Openness and COVID-19 induced xenophobia: The roles of trade and migration in sustainable development.

Along with the plight of the COVID-19 outbreak in 2020 come the xenophobic behaviors and hate crimes against people with Asian descent around the globe. The threat of a public health emergency catalyzed underlying xenophobic sentiments, manifesting them into racial discrimination of various degrees. With most discriminatory acts reported in liberal societies, this article investigates whether an economy more open to trade and migration can be more susceptible to xenophobia. Using our first-hand survey data of 1767 Chinese respondents residing overseas from 65 different countries during February of 2020, we adopt an instrumental variable strategy to identify the causal effect of openness to trade and migration of their residence country on the likelihood of them receiving discriminatory behaviors during the early stage of the COVID-19 outbreak. Our results show that greater openness to trade increases the likelihood of reported xenophobic behaviors, while openness to migration decreases it. On the other hand, stronger trade or immigration relationships with China are associated with less reported discrimination. And these effects primarily influence discriminatory behavior in interpersonal spaces, rather than through media outlets. Our findings highlight nuances of the effect of trade relations on the culture of a society.

1 Introduction

Discrimination is a major source of friction and disruption to cooperative relationships across cultural and national boundaries, that are increasingly important in shaping and supporting sustainable development [1]. Our increasingly globalized world sees ever more intense interactions through trade and migration, which, in turn, help to bring down barriers of misunderstanding to mitigate discrimination. On the other hand, globalization also heightens the conflict of interests, breeding hate and xenophobia. In extreme, such sentiments translate into bigotry and even hate crimes that carry extra social harm in addition to their harm to the individual victims [2]. Hate crimes against Asian Americans surged as COVID-19 spreads in the US. More incidents of hate against Asians were brought to light again following incidents in the US in early 2021.

Following the news on the COVID outbreak in China since January of 2020, numerous reputable news agencies reported incidents of racial discrimination targeting ethnically Chinese and other Asian residents even before the outbreak spreads outside China (He et al. 2020). Such behavior ranges from subtle responses of shunning people of Asian descent to blatant verbal abuses and even physical attacks in public. US major TV network NBC reported on March 26th, 2020, that an online forum, Stop AAPI Hate, received more than 650 direct reports of discrimination against primarily Asian Americans within one week. It appears that the threat of a public health emergency catalyzes underlying xenophobic sentiments, manifesting them into racial discrimination of various degrees.

It is a curious pattern arising from these reports that such shockingly xenophobic behaviors seem to be primarily reported in the most open and liberal economies: Australia, France, Germany, UK, and US. Is it possible that more open societies are more prone to racism in the face of a virus outbreak? Direct observation from the news reports would be misleading due to under-reporting in less liberal societies. To investigate this question, we designed a short survey aimed at ethnically Chinese residents living abroad to collect their observations around the globe.

We are particularly interested in the effect of a country’s openness to trade and migration on the likelihood of an ethnically Chinese resident observing anti-Asian discriminatory behaviors. Openness to trade and immigration are important components of the globalized world, and we choose to focus on these two aspects to provide a particular perspective regarding the general relationship between globalization and culture. They may mitigate or intensify xenophobic sentiments, and it is an important empirical question to explore the evidence. Openness to trade and migration facilitates more interactions with people outside one’s race, ethnicity and nationality, thus potentially improves mutual understanding and cultural exchanges, mitigating xenophobic sentiments [3-5]. In some instances, even low-skilled immigration can improve native unskilled wages, employment, and occupational mobility [6, 7]. They also increase the concentration of immigrant minorities that reduces the likelihood of harassment from the natives by deterring potential threats [8]. On the other hand, openness to trade and migration may threaten interests of native groups on economic grounds [9, 10] or cultural grounds [11], fueling xenophobic sentiments [12-14] and inducing conflicts [15]. It is a curious empirical question to find which effect dominates overall.

Based on an instrumental-variable strategy [16], one literature shows openness to trade and migration has a positive effect on long-run income per capita [17], is associated with more stringent antitrust laws [18], and has little harm on children’s health [19]. We closely follow their empirical strategy to address our research question. Our results show that greater openness to trade increases the likelihood of reported xenophobic behaviors.

In particular, consider two otherwise similar countries, with one country more open to trade (ranked at the 75th percentile in trade share) than the other (ranked at the 25th percentile in trade share). Our preferred estimates show that a respondent residing in the more open country sees an 80% increase in the likelihood of receiving COVID-19 induced discrimination. By contrast, stronger trade or immigration relationships with China are associated with less reported discrimination.

On the openness to immigration, we found general openness to migration decreases reported discrimination. A respondent residing in a country ranked at the 75th percentile of migration share in population (more open to migration) in our sample sees a 38% decrease in the likelihood of receiving COVID-19 induced discrimination compared to those in a country ranked at the 25th percentile. In addition, more immigration from China is associated with a lower level of reported discrimination in our specific context. These findings are in contrast with some previous work in the literature that argues that openness to immigration worsens xenophobic sentiments. An extensive literature at the intersection of psychology, politics and economics studies the determinants of native residents’ attitudes towards immigrants and immigration policies [11, 20]. In addition to the threat to natives’ own economic interests through labor market competition [21-23] and the threat of increasing burden of taxation [24, 25], immigration raises sociotropic concerns that induce anti-immigrant attitudes. In particular, anti-immigrant attitudes rise during times of national economic stress [26-29], and of greater anxiety [30]. In contrast to this literature that studies xenophobic attitudes of natives as reported by themselves, this article studies xenophobic behavior directed at a particular ethnic group that are reported by immigrants.

Our analysis further shows that these effects from trade and migration primarily work through discriminatory behavior observed in interpersonal spaces, rather than through media outlets. Given the increasingly stringent travel restrictions imposed during the pandemic, the world is quickly falling back to a more isolated structure. Re-opening trade and migration would become one of the critical discussions for the post-epidemic world, to which our study contributes a piece of evidence.

The rest of this article proceeds as the following. Section 2 introduces our survey and empirical model, Section 3 describes the data used to estimate our model, and Section 4 presents our main results. Finally, Section 5 discusses the robustness of our results and Section 6 concludes.

2 Survey and model specification

The research is conducted as an online survey where participants received the invitation on their cellphones via the mobile app Wechat. We clearly stated that the study is anonymous, and it follows relevant laws and regulations. By accepting the invitation and proceeding to fill the questionnaire and finally submitting their results, the survey subjects proceed to the questionnaire after reading the language describing the survey’s intent and purposes. They then filled out the survey questions with the understanding of the study. Our survey is anonymous and did not collect individual identifiers. Before the survey started, the study was approved by IRB at School of Ethnology and Sociology, Yunnan University.

We distributed this short 6-minute online survey through Wechat over the internet, and collected 1767 effective responses from ethnically Chinese residents currently living outside China in 65 different countries. All responses were collected within a window of 7 days from Feb 11th to Feb 17th, 2020—a period when the infection was mostly taking place within mainland China. On Feb 17th, 2020, WHO reported 70635 confirmed cases in mainland China, and 794 cases outside China (1% of total confirmed cases). In favor of time-sensitivity during the fast-changing outbreak period and wide geographical coverage, we chose not to adopt a prolonged random sampling process. The sample is, therefore, not fully representative, and all results should be interpreted with this caveat. However, we believe our results are reliable, and are complementary to future studies on a similar subject using alternative designs.

