Abstract
Purpose
This study aims to analyze the relationship between global value chain (GVC) participation and poverty levels. Additionally, it investigates the impact of education levels, specifically analyzing literacy rates and tertiary education rates, on the correlation between GVC participation and poverty in Organization for Economic Co-operation and Development (OECD) and Association of Southeast Asian Nations (ASEAN) countries.
Design/methodology/approach
Fixed effect and random effect models will be employed to quantify the relationships between the dependent and independent variables. The Hausman test is applied to determine the appropriate estimator between fixed and random effects. Also, in the model, time-fixed effect or two-way fixed effect has been used to control for unobserved heterogeneity both across entities and over time in panel data analysis.
Findings
The findings demonstrate that engagement in GVCs presents a promising avenue for stimulating development, advancing income per capita growth and facilitating job creation. Notably, the results illuminate that the poverty-alleviating impacts of GVC participation are most conspicuous in nations boasting elevated levels of educational attainment among their populace.
Originality/value
This research aims to promote a better understanding of the connection between GVC participation and the level of poverty, with GVC participation decomposed into forward participation and backward participation.
Keywords
Citation
Choi, Y.J. and Leelasribunjong, Y. (2024), "Asymmetric effects of GVC on poverty in ASEAN and OECD", International Trade, Politics and Development, Vol. 8 No. 2, pp. 82-95. https://doi.org/10.1108/ITPD-05-2024-0027
Publisher
:Emerald Publishing Limited
Copyright © 2024, Young Jun Choi and Yuwapak Leelasribunjong
License
Published in International Trade, Politics and Development. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Theoretical viewpoints propose that engaging in international trade enhances the distribution of resources, lowers consumer prices and fosters efficient production. Additionally, it promotes global integration and the adoption of advanced technology, resulting in increased productivity (Badinger, 2008). Trade liberalization, as a policy to bolster the growth and welfare of developing countries, has been advocated by international organizations. Empirical studies consistently demonstrate a notable positive correlation between trade liberalization, economic growth and the reduction of poverty (Edwards, 1993; Frankel and Romer, 1999; Winters et al., 2004). The potential drawbacks of trade openness are predominantly observed during short- and medium-term adjustment periods. Nonetheless, long-term effects on poverty and inequality due to trade openness have also been acknowledged. Numerous studies indicate that trade liberalization produces measurable economic benefits for developing countries (Dollar et al., 2013). However, these outcomes are contingent upon the nature of trade reforms and the livelihoods of impoverished individuals (Winters and Martuscelli, 2014). Historically, the relationship between globalization and economic growth has been influenced by a set of complementary factors that could impact trade gains (Meissner, 2014).
Global value chains (GVCs) have emerged as a defining feature of the contemporary global economy, reshaping the production, trade and consumption of goods and services. They involve breaking down production processes across borders, enabling countries to specialize in specific tasks and enhancing efficiency and competitiveness (OECD, 2015). GVCs are integral to the modern global economy, involving the international dispersal of design, production, assembly and distribution of goods and services across different nations. Thus, participating in GVCs provides avenues for developing nations to increase their involvement in global commerce and broaden their export spectrum. In the absence of GVCs, a developing country would need the capability to independently manufacture an entire product to enter a different sector (World Bank, 2019). This reduces the barrier to entry into international markets, allowing these countries to engage in specific production stages, leveraging their comparative advantages. Consequently, the expansion of GVCs enables developing countries to create employment, raise their per capita incomes and, subsequently, lift many people out of poverty (Dollar et al., 2013).
However, the benefits derived from GVCs are not uniformly distributed and can rely on various factors, including the level of education within a given population. Education, as a fundamental driver of human capital development, significantly influences a nation's economic growth and social well-being. The relationship between participation in GVCs and education levels is crucial, as it can significantly shape a country's economic and social landscape (Taglioni and Winkler, 2016). Education equips individuals with the skills and knowledge necessary to secure gainful employment and improve their quality of life.
