poverty reduction research paper

• Open supplemental data
• Reference Manager
• Simple TEXT file

People also looked at

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

Adams, R. H., and Page, J. (2005). Do international Migration and Remittances Reduce Poverty in Developing Countries? World Develop. 33, 1645–1669. doi:10.1016/j.worlddev.2005.05.004

CrossRef Full Text | Google Scholar

Addison, T., Hulme, D., and Kanbur, R. (2008), Poverty Dynamics: Measurement and Understanding from an Interdisciplinary Perspective. Working Paper No. 19. Brooks World Poverty Institute . Available at SSRN: https://ssrn.com/abstract=1246882 .

Google Scholar

Alix-Garcia, J., McIntosh, C., Sims, K. R. E., and Welch, J. R. (2013). The Ecological Footprint of Poverty Alleviation: Evidence from Mexico's Oportunidades Program. Rev. Econ. Stat. 95, 417–435. doi:10.1162/REST_a_00349

Alix-Garcia, J. M., Sims, K. R. E., and Yañez-Pagans, P. (2015). Only One Tree from Each Seed? Environmental Effectiveness and Poverty Alleviation in Mexico's Payments for Ecosystem Services Program. Am. Econ. J. Econ. Pol. 7, 1–40. doi:10.1257/pol.20130139

Amarante, V., Brun, M., and Rossel, C. (2020). Poverty and Inequality in Latin America's Research Agenda: A Bibliometric Review. Dev. Pol. Rev. 38, 465–482. doi:10.1111/dpr.12429

Banerjee, A., Duflo, E., Glennerster, R., and Kinnan, C. (2015). The Miracle of Microfinance? Evidence from a Randomized Evaluation. Am. Econ. J. Appl. Econ. 7, 22–53. doi:10.1257/app.20130533

Banerjee, A., Duflo, E., Goldberg, N., Karlan, D., Osei, R., Parienté, W., ..., , Shapiro, J., Thuysbaert, B., and Udry, C. (2015). A Multifaceted Program Causes Lasting Progress for the Very Poor: Evidence from Six Countries. Science 348, 1260799. doi:10.1126/science.1260799

PubMed Abstract | CrossRef Full Text | Google Scholar

Banerjee, A. V., and Duflo, E. (2007). The Economic Lives of the Poor. J. Econ. Perspect. 21, 141–167. doi:10.1257/089533007780095556

Bardhan, P., and Mookherjee, D. (2005). Decentralizing Antipoverty Program Delivery in Developing Countries. J. Public Econ. 89, 675–704. doi:10.1016/j.jpubeco.2003.01.001

Beck, T., Demirgüç-Kunt, A., and Levine, R. (2007). Finance, Inequality and the Poor. J. Econ. Growth 12, 27–49. doi:10.1007/s10887-007-9010-6

Benhassine, N., Devoto, F., Duflo, E., Dupas, P., and Pouliquen, V. (2015). Turning a Shove into a Nudge? A "Labeled Cash Transfer" for Education. Am. Econ. J. Econ. Pol. 7, 86–125. doi:10.1257/pol.20130225

Bibi, S., and Duclos, J.-Y. (2007). Equity and Policy Effectiveness with Imperfect Targeting. J. Develop. Econ. 83, 109–140. doi:10.1016/j.jdeveco.2005.12.001

Bourguignon, F., and Chakravarty, S. R. (2019). “The Measurement of Multidimensional Poverty,” in Poverty, Social Exclusion and Stochastic Dominance. Themes in Economics (Theory, Empirics, and Policy) . Editor S. Chakravarty (Singapore: Springer ), 83–107. doi:10.1007/978-981-13-3432-0_7

Callon, M., Courtial, J.-P., Turner, W. A., and Bauin, S. (1983). From Translations to Problematic Networks: An Introduction to Co-word Analysis. Soc. Sci. Inf. 22, 191–235. doi:10.1177/053901883022002003

Campos, F., Frese, M., Goldstein, M., Iacovone, L., Johnson, H. C., McKenzie, D., et al. (2017). Teaching Personal Initiative Beats Traditional Training in Boosting Small Business in West Africa. Science 357, 1287–1290. doi:10.1126/science.aan5329

Chamhuri, N. H., Karim, H. A., and Hamdan, H. (2012). Conceptual Framework of Urban Poverty Reduction: A Review of Literature. Proced. - Soc. Behav. Sci. 68, 804–814. doi:10.1016/j.sbspro.2012.12.268

Chen, S., and Ravallion, M. (2013). More Relatively-Poor People in a Less Absolutely-Poor World. Rev. Income Wealth 59 (1), 1–28. doi:10.1111/j.1475-4991.2012.00520.x

Collier, P., and Dollar, D. (2002). Aid Allocation and Poverty Reduction. Eur. Econ. Rev. 46, 1475–1500. doi:10.1016/S0014-2921(01)00187-8

Cornwall, A., and Brock, K. (2005). What Do Buzzwords Do for Development Policy? a Critical Look at 'participation', 'empowerment' and 'poverty Reduction'. Third World Q. 26, 1043–1060. doi:10.1080/01436590500235603

Datt, G., and Ravallion, M. (1992). Growth and Redistribution Components of Changes in Poverty Measures. J. Develop. Econ. 38, 275–295. doi:10.1016/0304-3878(92)90001-P

Daw, T., Brown, K., Rosendo, S., and Pomeroy, R. (2011). Applying the Ecosystem Services Concept to Poverty Alleviation: the Need to Disaggregate Human Well-Being. Envir. Conserv. 38, 370–379. doi:10.1017/S0376892911000506

Dollar, D., and Kraay, A. (2002). Growth Is Good for the Poor. J. Econ. Growth 7, 195–225. doi:10.1023/A:1020139631000

Foster, J., Greer, J., and Thorbecke, E. (1984). A Class of Decomposable Poverty Measures. Econometrica 52, 761–766. doi:10.2307/1913475

Galiani, S., and McEwan, P. J. (2013). The Heterogeneous Impact of Conditional Cash Transfers. J. Public Econ. 103, 85–96. doi:10.1016/j.jpubeco.2013.04.004

González-Alcaide, G., Calafat, A., Becoña, E., Thijs, B., and Glänzel, W. (2016). Co-Citation Analysis of Articles Published in Substance Abuse Journals: Intellectual Structure and Research Fields (2001-2012). J. Stud. Alcohol. Drugs 77, 710–722. doi:10.15288/jsad.2016.77.710

Grindle, M. S. (2004). Good Enough Governance: Poverty Reduction and Reform in Developing Countries. Governance 17, 525–548. doi:10.1111/j.0952-1895.2004.00256.x

Haushofer, J., and Shapiro, J. (2016). The Short-Term Impact of Unconditional Cash Transfers to the Poor: Experimental Evidence from Kenya*. Q. J. Econ. 131, 1973–2042. doi:10.1093/qje/qjw025

He, G., and Wang, S. (2017). Do college Graduates Serving as Village Officials Help Rural China. Am. Econ. J. Appl. Econ. 9, 186–215. doi:10.1257/app.20160079

Huang, D., and Ying, Z. (2018). A Review on Precise Poverty Alleviation by Introducing Market Mechanism in a Context Dominated by Government, 2nd International Forum on Management, Education and Information Technology Application . Paris: Atlantis Press , 110–117. doi:10.2991/ifmeita-17.2018.19

Hubacek, K., Baiocchi, G., Feng, K., and Patwardhan, A. (2017). Poverty Eradication in a Carbon Constrained World. Nat. Commun. 8 (1), 1–9. doi:10.1038/s41467-017-00919-4

Hulme, D., and Shepherd, A. (2003). Conceptualizing Chronic Poverty. World Develop. 31, 403–423. doi:10.1016/S0305-750X(02)00222-X

Hussain, I., and Hanjra, M. A. (2004). Irrigation and Poverty Alleviation: Review of the Empirical Evidence. Irrig. Drain. 53, 1–15. doi:10.1002/ird.114

Jalan, J., and Ravallion, M. (2003). Estimating the Benefit Incidence of an Antipoverty Program by Propensity-Score Matching. J. Business Econ. Stat. 21, 19–30. doi:10.1198/073500102288618720

Jones, H. (2016). More Education, Better Jobs? A Critical Review of CCTs and Brazil's Bolsa Família Programme for Long-Term Poverty Reduction. Soc. Pol. Soc. 15, 465–478. doi:10.1017/S1474746416000087

Karlan, D., and Valdivia, M. (2011). Teaching Entrepreneurship: Impact of Business Training on Microfinance Clients and Institutions. Rev. Econ. Stat. 93, 510–527. doi:10.1162/REST_a_00074

Karlan, D., and Zinman, J. (2011). Microcredit in Theory and Practice: Using Randomized Credit Scoring for Impact Evaluation. Science 332, 1278–1284. doi:10.1126/science.1200138

Karnani, A. (2007). The Mirage of Marketing to the Bottom of the Pyramid: How the Private Sector Can Help Alleviate Poverty. Calif. Manage. Rev. 49, 90–111. doi:10.2307/41166407

Kleven, H. J., and Kopczuk, W. (2011). Transfer Program Complexity and the Take-Up of Social Benefits. Am. Econ. J. Econ. Pol. 3, 54–90. doi:10.1257/pol.3.1.54

Kwan, C., Walsh, C. A., and Donaldson, R. (2018). Old Age Poverty: A Scoping Review of the Literature. Cogent Soc. Sci. 4, 1478479–1478521. doi:10.1080/23311886.2018.1478479

Li, R., Shan, Y., Bi, J., Liu, M., Ma, Z., Wang, J., et al. (2021). Balance between Poverty Alleviation and Air Pollutant Reduction in China. Environ. Res. Lett. 16 (9), 094019. doi:10.1088/1748-9326/ac19db

Linnenluecke, M. K., Marrone, M., and Singh, A. K. (2020). Conducting Systematic Literature Reviews and Bibliometric Analyses. Aust. J. Manage. 45, 175–194. doi:10.1177/0312896219877678

Ludwig, J., Duncan, G. J., Gennetian, L. A., Katz, L. F., Kessler, R. C., Kling, J. R., et al. (2012). Neighborhood Effects on the Long-Term Well-Being of Low-Income Adults. Science 337, 1505–1510. doi:10.1126/science.1224648

Mahembe, E., Odhiambo, N. M., and Read, R. (2019). Foreign Aid and Poverty Reduction: A Review of International Literature. Cogent Soc. Sci. 5, 1625741. doi:10.1080/23311886.2019.1625741

Mani, A., Mullainathan, S., Shafir, E., and Zhao, J. (2013). Poverty Impedes Cognitive Function. Science 341, 976–980. doi:10.1126/science.1238041

Maulu, S., Hasimuna, O. J., Mutale, B., Mphande, J., and Siankwilimba, E. (2021). Enhancing the Role of Rural Agricultural Extension Programs in Poverty Alleviation: A Review. Cogent Food Agric. 7, 1886663. doi:10.1080/23311932.2021.1886663

Mbuyisa, B., and Leonard, A. (2017). The Role of ICT Use in SMEs towards Poverty Reduction: A Systematic Literature Review. J. Int. Dev. 29, 159–197. doi:10.1002/jid.3258

McKernan, S.-M. (2002). The Impact of Microcredit Programs on Self-Employment Profits: Do Noncredit Program Aspects Matter. Rev. Econ. Stat. 84, 93–115. doi:10.1162/003465302317331946