During our sampling period, the chance of infection for an average resident in any other country remained relatively low and comparable. Therefore, after controlling for officially reported COVID case numbers in each country, relative geographical proximity to China, the risk of contracting the virus from any Asian-looking person is similar across different countries around the world. Thus, controlling for respondent and country characteristics, the variations in the reported discrimination behavior is primarily driven by differences in local characteristics, including openness to trade and migration. Under this conceptual framework, we seek to recover the effect of openness on trade and migration on xenophobic behavior, while addressing potential omitted variable biases using exogenous instruments for openness.

Our measures of coronavirus-related xenophobia come directly from our survey responses. Our questionnaire asked, “Since the virus outbreak in China, have you noticed any related discriminatory behavior in your working environment and daily life?” The choices include (i) yes; (ii) no; and (iii) not sure. We code an indicator variable “observed discrimination” that takes value 1 if the respondent chose “yes” to this question, and value 0 otherwise. We use this variable as our primary measure for xenophobia. If a respondent had answered “yes” or “not sure” in the question above, they were navigated to a multiple-choice question to select the primary type of discrimination that they noticed. Approximately 76% of respondents chose one of three categories: (i) racially discriminatory message against Chinese in the media (29%); (ii) racist rhetoric by native residents against Chinese in public (23%); and (iii) shunning (23%). We code three indicator variables, “racist message in media”, “racist rhetoric in public”, and “shunning”, that takes value 1 if a respondent selected the corresponding category. We use these three indicators as the secondary and measures of xenophobic behavior.

We follow [17] to measure openness to trade and migration. Given any country c, we measure the openness to trade by its total value of trade flow (import and export) relative to its national GDP, where j is an index for all trading partners of c. Similarly, the openness to migration is defined by its total immigration flow relative to its population where j is the origin country of immigration.

The structural equation of our interest is a linear-probability model where Discr is one of the indicator variables measuring xenophobia reported by respondent i in country c on date t. c is the number of confirmed COVID patients in country c on date t that controls for the level of threat of the virus outbreak at the local country. And X is a vector of individual and country characteristics that include (1) the usual individual demographics: female, age, level of education, occupation, whether the respondent obtained their highest degree of education overseas; and (2) country characteristics: logs of population, area and national GDP, as well as an indicator of whether the country is landlocked. Moreover, because all respondents are Chinese migrants, X also includes (3) controls for country c’s distance from China, including whether it shares the boarder with China, the log distance from China, and the time difference with China. In addition, to control for heterogeneity in migration history, X also includes (4) the respondent’s self-reported current migration or visa status (citizen, permanent resident, working visa, student visa, etc.), length of migration, and year of migration. To further control for potentially different discriminatory behavior in crowded public spaces, X also includes (5) an indicator of whether the respondent primarily relies on public transportation for their daily commute. Our variables of interest are β1 and β2, which measure the average marginal effects of a 1% increase in the trade and migration openness, respectively, on the linear probability of a respondent receiving COVID-19 induced discrimination of some form.

However, these key estimates may be biased from the true causal effects if TSH and MSH are endogenous. In particular, model (1) may suffer from an omitted variable bias. For instance, our sampling approach may attract more nationalistic respondents or those with a stronger personal tie to China. If such characteristics are correlated with openness to trade or migration or their residing country, then our OLS estimates would be biased. If such characteristics are correlated with openness to trade or migration or their residing country, then our OLS estimates would be biased. In addition, some unobservable country-level characteristics, such as a pro-trade and anti-migration ideology, may be correlated with TSH and MSH, while also increasing the likelihood of discrimination, thus leading to a spurious correlation between openness measures and discrimination. For example, [31, 32] argue that a stronger nationalistic sentiment is associated with more xenophobic attitudes.

To address such potential endogeneity concerns, we closely follow the empirical strategy by [17] to construct exogenous instruments for the openness measures that depend entirely on pairwise geographical, language and basic historical relationships. To construct the exogenous instruments, we first estimate the following gravity model at the country-dyad level using pairwise trade and immigration flow data where X and X are vectors of country characteristics of countries c and j, respectively. They each include the country’s log population, log area, and the indicator of whether the country is landlocked. X is a vector of pairwise characteristics capturing the geographical and cultural relationship between them, including: indicators of whether c and j have shared border, shared language, shared official language, shared time zone, colony history, prior hegemonic relationship, and the interactions of the shared-boarder indicator with log population, log area and landlocked indicator. We then use the estimates from (2) to construct predicted pairwise trade share PTSH and migration share PMSH, which depend exclusively on pairwise geographical, language and basic historical relationships. Finally, we aggregate all such pairwise shares over each focal country c to produce PTSH = ∑≠ PTSH and PMSH = ∑≠ PMSH as exogenous instrumental variables for TSH and MSH, and estimate model (1) with a standard 2-stage least-squares procedure.

Given that our context focuses on discrimination towards the ethnic Chinese, we further decompose the TSH and MSH measures and their instruments into China-related and non-China related measures. Specifically, we isolate out the TSH and MSH with China, and separately construct “leave-one-out” measures and their instruments by excluding trade and migration with China. In particular, it is highly plausible that the instrumental variables for the leave-one-out measures to be exogenous to our survey respondents’ unobservable characteristics because they are constructed purely on pairwise geographical relationship between the residence country and third countries that are different from China.

3 Data

The key dependent variable and all respondent characteristics come from our survey. Our sample includes a diverse set of respondents. The respondents are 65% female, 55% received their highest degree outside China, 32% routinely use public transportation, 65% below the age of 40 and an median age in the 31–40 year old group. The largest three groups of occupations are professionals (30%), students (27%) and workers (9%). 23% of the sample obtained citizenship in their residing country, 23% obtained permanent residency, and 13% were on working visas. 56% have been living outside China for more than 5 years. 45% started living aboard before 2010. The respondents are highly educated, with 89% with a college education or higher—a much higher level of education than the average Chinese citizen, but not as sizable a deviation among first-generation Chinese migrants of recent generations.

In addition to our survey, we rely on three other data sources for trade, migration and country-level information. The trade share is constructed from the mean trade flows during 1991 to 2016 from the Correlates of War, Trade 4.0 dataset [33, 34]. To construct our migration share, we use the mean immigration flow from 1991 to 2015 compiled by [35] that is based on data published by the World Bank and United Nations. All country-level characteristics are based on the data compiled by [18]. We use confirmed case data by country published by the World Health Organization (WHO) to control for the status of the virus spread at the country-by-day level. Table 1 reports the descriptive statistics of some key variables. The share of total trade measure may be greater than 1 because the numerator is the sum of total import and export combined, as opposed to net export. One country, United Arab Emirates (UAE), has an immigration flow that is greater than its domestic population. Removing the four responses from UAE does not change our results.

Table 1

Descriptive statistics.

