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Impact of Trade Liberalization on Household Welfare An Analysis Using Household Exposure to Trade Indices Vol (0123456789) Social Indicators Research (2021) 153 503–531 https doi org10 1007s11205 020 02499 1 1 3 O R I G I N A L R E S E A R C H Impact of Trade Liberalization on Household Welfare An Analysis Using Household Exposure‑to‑Trade Indices Thang T Vo1 Dinh X Nguyen1 Accepted 17 September 2020 Published online 26 September 2020 © Springer Nature B V 2020 Abstract This paper quantifi.
Social Indicators Research (2021) 153:503–531 https://doi.org/10.1007/s11205-020-02499-1 ORIGINAL RESEARCH Impact of Trade Liberalization on Household Welfare: An Analysis Using Household Exposure‑to‑Trade Indices Thang T. Vo1 · Dinh X. Nguyen1 Accepted: 17 September 2020 / Published online: 26 September 2020 © Springer Nature B.V 2020 Abstract This paper quantifies the impact of trade liberalization at the household level using data from the Vietnam Household Living Standard Survey from 2002 to 2016 Household welfare is measured using income, expenditure, and vulnerability to poverty Unlike previous studies, we address potential endogeneity at the household level by constructing household exposure-to-trade indices as a proxy for trade liberalization These indices are advantageous as they capture the influence of trade liberalization at the national level and the households’ ability to respond to new opportunities The results suggest that trade liberalization improves Vietnamese household income and expenditure via the export channel or the expansion of labor demand Tariff reduction for exported goods is less favorable to household welfare The impact of trade liberalization became smaller and less significant after the global downturn in 2008 Rural households suffered more vulnerability from trade, and the poor gained fewer benefits after the financial crisis in 2008 Keywords Trade liberalization · Trade exposure · Household · Vulnerability to poverty · Welfare · Vietnam 1 Introduction International trade has long been accepted as a critical driver of economic development, especially over the long run (Onafowora and Owoye 1998; WTO 2001; Winters et al 2004; Wacziarg and Welch 2007; George 2010; Naito 2017; Fukuda 2018), even in times of crisis (Falvey et al 2012) Trade provides developing countries opportunities to accelerate economic growth, improve welfare, and reduce poverty, especially when major exports are labor-intensive products such as agricultural and basic manufactured goods With fewer trade barriers, the domestic market is enlarged with various products and better choices in * Thang T Vo thangvt@ueh.edu.vn Dinh X Nguyen dinhnx@ueh.edu.vn Health and Agricultural Policy Research Institute, University of Economics Ho Chi Minh City, 279 Nguyen Tri Phuong, District 10, Ho Chi Minh City, Vietnam 13 Vol.:(0123456789) 504 T. T. Vo, D. X. Nguyen price and quality Exporting firms are more likely to employ additional low-skilled workers, which is expected to considerably impact poverty reduction However, the empirical impact of trade liberalization on poverty, welfare, and inequality is far from consistent First, while the impact of trade liberalization on income is often positive at the national level, it may vary between regions within a country (Law 2019; Popli 2010) Regions with a higher proportion of sectors exposed to trade openness tend to experience a greater decline in poverty (Kis-Katos and Sparrow 2015; Popli 2010; McCaig 2011) However, urban areas with a greater share of workers in high tariff industries might have a smaller reduction in poverty and inequality once tariffs are reduced (Castilho et al 2012) Second, the skill and education level of workers also influences the actual impact of trade openness on poverty and social welfare For instance, unskilled workers, and sectors that mostly employ unskilled labor, can suffer during the opening process (Kis-Katos and Sparrow 2015), and trade liberalization has been shown to widen the skilled-unskilled wage gap in Latin American countries (Chaudhuri et al 2002; Hanson and Harrison 1999; Beyer et al 1999) to the detriment of poorer/less-skilled households (Naude and Coetzee 2004) It is recognized that the influence of trade on households varies in correspondence with two factors: (i) the impact of trade openness on domestic markets where the household is located, and (ii) the exposure of the household to the goods and factors that are involved in the opening process (Nicita 2009) Some empirical studies indicate that not every household will benefit from trade liberalization (Nicita 2009; Marchand 2012), as trade openness can increase inequality while simultaneously reducing poverty overall (Winters et al 2004) Trade may even decrease welfare in the absence of insurance (Newbery and Stiglitz 1984) Therefore, a case study from an emerging country is required to enlarge the trade liberalization literature, wherein conclusive evidence at the household level is rare It is also important to empirically examine the channels through which trade openness contributes to household welfare (Castilho et al 2012; Marchand 2012) Vietnam provides an interesting case for trade literature as it has experienced successful economic development, major expansion of trade, and a history of trade policy reform and institutional change Since the start of Doi Moi, which began replacing the former inefficient centralized economic system with a market-oriented economy in 1986, Vietnam has achieved immense success in promoting trade and economic growth The country’s GDP rose from only 61 billion USD to 175 billion in 2017 at an average rate of around 6.1% Vietnam joined ASEAN in 1995; acceded to the WTO in 2007; and has signed trade agreements with a number of countries, including China, India, Japan, Korea, and Chile, which have greatly reduced the tariff barriers to Vietnamese goods entering these markets As a result, exports as a share of GDP increased from 6.6% in 1986 to 50% in 2000 before surpassing 100% in 2017 (World Bank 2019) However, only around 7% percent of Vietnamese firms are involved in exports to foreign markets; these exporters employ more than 90% of the workforce (SME 2015) Furthermore, most of Vietnam’s export industries are dominated by large firms Consequently, performance related to the expansion of employment is less satisfactory (Abbott et al 2009), unemployment remains at around 6.9% in urban areas, and underemployment persists in rural areas Therefore, whether wage workers have gained from the trade boom remains unclear To address this issue, our paper combines the household data extracted from the Vietnam Household Living