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Review article, poverty reduction of sustainable development goals in the 21st century: a bibliometric analysis.

• Institute of Blue and Green Development, Shandong University, Weihai, China

No Poverty is the top priority among 17 Sustainable Development Goals (SDGs). The research perspectives, methods, and subject integration of studies on poverty reduction have been greatly developed with the advance of practice in the 21st century. This paper analyses 2,459 papers on poverty reduction since 2000 using VOSviewer software and R language. Our conclusions show that (1) the 21st century has seen a sharp increase in publications of poverty reduction, especially the period from 2015 to date. (2) The divergence in research quantity and quality between China and Kenya is great. (3) Economic studies focus on inequality and growth, while environmental studies focus on protection and management mechanisms. (4) International cooperation is usually related to geographical location and conducted by developed countries with developing countries together. (5) Research on poverty reduction in different regions has specific sub-themes. Our findings provide an overview of the state of the research and suggest that there is a need to strengthen the integration of disciplines and pay attention to the contribution of marginal disciplines to poverty reduction research in the future.

Introduction

Global sustainable development is the common target of human society. “No Poverty” and “Zero Hunger” are two primary goals of the 2030 Agenda for Sustainable Development (SDGs) , along with important premises in the completion of the goals of “Decent Work and Economic Growth Industry” and “Innovation and Infrastructure.” China has made great efforts in meeting its No Poverty targets. To achieve the goal of eliminating extreme poverty in the rural areas by the end of2020 1 , China has been carrying out a basic strategy of targeted approach named Jingzhunfupin 2 , which refers to implementing accurate poverty identification, accurate support, accurate management and tracking. By 2021, China accomplished its poverty alleviation target for the new era on schedule and achieved a significant victory 3 .

However, the worldwide challenges are still arduous. On the one hand, the recent global poverty eradication process has been further hindered by the COVID-19 pandemic. The World Bank shows that global extreme poverty rose in 2020 for the first time in over 20 years, with the total expected to rise to about 150 million by the end of 2021 4 . People “return to poverty” are emerging around the world. On the other hand, people who got out of income poverty may still be trapped in deprivations in health or education. About 1.3 billion people (22%) still live in multidimensional poverty among 107 developing countries, according to the Global Multidimensional Poverty Index report released by the United Nations 5 . Meanwhile, the issue of inequality became more prominent, reflected by the number of people who are in relative poverty 6 .

In line with the dynamic poverty realities, the focusing of poverty research moved forward as well. Research frameworks have evolved from single dimension poverty to multidimensional poverty ( Bourguignon et al., 2019 ) and from income poverty to capacity poverty ( Zhou et al., 2021 ). Research perspectives concentrate on the macroscopic view, but have now turned to microscopic individual behavior analysis. Cross-integration of sociology, psychology, public management, and other disciplines also helps to expand and deepen the research ( Addison et al., 2008 ). Some cutting-edge researchers are making effort to shed light on the relationships between “No Poverty” and other SDGs. For example, Hubacek et al. (2017) verified the coherence of climate targets and achieving poverty eradication from a global perspective 7 . Li et al. (2021) discussed the impacts and synergies of achieving different poverty eradication goals on air pollutants in China. These novel papers give us insightful inspiration on combining poverty reduction with the resource or environmental problem including aspects like energy inequity, carbon emission. Hence, summarizing the research on different poverty realities and academic backgrounds should provide theoretical and empirical guidance for speeding up the elimination of poverty in the world ( Chen and Ravallion, 2013 ).

Previous review literature on poverty reduction all directed certain sub-themes. For example, Chamhuri et al. (2012) , Kwan et al. (2018) , Mahembe et al. (2019a) reviewed urban poverty, foreign aid, microfinance, and other topics, identifying the objects, causes, policies, and mechanisms of poverty and poverty reduction. Another feature of the review literature is that scholars often synthesize the articles and map the knowledge network manually, which constrains the amount of literature to be analyzed, leading to an inadequate understanding of poverty research. Manually literature review on specific fields of poverty reduction results in a research gap. Analysis delineating the general academic knowledge of poverty reduction is somewhat limited despite the abundance of research. Yet, following the trend toward scientific specialization and interdisciplinary viewpoints, the core and the periphery research fields and their connections have not been clearly described. Different studies are in a certain degree of segmentation because scholars have separately conducted studies based on their countries’ unique poverty background or their subdivision direction. Possibly, lacking communication and interaction will affect the overall development of poverty reduction research especially in the context of globalization. Less than 10 years are left to accomplish the UN sustainable development goals by 2030. It is urgent to view the previous literature from a united perspective in this turbulent and uncertain age.

Encouragingly, with advances in analytical technology, bibliometrics has become increasingly popular for developing representative summaries of the leading results ( Merediz-Solà and Bariviera, 2019 ). It has been widely applied in a variety of fields. In the domain of poverty study, Amarante et al. (2019) adopted the bibliometric method and reviewed thousands of papers on poverty and inequality in Latin America. Given above issues, we expand the scope of the literature and conduct a systematic bibliometric analysis to make a preliminary description of the research agenda on poverty reduction.

This paper presents an analysis of publications, keywords, citations, and the networks of co-authors, co-words, and co-citations, displaying the research status of the field, the hot spots, and evolution through time. We use R language and VOSviewer software to process and visualize data. Our contributions may be as follows. Firstly, we used the bibliometric method and reviewed thousands of papers together, helping keep pace with research advances in poverty alleviation with the rapid growth in the literature. Secondly, we clarified the core and periphery research areas, and their connections. These may be beneficial to handle the trend toward scientific specialization, as well as fostering communication and cooperation between disciplines, mitigating segmentation between the individual studies. Thirdly, we also provided insightful implications for future research directions. Discipline integration, intergenerational poverty, heterogeneous research are the directions that should be paid attention to.

The structure of this article is as follows. Methodology and Initial Statistics provides the methodology and initial statistics. Bibliometric Analysis and Network Analysis offer the bibliometric analysis and network visualization. The remaining sections offer discussions and conclusions.

Methodology and Initial Statistics

Bibliometrics, a library and information science, was first proposed by intelligence scientist Pritchard in 1969 ( Pritchard, 1969 ). It exploits information about the literature such as authors, keywords, citations, and institutions in the publication database. Bibliometric analyses can systematically and quantitatively analyze a large number of documents simultaneously. They can highlight research hotspots and detects research trends by exploring the time, source, and regional distribution of literature. Thus, bibliometric analyses have been widely used to help new researchers in a discipline quickly understand the extent of a topic ( Merediz-Solà and Bariviera, 2019 ).

Research tools such as Bibexcel, Histcite, Citespace, and Gephi have been created for bibliometric analysis. In this paper, R language and VOSviewer software are adopted. R language provides a convenient bibliometric analysis package for Web of Science, Scopus, and PubMed databases, by which mathematical statistics were performed on authors, journals, countries, and keywords. VOSviewer software provides a convenient tool for co-occurrence network visualization, helping map the knowledge structure of a scientific field ( Van Eck and Waltman, 2010 ).

Data Collection

The bibliometric data was selected and downloaded from the Web of Science database ( www.webofknowledge.com ). We choose the WoS Core Collection, which contained SCI-EXPANDED, SSCI, and A&HCI papers to focus on high-quality papers. The data was collected on March 19, 2021.

To identify the documents, we used verb phrases and noun phrases with the meaning of poverty reduction, such as “reduce poverty” and “poverty reduction,” as search terms, because there are several different expressions of “poverty reduction.” We also considered the combinations of “no poverty” and SDGs, “zero hungry” and “SDGs.” Because the search engine will pick up articles that have nothing to do with “poverty alleviation” depending on what words are used in the abstract, we employed keyword matching. Meanwhile, to prevent missing essential work that does not require author keywords, we also searched the title. Specifically, a retrieval formula can be written as [AK = (“search term”) OR TI = (“search term”)] in the advanced search box, where AK means author keywords and TI means title. Finally, we restricted the document types to “article” to obtain clear data. Thus, papers containing search phrases in headings or author keywords were marked and were guaranteed to be close to the desired topic.

A total of 2,551 studies were obtained, with 2,464 articles retained after removing duplicates. Table 1 presents the results for each search term. The phrasing of “poverty alleviation” and “poverty reduction” are written preferences.

TABLE 1 . Information of data collection.