The relationship between education and poverty can be complex, particularly in the context of GVCs. While trade liberalization plays a crucial role, it's not the sole solution. In parallel, the role of GVCs in the poverty-level debate remains largely unexplored. There are potential factors, such as GVC participation, that can potentially cause poverty reduction. The interplay between GVCs, education and poverty has garnered substantial interest in economics and development studies.
Therefore, this research aims to investigate the impact of participating in GVCs and the level of education on poverty in Organization for Economic Co-operation and Development (OECD) and Association of Southeast Asian Nations (ASEAN) countries. The OECD, comprising mostly high-income economies, presents a stark contrast to ASEAN countries, where economies are rapidly developing. This comparative analysis will provide valuable insights into how different economic contexts influence the relationship between GVC participation, education levels and poverty.
Through analyzing the extent of OECD and ASEAN member countries' involvement in GVCs and the role of education, this research aims to uncover the potential benefits and challenges associated with this participation. It will examine how engaging in GVCs and level of education in different groups of countries can create opportunities for employment generation, skill development and technological transfer, all of which can positively impact poverty levels.
The rest of the paper consists of the following follows: Section 2 reviews literature; Section 3 explains the empirical model as well as the variables and data sources and Section 4 provides empirical results and makes discussions. In conclusion, implication and limitations are suggested.
2. Literature reviews
Numerous empirical studies have attempted to examine the relationship between international trade and poverty levels, particularly in poorer, developing countries. Literature on trade openness and poverty concluded that greater trade openness is associated with higher poverty rates, particularly in impoverished countries (Le Goff and Singh, 2014). Winters et al. (2004) argue that poverty could rise when there is a greater demand for skilled labor compared to unskilled labor. Lopez-Gonzalez et al. (2015) suggested that offshoring high-skill tasks can lead to increased demand for high-skilled workers, resulting in increased wages for such workers but reduced wages for low-skilled workers, ultimately widening the wage gap and potentially causing a rise in poverty. Similarly, Carpa and Martinez-Zarzoso (2022) presents that an increase in skilled labor in offshoring countries can diminish the relative demand and wages for low-skilled labor, thereby contributing to increased poverty and income inequality in the short run. Moreover, Buracom's research (2021) indicates that the impact of trade on poverty is contingent on the level of income inequality within a country, with trade having a negative effect on poverty in low-income inequality nations and a positive effect in high-income inequality nations.
Conversely, Cai et al. (2022) propose that offshoring can potentially reduce inequality by elevating individuals in lower-income countries out of poverty and creating employment opportunities, particularly in developing countries. Lewandowski et al. (2021) introduce another facet to this discourse, emphasizing that the importation of intermediate goods can reduce costs and boost labor demand, thereby generating job opportunities for low-skilled workers. This, in turn, is expected to lower both poverty levels and wage inequality within developing countries.
The nexus between GVC participation and poverty can be rationalized based on the evidence that participation in GVC leads to increased economic growth in both developed and developing countries. When these countries join global production networks, they can benefit from technology transfer, access to global markets and foreign direct investment. This can boost industrialization and create job opportunities, potentially reducing poverty (OECD, 2015). GVC participation in developing countries can stimulate economic growth through knowledge transfer, foreign direct investment and employment opportunities, raising wages and potentially reducing poverty (OECD, 2015). Lopez-Gonzalez et al. (2015) found that countries with higher backward GVC participation tend to have lower wage inequality. Through the effects of knowledge transfer and investment in training and skills, GVC participation can reduce poverty and wage inequality in developing countries by raising wages for low-skilled labor, particularly when it relates to the participation of lower-skilled tasks. Jiang and Caraballo (2017) argue that participating in GVCs leads to higher domestic employment, especially in developing countries, where participating in GVCs increases the relative demand for low-skilled labor. Concerning poverty reduction, GVC participation initially concentrated on labor-intensive activities in developing countries, particularly influencing poverty reduction by positively impacting wages, notably in unskilled, labor-intensive sectors like the textile industry. For instance, the surge in exports to the United States following the USA-Vietnam Bilateral Trade Agreement in 2001 significantly contributed to poverty reduction in Vietnam by decreasing the skill premium and benefiting unskilled workers (Hollweg, 2019).