Standard Survey (VHLSS) and trade data from United Nations International Trade Statistics Database (UNCOM-TRADE) and Trade Analysis Information System (TRAINS) to investigate the impact of trade liberalization on household 13 505 Impact of Trade Liberalization on Household Welfare: An Analysis… welfare in Vietnam We construct two indices to represent household exposure to trade as two proxies for trade liberalization, while welfare is considered through the scope of household vulnerability, income, and expenditure The study makes a unique contribution to literature in four ways First, the households’ exposure to trade indices are unique and provide an incisive tool for empirical analysis The advantage of these indices is that they captures the influence of trade liberalization at the national level and the response of households Second, the study provides rare evidence regarding the impact of trade liberalization at the household level Third, using a wide range of data from 2002 to 2016, which mostly captures the period of trade policy reforms in Vietnam, we compare the impact of trade opening at different points in time Fourth, this study is the first attempt to compare the impact of trade liberalization on household vulnerability before and after the global financial crisis in 2008 The rest of this paper is organized as follows Section 2 reviews the analytical framework for trade liberalization and household welfare, including the research gaps Sections 3 and discuss empirical strategies and data description, respectively, while Sect. presents the results and findings The final section offers policy implications and concluding remarks 2 Analytical Framework for Trade Liberalization and Household Welfare 2.1 Impact Mechanisms Trade liberalization, in theory, should considerably alleviate poverty, but it is difficult to quantify the link between trade liberalization and poverty (Winters 1999; Justino et al 2008) Before Winters (2002) forwarded a conceptual framework linking trade and poverty, theoretical research was more prominent than empirical research (Niimi et al 2007) Winters (2002) argues that trade liberalization can affect household welfare, and therefore poverty, through three transmission channels: prices, factor markets, and government spending Households, distribution channels, factor markets, and governments are four broad groups of components that can shape the static effects of trade policy on poverty (details provided in Fig. 1) Each component has poverty promoting and reducing influences When all factors are considered together as a system, there is no a priori conclusion as to whether trade liberalization in a particular context will increase or reduce poverty 2.1.1 The Price Channel In the simplest form, the household welfare effect ( ΔWh ) is expressed as a function of the change in the price of a single good, i, used by the household h: ΔWh = (qi − ci ) ⋅ Δpi (1) where qi represents production, ci represents consumption, and pi represents the price of good i In pure accounting terms, the retail domestic price of good i is the product of a string of transmission from its world price, tariffs, exchange rates, etc., down to domestic taxes and distribution costs If the product’s final price is lower than its previous price, households that are net consumers of good i will gain, but households that are net producers of good 13 506 T. T. Vo, D. X. Nguyen Quantities Exchange rate Tariffs, Quotas Tariff Revenue Pass-through, competition Border price Taxes, regulation, distributors, procurement Enterprises Wholesale price Taxes Spending Distribution, taxes, regulation, cop-ops Retail price Factor markets Welfare Fig. 1 Winters’ framework for the effect of trade liberalization on household welfare i will suffer loss If imported products compete with local products and result in lower prices, the welfare effects will be similar Previous research investigating the effect of trade on welfare via this channel includes Porto (2006), Nicita (2009), and Marchand (2012) 2.1.2 The Factor Market Channel Trade can affect household welfare via the factor market, as illustrated in the left-hand side of Fig. The effect depends on the elasticity of factor supplies Under particular conditions, if the factor supplies are fixed, the real wage will increase if the production of goods increases By contrast, when factor supplies are infinitely elastic, the wage will be fixed and an adjustment will take place in terms of employment, and workers will switch between sectors in pursuit of higher wages In the real world, both these ends–wholly fixed or wholly elastic labor supplies–rarely exist Therefore, determining the elasticity of the labor supply is an important precondition of investigating the effects of trade on poverty Still, in both cases, if the conditions are met, workers always gain from trade liberalization Some studies that have investigated labor market mechanisms include Seshan (2013) and Kis-Katos and Sparrow (2015) 2.1.3 The Fiscal Channel Despite the welfare gained by poor households, trade liberalization definitely reduces government revenue by decreasing tariff, import regulations, imposing losses on state monopoly firms, and so on As government revenue is reduced, spending on social support may also decrease and, therefore, harm the poor However, for Winters (2002), falling revenue does not inevitably lead to reduced poverty reduction performance, as expected revenue loss is usually overstated by governments Even if revenue is lost, the government is primarily affected and not the poor The government’s ability to distribute revenue efficiently for anti-poverty campaigns before trade liberalization is often lacking In case of Vietnam, during the 1990s, aid for economic growth and direct aid for the poor was imbalanced, biased toward urban areas, and not conductive to poverty alleviation (Huong and Winters 2001) 13 Impact of Trade Liberalization on Household Welfare: An Analysis… 507 2.2 Measurements, Level of Analysis, and Research Gap 2.2.1 Measuring Trade Openness Empirical studies employ different definitions and measurements of trade openness due to the distinct context examined by each study However, according to McCulloch et al (2002), the concept of openness can be divided into two main facets: openness in practice and openness in policy The former focuses on the influence of trade on economic activities and actual price distortions, which may not be controlled Conversely, the latter focuses on policies designed to curb trade and is comprehensively controllable by the government The concepts of openness and trade openness have been the subject of heated debate in the last decade; hence, the appropriate