Meinzen-Dick, R., Quisumbing, A., Doss, C., and Theis, S. (2019). Women's Land Rights as a Pathway to Poverty Reduction: Framework and Review of Available Evidence. Agric. Syst. 172, 72–82. doi:10.1016/j.agsy.2017.10.009

Meng, L. (2013). Evaluating China's Poverty Alleviation Program: a Regression Discontinuity Approach. J. Public Econ. 101, 1–11. doi:10.1016/j.jpubeco.2013.02.004

Merediz-Solà, I., and Bariviera, A. F. (2019). A Bibliometric Analysis of Bitcoin Scientific Production. Res. Int. Business Finance 50, 294–305. doi:10.1016/j.ribaf.2019.06.008

Niehaus, P., Atanassova, A., Bertrand, M., and Mullainathan, S. (2013). Targeting with Agents. Am. Econ. J. Econ. Pol. 5, 206–238. doi:10.1257/pol.5.1.206

Pagiola, S., Arcenas, A., and Platais, G. (2005). Can Payments for Environmental Services Help Reduce Poverty? an Exploration of the Issues and the Evidence to Date from Latin America. World Develop. 33, 237–253. doi:10.1016/j.worlddev.2004.07.011

Park, A., Wang, S., and Wu, G. (2002). Regional Poverty Targeting in China. J. Public Econ. 86, 123–153. doi:10.1016/S0047-2727(01)00108-6

Pritchard, A. (1969). Oecologia. J. Doc. 50, 348–349. doi:10.2307/1934868

Sedlmayr, R., Shah, A., and Sulaiman, M. (2020). Cash-plus: Poverty Impacts of Alternative Transfer-Based Approaches. J. Develop. Econ. 144, 102418. doi:10.1016/j.jdeveco.2019.102418

Sen, A. (1999). “Development as freedom,” in The Globalization and Development Reader: Perspectives on Development and Global Change . Editors J. T. Roberts, A. B. Hite, and N. Chorev (New Jersey: John Wiley & Sons ), 525.

Sims, K. R. E., and Alix-Garcia, J. M. (2017). Parks versus PES: Evaluating Direct and Incentive-Based Land Conservation in Mexico. J. Environ. Econ. Manage. 86, 8–28. doi:10.1016/j.jeem.2016.11.010

Small, H. (1973). Co-citation in the Scientific Literature: A New Measure of the Relationship between Two Documents. J. Am. Soc. Inf. Sci. 24, 265–269. doi:10.1002/asi.4630240406

Stevenson, J. R., and Irz, X. (2009). Is Aquaculture Development an Effective Tool for Poverty Alleviation? A Review of Theory and Evidence. Cah. Agric. 18, 292–299. doi:10.1684/agr.2009.0286

Su, Y., Yu, Y., and Zhang, N. (2020). Carbon Emissions and Environmental Management Based on Big Data and Streaming Data: A Bibliometric Analysis. Sci. Total Environ. 733, 138984. doi:10.1016/j.scitotenv.2020.138984

Van Eck, N. J., and Waltman, L. (2010). Software Survey: VOSviewer, a Computer Program for Bibliometric Mapping. Scientometrics 84, 523–538. doi:10.1007/s11192-009-0146-3

Wilson, D. C., Velis, C., and Cheeseman, C. (2006). Role of Informal Sector Recycling in Waste Management in Developing Countries. Habitat Int. 30, 797–808. doi:10.1016/j.habitatint.2005.09.005

Zhang, J., Yu, Q., Zheng, F., Long, C., Lu, Z., and Duan, Z. (2016). Comparing Keywords Plus of WOS and Author Keywords: A Case Study of Patient Adherence Research. J. Assn Inf. Sci. Tec 67, 967–972. doi:10.1002/asi.23437

Zhang, X., Yu, Y., and Zhang, N. (2020). Sustainable Supply Chain Management under Big Data: a Bibliometric Analysis. Jeim 34, 427–445. doi:10.1108/JEIM-12-2019-0381

Zhou, D., Cai, K., and Zhong, S. (2021). A Statistical Measurement of Poverty Reduction Effectiveness: Using China as an Example. Soc. Indic. Res. 153, 39–64. doi:10.1007/s11205-020-02474-w

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

This article is part of the Research Topic

Sustainable Career Development in the Turbulent, Boundaryless and Internet Age

• Understanding Poverty

Poverty Reduction a Win-Win

Climate change adaptation and poverty reduction go hand in hand, a new World Bank book argues. So why not kill two birds with one stone?

Publication

Inequality in the Labor Market

The World Development Report of 2013 measures, perhaps for the first time, inequality of opportunity to labor market outcomes in a discrete setting. It focuses on Europe and Central Asia.

You have clicked on a link to a page that is not part of the beta version of the new worldbank.org. Before you leave, we’d love to get your feedback on your experience while you were here. Will you take two minutes to complete a brief survey that will help us to improve our website?

Feedback Survey

Thank you for agreeing to provide feedback on the new version of worldbank.org; your response will help us to improve our website.

Thank you for participating in this survey! Your feedback is very helpful to us as we work to improve the site functionality on worldbank.org.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• Advanced Search
• Journal List

Evaluating poverty alleviation strategies in a developing country

Pramod k. singh.

Institute of Rural Management Anand (IRMA), Anand, Gujarat, India

Harpalsinh Chudasama

Associated data.

All relevant data are within the manuscript and its Supporting Information files. The aggregated condensed matrix (social cognitive map) is given in S1 Table . One can replicate the findings of this study by analyzing this weight matrix.

A slew of participatory and community-demand-driven approaches have emerged in order to address the multi-dimensional nature of poverty in developing nations. The present study identifies critical factors responsible for poverty alleviation in India with the aid of fuzzy cognitive maps (FCMs) deployed for showcasing causal reasoning. It is through FCM-based simulations that the study evaluates the efficacy of existing poverty alleviation approaches, including community organisation based micro-financing, capability and social security, market-based and good governance. Our findings confirm, to some degree, the complementarity of various approaches to poverty alleviation that need to be implemented simultaneously for a comprehensive poverty alleviation drive. FCM-based simulations underscore the need for applying an integrated and multi-dimensional approach incorporating elements of various approaches for eradicating poverty, which happens to be a multi-dimensional phenomenon. Besides, the study offers policy implications for the design, management, and implementation of poverty eradication programmes. On the methodological front, the study enriches FCM literature in the areas of knowledge capture, sample adequacy, and robustness of the dynamic system model.

1. Introduction

1.1. poverty alleviation strategies.

Although poverty is a multi-dimensional phenomenon, poverty levels are often measured using economic dimensions based on income and consumption [ 1 ]. Amartya Sen’s capability deprivation approach for poverty measurement, on the other hand, defines poverty as not merely a matter of actual income but an inability to acquire certain minimum capabilities [ 2 ]. Contemplating this dissimilarity between individuals’ incomes and their inabilities is significant since the conversion of actual incomes into actual capabilities differs with social settings and individual beliefs [ 2 – 4 ]. The United Nations Development Programme (UNDP) also emphasises the capabilities’ approach for poverty measurement as propounded by Amartya Sen [ 5 ]. “ Ending poverty in all its forms everywhere ” is the first of the 17 sustainable development goals set by the United Nations with a pledge that no one will be left behind [ 6 ]. Development projects and poverty alleviation programmes all over the world are predominantly aimed at reducing poverty of the poor and vulnerable communities through various participatory and community-demand-driven approaches [ 7 , 8 ]. Economic growth is one of the principal instruments for poverty alleviation and for pulling the poor out of poverty through productive employment [ 9 , 10 ]. Studies from Africa, Brazil, China, Costa Rica, and Indonesia show that rapid economic growth lifted a significant number of poor people out of financial poverty between 1970 and 2000 [ 11 ]. According to Bhagwati and Panagariya, economic growth generates revenues required for expanding poverty alleviation programmes while enabling governments to spend on the basic necessities of the poor including healthcare, education, and housing [ 9 ]. Poverty alleviation strategies may be categorised into four types including community organisations based micro-financing, capability and social security, market-based, and good governance.

Micro-finance, aimed at lifting the poor out of poverty, is a predominant poverty alleviation strategy. Having spread rapidly and widely over the last few decades, it is currently operational across several developing countries in Africa, Asia, and Latin America [ 12 – 21 ]. Many researchers and policy-makers believe that access to micro-finance in developing countries empowers the poor (especially women) while supporting income-generating activities, encouraging the entrepreneurial spirit, and reducing vulnerability [ 15 , 21 – 25 ]. There are fewer studies, however, that show conclusive and definite evidence regarding improvements in health, nutrition, and education attributable to micro-finance [ 21 , 22 ]. For micro-finance to be more effective, services like skill development training, technological support, and strategies related to better education, health and sanitation, including livelihood enhancement measures need to be included [ 13 , 17 , 19 ].

Economic growth and micro-finance for the poor might throw some light on the financial aspects of poverty, yet they do not reflect its cultural, social, and psychological dimensions [ 11 , 21 , 26 ]. Although economic growth is vital for enhancing the living conditions of the poor, it does not necessarily help the poor exclusively tilting in favour of the non-poor and privileged sections of society [ 4 ]. Amartya Sen cites social exclusion and capability deprivation as reasons for poverty [ 4 , 27 ]. His capabilities’ approach is intended to enhance people’s well-being and freedom of choices [ 4 , 27 ]. According to Sen, development should focus on maximising the individual’s ability to ensure more freedom of choices [ 27 , 28 ]. The capabilities approach provides a framework for the evaluation and assessment of several aspects of the individual’s well-being and social arrangements. It highlights the difference between means and ends as well as between substantive freedoms and outcomes. An example being the difference between fasting and starving [ 27 – 29 ]. Improving capabilities of the poor is critical for improving their living conditions [ 4 , 10 ]. Improving individuals’ capabilities also helps in the pooling of resources while allowing the poor to engage in activities that benefit them economically [ 4 , 30 ]. Social inclusion of vulnerable communities through the removal of social barriers is as significant as financial inclusion in poverty reduction strategies [ 31 , 32 ]. Social security is a set of public actions designed to reduce levels of vulnerability, risk, and deprivation [ 11 ]. It is an important instrument for addressing the issues of inequality and vulnerability [ 32 ]. It also induces gender parity owing to the equal sovereignty enjoyed by both men and women in the context of economic, social, and political activities [ 33 ].

The World Development Report 1990 endorsed a poverty alleviation strategy that combines enhanced economic growth with provisions of essential social services directed towards the poor while creating financial and social safety nets [ 34 , 35 ]. Numerous social safety net programmes and public spending on social protection, including social insurance schemes and social assistance payments, continue to act as tools of poverty alleviation in many of the developing countries across the world [ 35 – 39 ]. These social safety nets and protection programmes show positive impacts on the reduction of poverty, extent, vulnerability, and on a wide range of social inequalities in developing countries. One major concern dogging these programmes, however, is their long-term sustainability [ 35 ].

Agriculture and allied farm activities have been the focus of poverty alleviation strategies in rural areas. Lately, though, much of the focus has shifted to livelihood diversification on the part of researchers and policy-makers [ 15 , 40 ]. Promoting non-farm livelihoods, along with farm activities, can offer pathways for economic growth and poverty alleviation in developing countries the world over [ 40 – 44 ]. During the early 2000s, the development of comprehensive value chains and market systems emerged as viable alternatives for poverty alleviation in developing countries [ 45 ]. Multi-sectoral micro-enterprises may be deployed for enhancing productivity and profitability through value chains and market systems, they being important for income generation of the rural poor while playing a vital role in inclusive poverty eradication in developing countries [ 46 – 48 ].