Descriptive Analysis

Figure 1 gives details of each year’s publications during the period 2000–2021. The cut-off points of 2006 and 2015 divide the publication trends into three stages. The first period is 2000–2006, with approximately 40 publications per year. The second period is 2007–2014, in which production is between 80 and 130 papers annually. The third period is 2015–2021, with an 18.31% annual growth rate, indicating a growing interest in this field among scholars. Perhaps this is because 2006 was the last year of the first decade for the International Eradication of Poverty, and 2015 is the year that eliminating all forms of poverty worldwide was formally adopted as the first goal in the United Nations Summit on Sustainable Development. Greater access to poverty reduction plan materials and data is a vital reason for the growth in papers as well.

FIGURE 1 . Annual scientific production.

We can notice that the milestone year is 1995 when we examine the time trend with broader horizons ( Figure 2 ). Before 1995, scant literature touches upon the topic of “poverty alleviation.” This confirms that in the time range we check the majority of the development of academic interest in this issue takes place. Thus, the 21st century has become a period of booming research on poverty reduction.

FIGURE 2 . Annual scientific production in a longer period.

Bibliometric Analysis

In this section, we offer the bibliometric analysis including the affiliation statistics, citation analysis and keywords analysis. Author analysis is not included because some authors’ abbreviations have led to statistical errors.

Affiliation Statistics

From 2000 to 2021, a total of 2,459 articles were published in 979 journals, a wide range. Table 2 lists the top ten journals, which together account for 439 (17.86%) of the articles in our data set. Development in Practice and World Development have the most publications, respectively 121 (4.92%) and 107 (4.35%), followed by Sustainability at 44 (1.8%). The top 10 journals mostly involve development or social issues, with some having high impact factors, including Food Policy (4.189) and Journal of Business Ethics (4.141).

TABLE 2 . Top 10 sources of publications.

Figure 3 presents the geographic distribution of the published articles on poverty reduction. As indicated in the legend, the white part on the map shows regions with zero published articles recorded in WoS. Darker shades indicate a greater number of articles published in the country or region. The US region is darkest on the map, with 593 articles published, followed by England, with 412 papers, and China, with 348 articles. Ranking fourth is South Africa, perhaps because South Africa is a pilot site for many poverty reduction projects. India, for the same reason, is similarly shaded.

FIGURE 3 . Spatial distribution of publication in all countries. Note: the data of all countries is from Web of Science.

Citation Analysis

The number of citations evaluates the influence and contribution of individual papers, authors, and nations. The top 10 countries in total citations are displayed in Table 3 . Consistent with the publication distribution, the leader is the United States (11,861), with the United Kingdom (8,735) and China (1,666) following. However, there is a broad gap between China and England in total citations. The average article citation ranks are quite different from the total citation list. Notably, Kenya takes first place based on its average citations per paper, though its total citations rank seventh, showing that Kenya’s poverty reduction practices and research are of great interest to a large number of scholars. By contrast, China’s average article citation is just roughly one-sixth of Kenya’s. The different pattern of the number of Chinese publications and citations shows that the quality of Chinese research must be improved even as it raises its publication quantity.

TABLE 3 . Top ten countries by total citations.

Table 4 lists the top 10 most cited articles with their first author, year, source, total citations, and total citations per year. Highly cited articles can be used as a benchmark for future research, and in some way signal the scientific excellence of each sub-field. For example, Wilson et al. (2006) reminded the importance of informal sector recycling to poverty alleviation. Daw et al. (2011) discussed the poverty alleviation benefits from ecosystem services (ES) with examples in developing countries. Pagiola et al. (2005) found that Payments for Environmental Services (PES) can alleviate poverty, and explored the key factors of this poverty mitigation effect using evidence from Latin America 8 . These three papers combined the environmental ecosystem with poverty alleviation. Beck et al. (2007) , Karnani (2007) explored the relationships between the SME sector and poverty alleviation and the private sector and poverty alleviation, respectively. Grindle (2004) discussed the necessary what, when, and how for good governance of poverty reduction. Cornwall and Brock (2005) took a critical look at how the three terms of “participation,” “empowerment” and “poverty reduction” have come to be used in international development policy. Adams and Page (2005) examined the impact of international migration and remittances on poverty. In the theory domain, Collier and Dollar (2002) derived a poverty-efficient allocation of aid. Hulme and Shepherd (2003) provided meaning for the term chronic poverty. Even from the present point of view, these scholars’ studies remain innovative and significant.

TABLE 4 . Top 10 papers with the highest total citations.

Keywords Analysis

The keywords clarify the main direction of the research and are regarded as a fine indicator for revealing the literature’s content ( Su et al., 2020 ). Two different types of keywords are provided by Web of Science. One is the author keywords, offered by the original authors, and another is the keywords plus, contrived by extracting from the cited reference. The frequency of both types of keywords in 2,459 papers is examined respectively in the whole sample and the sub-sample hereinafter for concentration and coverage.

Whole Sample

Table 5 lists the Top 10 most frequently used keywords and keyword-plus of total papers. Clearly author keywords are often repetitive, with “poverty,” “poverty reduction,” and “reduction” chosen as keywords for the same paper, but these do not dominate the keywords-plus. Hence, the keywords-plus may be more precise at identifying relevant content. However, we used author keywords for the literature screening.

TABLE 5 . Top 10 author keywords and keywords-plus with the highest frequency.

In addition to the terms “poverty” or “poverty reduction or alleviation,” we note that “China” and “Africa” occur frequently, with “India” and “Bangladesh” following when we expand the list from the Top 10 to Top 20 ( Supplementary Figure S1 ). The appearance of these places coincides with our speculation that the research was often conducted in Africa, East Asia, or South Asia once again, whereas the larger compositions are from developing countries or less developed countries.

The cumulative trend of TOP20 author keywords and keywords-plus is shown in Supplementary Figure S1 . The diagram also gives some information about other concerns bound up with anti-poverty programs, including “microfinance,” “food security,” “livelihoods,” “health,” “Economic-growth” and “income,” as numerous papers are focused on these aspects of poverty reduction.

Further, policy study and impact evaluation may be the core objectives of these papers. Vital evidence can be found in countless documents. Researchers measured the effect of policies or programs from various perspectives. In the study of Jalana and Ravallion (2003) , they indicated that ignoring foregone incomes overstated the benefits of the project when they estimated net gain from the Argentine workfare scheme. Meng (2013) found that the 8–7 plan increased rural income in China’s target counties by about 38% in 1994–2000, but had only a short-term impact 9 . Galiani and McEwan (2013) studied the heterogeneous influences of the Programa de Asignación Familiar (PRAF) program, in which implemented education cash transfer and health cash transfer to people of varying degrees of poverty in Honduran. Maulu et al. (2021) concluded that rural extension programs can provide a sustainable solution to poverty. Some studies also have drawn relatively fresh conclusions or advice on poverty reduction projects. Mahembe et al. (2019a) found that aid disbursed in production sectors, infrastructure and economic development was more effective in reducing poverty through retrospecting empirical studies of official development assistance (ODA) or foreign aid on poverty reduction. Meinzen-Dick et al. (2019) reviewed the literature on women’s land rights (WLR) and poverty reduction, but found no papers that directly investigate the link between WLR and poverty. Huang and Ying (2018) constructed a literature review that included the necessity and the ways of introducing a market mechanism to government poverty alleviation. Mbuyisa and Leonard (2017) demonstrated that information and communication technology (ICT) can be used as a tool for poverty reduction by Small and Medium Enterprises.

Web of Science provides the publications of each journal category ( Figure 4 ). Economics is the largest type of journal, followed by development studies and environmental studies. Education should be regarded as an important way to address the intergenerational poverty trap. However, we note that journals in education are only a fraction of the total number of journals. Psychology journals are in a similar position, though endogenous drivers of poverty reduction have been increasingly emphasized in recent research. The detailed data can be found in the supplementary documents. To investigate the differences between the subdivisions of the research, we chose economic and environmental journals as sub-samples for further analysis.

FIGURE 4 . Visualization of journal category from the web of science.

As Supplementary Figure S2 shows, the TOP10 author keywords in economic sample are similar to the whole sample. We note that microfinance is a real heated research domain both in economic and whole sample. The poor usually have multiple occupations or self-employment in very small businesses ( Banerjee and Duflo, 2007 ). The poor often have less access to formal credit. Karlan and Zinman (2011) examined a microcredit program in the Philippines and found that microcredit does expand access to informal credit and increase the ability against risk. Banerjee et al. (2015a) reported the results of an assessment of a random microcredit scheme in India, which increased the investment and profits of small-scale enterprises managed by the poor.