	N	mean	sd	min	max
Dependent Variables received discrimination	1767	0.24	0.43	0	1
racist message in media	1767	0.11	0.31	0	1
racist rhetoric in public	1767	0.084	0.28	0	1
shunning	1767	0.085	0.28	0	1
anti-discrimination advocacy	1767	0.26	0.44	0	1
Country Characteristics trade share	1767	0.54	0.43	0.2	2.5
immigration share	1767	0.047	0.064	0.002	1.1
trade with China share	1767	0.061	0.062	0.008	0.5
immigration from China share	1767	0.0031	0.0040	0	0.03
trade (all others) share	1767	0.47	0.39	0.2	2.1
immigration (all others) share	1767	0.044	0.062	0.002	1.1
land locked	1767	0.019	0.14	0	1
population (millions)	1767	123.0	146.8	0.4	1099.0
area (thousands of km2)	1767	4067.8	4450.2	0.3	17243.0
contiguous to China	1767	0.054	0.23	0	1
time difference with China	1767	5.39	3.70	0	12
population-weighted distance to China (km)	1767	7692.0	3954.5	1168.2	18884.5
Respondent Characteristics female	1767	0.65	0.48	0	1
highest degree obtained overseas	1767	0.55	0.50	0	1
routine public transport 1767 0.32 0.47 0 1

4 Results

4.1 Main results

Table 2 reports our main results from model (1), with the binary dependent variable being: any discrimination. Column (1) shows that a naive regression on openness to trade and migration alone shows no statistically significant association between reported xenophobia and openness to trade or migration. In column (2), after controlling for country and respondent characteristics, the same relationships remain. Column (3) reports the IV-estimates of the general openness to trade and migration. We find that, once instrumented for the possibly endogenous openness measures with our predicted trade share and migration share, the results are overturned: xenophobia is positively associated with openness to trade, but negatively associated with migration. The Kleibergen-Paap F-statistic for weak identification in the first stage is 10.4, greater than the Stock-Yogo weak ID test critical values at the most stringent 10% maximal IV size of 7.03. We present estimates of the equivalent probit model in Section 5.

Table 2

Discrimination, openness to trade and immigration.

	received discrimination
	(1)	(2)	(3)	(4)	(5)	(6)
ln(trade share)	0.00483 (0.0351)		0.727*** (0.262)
ln(immigration share)	0.0310 (0.0225)		-0.354** (0.145)
ln(trade (all others) share)		0.00891 (0.0340)		0.730*** (0.254)	0.204** (0.0901)
ln(immigration (all others) share)		0.0290 (0.0221)		-0.345** (0.138)		0.00373 (0.0496)
ln(trade with China share)					-0.370** (0.144)
ln(immigration from China share)						-0.111*** (0.0427)
Observations	1767	1767	1767	1767	1767	1767
Model	OLS	OLS	IV	IV	IV	IV
Kleibergen-Paap F-stat			10.4	11.1	26.9	17.2

Robust standard errors in parentheses

* p < 0.10

** p < 0.05

*** p < 0.01

Notes: Dependent variable: received discrimination. This table reports regression estimates based on model (1). Column (1) reports the naive OLS regression of the indicator outcome, whether the respondent observed any COVID-related racial discrimination, on two control variables, TSH and MSH. Column (2) reports the OLS estimates of model (1) with the full set of respondent and country controls. Columns (3) through (6) reports the 2SLS estimates of model (1) with different measures of TSH and MSH.

Column (4) presents our preferred estimates. In this regression, we replace the general TSH and MSH measures with the leave-one-out measures that exclude the trade and migration flows with China. The results are very similar to Column (3). Our estimates are both statistically significant and substantive in magnitudes. Specifically, for a one standard deviation increase in the log trade share in GDP, the likelihood of receiving any COVID-19 induced discrimination increases by 50% (0.73 × 0.68) or 1.1 standard deviations (0.73 × 0.68 / 0.44).

Put in a slightly different way, a respondent in the country ranked at the 75th percentile of trade share in GDP

in our sample sees an 80% increase in the likelihood of receiving COVID-19 induced discrimination compared to an otherwise identical country ranked at the 25th percentile at openness to trade. On the contrary, with a one standard deviation increase in the log migration share in population, the likelihood of receiving any COVID-19 induced discrimination decreases by 38% (-0.35 × 1.09) or 0.87 standard deviations (-0.35 × 1.09 / 0.44).

4.2 China-related trade and immigration

We further explore whether trade and migration with China have a different impact on xenophobic attitudes towards Chinese migrants, comparing to general openness to trade and migration. We analyze this relationship by studying the openness to trade and migration separately and decomposing the general openness into openness to trade to China and to all other countries. We treat both the shares with and excluding China as endogenous variables, and construct IVs for each variable separately. The results are presented in Table 2, columns (5) and (6). The first-stage estimates are presented in Table 3.

Table 3

Discrimination, openness to trade and immigration: 1st-stage result.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	ln(trade share)	ln(immigration share)	ln(trade (all others) share)	ln(immigration (all others) share)	ln(trade (all others) share)	ln(trade with China share)	ln(immigration (all others) share)	ln(immigration from China share)
ln predicted share—total trade	1.042*** (0.0598)	2.324*** (0.0829)
ln predicted share—total immigration	0.341*** (0.0355)	0.242*** (0.0445)
ln predicted share—trade (all others)			0.951*** (0.0569)	2.184*** (0.0730)	1.164*** (0.0506)	0.432*** (0.0564)
ln predicted share—immigration (all others)			0.340*** (0.0341)	0.263*** (0.0406)			0.935*** (0.0546)	-1.229*** (0.194)
ln predicted share—trade with China					0.635*** (0.0965)	0.825*** (0.0867)
ln predicted share—immigration from China							1.155*** (0.0546)	0.289 (0.258)
Observations	1767	1767	1767	1767	1767	1767	1767	1767

Robust standard errors in parentheses

* p < 0.10

** p < 0.05

*** p < 0.01

Notes: This table reports 1st stage estimates of the constructed instruments associated with Table 2. The Kleibergen-Paap F-statistics of the first-stage are reported in Table 2.

We make two observations from this analysis. First, after controlling for the openness to trade and migration with China, the effects of general openness to trade and migration are largely robust. Second, we found that, in contrast to the general openness to trade, more trade directly with China and more migration from China each contributes significantly to a lower level of xenophobic behavior in this context.

Specifically, a one standard deviation increase in the log trade share with China in GDP reduces the likelihood of receiving any COVID-19 induced discrimination by 28% (-0.37 × 0.76) or 0.6 standard deviations (-0.37 × 0.76 / 0.44). And a one standard deviation increase in the log migration share from China in population lowers dependent variable by 36% (0.11 × 3.3) or 0.82 standard deviations (0.11 × 3.3 / 0.44). In percentile terms of openness to trade (migration) with China, a respondent in the 75th-percentile country, on average, have a 35% (21%) lower likelihood of receiving COVID-19 induced discrimination comparing to an otherwise identical country ranked at 25th percentile on this measure. These results are consistent with general intuition that increased direct social and economic interactions reduce xenophobia.

4.3 Types of discrimination

Table 4 reports the IV estimates of our preferred model with our secondary dependent variables: racist rhetoric in public, shunning, and racist message in media. The results provide additional nuances behind our main result, but of another dimension. Columns (1) and (2) show that, as is consistent with our main result, general openness to trade (migration) is likely to increase (decrease) xenophobic behavior in personal spaces in the form of racist public comments or shunning. However, column (3) shows that openness to trade or migration has no effect on public media. These mixed effects suggest that the effects of trade and migration on reported xenophobic behavior are primarily driven by actions in interpersonal space, as opposed to through the public media.

Table 4

Received discrimination by type.

	(1)	(2)	(3)
	racist rhetoric in public	shunning	racist message in media
ln(trade (all others) share)	0.659*** (0.216)	0.296** (0.147)	-0.0659 (0.115)
ln(immigration (all others) share)	-0.375*** (0.117)	-0.137* (0.0799)	0.0768 (0.0626)
Observations	1767	1767	1767
Model	IV	IV	IV
Kleibergen-Paap F-stat	11.1	11.1	11.1

Robust standard errors in parentheses

* p < 0.10

** p < 0.05

*** p < 0.01

Notes: This table reports 2SLS estimates of model (1) using secondary measures of xenophobia as dependent variables.