Good governance relevant to poverty alleviation has gained top priority in development agendas over the past few decades [ 49 , 50 ]. Being potentially weak in the political and administrative areas of governance, developing countries have to deal with enormous challenges related to social services and security [ 49 , 51 ]. In order to receive financial aid from multinational donor agencies, a good governance approach towards poverty reduction has become a prerequisite for developing countries [ 49 , 50 ]. This calls for strengthening a participatory, transparent, and accountable form of governance if poverty has to be reduced while improving the lives of the poor and vulnerable [ 50 , 51 ]. Despite the importance of this subject, very few studies have explored the direct relationship between good governance and poverty alleviation [ 50 , 52 , 53 ]. Besides, evidence is available, both in India and other developing countries, of information and communication technology (ICT) contributing to poverty alleviation programmes [ 54 ]. Capturing, storing, processing, and transmitting various types of information with the help of ICT empowers the rural poor by increasing access to micro-finance, expanding the use of basic and advance government services, enabling the development of additional livelihood assets, and facilitating pro-poor market development [ 54 – 56 ].

1.2. Proposed contribution of the paper

Several poverty alleviation programmes around the world affirm that socio-political inclusion of the poor and vulnerable, improvement of social security, and livelihood enhancement coupled with activities including promoting opportunities for socio-economic growth, facilitating gender empowerment, improving facilities for better healthcare and education, and stepping up vulnerability reduction are central to reducing the overall poverty of poor and vulnerable communities [ 1 , 11 ]. These poverty alleviation programmes remain instruments of choice for policy-makers and development agencies even as they showcase mixed achievements in different countries and localities attributable to various economic and socio-cultural characteristics, among other things. Several poverty alleviation programmes continue to perform poorly despite significant investments [ 8 ]. The failure rate of the World Bank’s development projects was above 50% in Africa until 2000 [ 57 ]. Hence, identifying context-specific factors critical to the success of poverty alleviation programmes is vital.

Rich literature is available pertinent to the conceptual aspects of poverty alleviation. Extant literature emphasises the importance of enhancing capabilities and providing social safety, arranging high-quality community organisation based micro-financing, working on economic development, and ensuring good governance. However, the literature is scanty with regard to comparative performances of the above approaches. The paper tries to fill this gap. This study, through fuzzy cognitive mapping (FCM)-based simulations, evaluates the efficacy of these approaches while calling for an integrative approach involving actions on all dimensions to eradicate the multi-dimensional nature of poverty. Besides, the paper aims to make a two-fold contribution to the FCM literature: i) knowledge capture and sample adequacy, and ii) robustness of the dynamic system model.

The remainder of the paper proceeds as follows: We describe the methodology adopted in the study in section two. Section three illustrates key features of the FCM system in the context of poverty alleviation, FCM-based causal linkages, and policy scenarios for poverty alleviation with the aid of FCM-based simulations. We present our contribution to the extant literature relating to FCM and poverty alleviation. Finally, we conclude the paper and offer policy implications of the study.

2. Methodology

We conducted the study with the aid of the FCM-based approach introduced by Kosko in 1986 [ 58 ]. The process of data capture in the FCM approach is considered quasi-quantitative because the quantification of concepts and links may be interpreted in relative terms [ 59 ] allowing participants to debate the cause-effect relations between the qualitative concepts while generating quantitative data based on their experiences, knowledge, and perceptions of inter-relationships between concepts [ 60 – 64 , 65 – 68 ]. The FCM approach helps us visualise how interconnected factors/ variables/ concepts affect one another while representing self-loop and feedback within complex systems [ 62 , 63 , 69 ]. A cognitive map is a signed digraph with a series of feedback comprising concepts (nodes) that describe system behavior and links (edges) representing causal relationships between concepts [ 60 – 63 , 65 , 70 – 72 ]. FCMs may be created by individuals as well as by groups [ 60 , 72 , 73 ]. Individual cognitive mapping and group meeting approaches have their advantages and drawbacks [ 72 ]. FCMs allow the analysis of non-linear systems with causal relations, while their recurrent neural network behaviour [ 69 , 70 , 74 ] help in modelling complex and hard-to-model systems [ 61 – 63 ]. The FCM approach also provides the means to build multiple scenarios through system-based modelling [ 60 – 64 , 69 , 74 , 75 ].

The strengths and applications of FCM methodology, focussing on mental models, vary in terms of approach. It is important to remember, though, that (i) the FCM approach is not driven by data unavailability but is responsible for generating data [ 60 , 76 ]. Also that (ii) FCMs can model complex and ambiguous systems revealing hidden and important feedback within the systems [ 58 , 60 , 62 , 69 , 76 ] and (iii) FCMs have the ability to represent, integrate, and compare data–an example being expert opinion vis-à-vis indigenous knowledge–from multiple sources while divulging divergent viewpoints [ 60 ]. (iv) Finally, FCMs enable various policy simulations through an interactive scenario analysis [ 60 , 62 , 69 , 76 ].

The FCM methodology does have its share of weaknesses. To begin with: (i) Respondents’ misconceptions and biases tend to get encoded in the maps [ 60 , 62 ]. (ii) Possibility of susceptibility to group power dynamics in a group model-building setting cannot be ruled out; (iii) FCMs require a large amount of post-processing time [ 67 ]. (iv) The FCM-based simulations are non-real value and relative parameter estimates and lack spatial and temporal representation [ 60 , 77 , 78 ].

These drawbacks notwithstanding, we, along with many researchers, conceded that the strengths and applications of FCM methodology outweighed the former, particularly with regard to integrating data from multiple stakeholders with different viewpoints.

We adopted the multi-step FCM methodology discussed in the following sub-sections. We adopted the multi-step FCM methodology discussed in the following sub-sections. We obtained individual cognitive maps from the participants in two stages: ‘open-concept design’ approach followed by the ‘pre-concept design’ approach. We coded individual cognitive maps into adjacency matrices and aggregated individual cognitive maps to form a social cognitive map. FCM-based simulation was used to build policy scenarios for poverty alleviation using different input vectors.

2.1. Obtaining cognitive maps from the participants

A major proportion of the literature on fuzzy cognitive maps reflects an ‘open-concept design’ approach, while some studies also rely on a ‘pre-designed concept’ approach with regard to data collection.

In the case of the ‘open-concept design’ approach, concepts are determined entirely by participants and are unrestricted [ 59 , 60 , 62 , 63 , 65 – 67 , 79 , 80 ]. While the researcher determines the context of the model by specifying the system being modelled, including the boundaries of the system, participants are allowed to decide what concepts will be included. This approach provides very little restriction in the knowledge capture from participants and can be extremely beneficial especially if there is insufficient knowledge regarding the system being modelled.

In the case of the ‘pre-designed concept’ approach, concepts are pre-determined either by experts or by researchers using available literature [ 64 , 69 , 74 , 81 , 82 ]. In this approach, the researcher is able to exercise a higher degree of control over how the system is defined. The ‘pre-designed concept’ approach is likely to be more efficient compared to the ‘open-concept design’ in the context of time required for model building. However, it restricts the diversity of knowledge captured from participants and is able to influence more heavily the way in which this knowledge is contextualised based on input and interpretation.

We have adopted a ‘mixed-concept design’ approach for this study involving data collection in two stages:

2.1.1. Stage one: ‘Open-concept design’ approach

During the first stage, we engaged with the experts and national-level policy-makers who designed the Deendayal Antyodaya Yojana -National Rural Livelihoods Mission (DAY-NRLM), a centrally sponsored programme in India. The DAY-NRLM aims at abolishing rural poverty by promoting multiple livelihoods for the rural poor and vulnerable households. The programme is focussed on organising the rural poor and vulnerable communities into self-help groups (SHGs) while equipping them with means of self-employment. The four critical components of the programme viz ., (i) universal social mobilisation and institution building, (ii) financial inclusion, (iii) convergence and social development, and (iv) livelihood enhancement are designed to address the exclusions of these communities, eliminate their poverty, and bring them within the ambit of mainstream economic and social systems. Participants comprising three experts from the World Bank, nine experts from the National Mission Management Unit of the DAY-NRLM, and 25 monitoring and evaluation experts from 25 states of India created 37 FCMs. A sample map of FCMs obtained from these participants is provided in S1 Fig . We demonstrated the construction of fuzzy cognitive maps with the aid of a map from a neutral problem domain referring to direct and consequential impacts of deforestation, which had been approved by the ‘Research Ethics Committee’ of our Institute.

A group discussion was held with the participants regarding the issues under investigation subsumed under the title “critical factors required to ensure that people come out of poverty on a sustainable basis”. It prompted them to identify major concepts pertaining to the above. These were listed down on a whiteboard by the researchers. Once the participants had understood the process of drawing a fuzzy cognitive map and identified major concepts responsible for poverty alleviation, they were asked to draw a fuzzy cognitive map individually. The participants used the concepts listed on the whiteboard to draw fuzzy cognitive maps. Many participants added new concepts while drawing the maps. They then connected all the concepts through various links based on their personal understanding. The links, represented by arrows in between concepts, show the direction of influence between them.

The participants assigned weights to each link on a scale of 1–10 to describe the relationship strength between two concepts [ 60 ]. Ten denoted the highest strength and one the lowest; the numbers 1–3 signified relationships with low strength, 4–6 signified relationships with medium strength, and 7–10 signified relationships with high strength. After constructing the FCMs each participant made a presentation, which was video-recorded, explaining their map to the researchers. The researchers, based on causal relationships between the concepts, assigned positive and negative polarities to the weights of the links [ 59 , 60 , 62 – 64 , 66 – 68 , 72 ].

2.1.2. Stage two: ‘Pre-designed concept’ approach

During the second stage, an instrument depicting 95 concepts under 22 concept categories was prepared based on the FCMs obtained from participants during the first stage ( S2 Fig ). The instrument also contained links between the 22 concept categories. ‘Research Ethics Committee’ of our Institute approved this instrument as well. We used this instrument during the second stage to obtain FCMs. We obtained 123 additional FCMs, of which 20 FCMs were obtained from the Chief Executive Officers along with experts from livelihood, enterprise, and community development domains belonging to the National Mission Management Unit in the states of Bihar, Jharkhand, Madhya Pradesh, and Maharashtra. The remaining 103 FCMs were obtained from 103 district project coordinators, who had agreed to participate in the study. Unlike most FCM-based studies, which usually rely upon 30 to 50 participants, this study involved 174 experts and project implementers. Most participants produced FCMs individually and some in pairs. The 174 participants produced 160 FCMs.

The participants were given the instrument and were instructed to assign weights to each concept, wherever applicable, and leave other cells blank. These weights were assigned based on the concepts’ significance regarding poverty alleviation in India. The instrument was designed to allow participants to add new concepts and/or remove existing ones from the instrument based on their understanding and perceptions. Later, the participants were asked to assign weights to all pre-established links between the 22 concept categories. The instrument also allowed participants to draw new linkages between the categories and/or discard the existing relationships based on their understanding and perceptions. After constructing the FCMs each participant made a presentation to the researchers, which was video-recorded. During the process, participants added 55 new concepts within the pre-classified 22 concept categories. Five new concepts were added under a new category. The final data comprised 23 concept categories and 155 concepts ( S3 Fig ).

2.2. Coding individual cognitive maps into adjacency matrices

The individual FCMs were coded into separate excel sheets, with concepts listed in vertical and horizontal axes, forming an N x N adjacency matrix. The weights of the links, on a scale of 1−10, were normalised in the −1 to +1 range [ 62 , 63 ]. The values were then coded into a square adjacency matrix whenever a connection existed between any two concepts [ 60 , 62 – 64 , 66 ].