Several new keywords enter the TOP20 list in the economic field, including “targeting,” “income distribution,” “productivity,” “employment,” “rural poverty,” “access,” and “program.” “Targeting” is an essential topic in the economic field. It concerns the effectiveness of poverty reduction program and social fairness. Hence, an abundance of literature reviews the definitions of poverty that allow individuals to apply for poverty alleviation programs. Park et al. (2002) , Bibi and Duclos (2007) , Kleven and Kopczuk (2011) , discussed the inclusion error and exclusion error in programs’ targeting and identification under the criterion of poverty lines or specific tangible asset poverty agency indicators (e.g., whether households have color televisions, pumps or flooring, and so forth). In practice, Niehaus et al. (2013) tested the accuracy of different agency indicators to allocate Below Poverty Line (BPL) cards in India and found that using a greater number of poverty indicators led to a deterioration in targeting effectiveness while creating widespread violations in the implementation because less qualified families are more likely to pay bribes to investigators. Bardhan and Mookherjee (2005) explored the targeting effectiveness of decentralization in the implementation of anti-poverty projects. He and Wang (2017) assessed the targeting accuracy of the College Graduate Village Officials (CGVOs) project, a unique human capital redistribution policy in China, on poverty alleviation 10 .

The terms “inequality” and “growth” are first and second in the keywords-plus. This may be because inequality and growth are two of the major components in poverty changes in the economic field, which are stressed in the studies of Datt and Ravallion (1992) , Beck et al. (2007) . The ranking may also imply that the economics of the 21st century is more concerned with human welfare than the pursuit of rapid economic growth. Since a growing number of organizations are trying to build human capital to improve the livelihoods of their clients and further their mission of lifting themselves out of poverty. McKernan (2002) showed that social development programs are important components of microfinance program success. Similarly, Karlan and Valdivia (2006) argued that increasing business training can factually improve business knowledge, practice effectiveness, and revenue. Besides, cash transfers are widely adopted to reduce income inequality and improve education and the health status of poor groups ( Banerjee et al., 2015b ; Sedlmayr et al., 2020 ). Benhassine et al. (2015) noted that the Tayssir Project in Morocco, a cash transfer project, achieved an increasing improvement of school enrolment rate in the treatment group, especially for girls 11 .

We combine the journal types of “Environmental Studies” and “Environmental Sciences” into one unit for analysis ( Supplementary Figure S3 ). In the environmental field, the terms “conservation” and “management” are ranked first and second. This field also involves “ecosystem services,” “climate change,” “biodiversity conservation,” and “deforestation,” with rapid growth in recent years. These themes were discussed by Alix-Garcia et al. (2013) , Alix-Garcia et al. (2015) , Sims and Alix-Garcia (2017) in their investigations of the effect of conditional cash transfers on environmental degradation, the poverty alleviation benefits of the ecosystem service payment project, and comparison of the effects in protected areas and of ecosystem service payment on poverty reduction in Mexico. The differences in economic research in poverty reduction and environmental field show the necessity of strengthening cooperation between disciplines.

Network Analysis

Network relationship is established by the co-occurrence of two types of information. It enables mapping of the knowledge nodes with a joint perspective, instead of viewing scientific ideas in isolation. The data is imported into VOSviewer software after removing duplicates by R package. We then provide the co-authorship analysis, co-citation analysis, and co-keywords analysis.

Co-Authorship Analysis

Co-authorship may reflect international cooperation as shown by the country distribution ( Figure 5 ). When the authors of two countries have a cooperative relationship, a line is generated to connect the corresponding countries. The size of nodes reflects the number of countries of origin of the authors. The width of the line represents the cooperative frequency between them, and the different colors mark the partition of the countries.

FIGURE 5 . International networks of co-authorship.

The network includes a total of 1,449 countries, of which 92 meet the threshold of at least five instances of cooperation. The United States, United Kingdom, China, and South Africa have the strongest interlinkage with other countries or regions. Whether countries in each cluster demonstrate international academic cooperation on poverty reduction is sometimes based on geographic location. For example, the red cluster includes the United States, Mexico, Brazil, Chile, and Ecuador. These countries mainly lie in the Americas. The United Kingdom, Kenya, Uganda, and South Africa are in the yellow group, located in Europe and Africa. The green cluster includes China, Malaysia, and Bangladesh, all Asian countries. The distribution of countries on each cluster and the map as a whole show that research on poverty alleviation is usually conducted by developed and developing nations together. This may be due to anti-poverty programs in developed countries usually being subsidized by international non-governmental organizations, as shown by the branch literature devoted to foreign aid and poverty reduction ( Mahembe et al., 2019b ).

Co-Citation Analysis

Co-citation analysis can locate the core classical literature efficiently ( Zhang et al., 2020 ). Pioneering studies of co-citation analysis were performed by Small (1973) . When an article cited two other articles, a relationship of co-citation will be established between these two “cited” articles ( González-Alcaide et al., 2016 ). Since co-citation aims at reference, it targets the knowledge base for the past.

Figure 6 displays the co-citation network of the cited references. The functions of the sizes and colors are the same as in Figure 5 . The most cited papers in the co-citation relationship are the studies of Foster et al. (1984) , Sen et al. (1999) , Dollar and Kraay (2002) , which respectively explore poverty measures, globalization and development, and the growth impact for the poor.

FIGURE 6 . Cited reference network of co-citation.

Figure 7 gives the co-citation heat map of sources, based on their density. We set the threshold at 20, and 78 cited sources remained on the map. Different colors signify different clusters of co-citation. The lighter the color, the more frequently the journals are cited. There are four major categories. World development and the Journal of Development Economics have the largest influence on the red cluster, which mainly contains development and economic studies. The second cluster is green and includes the fields of energy, environment, and ecology, with Ecology Economics as its brightest star. The Journal of Business Ethics and Annals of Tourism Research are the most-cited journals in the third and fourth cluster, which represents the fields of business and tourism. Some psychology studies exist in transitional spaces between business studies and economic studies, suggesting a trend of interdisciplinary work. In the past 10 years, we checked manually that psychology and other interdisciplinary research performed well. Many papers were published in Science or Nature. In the research of Mani et al. (2013) , there was a causal relationship between poverty and psychological function. Poverty reduced the cognitive performance of the poor, because poverty consumes spiritual resources, leaving fewer cognitive resources to guide choices and actions. Another psychology-based experiment in Togo showed that personal proactive training increased the profits of poor businesses by 30%, while traditional training influence was not significant ( Campos et al., 2017 ). In the study of Ludwig et al. (2012) , they revealed that the shift from high-poverty to low-poverty communities resulted in significant long-term improvements in physical and mental health and subjective well-being and had a continuing impact on collective efficacy and neighborhood security.

FIGURE 7 . Cited source density network of co-citation.

Co-Words Analysis

The analysis of co-words was performed after the co-citation analysis. Since it is hard to explain the changes in cluster from year to another in a co-citation map, Callon et al. (1983) proposed co-word analysis to identify and visualize scientific networks and their evolution. Based on our keyword analysis and following the arguments of Zhang et al. (2016) , the knowledge structures of author keywords and keywords plus are similar, but keywords plus can mirror a large proportion of the author keywords when the threshold of the number of instances of a word exceeds 10. The merger of two types of keywords will inflate the total number of words, leaving unique words representing the latest hot spot with little chance to be selected. Therefore, we conduct the co-word analysis using keywords plus to map the structure.

We set the minimum number of occurrences to 15, and 100 words with the greatest link strength are selected from the total of 2,774. As shown in Figure 8 , keywords plus generates 4 clusters. To our delight, each cluster does reflect the research priorities of each region.

FIGURE 8 . Keywords-plus co-occurrence cluster map.

The first cluster (red) reveals studies concerning livelihood, conservation, management, climate change and agriculture. These topics have strong interlinkage to Africa, suggesting that poverty reduction in Africa is often related to basic livelihood and ecology. The poor in Africa rely on the ecological conditions heavily as they are facing a more disadvantaged climate and resources. Therefore, their poverty reduction process is sometimes highly unstable and subject to considerable internal and external constraints. Stevenson and Irz (2009) concluded that the numerous studies presented almost no evidence of aquaculture reducing poverty directly.