To summarize GVC and poverty, the relationship between GVCs and poverty is multifaceted. While GVC participation can promote economic growth and poverty reduction, it can also lead to income inequality, poor working conditions and economic vulnerability. The outcomes depend on various factors, including a country's policies, its institutional framework and its ability to adapt and diversify within GVCs. Balancing the potential benefits and challenges of GVC participation is a critical consideration for policymakers in addressing poverty in the context of the globalized economy.
3. Methodology and the model
3.1 Data and variables description
The empirical analysis is conducted by using a panel dataset for countries, which is collected by a group of 10 countries from the ASEAN; however, there are seven countries that this study can collect the data because some countries do not have a complete set of data between 1999 and 2018. Secondly, in a group of OECD countries, there are 38 countries. However, some countries also do not have a complete data set between 1999 and 2018. Therefore, this research is conducted using a panel dataset, covering 32 finalized countries, which have been selected to do the study. However, due to the limited available dataset, the dataset is not balanced.
This research uses poverty as the poverty headcount ratio at $2.15 a day (2017 PPP) by % of population in the country
3.2 The model
In this section, panel regression models are utilized to examine the impact of GVCs on poverty. GVC is disaggregated into two components: forward participation and backward participation, owing to their distinctive roles in global trade. Forward participation involves the production of high-value-added intermediate products that are more knowledge-intensive. In contrast, backward participation centers on activities such as assembly and packaging, which entail low-value-added products and rely on low-skilled labor.
The analysis also investigates the effects of education on poverty, with education categorized into two distinct types: literacy and tertiary education. The literacy rate is utilized as an indicator of the influence of foundational education on poverty, while tertiary education attainment reflects the impact of higher education. By employing these distinct measures, we aim to assess the varying impacts of educational attainment levels on poverty outcomes within the framework of GVC participation.
These models include a set of control variables commonly used in the poverty literature. To account for economic development, income per capita or gross domestic product (GDP) per capita is included in the model, while inflation, measured by the consumer price index, is used to address macroeconomic instability. Private credit, as a percentage of GDP, captures the financial deepening of the economy. The unemployment rate serves as a proxy for domestic policies, and control of corruption is included to evaluate public power and bureaucratic regulation within the government.
The model specifications for the estimation are as follows.
Fixed effect and random effect models will be employed to quantify the relationships between the dependent and independent variables. The Hausman test is applied to determine the appropriate estimator between fixed and random effects. Also, in the model, time-fixed effect or two-way fixed effect has been used to control for unobserved heterogeneity both across entities and over time in the panel data analysis.
3.3 Empirical results
Model 1, focusing on forward participation's impact on poverty, indicates that an increase in GVC participation raises poverty levels in developing countries, while decreasing them in developed ones. The results of the tests are presented in Table 1. These findings align with those of Carpa and Martinez-Zarzoso (2022), who found that forward participation leads to a reduction in income inequality and poverty in the short term. Due to the fact that forward participation frequently yields higher value-added products, resulting in increased demand for skilled workers, this can lead to higher wages and greater employment within the domestic country. Conversely, the estimated coefficient is found to be positive and significant at the 1% level for poverty in ASEAN countries, suggesting that increased forward participation is linked to higher poverty levels. These findings are consistent with those of Lopez-Gonzalez et al. (2015), who observed that greater forward participation is associated with higher income inequality and poverty.
This study examines the potential interaction between forward participation and poverty concerning the role of education. The findings reveal that higher levels of forward participation and increases in literacy rates are linked to poverty levels across all country groups. These results underscore the critical role of literacy rates in shaping the relationship between forward participation and poverty. The negative and statistically significant coefficient indicates that forward participation holds the potential to mitigate poverty levels. In the context of developing nations like those within ASEAN, education may mitigate the interaction between poverty and participation in GVCs. This is because an educated populace, equipped with appropriate learning skills, is better positioned to leverage the advantages associated with engagement in GVCs (Le Goff and Singh, 2014).