2.3. Aggregation of individual cognitive maps

There are various methods of aggregating individual FCMs; each method has advantages and disadvantages [ 83 ]. We aggregated individual adjacency matrices obtained by normalising each adjacency matrix element according to its decisional weight, w i , and the number of participants, k , who supported it. The following equation illustrates the augmentation of individual adjacency matrices:

M FCM is the aggregated adjacency matrix, where, k represents the number of participants interviewed; w i is the decisional weight of the expert i , where, ∑ i = 1 k w i = 1 ; and m i is the adjacency matrix written by the participant i .

This aggregation approach has been adopted by many researchers [ 59 , 60 , 63 – 67 , 74 , 79 , 84 – 87 ]. A large number of concepts in an aggregated (social/ group) fuzzy cognitive map with many interconnections and feedback form a complex system. Aggregation of all the 160 individual cognitive maps produced a social cognitive map ( S1 Table ). This shows the cumulative strength of the system.

2.4. Structural analysis of the system

Structural analysis of the final condensed social cognitive map was undertaken using the FCMapper software. The graph theory of a cognitive map provides a way of characterising FCM structures employing several indices in addition to the number of concepts (C) and links (W) such as in-degree, out-degree, centrality, complexity index, and density index [ 60 ].

The in-degree is the column sum of absolute values of a concept in the adjacency matrix. It shows the cumulative strength of links entering the concept ( w ji ). Where n = the total number of concepts:

The out-degree is the row sum of absolute values of a concept in the adjacency matrix. It shows the cumulative strengths of links exiting the concept ( w ij ). Where n = the total number of concepts:

The degree centrality of a concept is the summation of its in-degree and out-degree. The higher the value, the greater is the importance of a concept in the overall model [ 60 ].

Transmitter concepts (T) depict positive out-degree and zero in-degree. Receiver concepts (R) represent positive in-degree and zero out-degree. Ordinary concepts (O) have both a non-zero in-degree and out-degree [ 60 ].

The complexity index of a cognitive map is the ratio of receiver concepts (R) to transmitter concepts (T). Higher complexity indicates more complex systems thinking [ 60 ]:

The density index of a cognitive map is an index of connectivity showing how connected or sparse the maps are. It is a product of the number of concepts (C) and the number of links (W). Here the number of existing links is compared to the number of all possible links. Higher the density, greater the existence of potential management policies [ 60 ]:

2.5. Fuzzy cognitive maps-based simulations

The scenarios formed through FCM-based simulations can serve to guide managers and policy-makers during the decision-making process [ 62 – 64 , 66 , 69 , 82 , 88 – 90 ]. An FCM is formed out of the adjacency matrix and a state vector, representing the values of the connections between the concepts and the values of the system concepts [ 62 , 63 , 69 ]. The weighted adjacency matrix of an FCM forms a recurrent neural network, including concepts and interconnections for processing the information and feedback loops [ 88 , 91 ]. These have been used to analyse system behavior by running FCM-based simulations in order to determine possible future scenarios.

In order to understand FCM-based simulations, let us understand the FCM as a quadruple, i.e. M = (C n , W , A , f) , where, n is the set of all concepts ( C ) in the map, W : ( C i , C j ) → w ij is a function which defines the causal weight matrix, W M × M , A : ( C i ) → A ( t ) i is a function that computes the activation degree of each concept C i at the discrete-time step t ( t = 1, 2, …, T ), and f (.) is the transfer function [ 63 , 71 , 92 , 93 ]. Knowledge and experience of stakeholders regarding the system determine the type and number of concepts as well as the weights of the links in FCMs. The value A i of a concept C i , expresses the quantity of its corresponding value. With values assigned to the concepts and weights, the FCM converges to an equilibrium point [ 71 , 91 ]. At each step, the value A i of a concept is calculated, following an activation rule, which computes the influence of other concepts to a specific concept.

We have used an increasingly popular activation rule [ 61 – 64 , 90 , 91 ] introduced by Stylios [ 94 ], which is as follows:

Where, n is the total number of concepts, A i ( t +1) is the value of concept C i at simulation step t +1, A i ( t ) is the value of concept C i at simulation step t , A j ( t ) is the value of concept C j at simulation step t , w ji is the weight of the interconnection between concept C j and concept C i , and f is the transformation function [ 64 , 90 ]. The restriction i ≠ j is used when self-causation is assumed to be impossible [ 91 ].

The simulation outcomes also depend on the type of transformation function used. The most frequently used transformation functions (ƒ) are sigmoid and hyperbolic tangent functions [ 90 – 93 ]. When the values of concepts can only be positive, i.e. in the range of (0,1), the most common unipolar sigmoid transformation function is used [ 64 , 91 – 93 ]. Following is the mathematical equation of the sigmoid transformation function:

Where, 𝛌 is a real positive number (𝛌 > 0) and a constant value that determines the slope steepness factor, while, x is the value of concept A i ( t ) on the equilibrium point [ 64 , 93 ]. Higher values of 𝛌 increase the steepness and make it more sensitive to the changes of x . Hence, the derivative δ f δ x becomes higher when increasing the activation value [ 95 ].

2.5.1. Development of input vectors for policy scenarios

Identifying pivotal concepts is a traditional approach in scenario planning that helps linking storylines to the quantitative model [ 96 ]. In the FCM-based scenario analysis, recognition of such pivotal concepts, termed as input vectors, mainly relies upon participants’ perceptions along with the characteristics of the model. We identified four input vectors for four poverty alleviation policy scenarios based on existing literature on poverty alleviation strategies. The fifth input vector is based on the concepts with the highest weights identified by the participants. In the sixth input vector, the concept representing entrepreneurship is replaced by the concept representing livelihood diversification considering its importance based on existing literature [ 15 , 40 ]. All six scenarios are explained below:

Scenario 1 : High-quality community organisation based micro-financing —Input vector 1: C2, C3, C4, C5, C11, and C12 (strong institutions of the poor, community heroes driving the programme, capacity building of the community organisations, mainstream financial institutions supporting community organisations, need-based finance, and developing repayment culture). This scenario tries to examine how high-quality community organisation based micro-finance could alleviate poverty.

Scenario 2 : Capabilities and social security —Input vector 2: C19, C20, C21, C22, and C23 (affordable and approachable education and healthcare, social inclusion, building personal assets, adequate knowledge base, and vulnerability reduction). This scenario tries to estimate how improving the capabilities of the poor and providing them social security would help alleviate poverty.

Scenario 3 : Market-based approach —Input vector 3: C13, C14, C15, C16, and C17 (livelihood diversification, entrepreneurship, multi-sectoral collective enterprise, value addition by collectives, and market linkages). This scenario tries to evaluate how a market-based approach could alleviate poverty.

Scenario 4 : Good governance approach —Input vector 4: C6, C7, C8, C9, and C10 (good governance systems and processes, robust monitoring mechanisms, implementation process, linkages/ convergence/ partnerships, and enabling policy & political will). This scenario tries to evaluate how good governance is crucial for poverty alleviation.

Scenario 5 : Integrative approach 1 —Input vector 5: C2, C3, C6, C9, C10, C14, and C19 (strong institutions of the poor, community heroes driving the programme, sound governance systems and processes, enabling policy & political will, linkages/ convergence/ partnerships, entrepreneurship, and affordable and approachable education and healthcare). This scenario tries to assess how the most critical concepts, identified by the participants, are crucial for poverty alleviation.

Scenario 6 : Integrative approach 2 —Input vector 6: C2, C3, C6, C9, C10, C13, and C19 (strong institutions of the poor, community heroes driving the programme, good governance systems and processes, enabling policy & political will, linkages/ convergence/ partnerships, livelihood diversification, and affordable and approachable education and healthcare). This scenario tries to assess how the most important concepts, including livelihood diversification, are critical for the alleviation of poverty. Based on the relative weights, scenarios 4 to 6 also had alternative input vectors incorporating sensitive support structure (C1) without any demonstrable results.

2.5.2. Simulation process

Each concept in the system has an initial state vector A 0 that varies from 0 to |1|. which is associated with an activation vector, where 0 means ‘non-activated’ and |1| means ‘activated’ [ 65 , 80 ]. A new state of the concepts can be calculated by multiplying the adjacency matrix with the state vector [ 69 ]. When one or more concepts are ‘activated’ this activation spreads through the matrix following the weighted relationships. During the simulation process, each iteration produces a new state vector with ‘activated’ concepts and ‘non-activated’ concepts. Self-loops and feedback cause a repeated activation of concepts, introducing non-linearity to the model [ 61 , 70 , 88 ]. The activation of concepts is iterated, using a ‘squashing function’ to rescale concept values towards |1|, until the vector values stabilise and the model reaches equilibrium or steady-state [ 61 , 65 , 70 ]. The resulting concept values may be used to interpret outcomes of a particular scenario and to study the dynamics of the modeled system [ 61 – 63 , 70 ].

The simulation process is carried out with the initial state vector of the input vectors, identified in each scenario (1 to 6), clamped to 1 ( A 1 ) and the initial state vector of all the other concepts clamped to 0 ( A 0 ). We applied the activation rule proposed by Stylios [ 94 ], to run simulations because of its memory capabilities along with the sigmoid transformation function as the links have only positive values. The sensitivity of the system was analysed by clamping the concepts of each input vector to 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9 ( S4 Fig ) to determine whether the system behaves in a similar manner in each simulation [ 62 , 63 , 72 , 89 ].

3. Results and discussions

3.1. key features of the fcm system in the context of poverty alleviation.

The social cognitive map built by combining the individual FCMs comprises 23 concepts and 51 links ( Fig 2 and S1 Table ). This FCM system has a density index of 0.088, which signifies that 8.8% links are actually made of the maximum number of links that could theoretically exist between the 24 concepts. The FCM system has a complexity index of 0.125, which showcases more utility outcomes and less controlling forcing functions. However, unless the density and complexity values of the FCM system are compared to those of other FCM systems representing a similar topic, interpretation of these figures is challenging [ 75 ].

There are some autonomous concepts virtually disengaged from the system. Some dependent concepts although have a relatively low degree of influence, exhibit strong dependence. The contribution of a concept in a cognitive map can be understood by its degree centrality, which is the summation of in-degree and out-degree. Table 1 illustrates the in-degree and out-degree and degree centrality of the FCM system. Concepts have been depicted such as C2: strong institutions of the poor, C15: multi-sectoral collective enterprise development, C13: livelihood diversification and C14: entrepreneurship have higher degree centrality. These concepts should be interpreted as the greatest strength of poverty alleviation strategies. The most influential concepts (i.e., those with the highest out-degree) affecting the poverty alleviation strategies are C6: good governance systems and processes, C19: affordable and approachable education and healthcare, C18: climate-smart production systems, C2: strong institutions of the poor, and C5: mainstream financial institutions supporting CBOs. Scenario analysis results will later help us gain a deeper understanding of the connectivity and influencing concepts of poverty alleviation.

The participants also provided the state vector values (A) of all the concepts (C) based on their understanding of the relative significance of these concepts regarding poverty alleviation in India ( Fig 1 ).

The results show that participants assigned greater significance to the following concepts- C3: community heroes driving the programme, C1: quality support structure, C19: affordable and approachable education and healthcare, C6: good governance systems and processes, C2: strong institutions of the poor, C12: developing repayment culture, and C7: robust monitoring mechanisms.

