory procedures, like claiming Canadian canola oil was infected with pests, and subjecting other food products to long paperwork delays. Relatedly, after Australia passed a national security law and blocked Chinese companies from its 5G mobile networks, Australian exports of barley were hit with anti-dumping duties, import licenses on Australian beef, lobster, and copper were revoked, and directives were issued to stop buying Australian cotton and coal.

Bottom line: To the extent that the goal of the Chinese government was to retaliate against US tariffs on Chinese products by cutting imports from the US, this work reveals that non-tariff barriers to trade were more costly than tariffs alone, and the burden fell to Chinese consumers. Further, while this work offers important insights into the non-tariff costs associated with the recent US-China trade war, its analysis also provides a useful framework to examine similar effects of other trade disputes.

Investing With the Government: A Field Experiment in China

Government participation in the economy, via direct or indirect ownership of private sector firms, is pervasive around the world and is often characterized by two distinct models: the “grabbing hand” model, commonly used to describe Russia and Eastern Europe in the 1990s, where government interference by bureaucrats and politicians represents a key friction to the growth of private businesses; and the “helping hand” model where the government helps private sector firms overcome market failures.

The authors bring these models to an investigation of China’s massive and high-growth economy to determine whether market participants view the government as a grabbing or helping hand, in the context of the multi-trillion-dollar venture capital and private equity (VCPE) market. They combine a field experiment with new administrative and survey data to ask whether—all else equal—firms prefer to receive capital from the government vis-à-vis private investors. Specifically, the authors focus on the matching between capital investors, or Limited Partners (LPs), and profit-seeking firms, that is, the fund managers or General Partners (GPs), that manage invested capital by deploying it to high-growth entrepreneurs.

The authors characterize the role of government in the Chinese VCPE market by matching data on VCPE investments from 2015–2019 with administrative business registration records, through which they can observe the ownership structure of all firms (GPs) and investors (LPs) in the data, to establish four descriptive facts:

• The government—represented by central, provincial, and local government agencies as well as state-owned enterprises (SOEs)—is the leading investor, with the government as a majority owner of about half of LPs, and government LPs significantly larger investors than private LPs.
• The government is also a minority owner of a significant share (about a third) of GPs.
• Government-owned GPs perform worse than private GPs.
• There is a pattern of assortative matching, with government LPs investing disproportionally more in government-owned GPs.

These facts, while informative, can support many different interpretations, which motivates the authors to estimate actual firm demand for government capital. To do so, they conduct a field experiment in 2019 in collaboration with the leading VCPE industry service provider in China. This collaboration led to an experimental survey of 688 leading GPs in the market (with a response rate of 43 percent), which together manage nearly $1 trillion. GPs are asked to rate 20 profiles of LPs along two main dimensions: (i) how interested they would be in establishing an investment relationship with the LP (under the assumption the LP is interested); and (ii) the likelihood that the LP would be interested in entering an investment relationship with them if they had the chance. Importantly, there is no deception in this survey because GPs know the LP profiles are hypothetical. (Please see the working paper for more details on the survey instrument.)

The authors’ novel experimental survey finds the following:

• The negatives of receiving capital that is tied to the government outweigh the positive value GPs may obtain from establishing a link to a government-related politically connected investor.
• This finding is consistent with a “grabbing hand” interpretation of the government’s involvement in the market.
• Using both the administrative micro-data and follow-up surveys, and consistent with several anonymous discussions with active VCPE firms, the authors find support for an explanation according to which government connections of the investors lead to interference in decision-making that is due to political, rather than profit-maximizing, incentives.

This work has several implications. On the one hand, by providing direct evidence of the private sector perspective of the advantages and disadvantages of government investors, this research deepens our understanding of the nature of China’s model of economic growth grounded on the dominance of state economic actors. On the other hand, this work makes the simple point that the demand for government capital differs across different types of firms. As a result, to the extent that how capital is allocated depends on the agents receiving it, understanding the demand side is important to fully capture the efficiency implications of government participation, an aspect of the debate that the authors believe has been largely neglected but that is crucial for both theory and policy.

Is There an Industrial Land Discount in China? A Public Finance Perspective

China’s land market, a key driver of the country’s extraordinary economic growth over the past 40 years, does not provide revenues to local governments via property taxes, as do most developed economies. Rather, local governments serve as monopolistic sellers who control land supply and who rely heavily on land sales for fiscal revenue.

Rigid zoning restrictions in China classify different land parcels for different uses, with land zoned for residential use selling at roughly a ten-fold higher price than land zoned as industrial, which the authors term an industrial land discount (or industrial discount). Local governments, it would seem, face a tradeoff between selling residential property to raise revenues or selling industrial property at a discount to spur local economic growth for non-pecuniary reasons. At least, that is how conventional wisdom describes the tradeoff. This paper offers a different explanation by focusing, instead, on public finance rather than industrial subsidies to explain the industrial discount.