Examining Model (2), the results of the tests are presented in Table 2, which evaluates tertiary education levels instead of literacy, and the estimation results for forward participation indicate that the estimated coefficients for the OECD and all countries have a negative impact on poverty, suggesting that increased forward participation is associated with reduced poverty levels. This can be explained by the fact that forward participation benefits both low-skilled and high-skilled labor, particularly in developed countries, where OECD countries require both types of labor to enhance firm productivity and increase engagement in the value chain (Korwatanasakul et al., 2022). However, forward participation is found to exacerbate the level of poverty in ASEAN countries. This may be due to the fact that increased forward participation could lead to a relative increase in demand and wages for skilled labor, while decreasing demand for low-skilled or less-skilled labor within these countries, thereby lowering wages for those workers.
The interaction between higher education, forward participation and poverty is scrutinized. The interaction term of forward participation and tertiary education rates is observed to exacerbate poverty in ASEAN countries, highlighting the intricacies of labor market demands. Workers with tertiary education possess not only skills but also knowledge. Despite developing countries specializing in intermediate goods of higher added value, they may not necessitate knowledge-equipped workers. Therefore, contrary to the findings regarding literacy rates, tertiary education does not alleviate poverty through GVC participation. This underscores the potential inadequacy of tertiary enrollment alone for individuals in ASEAN to fully benefit from engagement in GVCs. Conversely, the interaction variable in OECD countries and all countries demonstrates a significant and negative impact on poverty. These outcomes indicate that individuals with robust human capital can better seize the new opportunities arising from participation in GVCs, particularly in developed countries.
The effects of backward GVC participation on poverty are examined in Models 3 and 4. Model 3 incorporates literacy as a proxy for basic education, while Model 4 employs the rate of tertiary education attainment. As explained, the literacy rate serves as an indicator of the impact of basic education on poverty, whereas tertiary education attainment reflects the influence of higher education. By employing these distinct measures, we can assess the differential impacts of educational attainment levels on poverty outcomes within the context of backward GVC participation.
The estimation results for Model 3 reveal that the estimated coefficients for the OECD, the ASEAN and all countries have positive effects on poverty, and the results of the tests are presented in Table 3. However, there are differences in significance; the coefficient for ASEAN is not significant at the level of p < 0.001, while those for the OECD and all countries are significant. This suggests that backward GVC participation may not obviously affect the poverty level in developing countries, whereas GVC participation increases poverty in developed countries. This is because an increase in backward GVC participation entails specialization in low-value-added products and increases demand for low-skilled workers. As a result, the wages of low-skilled workers in developed countries should decrease, while the wages of low-skilled workers in developing countries may increase due to the inclusion of lower-skilled workers at the margin.
This study also examines the role of education in reducing poverty under backward GVC participation. The results indicate that greater backward participation and increases in literacy rates are associated with lower levels of poverty. The interaction term of backward participation and literacy rate suggests that the beneficial impact of an increase in backward participation on poverty reduction is more pronounced when there is a stronger investment in human capital. Therefore, backward participation becomes increasingly advantageous for the poor (Olopade et al., 2019).
The estimation results for backward GVC participation in Model 4 reveal that the coefficient of backward participation is not significantly associated with OECD countries, and the results of the tests are presented in Table 4. However, after controlling for time dummies, backward participation is found to have a significant and positive impact on poverty levels in the OECD countries. Conversely, for the ASEAN countries, backward participation is associated with higher poverty levels. These findings suggest that backward GVC participation exacerbates the level of poverty in both developed and developing countries. These results are consistent with those of Lopez-Gonzalez et al. (2015) and Carpa and Martinez-Zarzoso (2022), who found that in both developed and developing countries, the significant effect of GVC participation on poverty and inequality is driven by backward participation. This can be attributed to the fact that backward participation can increase relative demand and wages for low-skilled labor, consequently leading to lower income inequality and poverty. Additionally, the interaction between backward participation and tertiary education has been found to be statistically significant and negatively impacts the poverty level for the OECD and ASEAN countries. The results suggest that increasing backward participation and tertiary education are associated with lower levels of poverty in OECD and ASEAN countries, provided individuals possess suitable learning skills that enable them to better utilize the benefits accrued from GVC participation. It is observed that GVC participation becomes favorable to the poor once human capital is strengthened (Tilak, 2007).