5 Robustness

Our findings are robust to a number of alternative analyses. In this section, we (1) interrogate the model specification by investigating potentially omitted variables; (2) use probit and IV probit models as alternatives to the linear probability models; (3) address concerns about our sample representativeness using Wave 6 data of the World Value Survey, in which two questions asked about respondents’ attitude toward other races and migrants; and (4) use alternative trade and migration data sources to construct openness to trade and migration.

One concern with our specification of model (1) is that more open societies may see more varieties of speech and opinions. Thus the relationship between observed discrimination and openness to trade and migration may be spurious—they are merely reflecting their effects on the diversity of speech, as opposed to xenophobic sentiment per se. Under this hypothesis, our estimated effects of openness to trade and migration would be mitigated once we control for some measure of the opposite speech. To investigate this possibility, we augment our baseline model by including respondents’ reports of anti-discrimination advocacy as the control of speech on the other extreme of the spectrum. Table 5 columns (1)-(3) reports the IV estimates. Contrary to the above hypothesis, the effects from openness to trade and migration are hardly mitigated compared to our baseline results in Table 2.

Table 5

Mitigating factors: Anti-discrimination advocacy, measures of disease control and prevention.

	received discrimination
	(1)	(2)	(3)	(4)	(5)	(6)
ln(trade (all others) share)	0.707*** (0.246)	0.206** (0.0898)		0.714*** (0.247)	0.196** (0.0909)
ln(trade with China share)		-0.370** (0.144)			-0.368** (0.147)
ln(immigration (all others) share)	-0.332** (0.134)		0.00318 (0.0492)	-0.340** (0.134)		-0.00712 (0.0498)
ln(immigration from China share)			-0.109*** (0.0423)			-0.116*** (0.0435)
anti-discrimination advocacy	0.119*** (0.0280)	0.0982*** (0.0244)	0.114*** (0.0289)	0.108*** (0.0284)	0.0866*** (0.0249)	0.0982*** (0.0292)
prev.: require travel history to China				-0.00320 (0.0272)	0.0166 (0.0249)	0.0131 (0.0285)
prev.: require travel history from Chinese				0.0646*(0.0381)	0.0697** (0.0347)	0.0568 (0.0447)
prev.: require isolation for traveler from China				0.0329 (0.0254)	0.0177 (0.0217)	0.0635** (0.0309)
prev.: require isolation for Chinese				0.0201 (0.0296)	0.0149 (0.0260)	0.00503 (0.0318)
prev.: reduce interactions with China				0.0449 (0.0311)	0.0466* (0.0274)	0.0234 (0.0372)
Observations	1767	1767	1767	1767	1767	1767
Model	IV	IV	IV	IV	IV	IV
Kleibergen-Paap F-stat	11.3	26.9	17.3	11.3	25.7	16.5

Robust standard errors in parentheses

* p < 0.10

** p < 0.05

*** p < 0.01

Notes: This table reports IV regression estimates based on a variation of model (1). Columns (1)-(3) add the indicator of whether the respondent observed any anti-discrimination advocacy in the media or from the residing country’s government; columns (4)-(6) add a set of indicator variables of local disease control and prevention measures.

A second concern is that local responses may drive the reported discriminatory responses to the virus outbreak, such as information provided on the outbreak, as opposed to the underlying xenophobia. If governments and employers in more open economies adopt more aggressive measures on travel restrictions, travel history tracking, or contact tracing, then such measures may prime native residents to behave more aggressively toward those of Asian descent. This hypothesis implies that controlling for local preventative measures to the virus would mitigate our key effects. To check this, we use information collected in our survey regarding the type of preventative measured adopted by their employer and local government as additional controls. Specifically, from a list of preventative measures, the respondent was asked to check all those applied to their situation. The options include (1) requiring travel history from those recently traveled to China; (2) requiring travel history from those of Chinese descent; (3) requiring self-isolation from those recently traveled to China; (4) requiring self-isolation from those of Chinese descent; and (5) requesting a reduction of business interactions with China. We create a list of indicator variables for each preventative measure and add them to our regression. Table 5 column (4)-(6) report the results. Our main results remain robust to the inclusion of controls for these preventative measures. Thus there is no evidence that our results are driven by priming from local policy responses.

A third concern regards our linear probability specification of the model. We favor the linear probability model for transparency and ease of interpretation, but the model could be mis-specified. To check this possibility, we estimate probit and IV probit counterparts of model 1. Tables 6 and 7 report the probit estimates as counterparts to Tables 2 and 4, and our key results remain robust.

Table 6

Discrimination, openness to trade and immigration (probit).

	received discrimination
	(1)	(2)	(3)	(4)	(5)	(6)
ln(trade share)	0.0237 (0.108)		2.379*** (0.684)
ln(immigration share)	0.114 (0.0776)		-1.143*** (0.379)
ln(trade (all others) share)		0.0368 (0.105)		2.390*** (0.670)	0.834** (0.343)
ln(immigration (all others) share)		0.107 (0.0762)		-1.110*** (0.367)		0.0145 (0.200)
ln(trade with China share)					-1.547** (0.633)
ln(immigration from China share)						-0.375** (0.161)
Observations	1767	1767	1767	1767	1767	1767
Model	Probit	Probit	IV Probit	IV Probit	IV Probit	IV Probit

Robust standard errors in parentheses

* p < 0.10

** p < 0.05

*** p < 0.01

Notes: This table reports results of the probit and IV-probit estimation that is equivalent to model (1). The table layout corresponds to that of Table 2.

Table 7

Received discrimination by type (probit).

	(1)	(2)	(3)
	racist rhetoric in public	shunning	racist message in media
ln(trade (all others) share)	3.255*** (0.882)	1.948** (0.836)	-0.666 (0.822)
ln(immigration (all others) share)	-1.872*** (0.487)	-0.937** (0.454)	0.657 (0.453)
Observations	1756	1763	1763
Model	IV Probit	IV Probit	IV Probit

Two-step standard errors in parentheses

* p < 0.10

** p < 0.05

*** p < 0.01

Notes: This table reports estimates of the probit models that is equivalent to model (1), using secondary measures of xenophobia as dependent variables. The model in each column is the counterpart of column (4) in Table 6.

A fourth concern relates to our sampling framework and the specific context we study. Because our response data is drawn from a non-random sampling framework, we would like to compare results with an alternative data source with individual-level cross-country information on general xenophobic measures. The best approximation we find is the Wave 6 data of the World Value Survey (WVS), which covers 55 countries with surveys conducted from 2010–2014. Their questionnaire asks, “On this list are various groups of people. Could you please mention any that you would not like to have as neighbors?” and two of the answers that approximate for a measure of xenophobia are: (1) people of a different race; and (2) immigrants/foreign workers. We use indicator variables for those who selected these options as dependent variables in a counterpart of model (1), while controlling for all the demographics information available in the WVS dataset. Tables 8 and 9 report the results. For our preferred model, reported in column (4), the Kleibergen-Paap F-statistic for weak identification in the first stage is 296.2, greater than the Stock-Yogo weak ID test critical values at the most stringent 10% maximal IV size of 7.03. The first-stage estimates shows that instruments are strong, we omit them here for brevity and the results are available upon request. These estimates confirm our key finding that openness to trade increases the likelihood of xenophobia, but yield different results on the effect of openness to migration. It is important to highlight that a close comparison of these results with ours should be interpreted with caution, given a number of differences between these two sets of surveys. (1) The question from WVS is hypothetical, while ours asked for observed behavior in recent history; (2) the respondents of WVS are those potentially display discriminatory behaviors toward others, while ours are potential victims to such behaviors; and (3) the question in WVS asks about the underlying willingness having someone as a neighbor, which is a starkly different context from acting aggressively toward others under the threat of a virus outbreak. These two sets of data, therefore, may be measuring different underlying phenomenon and are subject to different behavioral biases [36]. Overall, our interpretation of these mixed results is that there seemed to have a substantive and robust effect between openness to trade and migration, despite possible discrepancies in specific results. Above all, our findings call for further research in alternative scenarios, sampling framework designs, and empirical methods.