way to measure trade openness is not yet well-established The confusion and ambiguity surrounding trade openness can be seen in the numerous definitions used in various papers Some dominant measures include the share of trade (exports, imports, or both) in gross domestic product (Avelino et al 2005; Bussmann 2009; Aizenman and Noy 2009) and the adjusted trade share (Li et al 2004; Alcala and Ciccone 2004) Much effort has been made to improve these measurements throughout the years For example, some studies proposed using five trade openness criteria dummy variables to represent trade openness (Cigno et al 2002; Neumayer and de Soysa 2005) Other studies developed a Trade Restrictive Index, which measures the degree of trade protection for each country’s economy with respect to income, expenditure conditions, and balance of trade responses (Anderson and Peter Neary 1996; Bach and Martin 2001; Manole and Spatareanu 2010) One advanced measure of trade openness, called the Composite Trade Share, attempted to capture two dimensions of trade simultaneously, income from trade and interaction in trade activities (Squalli and Wilson 2011) Another study relied on the correlation of a region’s export with its value- added production and exports to calculate a Regional Openness Index (Marjit et al 2007) Finally, one study utilized various regional indices such as infrastructure, institutions, market efficiency, industrial labor share, retail sales, and a Provincial Competitive Index to represent regional trade openness (Le 2014) 2.2.2 Data and Level of Analysis Many types of data sources at different levels have been used to conduct empirical research on the links between trade liberalization and household welfare Several studies focus on the global level (Baye 2017; Falvey et al 2012; Onafowora and Owoye 1998; Wacziarg and Welch 2007; Tanaka and Hosoe 2011), while many others focus on the country level wherein each observation represents a geographic unit within the country such as a region, province, or district (Castilho et al 2012; Kis-Katos and Sparrow 2015; Law 2019; Marchand 2012; McCaig 2011; Nicita 2009; Topalova 2007) Far fewer analyses address the household level, and these tend to study trends in trade liberalization by identifying dates before and after the removal of trade barriers, evaluating the impact on household welfare rather than using actual trade data (Goldberg and Pavcnik 2007; Coello et al 2010; Popli 2010) This approach limits insights on how trade liberalization affects households through tariff reduction 13 508 T. T. Vo, D. X. Nguyen 2.2.3 Research Gaps Surprisingly, although trade liberalization probably exposes households to more risks, most studies overlook the possible impacts of trade opening process on households’ vulnerability to poverty Trade share measures are potentially biased as they only capture the income from trade effects (Squalli and Wilson 2011) and not account for their endogenous characteristics (Cavallo and Frankel 2008) Although a few studies have provided insightful empirical evidence on the links between trade and poverty alleviation, they fail to account for the elasticity of the labor supply This is particularly important, as the effects of trade might flow through wages if elasticity is low or through employment if elasticity is high (Winters 2002) Critically, despite improvements in constructing appropriate measurements of trade openness in recent years, none of the new measurements were applicable at the household level, thus omitting substantial household variations in response to new opportunities offered by trade reforms In the following section, we propose two indices to quantify household exposureto-trade openness through their labor supply in response to increased labor demand and tariff reduction Our study provides rare evidence regarding the impact of trade liberalization on vulnerability at the household level 3 Empirical Method 3.1 Measuring Vulnerability as Expected Poverty Vulnerability as expected poverty (VEP) is a measure of vulnerability first proposed by Chaudhuri (2003) and applied to Vietnam by Vo (2018) and Vo and Van (2019) Household vulnerability is defined as the likelihood that a household will fall into poverty in the next period VEP can be estimated using the following procedures, beginning with computing the consumption function: ln ci = 𝛼 + 𝛽 ⋅ Xi + ei (2) where ci is per capita consumption expenditure for household i; Xi represents a vector of observable household characteristics (age, gender, ethnic, and marital status of the household head, the household’s female ratio and dependent ratio, the highest educational qualification of family members, land ownership, and electricity access) and communal characteristics (household location, provincial poverty rate, interaction between provincial poverty rate and household location, and administrative region); 𝛽 is a vector of parameters to be estimated; and ei is a mean-zero disturbance term that captures idiosyncratic shocks that lead to different levels of per capita consumption With cross-sectional data, Chaudhuri (2003) suggests using a three-step Feasible General̂ ci |Xi ] , and the ized Least Squares technique to produce the expected log consumption, E[ln ̂ variance of log consumption, Var[ln ci |Xi ] Assuming that ln ci is normally distributed, the estimated probability that a household will be in poverty in the future (for example, at time t + 1 ) is given by: 13 Impact of Trade Liberalization on Household Welfare: An Analysis… � vi,Chaudhuri ⎞ ⎛ � ci �Xi ] ⎟ ⎜ ln z − E[ln � = Pr(ln ci < ln z�Xi ) = Φ⎜ � ⎟, ⎜ Var � [ln ci �Xi ] ⎟ ⎠ ⎝ 509 (3) where Φ(.) is the cumulative function of the standard normal and z is the actual poverty line The poverty lines used in this study are based on expenditure and use the definitions from the General Statistical Office—World Bank (World Bank 2018) Details of the poverty line are provided in Table 8 3.2 Estimation Strategy In this paper, we apply the framework described in Winters (2002) to investigate the link between trade exposure and household welfare through the factor market channel The baseline model is as follows: yht = 𝛼h + 𝛼t + 𝛽1 ⋅ Lht + 𝛾1 ⋅ Zht + 𝜖ht , (4) where yht are indicators of welfare outcomes for household h in year t, 𝛼h are the household fixed effects, 𝛼t are the year fixed effects, Zht is a vector of household controls (including age, gender, marital status, and years of education for the household head, dependent ratio, and location of household), and 𝜖ht is an error term assumed to be independently distributed normal The variable of interest Lht represents trade liberalization at the household level From Winters’ framework, Lht could be