4. Conclusion
In recent years, a growing body of literature has explored the impact of GVC participation on various economic indicators, including wage dynamics and economic growth. However, the predominant focus has been on the influence of GVC participation on income inequality, with limited attention given to its effects on poverty. This study seeks to address this gap by examining the relationship between GVC participation and poverty levels, with a particular emphasis on the role of education in mediating this relationship. Specifically, it aims to identify how different levels of education may moderate the impact of GVC participation on poverty outcomes. To investigate these dynamics, a fixed effect model is employed to analyze data from the OECD and ASEAN countries spanning the period 1999–2018.
Drawing upon regression analysis and a comprehensive review of existing literature, the study yields the following key findings. Firstly, the direction and magnitude of the impact of GVC participation on poverty levels vary depending on the nature of participation (i.e. forward or backward), the country group under consideration and the educational attainment of the workforce. In the OECD countries, forward participation in GVCs is found to have a consistently negative effect on poverty levels, suggesting that such participation benefits both low-skilled and high-skilled workers, thereby contributing to poverty alleviation. Conversely, in the ASEAN countries, forward participation tends to exacerbate poverty, likely due to its effects on wage dynamics and labor market structure in these economies. On the other hand, backward participation is consistently associated with higher poverty levels across all country groups, indicating that importing intermediate goods may displace domestic workers and negatively impact employment in the short term. Moreover, the importation of intermediate goods for the production of high-skilled labor in offshoring countries may further exacerbate wage differentials between skilled and unskilled workers.
Secondly, the study examines the interaction between GVC participation and education levels and its impact on poverty reduction. The empirical results reveal that the interaction between GVC participation (both forward and backward) and education levels significantly influences poverty outcomes, with nuanced effects observed across different educational categories. Specifically, forward participation combined with higher literacy rates is found to be associated with poverty reduction in both OECD and ASEAN countries. However, the impact of tertiary education on poverty reduction varies between the two country groups, with positive effects observed in the OECD countries and adverse effects in the ASEAN countries, reflecting disparities in educational attainment between the regions.
Furthermore, the analysis confirms the importance of other control variables in shaping poverty outcomes. Higher GDP per capita is associated with reduced poverty rates, while inflation exerts adverse effects on poverty levels. Additionally, private credit is found to play a crucial role in fostering economic growth and poverty reduction, highlighting the importance of financial inclusion and access to credit markets. Finally, the study underscores the significance of governance indicators, such as control of corruption, in creating an enabling environment for GVC trade and promoting inclusive economic growth. In summary, the findings of this study contribute to a deeper understanding of the complex relationship between GVC participation, education and poverty, emphasizing the need for targeted policy interventions to harness the potential benefits of GVCs while mitigating their adverse socioeconomic impacts.
The findings demonstrate that engagement in GVCs presents a promising avenue for stimulating development, advancing income per capita growth and facilitating job creation. This assertion is substantiated by a convergence of theoretical constructs and empirical data. Nevertheless, the study underscores the pivotal role of education in materializing the poverty-reducing potential inherent in GVC involvement. Notably, the results illuminate that the poverty-alleviating impacts of GVC participation are most conspicuous in nations boasting elevated levels of educational attainment among their populace.