Table 8

Discrimination, openness to trade and immigration (WV6).

	Dislike immigrant/foreigner as neighbor
	(1)	(2)	(3)	(4)	(5)	(6)
ln(trade share)	0.160*** (0.00433)		1.436*** (0.309)
ln(immigration share)	0.0513*** (0.00205)		-0.181** (0.0704)
ln(trade (all others) share)		0.151*** (0.00429)		0.0308 (0.0509)	0.519*** (0.0135)
ln(immigration (all others) share)		0.0480*** (0.00201)		0.101*** (0.00959)		0.644*** (0.0282)
ln(trade with China share)					-0.0256*** (0.00534)
ln(immigration from China share)						0.138*** (0.00639)
Observations	82527	82527	82527	82527	83765	82527
Model	OLS	OLS	IV	IV	IV	IV
Kleibergen-Paap F-stat			15.2	296.2	4720.8	301.8

Robust standard errors in parentheses

* p < 0.10

** p < 0.05

*** p < 0.01

Notes: This table reports regression estimates based on model (1) using the Wave 6 (2010–2012) data of the World Value Survey. The dependent variable is an indicator variable where the respondent mentioned that they did not want immigrants or foreign workers as their neighbor (based on question V39). The table layout is equivalent to that of Table 2.

Table 9

Discrimination, openness to trade and immigration (WV6).

	Dislike different race as neighbor
	(1)	(2)	(3)	(4)	(5)	(6)
ln(trade share)	0.192*** (0.00449)		1.274*** (0.292)
ln(immigration share)	0.0643*** (0.00215)		-0.112* (0.0667)
ln(trade (all others) share)		0.182*** (0.00447)		0.203*** (0.0528)	0.636*** (0.0145)
ln(immigration (all others) share)		0.0607*** (0.00211)		0.0947*** (0.0100)		0.781*** (0.0333)
ln(trade with China share)					-0.0527*** (0.00571)
ln(immigration from China share)						0.168*** (0.00749)
Observations	82527	82527	82527	82527	83765	82527
Model	OLS	OLS	IV	IV	IV	IV
Kleibergen-Paap F-stat			15.2	296.2	4720.8	301.8

Robust standard errors in parentheses

* p < 0.10

** p < 0.05

*** p < 0.01

Notes: This table reports regression estimates based on model (1) using the Wave 6 (2010–2012) data of the World Value Survey. The dependent variable is an indicator variable where the respondent mentioned that they did not want someone of a different race as their neighbor (based on question V37). The table layout is equivalent to that of Table 2.

Finally, we investigate whether our results are sensitive to alternative trade and immigration data sources. For alternative trade flow data, we use the Direction of Trade Statistics by IMF from 1991 to 2015. And for the migration flow data, we use the mean immigration flow during 1991 to 2000 in the World Bank Global Bilateral Migration Database [37] to construct our migration share. The results are reported in Tables 10 and 11, and our main results remain largely robust.

Table 10

Discrimination, openness to trade and immigration (alternative trade data from IMF).

	received discrimination
	(1)	(2)	(3)	(4)	(5)	(6)
ln(trade share)	0.0312 (0.0298)		0.522*** (0.202)
ln(immigration share)	-0.00284 (0.0181)		-0.248** (0.113)
ln(trade (all others) share)		0.0305 (0.0285)		0.406*** (0.149)	-5.071 (33.47)
ln(immigration (all others) share)		-0.00128 (0.0174)		-0.182** (0.0845)		-0.0694 (1.031)
ln(trade with China share)					7.198 (47.62)
ln(immigration from China share)						3.591 (26.51)
Observations	1764	1764	1764	1764	1764	1767
Model	OLS	OLS	IV	IV	IV	IV
Kleibergen-Paap F-stat			15.8	25.2	0.0	0.0

Robust standard errors in parentheses

* p < 0.10

** p < 0.05

*** p < 0.01

Notes: This table reports regression estimates based on model (1), using alternative data sources for bilateral trade and migration data. The table layout is equivalent to that of Table 2.

Table 11

Discrimination, openness to trade and immigration (alternative trade and migration data): 1st-stage result.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	ln(trade share)	ln(immigration share)	ln(trade (all others) share)	ln(immigration (all others) share)	ln(trade (all others) share)	ln(trade with China share)	ln(immigration (all others) share)	ln(immigration from China share)
ln predicted share—total trade	0.553*** (0.0552)	1.439*** (0.0842)
ln predicted share—total immigration	0.527*** (0.0492)	0.588*** (0.0697)
ln predicted share—trade (all others)			0.518*** (0.0531)	1.458*** (0.0739)	0.742*** (0.0517)	0.527*** (0.0491)
ln predicted share—immigration (all others)			0.568*** (0.0462)	0.574*** (0.0664)			0.973*** (0.0681)	0.0506 (0.236)
ln predicted share—trade with China					0.707*** (0.160)	0.471*** (0.141)
ln predicted share—immigration from China							1.301*** (0.0769)	0.00445 (0.350)
Observations	1764	1764	1764	1764	1764	1764	1767	1767

Robust standard errors in parentheses

* p < 0.10

** p < 0.05

*** p < 0.01

Notes: This table reports 1st stage estimates of the constructed instruments associated with Table 10. The Kleibergen-Paap F-statistics of the first-stage are reported in Table 10.

6 Concluding remarks

This paper analyzes a cross-country survey data during the early stage of the COVID-19 outbreak to study the effect of openness to trade and migration on the likelihood of xenophobic and racially discriminatory behavior. Our results show that greater openness to trade increases the likelihood of reported xenophobic behaviors, while openness to migration decreasing it. Our findings highlight previously unknown benefit for policies that foster openness to migration. Interestingly, we found that trade and migration with China mitigate reported xenophobia, and the effects of trade and migration on discrimination primarily manifest through actions in interpersonal spaces. These findings shed new light on the nuances of the interactions between economic, social and cultural interactions.

In an attempt to reach an extensive coverage across the globe in a fast-changing environment, our sampling framework is not fully representative. Yet we believe this article raises a critical issue in the discussions on the social impacts of COVID-19, and it calls for additional work on this topic to provide further evidence.

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23 Dec 2020

PONE-D-20-32641

Openness and COVID-19 induced xenophobia:

The roles of trade and migration in sustainable development

PLOS ONE

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Reviewer #1: This paper investigates whether more open economy in terms of trade and migration has an impact on observed discriminatory behaviors empirically. The theoretical frameworks build on existing research demonstrating that openness to trade and immigration facilitates more interactions with people outside one’s race, ethnicity and nationality, thus potentially improves mutual understanding and cultural exchanges and mitigating xenophobic sentiments. Using survey data on ethnically Chinese residents’ xenophobic experiences living abroad at the early stage of the pandemic in February, 2020, the authors find that greater openness to trade decreases the likelihood of reported xenophobic behaviors while openness to migration increases it.