The results acknowledge that building strong institutions of the poor for a community-demand-driven and community-managed poverty alleviation programme is likely to enjoy greater success. They also confirm that developing robust monitoring mechanisms can ensure better functioning of the community-based organisations (CBOs). Robust governance systems and processes are essential for vibrant CBOs. They can empower communities to have better access to affordable education and healthcare facilities. Better access to micro-finance for these CBOs could help alleviate the economic poverty of the poor and vulnerable communities.

The results, however, fail to capture the cultural and social dimensions of poverty.

3.2. Understanding the poverty alleviation strategy

This section summarises the views of participants across the concepts based on the presentations made by them to the researcher during both the stages of knowledge capture. Fig 2 illustrates the cognitive interpretive diagram formed using the social cognitive map. The concepts, represented by each node in the diagram, are connected by several links. These links establish relationships between the concepts representing the basis of degree centrality. The central concept is people coming out of poverty, which is depicted with yellow color in Fig 2 .

Participants indicated that setting up a quality and dedicated support structures at multiple levels (national, state, district, and block) is essential for poverty alleviation ( Fig 2 : C1). The support structures should be staffed with professionally competent and dedicated human resources. The crucial role of these support structures is to build and nurture strong institutions of the poor ( Fig 2 : C2) at multiple levels and evanesce when community heroes start driving the programme. Building and sustaining strong, inclusive, self-managed, and self-reliant institutions of the poor at various levels such as self-help groups (SHGs), village organisations (VOs), and cluster-level federations (CLFs) through training, handholding, and systematic guidance are crucial to the success of a poverty alleviation programme. However, superior CBOs are required to ensure the quality of primary-level institutions and their sustainability. Adherence to the five principles (regular meetings, regular savings, regular inter-loaning, timely repayment of the loans, and up-to-date books of accounts), co-ordination, and cohesiveness between the members would go a long way in building strong institutions of the poor.

Participants emphasised the importance of community heroes in driving the poverty alleviation programme ( Fig 2 : C3). The poverty eradication programme is likely to meet with greater success if it is entirely operated and managed by the community. Involving experienced community members for social mobilisation, capacity building and scaling-up of various processes within the project will ensure effective functioning and implementation of the programme. Participants believed that the capacity building of the CBOs, community resource persons, community cadres, and community service providers ( Fig 2 : C4) are essential for poverty alleviation. Apart from training in social and financial inclusion, these community members should be provided with knowledge, skills, and tools to improve their existing livelihoods and for managing innovative livelihood collectives and micro-enterprises. Providing access to financial services to society’s most vulnerable group in a cost-effective manner through mainstream financial institutions and allowing the poor to become preferred clients of the banking system is fundamental to the financial inclusion strategy of a poverty alleviation programme ( Fig 2 : C5). The SHG-bank linkage enables an easy access to micro-finance for the SHGs. It also serves to foster their faith towards the banking system.

Good governance systems and processes are crucial to building sensitive support structures and strong institutions of the poor ( Fig 2 : C6). A well-structured process for participatory identification of the poor by the community helps identify very poor, poor, vulnerable, tribal, differently-abled, and other marginalised communities in a village. A robust process for grading the quality of SHGs and their federations could help maintain a high standard for these institutions. Strong, robust, and transparent monitoring mechanisms ( Fig 2 : C7) could ensure good governance systems and processes. The process-oriented approach of the programme needs to undergo continuous review, assessment, and course-correction from the qualitative and quantitative progress achieved at various levels. Hence, participants suggested that a robust ICT-based monitoring and evaluation system remain in place for facilitating informed decision-making at all levels. The participants also indicated the urgency of robust implementation of institutional accountability and a self-monitoring process in institutions of the poor at all levels, including peer internal review mechanisms, external social auditing, public expenditure tracking, and community scorecards, in order to build stronger institutions of the poor ( Fig 2 : C8). Transparency in the functioning of human resources at all levels aided by regular meetings, reviews, and monitoring of progress could ensure effective implementation of the programme. Maintaining equity and transparency in releasing finances and ensuring effective fund utilisation across all eligible groups could also help focus on the most vulnerable groups.

The participants believed that a poverty alleviation programme should have a strong convergence with other welfare programmes ( Fig 2 : C9). Stronger emphasis should be placed on convergence for developing synergies directly and through the institutions of the poor. Participants suggested that the programme recognise the importance of engaging with industries to set up platforms for public-private-partnerships in farm and non-farm sectors while developing various sector-specific value chains to harness the comparative advantage of the micro-enterprise sector. The political will to support and encourage CBOs, enabling policies for smooth and efficient working of the institutions of the poor, diminished political influence in the decision-making of CBOs, and timely and adequate resource allocation on the part of government institutions is critical for poverty alleviation programmes ( Fig 2 : C10).

Participants acknowledged that livelihood augmentation requires customised need-based financing for the poor and vulnerable ( Fig 2 : C11). Access to micro-finance at affordable rates of interest coupled with desired amounts and convenient repayment terms are needed for the poverty reduction of communities. Providing interest subvention for all SHG loans availed from mainstream financial institutions, based on prompt loan repayment, helps develop a healthy loan repayment culture ( Fig 2 : C12).

Participants opined that diversification of livelihoods would ensure steady incomes for households ( Fig 2 : C13). The development of micro-enterprise in farm and non-farm sectors could encourage institutions of the poor in the aggregation of produce, value-addition, and marketing of finished goods. Therefore, it is imperative that more and more sustainable enterprises be created by the poor to improve their livelihood security. The demand-driven entrepreneurship ( Fig 2 : C14) programmes could be taken up through public-private-partnerships. Provisions could be made for incubation funds and start-up funds for the development of multi-sectoral livelihood collectives ( Fig 2 : C15) to foster a collective entrepreneurship spirit. Livelihood activities, in order to be commercially viable, would require economy of scale, enabling the adoption of available technologies while providing better bargaining power, offering a more significant political clout, and influencing public policy over time. Building specialised multi-sectoral collective institutions of the poor, such as producers’ companies and co-operatives could make the latter key players in the market. These livelihood institutions could carry out participatory livelihood mapping and integrated livelihood planning as well as build robust livelihood clusters, supply chains, and value chains. They could also identify gaps in the supply and value chains, create backward and forward linkages, and tap market opportunities for intervention and collectivisation for chosen livelihood activities ( Fig 2 : C16; C17). Developing adequate and productive infrastructure for processing, storage, packaging, and transportation is crucial for value addition ( Fig 2 : C16). The demand-based value chain development is currently evident in micro-investment planning processes. Identifying non-farm activities to support enterprises in a comprehensive way could also be crucial. Adequate market linkages and support services like branding, market research, market knowledge, market infrastructure, and backward linkages would go a long way in deriving optimum returns from the chosen livelihood activities ( Fig 2 : C17).

Several eco-friendly, climate-smart, and innovative approaches in agriculture production systems will ensure the sustainability of production systems even in the context of climate change ( Fig 2 : C18). Contemporary grassroots innovations supplemented by robust scientific analysis, mainly supported by various government programmes, are likely to ensure enhanced and efficient production systems. Focus on developing adequate infrastructure for processing, storing, and transporting for value addition would serve to reduce post-harvest losses.

Participants believed that affordable and approachable quality education up to the secondary level as well as affordable and quality healthcare facilities are crucial for poverty alleviation ( Fig 2 : C19). Convergence with mid-day-meal schemes will not only encourage communities to send their children to schools but also help curb malnutrition. An affordable and approachable healthcare system is likely to help reduce health-related vulnerabilities of the poor. Crucial is an approach that identifies all needy and poor households while primarily focussing on vulnerable sections like scheduled castes, scheduled tribes, particularly vulnerable tribal groups, single women and women-headed households, disabled, landless, migrant labor, isolated communities, and those living in disturbed areas. Equally crucial is including them in institutions of the poor ( Fig 2 : C20). Customised micro-financing coupled with adequate instruments on healthcare and education could aid vulnerability reduction ( Fig 2 : C23). The social, human, and personal assets created by developing institutions of the poor are crucial for sustaining and scaling-up of the poverty alleviation programme ( Fig 2 : C21). This will also allow women to articulate their problems and improve their self-confidence, enhance their respect in society, develop leadership qualities, inspire them to speak and express their feelings unhesitatingly, and empower them economically and socially. Developing an academic understanding of the factors that support community institutions is crucial for the social infrastructure developed to facilitate the social capital building of the poor and vulnerable communities ( Fig 2 : C22).

3.3. FCM-based simulations

In order to evaluate critical factors responsible for poverty alleviation, we used six input vectors for FCM-based simulations. For each scenario, causal propagation occurs in each iteration until the FCM system converges [ 62 – 65 , 67 , 70 , 91 ]. This happens when no change takes place in the values of a concept after a certain point, also known as the system steady-state; the conceptual vector at that point is called the final state vector [ 62 – 65 , 67 , 70 , 91 ]. Values of the final state vectors depend on the structure of the FCM system and concepts considered for input vectors. The larger the value of the final state vectors, the better the selected policies [ 62 – 65 ]. Comparisons between the final state vectors of the alternative simulations are drawn in order to assess the extent of the desired transition by activating each set of input vectors. The initial values and final state vectors of all the concepts for every scenario are presented in Table 2 . The graphical representation of various scenarios for poverty alleviation is provided in the S5 Fig .

*O = Ordinary; T = Transmitter; R = Receiver

The first scenario highlights the effects of high-quality community organisations based micro-financing approach. If strong institutions of the poor are built and community heroes start driving the poverty alleviation programme, capacity building of the CBOs gets underway. If mainstream financial institutions start supporting CBOs while customised need-based finance and a repayment culture is developed significant efforts would still be required for putting good governance systems and processes in place along with linkages/ convergences/ partnerships along with other schemes while building capabilities of the poor. In the case of successful micro-financing, there will be opportunities for livelihood diversification, entrepreneurship, multi-sectoral collective enterprise development, value addition by collectives, and market linkages.

The second scenario highlights the effects of the capabilities approach and social security. In this case, affordable and approachable education and healthcare, social inclusion, the building of personal assets, adequate knowledge base, and vulnerability reduction are ensured. In this context, ample efforts will be required for mainstream financial institutions supporting CBOs, good governance systems and processes, and linkages/ convergences/ partnerships with other schemes. Efforts will also be required for a quality support structure and customised need-based finance. The capability and social security enhancement could have prospects for strong institutions of the poor, better implementation processes, livelihood diversification, entrepreneurship, value addition by collectives, multi-sectoral collective enterprise development, and vulnerability reduction.

The third scenario highlights the outcomes of the market-based approach. Here, livelihood diversification, entrepreneurship, multi-sectoral collective enterprise development, value addition by collectives, and market linkages are activated. In such a situation, adequate efforts will be required for mainstream financial institutions supporting CBOs, good governance systems and processes, and linkages/ convergences/ partnerships with other schemes. Efforts will also be required for continuous capacity building of the CBOs, customised need-based finance, affordable and approachable education and healthcare, and vulnerability reduction.

The fourth scenario highlights the outcomes of good governance. Here, good governance systems and processes, robust monitoring mechanisms, implementation processes, enabling policies and political will, and linkages/ convergence/ partnership with other governmental schemes are ensured. In such a situation, plentiful efforts will be required for mainstream financial institutions to lend their support to CBOs and for the building of personal assets. Efforts will also be required for developing a repayment culture, climate-smart production systems, and vulnerability reduction. Good governance is likely to ensure strong institutions of the poor, development of collective enterprises, livelihood diversification, entrepreneurship, value addition by collectives, and market linkages.