Determinant of poverty rate by type of forward participation and literacy
| Variable | OECD | ASEAN | ALL | |||
|---|---|---|---|---|---|---|
| FE | FE | FE | FE | FE | FE | |
| Fward | −1.333*** (0.217) | −1.152*** (0.225) | 6.635*** (1.884) | 11.807*** (2.130) | −3.788*** (0.554) | −3.811*** (0.551) |
| Lit | −0.341** (0.044) | −0.399*** (0.049) | −0.674* (0.444) | −2.133*** (0.540) | −1.493*** (0.136) | −1.502*** (0.135) |
| PGDP | −0.657*** (0.090) | −1.082*** (0.049) | −4.582*** (0.837) | −6.822** (2.164) | −3.558*** (0.311) | −5.869*** (0.537) |
| Inf | 0.011 (0.007) | 0.002 (0.007) | 0.087 (0.084) | 0.028 (0.063) | −0.105 (0.004) | 0.131*** (0.022) |
| PriCre | −0.002** (0.001) | −0.001 (0.001) | −0.179 (0.024) | −0.164*** (0.029) | −0.0067** (0.021) | −0.010*** (0.004) |
| Unem | 0.019* (0.010) | −0.005 (0.012) | 0.123** (0.300) | 0.375 (0.307) | −0.010 (0.042) | 0.078* (0.042) |
| Cor | −0.397** (0.137) | −0.431*** (0.167) | 5.991** (3.376) | 4.202* (2.456) | −2.249*** (0.612) | −1.584** (0.624) |
| Fward* Lit | −0.013*** (0.002) | −0.011*** (0.002) | −0.074*** (0.041) | −0.139*** (0.024) | −0.042*** (0.005) | −0.041*** (0.005) |
| Observations | 500 | 500 | 140 | 140 | 640 | 640 |
| Year dummy | No | Yes | No | Yes | No | Yes |
| R-squared within | 0.3075 | 0.3288 | 0.6918 | 0.7703 | 0.4676 | 0.5051 |
| Hausman test (p-value) | 0.0022 | 0.0004 | 0.000 | 0.0257 | 0.0009 | 0.0001 |
Note(s): *refers to significant factor to the regression results, ***p < 0.001, **p < 0.05, *p < 0
Source(s): Table by authors
Determinant of poverty rate by type of forward participation and tertiary
| Variable | OECD | ASEAN | ALL | |||
|---|---|---|---|---|---|---|
| FE | FE | FE | FE | FE | FE | |
| Fward | −0.069*** (0.015) | −0.136*** (0.021) | 1.308** (0.225) | 1.3402*** (0.234) | −0.251** (0.092) | −0.437*** (0.096) |
| Tert | −1.465** (0.265) | −1.645*** (0.005) | −1.860*** (0.231) | −1.883*** (0.239) | −0.135*** (0.029) | −0.135*** (0.029) |
| PGDP | −0.462*** (0.110) | −0.321 (0.205) | −0.121 (0.964) | −5.150** (1.871) | −5.149** (0.338) | −6.728*** (0.558) |
| Inf | 0.019** (0.006) | 0.021** (0.206) | 0.015 (0.047) | 0.108** (0.059) | 0.062** (0.022) | 0.087** (0.022) |
| PriCre | −0.003** (0.001) | −0.003*** (0.001) | −0.108*** (0.023) | −0.105*** (0.031) | −0.007 (0.004) | −0.011** (0.004) |
| Unem | 0.030** (0.010) | 0.038** (0.013) | 0.591** (0.232) | 0.978** (0.238) | −0.006 (0.045) | 0.054 (0.049) |
| Cor | −0.352** (0.144) | −0.437** (0.164) | 1.651 (2.844) | 1.151 (1.771) | −0.791 (0.639) | −0.488 (0.649) |
| Fward*Tert | −0.0004** (0.0001) | −0.0003* (0.0002) | 0.049*** (0.016) | 0.056*** (0.008) | −0.008*** (0.001) | −0.009*** (0.001) |
| Observations | 500 | 500 | 140 | 140 | 640 | 640 |
| Year dummy | No | Yes | No | Yes | No | Yes |
| R-squared within | 0.2944 | 0.3132 | 0.7627 | 0.8026 | 0.4058 | 0.4555 |
| Hausman test (p-value) | 0.0242 | 0.0136 | 0.000 | 0.000 | 0.0052 | 0.0138 |
Note(s): *refers to significant factor to the regression results, ***p < 0.001, **p < 0.05 and *p < 0.1