the household’s participation in international trade, which is likely to be correlated with welfare related outcomes captured in the dependent variable yht We, therefore, construct a proxy variable measuring changing household trade participation resulting from factors exogenous to the household We first construct the index of labor demand due to exports for industry i in year t: LDit = Exportit − Exportit−1 (5) All households are exposed to the same labor demand due to exports in industry i However, the household’s decision to join the labor market varies in accordance with their capability to respond to the opportunities created by trade liberalization Additionally, the distance from their home to urban areas where most trade-related activities occur may affect their decision to join the labor force Therefore, in this study, we weight the labor demand index by the percentage of the household’s members working in industry i and the household’s distance from urban areas Only individuals 15 years or older and who have income from their first main job are considered National trade shocks are transformed into household trade shocks, and we call this index the household exposure to exports: HHExposureEXht = ∑ Workeriht ⋅ LDit ⋅ Distanceht i Total Workerht (6) Another indicator that we use as a proxy for household exposure to trade is the tariff index In this case, national labor demand is modified by the average value of the Most Favored Nation tariff rate applied to Vietnamese goods exported to China, the U.S., Japan, and Korea, the four main destinations for Vietnamese exports over the last 10 years, which is represented by TariffMFN 13 510 T. T. Vo, D. X. Nguyen HHExposureTRht = ∑ Workeriht 1 ⋅ ⋅ Distanceht i TariffMFNi Total Workerht (7) With these two exposure-to-trade indices in the place of Lht , we expect to capture the effects of trade liberalization through labor demand at the industry level and labor supply at the household level The higher the household exposure index, the higher is the exposure of that household to trade Following the theory of international trade, we expect that, as exposure to trade increases, the household gains more benefits from trade, including higher income, higher expenditure, and lower vulnerability to poverty 4 Data Household data for this study are drawn from the Vietnam Household Living Standard Surveys (VHLSS) These nationally representative surveys have been conducted by the General Statistics Office of Vietnam every two years since 2000 with technical assistance from the World Bank with both household and commune survey The household survey collects detailed information about households, such as demographic information, education, employment and labor force participation, income, expenditure, health, housing, durable goods, fixed assets, and participation in poverty programs The commune survey collects basic information regarding demography, socioeconomic characteristics, and the infrastructure of communes The surveys cover approximately 9300 households biennially 2002 onward The sample sizes and descriptive statistics of the basic variables in this study are provided in Table 9 and Table 10 Panel data for the VHLSS is only viable when the investigation spans over two years, as the VHLSS re-surveys only 50% of its observations every two years For panel data extended for more than two periods, the number of valid panel observations is reduced massively The inconsistency of matching between VHLSS waves prior to 2008 were addressed using McCaig (2009) revised version of the identification codes The number of observations for the panels used in this study are provided in Table 11 The trade value in goods for all the trade partners of Vietnam and tariff data, hereafter referred to as Vietnam Trade Data, were taken from UNCOMTRADE (United Nation 2019) and TRAINS (UNCTAD and WB 2019), respectively Both data are coded based on the Harmonized Commodity Description and Coding System (HS code) by degree of aggregation, starting from sections (one digit) to Chapters (two digits), Headings (four digits), and Subheadings (six digits) However, the levels beyond six digits (eight and ten digits) are not harmonized between countries In this study, we use the data aggregated at the two-digit level The tariff data used in this study is the mean rate of tariff Vietnam’s main trade partners–China, Japan, South Korea, and the U.S.–to imported Vietnamese goods This collective and comprehensive measure of import tariff rates for more than 9828 tariff lines is expected to represent a less-biased measure of openness than using Vietnam’s import tariff rate for foreign goods 13 Impact of Trade Liberalization on Household Welfare: An Analysis… 511 5 Results 5.1 Descriptive Statistics Figure 2a illustrates the evolution of Vietnam’s exports to its top five partners from the early 2000s After the Vietnam-U.S BTA was signed in 2001, Vietnam’s exports increased dramatically until the global financial crisis in 2008 In 2008–2009, exports to major partners, except China, significantly declined Exporting turnover recovered and expanded quickly after 2010, especially to the U.S., China, Japan, and Korea However, data from the period show that the average Most Favored Nation tariff rates of the U.S and Japan varied only slightly Korea’s MFN tariff fluctuated, while that of China fell sharply between 2000 and 2006 and has remained around 10% since then (Fig. 2b) Figure depicts the evolution of Vietnamese household welfare from 2002 to 2016 Income and expenditure increased in this period, but there was a disparity between households in urban and rural areas Growth rates for income and expenditure in urban areas were faster The gap between income and expenditure was also wider for urban households, representing a higher level of saving These differences partly explain urban households’ slightly lower levels of income inequality, as measured by the GINI index and much lower vulnerability to poverty (Ward 2016) The figure also indicates that the economic crisis affected income inequality and vulnerability in urban and rural areas However, rural households tended to suffer greater losses from the economic downturn than their peers in urban areas, as they had less effective means to cope with shocks Panel estimation from VHLSS confirms that household vulnerability is inversely associated with income and expenditure (Table 12) Table 1 describes the mean of the two exposure indices across surveys by region, poverty status, and industry sector In general, tariff reduction and exposure to exports created more opportunities for households over time, but the former shrunk and the latter subsequently fluctuated following the 2008 