In the fifth and sixth scenarios, we activated the most important concepts identified by the participants. The sixth scenario is similar to the fifth one except that the concept C14: entrepreneurship has been replaced by the concept C13: livelihood diversification. The simulation results reveal that quality of CBOs, strong institutions of the poor, community heroes driving the programme, good governance systems and processes, convergence with other schemes/ programmes, enabling policies and political will, and livelihood diversification are very critical for poverty alleviation in a developing nation.

The participants judged a relatively higher weight for the concept C1 (sensitive support structure) ( Fig 1 ). This could be attributed to a conflict of interest on the part of the participants. Even after activating the concept C1 across policy scenarios 4 to 6, the outcome does not change. This also justifies the fact that any community-demand-driven and community-managed poverty alleviation programme has to be self-sustainable in the long-term. Therefore, while a poverty alleviation programme may make use of a support structure in its initial phase, it should persist at thriving even after the support structure has been withdrawn.

4. Contributions to FCM and poverty literature and future research directions

This section deals with contributions of the paper to FCM and poverty literature while offering a practical approach to address multi-dimensional poverty. The paper makes a two-fold contribution to FCM literature: i) knowledge capture and sample adequacy and ii) robustness of the dynamic system model. FCM sampling is often extended if additional maps keep adding new dimensions/ insights. The saturation of FCM sampling is formally measured by tracking the number of new concepts introduced in subsequent exercises and estimating an accumulation curve of concepts. When the point of saturation is reached data collection is stopped. In most studies, the saturation of FCM sampling is reported at 30–32 maps [ 60 , 62 , 63 , 66 , 72 ]. This study does demonstrate, however, that in the event of a ‘mixed-concept design’ approach when the participants gain access to concepts already identified by other sets of participant groups the latter participants continue to add new concepts, making the system much more complex and the data richer. Most FCM-based case studies published in scientific journals have taken weights of the causal interactions between the concepts. This study has not only obtained weights of the causal interactions between the concepts but also obtained weights of each concept. Results of the FCM-based simulations, by and large, match with the most critical concepts identified by participants represented by higher relative weights. This demonstrates in-depth understanding of participants of the subject matter and robustness of the system.

Scenarios are defined as ‘a plausible description of how the future may develop based on a coherent and internally consistent set of assumptions’ [ 97 ]. It also represents uncertainty as a range of plausible futures. Hence, in order to establish proper causal pathways of various poverty eradication approaches, it may be necessary to design random control trial experiments along each of the poverty eradication approaches and carry out the efficacy of each approach delineated above using the difference-in-difference micro-econometric model.

5. Conclusions

The results of our FCM-based simulations reveal that in order to eradicate poverty one needs to provide micro-finance through high-quality community organisations, enhance capabilities of the poor while providing social safety nets to the poor and vulnerable, ensure good governance within community organisations and institutions supporting them, continue to diversify livelihood options, and provide market linkages to small producers. Our findings confirm that various approaches to poverty alleviation are rather complementary and need to be implemented simultaneously for a comprehensive poverty alleviation drive. However, in relative terms, factors like good governance within community organisations and supporting institutions, high-quality community organisations based micro-financing, and enhancement of capabilities coupled with social security assurance seem to work better than a market-based approach. There is rich literature available on radical approaches like land reforms, decentralisation and poverty alleviation that have not been evaluated in this study. Nevertheless, findings of the study lead us to conclude that in order to address multi-dimensional poverty an integrated and multi-dimensional poverty alleviation approach is needed. Findings of the study are likely to help improve the design, management, and implementation of poverty eradication programmes in developing countries.

Supporting information

Acknowledgments.

We thank the World Bank team and functionaries of DAY-NRLM at national, state, and district levels for participating in the study. Indrani Talukdar is acknowledged for language editing. We thank the Academic Editor and the two anonymous reviewers for providing insightful comments and constructive suggestions.

Funding Statement

The World Bank and the Ministry of Rural Development, Government of India

Data Availability

• PLoS One. 2020; 15(1): e0227176.

Decision Letter 0

14 Nov 2019

PONE-D-19-26538

Evaluating Poverty Alleviation Strategies in a Developing Country

Dear Dr. Singh,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The manuscript requires further revisions are regards the abstract, the contribution to the related literature, literature, research methodology, outcomes’ reporting style. Not least, an extensive English language is required.

We would appreciate receiving your revised manuscript by Dec 22 2019 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Stefan Cristian Gherghina, PhD

Academic Editor

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Thank you for including your ethics statement:  "Research Ethics Committee of our Institute approved the methodology of this paper, including the neutral problem domain— direct and consequential impacts of deforestation.

Collection of plant, animal, or other materials are not involved in this study"

a) Please amend your current ethics statement to include the full name of the ethics committee/institutional review board(s) that approved your specific study.

b) Once you have amended this/these statement(s) in the Methods section of the manuscript, please add the same text to the “Ethics Statement” field of the submission form (via “Edit Submission”).

For additional information about PLOS ONE ethical requirements for human subjects research, please refer to http://journals.plos.org/plosone/s/submission-guidelines#loc-human-subjects-research .

2. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability .

Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories . Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions . Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

2. Has the statistical analysis been performed appropriately and rigorously?

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The is an interesting research which identifies the critical factors for poverty alleviation in India with the aid of fuzzy cognitive maps (FCMs). This paper has many strengths and some opportunities for improvement, which I will elaborate below:

Abstract has inappropriate structure. I suggest to answer the following aspects: - general context - novelty of the work - methodology used - main results

Section 1 presents interesting information. However, it fails to set out any specific interest to a broader audience. There is nothing more than a sort of putting forward the topic. However, what about contribution to relevant literature? Which gaps do you want to fill and how?

Methodology is unclear. Initially a short resume can be proposed to explain several steps. The methodology used must be linked to the existing literature on FCM. what is its potential? its limit?

Results must be linked to the methodology. Please define the relationship and relate your finding with the relevant literature.

Finally, an extensive editing of English language and style is required.

Suggested references:

https://doi.org/10.1016/j.techfore.2019.07.012

https://doi.org/10.1016/j.eist.2015.06.006

https://doi.org/10.1016/j.jenvman.2015.10.038

Reviewer #2: The paper is accurate in the description of the methodology; however some steps can be explained better.

In 189-190 you explained that the concepts were elicited asking the participants the “critical 190 factors required to ensure that people come out of poverty on a sustainable basis”. Was this enough to prompt the contribution of the participants or did you give some other information to elicit their contribution. The quantity of information given before the participant tasks is a question that matter in FCMs building since a great quantity of information could lead to bias while very little information can lead to scanty results. How did you reach the correct trade-off?

523-524 “The 524 larger the value of the final state vectors, better the selected policies.” This means that all the concept give a desired and positive contribution to the poverty alleviation. Did all respondent give positive concept or did you declined all in a positive way to make them handy?

The paper aims also at giving a methodological contribution. I suggest some recent paper to enrich this part:

Falcone, P. M., Lopolito, A., & Sica, E. (2019). Instrument mix for energy transition: A method for policy formulation. Technological Forecasting and Social Change, 148, 119706.

Morone, P., Falcone, P. M., & Lopolito, A. (2019). How to promote a new and sustainable food consumption model: A fuzzy cognitive map study. Journal of cleaner production, 208, 563-574.

Falcone, P. M., Lopolito, A., & Sica, E. (2018). The networking dynamics of the Italian biofuel industry in time of crisis: Finding an effective instrument mix for fostering a sustainable energy transition. Energy Policy, 112, 334-348.

Falcone, P. M., Lopolito, A., & Sica, E. (2017). Policy mixes towards sustainability transition in the Italian biofuel sector: Dealing with alternative crisis scenarios. Energy research & social science, 33, 105-114.

ln 192 why do you mention only two concepts?

The diagrams of the various scenarios are hard to read. I suggest bar diagrams showing differences with the steady state values of each variable under each scenario.

6. PLOS authors have the option to publish the peer review history of their article ( what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at gro.solp@serugif . Please note that Supporting Information files do not need this step.

Author response to Decision Letter 0

Uploaded as separate file namely, Response to Reviewers

Submitted filename: Response to Reviewers.docx

Decision Letter 1

16 Dec 2019

PONE-D-19-26538R1

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/ , click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at gro.solp@gnillibrohtua .

If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact gro.solp@sserpeno .

With kind regards,

Additional Editor Comments (optional):

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

2. Is the manuscript technically sound, and do the data support the conclusions?

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: N/A

4. Have the authors made all data underlying the findings in their manuscript fully available?

5. Is the manuscript presented in an intelligible fashion and written in standard English?

6. Review Comments to the Author

Reviewer #1: The authors accurately addressed all my previous comments. I recommend the article for publication in PLOS ONE Journal.

Reviewer #2: The author(s) has/have addressed all the point raised in the review. The paper has been much improved and suits for the publication in the Journal

7. PLOS authors have the option to publish the peer review history of their article ( what does this mean? ). If published, this will include your full peer review and any attached files.

Acceptance letter

20 Dec 2019

Dear Dr. Singh:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact gro.solp@sserpeno .

For any other questions or concerns, please email gro.solp@enosolp .

Thank you for submitting your work to PLOS ONE.

PLOS ONE Editorial Office Staff

on behalf of

Dr. Stefan Cristian Gherghina

The Household Multidimensional Poverty Reduction Effects of Digital Financial Inclusion: A Financial Environment Perspective

• Original Research
• Published: 08 February 2024

Cite this article

• Fang Wang 1 ,
• Xixi Zhang 2 ,
• Chuwen Ye 3 &
• Qihua Cai   ORCID: orcid.org/0000-0003-4617-4682 1  

141 Accesses

Explore all metrics

The poverty alleviation effects of digital financial inclusion are widely recognized, while these effects are dependent on the financial environment. However, little is known about how the financial environment influences digital financial inclusion, which leads to a reduction in household multidimensional poverty. Using matching data from Peking University Digital Financial Inclusion Index of China and Chinese Family Panel Studies for 2012, 2014, 2016, and 2018, this paper estimates the impact of digital financial inclusion on household multidimensional poverty, considering the financial environment. The results show that: (1) Digital financial inclusion alleviates household multidimensional poverty and has significant alleviation effects on different dimensions of poverty. (2) Digital financial inclusion plays a particularly significant role in reducing household multidimensional poverty in China’s rural areas and central-western regions. (3) Credit availability, social capital, and non-agricultural employment are important channels through which digital financial inclusion reduces household multidimensional poverty. (4) A single threshold effect governs the influence of the financial environment on the impact of digital financial inclusion on household multidimensional poverty. When the financial environment crosses the threshold value, the poverty reduction effect of digital financial inclusion is enhanced. For the components of the financial environment, a single threshold effect governs the influence of both traditional financial foundations and household financial literacy on the impact of digital financial inclusion on multidimensional poverty, whereas government financial supervision does so via a double threshold effect.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Aaberge, R., & Brandolini, A. (2015). Multidimensional poverty and inequality. In A. B. Atkinson, & F. Bourguignon (Eds.), Handbook of income distribution (Vol. 2, pp. 141–216). Elsevier.

Alkire, S., & Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of Public Economics , 95 (7–8), 476–487.

Article   Google Scholar  

Autor, D. H., & Dorn, D. (2013). The growth of low-skill service jobs and the polarization of the US labor market. American Economic Review , 103 (5), 1553–1597.

Beck, T., Demirguc-Kunt, A., & Peria, M. M. (2007). Reaching out: Access to and use of banking services across countries. Social Science Electronic Publishing , 85 (1), 234–266.