Source(s): Table by authors
Determinant of poverty rate by type of backward participation and literacy
| Variable | OECD | ASEAN | ALL | |||
|---|---|---|---|---|---|---|
| FE | FE | FE | FE | FE | FE | |
| Bward | 0.371** (0.015) | 0.385** (0.191) | 0.339* (0.148) | 0.247* (0.148) | 1.236** (0.462) | 1.739*** (0.464) |
| Lit | −0.124* (0.066) | −0.112* (0.067) | −0.584** (1.204) | −0.550** (1.204) | −0.517*** (0.149) | −0.430** (0.148) |
| PGDP | −0.767** (0.094) | −0.740*** (0.187) | −3.482*** (0.820) | 1.492 (2.404) | −3.174*** (0.321) | −6.038*** (0.549) |
| Inf | 0.008 (0.008) | 0.004 (0.007) | 0.072 (0.054) | 0.037 (0.072) | 0.049** (0.022) | 0.122** (0.022) |
| PriCre | −0.002 (0.001) | −0.002 (0.001) | −0.172*** (0.026) | −0.204*** (0.032) | −0.010*** (0.004) | −0.015*** (0.004) |
| Unem | 0.013** (0.010) | 0.012 (0.010) | 0.251 (0.301) | 0.621* (0.349) | −0.004 (0.043) | 0.092* (0.048) |
| Cor | −0.428** (0.160) | −0.471** (0.168) | 3.436** (3.336) | 2.208** (2.184) | −2.288*** (0.639) | −1.195* (0.626) |
| Bward*Lit | −0.003* (0.001) | −0.003* (0.001) | −0.042** (0.020) | −0.035*** (0.021) | −0.011*** (0.004) | −0.017*** (0.004) |
| Observations | 500 | 500 | 140 | 140 | 640 | 640 |
| Year dummy | No | Yes | No | Yes | No | Yes |
| R-squared within | 0.3192 | 0.3242 | 0.6716 | 0.7076 | 0.4342 | 0.4814 |
| Hausman test (p-value) | 0.0000 | 0.0000 | 0.000 | 0.0004 | 0.0000 | 0.0000 |
Note(s): *refers to significant factor to the regression results, ***p < 0.001, **p < 0.05, *p < 0.1
Source(s): Table by authors
Determinant of poverty rate by type of backward participation and tertiary
| Variable | OECD | ASEAN | ALL | |||
|---|---|---|---|---|---|---|
| FE | FE | FE | FE | FE | FE | |
| Bward | 0.022 (0.020) | 0.071** (0.015) | 0.809*** (0.207) | 0.987*** (0.242) | −0.095** (0.462) | −0.146** (0.063) |
| Tert | −2.718*** (0.365) | −2.718*** (0.370) | −0.105*** (1.204) | −0.202*** (1.101) | −0.011*** (0.027) | −0.040* (0.028) |
| PGDP | −0.443** (0.106) | −0.111 (0.190) | 0.034 (1.237) | 8.773*** (2.551) | −4.825*** (0.346) | −6.634*** (0.585) |
| Inf | 0.003 (0.007) | 0.007 (0.007) | 0.030 (0.071) | 0.124 (0.087) | 0.044* (0.022) | 0.065* (0.023) |
| PriCre | 0.001 (0.0009) | 0.001 (0.001) | −0.121*** (0.022) | −0.132*** (0.023) | −0.007 (0.004) | −0.011** (0.004) |
| Unem | 0.017** (0.010) | 0.030** (0.012) | 0.127 (0.212) | 0.297 (0.220) | 0.025 (0.043) | 0.086** (0.057) |
| Cor | −0.377** (0.150) | −0.526** (0.157) | 7.509** (2.518) | 6.368** (2.460) | −1.309** (0.639) | 0.653 (0.663) |
| Bward*Tert | −0.0006** (0.0002) | −0.0004** (0.0002) | −0.016*** (0.005) | −0.018*** (0.005) | 0.0002 (0.0009) | 0.0006 (0.0009) |
| Observations | 500 | 500 | 140 | 140 | 640 | 640 |
| Year dummy | No | Yes | No | Yes | No | Yes |
| R-squared within | 0.3724 | 0.4082 | 0.6927 | 0.7361 | 0.3688 | 0.4121 |
| Hausman test (p-value) | 0.0097 | 0.0002 | 0.000 | 0.000 | 0.0000 | 0.0000 |
Note(s): * refers to significant factor to the regression results, ***p < 0.001, **p < 0.05 and *p < 0.1