The second cluster (green) represents studies focused on economic growth and income inequality, common in China and India. This pattern may imply that papers of this cluster focus on the economic conditions of the poor. Other studies in this cluster are related to migration, health, and welfare. The third cluster (blue) is the poverty reduction strategies on microfinance and empowerment, which are associated with Bangladesh where the Grameen Bank, one of the most notable and intensely researched microcredit programs, was founded ( McKernan, 2002 ). This cluster’s studies are interested in approaches such as business, markets, and education, to help the poor rise from poverty. The fourth cluster (yellow) contains studies of poverty reduction programs on environmental services in Latin America, where the environmental problem is intertwined with poverty traps.

Figure 9 shows the time trend of keywords-plus co-occurrence. Because the keywords plus are extracted from the cited references, they can reflect the changes in hotspots from relatively early to the most recent years. As can be seen, education, technology, and environmental services are the latest keywords in research on poverty reduction.

FIGURE 9 . Keywords-plus co-occurrence time trend map.

There are several limitations to our bibliometric analysis, though we undertake an extensive review of the literature. First, we inevitably lose a fraction of the literature. keywords and title are chosen as the criteria for helping precisely concentrate the search results on our subject. However, the Web of Science core collection on which our study relied is weak in the coverage of literature to some degree. Hence, there is a trade-off between the quantity and the quality of literature. We choose the latter, leading to an unclear restriction of the comprehensiveness of research. Second, we can identify recent research status but are not able to locate the Frontier accurately. Network mapping requires selecting a minimum occurrence threshold for including corresponding authors, keywords, and citations into the network. Because a certain number of citations or new hotspots take several years to be widely used and studied, this threshold may neglect these important data ( Linnenluecke et al., 2020 ). One possible solution is to manually examine the latest published papers in high-quality journals. Third, the mining of subfields is not deep enough. In other words, bibliometrics cannot sort out the main conclusions of literature on poverty reduction. For instance, we do not know whether the conclusion of different studies are consistent for the same poverty alleviation project. Neither do we know the exact mechanism of the anti-poverty program through bibliometric analysis, which limits the possibility of finding research points from controversial conclusions or mechanisms.

However, several points are worth taking into consideration for the future. To start with, poverty reduction is a natural interdisciplinary social science problem. Interdisciplinary has become a major research trend. Except applying cash transfer to ecological programs, associations are raised. We may discuss whether the combination of finance and ecology will bring positive benefits to financial stability, ecological protection, and poverty reduction by the means of capitalization of ecological resources or establishing the ecological bank. Our analysis suggests that some unheeded branch disciplines like human ethology are contributing to poverty reduction research as well. Thus, we need to investigate the interdisciplinary integration and the contribution of marginal disciplines on poverty reduction.

Then, more attention should be paid the intergenerational poverty. It requires researchers to extend the time span of observation and questionnaire investigation. Some work has been done. One example is the research of Hussain and Hanjra (2004) . They reviewed literature and clarified that advances in irrigation technologies, such as micro-irrigation systems, have strong anti-poverty potential, alleviating both temporary and chronic poverty. Another example is the research of Jones (2016) , which indicated that conditional cash transfers (CCTs) could indeed interrupt the intergenerational cycle of poverty through human capital investments. However, there remains a lot of work to be done for preventing the next generation from returning to poverty in this turbulent period. In a related matter, the role of education in isolating intergenerational poverty or returning to the poverty trap should be highlighted. What kind of education would more effectively help families out of poverty, quality education or vocational skill education? How to allocate educational resources effectively? For poor students, what kind of psychological intervention in education is needed to mitigate the impact of native families and help them grow up confidently? Lots of questions waiting for empirical answering, yet we note that the educational journal only took a little fraction of the total journals in Section 4.3.2.

Next, poverty does exist in prosperous conurbations though the focal point obtained from keywords analysis is “rural area”. Nevertheless, both the slums in the center of big cities and circulative flowing refugees are experiencing more relative deprivation, representing a state of instability. Chamhuri et al. (2012) reviewed the objects, causes, and policies of urban poverty. Exploring how to lift a particular small economic low-lying area out of poverty is also of great significance. Follow-up researches should keep up.

Moreover, poverty alleviation needs to be based on individual or group-specific characteristics to some degree. It is not feasible to implement a unified poverty alleviation policy on a large scale. Exquisitely designed randomized controlled trials are used to reveal the heterogeneous influence of poverty alleviation programs. Haushofer and Shapiro (2016) compared the difference between monthly transfers and one-time lump-sum transfers. The subdivision research on the effect of poverty reduction programs should be strengthened. We imagine that a model may be formed to predict the total poverty reduction effects of different policies in the various region to obtain an optimized strategy of “No Poverty” in the future.

Lastly, exploring whether poverty reduction will be contradictory or coordinate with other SDGs might be a popular direction. About the literature review, two aspects can be improved. The first is merging with other databases to compare the loss of the trade-off between quality and quantity. Next, subsequent literature reviews need to explore how to better combine manual literature collation and bibliometrics, especially when the subject is a large topic.

Poverty reduction is one of the objectives of welfare economics and development economics. It is a classic and lasting topic and has recently come into the limelight. Poverty reduction studies in the 21st century are usually based on specific poverty alleviation projects or policies in developing countries. Researchers examine numerous topics, including whether the target audience has been precisely identified and covered in the design and implementation process, whether poverty reduction projects have been proved effective, what mechanisms have contributed to the success of poverty reduction projects, and what caused their failure. The aim of this paper is to summarize the amount, growth trajectory, citation, and geographic distribution of the poverty reduction literature, map the intellectual structure, and highlight emerging key areas in the research domain using the bibliometric method. We use the VOSviewer software and the R language as tools to analyze 2,459 articles published since 2000.

We have several conclusions. First, the 21st century is a period of booming research on poverty reduction, and the number of publications has increased sharply since 2015. Second, in affiliation analysis, Development in Practice and World Development are the top publications. The most frequently cited source of co-citations are World Development , Ecology Economics, Journal of Business Ethics, and Annals of Tourism Research , respectively the centers of the fields of economics, energy, the environment, and ecology, business, and tourism. Third, there are differences in the national and regional distribution of literature, based on the number of publications and citations. The United States led both the publication list and the total citation list, followed by the United Kingdom, China, and South Africa. Yet, there is a huge variation in the number of citations, with the United States and the United Kingdom having almost 5 to 6 times more citations than China and South Africa. In terms of average citations, Kenya is the best performer. The average citation amount in China is low, implying that Chinese scholars need to improve the quality of their literature. Fourth, in the keyword analysis, policy discussion and impact estimation are the two major themes. The keywords related to poverty reduction are different among different disciplines. Economics pays more attention to inequality and growth, while environmental disciplines pay more attention to protection and management. This may suggest that strengthening the cooperation between disciplines will lead to more diversified research perspectives. Fifth, in the co-author analysis, international cooperation is usually related to geographical location. For example, there is a large amount of cooperation between Europe and Africa, within Asia, and between North and South America. At the same time, poverty reduction research often shows the cooperative patterns of developed and developing countries. Last, in the co-keyword analysis, four clusters reflect the research priorities of each region. Poverty reduction in Africa is often related to basic livelihood and ecology. The economic conditions of the poor are the concerns of research in China and India. The South Asia region is also the location of microcredit program experiments. Poverty traps are intertwined with environmental problems in Latin America’s literature.

Our findings also offer inspiration for the future. There may be a need to investigate the interdisciplinary integration. Intergenerational and urban poverty deserve attention. The heterogeneous design of poverty alleviation strategies needs to be further deepened. It might be a popular direction to figure out whether poverty reduction will be contradictory with other SDGs and conduct scenario simulation. We identify shortcomings as well. Finally, precisely identifying research frontiers requires further exploration.