I think that the paper will be interesting to scholars in political economy and public opinion. The paper addresses an interesting but unexplored question. I recommend revisions before the paper appears in Polis-one or another journal.

1. Theory

The objective of this paper is to examine whether there is a causal effect of trade and immigration policy on discriminatory behaviors observed by ethnically Chinese residents living abroad at the early time of the covid-19 pandemic. Although the paper is empirical-oriented one, we still need some theoretical explanations of how changes in trade and immigration affect discriminatory behaviors observed. In this sense, the paper has some issues to be addressed. First, it does not explain why we focus on the effects of immigration and trade on discriminatory behaviors observed. Globalization can have various dimensions such as trade, foreign direct investment, immigration, and capital market liberalization. Given the various aspects of globalization, I am wondering why the authors pay particular attention to the two dimensions of globalization – trade and immigration, not trade and FDI or trade capital market liberalization, etc. The authors would justify this point well in the main text.

Second, I am wondering how trade and immigration influence discriminatory behaviors observed among ethnically Chinese residents living abroad. I think that a large body of literature in international political economy has explained underlying mechanisms linking trade and immigration to xenophobic behaviors. Yet the authors do not present these mechanisms in a systematic way, while just describing some selective studies supporting their theoretical frameworks. In doing so, the authors do not describe whether trade and immigration have a negative, positive, or null impact on xenophobic behavior observed. The authors would elaborate some testable hypotheses on the effect of trade and immigration on xenophobic behavior observed.

2. Empirics

The authors predict that an increase in trade and immigration can have a negative, positive, or null effect on xenophobic behaviors observed. The questionnaire is “Since the virus outbreak in China, have you noticed any related discriminatory behavior in your working environment and daily life?.” The underlying logic behind the hypothesis is that openness to trade and immigration facilitates more interactions with people outside one’s race, ethnicity and nationality, thus potentially improves mutual understanding and cultural exchanges and mitigating xenophobic sentiments. Given this logic, how can we know that trade and immigration affect local natives in a way that the authors expect? To figure out the mechanisms, wouldn’t it be better to do some survey experiments to local natives rather than ethnically Chinese residents? Simply asking ethnically Chinese residents living abroad does not show that they identify the causal mechanisms between trade and immigration and xenophobic behaviors as local natives are main actors under their theoretical framework.

Related to the above point, another concern is that the authors use trade and immigration indicators at the national level. Employing the national-level indicators capturing trade and immigration does not tell that individuals are affected by them. Although countries may be more economically opened in terms of trade and immigration, it does not necessarily mean that individuals know, experience, and perceive them objectively and subjectively. If the authors attempt to uncover the causal mechanisms, it would be better to use some survey questionnaires to measure how individual respondents know, experience, and perceive the degree of trade and immigration at the individual level.

Reviewer #2: This paper traces the country-specific factors that contribute to anti-Chinese discrimination in other countries under the shadow of a pandemic first spread out in China. There are many merits in this paper. Methodologically, this study is carefully done following Ortega and Peri (2014)'s IV approach using the dyadic bilateral geographical and cultural distance. The topic is very timely and critical as the pandemic has been prevailing around the world. Nonetheless, I have several points of reservation and concern regarding this research.

1. It is entirely in a black box how the respondents are recruited. Even a snowballing sampling, the readers need to know how the respondents are sampled, and who they are, how the survey was done in which language. For instance, it might be possible that these respondents were recruited from the more concerned or more nationalistic population of Chinese immigrants. The platform used in recruitment (WeChat) makes this more likely. Also, it appears that there is no compensation for the survey to the respondents. If you are not paid, what would have been the motivation for the survey-takers to participate in this survey, other than they are particularly concerned about the anti-Chinese atmosphere or feel patriotic about the difficulties their home country was going through? I do not think the WVS analysis remedies this issue.

2. Conceptually, I was not entirely clear whether the paper is about anti-immigrants, anti-China, anti-Chinese, or anti-Asian, or just broad xenophobia: all these have different implications for hypothesizing and analyses. The timing of the survey was 7 days from Feb 11th to Feb 17th, 2020, which the authors described it was a period when the infection was mostly taking place within mainland China. However, the epidemic already took place in South Korea and Japan (in the cruise ship) on a massive scale and in Taiwan and Hong Kong as well. I think this makes Anti-Asian sentiment a better angle, but at least I hope this issue can be discussed and clarified at the beginning.

3. Empirically, the primary issues I had were related to how to adopt Ortega and Peri's approach. I think this paper's setup is rather China- or East Asia- specific and different from the general perspective in Ortega and Peri. So the right approach would be to take the share of trade with "China" and the share of "Chinese immigrants" in the population, rather than general trade or immigration. Many European countries probably have many immigrants from neighboring European states, but a few from China. I could not think of why and how this would matter in the same way as, say, in South Korea, where a large proportion of immigrants must be from China.

4. Second, I am very concerned about the correlation between trade and immigration and the fact that the authors use these variables together in all models. I suspect this might drive the results of the paper. First, the authors use the same IV for both trade and immigration. Second, as shown in Figure 1, the vast majority of responses came from the US, Australia, and Canada: all of them are high immigration and high trade countries. Third, Ortega and Peri (2014) use the two variables separately and together, which I believe this paper should do. Also, the authors need to report the first stage of 2SLS, at least in the appendix.

5. Finally, because all survey was taken after the outbreak of the epidemic and the question was specifically about "since the outbreak," I was not sure if the discrimination got worse than or the same as before. Especially, due the deteriorating relationship between China and the US along with some western countries since at least 2018, maybe the discrimination was rising even before the pandemic.

6. I have questions about the data sources: Why trade data (2012-2016) is from CoW, not the World Bank or the WTO? Why are migration data from 1991-2000? There was almost no migration from China to Africa back then?

7. Some minor points:

a. P.6. says "approximately 76% of respondents chose one of three categories: (i) racially discriminatory message against Chinese in the media (29%); (ii) racist rhetoric by native residents against Chinese in public (23%); and (iii) shunning (23%)." 76% means these categories are exclusive to each other? What if one respondent experienced many of these?

b. On P.4 the authors provide some key descriptive statistics, which is very confusing. "In particular, our preferred estimates show that a respondent residing in a country ranked at the 75th percentile of trade share in GDP in our sample sees a 43% decrease in the likelihood of receiving COVID-19 induced discrimination compared to those in a country ranked at the 25th percentile. In contrast, a respondent in the 75th-percentile country in the share of immigrants in population, on average, has an 86% greater likelihood of receiving COVID-19 induced discrimination compared to those in the counterpart ranked at the 25th percentile." It was unclear because I could not figure out whether the percentile was ascending or descending order. It was only evident after I saw the empirical results in later pages.

c. Isn't it also possible that observed discrimination can be subject to the fear that the respondents at the time?

d. Table1: how about age? Why public transportation?

e. Local media on P. 10 mean domestic media?

f. Can the authors provide full survey questions and an screenshot of the survey in the appendix?

**********

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Reviewer #1: No

Reviewer #2: No

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Submitted filename: Plos one_review December 2020.docx

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17 Feb 2021

Response to reviewer comments

We thank the editor and the two reviewers for their detailed and insightful comments.