Source(s): Table by authors
References
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Further reading
Awan, M.S., Malik, N., Sarwar, H. and Waqas, M. (2011), “Impact of education on poverty reduction”, available at: https://ideas.repec.org/p/pra/mprapa/31826.html
Bakare, A.S. (2011), “A critical appraisal of the linkage between literacy rate and the incidence of poverty in Nigeria”, Journal of Emerging Trends in Educational Research and Policy Studies, Vol. 2, pp. 450-456, available at: https://www.semanticscholar.org/paper/A-critical-appraisal-of-the-linkage-between-rate-of-Bakare/179aedb7cf845d770692b36a8be4ac52673b2980?utm_source=direct_link
Batul, E., Haseeb, M.A. and Satter, S.A. (2019), “Examining the relationship between literacy rate and poverty in Pakistan”, International Journal of Education Humanities and Social Science, Vol. 2 No. 5, available at: http://ijehss.com/uploads2019/EHS_2_55.pdf
Chetwynd, E., Chetwynd, F. and Spector, B.I. (2004), “Corruption and poverty : a review of recent literature”, available at: https://www.eldis.org/document/A14857
Dollar, D. and Kraay, A. (2002), “Growth is good for the poor”, Journal of Economic Growth, Vol. 7 No. 3, pp. 195-225, doi: 10.1023/a:1020139631000, available at: https://www.jstor.org/stable/40216063
Fernandes, A.M., Kee, H.L. and Winkler, D. (2022), “Determinants of global value chain participation: cross-country evidence”, The World Bank Economic Review, Vol. 36 No. 2, pp. 329-360, doi: 10.1093/wber/lhab017.
Fryer, D. and Fagan, R. (2003), “Poverty and unemployment”, Poverty and Psychology, pp. 87-101, doi: 10.1007/978-1-4615-0029-2_5.
Grossman, G.M. and Rossi-Hansberg, E. (2006), “Trading tasks: a simple theory of offshoring”, (Working Paper No. 12721). National Bureau of Economic Research. doi:10.3386/w12721.
Jouanjean, M.-A., Gourdon, J. and Korinek, J. (2017), “GVC participation and economic transformation: lessons from three sectors”, OECD Trade Policy Papers, Vol. 207, doi: 10.1787/617d7a19-en.
Koopman, R., Wang, Z. and Wei, S.-J. (2014), “Tracing value-added and double counting in gross exports”, American Economic Review, Vol. 104 No. 2, pp. 459-494, doi: 10.1257/aer.104.2.459.
Kunroo, M.H. and Ahmad, I. (2023), “Heckscher-ohlin theory or the modern trade theory: how the overall trade characterizes at the global level?”, Journal of Quantitative Economics, Vol. 21 No. 1, pp. 151-174, doi: 10.1007/s40953-022-00330-x.
Lee, K. (2014), “Globalization, income inequality and poverty: theory and empirics”, available at: https://www.semanticscholar.org/paper/Globalization%2C-Income-Inequality-and-Poverty%3A-and-Lee/d6a5e8a2784ed2f1d348140da0c05c0c435af01d?utm_source=direct_link
Tilak, J.B. (2002), “Education and poverty”, Journal of Human Development, Vol. 3 No. 2, pp. 191-207, doi: 10.1080/14649880220147301.
Yanikkaya, H. and Altun, A. (2020), “The impact of global value chain participation on sectoral growth and productivity”, Sustainability, Vol. 12 No. 12, 4848, doi: 10.3390/su12124848.