Author Contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

This work is supported by National Natural Science Foundation of China (NSFC) (grant number 72022009).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcomm.2021.754181/full#supplementary-material

1 The extreme poverty criterion set by World Bank is 1.9$ a day in purchasing power parties (PPP), https://www.worldbank.org/en/research/brief/policy-research-note-03-ending-extreme-poverty-and-sharing-prosperity-progress-and-policies . The China poverty alleviation target in 2020 is to eliminate absolute poverty, which is defined as living less than 2,300 yuan per person per day at 2010 constant prices. In addition to living above the absolute poverty line, people who must reached other five qualitative criteria can be calculated getting rid of absolute poverty, which is no worries about food, clothing, basic medical care, compulsory education and housing safety

2 Jingzhunfupin is a general term of Chinese targeted poverty alleviation work model. Opposite to the haploid poverty alleviation, different assistance policies will be formulated according to the different category of poverty, distinctive causes, dissimilar background of poor households and their divergent living environment

3 https://enapp.chinadaily.com.cn/a/202102/26/AP60382a17a310f03332f97555.html . https://www.bbc.com/news/56213271

4 https://www.worldbank.org/en/topic/poverty/overview

5 The global Multidimensional Poverty Index (MPI) is developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) since 2010. It has been published annually by OPHI and in the Human Development Reports (HDRs) ever since. https://ophi.org.uk/multidimensional-poverty-index/

6 Relative poverty is another poverty measurement to reflect the underlying economic gradient. It is induced from the relative deprivation theory. Countries set the relative poverty line at a constant proportion of the country or year-specific mean (or median) income in practice ( https://doi.org/10.1162/REST_a_00127 )

7 This paper mainly found that eradicating extreme poverty, i.e., moving people to an income above $1.9 purchasing power parity (PPP) a day, does not jeopardize the climate target. That is to say, the climate target and no poverty goal is consistent and coordinated

8 This paper indicated that Payments for Environmental Services may reduce poverty mainly by making payments to poor natural resource managers in upper watersheds. The effects depend on how many participants are poor, the poor’s ability to participate, and the amounts paid

9 8–7 plan is the second wave of China’s poverty alleviation program. The Leading Group renewed poverty line and the National Poor Counties list in 1993. Targeted counties received three major interventions: credit assistance, budgetary grants for investment and public employed projects (i.e., Food-for-Work).

10 In the College Graduate Village Officials (CGVOs) program, the government hire outstanding graduates to work in the rural areas, for example as the village committee secretary, to help rural development and alleviate poverty. In this paper, the College Graduate Village Officials assisted eligible poor households to understand and apply for relevant subsidies, which reduced elite capture of pro-poor programs and move forward poverty alleviation process

11 The Tayssir Project was labeled the Education Support Program, sending a positive signal of its educative value

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Keywords: poverty reduction, bibliometric analysis, VOSviewer, sustainable development goals, 21st century

Citation: Yu Y and Huang J (2021) Poverty Reduction of Sustainable Development Goals in the 21st Century: A Bibliometric Analysis. Front. Commun. 6:754181. doi: 10.3389/fcomm.2021.754181

Received: 06 August 2021; Accepted: 01 October 2021; Published: 18 October 2021.

Reviewed by:

Copyright © 2021 Yu and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yanni Yu, [email protected] ; Jinghong Huang, [email protected]

† These authors have contributed equally to this work

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The Impact of Education and Culture on Poverty Reduction: Evidence from Panel Data of European Countries

1 Department of Economics, University of Foggia, 71121 Foggia, Italy

2 Department of Agricultural, Food and Forest Sciences, University of Palermo, Viale Delle Scienze, 90128 Palermo, Italy

The 2030 Agenda has among its key objectives the poverty eradication through increasing the level of education. A good level of education and investment in culture of a country is in fact necessary to guarantee a sustainable economy, in which coexists satisfactory levels of quality of life and an equitable distribution of income. There is a lack of studies in particular on the relations between some significant dimensions, such as education, culture and poverty, considering time lags for the measurement of impacts. Therefore, this study aims to fill this gap by focusing on the relationship between education, culture and poverty based on a panel of data from 34 European countries, over a 5-year period, 2015–2019. For this purpose, after applying principal component analysis to avoid multicollinearity problems, the authors applied three different approaches: pooled-ordinary least squares model, fixed effect model and random effect model. Fixed-effects estimator was selected as the optimal and most appropriate model. The results highlight that increasing education and culture levels in these countries reduce poverty. This opens space to new research paths and policy strategies that can start from this connection to implement concrete actions aimed at widening and improving educational and cultural offer.

Introduction

Poverty eradication has been the key objective for spans in many countries since that has been recognized as the greatest hostile issues ‘jeopardising balanced society socio-economic development’ (Balvociute, 2020 ). Poverty can be considered one of the core features of unsustainable socio-economic development and as a persistent phenomenon that can have upsetting effect on peoples’ lives (Bossert et al., 2022 ). For this reason, the extreme poverty removal, as well as the fight against inequalities and injustices, have been placed at the center, with climate change, of the 2030 Sustainable Development Goals. The nature of poverty is multidimensional and inequalities within and among countries is an obstinate origin for concern (Fund, 2015 ; Alvaredo et al., 2017 ; Alkire & Seth, 2015 ; Kwadzo, 2015 ). For its interpretation and measurement, the literature has added to the monetary approach of material deprivation, the social and subjective dimension of the human being (Bellani & D’Ambrosio, 2014 ; Maggino, 2015 ). As stated by Kwadzo ( 2015 ), it is possible to define three poverty measurements: monetary poverty, social exclusion, and capability poverty. Similarly, there are a lot of indicators measuring well-being and quality of life: Index of Happiness, Human Poverty Index and Human Development Index (Senasu et al., 2019 ; Spada et al., 2020 ; UNDP, 1990 ; Veenhoven, 2012 ; Watkins, 2007 ). All these indicators focus and start from education. For example, the Human Poverty Index (HPI) was introduced by the United Nations to complement the Human Development Index (HDI) and used, for the first time, in the 1997 Human Development Report. In 2010, it was replaced by the Multidimensional Poverty Index. The HPI focuses on the deprivation of three essential parameters of human life, already taken into account by the Human Development Index: life expectancy, education and standard of living (Alkire et al., 2015 ; UNDP, 1990 ).

Previous studies shown that education indicators have a large impact on a country’s poverty (Bakhtiari & Meisami, 2010 ; UNDP, 1990 ; Watkins, 2007 ) and that investing in health and education is a way to reduce income inequality and poverty. In addition, studies highlight that increasing equality and the quality of education is essential to combat economic and gender inequality within society (Walker et al., 2019 ). However, few studies provide empirical evidence on how education impacts on income inequality (Liu et al., 2021 ; Santos, 2011 ; Walker et al., 2019 ) and most of these studies analyses the poverty phenomenon neglecting the combined effect of various variables. Different dimensions of poverty have also empirically demonstrated a high degree of correlation (Kwadzo, 2015 ). In addition, the literature review analysis highlighted a gap in quantitative studies, especially on the paths between some relevant dimensions, such as education, culture and poverty, considering time lags for the measurement of impacts. In light of this, the main objectives of this study are: (i) To identify over the five-year period considered (2015–2019), with what delay and with what magnitude and sign, the poverty is influenced by some indicators representative of the educational and cultural dimension; and (ii) Consequently, better calibrate education policies in European countries, in order to achieve a reduction in the poverty rate in the short term, in compliance with the objectives of the 2030 Agenda.

The rest of the paper is organized as follows. A literature review regarding the relation between poverty, education and inequalities is presented in Sect.  2 . The Sect.  3 enlightens research gaps linked to the aims of this study and hypothesis to corroborate. Section  4 defines data and summarizes the methodological approach used to reach the work’s aims. Results are presented and discussed in Sect.  5 . Finally, the last section sets out our main conclusions by highlighting limitations of the study and future directions.

Theoretical Framework

The core role of education.