The authors propose that the choice between residential and industrial land sales involves an intertemporal revenue tradeoff. Chinese local governments are predominately funded through a combination of corporate tax revenues and land sale revenues, which together account for roughly 60% of local government revenue. Industrial land generates future tax flows, since industrial firms pay value-added taxes and income taxes along with various fees; residential land does not. This simple fact leads to a new description of the tradeoff described above:

• Local governments face a choice between selling residential land, which pays larger upfront revenues from higher sale prices, versus selling industrial land, which pays smaller upfront revenues but comes with a stream of future cash flows from tax revenues over time.

This dynamic perspective suggests that local governments are not necessarily subsidizing industry through cheap land; in fact, the authors show that future tax revenues from industrial land more than compensate for the upfront discount on industrial land sales. This result has strong implications for understanding the drivers of land prices in China, and how they are linked to the tax sharing scheme with the central government, as well as local governments’ intertemporal revenue tradeoffs. From the central government’s perspective, the tax sharing scheme between the central and local governments can be carefully designed to counteract the effect of the local governments’ differential market power in local land markets to achieve desired land allocation outcomes.

Taking stock, this paper shows that local governments’ financing needs affect land supply to the whole industry sector in China, which implies that local public finance plays an underappreciated role in shaping the path of China’s economic growth through the land allocation channel.

Share Pledging in China: Funding Listed Firms or Funding Entrepreneurship?

Policy Experimentation in China: The Political Economy of Policy Learning

Determining which policies to implement and how to implement them is an essential government task. However, policy learning is complicated by a host of factors, encouraging countries to engage in various policy experiments to help resolve policy uncertainty and to facilitate policy learning. This paper analyzes systematic policy experimentation in China since the 1980s, where the government has systematically tried out different policies across regions and often over multiple waves before deciding whether to roll out the policies to the entire nation.

China is an important case study for two reasons. First, the systematic policy experimentation in China is unparalleled in terms of its depth, breadth, and duration. Second, scholars have argued that policy experimentation was a critical mechanism leading to China’s economic rise over the past four decades. Even so, surprisingly little is understood about the characteristics of such policy experimentation, or how the structure of experimentation may affect policy learning and policy outcomes.

The authors focus on two characteristics of policy experimentation to assess whether it provides informative and accurate signals on general policy effectiveness. First, to the extent that policy effects are often heterogeneous across localities, representative selection of experimentation sites is critical to ensure unbiased learning of the policy’s average effects. Second, to the extent that the efforts of the key actors (such as local politicians) can play important roles in shaping policy outcomes, experiments that induce excessive efforts through local political incentives can result in exaggerated signals of policy effectiveness.

Motivated by questions that address these concerns, the authors collect 19,812 government documents on policy experimentation in China between 1980 and 2020 and construct a database of 633 policy experiments initiated by 98 central ministries and commissions. The authors describe their methodology in detail within the paper, but broadly speaking they link the central government document that outlines the overall experimentation guidelines with all corresponding local government documents to record its implementation throughout the country. They measure numerous characteristics of policy experiments, including ex-ante uncertainty about policy effectiveness, career trajectories of central and local politicians involved in the experiment, the bureaucratic structure of the policy-initiating ministries, the degree of differentiation in policy implementation across local governments, and local socioeconomic conditions. 

The authors find the following:

• Policy experimentation sites are substantially positively selected in terms of a locality’s level of economic development, and misaligned incentives across political hierarchies account for much of the observed positive selection. 
• Experimental situation during policy experimentation is unrepresentative: local politicians exert strategic efforts and allocate more resources during experimentation that may exaggerate policy effectiveness, and such strategic efforts are not replicable when the policy eventually rolls out to the rest of the country.
• The positive sample selection and unrepresentative experimental situation are not fully accounted for when the central government evaluates experimentation outcomes, which would bias policy learning and national policies originated from the experiments.

Among its important implications, this research offers insights into the fundamental trade-off facing a central government: structuring political incentives to stimulate politicians’ effort to improve policy outcomes, while making sure that such incentives are not exaggerated during the experimentation phase, so that policy learning remains unbiased. Solutions that improve mechanism design could improve the efficiency of policy learning and, likewise, could be of valuable policy relevance and importance.

In developing countries, indirect impacts of COVID-19 may be greater than direct health impacts

The poorest countries of the world have so far avoided the worst of Covid-19, a result we attribute to a younger population and limited obesity. In the long run, the highest costs may be due to the indirect effects of virus containment policies, especially for girls.

Early in 2020, the general expectation was that the coronavirus pandemic’s effects would be more severe in developing countries than in advanced economies, both on the public health and economic fronts. Preliminary evidence as of July 2020 supports a more optimistic assessment. To date, most low- and middle-income countries have a significantly lower death toll per capita than richer countries, a pattern that can be partially explained by younger population and limited obesity. On the economic front, emerging market and developing economies (EMDEs) have seen massive capital outflows and large price declines for certain commodities, especially oil and non-precious metals, but net capital outflows are in line with earlier commodity price shocks. While there is considerable heterogeneity in how specific countries will be affected in the short and medium run, we are cautiously optimistic that financial markets in the largest EMDEs, especially those not reliant on energy and metal exports, could recover quickly – assuming the disease burden is ultimately not as dire in these countries. In the long run, the highest costs may be due to the indirect effects of virus containment policies on poverty, health and education as well as the effects of accelerating deglobalization on EMDEs. An important caveat is that there is still considerable uncertainty about the future course of the pandemic and the consequences of new waves of infections.