crisis Both indices indicate that those above the poverty line were more exposed to international trade than the poor Households in urban areas were more likely to be exposed to international trade opportunities created by tariff reduction than their peers in rural areas They, therefore, had more chances to consume less-expensive imported products However, urban households might likewise have been subject to greater competitive pressure from imported goods Similarly, urban households responded to opportunities in the labor force created by export industries In term of workforce sectors, those who worked in mining and quarries responded better to tariff reduction while households having jobs in agriculture, forestry, aquaculture, and manufacturing industries were more exposed to exports Our decomposition of the two indices in Table 2 shows that the shares of labor supply in the export index are higher than those figures in the tariff index, implying that households are more responsive to export increases than to tariff reductions Notably, exports generated more opportunities for the poor as the export increase in Vietnam is mostly related to agriculture and related services (Table 13) and the poor’s income mostly depend on this dominant sector (Table 14) 5.2 Impacts of Trade on Vulnerability, Income and Expenses Table 3a reports the impact of trade liberalization on vulnerability, income, and expenses at the household level In general, the average effects of trade on Vietnamese households 13 Australia China 20000 Hong Kong Japan Korea Malaysia Singapore 15000 Switzerland 5000 10000 USA Top five in total export value Germany Economic crisis Assession to WTO T. T. Vo, D. X. Nguyen 25000 512 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 20 (a) Vietnam’s exports to top five countries ($ million, 2010’s value) China Japan Rep of Korea 15 10 Average taiff of all industries (%) USA 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 (b) Most Favored Nation tariff rates of Vietnam’s major export partners Fig. 2 Export values and tariff rates 13 Impact of Trade Liberalization on Household Welfare: An Analysis… 517 Table 3 (continued) Per capita income and expenditure are deflated annual values Control variables are suppressed from the tables t-statistic in parentheses * p < 1 , **p < 05 , ***p < 01 Our findings on the export channel are consistent with those reported in other studies available in the literature (Niimi et al 2007; Justino et al 2008; Heo and Doanh 2009; Seshan 2013) Export expansion could augment the employment opportunity outside of agricultural sector, raise the relative wage of both skilled and unskilled worker in more exposed-to-trade provinces (Fukase 2012) However, tariff reduction is less beneficial at the household level although provinces that were more exposed to tariff cuts enjoyed an income increase, similar to the findings of McCaig (2011) on tariff cuts and poverty Our study confirms the findings of Magrini et al (2018) that households engaged in farm activities, which is the main activity of rural and poor households in Vietnam, are more exposed to trade risks and may require supports Additionally, the distributional effects of trade in our study are in line with Cling et al (2009), who show that inequalities between rural and urban areas could possibly increase even if trade liberalization results in welfare gains for all households 6 Conclusion After centuries of technological progress and advances in international cooperation, globalization and trade liberalization are inevitable as they tend to enhance the total welfare of the world economy However, the wide-ranging effects of globalization are complex and subject to political dispute Globalization benefits society as a whole, although it harms certain groups Understanding relative costs and benefits can pave the way for alleviating problems while sustaining the wider payoffs Unfortunately, limited studies measure the impact of trade liberalization at the household level due to data unavailability and inappropriate approaches It is difficult to find a trade liberalization variable that accounts for the incomplete characteristic of the labor market and is exogenous with households’ welfare This study bridges this research gap by developing new measures of trade liberalization, which we call the “household exposure-to-trade indices” These measures combine labor demand generated by trade shocks at the industry level with labor supply capability at the household level The advantages of these indices are that they capture the influence of trade liberalization at the national level and households’ ability to respond to new opportunities Vietnam is a compelling case study, as it has opened itself with a comprehensive transformation from a state-centered to a market-oriented economy With household survey data from 2002 to 2016, the case of Vietnam allows the examination of the impact of trade across different points in time Our method of combining trade data from international datasets with household surveys in this study may prove beneficial for future research Our results suggest that trade liberalization improves Vietnamese households’ income and expenditure via the export channel or the expansion of labor demand Tariff reduction for exported goods is less beneficial to household welfare The impact of trade liberalization became smaller and less significant after the global downturn in 2008 Among working households, rural households suffered more vulnerability from trade and the poor gained less after the financial crisis in 2008 These results partly explain urban households’ lower probability of becoming poor and broader gap between income and expenditure versus rural households The results also imply that trade liberalization had different impact 13 518 T. T. Vo, D. X. Nguyen Table 4 Fixed effects on vulnerability, income and expense at the household level—two stages regression Export exposure 2002–2004 2004–2006 2006–2008 2010–2012 2012–2014 2014–2016 2.864*** 1.674*** 3.183*** 1.639*** 1.458*** 1.745*** (63.02) (22.75) (23.28) (73.35) (58.85) (26.92) (a) First stage regression for household’s tariff and export exposure Under ID.: Kleibergen-Paap rk LM 1154.8 206.1 1086.5 573.0 606.8 198.4 Kleibergen-Paap rk Wald F 3967.9 517.2 541.2 5374.4 3458.9 724.1 Over ID.: Hansen J 0 0 0 Tariff exposure 0.525*** 3.054*** 3.158*** 1.675*** 1.555*** 1.680*** (7.04) (11.23) (8.77) (8.87) (4.21) (9.58) 39.73 26.28 9.994 18.15 5.556 9.065 Kleibergen-Paap rk Wald F 49.57 126.0 76.92 78.54 17.71 91.77 Over ID.: Hansen J 0 0 0 N 6134 6072 5924 5526 5426 5532 Weak ID.: Under ID.: Kleibergen-Paap rk LM Weak ID.: (b) Second stage VEP Export exposure Tariff