Google Scholar  

Behtoui, A., & Neergaard, A. (2012). Social capital, status and income attainment in the workplace. International Journal of Sociology and Social Policy .

Bei, D., & Zhang, R. (2017). Financial inclusion strategies in the context of inclusive growth. Economic Theory and Business Management , 36 (2), 5.

Berry, H. L., & Welsh, J. A. (2010). Social capital and health in Australia: An overview from the household, income and labour dynamics in Australia survey. Social Science & Medicine , 70 (4), 588–596.

Bogan, V. (2008). Stock market participation and the internet. Journal of Financial and Quantitative Analysis , 43 (1), 191–211.

Bosco, B. (2016). Corruption, fatigued democracy and bad governance: Are they co-determinants of poverty risk and social exclusion in Europe? A cross-country macro-level comparison. University of Milan Bicocca Department of Economics , Management and Statistics Working Paper (323).

Bourguignon, F., & Chakravarty, S. R. (2019). The measurement of multidimensional poverty. In S. R. Chakravarty (Ed.), Poverty, social exclusion and stochastic dominance (pp. 83–107). Springer.

Bruhn, M., & Love, I. (2014). The real impact of improved access to finance: Evidence from Mexico. The Journal of Finance , 69 (3), 1347–1376. https://doi.org/10.1111/jofi.12091 .

Cennamo, C., & Santalo, J. (2013). Platform competition: Strategic trade-offs in platform markets. Strategic Management Journal , 34 (11), 1331–1350.

Chibba, M. (2009). Financial inclusion, poverty reduction and the millennium development goals. The European Journal of Development Research , 21 (2), 213–230.

Collins, D., Morduch, J., Rutherford, S., & Ruthven, O. (2009). Portfolios of the poor . Princeton University Press.

De Gasperin, C., Rotondi, V., & Stanca, L. (2019). Mobile money and the labor market: Evidence from developing countries. University of Milan Bicocca Department of Economics, Management and Statistics Working Paper (403).

Delis, M. D., Hasan, I., & Kazakis, P. (2014). Bank regulations and income inequality: Empirical evidence. Review of Finance , 18 (5), 1811–1846.

Dohmen, T., Falk, A., Huffman, D., & Sunde, U. (2010). Are risk aversion and impatience related to cognitive ability? American Economic Review , 100 (3), 1238–1260.

Du, C. Y., & Han, L. Y. (2013). International comparative study on household asset allocation. Global Finance , 6 , 44–55.

Fan, S., & Cho, E. E. (2021). Paths out of poverty: International experience. Journal of Integrative Agriculture , 20 (4), 857–867.

Fang, G. F., & Xu, J. Y. (2020). Does digital financial inclusion promote employment?—evidence from a follow-up investigation on Chinese families. Financial Economics Research , 2 , 75–86.

Feng, G., Jingyi, W., Zhiyun, C., Yongguo, L., Fang, W., & Aiyong, W. (2019). The Peking University Digital financial inclusion index of China (2011–2018) . Institute of Digital Finance, Peking University.

Fu, Q. Z., & Huang, Y. P. (2018). Digital finance’s heterogeneous effects on rural financial demand: Evidence from China household finance survey and inclusive digital finance index. Journal of Financial Research , 11 , 68–84.

Gan, X. L., Yuan, S. J., & Wu, J. (2019). The impact of financial environment on the choice of household financial assets: Evidence from China household finance survey in a perspective of micro-measurement. Journal of Accounting and Economics , 2 , 83–100. https://doi.org/10.16314/j.cnki.31-2074/f.2019.02.006 .

Ge, Y. Q. (2020). Discussion on the ecological framework and implementation path of rural digital inclusive finance development. Financial Theory and Practice , 3 , 32–39.

Goldsmith, R. W. (1969). Financial structure and development .

González Páramo, J. M. (2017). Financial innovation in the digital age: Challenges for regulation and supervision. Revista De Estabilidad Financiera , 32 , 9–37.

Goodhart, C., Hartmann, P., Llewellyn, D. T., Rojas-Suarez, L., & Weisbrod, S. (2013). Financial regulation: Why, how and where now? Routledge.

Guo, F., & Qiong, Y. P. (2021). Measurement and impact of digital financial inclusion in China: A literature review. Chinese Review of Financial Studies , 6 , 12–23.

Guo, F., & Wang, Y. P. (2020). Traditional financial basis, knowledge threshold and digital finance for farmers. Journal of Finance and Economics , 46 (1), 19–33. https://doi.org/16538/j.cnki.jfe.2020.01.002.

Guo, X., & Zhou, Q. (2016). Chronic multidimensional poverty, inequality and causes of poverty. Economic Research Journal , 6 , 143–156.

Guo, F., Kong, S. T., & Wang, J. (2016). General patterns and regional disparity of internet finance development in China: Evidence from the Peking University Internet Finance Development Index. China Economic Journal , 9 (3), 253–271.

Guo, F., Wang, J., Wang, F., Kong, T., Zhang, X., & Cheng, Z. (2020). Measuring China’s digital financial inclusion: Index compilation and spatial characteristics. China Economic Quarterly , 19 (4), 1401–1418.

Hansen, B. E. (2001). The new econometrics of structural change: Dating breaks in US labour productivity. Journal of Economic Perspectives , 15 (4), 117–128.

He, Z., Zhang, X., & Wan, G. (2020). Digital finance, digital divide, and multidimensional poverty. Statistical Research , 37 , 79–89. https://doi.org/10.19343/j.cnki.11-1302/c.2020.10.007 .

Huang, H., & Liu, Y. (2007). An evaluation system on Chinese financial ecologies. Systems Engineering Theory & Practice , 7–14.

Huang, Q., Li, Z., & Xiong, D. P. (2019). Poverty reduction effect of digital financial inclusion and its transmission mechanism. Reform , 11 , 90–101.

Hung, A., Parker, A. M., & Yoong, J. (2009). Defining and measuring financial literacy. RAND Working Paper Series WR-708. Available at SSRN: https://doi.org/10.2139/ssrn.1498674 .

Huston, S. J. (2010). Measuring financial literacy. Journal of Consumer Affairs , 44 (2), 296–316.

Jiang, C., & Xu, L. C. (2005). Financial regulation and financial development: Theoretical framework and empirical test. Journal of Financial Research , 4 , 79–88.

Jie, F., Kan, Z., & Jianxiong, W. (2020). Typical study on sustainable development in relative poverty areas and policy outlook of China. Bulletin of Chinese Academy of Sciences (Chinese Version) , 35 (10), 1249–1263.

Karaa, I. E., & Kuğu, T. D. (2016). Determining advanced and basic financial literacy relations and overconfidence, and informative social media association of university students in Turkey. Educational Sciences: Theory & Practice , 16 (6).

Karlan, D., Kendall, J., Mann, R., Pande, R., Suri, T., & Zinman, J. (2016). Research and impacts of digital financial services . NBER Working Paper Series 22633. Available at: https://www.nber.org/system/files/working_papers/w22633/w22633.pdf .

Klapper, L., El-Zoghbi, M., & Hess, J. (2016, April). Achieving the sustainable development goals. Retrieved April 14, 2023, from https://www.cgap.org/research/publication/achieving-sustainable-development-goals .

Koomson, I., Villano, R. A., & Hadley, D. (2020). Effect of financial inclusion on poverty and vulnerability to poverty: Evidence using a multidimensional measure of financial inclusion. Social Indicators Research , 149 (2), 613–639. https://doi.org/10.1007/s11205-019-02263-0 .

Lauer, K., & Lyman, T. (2015, February). Digital financial inclusion: Implications for customers, regulators, supervisors, and standard-setting bodies . CGAP Brief . Available from: https://www.cgap.org/sites/default/files/Brief-Digital-Financial-Inclusion-Feb-2015.pdf .

Li, J. J., Peng, Y. C., & Ma, S. C. (2020). Inclusive finance and economic development in China: Multidimensional connotation and empirical analysis. Economic Research Journal , 4.

Li, J., Guo, D. W., Yang, F., & Zhang, R. (2022). Evolution game study of internet finance supervision under the regulatory technology. Journal of Systems Engineering , 37 (6), 721–735. https://doi.org/10.13383/j.cnki.jse.2022.06.001 .

Liang, B., & Li, X. L. (2021). Digital financial inclusion, poverty alleviation, and income distribution—empirical analysis from Chinese microdata. Shanghai Finance , 5 , 12–24. https://doi.org/10.13910/j.cnki.shjr.2021.05.002 .

Lin, C. H. (2020). The influence path mechanism of financial development on the high-quality development of China’s economy. Credit Reference , 38 (2), 77–84.

Liu, G. Y., & Wang, Z. (2020). Poverty reduction effect and mechanism of land transfer from multidimensional poverty perspective: Empirical study based on micro data of CFPS. Rural Economy , 12 , 58–68.

Liu, Y., Liu, J., & Zhou, Y. (2017). Spatio-temporal patterns of rural poverty in China and targeted poverty alleviation strategies. Journal of Rural Studies , 52 , 66–75.

Article   ADS   Google Scholar  

Ludvigson, S. (1999). Consumption and credit: A model of time-varying liquidity constraints. The Review of Economics and Statistics , 81 (3), 434–447.

Luo, M. Z., & Zhou, J. Y. (2011). A review of the factors affecting farmers’ entrepreneurship. Economic Perspectives , 8 , 133–136.

Luo, C., Li, S., & Sicular, T. (2020). The long-term evolution of national income inequality and rural poverty in China. China Economic Review , 62 , 101465.

Lyons, A., Kass-Hanna, J., & Greenlee, A. (2020). Impacts of financial and digital inclusion on poverty in South Asia and Sub-Saharan Africa. Available at SSRN: https://doi.org/10.2139/ssrn.3684265 .

Mariscal, J., Mayne, G., Aneja, U., & Sorgner, A. (2019). Bridging the gender digital gap. Economics , 13 (1).

McKinnon, R. I. (1974). Money and capital in economic development. American Journal of Agricultural Economics , 56 (1), 201–201. https://doi.org/10.2307/1239377 .

Miled, K. B. H., & Rejeb, J. E. B. (2015). Microfinance and poverty reduction: A review and synthesis of empirical evidence. Procedia - Social and Behavioral Sciences , 195 , 705–712. https://doi.org/10.1016/j.sbspro.2015.06.339 .

Morgan, P. J., Huang, B., & Trinh, L. Q. (2019). The need to promote digital financial literacy for the digital age . G20 2019 Japan / T-20 Japan 2019 series on The future of work and education in the digital age.

Morris, A., Goodman, J., & Brading, H. (2007). Internet use and non-use: Views of older users. Universal Access in the Information Society , 6 , 43–57.

Munyegera, G. K., & Matsumoto, T. (2016). Mobile money, remittances, and household welfare: Panel evidence from rural Uganda. World Development , 79 , 127–137.

Orlova, I. S., & Tsinchenko, G. M. (2020). Russia, China: Poverty alleviation (comparative analysis). Administrative Consulting .

Ozili, P. K. (2018). Impact of digital finance on financial inclusion and stability. Borsa Istanbul Review , 18 (4), 329–340.

Page, L., & Pande, R. (2018). Ending global poverty: Why money isn’t enough. Journal of Economic Perspectives , 32 (4), 173–200.

Park, C. Y., & Mercado, R. V. (2016). Does financial inclusion reduce poverty and income inequality in developing Asia? In S. Gopalan, & T. Kikuchired Financial inclusion in Asia: Issues and policy concerns (pp. 61–92). Springer.