Over the last decades it is possible to individuate in the EU-28 a quickly growing portion of the population having income below 60% of the median disposable income. In addition, there is a share of the population has been becoming more impoverished (Balvociute, 2020 ; EUROSTAT Statistic Explained, 2019 ). In same way, it is possible to speak about “poverty trap”, a mechanisms whereby countries are poor and persist poor: existing poverty appears a straight cause of poverty in the future (Knight et al., 2009 ; Kraay & McKenzie, 2014 ). Aspects such as accommodation, education, medical and material services are considered essential. In particular, an increasing number of empirical studies have supported the positive effects of education on the creation of wealth by individuals and on promoting economic effective and fair development (UNESCO & Global Education Monitoring Report, 2017 ; Walker et al., 2019 ; Xu, 2016 ; Zhang, 2020 ). A research note by European Commission ( 2015 ) shows that individuals with primary education remain the most vulnerable in all EU countries (with a risk of poverty ranging from 13%—Netherlands—to 56% Romania). Even the Millennium Development Goals (MDGs), the Poverty Reduction Strategy Papers (PRSP) endorsed by the World Bank and ‘Education for All’ program (UNESCO, 2007 ) emphases the significant role of education (Awan et al., 2011 ). A diverse balance can be possible and policy efforts to interrupt the poverty trap might have long-term effects. In this framework, the model proposed by Santos ( 2011 ) shows that a policy oriented towards aligning the quality of education would reduce initial inequalities. In light of this, Shi & Qamruzzaman, ( 2022 ) in a recent work, study, by means of numerous econometrical methods, the tie between investments in education, financial inclusion, and poverty decrease for the period 1995–2018 in 68 nations, underlining the role of education-backed poverty mitigation public policies that need to be more targeted. Several studies demonstrate that level of poverty and education are strictly related. For instance, Bossert et al. ( 2022 ) by focusing on Atkinson-Kolm-Sen index, that measures the percentage income gap of the poor that can be attributed to inequality among the poor (Sen, 1973 , 1976 ), emphasized the close relation between poverty and inequality. Consistent with previous studies, Lenzi and Perruca ( 2022 ) demonstrate that tertiary educated people report higher ranks of life satisfaction. This link is even more marked in rural territories where education is recognised as an important tool for reducing poverty as it allows the acquisition of skills and productive knowledges which increase people’s productivity and their earnings (Tilak, 2002 ). A recent report of the United Nations ( 2021 ) underlines how the reduced access to educational and health services in rural areas becomes a barrier, determining the difficulty of people living in these areas to found employment in well-paid professions contributing to economic growth (Chmelewska and Zegar, 2018 ). However, as Liu and colleagues ( 2021 ) find, different levels of education have distinct effects on poverty in rural areas of China and that the latter is driven not only by factors within the region but also by the level of poverty in the surrounding regions. In addition, numerous empirical evidences reveal a link between educational level and income inequalities in several geopolitical contexts. Bakhtiari and Meisami ( 2010 ), in a work of over 10 years ago, makes use of a panel data set of 37 Islamic countries (eight time periods) to study income inequality along with a model of poverty, with the main variables as income level, health status, education and savings. Findings show that enhancing the health and education can reduce income inequality and poverty. Likewise, as Arafat and Khan ( 2022 ) underline the high level of education not only contributes to reducing the degree of poverty but improves the conditions of mental, social and emotional well-being compared to poorly educated families. After about 10 years, similar works by Wani and Dhami ( 2021 ) and Sabir and Aziz ( 2018 ) reach the same results investigating the SAARC (South Asian Association for Regional Cooperation) countries and 31 developing countries (by employing the System Generalized Method of Moments). In several cases, and especially in rural areas, poverty is linked to the lower level of household income compared to urban areas, resulting in differences in access to basic goods and services to meet personal needs (Chmelewska and Zegar, 2018 ). In this territories household income level is directly associated with food security, in fact, an increase in the level of income reduces food insecurity (Chegini et al., 2021 ). However, as evidenced by other authors (Kirkpatrick et al., 2020 ; Kusio & Fiore, 2022 ), access to education can help to overcome the migration of young people and geographical isolation and inaccessibility that characterize the poor areas (Kvedaraite et al., 2011 ). In turn, young, educated people affect entrepreneurial attitudes. Walker et al. ( 2019 ) in the recent report ‘ The Power of Education to Fight Inequality. How increasing educational equality and quality is crucial to fighting economic and gender inequality ’ show how education can be emancipating for individuals, and it can play the role of a ‘leveler and equalizer within society’. Education interrupts obstinate and rising inequality by promoting the development of more decent work, rising incomes for the poorest people: it can aid to endorse long-lasting, wide-ranging economic growth and social cohesion.

Gradstein and Justman ( 2002 ) underlined the role of education in shaping the social cohesion that can assure equality between individuals. Universal free education enhances people’s earning power, and can bring them out of poverty. Low levels of education hamper economic growth, which in turn slows down poverty reduction (UNESCO, 2017 ; Global Education Monitoring Report, 2019 ) estimates that each year of schooling raises earnings by around 10%;53 this figure is even higher for women. In Tanzania, having a secondary education reduces the chances of being poor as a working adult by almost 60%. According to a study by UNESCO and the Global Education Monitoring Report ( 2019 ), if all adults finished secondary school, 420 million individuals would be lifted out of poverty. The convergence of crises deriving first from COVID-19 then from climate change, and conflicts, are generating extra impacts above all on poverty, nutrition, health and education affecting all the Sustainable Development Goals (SDGs).

Equilience, a synchratic neologism composed of Equity + Resilience, that is resilient systems in respect of equity as a balancing of the different interests of the parties. Recent research (Berbés-Blázquez et al., 2021 ; Williams et al., 2020 ; Contò and Fiore, 2020 ) highlight the crucial importance to promote the ‘marriage’ between equity and resilience.

Aims of Study and Hypothesis

This research is potentially the first study to investigate the relationship between educational, cultural factors and poverty in European countries.

The main research directions are as follows: (i) To assess the impact of education and culture (expressed by the following indicators: Cultural employment, Total educational expenditure, Graduates in tertiary education, Number of enterprises in the cultural sectors, Tertiary educational attainment ) upon poverty (indicated by Persons at risk of poverty or social ); (ii) To compare the strength and direction of the relationships between the variables considered in two temporal situations, i.e. with zero lag, and with lag equal to one year. The data cover the period 2015–2019 and were extracted from the Eurostat database.

In the light of the above discussion, of the literature review analysis, and of the theoretical frameworks examined this study explores the following research hypotheses with regard to the European context:

Education and culture have an inverse impact on the levels of poverty.

Our second hypothesis states:

The association between cultural, educational variables and poverty, in the short term is more intense if we consider a delay of one-year.

The dataset is a balanced panel of annual observations for 34 European countries and covers the period from 2015 to 2019. On the basis of literature findings, our analysis focused on the following dimensions: education, income inequality and poverty.

Thereby, the variables considered for our investigation are as follows:

• Poverty indicator: Persons at risk of poverty or social exclusion (% of population, thousand persons; hereinafter labelled with PRP);
• Education and cultural indicators: Cultural employment (thousand persons); Total educational expenditure (million euros); Graduates in tertiary education (‰ of population;); Number of enterprises in the cultural sectors (number) Tertiary educational attainment (‰ of population). Respectively, hereinafter they will be labelled with CE, TEE, GTE, NEC and TEA.

The indicators have been extracted from the Eurostat database. The summary statistics are reported in Table ​ Table1. 1 . In the selected time period, Iceland is the country that shows the lowest values with respect PRP (12.08%). Instead, the country showing the worst performance is Romania (PRP = 41.60%). With regard to the education indicators, Germany holds the highest values for both CE (81,661.48 thousand persons) and TEE (30.588.86 million euros), highlighting great attention to education issues. Instead, in Eastern Europe (Montenegro, Romania, and Hungary) the indicators pertaining to the education area take on more penalized values. Italy is the country that boasts the largest number of enterprises in the cultural sector (NEC = 179,136.8), thanks also to the artistic beauties of which this country is rich. As far as the tertiary education level is concerned, the highest value of is held by Cyprus while the lowest by Romania (respectively TEA = 57.34 and TEA = 25.26). For subsequent processing, since the variables considered are both in the form of ratios and counts, all data were converted to natural logarithms.

Summary statistics for the Eurostat datasets, 2015–2019

Methodology

The methodological approach used is based on linear panel data models including the simple Pooled Ordinary Least Square (pooled OLS) model, the Fixed Effects (FE) model and the Random Effects (RE) model. Before proceeding with the application of the linear models, the correlation matrix between the variables taken into consideration was performed and subsequently, to avoid multicollinearity problems and distorted estimates, the study, based on the principal component analysis (PCA), used two indicators related to education and culture. According to Jolliffe and Cadima ( 2016 ), through PCA starting from a set of correlated variables, a set of uncorrelated variables is obtained, known as Principal Components (PC). In PCA, only common factors that have an eigenvalue greater than one or greater than the mean should be kept (Jolliffe, 2002 ; Kaiser, 1974 ). In this study PCA allowed to obtain the following indicators: EDU1, which includes CE, NEC, TEE, and EDU2, composed of TEA and GTE. These indicators have been incorporated into the panel data models, replacing the original variables.