First and foremost, we’d like to note that the result in this revision has changed dramatically from the previously submitted version. However, our research question and empirical model remain the same.

Two main reasons led to the change in results. First, in the process of revising the submitted manuscript, we identified a coding error in the data processing process, the correction of the error led to major changes in the result. In addition, based on the suggestion of the reviewer 2, we collected more recent bilateral immigration data and re-estimated our model. The updated data also led to changes in the result. Despite the changes, we are more confident in the results presented in this revised version.

In the remaining part of this note, we offer a more detailed response to the reviewers’ comments.

Reviewer 1:

1. Theory

The objective of this paper is to examine whether there is a causal effect of trade and immigration policy on discriminatory behaviors observed by ethnically Chinese residents living abroad at the early time of the covid-19 pandemic. Although the paper is empirical-oriented one, we still need some theoretical explanations of how changes in trade and immigration affect discriminatory behaviors observed. In this sense, the paper has some issues to be addressed. First, it does not explain why we focus on the effects of immigration and trade on discriminatory behaviors observed. Globalization can have various dimensions such as trade, foreign direct investment, immigration, and capital market liberalization. Given the various aspects of globalization, I am wondering why the authors pay particular attention to the two dimensions of globalization – trade and immigration, not trade and FDI or trade capital market liberalization, etc. The authors would justify this point well in the main text.

Response: It is well noted that the impact of globalization is multifaceted, and trade and immigration are only part of the big picture. We now note, in the 2nd sentence in the 4th paragraph in introduction, that “Openness to trade and immigration are important components of the globalized world, and we choose to focus on these two aspects to provide a particular perspective regarding the general relationship between globalization and culture.”

Second, I am wondering how trade and immigration influence discriminatory behaviors observed among ethnically Chinese residents living abroad. I think that a large body of literature in international political economy has explained underlying mechanisms linking trade and immigration to xenophobic behaviors. Yet the authors do not present these mechanisms in a systematic way, while just describing some selective studies supporting their theoretical frameworks. In doing so, the authors do not describe whether trade and immigration have a negative, positive, or null impact on xenophobic behavior observed. The authors would elaborate some testable hypotheses on the effect of trade and immigration on xenophobic behavior observed.

Response: Our reading of the literature regarding trade and immigration suggests that the field is mixed with theories and evidence pointing on both directions. We are not aware of any theory or evidence that directly studies the question we investigate in this project. And we try to cite studies that are most closely related to our specific research question in this context. As a result, we do not have a strong preference toward any particular theory in this context, nor do we have a prior study against which to compare closely. Therefore, we did not set out to seek evidence to validate or invalidate any particular theory. We deliberately try not to frame a specific testable hypothesis that predicts a particular relationship. However, if there are critical omissions on our part regarding the literature review, please kindly let us know, and we are more than happy to address them.

2. Empirics

The authors predict that an increase in trade and immigration can have a negative, positive, or null effect on xenophobic behaviors observed. The questionnaire is “Since the virus outbreak in China, have you noticed any related discriminatory behavior in your working environment and daily life?.” The underlying logic behind the hypothesis is that openness to trade and immigration facilitates more interactions with people outside one’s race, ethnicity and nationality, thus potentially improves mutual understanding and cultural exchanges and mitigating xenophobic sentiments. Given this logic, how can we know that trade and immigration affect local natives in a way that the authors expect? To figure out the mechanisms, wouldn’t it be better to do some survey experiments to local natives rather than ethnically Chinese residents? Simply asking ethnically Chinese residents living abroad does not show that they identify the causal mechanisms between trade and immigration and xenophobic behaviors as local natives are main actors under their theoretical framework.

Response: Our view is that openness to trade and migration may have either a positive or a negative effect. “Openness to trade and migration facilitates more interactions with people outside one's race, ethnicity and nationality, thus potentially improves mutual understanding and cultural exchanges, mitigating xenophobic sentiments”, as we noted in the 4th paragraph of the article, is our synthesis of the views argued in some cited literature (McLaren, 2003; Ellison et al., 2011; Bove and Elia, 2017). It is a plausible mechanism through which openness can mitigate discrimination. But it is not our hypothesis or prediction on the empirical results. To further clarify this point, we now note, in the 3rd sentence of the 4th paragraph, that “They (openness to trade and migration) may mitigate or intensify xenophobic sentiments, and it is an important empirical question to explore the evidence.”

Related to the above point, another concern is that the authors use trade and immigration indicators at the national level. Employing the national-level indicators capturing trade and immigration does not tell that individuals are affected by them. Although countries may be more economically opened in terms of trade and immigration, it does not necessarily mean that individuals know, experience, and perceive them objectively and subjectively. If the authors attempt to uncover the causal mechanisms, it would be better to use some survey questionnaires to measure how individual respondents know, experience, and perceive the degree of trade and immigration at the individual level.

Response: Indeed, the respondents do not necessarily know or experience the openness to trade and migration directly. But it is not what our survey set out to measure, nor is it necessary for our empirical model. The survey was designed to measure the respondent’s experience and perception regarding COVID-related discriminatory behavior. Our empirical model was used to estimate and test whether respondents living in countries with a greater degree of openness systematically experience more or less discrimination. We believe that our empirical approach remains valid as long as the constructed instrumental variables based on country-pairwise geographical relationships are independent of the respondents’ observable characteristics.

Reviewer 2:

This paper traces the country-specific factors that contribute to anti-Chinese discrimination in other countries under the shadow of a pandemic first spread out in China. There are many merits in this paper. Methodologically, this study is carefully done following Ortega and Peri (2014)'s IV approach using the dyadic bilateral geographical and cultural distance. The topic is very timely and critical as the pandemic has been prevailing around the world. Nonetheless, I have several points of reservation and concern regarding this research.

1. It is entirely in a black box how the respondents are recruited. Even a snowballing sampling, the readers need to know how the respondents are sampled, and who they are, how the survey was done in which language. For instance, it might be possible that these respondents were recruited from the more concerned or more nationalistic population of Chinese immigrants. The platform used in recruitment (WeChat) makes this more likely. Also, it appears that there is no compensation for the survey to the respondents. If you are not paid, what would have been the motivation for the survey-takers to participate in this survey, other than they are particularly concerned about the anti-Chinese atmosphere or feel patriotic about the difficulties their home country was going through? I do not think the WVS analysis remedies this issue.

Response: The respondents are not compensated for taking the survey. Indeed, it is certainly possible that our sampling approach may recruit respondents that are systematically more or less nationalistic. If this potential bias from sample selection is independent across countries, then the OLS estimates would be valid. Otherwise, if the bias is not independent across countries, then it would invalidate the OLS estimates. However, as long as such biases are independent of the instrumental variables we constructed, then our IV estimates remain valid. Because our instruments are constructed using country-pairwise geographical information, we think it is unlikely that the sample selection is correlated with our instruments. In particular, in this revision, we added an additional measure of openness based on shares of trade and migration excluding those from China. We think it is highly plausible that the instruments constructed by leaving China out would be independent of the respondents’ observable characteristics.

We now note, in the 2nd and 3rd sentence in the 2nd paragraph of page 5, that “For instance, our sampling approach may attract more nationalistic respondents or those with a stronger personal tie to China. If such characteristics are correlated with openness to trade or migration or their residing country, then our OLS estimates would be biased.” The discussion in the following paragraph regarding our IV estimation strategy then serve to address this concern.