exposure Constant − 0.153*** 0.0153 − 0.0187*** − 0.00435 0.00415 − 0.00765** (− 10.47) (1.17) (− 5.51) (− 1.05) (0.97) (− 2.37) 0.0151*** − 0.000184 0.00142** 0.000293 0.000539** 0.000379 (9.32) (− 0.36) (1.98) (0.64) (2.51) (1.01) 0.488*** 0.155*** 0.259*** 0.408*** 0.348*** 0.232*** (7.19) (6.60) (5.53) (7.40) (8.32) (5.11) Per capita expend (log) Export exposure Tariff exposure Constant 0.352*** 0.0102 0.0594*** 0.0463** 0.0276 − 0.0293 (11.06) (0.11) (4.06) (2.37) (1.11) (− 1.55) − 0.00507 0.00294 0.00113 − 0.00341 − 0.00287** 0.00181 (− 1.12) (0.58) (0.24) (− 1.16) (− 2.02) (0.90) 8.693*** 9.010*** 9.062*** 9.526*** 9.476*** 9.669*** (68.93) (75.72) (59.79) (63.54) (71.36) (66.38) Per capita income (log) Export exposure Tariff exposure Constant N 0.371*** 0.165 0.0118 0.0836*** − 0.0251 0.0479** (10.63) (1.49) (1.05) (3.73) (− 1.12) (2.55) − 0.0116 0.000258 0.00291 − 0.00173 0.00118 0.000329 (− 1.56) (0.04) (1.14) (− 0.62) (0.95) (0.10) 8.691*** 9.062*** 9.209*** 9.249*** 9.402*** 9.685*** (40.48) (69.46) (65.58) (49.37) (55.93) (62.08) 6134 6072 5924 5526 5426 5532 Stock–Yogo weak ID test critical values for 10%, 15%, 20% and 25% maximal IV size: 16.38, 8.96, 6.66, 5.53 Per capita income and expenditure are deflated annual values Control variables are suppressed from the tables t-statistic in parentheses * p < 1 , **p < 05 , ***p < 01 13 519 Impact of Trade Liberalization on Household Welfare: An Analysis… Table 5 Fixed effects on vulnerability, income and expense at the household level by living location—two stages regression 2002–2004 2004–2006 2006–2008 2010–2012 2012–2014 2014–2016 − 0.0218 (− 1.49) 0.000377 (1.46) 0.0175 (0.79) − 0.000883** (− 2.25) 0.0000572 (0.19) 0.0145* (1.89) − 0.00789** (− 2.19) 0.0000140 (0.07) − 0.00318 (− 0.05) − 0.00134 (− 0.41) 0.000284** (2.43) 0.0235 (0.41) − 0.00371 (− 1.32) 0.000321 (1.34) − 0.115 (− 1.35) − 0.0635 (− 0.52) 0.00142 (0.24) 9.327*** (25.57) 0.0551*** (3.00) 0.00358 (0.63) 10.35*** (29.91) 0.0540*** (2.81) − 0.00369 (− 1.22) 10.96*** (35.49) 0.0191 (0.74) − 0.00320** (− 2.29) 10.12*** (35.33) − 0.0211 (− 1.18) 0.00192 (0.92) 9.510*** (21.01) 0.0431 (0.36) 0.00197 (0.29) 9.803*** (31.32) 784 0.0100 (0.62) 0.00927* (1.81) 10.10*** (29.89) 734 0.0639*** (2.79) − 0.00138 (− 0.53) 10.24*** (23.32) 694 − 0.0322 (− 1.35) 0.000811 (0.65) 9.935*** (30.18) 876 0.0230 (1.18) 0.00253 (1.28) 9.736*** (30.56) 866 0.0666*** (2.68) − 0.00485*** (− 2.85) 0.171*** (6.38) − 0.0432*** (− 4.24) 0.00237 (1.42) 0.291*** (5.28) − 0.0537*** (− 2.99) − 0.000860 (− 0.34) 0.448*** (7.12) 0.0833*** (3.61) − 0.0109* (− 1.96) 0.372*** (7.72) − 0.0236** (− 1.98) 0.00535 (1.29) 0.261*** (5.12) 0.241 (1.41) 0.0112 (0.94) 8.953*** (75.10) 0.0749*** (2.90) 0.0000117 (0.00) 8.840*** (52.28) 0.132 (1.45) 0.00903 (0.55) 9.290*** (57.70) 0.0717 (0.77) − 0.00672 (− 0.27) 9.317*** (61.53) − 0.0802 (− 1.43) 0.0130 (0.58) 9.738*** (65.22) 0.408* (1.75) − 0.00887 (− 0.68) 0.0163 (1.01) − 0.000748 (− 0.26) 0.381*** (5.27) 0.00593 (0.23) 0.0103 (0.11) 0.0127 (0.50) 0.281*** (2.72) − 0.162 (− 1.27) (a) Urban households VEP Export − 0.00523 exposure (− 1.11) Tariff − 0.00204*** exposure (− 2.70) Constant − 0.0859* (− 1.75) Per capita expend (log) Export 0.118** exposure (2.29) Tariff 0.0314*** exposure (3.89) Constant 9.525*** (19.35) Per capita income (log) Export 0.191*** exposure (3.04) Tariff 0.0101 exposure (0.73) Constant 7.930*** (5.74) N 726 (b) Rural households VEP Export − 0.277*** exposure (− 8.18) Tariff 0.00806** exposure (2.27) Constant 0.512*** (7.05) Per capita expend (log) Export 0.477*** exposure (8.21) Tariff − 0.00884 exposure (− 1.13) Constant 8.629*** (69.68) Per capita income (log) Export 0.548*** exposure (8.83) Tariff 0.000555 exposure (0.07) 13 520 T. T. Vo, D. X. Nguyen Table 5 (continued) Constant N 2002–2004 2004–2006 2006–2008 2010–2012 2012–2014 2014–2016 8.820*** 8.924*** 9.081*** 9.130*** 9.278*** 9.676*** (61.88) 5408 (63.13) 5288 (58.27) 5190 (45.18) 4832 (48.42) 450 (54.41) 4666 Living location is household’s location in the base year Per capita income and expenditure are deflated annual values Control variables are suppressed from the tables First stage result is in Table 4a t-statistic in parentheses * p < 1 , **p < 05 , ***p < 01 on households over time, although opening process had positive impact on the entire population over the long term Our study highlights that trade liberalization can have adverse effects on an economy after an external economic shock and that the government should also focus on the disadvantages of rural households and the poor Programs or policies that enable these groups to respond to changes in the labor market or to cope with potential risks from trade would be beneficial 13 521 Impact of Trade Liberalization on Household Welfare: An Analysis… Table 6 Two-stage regression—fixed effects on vulnerability, income and expense at the household level by poverty situation 2002–2004 2004–2006 2006–2008 2010–2012 2012–2014 2014–2016 − 0.0425 (− 0.53) − 0.00265 (− 0.76) 0.337*** (5.30) − 0.134*** (− 4.46) 0.00236 (1.15) 0.412*** (3.03) − 0.0514*** (− 2.92) 0.00513 (0.83) 0.585*** (7.97) 0.00781 (0.47) − 0.00246 (− 0.40) 0.430*** (6.09) − 0.0717** (− 2.17) 0.0107* (1.80) 0.354*** (3.40) − 0.304 (− 0.84) − 0.00998 (− 0.36) 8.700*** (40.29) 0.255*** (4.54) 0.0170*** (5.07) 8.649*** (30.99) 0.0893 (1.50) − 0.0103 (− 0.25) 9.095*** (34.24) − 0.0317 (− 0.57) − 0.00233 (− 0.06) 9.181*** (44.08) − 0.0997 (− 1.42) − 0.0105 (− 0.57) 9.348*** (34.77) 0.552 (1.31) − 0.0331 (− 1.01) 8.799*** (34.81) 1430 0.0932*** (2.63) 0.00534*** (2.75) 8.777*** (34.62) 1200 0.106* (1.78) 0.0405 (0.99) 8.813*** (31.03) 2326 − 0.0833 (− 0.98) − 0.00207 (− 0.05) 9.096*** (40.99) 2098 − 0.00731 (− 0.11) − 0.0170 (− 0.94) 9.122*** (32.01) 1760 0.0165** (2.00) − 0.000202 (− 0.53) 0.0832*** (3.63) − 0.00792*** (− 4.26) 0.000679 (1.07) 0.211*** (5.58) 0.00390 (1.29) 0.000469 (1.45) 0.237*** (2.93) 0.00164 − 0.00178 (0.49) (− 0.99) 0.000393** 0.000216 (2.43) (0.85) 0.246*** 0.153*** (5.95) (3.39) 0.0451 (0.47) 0.00287 (0.56) 9.108*** (65.01) 0.0466*** (3.58) − 0.00460* (− 1.67) 9.124*** (51.54) 0.0429** (2.26) − 0.00339 (− 1.09) 9.921*** (58.78) 0.0460* (1.82) − 0.00273* (− 1.86) 9.678*** (59.82) − 0.0224 (− 1.19) 0.00197 (0.98) 9.856*** (58.31) 0.131 (1.18) 0.00160 (0.24) 0.00375 (0.31) 0.00231 (0.70) 0.0742*** (3.14) − 0.00194 (− 0.74) − 0.00531 (− 0.24) 0.00126 (1.02) 0.0537*** (2.73) 0.000849 (0.27) (a) Poor households VEP Export exposure − 0.333*** (− 6.01) Tariff expo0.0201*** sure (4.31) Constant 0.703*** (4.92) Per capita expend (log) Export expo- 0.609*** sure (8.17) Tariff expo0.00288 sure (0.28) Constant 8.461*** (42.59) Per capita income (log) Export expo- 0.500*** sure (6.24) Tariff expo0.00655 sure (0.64) Constant 8.520*** (41.24) N 2118 (b) Non-poor households VEP Export expo- − 0.0896*** sure (− 8.02) Tariff expo0.00969*** sure (8.52) Constant 0.337*** (5.93) Per capita expend (log) Export expo- 0.261*** sure (7.50) Tariff expo− 0.00251 sure (− 0.49) Constant 8.853*** (54.69) Per capita income (log) Export expo- 0.334*** sure (8.54) Tariff expo− 0.0151* sure (− 1.65) 13 522 T. T. Vo, D. X. Nguyen Table 6 (continued) Constant N 2002–2004 2004–2006 2006–2008 2010–2012 2012–2014 2014–2016 8.784*** 9.143*** 9.296*** 9.519*** 9.602*** 9.992*** (26.77) 4016 (62.89) 4642 (55.85) 4724 (35.90) 3200 (38.90) 3328 (52.94) 3772 Poverty status is household’s poverty status in the base year Per capita income and expenditure are deflated annual values Control variables are suppressed from the tables t-statistic in parentheses, first stage result in Table 4a * p < 1 , **p < 05 , ***p < 01 Table 7 Fixed effects on vulnerability, income and expense at the provincial level Labor demand at the household level Labor demand at the commune level 2002–2008 2010–2016 2002–2008 2010–2016 − 0.160*** (− 7.09) 0.000147 (0.02) 0.156*** (12.65) − 0.0398*** (− 3.00) − 0.0110 (− 1.27) 0.200*** (12.78) − 0.707*** (− 6.02) 0.0158 (0.62) 0.146*** (9.77) − 0.0389 (− 1.20) − 0.0179 (− 0.82) 0.184*** (14.82) 0.429*** (11.31) 0.0188 (1.40) 8.697*** (371.28) 0.119*** (3.84) 0.0280** (2.55) 9.351*** (473.84) 1.931*** (10.57) − 0.0387 (− 1.13) 8.763*** (417.99) 0.185** (2.20) 0.0108 (0.34) 9.408*** (530.69) 0.311*** (7.91) 0.0304* (1.76) 8.913*** (255.77) 249 0.230*** (3.90) 0.0405** (2.07) 9.430*** (264.50) 252 1.560*** (8.35) − 0.0545 (− 1.34) 9.002*** (330.01) 249 0.352*** (2.77) 0.0217 (0.47) 9.516*** (355.44) 252 VEP Tariff exposure Tariff exposure Constant Per capita expend (log) Tariff exposure Tariff exposure Constant Per capita income (log) Tariff exposure Tariff exposure Constant N Per capita income and expenditure are deflated annual values Control variables are suppressed from the tables t-statistic in parentheses * p < 1 , **p < 05 , ***p < 01 Acknowledgements We would like to thank the editor and two anonymous reviewers for their constructive comments and suggestions, which helped us to improve the manuscript Additionally, we thank the University of Economics Ho Chi Minh City for its generosity in financing this paper’s submission Author Contributions TV contributed to the study conception, methodology, formal analysis, and writing DN collected data and processed the data TV prepared the draft manuscript and approved the final manuscript 13 523 Impact of Trade Liberalization on Household Welfare: An Analysis… Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors Availability of data and material The datasets used and analysed during the current study are available from the corresponding author on reasonable request Compliance with ethical standards Conflict of interest The authors declare that they have no conflict of interest Appendix: Additional Statistics and Results See Tables 8, 9, 10, 11, 12, 13 and 14 Table 8 Poverty line in Vietnam by measurement Year MOLISA GSO-WB Monthly Monthly Yearly Urban Rural Urban Rural 150 220 260 370 500 660 750 900 935 975 100 170 200 290 400 530 605 700 725 755 1800 2640 3120 4440 6000 7920 9000 10,800 11,220 11,700 1200 2040 2400 3480 4800 6360 7260 8400 8700 9060 1998 2002 2004 2006 2008 2010 2012 2014 2016 2017 2018 Yearly 149 1788 160 173 213 280 653 871 964 969 1920 2076 2556 3360 7836 10,456 11,568 11,628 Measurement unit is ’000 VND In this paper, we use the General Statistics Oce and the World Bank (GSO-WB) poverty line as our poverty measure The other measure of the Ministry of Labor, Invalids, and Social Aairs (MOLISA) is added for comparison The GSO-WB absolute poverty line is constructed using a standard Cost-of-BasicNeeds approach, based on the observed consumption pattern of the poor, as reported in the Vietnam Household Living Standards Survey (VHLSS) It consists of an allowance for food and non-food spending The GSO-WB line has been kept roughly constant in real purchasing power and applied to per capita consumption measured in successive rounds of VHLSS to monitor changes in poverty over time at the national, urban/rural, and regional level since 1998 (World Bank 2013) 13 13 49.09 0.76 0.82 6.86 0.39 2.90 0.24 0.08 8.87 9.08 9188 47.55 0.76 0.82 6.65 0.41 2.86 0.23 0.19 8.67 8.90 29,530 2004 0.08 8.95 9.21 9189 0.37 2.90 0.25 49.36 0.75 0.82 6.97 2006 0.03 9.10 9.25 9189 0.35 2.88 0.26 49.96 0.76 0.82 7.10 2008 0.19 9.40 9.48 9398 0.35 2.71 0.28 48.35 0.75 0.82 7.14 2010 0.17 9.44 9.58 9398 0.35 2.69 0.29 49.75 0.75 0.81 7.18 2012 0.14 9.50 9.67 9399 0.34 2.63 0.30 50.74 0.74 0.80 7.30 2014 0.09 9.58 9.79 9398 0.35 2.58 0.30 51.70 0.75 0.80 7.35 2016 Mean values are provided Gender is male or female, Marital status is married or single, Grade finished is an integer ranging from to 12, Dependent ratio is the share of household members that are aged over 16 and under 65 years old, Labor force is the number of members within the working age (between 15 and 65 years), Urban indicates whether the household is living in the urban or rural area Income and Expenditure per capita are deflated annual values Age Gender Marital status Grade finished Household characteristics Dependent ratio Labor force Urban Main dependent variables VEP Per capita expenditure (log) Per capita income (log) N Household head 2002 Table 9 Descriptive statistics of basic variables Source: Author’s calculation from the VHLSSs 524 T. T. Vo, D. X. Nguyen Female Male Marital status Single Married Urban Rural Urban Poverty status Poor Non-Poor Total Gender 23.6 76.4 18.1 81.9 76.6 23.4 26.3 73.7 100 6974 22,556 5341 24,189 22,621 6909 7768 21,762 29,530 1283 7905 9188 6938 2250 1693 7495 2231 6957 No No % 2004 2002 14.0 86.0 100 75.5 24.5 18.4 81.6 24.3 75.7 % 1271 7918 9189 6882 2307 1674 7515 2256 6933 No 2006 13.8 86.2 100 74.9 25.1 18.2 81.8 24.6 75.4 % 647 8542 9189 6837 2352 1673 7516 2250 6939 No 2008 7.0 93.0 100 74.4 25.6 18.2 81.8 24.5 75.5 % Table 10 Distributions of categorical variables Source: Author’s calculation from the VHLSSs 2321 7077 9398 6749 2649 1698 7700 2326 7072 No 2010 24.7 75.3 100 71.8 28.2 18.1 81.9 24.7 75.3 % 2188 7210 9398 6695 2703 1774 7624 2331 7067 No 2012 23.3 76.7 100 71.2 28.8 18.9 81.1 24.8 75.2 % 1871 7528 9399 6618 2781 1841 7558 2399 7000 No 2014 19.9 80.1 100 70.4 29.6 19.6 80.4 25.5 74.5 % 1454 7944 9398 6569 2829 1893 7505 2353 7045 No 2016 15.5 84.5 100 69.9 30.1 20.1 79.9 25.0 75.0 % Impact of Trade Liberalization on Household Welfare: An Analysis… 525 13 526 T. T. Vo, D. X. Nguyen Table 11 Numbers of observation by panels Source: Author’s calculation