The first linear panel data model adopted is the pooled OLS, which assumes no heterogeneity between countries, whose equation is as follows:

where ln PRP is the natural logarithm of the poverty indicator, α is the intercept, EDU is composed of the principal components extracted, ε is the error term, i denotes statistical units, in this case countries, and t denotes the time index.

The second model adopted is FE which controls for cross-country heterogeneity and is expressed as:

where α i is the regional specific parameter denoting the fixed effect. The basic intuition of the FE model is that α i does not change over time.

Finally, the third model is RE denoted as;

In the RE model, variations between units are assumed to be random and uncorrelated with the independent variables in the model.

To verify the two research hypotheses, for each of the three models (pooled OLS, FE and RE) two versions were calculated, with lag 0 and lag 1 year. In the model at lag 0 the variables are synchronous, while in the model at lag 1 principal components enter the equation with a one-year lag compared to PRP. The choice of the reference model between pooled OLS, FE and RE is based on several tests. In choosing between FE and pooled OLS, the study applies the F-test. A p-value of less than 5% indicates that there are important country effects that OLS fails to detect, and that thus neglecting unobserved heterogeneity in the model can lead to estimation errors and inconsistencies. The study also tests which is better between the OLS and RE model using the Breusch-Pagan (BP)-Langragian Multiplier (LM) test. The null hypothesis of the BP-LM test is that there is no substantial variance between regions. A probability value of less than 5% for the BP-LM test indicates that the RE model is appropriate and the OLS pool is not. Finally, the Hausman test χ 2 is also performed to compare the FE model and the RE model. According to Algieri and Mannarino ( 2013 ), the Hausman test χ 2 aims to identify a violation of the RE modelling hypothesis. In this test, the alternative hypothesis is that the FE model is preferable to the RE model, while the null hypothesis is that both models produce similar coefficients. A p-value greater than 5% denotes that both FE and RE are reliable, but the RE model is more efficient because it uses a lower degree of freedom. We also test for heteroskedasticity in the FE model using the modified Wald test developed by Lasker and King ( 1997 ). The null hypothesis of this test is that the variance of the error is similar for all countries (Amaz et al., 2012 ). All statistical analyses were conducted in Stata 17.0 (Stata Corp LP, College Station, Texas, USA). A critical value of p  < 0.05 was specified a priori as the threshold of statistical significance for all analyses.

The relationships between the variables, measured by Pearson’s linear correlation coefficient, is shown in Table ​ Table2. 2 . It is noted that the PRP variable is negatively correlated with all the other panel variables, albeit with modest correlations. Instead, TEE shows a high positive correlation with NEC ( r  = 0.963 and r  = 0.903, respectively). There is also a high correlation between NEC and TEE ( r  = 0.857). Therefore, in the light of the results, to exclude the problem of multicollinearity between the covariates, we proceeded to analyse the principal components.

Pearson correlation coefficient

* p  < 0.05, ** p  < 0.01, *** p  < 0.001

Table ​ Table3 3 shows the results of principal component analysis. On the basis of these results, the need to maintain the first two principal components is highlighted, since their eigenvalues are greater or very close to 1 and cumulatively represent the 84% of the information. They will be labelled as EDU1 and EDU2 respectively. EDU1 refers to TEE, CE and NEC, i.e. it refers to a cultural dimension of the country and therefore, even if not strictly connected to the school environment, with an important educational role, while the EDU2 component referring to GTE and TEA, is more closely related to the school.

Principal component analysis: factor loading, eigen value and variance explained

Table ​ Table4 4 shows the results of the three econometric models (pooled OLS, FE, RE) on the link between education, culture and poverty. It is observed that all models converge in showing that poverty decreases with increasing education and culture. In particular, the EDU1 indicator always shows a negative coefficient, and this relationship is statistically significant in the model fixed at lag 0 and lag 1 (respectively b  = − 0.3804, p  < 0.001; b  = − 0.3925, p  < 0.001). Furthermore, for EDU1, in all three econometric models it can be noted that the coefficients are higher in absolute value passing from lag 0 to lag 1, highlighting that the impact between cultural and educational tools and poverty reduction occurs with a delay, perhaps necessary to have positive results. Also, the EDU2 indicator always shows a negative coefficient and this relationship is statistically significant in all three models, both at lag 0 and at lag 1 (for all p  < 0.001). To discern the econometric model that best fits the data, as a first step the F-test allows you to choose between the OLS and FE models. The value F = 80.09 for lag 0 and F = 109.61for lag = 1, (for all p -value < 0.001), indicates in both cases that the FE model is more suitable than the pooled OLS. This demonstrates that in the relationships examined time plays an important role, which a simple OLS model may fail to capture, i.e. EDU1 and EDU2 have an effect on poverty decrease that changes over time. The choice between the RE model and the pooled OLS was instead based on the BP LM test, which suggests that the RE model is more suitable than the pooled OLS. Finally, the Hausman test χ 2 allows to identify which between FE and RE is more suitable: The value χ 2  = 15.95 at lag 0 and χ 2  = 13.40 at lag = 1, (for all p -value < 0.001) suggests that the FE model is more suitable than the RE model, indicating the presence of non-random differences between countries or over time. The model that best fits the examined panel of data is therefore the FE model.

Pooled OLS, Fixed Model, Random Model, at lag 0 and at lag 1

** p  < 0.01, *** p  < 0.001

In light of these results, as supposed in hypothesis H 1 , it is evident that education and culture play a significant role in poverty reduction. Furthermore, as supposed by hypothesis H 2  and based on the FE model which was found to be the most suitable, this impact is more intense if one considers a year of delay, above all for cultural and educational variables relating to a dimension that is not strictly scholastic.

Discussions and Conclusions

The present study analysed the relationship between education, culture and poverty for 34 countries, over the period 2015–2019. The findings indicate that rising education and culture levels in these nations reduce poverty. The model also highlighted that this relationship is weaker if we consider a contemporaneity of the values of the variables (at lag 0), while it is strengthened if we consider a time interval of one year.

As policy-makers regularly disclose the consequences of unfair development by identifying problems requiring solutions built on evidence-based guidelines, these results can have interesting and fruitful implications. By concluding, education appears, in line with other studies (Sabir & Aziz, 2018 ; Xu, 2016 ), one of the best effective methods to eradicate poverty. In line with the work by Walker et al., ( 2019 ), investing in universal-free-public education for all the persons can close different circles: the gap between rich and poor people, between women and men, between poor and rich areas within a country and among countries. In addition, education appears crucial to fight inequalities across the world. The results appear also consistent with the UN report ( 2021 ) that emphasizes the importance of the access to educational and health services in marginal poor areas to improve and contribute equal economic growth and reduce poverty (Chmelewska and Zegar, 2018 ; Bakhtiari & Meisami, 2010 ; Wani & Dhami, 2021 ). The same findings come from the work by Peng ( 2019 ) based on data from poor Chinese provinces showing that education has steady and positive impacts on farmers’ income, and the outcome of growing income in poor zones is higher than in other areas.

All in all, as evidenced by the European Commission ( 2015 ), the means to diminish the risk of poverty appears ‘straight-forward: go to school, get a job’. Clearly, these implications have to consider conditions and country environment. In line with previous research (Noper Ardi & Isnayanti, 2020 ; Walker et al., 2019 ), these results highlight that education can have an immediate impact on income inequalities and poverty; on the other hand, education (and public spending on it) has a longer-term impact on inequality through its effects in enhancing future salaries and chances. Indeed, as stated by some notable researchers (Kraay & McKenzie, 2014 ), the ‘more-likely poverty traps’ need action in less-traditional policy areas. The scholars have to further perfect the theoretical concepts and policy standards of poverty alleviation through education (Shi & Qamruzzaman, 2022 ).

This paper reinforces the conclusions deriving from other research (Mou and Xu, 2020 ; Assari et al., 2018 ; Batool and Batool, 2018 ) that are to give evidence of how education can forecast coming ‘Emotional Well-Being’ thus decreasing the inequalities by means of more generous policies and strategies. The latter can support international experience-based education (Xu, 2016 ).