This paper is part of the Summer 2020 special edition of the Brookings Papers on Economic Activity.

Healthcare Crowd-out During COVID-19

During COVID, the issue of healthcare crowd-out is critical even in non-hotspot areas. The alpha-reserve capacity reallocation through tele-medicine could mitigate this.

There are lots of anecdotic evidence and news articles talking about large amount of healthcare needs have remained unmet during COVID-19, including whether COVID-related care may have displaced non-COVID care (so-called crowd-out effect). If so, efficient healthcare resource allocation is required during such a critical period. We aim to understand the extent healthcare demand may be shifted and can be rearranged. The non-availability of timely and reliable medical claim data during a public health crisis like COVID, makes this task difficult. To surmount this practical challenge, we leverage the dataset of online drug retailing transactions across geographic regions in Mainland China during the pandemic’s first wave. The key data aspect that allows us to infer crowd-out regards demand differences between prescription (Rx) and over-the-counter (OTC) drugs. Specifically, because a provider-written prescription is required for Rx but not for OTC purchases, relative Rx/OTC demand changes reflect changes in the amount of used medical consultations. Since most sample drugs target non-C19 symptoms, changes in the amount of used non-COVID care can be inferred from this variation.

We embed the above insights in a differences-in-differences-in-differences (DDD) identification framework. We find COVID-fueled relative surge of online Rx/OTC demand in lower-capacity regions. Given that the Chinese system implicitly bundles utilization with offline Rx demand, this suggests that patients from lower-capacity regions were less likely to receive care as demand for COVID-related care strained the system. At the pandemic’s peak, this crowd-out effect is equivalent to 10% decrease of non-C19 care. We propose and evaluate an alpha-reserve capacity reallocation policy. Relying on tele-health infrastructure, this policy would reallocate healthcare supply across regions on a spot basis, aiming to minimize aggregate crowd-out. Significant crowd-out reduction is achieved without drastically undercutting any region’s healthcare capacity. This provides meaningful managerial and policy suggestions to lessen the adverse impacts of such a public health crisis.

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A Large-scale Policy Experiment to Stimulate Consumption post COVID-19 outbreak

The small-value short-duration digital coupon issued by local governments in China is highly effective in driving excess consumption.

In response to the significant economic contraction caused by the COVID-19 pandemic, many local governments in China have experimented with an innovative policy tool to stimulate consumption, the digital consumption coupon program. The program departs from other commonly adopted fiscal stimulus programs such as cash payment or tax rebate in several salient ways. First, the coupon typically takes the form of saving with certain amount of spending, e.g. “spend RMB 40, get RMB 10 off,” and hence has the nature of “use-it-or-lose-it.” Second, the coupons are of small face value and short duration, and involve a small amount of government subsidy per voucher. Third, the coupons are disbursed through mobile payment platform with limited quantity in each round.

Using the high-frequency transaction-level data of more than one million de-identified consumers, the authors evaluate the effectiveness of the digital coupon program in a major city in China. Exploiting a difference-in-differences approach, they find that the consumers who successfully acquired the government coupon spent significantly more during the coupon redemption week compared to similar individuals who applied but failed to acquire the coupon due to limited quantity. Defining MPC as the ratio of excess spending over the effective amount of government subsidy, they find the MPC ranges from 3.4 to 5.8 across several waves of coupon issuance. The excess spending mostly concentrates in the catering services and food and drinks purchases. The authors do not find significant intertemporal substitution of consumption. In addition, the coupon effect does not wear out over multiple waves of the program. Behavioral factors such as mental accounting and loss framing are likely to play a role in the underlying mechanism.

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Response to Stimulus Checks Driven Primarily by Liquidity

Households with low levels of liquidity spent about a third of their stimulus checks within the first two weeks, while households with ample liquidity spent approximately zero.

The 2020 CARES Act directed large cash payments to households. With policy-makers considering future stimulus programs, we work to analyze how household spending responded to the first checks using high-frequency transaction data from a FinTech nonprofit. Households respond rapidly to the receipt of stimulus payments, with spending increasing by $0.25-$0.30 per dollar of stimulus during the first weeks across a range of categories.

The authors also explore heterogeneity across households along dimensions like income levels, recent income declines, and liquidity. Households with lower incomes, greater income drops, and lower levels of liquidity display stronger responses, highlighting the importance of targeting when thinking about fiscal multipliers. Liquidity plays the most important role, with no observed spending response for households with high levels of bank account balances.

Relative to the effects of previous economic stimulus programs in 2001 and 2008, the authors see faster effects, smaller increases in durables spending, and larger increases in spending on food, likely reflecting the impact of shelter-in-place orders and supply disruptions. Additionally, they see substantial increases in payments like rents, mortgages, and credit cards reflecting a short-term debt overhang. The authors formally show that these differences can make direct payments less effective in stimulating aggregate consumption.

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