from the VHLSSs 2002 2004 2006 2008 2010 2012 2014 2016 Total 2002–2004 3067 3067 – – – – – – 6134 2004–2006 2006–2008 2010–2012 2012–2014 2014–2016 – – – – – 3036 – – – – 3036 2962 – – – – 2962 – – – – – 2763 – – – – 2763 2713 – – – – 2713 2766 – – – – 2766 6072 5924 5526 5426 5532 Number of observations for each panel vary according to the available of data Table 12 VEP and household welfare 2002–2004 2004–2006 2006–2008 2010–2012 2012–2014 2014–2016 − 0.605*** (− 6.98) − 0.428*** (− 5.61) − 0.702*** (− 8.74) − 0.472*** (− 6.82) − 0.469*** (− 5.04) − 0.354*** (− 4.50) 5426 − 0.689*** (− 8.18) − 0.465*** (− 6.54) 5532 Independent variable: VEP (without control variables) Per capita income (log) Per capita expenditure (log) Per capita income (log) Per capita expenditure (log) N − 0.765*** − 0.378*** − 0.206*** − 0.384*** (− 17.68) (− 4.60) (− 3.52) (− 5.29) − 0.595*** − 0.428*** − 0.675*** − 0.367*** (− 16.38) (− 5.98) (− 13.82) (− 6.10) Independent variable: VEP (with control variables) − 0.719*** − 0.268*** − 0.163*** − 0.320*** (− 15.14) (− 3.06) (− 2.74) (− 3.92) − 0.558*** − 0.354*** − 0.628*** − 0.318*** (− 14.48) (− 4.68) (− 12.66) (− 4.87) 6134 6072 5924 5526 Panel fixed effects regression Control variables are suppressed from the tables * p < 1 , **p < 05 , ***p < 01 13 Forestry and related services Aquaculture production and exploitation Exploitation of crude oil and natural gas Exploitation of metal ores Other mining and quarrying Foodstuff production and processing Textiles Costume production Production of leather and related products Wood-processing and making (except beds, wardrobes, desks, chairs); products from straw and plaiting materials Producing paper and paper-based products Production of chemicals and chemical products Production of medicines, pharmaceutical chemicals and materials Manufacturing of rubber and plastic products Manufacturing of products from other non-metallic minerals Production of metals Mfg of products from cast metal (except machines and equipment) Mfg of electronic products, PCs and optical products Mfg of electrical equipment Mfg of unclassified machines and equipment Mfg of motorized vehicles and truck trailers 10 13 14 15 16 26 27 28 29 24 25 22 23 17 20 21 Agriculture and related services Code % No 2006 % No 2008 % No 2010 % No 2012 % No 2014 % No 2016 % 0.1 1.0 0.1 0.9 0.2 0.2 0.0 18 0.1 20 0.1 20 0.1 0.0 19 236 34 215 53 53 104 0.4 992 4.2 0.0 15 0.1 115 0.5 688 2.9 241 1.0 532 2.3 123 0.5 370 1.6 10 10 56 19 68 14 24 31 311 30 223 46 190 46 104 0.1 0.1 0.1 0.1 0.1 0.8 0.3 0.9 0.2 0.3 0.0 0.4 4.3 0.1 0.0 0.4 3.1 0.6 2.7 0.6 1.5 7 64 22 68 20 18 39 311 32 249 44 221 55 113 0.1 0.1 0.1 0.0 0.1 0.9 0.3 1.0 0.3 0.3 0.0 0.6 4.4 0.1 0.0 0.5 3.6 0.6 3.2 0.8 1.6 10 18 60 16 67 28 11 47 310 18 249 48 242 52 123 0.1 0.1 0.1 0.0 0.3 0.9 0.2 1.0 0.4 0.1 0.2 0.7 4.5 0.1 0.1 0.3 3.6 0.7 3.5 0.8 1.8 18 10 67 27 80 27 15 15 41 296 32 568 37 270 87 117 0.3 0.1 0.1 0.0 0.1 1.0 0.4 1.2 0.4 0.2 0.2 0.6 4.4 0.1 0.1 0.5 8.5 0.6 4.0 1.3 1.8 21 17 67 36 72 19 20 13 58 289 18 449 36 288 102 108 0.3 0.1 0.1 0.0 0.3 1.0 0.5 1.1 0.3 0.3 0.2 0.9 4.3 0.0 0.1 0.3 6.7 0.5 4.3 1.5 1.6 14 8 16 82 37 64 25 18 16 40 310 15 311 38 324 109 101 0.2 0.1 0.1 0.1 0.2 1.2 0.6 1.0 0.4 0.3 0.2 0.6 4.7 0.0 0.1 0.2 4.7 0.6 4.9 1.6 1.5 47 15 14 86 40 71 20 23 64 312 20 282 40 345 144 78 0.7 0.2 0.1 0.1 0.2 1.3 0.6 1.1 0.3 0.4 0.1 1.0 4.8 0.0 0.1 0.3 4.3 0.6 5.3 2.2 1.2 19,563 83.4 5945 83.0 5699 81.4 5557 80.5 4926 73.8 5022 75.3 5061 76.4 4891 74.9 No No % 2004 2002 Table 13 Employment by industry Source: Author’s calculation from the VHLSSs Impact of Trade Liberalization on Household Welfare: An Analysis… 527 13 13 Mfg of other transport vehicles Cinematographic activities, production of TV programs, recording and musical publication Total Mfg manufacturing 30 59 Code Table 13 (continued) 0.1 0.0 23,452 100 28 0.2 0.0 % 7163 100 11 No No % 2004 2002 0.1 0.0 % 7001 100 No 2006 0.2 0.0 % 6904 100 12 No 2008 0.1 0.0 % 6671 100 No 2010 0.1 0.0 % 6670 100 No 2012 0.2 0.0 % 6626 100 14 No 2014 0.2 0.0 % 6533 100 12 No 2016 528 T. T. Vo, D. X. Nguyen 529 Impact of Trade Liberalization on Household Welfare: An Analysis… Table 14 Percentage of employment in selected industries by poverty status Source: Author’s calculation from the VHLSSs Code 10 14 16 – 2002 2004 2006 2008 2010 2012 2014 2016 88.41 58.36 74.41 45.20 75.73 46.67 80.06 47.33 80.26 46.88 0.46 0.52 0.99 0.25 1.05 0.49 0.91 0.31 1.31 0.57 2.78 3.42 3.49 3.04 2.70 3.19 2.41 3.52 2.54 3.46 0.93 2.84 7.88 5.44 4.48 4.87 1.39 3.79 1.24 3.32 0.15 2.82 1.42 3.35 1.05 3.68 1.28 3.99 1.10 4.14 0.62 1.39 0.90 1.36 1.19 1.14 1.12 1.06 0.62 0.87 6.65 30.65 10.90 41.36 13.80 39.97 12.83 40.01 12.93 40.76 Agriculture and related services (%) Poor 87.58 89.87 87.57 Non-poor 58.63 60.62 57.92 Forestry and related services (%) Poor 0.35 0.31 0.71 Non-poor 0.35 0.34 0.38 Aquaculture production and exploitation (%) Poor 3.05 2.57 3.07 Non-poor 3.47 3.52 3.44 Foodstuff production and processing (%) Poor 0.75 0.31 0.24 Non-poor 2.89 2.77 3.11 Costume production (%) Poor 0.30 0.08 0.08 Non-poor 2.34 2.39 2.78 Wood-processing (%) Poor 0.95 0.70 0.94 Non-poor 1.36 1.20 1.28 Other (%) Poor 7.03 6.16 7.40 Non-poor 30.95 29.16 31.11 References Abbott, P., Bentzen, J., & Tarp, F (2009) Trade and development: lessons from Vietnam’s past trade agreements World Development, 37(2), 341–353 Aizenman, J., & Noy, I (2009) Endogenous financial and trade openness Review of Development Economics, 13(2), 175–189 Alcala, F., & Ciccone, A (2004) Trade and productivity The Quarterly Journal of Economics, 119(2), 613–646 Anderson, J E., & Peter Neary, J (1996) A new approach to evaluating trade policy The Review of Economic Studies, 63(1), 107 Avelino, G., Brown, D S., & Hunter, W (2005) The effects of capital mobility, trade openness, and democracy on social spending in Latin America, 1980–1999 American Journal of Political Science, 49(3), 625–641 Bach, C F., & Martin, W (2001) Would the right tariff aggregator for policy analysis please stand up? 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International Trade Statistics Database (UNCOM -TRADE) and Trade Analysis Information System (TRAINS) to investigate the impact of trade liberalization on household 13 505 Impact of? ?Trade Liberalization. .. the poor was imbalanced, biased toward urban areas, and not conductive to poverty alleviation (Huong and Winters 2001) 13 Impact of? ?Trade Liberalization on? ?Household Welfare: An? ?Analysis? ?? 507 2.2 Measurements,... Export exposure Tariff exposure Constant Per capita income (log) Export exposure Tariff exposure Constant N 13 Impact of? ?Trade Liberalization on? ?Household Welfare: An? ?Analysis? ??