Park, C. Y., & Mercado, R. (2018). Financial inclusion, poverty, and income inequality. The Singapore Economic Review , 63 (1), 185–206.

Peng, P., & Xu, Z. G. (2021). Can digital financial inclusion reduce the vulnerability of peasant households? Economic Review , 1 , 82–95. https://doi.org/10.19361/j.er.2021.01.06 .

Prete, A. L. (2022). Digital and financial literacy as determinants of digital payments and personal finance. Economics Letters , 213 , 110378.

Qu, Y. C. (2021). Rural relative poverty control: Measure principle and path choice. Theory Journal , 4 , 142–149. https://doi.org/10.14110/j.cnki.cn-37-1059/d.2021.04.016 .

Quan-Haase, A., Mo, G. Y., & Wellman, B. (2017). Connected seniors: How older adults in East York exchange social support online and offline. Information Communication & Society , 20 (7), 967–983.

Rajan, R., & Zingales, L. (1998). Financial development and growth. American Economic Review , 88 (3), 559–586.

Ren, T. Z., & Yin, Z. G. (2022). Digital financial inclusion and inclusive growth of Chinese economy: Theoretical analysis and empirical evidence. Journal of Management , 1 , 23–35. https://doi.org/10.19808/j.cnki.41-1408/f.2022.0003 .

Sander, T., Pauzuoliene, J., & Sloka, B. (2016). Human resources management use of social network sites to recruit employees. New Challenges of Economic and Business Development–2016 , 623.

Scholtens, B. (2017). Why finance should care about ecology. Trends in Ecology & Evolution , 32 (7), 500–505.

Schultz, T. W. (1990). Investment in human capital: The role of education and of research . Commercial Press.

Senyo, P. K., & Osabutey, E. (2020). Unearthing antecedents to financial inclusion through FinTech innovations. Technovation , 98 , 102155.

Sheng, S. H., & Li, H. (2022). Overconfidence, financial environment and family entrepreneurship options. Wuhan Finance Monthly , 2 , 62–69.

Siaw, A., Jiang, Y., Ankrah Twumasi, M., Agbenyo, W., Ntim-Amo, G., Danquah, O., F., & Ankrah, E. K. (2021). The ripple effect of credit accessibility on the technical efficiency of maize farmers in Ghana. Agricultural Finance Review , 81 (2), 189–203.

Song, X., & Guo, H. (2017). Influence factors of the urban-rural residents’ income gap: A restudy with the digital inclusive finance. Proceedings of the 2017 4th International Conference on Business, Economics and Management, Qingdao, China .

Stern, C., Makinen, M., & Qian, Z. (2017). FinTechs in China–with a special focus on peer to peer lending. Journal of Chinese Economic and Foreign Trade Studies .

Sun, T. Q. (2016). G20 advanced principles of digital financial inclusion: Background, framework and outlook. Tsinghua Financial Review , 12 , 29–33. https://doi.org/10.19409/j.cnki.thf-review.2016.12.007 .

Article   CAS   Google Scholar  

Sun, B. W., Chen, Q. Y., & Chen, X. X. (2023). Market mechanism, social capital and common prosperity—from the perspective of China. China Journal of Economics , 1–32. https://doi.org/10.16513/j.cnki.cje.20230320.002 .

Suppa, N. (2018). Towards a multidimensional poverty index for Germany. Empirica , 45 (4), 655–683.

Tadesse, G., & Bahiigwa, G. (2015). Mobile phones and farmers’ marketing decisions in Ethiopia. World Development , 68 , 296–307.

Tang, S., Wu, X., & Zhu, J. (2020). Digital finance and enterprise technology innovation: Structural feature, mechanism identification and effect difference under financial supervision. Management World , 36 (5), 52–66.

Toffler, A. (2022). Powershift: Knowledge, wealth, and power at the edge of the 21st century . Bantam.

Treleaven, P. (2015). Financial regulation of FinTech. Journal of Financial Perspectives , 3 (3).

UNDP & Oxford Poverty and Human Development Initiative (2021). Global Multidimensional Poverty Index 2021 : Unmasking disparities by ethnicity, caste and gender United Nations Development Programme.

Valletta, R. G. (2006). The ins and outs of poverty in advanced economies: Government policy and poverty dynamics in Canada, Germany, Great Britain, and the United States. Review of Income and Wealth , 52 (2), 261–284.

Wan, Q., & Liu, L. (2010). Regional financial environment and SME’s financing behavior—based on the questionnaire of the SMEs in Suzhou and Wuxi. Finance Forum , (10), 73–88. https://doi.org/10.16529/j.cnki.11-4613/f.2010.10.011 .

Wang, Y. L. (2007). The evolution and review of financial ecology and financial ecological environment research. Ecological Economy , 6 , 90–93.

Wang, X., & He, G. (2020). Digital financial inclusion and farmers’ vulnerability to poverty: Evidence from rural China. Sustainability , 12 (4), 1668.

Article   MathSciNet   Google Scholar  

Wang, Z., Chen, Y. M., & Zhang, M. (2021). Traditional financial supply and digital finance development: Supplement or substitute? Based on the perspective of regional system differences. Economic Management , 5 , 5–23. https://doi.org/10.19616/j.cnki.bmj.2021.05.001 .

Wiener, R. L., Baron-Donovan, C., Gross, K., & Block‐Lieb, S. (2005). Debtor education, financial literacy, and pending bankruptcy legislation. Behavioral Sciences & the Law , 23 (3), 347–366.

World Bank (2018). Decline of global extreme poverty continues but has slowed: World Bank [press release]. Retrieved 14 April, 2023, from: https://www.worldbank.org/en/news/press-release/2018/09/19/decline-of-global-extreme-poverty-continues-but-has-slowed-world-bank .

World Bank. (2013). Risk and opportunity: Managing risk for development . World Bank.

Wu, Z. Y., & Gan, W. Y. (2022). On the relative poverty of left-behind families in the context of rural revitalization from the perspective of social capital theory. Social Sciences in Yunnan , 1 , 147–153.

Wu, H. M., & Jin, T. T. (2017). The financial eco-environment and the efficiency of firm innovation. Finance Forum , 12 , 57–67. https://doi.org/10.16529/j.cnki.11-4613/f.2017.12.006 .

Xiao, L. D., & Zhang, B. (2017). Financial availability, off-farm employment, and farmer’s income—empirical study based on CHFS data. Economic Science , 2 , 74–87. https://doi.org/10.19523/j.jjkx.2017.02.006 .

Xie, D., & Su, B. (2021). Digital financial inclusion helps rural revitalization and development: Theoretical analysis and empirical test. Shandong Social Sciences , 4 , 121–127. https://doi.org/10.14112/j.cnki.37-1053/c.2021.04.019 .

Xie, J. Z., & Wang, W. Z. (2016). Social structure change, social capital transition, and income inequality in rural China. China Soft Science , 10 , 20–36.

Xing, Y. (2021). The dividend and gap of rural digital inclusive finance. Economist , 2 , 102–111.

Yang, L., & Wang, S. (2015). Households’ vulnerability and determinants analysis in poor areas. China Population Resources and Environment , 25 , 150–156.

Ye, X. Q., & Xu, J. (2021). Demographic structure, education, and poverty rate in minority areas. Statistics & Decision , 4 , 86–90. https://doi.org/10.13546/j.cnki.tjyjc.2021.04.019 .

Yin, Z., & Zhang, H. (2018). Financial availability, internet finance and households’ credit constraints: Evidence from CHFS data. Journal of Financial Research , 11 , 188–206.

Yip, W., Subramanian, S. V., Mitchell, A. D., Lee, D. T., Wang, J., & Kawachi, I. (2007). Does social capital enhance health and well-being? Evidence from rural China. Social Science & Medicine , 64 (1), 35–49.

Zeng, F. S., & Zheng, Z. Z. (2021). Search on poverty reduction effect of rural digital financial inclusion from multi-dimensional perspective. Rural Economy , 4 , 70–77.

Zhang, L. (2021). Digital financial inclusion, industrial upgrading and farmers’ income growth at county level. Research on Financial and Economic Issues , 6 , 51–59. https://doi.org/10.19654/j.cnki.cjwtyj.2021.06.005 .

Zhang, B., & Li, N. (2022). Digital financial inclusion, non-agricultural employment, and farmers’ income growth: Empirical analysis based on the mediation effect model. Research of Agricultural Modernization , 2 , 249–260. https://doi.org/10.13872/j.1000-0275.2022.0007 .

Zhang, D. H., Yin, Z., & Sui, Y. (2020). Can financial inclusion improve the quality of poverty reduction: Analysis based on multidimensional poverty. South China Journal of Economics , 10 , 56–75.

CAS   Google Scholar  

Zhang, X., Wan, G. H., & Wu, H. T. (2021). Narrowing the digital divide: The development of digital finance with Chinese characteristics. Social Sciences in China , 8 , 35–51.

ADS   Google Scholar  

Zhang, X. Y., Wu, J. J., & Wang, Y. H. (2022). Development status and countermeasures of digital financial inclusion in China. Hebei Finance , 3 , 59–62. https://doi.org/10.14049/j.cnki.hbjr.2022.03.004 .

Zhang, J. Y., Shen, J., & Lei, F. (2023). Household multidimensional relative poverty: Measurement, causes, and spatial variation. Journal of Social Science of Hunan Normal University , 52 (1), 121–129. https://doi.org/10.19503/j.cnki.1000-2529.2023.01.014 .

Zhong, F. N., & He, J. (2007). To generate more off-farm job opportunities as the key to increase the farmers’ income. Issues in Agricultural Economics .

Zhou, L., & Wang, Z. L. (2002). Empirical research of regional financial development and economic growth in China. Journal of Financial Research , 10 , 1–13.

Download references

Acknowledgements

The authors wish to acknowledge support from Philosophy and Social Science Planning Program of Henan Province (No. 2021BJJ092); the Plan for Key Research Projects of Henan Province Higher Education Institutions (No. 24B790026).

Author information

Authors and affiliations.

School of Business, Zhengzhou University, Zhengzhou, China

Fang Wang & Qihua Cai

Business School, Hohai University, Nanjing, China

Education Center for Master of Laws, Zhongnan University of Economics and Law, Wuhan, China

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Qihua Cai .

Ethics declarations

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.

Additional information

Publisher’s note.

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Wang, F., Zhang, X., Ye, C. et al. The Household Multidimensional Poverty Reduction Effects of Digital Financial Inclusion: A Financial Environment Perspective. Soc Indic Res (2024). https://doi.org/10.1007/s11205-023-03298-0

Download citation

Accepted : 24 December 2023

Published : 08 February 2024

DOI : https://doi.org/10.1007/s11205-023-03298-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Digital financial inclusion
• Household multidimensional poverty
• Poverty reduction
• Financial environment
• Heterogeneity
• Threshold effect
• Find a journal
• Publish with us
• Track your research

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

•  We're Hiring!
•  Help Center

Poverty Reduction

• Most Cited Papers
• Most Downloaded Papers
• Newest Papers
• Save to Library
• Last »
• Poverty Alleviation Programmes Follow Following
• Civic Engagement Follow Following
• Poverty Alleviation Follow Following
• Service Learning Follow Following
• Micro and small busines Follow Following
• Informal sector business development Follow Following
• G20 Follow Following
• Financial and Economic Crisis Follow Following
• Us Social Forum Follow Following
• Washington consensus Follow Following

Enter the email address you signed up with and we'll email you a reset link.

• Academia.edu Publishing
•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024