2. Conceptually, I was not entirely clear whether the paper is about anti-immigrants, anti-China, anti-Chinese, or anti-Asian, or just broad xenophobia: all these have different implications for hypothesizing and analyses. The timing of the survey was 7 days from Feb 11th to Feb 17th, 2020, which the authors described it was a period when the infection was mostly taking place within mainland China. However, the epidemic already took place in South Korea and Japan (in the cruise ship) on a massive scale and in Taiwan and Hong Kong as well. I think this makes Anti-Asian sentiment a better angle, but at least I hope this issue can be discussed and clarified at the beginning.

Response: The point is well noted. We now revise the 1st sentence in the 4th paragraph of the introduction to read “We are particularly interested in the effect of a country's openness to trade and migration on the likelihood of an ethnically Chinese resident observing anti-Asian discriminatory behaviors.”

3. Empirically, the primary issues I had were related to how to adopt Ortega and Peri's approach. I think this paper's setup is rather China- or East Asia- specific and different from the general perspective in Ortega and Peri. So the right approach would be to take the share of trade with "China" and the share of "Chinese immigrants" in the population, rather than general trade or immigration. Many European countries probably have many immigrants from neighboring European states, but a few from China. I could not think of why and how this would matter in the same way as, say, in South Korea, where a large proportion of immigrants must be from China.

Response: The point is also well taken and very much appreciated. We now expand the main analysis by further decomposing the openness measures into trade and migration with China and those other than China. The analysis lends much richer insights into the paper (results presented in Table 2, and discussed in the results section). We acknowledge your suggestion in footnote 11 on page 6.

4. Second, I am very concerned about the correlation between trade and immigration and the fact that the authors use these variables together in all models. I suspect this might drive the results of the paper. First, the authors use the same IV for both trade and immigration. Second, as shown in Figure 1, the vast majority of responses came from the US, Australia, and Canada: all of them are high immigration and high trade countries. Third, Ortega and Peri (2014) use the two variables separately and together, which I believe this paper should do. Also, the authors need to report the first stage of 2SLS, at least in the appendix.

Response: The point is also well taken and very much appreciated. We expand the main analysis by analyzing openness to trade and migration separately. The results are consistent with findings of the model with both openness measures, and reported in columns (5) and (6) of Table 2. The first-stage estimates on the instruments are also reported now in Table 3, which shows statistically significant relationship between the constructed instruments and the endogenous regressors.

5. Finally, because all survey was taken after the outbreak of the epidemic and the question was specifically about "since the outbreak," I was not sure if the discrimination got worse than or the same as before. Especially, due the deteriorating relationship between China and the US along with some western countries since at least 2018, maybe the discrimination was rising even before the pandemic.

Response: It is a possible that the perceived discriminatory behavior by the respondents was in fact starting before the COVID crisis. Nonetheless, we think the follow up question regarding the type of COVID-related discrimination is very specifically pointed at COVID, and the results based on those measures (Table 4) are largely consistent with our main results.

6. I have questions about the data sources: Why trade data (2012-2016) is from CoW, not the World Bank or the WTO? Why are migration data from 1991-2000? There was almost no migration from China to Africa back then?

Response: In the choice of trade and immigration data, we followed Bradford and Chilton (2019, J. Law and Econ) that adopts the Ortega and Peri (2014) framework. However, the comment is well noted that alternative data sources, especially more recent immigration data would improve the credibility of the results. To address this issue, we explored alternative data sources on both trade and immigration.

On bilateral trade data flows, we were not able to obtain the UN dataset. The WTO bilateral trade data only contains import, but not exports. Our trade measure requires the sum of both in the share of the total GDP. As an alternative, we obtained the IMF bilateral trade data and re-constructed and instruments and re-calculated the estimates. In a nutshell, the results are largely consistent with those obtained based on the CoW trade data, but the instruments based on this dataset is too weak. We report this set of results at the end of the robustness check section.

On bilateral immigration flows, we obtained a more recent dataset (Abel and Cohen, 2019, Scientific Data) which contains immigration flows since 2000 and up to 2015. We now use this dataset as the primary immigration data, and all results are updated. The results based on the original immigration dataset (World Bank) is now relegated to the robustness check section.

7. Some minor points:

a. P.6. says "approximately 76% of respondents chose one of three categories: (i) racially discriminatory message against Chinese in the media (29%); (ii) racist rhetoric by native residents against Chinese in public (23%); and (iii) shunning (23%)." 76% means these categories are exclusive to each other? What if one respondent experienced many of these?

Response: Yes, the question only allowed for one answer from each respondent. In hindsight, we wish it was set to allow for multiple selections. But given the responses, we make the assumption that the respondent selected the most prominent type of discrimination they experienced. We noted this in footnote 5.

b. On P.4 the authors provide some key descriptive statistics, which is very confusing. "In particular, our preferred estimates show that a respondent residing in a country ranked at the 75th percentile of trade share in GDP in our sample sees a 43% decrease in the likelihood of receiving COVID-19 induced discrimination compared to those in a country ranked at the 25th percentile. In contrast, a respondent in the 75th-percentile country in the share of immigrants in population, on average, has an 86% greater likelihood of receiving COVID-19 induced discrimination compared to those in the counterpart ranked at the 25th percentile." It was unclear because I could not figure out whether the percentile was ascending or descending order. It was only evident after I saw the empirical results in later pages.

Response: Point well taken. In the 3rd paragraph on page 2, where we first mention the 75-25 percentile comparison, we revised the sentences to read “Our results show that greater openness to trade increases the likelihood of reported xenophobic behaviors. In particular, consider two otherwise similar countries, with one country more open to trade (ranked at the 75th percentile in trade share) than the other (ranked at the 25th percentile in trade share). Our preferred estimates show that a respondent residing in the more open country sees an 80% increase in the likelihood of receiving COVID-19 induced discrimination.”

c. Isn't it also possible that observed discrimination can be subject to the fear that the respondents at the time?

Response: It is indeed possible that fear renders respondents more sensitive to discriminatory behavior. But we think it is unlikely that the underlying level of fear would be correlated with our constructed instruments. Therefore, we are confident in our reported estimates.

d. Table1: how about age? Why public transportation?

Response: We do not report age in the descriptive statics table because the survey only collects a categorical variable on age. The median of our sample lies in the age group of 31-40 year old. We now note this in the text, in the first paragraph under the data section that “The respondents are 65% female, 55% received their highest degree outside China, 32% routinely use public transportation, 65% below the age of 40 and an median age in the 31-40 year old group.”

Public transportation is a proxy to measure the respondent’s intensity of daily contact with strangers and their residential setting, which may be correlated with the level of discriminatory behavior they are exposed to.

e. Local media on P. 10 mean domestic media?

Response: Local media meant media in the respondent’s residing country. We avoided using the domestic media to avoid the confusion that it might be referring to Chinese domestic media. This set of results are now removed from the revision.

f. Can the authors provide full survey questions and an screenshot of the survey in the appendix?

Response: Yes. The survey questionnaire (a pdf file) is provided with this revision as supplementary.

Submitted filename: Response to Reviewer Comments.docx

Click here for additional data file.

22 Mar 2021

Openness and COVID-19 induced xenophobia:

The roles of trade and migration in sustainable development

PONE-D-20-32641R1

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29 Mar 2021

PONE-D-20-32641R1

Openness and COVID-19 induced xenophobia: The roles of trade and migration in sustainable development

Dear Dr. He:

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