In the following research phases, other variables can be inserted to improve the specifications of the model and also verify the existence of homogeneous groups of countries. In addition, a distinction between urban and rural areas to highlight the link between income, education and poverty and differences could enrich the literature and provide useful information to guide national policies in a targeted way. Regarding possible limitations of the paper, it is possible to notice a time period limited for missing data and health variables are missing.

The ‘dark’ side of this conclusions is considering the effects of the COVID19 pandemic that has increased on one hand the online teaching and training: on the other hand, education has become more difficult in remote, rural and/or marginal areas due to connections and hardware limitations.

Therefore, nowadays strategies, models and polices focusing on equi-lience (equity and resilience) processes can promote the creation of a different balance between the needs of sustainable growth and those of social, fair and environmental development (Fiore, 2022 ). Therefore, developing a strategy to convey a trained, skilled and well-supported workforce, investing in relevant and fair teaching resources, ensuring funds and building better liability mechanisms from national to local levels can be significant and fair paths to reduce poverty and inequalities. These strategies have to be aimed at developing national education plans that try to identify pre-education existing inequalities in order to arrange actions in poorer rural and marginalized districts or regions.

Open access funding provided by Università di Foggia within the CRUI-CARE Agreement.

Declarations

Authors disclose financial or non-financial interests directly or indirectly related to the work submitted for publication.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Drought shocks, adaptive strategies, and vulnerability to relative poverty

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• Published: 29 May 2024

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• Liu Lijin   ORCID: orcid.org/0000-0003-2720-3265 1 &
• Yilin Wu 1 , 2  

This paper explores the impact of drought shocks on the vulnerability to relative poverty of farm households based on the fixed-effects model using Chinese micro-household data. Further, we examine the effectiveness of different adaptive strategies in mitigating the adversely impacts of drought. The results show that drought shocks suffered by farm households during the growth season will significantly increase household vulnerability to relative poverty and the probability of farm households falling into relative poverty in the future increases. The results of the mechanism analysis indicate that drought shocks affect household vulnerability to relative poverty mainly by affecting household income, health status, and production behavior. The results of heterogeneity analysis revealed that households in poor areas as well as low-income households are more affected by drought shocks. With adaptive strategies such as engaging in off-farm employment, joining farmer cooperatives, and using digital financial inclusion tools, the negative impacts of drought shocks are significantly mitigated.

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Economists report on a modest intervention that helps low-income families beat the poverty trap

by Peter Dizikes, Massachusetts Institute of Technology

Many low-income families might desire to move into different neighborhoods—places that are safer, quieter, or have more resources in their schools. In fact, not many do relocate. But it turns out they are far more likely to move when someone is on hand to help them do it.

That's the outcome of a high-profile experiment by a research team including MIT economists, which shows that a modest amount of logistical assistance dramatically increases the likelihood that low-income families will move into neighborhoods providing better economic opportunity.

A paper describing this work is published in the journal American Economic Review .

The randomized field experiment, set in the Seattle area, showed that the number of families using vouchers for new housing jumped from 15% to 53% when they had more information, some financial support, and, most of all, a navigator who helped them address logistical challenges.

"The question we were after is really what drives residential segregation," says Nathaniel Hendren, an MIT economist and co-author of the paper detailing the results. "Is it due to preferences people have, due to having family or jobs close by? Or are there constraints on the search process that make it difficult to move?" As the study clearly shows, he says, "Just pairing people with [navigators] broke down search barriers and created dramatic changes in where they chose to live. This was really just a very deep need in the search process."

The study's results have prompted U.S. Congress to twice allocate $25 million in funds, allowing eight other U.S. cities to run their own versions of the experiment and measure the impact.

That is partly because the result "represented a bigger treatment effect than any of us had really ever seen," says Christopher Palmer, an MIT economist and a co-author of the paper. "We spend a little bit of money to help people take down the barriers to moving to these places, and they are happy to do it."

New research renews an idea

The study follows other prominent work about the geography of economic mobility. In 2018, Chetty and Hendren released an "Opportunity Atlas" of the U.S., a comprehensive national study showing that other things being equal, some areas provide greater long-term economic mobility for people who grow up there. The project brought renewed attention to the influence of place on economic outcomes.

The Seattle experiment also follows a 1990s federal government program called Moving to Opportunity, a test in five U.S. cities helping families seek new neighborhoods. That intervention had mixed results: Participants who moved reported better mental health, but there was no apparent change in income levels.

Still, in light of the Opportunity Atlas data, the scholars decided to revisit the concept, with a program they call Creating Moves to Opportunity (CMTO). This provides housing vouchers along with a bundle of other things: Short-term financial assistance of about $1,000 on average, more information, and the assistance of a navigator, a caseworker who would help troubleshoot issues that families encountered.

The experiment was implemented by the Seattle and King County Housing Authorities, along with MDRC, a nonprofit policy research organization, and J-PAL North America. The latter is one of the arms of the MIT-based Abdul Latif Jameel Poverty Action Lab (J-PAL), a leading center promoting randomized, controlled trials in the social sciences.

The experiment included 712 families and two phases. In the first, all participants were issued housing vouchers worth a little more than $1,500 per month on average, and divided into treatment and control groups. Families in the treatment group also received the CMTO bundle of services, including the navigator.

In this phase, lasting from 2018 to 2019, 53% of families in the treatment group used the housing vouchers, while only 15% of those in the control group used the vouchers. Families who moved dispersed to 46 different neighborhoods, defined by U.S. Census Bureau tracts, meaning they were not just shifting en masse from one location to one other.

Families who moved were very likely to want to renew their leases, and expressed satisfaction with their new neighborhoods. All told, the program cost about $2,670 per family. Additional research scholars in the group have conducted about changes in income suggest the program's direct benefits are 2.5 times greater than its costs.

"Our sense is that's a pretty reasonable return for the money compared to other strategies we have to combat intergenerational poverty," Hendren says.

Logistical and emotional support

In the second phase of the experiment, lasting from 2019 to 2020, families in a treatment group received individual components of the CMTO support, while the control group again only received the housing vouchers. This way, the researchers could see which parts of the program made the biggest difference. The vast majority of the impact, it turned out, came from receiving the full set of services, especially the "customized" help of navigators.

"What came out of the phase two results was that the customized search assistance was just invaluable to people," Palmer says. "The barriers are so heterogenous across families." Some people might have trouble understanding lease terms; others might want guidance about schools; still others might have no experience renting a moving truck.

The research turned up a related phenomenon: In 251 follow-up interviews, families often emphasized that the navigators mattered partly because moving is so stressful.

"When we interviewed people and asked them what was so valuable about that, they said things like, "Emotional support,'" Palmer observes. He notes that many families participating in the program are "in distress," facing serious problems such as the potential for homelessness.

Moving the experiment to other cities

The researchers say they welcome the opportunity to see how the Creating Moves to Opportunity program, or at least localized replications of it, might fare in other places. Congress allocated $25 million in 2019, and then again in 2022, so the program could be tried out in eight metro areas : Cleveland, Los Angeles, Minneapolis, Nashville, New Orleans, New York City, Pittsburgh, and Rochester. With the COVID-19 pandemic having slowed the process, officials in those places are still examining the outcomes.

"It's thrilling to us that Congress has appropriated money to try this program in different cities, so we can verify it wasn't just that we had really magical and dedicated family navigators in Seattle," Palmer says. "That would be really useful to test and know."

Seattle might feature a few particularities that helped the program succeed. As a newer city than many metro areas, it may contain fewer social roadblocks to moving across neighborhoods, for instance.

"It's conceivable that in Seattle, the barriers for moving to opportunity are more solvable than they might be somewhere else." Palmer says. "That's [one reason] to test it in other places."

Still, the Seattle experiment might translate well even in cities considered to have entrenched neighborhood boundaries and racial divisions. Some of the project's elements extend earlier work applied in the Baltimore Housing Mobility Program, a voucher plan run by the Baltimore Regional Housing Partnership. In Seattle, though, the researchers were able to rigorously test the program as a field experiment, one reason it has seemed viable to try to replicate it elsewhere.

"The generalizable lesson is there's not a deep-seated preference for staying put that's driving residential segregation," Hendren says. "I think that's important to take away from this. Is this the right policy to fight residential segregation? That's an open question, and we'll see if this kind of approach generalizes to other cities."

Journal information: American Economic Review

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This story is republished courtesy of MIT News ( web.mit.edu/newsoffice/ ), a popular site that covers news about MIT research, innovation and teaching.

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